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Feng Y, Li C, Cheng B, Chen Y, Chen P, Wang Z, Zheng X, He J, Zhu F, Wang W, Liang W. Identifying genetically-supported drug repurposing targets for non-small cell lung cancer through mendelian randomization of the druggable genome. Transl Lung Cancer Res 2024; 13:1780-1793. [PMID: 39263038 PMCID: PMC11384480 DOI: 10.21037/tlcr-24-65] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/18/2024] [Accepted: 07/04/2024] [Indexed: 09/13/2024]
Abstract
Background Lung cancer is responsible for most cancer-related deaths, and non-small cell lung cancer (NSCLC) accounts for the majority of cases. Targeted therapy has made promising advancements in systemic treatment for NSCLC over the last two decades, but inadequate drug targets with clinically proven survival benefits limit its universal application in clinical practice compared to chemotherapy and immunotherapy. There is an urgent need to explore new drug targets to expand the beneficiary group. This study aims to identify druggable genes and to predict the efficacy and prognostic value of the corresponding targeted drugs in NSCLC. Methods Two-sample mendelian randomization (MR) of druggable genes was performed to predict the efficacy of their corresponding targeted therapy for NSCLC. Subsequent sensitivity analyses were performed to assess potential confounders. Accessible RNA sequencing data were incorporated for subsequent verifications, and Kaplan-Meier survival curves of different gene expressions were used to explore the prognostic value of candidate druggable genes. Results MR screening encompassing 4,863 expression quantitative trait loci (eQTL) and 1,072 protein quantitative trait loci (pQTL, with 453 proteins overlapping) were performed. Seven candidate druggable genes were identified, including CD33, ENG, ICOSLG and IL18R1 for lung adenocarcinoma, and VSIR, FSTL1 and TIMP2 for lung squamous cell carcinoma. The results were validated by further transcriptomic investigations. Conclusions Drugs targeting genetically supported genomes are considerably more likely to yield promising efficacy and succeed in clinical trials. We provide compelling genetic evidence to prioritize drug development for NSCLC.
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Affiliation(s)
- Yi Feng
- Dpartment of Thoracic Surgery and Oncology, China State Key Laboratory of Respiratory Disease & National Clinical Research Center for Respiratory Disease, The First Affiliated Hospital of Guangzhou Medical University, Guangzhou, China
- Guangzhou Institute of Respiratory Health, Guangzhou, China
| | - Caichen Li
- Dpartment of Thoracic Surgery and Oncology, China State Key Laboratory of Respiratory Disease & National Clinical Research Center for Respiratory Disease, The First Affiliated Hospital of Guangzhou Medical University, Guangzhou, China
- Guangzhou Institute of Respiratory Health, Guangzhou, China
| | - Bo Cheng
- Dpartment of Thoracic Surgery and Oncology, China State Key Laboratory of Respiratory Disease & National Clinical Research Center for Respiratory Disease, The First Affiliated Hospital of Guangzhou Medical University, Guangzhou, China
- Guangzhou Institute of Respiratory Health, Guangzhou, China
| | - Ying Chen
- Dpartment of Thoracic Surgery and Oncology, China State Key Laboratory of Respiratory Disease & National Clinical Research Center for Respiratory Disease, The First Affiliated Hospital of Guangzhou Medical University, Guangzhou, China
- Guangzhou Institute of Respiratory Health, Guangzhou, China
| | - Peiling Chen
- Dpartment of Thoracic Surgery and Oncology, China State Key Laboratory of Respiratory Disease & National Clinical Research Center for Respiratory Disease, The First Affiliated Hospital of Guangzhou Medical University, Guangzhou, China
- Guangzhou Institute of Respiratory Health, Guangzhou, China
| | - Zixun Wang
- Dpartment of Thoracic Surgery and Oncology, China State Key Laboratory of Respiratory Disease & National Clinical Research Center for Respiratory Disease, The First Affiliated Hospital of Guangzhou Medical University, Guangzhou, China
- Guangzhou Institute of Respiratory Health, Guangzhou, China
- Nanshan School, Guangzhou Medical University, Guangzhou, China
| | - Xiangyuan Zheng
- Dpartment of Thoracic Surgery and Oncology, China State Key Laboratory of Respiratory Disease & National Clinical Research Center for Respiratory Disease, The First Affiliated Hospital of Guangzhou Medical University, Guangzhou, China
- Guangzhou Institute of Respiratory Health, Guangzhou, China
| | - Juan He
- Dpartment of Thoracic Surgery and Oncology, China State Key Laboratory of Respiratory Disease & National Clinical Research Center for Respiratory Disease, The First Affiliated Hospital of Guangzhou Medical University, Guangzhou, China
- Guangzhou Institute of Respiratory Health, Guangzhou, China
- Nanshan School, Guangzhou Medical University, Guangzhou, China
| | - Feng Zhu
- Internal Medicine Department, Detroit Medical Center Sinai-Grace Hospital, Detroit, MI, USA
| | - Wei Wang
- Dpartment of Thoracic Surgery and Oncology, China State Key Laboratory of Respiratory Disease & National Clinical Research Center for Respiratory Disease, The First Affiliated Hospital of Guangzhou Medical University, Guangzhou, China
- Guangzhou Institute of Respiratory Health, Guangzhou, China
| | - Wenhua Liang
- Dpartment of Thoracic Surgery and Oncology, China State Key Laboratory of Respiratory Disease & National Clinical Research Center for Respiratory Disease, The First Affiliated Hospital of Guangzhou Medical University, Guangzhou, China
- Guangzhou Institute of Respiratory Health, Guangzhou, China
- Department of Oncology Medical Center, The First People's Hospital of Zhaoqing, Zhaoqing, China
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Zhang Y, Xu F, Wang T, Han Z, Shang H, Han K, Zhu P, Gao S, Wang X, Xue Y, Huang C, Chen Y, Liu G. Shared genetics and causal association between plasma levels of SARS-CoV-2 entry receptor ACE2 and Alzheimer's disease. CNS Neurosci Ther 2024; 30:e14873. [PMID: 39056224 PMCID: PMC11273102 DOI: 10.1111/cns.14873] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/04/2024] [Revised: 06/26/2024] [Accepted: 07/11/2024] [Indexed: 07/28/2024] Open
Abstract
BACKGROUND Alzheimer's disease (AD) is the highest risk of COVID-19 infection, hospitalization, and mortality. However, it remains largely unclear about the link between AD and COVID-19 outcomes. ACE2 is an entry receptor for SARS-CoV-2. Circulating ACE2 is a novel biomarker of death and associated with COVID-19 outcomes. METHODS Here, we explored the shared genetics and causal association between AD and plasma ACE2 levels using large-scale genome-wide association study, gene expression, expression quantitative trait loci, and high-throughput plasma proteomic profiling datasets. RESULTS We found a significant causal effect of genetically increased circulating ACE2 on increased risk of AD. Cross-trait association analysis identified 19 shared genetic variants, and three variants rs3104412, rs2395166, and rs3135344 at chromosome 6p21.32 were associated with COVID-19 infection, hospitalization, and severity. We mapped 19 variants to 117 genes, which were significantly upregulated in lung, spleen, and small intestine, downregulated in brain tissues, and involved in immune system, immune disease, and infectious disease pathways. The plasma proteins corresponding to LST1, AGER, TNXB, and APOC1 were predominantly associated with COVID-19 infection, ventilation, and death. CONCLUSION Together, our findings suggest the shared genetics and causal association between AD and plasma ACE2 levels, which may partially explain the link between AD and COVID-19.
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Affiliation(s)
- Yan Zhang
- Department of PathologyThe Affiliated Hospital of Weifang Medical UniversityWeifangChina
| | - Fang Xu
- Department of Neurology, Xuanwu Hospital, National Center for Neurological DisordersCapital Medical UniversityBeijingChina
| | - Tao Wang
- Academy for Advanced Interdisciplinary StudiesPeking UniversityBeijingChina
- Chinese Institute for Brain ResearchBeijingChina
| | - Zhifa Han
- Center of Respiratory Medicine, China–Japan Friendship Hospital, National Center for Respiratory Medicine, Institute of Respiratory MedicineChinese Acadamy of Medical Sciences, National Clinical Research Center for Respiratory DiseasesBeijingChina
| | - Hong Shang
- Department of NeurologyThe Fourth Affiliated Hospital of Harbin Medical UniversityHarbinChina
| | - Kevin Han
- Department of StatisticsStanford UniversityStanfordCaliforniaUSA
| | - Ping Zhu
- Beijing Institute of Brain Disorders, Laboratory of Brain Disorders, Ministry of Science and Technology, Collaborative Innovation Center for Brain DisordersCapital Medical UniversityBeijingChina
| | - Shan Gao
- Beijing Institute of Brain Disorders, Laboratory of Brain Disorders, Ministry of Science and Technology, Collaborative Innovation Center for Brain DisordersCapital Medical UniversityBeijingChina
| | - Xiaojie Wang
- Department of NeurologyShenzhen Qianhai Shekou Free Trade Zone HospitalShenzhenChina
| | - Yanli Xue
- School of Biomedical EngineeringCapital Medical UniversityBeijingChina
| | - Chen Huang
- Dr. Neher's Biophysics Laboratory for Innovative Drug Discovery, State Key Laboratory of Quality Research in Chinese MedicineMacau University of Science and TechnologyMacao SARChina
| | - Yan Chen
- Department of Epidemiology and Biostatistics, School of Public HealthWannan Medical CollegeWuhuChina
- Institute of Chronic Disease Prevention and ControlWannan Medical CollegeWuhuChina
| | - Guiyou Liu
- Beijing Institute of Brain Disorders, Laboratory of Brain Disorders, Ministry of Science and Technology, Collaborative Innovation Center for Brain DisordersCapital Medical UniversityBeijingChina
- Department of Epidemiology and Biostatistics, School of Public HealthWannan Medical CollegeWuhuChina
- Institute of Chronic Disease Prevention and ControlWannan Medical CollegeWuhuChina
- Beijing Key Laboratory of Hypoxia Translational Medicine, National Engineering Laboratory of Internet Medical Diagnosis and Treatment Technology, Xuanwu HospitalCapital Medical UniversityBeijingChina
- Taishan Vocational College of NursingTaianChina
- Brain HospitalShengli Oilfield Central HospitalDongyingChina
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3
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Xu H, Yang Z, Hu W, Zhou X, Zhang Z, Zhang X. CSPG4P12 polymorphism served as a susceptibility marker for esophageal cancer in Chinese population. BMC Cancer 2024; 24:729. [PMID: 38877481 PMCID: PMC11177360 DOI: 10.1186/s12885-024-12475-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/24/2023] [Accepted: 06/04/2024] [Indexed: 06/16/2024] Open
Abstract
BACKGROUND Chondroitin sulfate proteoglycan 4 pseudogene 12 (CSPG4P12) has been implicated in the pathogenesis of various cancers. This study aimed to evaluate the association of the CSPG4P12 polymorphism with esophageal squamous cell carcinoma (ESCA) risk and to explore the biological impact of CSPG4P12 expression on ESCA cell behavior. METHODS A case-control study was conducted involving 480 ESCA patients and 480 healthy controls to assess the association between the rs8040855 polymorphism and ESCA risk. The CSPG4P12 rs8040855 genotype was identified using the TaqMan-MGB probe method. Logistic regression model was used to evaluate the association of CSPG4P12 SNP with the risk of ESCA by calculating the odds ratios (OR) and 95% confidence intervals (95%CI ). The effects of CSPG4P12 overexpression on cell proliferation, migration, and invasion were examined in ESCA cell lines. Co-expressed genes were identified via the CBioportal database, with pathway enrichment analyzed using SangerBox. The binding score of CSPG4P12 to P53 was calculated using RNA protein interaction prediction (RPISeq). Additionally, Western Blot analysis was performed to investigate the impact of CSPG4P12 overexpression on the P53/PI3K/AKT signaling pathway. RESULTS The presence of at least one rs8040855 G allele was associated with a reduced susceptibility to ESCA compared to the CC genotype (OR = 0.51, 95%CI = 0.28-0.93, P = 0.03). Stratification analysis revealed that the CSPG4P12 rs8040855 C allele significantly decreased the risk of ESCA among younger individuals (≤ 57 years) and non-drinkers (OR = 0.31, 95%CI = 0.12-0.77, P = 0.01; OR = 0.42, 95%CI=0.20-0.87, P = 0.02, respectively). CSPG4P12 expression was found to be downregulated in ESCA tissues compared to adjacent normal tissues. Overexpression of CSPG4P12 in ESCA cells inhibited their proliferation, migration, and invasion capabilities. Furthermore, Western Blot analysis indicated that CSPG4P12 overexpression led to a reduction in PI3K and p-AKT protein expression levels. P53 silencing rescues the inhibitory effect of CSPG4P12 on p-AKT. CONCLUSION The CSPG4P12 rs8040855 variant is associated with reduced ESCA risk and the overexpression of CSPG4P12 inhibited the migration and invasion of ESCA cells by P53/PI3K/AKT pathway. These findings suggest that CSPG4P12 may serve as a novel biomarker for ESCA susceptibility and a potential target for therapeutic intervention.
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Affiliation(s)
- Hongxue Xu
- School of Public Health, North China University of Science and Technology, Tangshan, China
- College of Life Sciences, North China University of Science and Technology, Tangshan, China
- Hebei Key Laboratory of Occupational Health and Safety for Coal Industry, North China University of Science and Technology, Tangshan, China
| | - Zhenbang Yang
- School of Public Health, North China University of Science and Technology, Tangshan, China
| | - Wenqian Hu
- School of Public Health, North China University of Science and Technology, Tangshan, China
| | - Xianlei Zhou
- School of Public Health, North China University of Science and Technology, Tangshan, China
| | - Zhi Zhang
- Affiliated Tangshan Gongren Hospital , North China University of Science and Technology, Tangshan, China
| | - Xuemei Zhang
- School of Public Health, North China University of Science and Technology, Tangshan, China.
- College of Life Sciences, North China University of Science and Technology, Tangshan, China.
- Hebei Key Laboratory of Occupational Health and Safety for Coal Industry, North China University of Science and Technology, Tangshan, China.
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Xu H, Gupta S, Dinsmore I, Kollu A, Cawley AM, Anwar MY, Chen HH, Petty LE, Seshadri S, Graff M, Below P, Brody JA, Chittoor G, Fisher-Hoch SP, Heard-Costa NL, Levy D, Lin H, Loos RJF, Mccormick JB, Rotter JI, Mirshahi T, Still CD, Destefano A, Cupples LA, Mohlke KL, North KE, Justice AE, Liu CT. Integrating Genetic and Transcriptomic Data to Identify Genes Underlying Obesity Risk Loci. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2024:2024.06.11.24308730. [PMID: 38903089 PMCID: PMC11188121 DOI: 10.1101/2024.06.11.24308730] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/22/2024]
Abstract
Genome-wide association studies (GWAS) have identified numerous body mass index (BMI) loci. However, most underlying mechanisms from risk locus to BMI remain unknown. Leveraging omics data through integrative analyses could provide more comprehensive views of biological pathways on BMI. We analyzed genotype and blood gene expression data in up to 5,619 samples from the Framingham Heart Study (FHS). Using 3,992 single nucleotide polymorphisms (SNPs) at 97 BMI loci and 20,692 transcripts within 1 Mb, we performed separate association analyses of transcript with BMI and SNP with transcript (PBMI and PSNP, respectively) and then a correlated meta-analysis between the full summary data sets (PMETA). We identified transcripts that met Bonferroni-corrected significance for each omic, were more significant in the correlated meta-analysis than each omic, and were at least nominally associated with BMI in FHS data. Among 308 significant SNP-transcript-BMI associations, we identified seven genes (NT5C2, GSTM3, SNAPC3, SPNS1, TMEM245, YPEL3, and ZNF646) in five association regions. Using an independent sample of blood gene expression data, we validated results for SNAPC3 and YPEL3. We tested for generalization of these associations in hypothalamus, nucleus accumbens, and liver and observed significant (PMETA<0.05 & PMETA
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Affiliation(s)
- Hanfei Xu
- Department of Biostatistics, School of Public Health, Boston University, 801 Massachusettes Ave, Boston, MA, 02118, USA
| | - Shreyash Gupta
- Department of Population Health Sciences, Geisinger, 100 N. Academy Ave., Danville, PA, 17822, USA
| | - Ian Dinsmore
- Department of Genomic Health, Geisinger, 100 N. Academy Ave., Danville, PA, 17822, USA
| | - Abbey Kollu
- Department of Psychology and Neuroscience, University of North Carolina, 235 E. Cameron Avenue, Chapel Hill, NC, 27599, USA
| | - Anne Marie Cawley
- Marsico Lung Institute, University of North Carolina, 125 Mason Farm Rd, Chapel Hill, NC, 27599, USA
| | - Mohammad Y. Anwar
- Department of Epidemiology, Gillings School of Global Public Health, University of North Carolina, 135 Dauer Drive, Chapel Hill, NC, 27599, USA
| | - Hung-Hsin Chen
- Institute of Biomedical Sciences, Academia Sinica, No. 128, Section 2, Academia Rd., Taipei, Nangang District, 115201, Taiwan
- Vanderbilt Genetics Institute, Division of Genetic Medicine, Vanderbilt University Medical Center, 1161 21st Ave S, Nashville, TN, 37232, USA
| | - Lauren E. Petty
- Vanderbilt Genetics Institute, Division of Genetic Medicine, Vanderbilt University Medical Center, 1161 21st Ave S, Nashville, TN, 37232, USA
| | - Sudha Seshadri
- Department of Biostatistics, School of Public Health, Boston University, 801 Massachusettes Ave, Boston, MA, 02118, USA
- Department of Neurology, School of Medicine, Boston University, 85 East Concord Street, Boston, MA, 02118, USA
- Glenn Biggs Institute for Alzheimer’s & Neurodegenerative Diseases, UT Health San Antonio, 8300 Floyd Curl Drive, San Antonio, TX, 78229, USA
| | - Misa Graff
- Department of Epidemiology, Gillings School of Global Public Health, University of North Carolina, 135 Dauer Drive, Chapel Hill, NC, 27599, USA
| | - Piper Below
- Vanderbilt Genetics Institute, Division of Genetic Medicine, Vanderbilt University Medical Center, 1161 21st Ave S, Nashville, TN, 37232, USA
| | - Jennifer A. Brody
- Department of Medicine, Cardiovascular Health Research Unit, University of Washington, 1730 Minor Ave, Seattle, WA, 98101, USA
| | - Geetha Chittoor
- Department of Population Health Sciences, Geisinger, 100 N. Academy Ave., Danville, PA, 17822, USA
| | - Susan P. Fisher-Hoch
- Department of Epidemiology, School of Public Health, UT Health Houston, Regional Academic Health Center, One West University Blvd, Brownsville, TX, 78520, USA
| | - Nancy L. Heard-Costa
- Framingham Heart Study, 73 Mt Wayte Ave, Framingham, MA, 01702, USA
- Department of Neurology, Chobanian & Avedisian School of Medicine, Boston University, 72 E Concord St, Boston, MA, 02118, USA
| | - Daniel Levy
- Population Sciences Branch, National Heart, Lung, and Blood Institute of the National Institutes of Health, 6701 Rockledge Drive, Bethesda, MD, 20892, USA
| | - Honghuang Lin
- Department of Medicine, University of Massachusetts Chan Medical School, 55 N Lake Ave, Worcester, MA, 01655, USA
| | - Ruth JF. Loos
- Charles Bronfman Institute for Personalized Medicine at Mount Sinai, Icahn School of Medicine at Mount Sinai, 1 Gustave L. Levy Pl, New York, NY, 10029, USA
- Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medical Sciences, University of Copenhagen, Blegdamsvej 3A, 2200, Copenhagen, Denmark
| | - Joseph B. Mccormick
- Department of Epidemiology, School of Public Health, UT Health Houston, Regional Academic Health Center, One West University Blvd, Brownsville, TX, 78520, USA
| | - Jerome I. Rotter
- Department of Pediatrics, The Institute for Translational Genomics and Population Sciences, The Lundquist Institute for Biomedical Innovation at Harbor-UCLA Medical Center, 1124 West Carson Street, Torrance, CA, 90502, USA
| | - Tooraj Mirshahi
- Department of Genomic Health, Geisinger, 100 N. Academy Ave., Danville, PA, 17822, USA
| | - Christopher D. Still
- Center for Obesity and Metabolic Health, Geisinger, 100 N. Academy Ave., Danville, PA, 17822, USA
| | - Anita Destefano
- Department of Biostatistics, School of Public Health, Boston University, 801 Massachusettes Ave, Boston, MA, 02118, USA
- Department of Neurology, School of Medicine, Boston University, 85 East Concord Street, Boston, MA, 02118, USA
| | - L. Adrienne Cupples
- Department of Biostatistics, School of Public Health, Boston University, 801 Massachusettes Ave, Boston, MA, 02118, USA
| | - Karen L Mohlke
- Department of Genetics, School of Medicine, University of North Carolina, Chapel Hill, NC, 27599, USA
| | - Kari E. North
- Department of Epidemiology, Gillings School of Global Public Health, University of North Carolina, 135 Dauer Drive, Chapel Hill, NC, 27599, USA
| | - Anne E. Justice
- Department of Population Health Sciences, Geisinger, 100 N. Academy Ave., Danville, PA, 17822, USA
| | - Ching-Ti Liu
- Department of Biostatistics, School of Public Health, Boston University, 801 Massachusettes Ave, Boston, MA, 02118, USA
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Shellard EM, Rane SS, Eyre S, Warren RB. Functional Genomics and Insights into the Pathogenesis and Treatment of Psoriasis. Biomolecules 2024; 14:548. [PMID: 38785955 PMCID: PMC11117854 DOI: 10.3390/biom14050548] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/11/2024] [Revised: 04/17/2024] [Accepted: 04/24/2024] [Indexed: 05/25/2024] Open
Abstract
Psoriasis is a lifelong, systemic, immune mediated inflammatory skin condition, affecting 1-3% of the world's population, with an impact on quality of life similar to diseases like cancer or diabetes. Genetics are the single largest risk factor in psoriasis, with Genome-Wide Association (GWAS) studies showing that many psoriasis risk genes lie along the IL-23/Th17 axis. Potential psoriasis risk genes determined through GWAS can be annotated and characterised using functional genomics, allowing the identification of novel drug targets and the repurposing of existing drugs. This review is focused on the IL-23/Th17 axis, providing an insight into key cell types, cytokines, and intracellular signaling pathways involved. This includes examination of currently available biological treatments, time to relapse post drug withdrawal, and rates of primary/secondary drug failure, showing the need for greater understanding of the underlying genetic mechanisms of psoriasis and how they can impact treatment. This could allow for patient stratification towards the treatment most likely to reduce the burden of disease for the longest period possible.
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Affiliation(s)
- Elan May Shellard
- Faculty of Biology, Medicine and Health, Division of Musculoskeletal and Dermatological Sciences, School of Biological Sciences, The University of Manchester, Manchester M13 9PT, UK
| | - Shraddha S. Rane
- Centre for Genetics and Genomics Versus Arthritis, Centre for Musculoskeletal Research, NIHR Manchester Biomedical Research Centre, The University of Manchester, Manchester M13 9PT, UK; (S.S.R.); (S.E.)
| | - Stephen Eyre
- Centre for Genetics and Genomics Versus Arthritis, Centre for Musculoskeletal Research, NIHR Manchester Biomedical Research Centre, The University of Manchester, Manchester M13 9PT, UK; (S.S.R.); (S.E.)
| | - Richard B. Warren
- Dermatology Centre, Northern Care Alliance NHS Foundation Trust, Manchester M6 8HD, UK;
- NIHR Manchester Biomedical Research Centre, Manchester University NHS Foundation Trust, Manchester Academic Health Science Centre, Manchester M23 9LT, UK
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Rudra P, Zhou YH, Nobel A, Wright FA. Control of false discoveries in grouped hypothesis testing for eQTL data. BMC Bioinformatics 2024; 25:147. [PMID: 38605284 PMCID: PMC11007981 DOI: 10.1186/s12859-024-05736-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/18/2023] [Accepted: 03/08/2024] [Indexed: 04/13/2024] Open
Abstract
BACKGROUND Expression quantitative trait locus (eQTL) analysis aims to detect the genetic variants that influence the expression of one or more genes. Gene-level eQTL testing forms a natural grouped-hypothesis testing strategy with clear biological importance. Methods to control family-wise error rate or false discovery rate for group testing have been proposed earlier, but may not be powerful or easily apply to eQTL data, for which certain structured alternatives may be defensible and may enable the researcher to avoid overly conservative approaches. RESULTS In an empirical Bayesian setting, we propose a new method to control the false discovery rate (FDR) for grouped hypotheses. Here, each gene forms a group, with SNPs annotated to the gene corresponding to individual hypotheses. The heterogeneity of effect sizes in different groups is considered by the introduction of a random effects component. Our method, entitled Random Effects model and testing procedure for Group-level FDR control (REG-FDR), assumes a model for alternative hypotheses for the eQTL data and controls the FDR by adaptive thresholding. As a convenient alternate approach, we also propose Z-REG-FDR, an approximate version of REG-FDR, that uses only Z-statistics of association between genotype and expression for each gene-SNP pair. The performance of Z-REG-FDR is evaluated using both simulated and real data. Simulations demonstrate that Z-REG-FDR performs similarly to REG-FDR, but with much improved computational speed. CONCLUSION Our results demonstrate that the Z-REG-FDR method performs favorably compared to other methods in terms of statistical power and control of FDR. It can be of great practical use for grouped hypothesis testing for eQTL analysis or similar problems in statistical genomics due to its fast computation and ability to be fit using only summary data.
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Affiliation(s)
- Pratyaydipta Rudra
- Department of Statistics, Oklahoma State University, Stillwater, OK, USA.
| | - Yi-Hui Zhou
- Bioinformatics Research Center, Departments of Statistics and Biological Sciences, North Carolina State University, Raleigh, NC, USA
| | - Andrew Nobel
- Department of Statistics and Operations Research, University of North Carolina, Chapel Hill, NC, USA
| | - Fred A Wright
- Bioinformatics Research Center, Departments of Statistics and Biological Sciences, North Carolina State University, Raleigh, NC, USA.
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7
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Pott J, Kheirkhah A, Gadin JR, Kleber ME, Delgado GE, Kirsten H, Forer L, Hauck SM, Burkhardt R, Scharnagl H, Loeffler M, März W, Thiery J, Gieger C, Peters A, Silveira A, Hooft FV, Kronenberg F, Scholz M. Sex and statin-related genetic associations at the PCSK9 gene locus: results of genome-wide association meta-analysis. Biol Sex Differ 2024; 15:26. [PMID: 38532495 DOI: 10.1186/s13293-024-00602-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/28/2023] [Accepted: 03/13/2024] [Indexed: 03/28/2024] Open
Abstract
BACKGROUND Proprotein convertase subtilisin/kexin type 9 (PCSK9) is a key player of lipid metabolism with higher plasma levels in women throughout their life. Statin treatment affects PCSK9 levels also showing evidence of sex-differential effects. It remains unclear whether these differences can be explained by genetics. METHODS We performed genome-wide association meta-analyses (GWAS) of PCSK9 levels stratified for sex and statin treatment in six independent studies of Europeans (8936 women/11,080 men respectively 14,825 statin-free/5191 statin-treated individuals). Loci associated in one of the strata were tested for statin- and sex-interactions considering all independent signals per locus. Independent variants at the PCSK9 gene locus were then used in a stratified Mendelian Randomization analysis (cis-MR) of PCSK9 effects on low-density lipoprotein cholesterol (LDL-C) levels to detect differences of causal effects between the subgroups. RESULTS We identified 11 loci associated with PCSK9 in at least one stratified subgroup (p < 1.0 × 10-6), including the PCSK9 gene locus and five other lipid loci: APOB, TM6SF2, FADS1/FADS2, JMJD1C, and HP/HPR. The interaction analysis revealed eight loci with sex- and/or statin-interactions. At the PCSK9 gene locus, there were four independent signals, one with a significant sex-interaction showing stronger effects in men (rs693668). Regarding statin treatment, there were two significant interactions in PCSK9 missense mutations: rs11591147 had stronger effects in statin-free individuals, and rs11583680 had stronger effects in statin-treated individuals. Besides replicating known loci, we detected two novel genome-wide significant associations: one for statin-treated individuals at 6q11.1 (within KHDRBS2) and one for males at 12q24.22 (near KSR2/NOS1), both with significant interactions. In the MR of PCSK9 on LDL-C, we observed significant causal estimates within all subgroups, but significantly stronger causal effects in statin-free subjects compared to statin-treated individuals. CONCLUSIONS We performed the first double-stratified GWAS of PCSK9 levels and identified multiple biologically plausible loci with genetic interaction effects. Our results indicate that the observed sexual dimorphism of PCSK9 and its statin-related interactions have a genetic basis. Significant differences in the causal relationship between PCSK9 and LDL-C suggest sex-specific dosages of PCSK9 inhibitors.
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Affiliation(s)
- Janne Pott
- Institute for Medical Informatics, Statistics and Epidemiology, University of Leipzig, Leipzig, Germany.
- MRC Biostatistics Unit, University of Cambridge, Cambridge, UK.
| | - Azin Kheirkhah
- Institute of Genetic Epidemiology, Medical University of Innsbruck, Innsbruck, Austria
| | - Jesper R Gadin
- Division of Cardiovascular Medicine, Department of Medicine Solna, Karolinska Institutet, Karolinska University Hospital Solna, Solna, Sweden
| | - Marcus E Kleber
- Vth Department of Medicine, Medical Faculty Mannheim, University of Heidelberg, Mannheim, Germany
- SYNLAB MVZ Humangenetik Mannheim, Mannheim, Germany
| | - Graciela E Delgado
- Vth Department of Medicine, Medical Faculty Mannheim, University of Heidelberg, Mannheim, Germany
| | - Holger Kirsten
- Institute for Medical Informatics, Statistics and Epidemiology, University of Leipzig, Leipzig, Germany
- LIFE Research Center for Civilization Diseases, University of Leipzig, Leipzig, Germany
| | - Lukas Forer
- Institute of Genetic Epidemiology, Medical University of Innsbruck, Innsbruck, Austria
| | - Stefanie M Hauck
- Metabolomics and Proteomics Core, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany
| | - Ralph Burkhardt
- LIFE Research Center for Civilization Diseases, University of Leipzig, Leipzig, Germany
- Institute of Clinical Chemistry and Laboratory Medicine, University Hospital Regensburg, Regensburg, Germany
| | - Hubert Scharnagl
- Clinical Institute of Medical and Chemical Laboratory Diagnostics, Medical University of Graz, Graz, Austria
| | - Markus Loeffler
- Institute for Medical Informatics, Statistics and Epidemiology, University of Leipzig, Leipzig, Germany
- LIFE Research Center for Civilization Diseases, University of Leipzig, Leipzig, Germany
| | - Winfried März
- Vth Department of Medicine, Medical Faculty Mannheim, University of Heidelberg, Mannheim, Germany
- Clinical Institute of Medical and Chemical Laboratory Diagnostics, Medical University of Graz, Graz, Austria
- SYNLAB Academy, Synlab Holding Deutschland GmbH, Mannheim and Augsburg, Germany
| | - Joachim Thiery
- Institute of Laboratory Medicine, Clinical Chemistry and Molecular Diagnostics, University Hospital Leipzig, Leipzig, Germany
- Faculty of Medicine, University of Kiel, Kiel, Germany
| | - Christian Gieger
- Institute of Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany
| | - Annette Peters
- Institute of Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany
| | - Angela Silveira
- Division of Cardiovascular Medicine, Department of Medicine Solna, Karolinska Institutet, Karolinska University Hospital Solna, Solna, Sweden
| | - Ferdinand Van't Hooft
- Division of Cardiovascular Medicine, Department of Medicine Solna, Karolinska Institutet, Karolinska University Hospital Solna, Solna, Sweden
| | - Florian Kronenberg
- Institute of Genetic Epidemiology, Medical University of Innsbruck, Innsbruck, Austria
| | - Markus Scholz
- Institute for Medical Informatics, Statistics and Epidemiology, University of Leipzig, Leipzig, Germany.
- LIFE Research Center for Civilization Diseases, University of Leipzig, Leipzig, Germany.
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8
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Huang Y, Chen Q, Wang Z, Wang Y, Lian A, Zhou Q, Zhao G, Xia K, Tang B, Li B, Li J. Risk factors associated with age at onset of Parkinson's disease in the UK Biobank. NPJ Parkinsons Dis 2024; 10:3. [PMID: 38167894 PMCID: PMC10762149 DOI: 10.1038/s41531-023-00623-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/31/2023] [Accepted: 12/12/2023] [Indexed: 01/05/2024] Open
Abstract
Substantial evidence shown that the age at onset (AAO) of Parkinson's disease (PD) is a major determinant of clinical heterogeneity. However, the mechanisms underlying heterogeneity in the AAO remain unclear. To investigate the risk factors with the AAO of PD, a total of 3156 patients with PD from the UK Biobank were included in this study. We evaluated the effects of polygenic risk scores (PRS), nongenetic risk factors, and their interaction on the AAO using Mann-Whitney U tests and regression analyses. We further identified the genes interacting with nongenetic risk factors for the AAO using genome-wide environment interaction studies. We newly found physical activity (P < 0.0001) was positively associated with AAO and excessive daytime sleepiness (P < 0.0001) was negatively associated with AAO, and reproduced the positive associations of smoking and non-steroidal anti-inflammatory drug intake and the negative association of family history with AAO. In the dose-dependent analyses, smoking duration (P = 1.95 × 10-6), coffee consumption (P = 0.0150), and tea consumption (P = 0.0008) were positively associated with AAO. Individuals with higher PRS had younger AAO (P = 3.91 × 10-5). In addition, we observed a significant interaction between the PRS and smoking for AAO (P = 0.0316). Specifically, several genes, including ANGPT1 (P = 7.17 × 10-7) and PLEKHA6 (P = 4.87 × 10-6), may influence the positive relationship between smoking and AAO. Our data suggests that genetic and nongenetic risk factors are associated with the AAO of PD and that there is an interaction between the two.
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Affiliation(s)
- Yuanfeng Huang
- National Clinical Research Centre for Geriatric Disorders, Department of Geriatrics, Xiangya Hospital, Central South University, Changsha 410008, Hunan, China
- Bioinformatics Center, Xiangya Hospital & Furong Laboratory, Central South University, Changsha 410008, Hunan, China
| | - Qian Chen
- National Clinical Research Centre for Geriatric Disorders, Department of Geriatrics, Xiangya Hospital, Central South University, Changsha 410008, Hunan, China
- Bioinformatics Center, Xiangya Hospital & Furong Laboratory, Central South University, Changsha 410008, Hunan, China
- Department of Oncology, Xiangya Hospital, Central South University, Changsha 410008, Hunan, China
| | - Zheng Wang
- National Clinical Research Centre for Geriatric Disorders, Department of Geriatrics, Xiangya Hospital, Central South University, Changsha 410008, Hunan, China
- Bioinformatics Center, Xiangya Hospital & Furong Laboratory, Central South University, Changsha 410008, Hunan, China
- Department of Neurology, Xiangya Hospital, Central South University, Changsha 410008, Hunan, China
| | - Yijing Wang
- National Clinical Research Centre for Geriatric Disorders, Department of Geriatrics, Xiangya Hospital, Central South University, Changsha 410008, Hunan, China
- Bioinformatics Center, Xiangya Hospital & Furong Laboratory, Central South University, Changsha 410008, Hunan, China
| | - Aojie Lian
- National Health Commission Key Laboratory of Birth Defect Research and Prevention, Hunan Provincial Maternal and Child Health Care Hospital, Changsha 410008, Hunan, China
| | - Qiao Zhou
- National Clinical Research Centre for Geriatric Disorders, Department of Geriatrics, Xiangya Hospital, Central South University, Changsha 410008, Hunan, China
- Bioinformatics Center, Xiangya Hospital & Furong Laboratory, Central South University, Changsha 410008, Hunan, China
| | - Guihu Zhao
- National Clinical Research Centre for Geriatric Disorders, Department of Geriatrics, Xiangya Hospital, Central South University, Changsha 410008, Hunan, China
- Bioinformatics Center, Xiangya Hospital & Furong Laboratory, Central South University, Changsha 410008, Hunan, China
- Department of Neurology, Xiangya Hospital, Central South University, Changsha 410008, Hunan, China
| | - Kun Xia
- Centre for Medical Genetics & Hunan Key Laboratory of Medical Genetics, School of Life Sciences, Central South University, Changsha 410008, Hunan, China
| | - Beisha Tang
- National Clinical Research Centre for Geriatric Disorders, Department of Geriatrics, Xiangya Hospital, Central South University, Changsha 410008, Hunan, China
- Department of Neurology, Xiangya Hospital, Central South University, Changsha 410008, Hunan, China
- Centre for Medical Genetics & Hunan Key Laboratory of Medical Genetics, School of Life Sciences, Central South University, Changsha 410008, Hunan, China
| | - Bin Li
- National Clinical Research Centre for Geriatric Disorders, Department of Geriatrics, Xiangya Hospital, Central South University, Changsha 410008, Hunan, China.
- Bioinformatics Center, Xiangya Hospital & Furong Laboratory, Central South University, Changsha 410008, Hunan, China.
- Department of Neurology, Xiangya Hospital, Central South University, Changsha 410008, Hunan, China.
| | - Jinchen Li
- National Clinical Research Centre for Geriatric Disorders, Department of Geriatrics, Xiangya Hospital, Central South University, Changsha 410008, Hunan, China.
- Bioinformatics Center, Xiangya Hospital & Furong Laboratory, Central South University, Changsha 410008, Hunan, China.
- Department of Neurology, Xiangya Hospital, Central South University, Changsha 410008, Hunan, China.
- Centre for Medical Genetics & Hunan Key Laboratory of Medical Genetics, School of Life Sciences, Central South University, Changsha 410008, Hunan, China.
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9
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Thompson LI, Cummings M, Emrani S, Libon DJ, Ang A, Karjadi C, Au R, Liu C. Digital Clock Drawing as an Alzheimer's Disease Susceptibility Biomarker: Associations with Genetic Risk Score and APOE in Older Adults. J Prev Alzheimers Dis 2024; 11:79-87. [PMID: 38230720 PMCID: PMC10794851 DOI: 10.14283/jpad.2023.48] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/23/2022] [Accepted: 03/15/2023] [Indexed: 01/18/2024]
Abstract
BACKGROUND Alzheimer's disease (AD) is the leading cause of dementia in older adults, but most people are not diagnosed until significant neuronal loss has likely occurred along with a decline in cognition. Non-invasive and cost-effective digital biomarkers for AD have the potential to improve early detection. OBJECTIVE We examined the validity of DCTclockTM (a digitized clock drawing task) as an AD susceptibility biomarker. DESIGN We used two primary independent variables, Apolipoprotein E (APOE) ε4 allele carrier status and polygenic risk score (PRS). We examined APOE and PRS associations with DCTclockTM composite scores as dependent measures. SETTING We used existing data from the Framingham Heart Study (FHS), a community-based study with the largest dataset of digital clock drawing data to date. PARTICIPANTS The sample consisted of 2,398 older adults ages 60-94 with DCTclockTM data (mean age of 72.3, 55% female and 92% White). MEASUREMENTS PRS was calculated using 38 variants identified in a recent large genome-wide association study (GWAS) and meta-analysis of late-onset AD (LOAD). RESULTS Results showed that DCTclockTM performance decreased with advancing age, lower education, and the presence of one or more copies of APOE ε4. Lower DCTclockTM Total Score as well as lower composite scores for Information Processing Speed (both command and copy conditions) and Drawing Efficiency (command condition) were significantly associated with higher PRS levels and more copies of APOE ε4. APOE and PRS associations displayed similar effect sizes in both men and women. CONCLUSIONS Our results indicate that higher AD genetic risk is associated with poorer DCTclockTM performance in older adults without dementia. This is the first study to demonstrate significant differences in clock drawing performance on the basis of APOE status or PRS.
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Affiliation(s)
- L I Thompson
- Louisa Thompson, Department of Psychiatry, Alpert Medical School, Brown University, Providence, RI. Address: 345 Blackstone Blvd., Providence, RI 02906, USA. Phone: 401-455-6402. E-mail:
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10
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Liu T, Gu J, Li C, Guo M, Yuan L, Lv Q, Qin C, Du M, Chu H, Liu H, Zhang Z. Alternative polyadenylation-related genetic variants contribute to bladder cancer risk. J Biomed Res 2023; 37:405-417. [PMID: 37936490 PMCID: PMC10687529 DOI: 10.7555/jbr.37.20230063] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/20/2023] [Revised: 05/26/2023] [Accepted: 05/28/2023] [Indexed: 11/09/2023] Open
Abstract
Aberrant alternative polyadenylation (APA) events play an important role in cancers, but little is known about whether APA-related genetic variants contribute to the susceptibility to bladder cancer. Previous genome-wide association study performed APA quantitative trait loci (apaQTL) analyses in bladder cancer, and identified 17 955 single nucleotide polymorphisms (SNPs). We found that gene symbols of APA affected by apaQTL-associated SNPs were closely correlated with cancer signaling pathways, high mutational burden, and immune infiltration. Association analysis showed that apaQTL-associated SNPs rs34402449 C>A, rs2683524 C>T, and rs11540872 C>G were significantly associated with susceptibility to bladder cancer (rs34402449: OR = 1.355, 95% confidence interval [CI]: 1.159-1.583, P = 1.33 × 10 -4; rs2683524: OR = 1.378, 95% CI: 1.164-1.632, P = 2.03 × 10 -4; rs11540872: OR = 1.472, 95% CI: 1.193-1.815, P = 3.06 × 10 -4). Cumulative effect analysis showed that the number of risk genotypes and smoking status were significantly associated with an increased risk of bladder cancer ( P trend = 2.87 × 10 -12). We found that PRR13, being demonstrated the most significant effect on cell proliferation in bladder cancer cell lines, was more highly expressed in bladder cancer tissues than in adjacent normal tissues. Moreover, the rs2683524 T allele was correlated with shorter 3' untranslated regions of PRR13 and increased PRR13 expression levels. Collectively, our findings have provided informative apaQTL resources and insights into the regulatory mechanisms linking apaQTL-associated variants to bladder cancer risk.
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Affiliation(s)
- Ting Liu
- Department of Environmental Genomics, Jiangsu Key Laboratory of Cancer Biomarkers, Prevention and Treatment, Collaborative Innovation Center for Cancer Personalized Medicine, School of Public Health, Nanjing Medical University, Nanjing, Jiangsu 211166, China
- Department of Genetic Toxicology, the Key Laboratory of Modern Toxicology of Ministry of Education, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing, Jiangsu 211166, China
| | - Jingjing Gu
- Department of Environmental Genomics, Jiangsu Key Laboratory of Cancer Biomarkers, Prevention and Treatment, Collaborative Innovation Center for Cancer Personalized Medicine, School of Public Health, Nanjing Medical University, Nanjing, Jiangsu 211166, China
- Department of Genetic Toxicology, the Key Laboratory of Modern Toxicology of Ministry of Education, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing, Jiangsu 211166, China
| | - Chuning Li
- Department of Environmental Genomics, Jiangsu Key Laboratory of Cancer Biomarkers, Prevention and Treatment, Collaborative Innovation Center for Cancer Personalized Medicine, School of Public Health, Nanjing Medical University, Nanjing, Jiangsu 211166, China
- Department of Genetic Toxicology, the Key Laboratory of Modern Toxicology of Ministry of Education, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing, Jiangsu 211166, China
| | - Mengfan Guo
- Department of Environmental Genomics, Jiangsu Key Laboratory of Cancer Biomarkers, Prevention and Treatment, Collaborative Innovation Center for Cancer Personalized Medicine, School of Public Health, Nanjing Medical University, Nanjing, Jiangsu 211166, China
- Department of Genetic Toxicology, the Key Laboratory of Modern Toxicology of Ministry of Education, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing, Jiangsu 211166, China
| | - Lin Yuan
- Department of Urology, Jiangsu Province Hospital of Traditional Chinese Medicine, Nanjing, Jiangsu 210029, China
| | - Qiang Lv
- Department of Urology, the First Affiliated Hospital of Nanjing Medical University, Nanjing, Jiangsu 210029, China
| | - Chao Qin
- Department of Urology, the First Affiliated Hospital of Nanjing Medical University, Nanjing, Jiangsu 210029, China
| | - Mulong Du
- Department of Environmental Genomics, Jiangsu Key Laboratory of Cancer Biomarkers, Prevention and Treatment, Collaborative Innovation Center for Cancer Personalized Medicine, School of Public Health, Nanjing Medical University, Nanjing, Jiangsu 211166, China
- Department of Genetic Toxicology, the Key Laboratory of Modern Toxicology of Ministry of Education, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing, Jiangsu 211166, China
| | - Haiyan Chu
- Department of Environmental Genomics, Jiangsu Key Laboratory of Cancer Biomarkers, Prevention and Treatment, Collaborative Innovation Center for Cancer Personalized Medicine, School of Public Health, Nanjing Medical University, Nanjing, Jiangsu 211166, China
- Department of Genetic Toxicology, the Key Laboratory of Modern Toxicology of Ministry of Education, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing, Jiangsu 211166, China
| | - Hanting Liu
- Department of Environmental Genomics, Jiangsu Key Laboratory of Cancer Biomarkers, Prevention and Treatment, Collaborative Innovation Center for Cancer Personalized Medicine, School of Public Health, Nanjing Medical University, Nanjing, Jiangsu 211166, China
- Department of Genetic Toxicology, the Key Laboratory of Modern Toxicology of Ministry of Education, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing, Jiangsu 211166, China
| | - Zhengdong Zhang
- Department of Environmental Genomics, Jiangsu Key Laboratory of Cancer Biomarkers, Prevention and Treatment, Collaborative Innovation Center for Cancer Personalized Medicine, School of Public Health, Nanjing Medical University, Nanjing, Jiangsu 211166, China
- Department of Genetic Toxicology, the Key Laboratory of Modern Toxicology of Ministry of Education, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing, Jiangsu 211166, China
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11
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Sarnowski C, Huan T, Ma Y, Joehanes R, Beiser A, DeCarli CS, Heard-Costa NL, Levy D, Lin H, Liu CT, Liu C, Meigs JB, Satizabal CL, Florez JC, Hivert MF, Dupuis J, De Jager PL, Bennett DA, Seshadri S, Morrison AC. Multi-tissue epigenetic analysis identifies distinct associations underlying insulin resistance and Alzheimer's disease at CPT1A locus. Clin Epigenetics 2023; 15:173. [PMID: 37891690 PMCID: PMC10612362 DOI: 10.1186/s13148-023-01589-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/17/2023] [Accepted: 10/20/2023] [Indexed: 10/29/2023] Open
Abstract
BACKGROUND Insulin resistance (IR) is a major risk factor for Alzheimer's disease (AD) dementia. The mechanisms by which IR predisposes to AD are not well-understood. Epigenetic studies may help identify molecular signatures of IR associated with AD, thus improving our understanding of the biological and regulatory mechanisms linking IR and AD. METHODS We conducted an epigenome-wide association study of IR, quantified using the homeostatic model assessment of IR (HOMA-IR) and adjusted for body mass index, in 3,167 participants from the Framingham Heart Study (FHS) without type 2 diabetes at the time of blood draw used for methylation measurement. We identified DNA methylation markers associated with IR at the genome-wide level accounting for multiple testing (P < 1.1 × 10-7) and evaluated their association with neurological traits in participants from the FHS (N = 3040) and the Religious Orders Study/Memory and Aging Project (ROSMAP, N = 707). DNA methylation profiles were measured in blood (FHS) or dorsolateral prefrontal cortex (ROSMAP) using the Illumina HumanMethylation450 BeadChip. Linear regressions (ROSMAP) or mixed-effects models accounting for familial relatedness (FHS) adjusted for age, sex, cohort, self-reported race, batch, and cell type proportions were used to assess associations between DNA methylation and neurological traits accounting for multiple testing. RESULTS We confirmed the strong association of blood DNA methylation with IR at three loci (cg17901584-DHCR24, cg17058475-CPT1A, cg00574958-CPT1A, and cg06500161-ABCG1). In FHS, higher levels of blood DNA methylation at cg00574958 and cg17058475 were both associated with lower IR (P = 2.4 × 10-11 and P = 9.0 × 10-8), larger total brain volumes (P = 0.03 and P = 9.7 × 10-4), and smaller log lateral ventricular volumes (P = 0.07 and P = 0.03). In ROSMAP, higher levels of brain DNA methylation at the same two CPT1A markers were associated with greater risk of cognitive impairment (P = 0.005 and P = 0.02) and higher AD-related indices (CERAD score: P = 5 × 10-4 and 0.001; Braak stage: P = 0.004 and P = 0.01). CONCLUSIONS Our results suggest potentially distinct epigenetic regulatory mechanisms between peripheral blood and dorsolateral prefrontal cortex tissues underlying IR and AD at CPT1A locus.
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Affiliation(s)
- Chloé Sarnowski
- Human Genetics Center, Department of Epidemiology, Human Genetics, and Environmental Sciences, School of Public Health, The University of Texas Health Science Center at Houston, Houston, TX, USA.
| | - Tianxiao Huan
- Population Sciences Branch, National Heart, Lung and Blood Institutes of Health, Bethesda, MD, USA
| | - Yiyi Ma
- Center for Translational and Computational Neuroimmunology, Department of Neurology, Columbia University Irving Medical Center, New York, NY, USA
| | - Roby Joehanes
- Population Sciences Branch, National Heart, Lung and Blood Institutes of Health, Bethesda, MD, USA
- The Framingham Heart Study, Framingham, MA, USA
| | - Alexa Beiser
- The Framingham Heart Study, Framingham, MA, USA
- Department of Biostatistics, School of Public Health, Boston University, Boston, MA, USA
- Department of Neurology, Boston University School of Medicine, Boston, MA, USA
| | | | - Nancy L Heard-Costa
- The Framingham Heart Study, Framingham, MA, USA
- Department of Biostatistics, School of Public Health, Boston University, Boston, MA, USA
| | - Daniel Levy
- Center for Translational and Computational Neuroimmunology, Department of Neurology, Columbia University Irving Medical Center, New York, NY, USA
- The Framingham Heart Study, Framingham, MA, USA
| | - Honghuang Lin
- Department of Medicine, University of Massachusetts Chan Medical School, Worcester, MA, USA
| | - Ching-Ti Liu
- Department of Biostatistics, School of Public Health, Boston University, Boston, MA, USA
| | - Chunyu Liu
- Department of Biostatistics, School of Public Health, Boston University, Boston, MA, USA
| | - James B Meigs
- Division of General Internal Medicine, Massachusetts General Hospital, Boston, MA, USA
- Department of Medicine, Harvard Medical School, Boston, MA, USA
- Programs in Metabolism and Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Claudia L Satizabal
- The Framingham Heart Study, Framingham, MA, USA
- Department of Neurology, Boston University School of Medicine, Boston, MA, USA
- Glenn Biggs Institute for Alzheimer's and Neurodegenerative Diseases, The University of Texas Health Science Center at San Antonio, San Antonio, TX, USA
- Department of Population Health Sciences, The University of Texas Health Science Center at San Antonio, San Antonio, TX, USA
| | - Jose C Florez
- Department of Medicine, Harvard Medical School, Boston, MA, USA
- Programs in Metabolism and Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Center for Genomic Medicine and Diabetes Unit, Massachusetts General Hospital, Boston, MA, USA
| | - Marie-France Hivert
- Department of Population Medicine, Harvard Medical School and Harvard Pilgrim Health Care Institute, Harvard University, Boston, MA, USA
- Diabetes Unit, Massachusetts General Hospital, Boston, MA, USA
- Department of Medicine, Université de Sherbrooke, Sherbrooke, QC, Canada
| | - Josée Dupuis
- Department of Biostatistics, School of Public Health, Boston University, Boston, MA, USA
- Department of Epidemiology, Biostatistics and Occupational Health, School of Population and Global Health, McGill University, Montreal, Canada
| | - Philip L De Jager
- Center for Translational and Computational Neuroimmunology, Department of Neurology, Columbia University Irving Medical Center, New York, NY, USA
- Taub Institute for Research on Alzheimer's Disease and the Aging Brain, Columbia University Irving Medical Center, New York, NY, USA
| | - David A Bennett
- Rush Alzheimer's Disease Center, Rush University Medical Center, Chicago, IL, USA
| | - Sudha Seshadri
- The Framingham Heart Study, Framingham, MA, USA
- Department of Neurology, Boston University School of Medicine, Boston, MA, USA
- Glenn Biggs Institute for Alzheimer's and Neurodegenerative Diseases, The University of Texas Health Science Center at San Antonio, San Antonio, TX, USA
| | - Alanna C Morrison
- Human Genetics Center, Department of Epidemiology, Human Genetics, and Environmental Sciences, School of Public Health, The University of Texas Health Science Center at Houston, Houston, TX, USA
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12
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Accordini S, Lando V, Calciano L, Bombieri C, Malerba G, Margagliotti A, Minelli C, Potts J, van der Plaat DA, Olivieri M. SNPs in FAM13Aand IL2RBgenes are associated with FeNO in adult subjects with asthma. J Breath Res 2023; 18:016001. [PMID: 37733009 DOI: 10.1088/1752-7163/acfbf1] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/05/2023] [Accepted: 09/21/2023] [Indexed: 09/22/2023]
Abstract
Nitric oxide has different roles in asthma as both an endogenous modulator of airway function and a pro-inflammatory mediator. Fractional exhaled nitric oxide (FeNO) is a reliable, quantitative, non-invasive, simple, and safe biomarker for assessing airways inflammation in asthma. Previous genome-wide and genetic association studies have shown that different genes and single nucleotide polymorphisms (SNPs) are linked to FeNO. We aimed at identifying SNPs in candidate genes or gene regions that are associated with FeNO in asthma. We evaluated 264 asthma cases (median age 42.8 years, female 47.7%) who had been identified in the general adult population within the Gene Environment Interactions in Respiratory Diseases survey in Verona (Italy; 2008-2010). Two hundred and twenty-one tag-SNPs, which are representative of 50 candidate genes, were genotyped by a custom GoldenGate Genotyping Assay. A two-step association analysis was performed without assuming ana priorigenetic model: step (1) a machine learning technique [gradient boosting machine (GBM)] was used to select the 15 SNPs with the highest variable importance measure; step (2) the GBM-selected SNPs were jointly tested in a linear regression model with natural log-transformed FeNO as the normally distributed outcome and with age, sex, and the SNPs as covariates. We replicated our results within an independent sample of 296 patients from the European Community Respiratory Health Survey III. We found that SNP rs987314 in family with sequence similarity 13 member A (FAM13A) and SNP rs3218258 in interleukin 2 receptor subunit beta (IL2RB) gene regions are significantly associated with FeNO in adult subjects with asthma. These genes are involved in different mechanisms that affect smooth muscle constriction and endothelial barrier function responses (FAM13A), or in immune response processes (IL2RB). Our findings contribute to the current knowledge on FeNO in asthma by identifying two novel SNPs associated with this biomarker of airways inflammation.
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Affiliation(s)
- Simone Accordini
- Unit of Epidemiology and Medical Statistics, Department of Diagnostics and Public Health, University of Verona, Verona 37134, Italy
| | - Valentina Lando
- Unit of Epidemiology and Medical Statistics, Department of Diagnostics and Public Health, University of Verona, Verona 37134, Italy
| | - Lucia Calciano
- Unit of Epidemiology and Medical Statistics, Department of Diagnostics and Public Health, University of Verona, Verona 37134, Italy
| | - Cristina Bombieri
- Biology and Genetics Section, Department of Neurosciences, Biomedicine and Movement Sciences, University of Verona, Verona 37134, Italy
| | - Giovanni Malerba
- Biology and Genetics Section, Department of Neurosciences, Biomedicine and Movement Sciences, University of Verona, Verona 37134, Italy
| | - Antonino Margagliotti
- Unit of Epidemiology and Medical Statistics, Department of Diagnostics and Public Health, University of Verona, Verona 37134, Italy
| | - Cosetta Minelli
- National Heart and Lung Institute, Imperial College London, London SW3 6LR, United Kingdom
| | - James Potts
- National Heart and Lung Institute, Imperial College London, London SW3 6LR, United Kingdom
| | - Diana A van der Plaat
- National Heart and Lung Institute, Imperial College London, London SW3 6LR, United Kingdom
| | - Mario Olivieri
- Retired Professor of Occupational Medicine, University of Verona, Verona, Italy
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13
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Wang Y, Selvaraj MS, Li X, Li Z, Holdcraft JA, Arnett DK, Bis JC, Blangero J, Boerwinkle E, Bowden DW, Cade BE, Carlson JC, Carson AP, Chen YDI, Curran JE, de Vries PS, Dutcher SK, Ellinor PT, Floyd JS, Fornage M, Freedman BI, Gabriel S, Germer S, Gibbs RA, Guo X, He J, Heard-Costa N, Hildalgo B, Hou L, Irvin MR, Joehanes R, Kaplan RC, Kardia SL, Kelly TN, Kim R, Kooperberg C, Kral BG, Levy D, Li C, Liu C, Lloyd-Jone D, Loos RJ, Mahaney MC, Martin LW, Mathias RA, Minster RL, Mitchell BD, Montasser ME, Morrison AC, Murabito JM, Naseri T, O'Connell JR, Palmer ND, Preuss MH, Psaty BM, Raffield LM, Rao DC, Redline S, Reiner AP, Rich SS, Ruepena MS, Sheu WHH, Smith JA, Smith A, Tiwari HK, Tsai MY, Viaud-Martinez KA, Wang Z, Yanek LR, Zhao W, Rotter JI, Lin X, Natarajan P, Peloso GM. Rare variants in long non-coding RNAs are associated with blood lipid levels in the TOPMed whole-genome sequencing study. Am J Hum Genet 2023; 110:1704-1717. [PMID: 37802043 PMCID: PMC10577076 DOI: 10.1016/j.ajhg.2023.09.003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/22/2023] [Revised: 09/01/2023] [Accepted: 09/01/2023] [Indexed: 10/08/2023] Open
Abstract
Long non-coding RNAs (lncRNAs) are known to perform important regulatory functions in lipid metabolism. Large-scale whole-genome sequencing (WGS) studies and new statistical methods for variant set tests now provide an opportunity to assess more associations between rare variants in lncRNA genes and complex traits across the genome. In this study, we used high-coverage WGS from 66,329 participants of diverse ancestries with measurement of blood lipids and lipoproteins (LDL-C, HDL-C, TC, and TG) in the National Heart, Lung, and Blood Institute (NHLBI) Trans-Omics for Precision Medicine (TOPMed) program to investigate the role of lncRNAs in lipid variability. We aggregated rare variants for 165,375 lncRNA genes based on their genomic locations and conducted rare-variant aggregate association tests using the STAAR (variant-set test for association using annotation information) framework. We performed STAAR conditional analysis adjusting for common variants in known lipid GWAS loci and rare-coding variants in nearby protein-coding genes. Our analyses revealed 83 rare lncRNA variant sets significantly associated with blood lipid levels, all of which were located in known lipid GWAS loci (in a ±500-kb window of a Global Lipids Genetics Consortium index variant). Notably, 61 out of 83 signals (73%) were conditionally independent of common regulatory variation and rare protein-coding variation at the same loci. We replicated 34 out of 61 (56%) conditionally independent associations using the independent UK Biobank WGS data. Our results expand the genetic architecture of blood lipids to rare variants in lncRNAs.
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Affiliation(s)
- Yuxuan Wang
- Department of Biostatistics, Boston University School of Public Health, Boston, MA, USA
| | - Margaret Sunitha Selvaraj
- Cardiovascular Research Center and Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA; Program in Medical and Population Genetics, Broad Institute of Harvard and MIT, Cambridge, MA, USA; Department of Medicine, Harvard Medical School, Boston, MA, USA
| | - Xihao Li
- Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA; Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Zilin Li
- School of Mathematics and Statistics, Northeast Normal University, Changchun, Jilin, China; Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Jacob A Holdcraft
- Department of Biostatistics, Boston University School of Public Health, Boston, MA, USA
| | - Donna K Arnett
- Provost Office, University of South Carolina, Columbia, SC, USA; Department of Epidemiology and Biostatistics, University of South Carolina Arnold School of Public Health, Columbia, SC, USA
| | - Joshua C Bis
- Cardiovascular Health Research Unit, Department of Medicine, University of Washington, Seattle, WA, USA
| | - John Blangero
- Department of Human Genetics and South Texas Diabetes and Obesity Institute, University of Texas Rio Grande Valley School of Medicine, Brownsville, TX, USA
| | - Eric Boerwinkle
- Human Genetics Center, Department of Epidemiology, Human Genetics, and Environmental Sciences, School of Public Health, The University of Texas Health Science Center at Houston, Houston, TX, USA
| | - Donald W Bowden
- Department of Biochemistry, Wake Forest University School of Medicine, Winston-Salem, NC, USA
| | - Brian E Cade
- Department of Medicine, Brigham and Women's Hospital, Boston, MA, USA; Division of Sleep Medicine, Harvard Medical School, Boston, MA, USA
| | - Jenna C Carlson
- Department of Human Genetics, School of Public Health, University of Pittsburgh, Pittsburgh, PA, USA; Department of Biostatistics, School of Public Health, University of Pittsburgh, Pittsburgh, PA, USA
| | - April P Carson
- Department of Medicine, University of Mississippi Medical Center, Jackson, MS, USA
| | - Yii-Der Ida Chen
- The Institute for Translational Genomics and Population Sciences, Department of Pediatrics, The Lundquist Institute for Biomedical Innovation at Harbor-UCLA Medical Center, Torrance, CA, USA
| | - Joanne E Curran
- Department of Human Genetics and South Texas Diabetes and Obesity Institute, University of Texas Rio Grande Valley School of Medicine, Brownsville, TX, USA
| | - Paul S de Vries
- Human Genetics Center, Department of Epidemiology, Human Genetics, and Environmental Sciences, School of Public Health, The University of Texas Health Science Center at Houston, Houston, TX, USA
| | - Susan K Dutcher
- The McDonnell Genome Institute, Washington University School of Medicine, St. Louis, MO, USA
| | - Patrick T Ellinor
- Cardiovascular Research Center, Massachusetts General Hospital, Boston, MA, USA; Cardiovascular Disease Initiative, The Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - James S Floyd
- Cardiovascular Health Research Unit, Department of Medicine, University of Washington, Seattle, WA, USA; Department of Epidemiology, University of Washington, Seattle, WA, USA
| | - Myriam Fornage
- Center for Human Genetics, University of Texas Health at Houston, Houston, TX, USA
| | - Barry I Freedman
- Department of Internal Medicine, Nephrology, Wake Forest University School of Medicine, Winston-Salem, NC, USA
| | | | | | - Richard A Gibbs
- Baylor College of Medicine Human Genome Sequencing Center, Houston, TX, USA
| | - Xiuqing Guo
- The Institute for Translational Genomics and Population Sciences, Department of Pediatrics, The Lundquist Institute for Biomedical Innovation at Harbor-UCLA Medical Center, Torrance, CA, USA
| | - Jiang He
- Department of Epidemiology, Tulane University School of Public Health and Tropical Medicine, New Orleans, LA, USA; Tulane University Translational Science Institute, New Orleans, LA, USA
| | - Nancy Heard-Costa
- Framingham Heart Study, Framingham, MA, USA; Department of Neurology, Boston University Chobanian & Avedisian School of Medicine, Boston, MA, USA
| | - Bertha Hildalgo
- Department of Epidemiology, University of Alabama at Birmingham School of Public Health, Birmingham, AL, USA
| | - Lifang Hou
- Department of Preventive Medicine, Northwestern University, Chicago, IL, USA
| | - Marguerite R Irvin
- Department of Epidemiology, University of Alabama at Birmingham School of Public Health, Birmingham, AL, USA
| | - Roby Joehanes
- Population Sciences Branch, Division of Intramural Research, National Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, MD, USA
| | - Robert C Kaplan
- Department of Epidemiology and Population Health, Albert Einstein College of Medicine, Bronx, NY, USA; Division of Public Health Sciences, Fred Hutchinson Cancer Center, Seattle, WA, USA
| | - Sharon Lr Kardia
- Department of Epidemiology, University of Michigan, Ann Arbor, MI, USA
| | - Tanika N Kelly
- Department of Medicine, Division of Nephrology, University of Illinois Chicago, Chicago, IL, USA
| | - Ryan Kim
- Psomagen, Inc. (formerly Macrogen USA), Rockville, MD, USA
| | - Charles Kooperberg
- Division of Public Health Sciences, Fred Hutchinson Cancer Center, Seattle, WA, USA
| | - Brian G Kral
- GeneSTAR Research Program, Department of Medicine, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Daniel Levy
- Framingham Heart Study, Framingham, MA, USA; Population Sciences Branch, Division of Intramural Research, National Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, MD, USA
| | - Changwei Li
- Tulane University Translational Science Institute, New Orleans, LA, USA; Department of Epidemiology, Tulane University School of Public Health and Tropical Medicine, New Orleans, LA, USA
| | - Chunyu Liu
- Department of Biostatistics, Boston University School of Public Health, Boston, MA, USA; Framingham Heart Study, Framingham, MA, USA
| | - Don Lloyd-Jone
- Department of Preventive Medicine, Northwestern University, Chicago, IL, USA
| | - Ruth Jf Loos
- The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA; NNF Center for Basic Metabolic Research, University of Copenhagen, Cophenhagen, Denmark
| | - Michael C Mahaney
- Department of Human Genetics and South Texas Diabetes and Obesity Institute, University of Texas Rio Grande Valley School of Medicine, Brownsville, TX, USA
| | - Lisa W Martin
- George Washington University School of Medicine and Health Sciences, Washington, DC, USA
| | - Rasika A Mathias
- GeneSTAR Research Program, Department of Medicine, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Ryan L Minster
- Department of Human Genetics, School of Public Health, University of Pittsburgh, Pittsburgh, PA, USA
| | - Braxton D Mitchell
- Department of Medicine, University of Maryland School of Medicine, Baltimore, MD, USA
| | - May E Montasser
- Department of Medicine, University of Maryland School of Medicine, Baltimore, MD, USA
| | - Alanna C Morrison
- Human Genetics Center, Department of Epidemiology, Human Genetics, and Environmental Sciences, School of Public Health, The University of Texas Health Science Center at Houston, Houston, TX, USA
| | - Joanne M Murabito
- Framingham Heart Study, Framingham, MA, USA; Department of Medicine, Boston Medical Center, Boston University Chobanian and Avedisian School of Medicine, Boston, MA, USA
| | - Take Naseri
- Naseri & Associates Public Health Consultancy Firm and Family Health Clinic, Apia, Samoa; International Health Institute, School of Public Health, Brown University, Providence, RI, USA
| | - Jeffrey R O'Connell
- Department of Medicine, University of Maryland School of Medicine, Baltimore, MD, USA
| | - Nicholette D Palmer
- Department of Biochemistry, Wake Forest University School of Medicine, Winston-Salem, NC, USA
| | - Michael H Preuss
- The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Bruce M Psaty
- Cardiovascular Health Research Unit, Department of Medicine, University of Washington, Seattle, WA, USA; Department of Epidemiology, University of Washington, Seattle, WA, USA; Department of Health Systems and Population Health, University of Washington, Seattle, WA, USA
| | - Laura M Raffield
- Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Dabeeru C Rao
- Division of Biostatistics, Washington University School of Medicine, St. Louis, MO, USA
| | - Susan Redline
- Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | | | - Stephen S Rich
- Center for Public Health Genomics, University of Virginia, Charlottesville, VA, USA
| | | | - Wayne H-H Sheu
- Institute of Molecular and Genomic Medicine, National Health Research Institute (NHRI), Miaoli County, Taiwan
| | - Jennifer A Smith
- Department of Epidemiology, University of Michigan, Ann Arbor, MI, USA
| | - Albert Smith
- Department of Biostatistics, University of Michigan, Ann Arbor, MI, USA
| | - Hemant K Tiwari
- Department of Biostatistics, University of Alabama, Birmingham, AL, USA
| | - Michael Y Tsai
- Department of Laboratory Medicine and Pathology, University of Minnesota, Minneapolis, MN, USA
| | | | - Zhe Wang
- The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Lisa R Yanek
- GeneSTAR Research Program, Department of Medicine, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Wei Zhao
- Department of Epidemiology, University of Michigan, Ann Arbor, MI, USA
| | - Jerome I Rotter
- The Institute for Translational Genomics and Population Sciences, Department of Pediatrics, The Lundquist Institute for Biomedical Innovation at Harbor-UCLA Medical Center, Torrance, CA, USA
| | - Xihong Lin
- Program in Medical and Population Genetics, Broad Institute of Harvard and MIT, Cambridge, MA, USA; Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, USA; Department of Statistics, Harvard University, Cambridge, MA, USA
| | - Pradeep Natarajan
- Cardiovascular Research Center and Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA; Program in Medical and Population Genetics, Broad Institute of Harvard and MIT, Cambridge, MA, USA; Department of Medicine, Harvard Medical School, Boston, MA, USA
| | - Gina M Peloso
- Department of Biostatistics, Boston University School of Public Health, Boston, MA, USA.
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14
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Shapiro D, Lee K, Asmussen J, Bourquard T, Lichtarge O. Evolutionary Action-Machine Learning Model Identifies Candidate Genes Associated With Early-Onset Coronary Artery Disease. J Am Heart Assoc 2023; 12:e029103. [PMID: 37642027 PMCID: PMC10547338 DOI: 10.1161/jaha.122.029103] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/05/2022] [Accepted: 07/11/2023] [Indexed: 08/31/2023]
Abstract
Background Coronary artery disease is a primary cause of death around the world, with both genetic and environmental risk factors. Although genome-wide association studies have linked >100 unique loci to its genetic basis, these only explain a fraction of disease heritability. Methods and Results To find additional gene drivers of coronary artery disease, we applied machine learning to quantitative evolutionary information on the impact of coding variants in whole exomes from the Myocardial Infarction Genetics Consortium. Using ensemble-based supervised learning, the Evolutionary Action-Machine Learning framework ranked each gene's ability to classify case and control samples and identified 79 significant associations. These were connected to known risk loci; enriched in cardiovascular processes like lipid metabolism, blood clotting, and inflammation; and enriched for cardiovascular phenotypes in knockout mouse models. Among them, INPP5F and MST1R are examples of potentially novel coronary artery disease risk genes that modulate immune signaling in response to cardiac stress. Conclusions We concluded that machine learning on the functional impact of coding variants, based on a massive amount of evolutionary information, has the power to suggest novel coronary artery disease risk genes for mechanistic and therapeutic discoveries in cardiovascular biology, and should also apply in other complex polygenic diseases.
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Affiliation(s)
- Dillon Shapiro
- Department of Molecular and Human GeneticsBaylor College of MedicineHoustonTXUSA
| | - Kwanghyuk Lee
- Department of Molecular and Human GeneticsBaylor College of MedicineHoustonTXUSA
| | - Jennifer Asmussen
- Department of Molecular and Human GeneticsBaylor College of MedicineHoustonTXUSA
| | - Thomas Bourquard
- Department of Molecular and Human GeneticsBaylor College of MedicineHoustonTXUSA
| | - Olivier Lichtarge
- Department of Molecular and Human GeneticsBaylor College of MedicineHoustonTXUSA
- Computational & Integrative Biomedical Research CenterBaylor College of MedicineHoustonTXUSA
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15
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Recto K, Kachroo P, Huan T, Van Den Berg D, Lee GY, Bui H, Lee DH, Gereige J, Yao C, Hwang SJ, Joehanes R, Weiss ST, O'Connor GT, Levy D, DeMeo DL. Epigenome-wide DNA methylation association study of circulating IgE levels identifies novel targets for asthma. EBioMedicine 2023; 95:104758. [PMID: 37598461 PMCID: PMC10462855 DOI: 10.1016/j.ebiom.2023.104758] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/14/2023] [Revised: 08/01/2023] [Accepted: 08/02/2023] [Indexed: 08/22/2023] Open
Abstract
BACKGROUND Identifying novel epigenetic signatures associated with serum immunoglobulin E (IgE) may improve our understanding of molecular mechanisms underlying asthma and IgE-mediated diseases. METHODS We performed an epigenome-wide association study using whole blood from Framingham Heart Study (FHS; n = 3,471, 46% females) participants and validated results using the Childhood Asthma Management Program (CAMP; n = 674, 39% females) and the Genetic Epidemiology of Asthma in Costa Rica Study (CRA; n = 787, 41% females). Using the closest gene to each IgE-associated CpG, we highlighted biologically plausible pathways underlying IgE regulation and analyzed the transcription patterns linked to IgE-associated CpGs (expression quantitative trait methylation loci; eQTMs). Using prior UK Biobank summary data from genome-wide association studies of asthma and allergy, we performed Mendelian randomization (MR) for causal inference testing using the IgE-associated CpGs from FHS with methylation quantitative trait loci (mQTLs) as instrumental variables. FINDINGS We identified 490 statistically significant differentially methylated CpGs associated with IgE in FHS, of which 193 (39.3%) replicated in CAMP and CRA (FDR < 0.05). Gene ontology analysis revealed enrichment in pathways related to transcription factor binding, asthma, and other immunological processes. eQTM analysis identified 124 cis-eQTMs for 106 expressed genes (FDR < 0.05). MR in combination with drug-target analysis revealed CTSB and USP20 as putatively causal regulators of IgE levels (Bonferroni adjusted P < 7.94E-04) that can be explored as potential therapeutic targets. INTERPRETATION By integrating eQTM and MR analyses in general and clinical asthma populations, our findings provide a deeper understanding of the multidimensional inter-relations of DNA methylation, gene expression, and IgE levels. FUNDING US NIH/NHLBI grants: P01HL132825, K99HL159234. N01-HC-25195 and HHSN268201500001I.
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Affiliation(s)
- Kathryn Recto
- The Population Sciences Branch, National Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, MD 20892, USA; The Framingham Heart Study, Framingham, MA 01702, USA
| | - Priyadarshini Kachroo
- Brigham and Women's Hospital, Channing Division of Network Medicine, Boston, MA 02115, USA
| | - Tianxiao Huan
- The Population Sciences Branch, National Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, MD 20892, USA; The Framingham Heart Study, Framingham, MA 01702, USA
| | - David Van Den Berg
- University of Southern California Methylation Characterization Center, University of Southern California, Los Angeles, CA 90033, USA
| | - Gha Young Lee
- The Population Sciences Branch, National Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, MD 20892, USA; The Framingham Heart Study, Framingham, MA 01702, USA
| | - Helena Bui
- The Population Sciences Branch, National Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, MD 20892, USA; The Framingham Heart Study, Framingham, MA 01702, USA
| | - Dong Heon Lee
- The Population Sciences Branch, National Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, MD 20892, USA; The Framingham Heart Study, Framingham, MA 01702, USA
| | - Jessica Gereige
- Boston University School of Medicine, Pulmonary Center, Boston, MA 02118, USA
| | - Chen Yao
- The Population Sciences Branch, National Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, MD 20892, USA; The Framingham Heart Study, Framingham, MA 01702, USA
| | - Shih-Jen Hwang
- The Population Sciences Branch, National Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, MD 20892, USA; The Framingham Heart Study, Framingham, MA 01702, USA
| | - Roby Joehanes
- The Population Sciences Branch, National Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, MD 20892, USA; The Framingham Heart Study, Framingham, MA 01702, USA
| | - Scott T Weiss
- Brigham and Women's Hospital, Channing Division of Network Medicine, Boston, MA 02115, USA
| | - George T O'Connor
- The Framingham Heart Study, Framingham, MA 01702, USA; Boston University School of Medicine, Pulmonary Center, Boston, MA 02118, USA
| | - Daniel Levy
- The Population Sciences Branch, National Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, MD 20892, USA; The Framingham Heart Study, Framingham, MA 01702, USA.
| | - Dawn L DeMeo
- Brigham and Women's Hospital, Channing Division of Network Medicine, Boston, MA 02115, USA.
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16
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Khan AH, Bagley JR, LaPierre N, Gonzalez-Figueroa C, Spencer TC, Choudhury M, Xiao X, Eskin E, Jentsch JD, Smith DJ. Genetic pathways regulating the longitudinal acquisition of cocaine self-administration in a panel of inbred and recombinant inbred mice. Cell Rep 2023; 42:112856. [PMID: 37481717 PMCID: PMC10530068 DOI: 10.1016/j.celrep.2023.112856] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/16/2022] [Revised: 06/06/2023] [Accepted: 07/10/2023] [Indexed: 07/25/2023] Open
Abstract
To identify addiction genes, we evaluate intravenous self-administration of cocaine or saline in 84 inbred and recombinant inbred mouse strains over 10 days. We integrate the behavior data with brain RNA-seq data from 41 strains. The self-administration of cocaine and that of saline are genetically distinct. We maximize power to map loci for cocaine intake by using a linear mixed model to account for this longitudinal phenotype while correcting for population structure. A total of 15 unique significant loci are identified in the genome-wide association study. A transcriptome-wide association study highlights the Trpv2 ion channel as a key locus for cocaine self-administration as well as identifying 17 additional genes, including Arhgef26, Slc18b1, and Slco5a1. We find numerous instances where alternate splice site selection or RNA editing altered transcript abundance. Our work emphasizes the importance of Trpv2, an ionotropic cannabinoid receptor, for the response to cocaine.
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Affiliation(s)
- Arshad H Khan
- Department of Molecular and Medical Pharmacology, Geffen School of Medicine, UCLA, Los Angeles, CA 90095, USA
| | - Jared R Bagley
- Department of Psychology, Binghamton University, Binghamton, NY, USA
| | - Nathan LaPierre
- Department of Computer Science, UCLA, Los Angeles, CA 90095, USA
| | | | - Tadeo C Spencer
- Department of Integrative Biology and Physiology, UCLA, Los Angeles, CA 90095, USA
| | - Mudra Choudhury
- Department of Integrative Biology and Physiology, UCLA, Los Angeles, CA 90095, USA
| | - Xinshu Xiao
- Department of Integrative Biology and Physiology, UCLA, Los Angeles, CA 90095, USA
| | - Eleazar Eskin
- Department of Computational Medicine, UCLA, Los Angeles, CA 90095, USA
| | - James D Jentsch
- Department of Psychology, Binghamton University, Binghamton, NY, USA
| | - Desmond J Smith
- Department of Molecular and Medical Pharmacology, Geffen School of Medicine, UCLA, Los Angeles, CA 90095, USA.
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17
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Recto KA, Huan T, Lee DH, Lee GY, Gereige J, Yao C, Hwang SJ, Joehanes R, Kelly RS, Lasky-Su J, O’Connor G, Levy D. Transcriptome-wide association study of circulating IgE levels identifies novel targets for asthma and allergic diseases. Front Immunol 2023; 14:1080071. [PMID: 36793728 PMCID: PMC9922991 DOI: 10.3389/fimmu.2023.1080071] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/26/2022] [Accepted: 01/13/2023] [Indexed: 01/31/2023] Open
Abstract
Measurement of circulating immunoglobulin E (IgE) concentration is helpful for diagnosing and treating asthma and allergic diseases. Identifying gene expression signatures associated with IgE might elucidate novel pathways for IgE regulation. To this end, we performed a discovery transcriptome-wide association study to identify differentially expressed genes associated with circulating IgE levels in whole-blood derived RNA from 5,345 participants in the Framingham Heart Study across 17,873 mRNA gene-level transcripts. We identified 216 significant transcripts at a false discovery rate <0.05. We conducted replication using the meta-analysis of two independent external studies: the Childhood Asthma Management Program (n=610) and the Genetic Epidemiology of Asthma in Costa Rica Study (n=326); we then reversed the discovery and replication cohorts, which revealed 59 significant genes that replicated in both directions. Gene ontology analysis revealed that many of these genes were implicated in immune function pathways, including defense response, inflammatory response, and cytokine production. Mendelian randomization (MR) analysis revealed four genes (CLC, CCDC21, S100A13, and GCNT1) as putatively causal (p<0.05) regulators of IgE levels. GCNT1 (beta=1.5, p=0.01)-which is a top result in the MR analysis of expression in relation to asthma and allergic diseases-plays a role in regulating T helper type 1 cell homing, lymphocyte trafficking, and B cell differentiation. Our findings build upon prior knowledge of IgE regulation and provide a deeper understanding of underlying molecular mechanisms. The IgE-associated genes that we identified-particularly those implicated in MR analysis-can be explored as promising therapeutic targets for asthma and IgE-related diseases.
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Affiliation(s)
- Kathryn A. Recto
- The Population Sciences Branch, National Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, MD, United States
- The Framingham Heart Study, Framingham, MA, United States
| | - Tianxiao Huan
- The Population Sciences Branch, National Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, MD, United States
- The Framingham Heart Study, Framingham, MA, United States
| | - Dong Heon Lee
- The Population Sciences Branch, National Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, MD, United States
- The Framingham Heart Study, Framingham, MA, United States
| | - Gha Young Lee
- The Population Sciences Branch, National Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, MD, United States
- The Framingham Heart Study, Framingham, MA, United States
| | - Jessica Gereige
- Pulmonary Center, Boston University School of Medicine, Boston, MA, United States
| | - Chen Yao
- The Population Sciences Branch, National Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, MD, United States
- The Framingham Heart Study, Framingham, MA, United States
| | - Shih-Jen Hwang
- The Population Sciences Branch, National Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, MD, United States
- The Framingham Heart Study, Framingham, MA, United States
| | - Roby Joehanes
- The Population Sciences Branch, National Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, MD, United States
- The Framingham Heart Study, Framingham, MA, United States
| | - Rachel S. Kelly
- Brigham and Women’s Hospital, Channing Division of Network Medicine, Boston, MA, United States
| | - Jessica Lasky-Su
- Brigham and Women’s Hospital, Channing Division of Network Medicine, Boston, MA, United States
| | - George O’Connor
- Pulmonary Center, Boston University School of Medicine, Boston, MA, United States
| | - Daniel Levy
- The Population Sciences Branch, National Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, MD, United States
- The Framingham Heart Study, Framingham, MA, United States
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18
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Ma J, Huang A, Yan K, Li Y, Sun X, Joehanes R, Huan T, Levy D, Liu C. Blood transcriptomic biomarkers of alcohol consumption and cardiovascular disease risk factors: the Framingham Heart Study. Hum Mol Genet 2023; 32:649-658. [PMID: 36130209 PMCID: PMC9896471 DOI: 10.1093/hmg/ddac237] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/11/2022] [Revised: 08/19/2022] [Accepted: 09/15/2022] [Indexed: 02/07/2023] Open
Abstract
BACKGROUND The relations of alcohol consumption and gene expression remain to be elucidated. MATERIALS AND METHODS We examined cross-sectional associations between alcohol consumption and whole blood derived gene expression levels and between alcohol-associated genes and obesity, hypertension, and diabetes in 5531 Framingham Heart Study (FHS) participants. RESULTS We identified 25 alcohol-associated genes. We further showed cross-sectional associations of 16 alcohol-associated genes with obesity, nine genes with hypertension, and eight genes with diabetes at P < 0.002. For example, we observed decreased expression of PROK2 (β = -0.0018; 95%CI: -0.0021, -0.0007; P = 6.5e - 5) and PAX5 (β = -0.0014; 95%CI: -0.0021, -0.0007; P = 6.5e - 5) per 1 g/day increase in alcohol consumption. Consistent with our previous observation on the inverse association of alcohol consumption with obesity and positive association of alcohol consumption with hypertension, we found that PROK2 was positively associated with obesity (OR = 1.42; 95%CI: 1.17, 1.72; P = 4.5e - 4) and PAX5 was negatively associated with hypertension (OR = 0.73; 95%CI: 0.59, 0.89; P = 1.6e - 3). We also observed that alcohol consumption was positively associated with expression of ABCA13 (β = 0.0012; 95%CI: 0.0007, 0.0017; P = 1.3e - 6) and ABCA13 was positively associated with diabetes (OR = 2.57; 95%CI: 1.73, 3.84; P = 3.5e - 06); this finding, however, was inconsistent with our observation of an inverse association between alcohol consumption and diabetes. CONCLUSIONS We showed strong cross-sectional associations between alcohol consumption and expression levels of 25 genes in FHS participants. Nonetheless, complex relationships exist between alcohol-associated genes and CVD risk factors.
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Affiliation(s)
- Jiantao Ma
- Division of Nutrition Epidemiology and Data Science, Friedman School of Nutrition Science and Policy, Tufts University, Boston, MA 02111, USA
- Population Sciences Branch, National Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, MD 20892, USA
| | - Allen Huang
- Department of Electrical Engineering and Computer Science, MIT, Cambridge, MA 02142, USA
| | - Kaiyu Yan
- Department of Biostatistics, Boston University, Boston, MA 02118, USA
| | - Yi Li
- Department of Biostatistics, Boston University, Boston, MA 02118, USA
| | - Xianbang Sun
- Department of Biostatistics, Boston University, Boston, MA 02118, USA
| | - Roby Joehanes
- Population Sciences Branch, National Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, MD 20892, USA
| | - Tianxiao Huan
- Department of Ophthalmology and Visual Sciences, University of Massachusetts Medical School, Worcester, MA 01605, USA
| | - Daniel Levy
- Population Sciences Branch, National Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, MD 20892, USA
- Boston University’s and National Heart, Lung, and Blood Institute’s Framingham Heart Study, Framingham, MA 01702, USA
| | - Chunyu Liu
- Department of Biostatistics, Boston University, Boston, MA 02118, USA
- Boston University’s and National Heart, Lung, and Blood Institute’s Framingham Heart Study, Framingham, MA 01702, USA
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19
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Sha Z, Hou T, Zhou T, Dai Y, Bao Y, Jin Q, Ye J, Lu Y, Wu L. Causal relationship between atrial fibrillation and leukocyte telomere length: A two sample, bidirectional Mendelian randomization study. Front Cardiovasc Med 2023; 10:1093255. [PMID: 36873417 PMCID: PMC9975167 DOI: 10.3389/fcvm.2023.1093255] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/08/2022] [Accepted: 01/12/2023] [Indexed: 02/17/2023] Open
Abstract
Background Atrial fibrillation (AF) is an age-related disease, while telomeres play a central role in aging. But the relationship between AF and telomere length (LTL) is still controversial. This study aims to examine the potential causal association between AF and LTL by using Mendelian randomization (MR). Methods Bidirectional two-sample MR, expression and protein quantitative trait loci (eQTL and pQTL)-based MR were performed using genetic variants from United Kingdom Biobank, FinnGen, and a meta-analysis study, which comprised nearly 1 million participants in the Atrial Fibrillation Study and 470,000 participants in the Telomere Length Study. Apart from the inverse variance weighted (IVW) approach as the main MR analysis, complementary analysis approaches and sensitivity analysis were applied. Results The forward MR revealed a significant causal estimate for the genetically predicted AF with LTL shortening [IVW: odds ratio (OR) = 0.989, p = 0.007; eQTL-IVW: OR = 0.988, p = 0.005; pQTL-IVW: OR = 0.975, p < 0.005]. But in the reverse MR analysis, genetically predicted LTL has no significant correlation with AF (IVW: OR = 0.995, p = 0.916; eQTL-IVW: OR = 0.999, p = 0.995; pQTL-IVW: OR = 1.055, p = 0.570). The FinnGen replication data yielded similar findings. Sensitivity analysis ensured the stability of the results. Conclusion The presence of AF leads to LTL shortening rather than the other way around. Aggressive intervention for AF may delay the telomere attrition.
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Affiliation(s)
- Zimo Sha
- Department of Cardiology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Tianzhichao Hou
- Department of Endocrine and Metabolic Diseases, Shanghai Institute of Endocrine and Metabolic Diseases, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Taojie Zhou
- Department of Cardiology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Yang Dai
- Department of Cardiology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.,Institute of Cardiovascular Diseases, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Yangyang Bao
- Department of Cardiology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Qi Jin
- Department of Cardiology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Jing Ye
- Department of Geriatrics, Medical Center on Aging of Shanghai Ruijin Hospital, Shanghai Jiaotong University School of Medicine, Shanghai, China.,International Laboratory in Hematology and Cancer, Shanghai Jiao Tong University School of Medicine/Ruijin Hospital/CNRS/Inserm/Côte d'Azur University, Shanghai, China
| | - Yiming Lu
- Department of Geriatrics, Medical Center on Aging of Shanghai Ruijin Hospital, Shanghai Jiaotong University School of Medicine, Shanghai, China.,International Laboratory in Hematology and Cancer, Shanghai Jiao Tong University School of Medicine/Ruijin Hospital/CNRS/Inserm/Côte d'Azur University, Shanghai, China
| | - Liqun Wu
- Department of Cardiology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
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20
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Common and rare variant associations with clonal haematopoiesis phenotypes. Nature 2022; 612:301-309. [PMID: 36450978 PMCID: PMC9713173 DOI: 10.1038/s41586-022-05448-9] [Citation(s) in RCA: 91] [Impact Index Per Article: 45.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/23/2021] [Accepted: 10/14/2022] [Indexed: 12/02/2022]
Abstract
Clonal haematopoiesis involves the expansion of certain blood cell lineages and has been associated with ageing and adverse health outcomes1-5. Here we use exome sequence data on 628,388 individuals to identify 40,208 carriers of clonal haematopoiesis of indeterminate potential (CHIP). Using genome-wide and exome-wide association analyses, we identify 24 loci (21 of which are novel) where germline genetic variation influences predisposition to CHIP, including missense variants in the lymphocytic antigen coding gene LY75, which are associated with reduced incidence of CHIP. We also identify novel rare variant associations with clonal haematopoiesis and telomere length. Analysis of 5,041 health traits from the UK Biobank (UKB) found relationships between CHIP and severe COVID-19 outcomes, cardiovascular disease, haematologic traits, malignancy, smoking, obesity, infection and all-cause mortality. Longitudinal and Mendelian randomization analyses revealed that CHIP is associated with solid cancers, including non-melanoma skin cancer and lung cancer, and that CHIP linked to DNMT3A is associated with the subsequent development of myeloid but not lymphoid leukaemias. Additionally, contrary to previous findings from the initial 50,000 UKB exomes6, our results in the full sample do not support a role for IL-6 inhibition in reducing the risk of cardiovascular disease among CHIP carriers. Our findings demonstrate that CHIP represents a complex set of heterogeneous phenotypes with shared and unique germline genetic causes and varied clinical implications.
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21
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Whole genome DNA and RNA sequencing of whole blood elucidates the genetic architecture of gene expression underlying a wide range of diseases. Sci Rep 2022; 12:20167. [PMID: 36424512 PMCID: PMC9686236 DOI: 10.1038/s41598-022-24611-w] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/26/2022] [Accepted: 11/17/2022] [Indexed: 11/27/2022] Open
Abstract
To create a scientific resource of expression quantitative trail loci (eQTL), we conducted a genome-wide association study (GWAS) using genotypes obtained from whole genome sequencing (WGS) of DNA and gene expression levels from RNA sequencing (RNA-seq) of whole blood in 2622 participants in Framingham Heart Study. We identified 6,778,286 cis-eQTL variant-gene transcript (eGene) pairs at p < 5 × 10-8 (2,855,111 unique cis-eQTL variants and 15,982 unique eGenes) and 1,469,754 trans-eQTL variant-eGene pairs at p < 1e-12 (526,056 unique trans-eQTL variants and 7233 unique eGenes). In addition, 442,379 cis-eQTL variants were associated with expression of 1518 long non-protein coding RNAs (lncRNAs). Gene Ontology (GO) analyses revealed that the top GO terms for cis-eGenes are enriched for immune functions (FDR < 0.05). The cis-eQTL variants are enriched for SNPs reported to be associated with 815 traits in prior GWAS, including cardiovascular disease risk factors. As proof of concept, we used this eQTL resource in conjunction with genetic variants from public GWAS databases in causal inference testing (e.g., COVID-19 severity). After Bonferroni correction, Mendelian randomization analyses identified putative causal associations of 60 eGenes with systolic blood pressure, 13 genes with coronary artery disease, and seven genes with COVID-19 severity. This study created a comprehensive eQTL resource via BioData Catalyst that will be made available to the scientific community. This will advance understanding of the genetic architecture of gene expression underlying a wide range of diseases.
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22
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Hawe JS, Saha A, Waldenberger M, Kunze S, Wahl S, Müller-Nurasyid M, Prokisch H, Grallert H, Herder C, Peters A, Strauch K, Theis FJ, Gieger C, Chambers J, Battle A, Heinig M. Network reconstruction for trans acting genetic loci using multi-omics data and prior information. Genome Med 2022; 14:125. [PMID: 36344995 PMCID: PMC9641770 DOI: 10.1186/s13073-022-01124-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/15/2022] [Accepted: 10/11/2022] [Indexed: 11/09/2022] Open
Abstract
BACKGROUND Molecular measurements of the genome, the transcriptome, and the epigenome, often termed multi-omics data, provide an in-depth view on biological systems and their integration is crucial for gaining insights in complex regulatory processes. These data can be used to explain disease related genetic variants by linking them to intermediate molecular traits (quantitative trait loci, QTL). Molecular networks regulating cellular processes leave footprints in QTL results as so-called trans-QTL hotspots. Reconstructing these networks is a complex endeavor and use of biological prior information can improve network inference. However, previous efforts were limited in the types of priors used or have only been applied to model systems. In this study, we reconstruct the regulatory networks underlying trans-QTL hotspots using human cohort data and data-driven prior information. METHODS We devised a new strategy to integrate QTL with human population scale multi-omics data. State-of-the art network inference methods including BDgraph and glasso were applied to these data. Comprehensive prior information to guide network inference was manually curated from large-scale biological databases. The inference approach was extensively benchmarked using simulated data and cross-cohort replication analyses. Best performing methods were subsequently applied to real-world human cohort data. RESULTS Our benchmarks showed that prior-based strategies outperform methods without prior information in simulated data and show better replication across datasets. Application of our approach to human cohort data highlighted two novel regulatory networks related to schizophrenia and lean body mass for which we generated novel functional hypotheses. CONCLUSIONS We demonstrate that existing biological knowledge can improve the integrative analysis of networks underlying trans associations and generate novel hypotheses about regulatory mechanisms.
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Affiliation(s)
- Johann S Hawe
- Institute of Computational Biology, German Research Center for Environmental Health, HelmholtzZentrum München, Neuherberg, Germany.,German Heart Centre Munich, Department of Cardiology, Technical University Munich, Munich, Germany.,Department of Informatics, Technical University of Munich, Garching, Germany
| | - Ashis Saha
- Department of Computer Science, Johns Hopkins University, Baltimore, MD, USA
| | - Melanie Waldenberger
- Research Unit of Molecular Epidemiology, German Research Center for Environmental Health, HelmholtzZentrum München, Neuherberg, Germany
| | - Sonja Kunze
- Research Unit of Molecular Epidemiology, German Research Center for Environmental Health, HelmholtzZentrum München, Neuherberg, Germany
| | - Simone Wahl
- Research Unit of Molecular Epidemiology, German Research Center for Environmental Health, HelmholtzZentrum München, Neuherberg, Germany
| | - Martina Müller-Nurasyid
- Institute of Genetic Epidemiology, German Research Center for Environmental Health, HelmholtzZentrum München, Neuherberg, Germany.,IBE, Faculty of Medicine, LMU Munich, 81377, Munich, Germany.,Institute of Medical Biostatistics, Epidemiology and Informatics (IMBEI), University Medical Center, Johannes Gutenberg University, Mainz, Germany.,Department of Internal Medicine I (Cardiology), Hospital of the Ludwig-Maximilians-University (LMU) Munich, Munich, Germany
| | - Holger Prokisch
- Institute of Human Genetics, School of Medicine, Technische Universität München, Munich, Germany
| | - Harald Grallert
- Research Unit of Molecular Epidemiology, German Research Center for Environmental Health, HelmholtzZentrum München, Neuherberg, Germany.,Institute of Epidemiology, German Research Center for Environmental Health, HelmholtzZentrum München, Neuherberg, Germany.,German Center for Diabetes Research (DZD), Neuherberg, Germany
| | - Christian Herder
- German Center for Diabetes Research (DZD), Neuherberg, Germany.,Institute for Clinical Diabetology, German Diabetes Center, Leibniz Center for Diabetes Research at Heinrich Heine University, Düsseldorf, Germany.,Division of Endocrinology and Diabetology, Medical Faculty, Heinrich Heine University, Düsseldorf, Germany
| | - Annette Peters
- Institute of Epidemiology, German Research Center for Environmental Health, HelmholtzZentrum München, Neuherberg, Germany
| | - Konstantin Strauch
- Institute of Genetic Epidemiology, German Research Center for Environmental Health, HelmholtzZentrum München, Neuherberg, Germany.,Institute of Medical Biostatistics, Epidemiology and Informatics (IMBEI), University Medical Center, Johannes Gutenberg University, Mainz, Germany.,Chair of Genetic Epidemiology, IBE, Faculty of Medicine, LMU Munich, Munich, Germany
| | - Fabian J Theis
- Department of Informatics, Technical University of Munich, Garching, Germany.,Department of Mathematics, Technical University of Munich, Garching, Germany
| | - Christian Gieger
- Research Unit of Molecular Epidemiology, German Research Center for Environmental Health, HelmholtzZentrum München, Neuherberg, Germany.,Institute of Epidemiology, German Research Center for Environmental Health, HelmholtzZentrum München, Neuherberg, Germany.,German Center for Diabetes Research (DZD), Neuherberg, Germany
| | - John Chambers
- Department of Epidemiology and Biostatistics, MRC-PHE Centre for Environment and Health, School of Public Health, Imperial College London, London, UK.,Lee Kong Chian School of Medicine, Nanyang Technological University, 308232, Singapore, Singapore
| | - Alexis Battle
- Department of Computer Science, Johns Hopkins University, Baltimore, MD, USA.,Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, USA
| | - Matthias Heinig
- Institute of Computational Biology, German Research Center for Environmental Health, HelmholtzZentrum München, Neuherberg, Germany. .,Department of Informatics, Technical University of Munich, Garching, Germany. .,Munich Heart Association, Partner Site Munich, DZHK (German Centre for Cardiovascular Research), 10785, Berlin, Germany.
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23
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Keshawarz A, Joehanes R, Guan W, Huan T, DeMeo DL, Grove ML, Fornage M, Levy D, O’Connor G. Longitudinal change in blood DNA epigenetic signature after smoking cessation. Epigenetics 2022; 17:1098-1109. [PMID: 34570667 PMCID: PMC9542417 DOI: 10.1080/15592294.2021.1985301] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/07/2021] [Revised: 08/20/2021] [Accepted: 09/21/2021] [Indexed: 12/14/2022] Open
Abstract
Cigarette smoking is associated with epigenetic changes that may be reversible following smoking cessation. Whole blood DNA methylation was evaluated in Framingham Heart Study Offspring (n = 169) and Third Generation (n = 30) cohort participants at two study visits 6 years apart and in Atherosclerosis Risk in Communities (ARIC) study (n = 222) participants at two study visits 20 years apart. Changes in DNA methylation (delta β values) at 483,565 cytosine-phosphate-guanine (CpG) sites and differentially methylated regions (DMRs) were compared between participants who were current, former, or never smokers at both visits (current-current, former-former, never-never, respectively), versus those who quit in the interim (current-former). Interim quitters had more hypermethylation at four CpGs annotated to AHRR, one CpG annotated to F2RL3, and one intergenic CpG (cg21566642) compared with current-current smokers (FDR < 0.02 for all), and two significant DMRs were identified. While there were no significant differentially methylated CpGs in the comparison of interim quitters and former-former smokers, 106 DMRs overlapping with small nucleolar RNA were identified. As compared with all non-smokers, current-current smokers additionally had more hypermethylation at two CpG sites annotated to HIVEP3 and TMEM126A, respectively, and another intergenic CpG (cg14339116). Gene transcripts associated with smoking cessation were implicated in immune responses, cell homoeostasis, and apoptosis. Smoking cessation is associated with early reversion of blood DNA methylation changes at CpG sites annotated to AHRR and F2RL3 towards those of never smokers. Associated gene expression suggests a role of longitudinal smoking-related DNA methylation changes in immune response processes.
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Affiliation(s)
- Amena Keshawarz
- Population Sciences Branch, Division of Intramural Research, National Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, MD, USA
- Framingham Heart Study, Framingham, MA, USA
| | - Roby Joehanes
- Population Sciences Branch, Division of Intramural Research, National Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, MD, USA
- Framingham Heart Study, Framingham, MA, USA
| | - Weihua Guan
- Division of Biostatistics, School of Public Health, University of Minnesota, Minneapolis, MN, USA
| | - Tianxiao Huan
- Framingham Heart Study, Framingham, MA, USA
- Department of Ophthalmology and Visual Sciences, University of Massachusetts Medical School, Worcester, MA, USA
| | - Dawn L. DeMeo
- Channing Division of Network Medicine, Brigham and Women’s Hospital, Boston, MA, USA
- Division of Pulmonary and Critical Care Medicine, Brigham and Women’s Hospital, Boston, MA, USA
| | - Megan L. Grove
- Human Genetics Center, Department of Epidemiology, Human Genetics, and Environmental Sciences, School of Public Health, The University of Texas Health Science Center at Houston, Houston, TX, USA
| | - Myriam Fornage
- McGovern Medical School and Human Genetics Center, School of Public Health, The University of Texas Health Science Center at Houston, Brown Foundation Institute of Molecular Medicine, Houston, TX, USA
| | - Daniel Levy
- Population Sciences Branch, Division of Intramural Research, National Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, MD, USA
| | - George O’Connor
- Pulmonary Center, Boston University School of Medicine, Boston, MA, USA
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24
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Bankier S, Michoel T. eQTLs as causal instruments for the reconstruction of hormone linked gene networks. Front Endocrinol (Lausanne) 2022; 13:949061. [PMID: 36060942 PMCID: PMC9428692 DOI: 10.3389/fendo.2022.949061] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/20/2022] [Accepted: 07/25/2022] [Indexed: 11/17/2022] Open
Abstract
Hormones act within in highly dynamic systems and much of the phenotypic response to variation in hormone levels is mediated by changes in gene expression. The increase in the number and power of large genetic association studies has led to the identification of hormone linked genetic variants. However, the biological mechanisms underpinning the majority of these loci are poorly understood. The advent of affordable, high throughput next generation sequencing and readily available transcriptomic databases has shown that many of these genetic variants also associate with variation in gene expression levels as expression Quantitative Trait Loci (eQTLs). In addition to further dissecting complex genetic variation, eQTLs have been applied as tools for causal inference. Many hormone networks are driven by transcription factors, and many of these genes can be linked to eQTLs. In this mini-review, we demonstrate how causal inference and gene networks can be used to describe the impact of hormone linked genetic variation upon the transcriptome within an endocrinology context.
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Affiliation(s)
- Sean Bankier
- Computational Biology Unit, Department of Informatics, University of Bergen, Bergen, Norway
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25
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Liu C, Joehanes R, Ma J, Wang Y, Sun X, Keshawarz A, Sooda M, Huan T, Hwang SJ, Bui H, Tejada B, Munson PJ, Cumhur D, Heard-Costa NL, Pitsillides AN, Peloso GM, Feolo M, Sharopova N, Vasan RS, Levy D. Whole Genome DNA and RNA Sequencing of Whole Blood Elucidates the Genetic Architecture of Gene Expression Underlying a Wide Range of Diseases. RESEARCH SQUARE 2022:rs.3.rs-1598646. [PMID: 35664994 PMCID: PMC9164515 DOI: 10.21203/rs.3.rs-1598646/v1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 01/13/2023]
Abstract
To create a scientific resource of expression quantitative trail loci (eQTL), we conducted a genome-wide association study (GWAS) using genotypes obtained from whole genome sequencing (WGS) of DNA and gene expression levels from RNA sequencing (RNA-seq) of whole blood in 2622 participants in Framingham Heart Study. We identified 6,778,286 cis -eQTL variant-gene transcript (eGene) pairs at p < 5x10 - 8 (2,855,111 unique cis -eQTL variants and 15,982 unique eGenes) and 1,469,754 trans -eQTL variant-eGene pairs at p < 1e-12 (526,056 unique trans -eQTL variants and 7,233 unique eGenes). In addition, 442,379 cis -eQTL variants were associated with expression of 1518 long non-protein coding RNAs (lncRNAs). Gene Ontology (GO) analyses revealed that the top GO terms for cis- eGenes are enriched for immune functions (FDR < 0.05). The cis -eQTL variants are enriched for SNPs reported to be associated with 815 traits in prior GWAS, including cardiovascular disease risk factors. As proof of concept, we used this eQTL resource in conjunction with genetic variants from public GWAS databases in causal inference testing (e.g., COVID-19 severity). After Bonferroni correction, Mendelian randomization analyses identified putative causal associations of 60 eGenes with systolic blood pressure, 13 genes with coronary artery disease, and seven genes with COVID-19 severity. This study created a comprehensive eQTL resource via BioData Catalyst that will be made available to the scientific community. This will advance understanding of the genetic architecture of gene expression underlying a wide range of diseases.
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26
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Ratios of Acetaminophen Metabolites Identify New Loci of Pharmacogenetic Relevance in a Genome-Wide Association Study. Metabolites 2022; 12:metabo12060496. [PMID: 35736429 PMCID: PMC9228664 DOI: 10.3390/metabo12060496] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/03/2022] [Revised: 05/24/2022] [Accepted: 05/25/2022] [Indexed: 11/17/2022] Open
Abstract
Genome-wide association studies (GWAS) with non-targeted metabolomics have identified many genetic loci of biomedical interest. However, metabolites with a high degree of missingness, such as drug metabolites and xenobiotics, are often excluded from such studies due to a lack of statistical power and higher uncertainty in their quantification. Here we propose ratios between related drug metabolites as GWAS phenotypes that can drastically increase power to detect genetic associations between pairs of biochemically related molecules. As a proof-of-concept we conducted a GWAS with 520 individuals from the Qatar Biobank for who at least five of the nine available acetaminophen metabolites have been detected. We identified compelling evidence for genetic variance in acetaminophen glucuronidation and methylation by UGT2A15 and COMT, respectively. Based on the metabolite ratio association profiles of these two loci we hypothesized the chemical structure of one of their products or substrates as being 3-methoxyacetaminophen, which we then confirmed experimentally. Taken together, our study suggests a novel approach to analyze metabolites with a high degree of missingness in a GWAS setting with ratios, and it also demonstrates how pharmacological pathways can be mapped out using non-targeted metabolomics measurements in large population-based studies.
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27
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Pott J, Garcia T, Hauck SM, Petrera A, Wirkner K, Loeffler M, Kirsten H, Peters A, Scholz M. Genetically regulated gene expression and proteins revealed discordant effects. PLoS One 2022; 17:e0268815. [PMID: 35604899 PMCID: PMC9126407 DOI: 10.1371/journal.pone.0268815] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/18/2021] [Accepted: 05/10/2022] [Indexed: 11/29/2022] Open
Abstract
Background Although gene-expression (GE) and protein levels are typically strongly genetically regulated, their correlation is known to be low. Here we investigate this phenomenon by focusing on the genetic background of this correlation in order to understand the similarities and differences in the genetic regulation of these omics layers. Methods and results We performed locus-wide association studies of 92 protein levels measured in whole blood for 2,014 samples of European ancestry and found that 66 are genetically regulated. Three female- and one male-specific effects were detected. We estimated the genetically regulated GE for all significant genes in 49 GTEx v8 tissues. A total of 7 proteins showed negative correlations with their respective GE across multiple tissues. Finally, we tested for causal links of GE on protein expression via Mendelian Randomization, and confirmed a negative causal effect of GE on protein level for five of these genes in a total of 63 gene-tissue pairs: BLMH, CASP3, CXCL16, IL6R, and SFTPD. For IL6R, we replicated the negative causal effect on coronary-artery disease (CAD), while its GE was positively linked to CAD. Conclusion While total GE and protein levels are only weakly correlated, we found high correlations between their genetically regulated components across multiple tissues. Of note, strong negative causal effects of tissue-specific GE on five protein levels were detected. Causal network analyses revealed that GE effects on CAD risks was in general mediated by protein levels.
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Affiliation(s)
- Janne Pott
- Institute for Medical Informatics, Statistics and Epidemiology, Medical Faculty, University of Leipzig, Leipzig, Germany
- LIFE Research Center for Civilization Diseases, Medical Faculty, University of Leipzig, Leipzig, Germany
- * E-mail: (JP); (MS)
| | - Tarcyane Garcia
- Institute for Medical Informatics, Statistics and Epidemiology, Medical Faculty, University of Leipzig, Leipzig, Germany
| | - Stefanie M. Hauck
- Research Unit Protein Science and Metabolomics and Proteomics Core Facility, Helmholtz Zentrum Munich - German Research Center for Environmental Health, Neuherberg, Germany
| | - Agnese Petrera
- Research Unit Protein Science and Metabolomics and Proteomics Core Facility, Helmholtz Zentrum Munich - German Research Center for Environmental Health, Neuherberg, Germany
| | - Kerstin Wirkner
- Institute for Medical Informatics, Statistics and Epidemiology, Medical Faculty, University of Leipzig, Leipzig, Germany
- LIFE Research Center for Civilization Diseases, Medical Faculty, University of Leipzig, Leipzig, Germany
| | - Markus Loeffler
- Institute for Medical Informatics, Statistics and Epidemiology, Medical Faculty, University of Leipzig, Leipzig, Germany
- LIFE Research Center for Civilization Diseases, Medical Faculty, University of Leipzig, Leipzig, Germany
| | - Holger Kirsten
- Institute for Medical Informatics, Statistics and Epidemiology, Medical Faculty, University of Leipzig, Leipzig, Germany
- LIFE Research Center for Civilization Diseases, Medical Faculty, University of Leipzig, Leipzig, Germany
| | - Annette Peters
- Research Unit Protein Science and Metabolomics and Proteomics Core Facility, Helmholtz Zentrum Munich - German Research Center for Environmental Health, Neuherberg, Germany
- German Center for Diabetes Research (DZD), München-Neuherberg, Germany
- Chair of Epidemiology, Institute for Medical Information Processing, Biometry and Epidemiology, Medical Faculty, Ludwig-Maximilians-Universität München, Munich, Germany
| | - Markus Scholz
- Institute for Medical Informatics, Statistics and Epidemiology, Medical Faculty, University of Leipzig, Leipzig, Germany
- LIFE Research Center for Civilization Diseases, Medical Faculty, University of Leipzig, Leipzig, Germany
- * E-mail: (JP); (MS)
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28
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Zhang C, Li X, Zhao L, Liang R, Deng W, Guo W, Wang Q, Hu X, Du X, Sham PC, Luo X, Li T. Comprehensive and integrative analyses identify TYW5 as a schizophrenia risk gene. BMC Med 2022; 20:169. [PMID: 35527273 PMCID: PMC9082878 DOI: 10.1186/s12916-022-02363-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/10/2021] [Accepted: 04/04/2022] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND Identifying the causal genes at the risk loci and elucidating their roles in schizophrenia (SCZ) pathogenesis remain significant challenges. To explore risk variants associated with gene expression in the human brain and to identify genes whose expression change may contribute to the susceptibility of SCZ, here we report a comprehensive integrative study on SCZ. METHODS We systematically integrated the genetic associations from a large-scale SCZ GWAS (N = 56,418) and brain expression quantitative trait loci (eQTL) data (N = 175) using a Bayesian statistical framework (Sherlock) and Summary data-based Mendelian Randomization (SMR). We also measured brain structure of 86 first-episode antipsychotic-naive schizophrenia patients and 152 healthy controls with the structural MRI. RESULTS Both Sherlock (P = 3. 38 × 10-6) and SMR (P = 1. 90 × 10-8) analyses showed that TYW5 mRNA expression was significantly associated with risk of SCZ. Brain-based studies also identified a significant association between TYW5 protein abundance and SCZ. The single-nucleotide polymorphism rs203772 showed significant association with SCZ and the risk allele is associated with higher transcriptional level of TYW5 in the prefrontal cortex. We further found that TYW5 was significantly upregulated in the brain tissues of SCZ cases compared with controls. In addition, TYW5 expression was also significantly higher in neurons induced from pluripotent stem cells of schizophrenia cases compared with controls. Finally, combining analysis of genotyping and MRI data showed that rs203772 was significantly associated with gray matter volume of the right middle frontal gyrus and left precuneus. CONCLUSIONS We confirmed that TYW5 is a risk gene for SCZ. Our results provide useful information toward a better understanding of the genetic mechanism of TYW5 in risk of SCZ.
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Affiliation(s)
- Chengcheng Zhang
- Mental Health Center and Psychiatric Laboratory, the State Key Laboratory of Biotherapy, West China Hospital of Sichuan University, Chengdu, Sichuan, China
| | - Xiaojing Li
- Mental Health Center and Psychiatric Laboratory, the State Key Laboratory of Biotherapy, West China Hospital of Sichuan University, Chengdu, Sichuan, China
| | - Liansheng Zhao
- Mental Health Center and Psychiatric Laboratory, the State Key Laboratory of Biotherapy, West China Hospital of Sichuan University, Chengdu, Sichuan, China
| | - Rong Liang
- Department of Neurobiology, Affiliated Mental Health Center & Hangzhou Seventh People's Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, 310013, People's Republic of China
| | - Wei Deng
- Department of Neurobiology, Affiliated Mental Health Center & Hangzhou Seventh People's Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, 310013, People's Republic of China
| | - Wanjun Guo
- Mental Health Center and Psychiatric Laboratory, the State Key Laboratory of Biotherapy, West China Hospital of Sichuan University, Chengdu, Sichuan, China
| | - Qiang Wang
- Mental Health Center and Psychiatric Laboratory, the State Key Laboratory of Biotherapy, West China Hospital of Sichuan University, Chengdu, Sichuan, China
| | - Xun Hu
- The Clinical Research Center and Department of Pathology, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China
| | - Xiangdong Du
- Suzhou Psychiatric Hospital, The Affiliated Guangji Hospital of Soochow University, Suzhou, China
| | - Pak Chung Sham
- Department of Psychiatry, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong, SAR, China.,Centre for PanorOmic Sciences, The University of Hong Kong, Hong Kong, SAR, China.,State Key Laboratory of Brain and Cognitive Sciences, The University of Hong Kong, Hong Kong, SAR, China
| | - Xiongjian Luo
- Kunming College of Life Science, University of Chinese Academy of Sciences, Kunming, Yunnan, China
| | - Tao Li
- Department of Neurobiology, Affiliated Mental Health Center & Hangzhou Seventh People's Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, 310013, People's Republic of China. .,NHC and CAMS Key Laboratory of Medical Neurobiology, MOE Frontier Science Center for Brain Science and Brain-machine Integration, School of Brain Science and Brain Medicine, Zhejiang University, Hangzhou, Zhejiang, China.
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29
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Fauman EB, Hyde C. An optimal variant to gene distance window derived from an empirical definition of cis and trans protein QTLs. BMC Bioinformatics 2022; 23:169. [PMID: 35527238 PMCID: PMC9082853 DOI: 10.1186/s12859-022-04706-x] [Citation(s) in RCA: 18] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/15/2022] [Accepted: 04/27/2022] [Indexed: 01/01/2023] Open
Abstract
Background A genome-wide association study (GWAS) correlates variation in the genotype with variation in the phenotype across a cohort, but the causal gene mediating that impact is often unclear. When the phenotype is protein abundance, a reasonable hypothesis is that the gene encoding that protein is the causal gene. However, as variants impacting protein levels can occur thousands or even millions of base pairs from the gene encoding the protein, it is unclear at what distance this simple hypothesis breaks down. Results By making the simple assumption that cis-pQTLs should be distance dependent while trans-pQTLs are distance independent, we arrive at a simple and empirical distance cutoff separating cis- and trans-pQTLs. Analyzing a recent large-scale pQTL study (Pietzner in Science 374:eabj1541, 2021) we arrive at an estimated distance cutoff of 944 kilobasepairs (95% confidence interval: 767–1,161) separating the cis and trans regimes. Conclusions We demonstrate that this simple model can be applied to other molecular GWAS traits. Since much of biology is built on molecular traits like protein, transcript and metabolite abundance, we posit that the mathematical models for cis and trans distance distributions derived here will also apply to more complex phenotypes and traits. Supplementary Information The online version contains supplementary material available at 10.1186/s12859-022-04706-x.
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30
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Liu C, Joehanes R, Ma J, Wang Y, Sun X, Keshawarz A, Sooda M, Huan T, Hwang SJ, Bui H, Tejada B, Munson PJ, Cumhur D, Heard-Costa NL, Pitsillides AN, Peloso GM, Feolo M, Sharopova N, Vasan RS, Levy D. Whole Genome DNA and RNA Sequencing of Whole Blood Elucidates the Genetic Architecture of Gene Expression Underlying a Wide Range of Diseases. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2022:2022.04.13.22273841. [PMID: 35547845 PMCID: PMC9094109 DOI: 10.1101/2022.04.13.22273841] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 01/13/2023]
Abstract
To create a scientific resource of expression quantitative trail loci (eQTL), we conducted a genome-wide association study (GWAS) using genotypes obtained from whole genome sequencing (WGS) of DNA and gene expression levels from RNA sequencing (RNA-seq) of whole blood in 2622 participants in Framingham Heart Study. We identified 6,778,286 cis -eQTL variant-gene transcript (eGene) pairs at p <5×10 -8 (2,855,111 unique cis -eQTL variants and 15,982 unique eGenes) and 1,469,754 trans -eQTL variant-eGene pairs at p <1e-12 (526,056 unique trans -eQTL variants and 7,233 unique eGenes). In addition, 442,379 cis -eQTL variants were associated with expression of 1518 long non-protein coding RNAs (lncRNAs). Gene Ontology (GO) analyses revealed that the top GO terms for cis- eGenes are enriched for immune functions (FDR <0.05). The cis -eQTL variants are enriched for SNPs reported to be associated with 815 traits in prior GWAS, including cardiovascular disease risk factors. As proof of concept, we used this eQTL resource in conjunction with genetic variants from public GWAS databases in causal inference testing (e.g., COVID-19 severity). After Bonferroni correction, Mendelian randomization analyses identified putative causal associations of 60 eGenes with systolic blood pressure, 13 genes with coronary artery disease, and seven genes with COVID-19 severity. This study created a comprehensive eQTL resource via BioData Catalyst that will be made available to the scientific community. This will advance understanding of the genetic architecture of gene expression underlying a wide range of diseases.
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Affiliation(s)
- Chunyu Liu
- Department of Biostatistics, School of Public Health, Boston University, Boston, MA, USA
- Framingham Heart Study, Framingham, MA, USA
| | - Roby Joehanes
- Population Sciences Branch, Division of Intramural Research, National Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, MD, USA
| | - Jiantao Ma
- Nutrition Epidemiology and Data Science, Friedman School of Nutrition Science and Policy, Tufts University, Boston, MA, USA
| | - Yuxuan Wang
- Department of Biostatistics, School of Public Health, Boston University, Boston, MA, USA
| | - Xianbang Sun
- Department of Biostatistics, School of Public Health, Boston University, Boston, MA, USA
| | - Amena Keshawarz
- Population Sciences Branch, Division of Intramural Research, National Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, MD, USA
| | - Meera Sooda
- Population Sciences Branch, Division of Intramural Research, National Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, MD, USA
| | - Tianxiao Huan
- University of Massachusetts Medical School, Worcester, MA, USA
| | - Shih-Jen Hwang
- Population Sciences Branch, Division of Intramural Research, National Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, MD, USA
| | - Helena Bui
- Population Sciences Branch, Division of Intramural Research, National Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, MD, USA
| | - Brandon Tejada
- Population Sciences Branch, Division of Intramural Research, National Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, MD, USA
| | - Peter J Munson
- Population Sciences Branch, Division of Intramural Research, National Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, MD, USA
| | | | - Nancy L Heard-Costa
- Framingham Heart Study, Framingham, MA, USA
- Departments of Medicine and Epidemiology, Boston University Schools of Medicine and Public Health, Boston, MA, USA
| | | | - Gina M Peloso
- Department of Biostatistics, School of Public Health, Boston University, Boston, MA, USA
| | - Michael Feolo
- University of Massachusetts Medical School, Worcester, MA, USA
| | | | - Ramachandran S Vasan
- Framingham Heart Study, Framingham, MA, USA
- Departments of Medicine and Epidemiology, Boston University Schools of Medicine and Public Health, Boston, MA, USA
| | - Daniel Levy
- Framingham Heart Study, Framingham, MA, USA
- Population Sciences Branch, Division of Intramural Research, National Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, MD, USA
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31
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Olayinka OA, O'Neill NK, Farrer LA, Wang G, Zhang X. Molecular Quantitative Trait Locus Mapping in Human Complex Diseases. Curr Protoc 2022; 2:e426. [PMID: 35587224 PMCID: PMC9186089 DOI: 10.1002/cpz1.426] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
Mapping quantitative trait loci (QTLs) for molecular traits from chromatin to metabolites (i.e., xQTLs) provides insight into the locations and effect modes of genetic variants that influence these molecular phenotypes and the propagation of functional consequences of each variant. xQTL studies indirectly interrogate the functional landscape of the molecular basis of complex diseases, including the impact of non-coding regulatory variants, the tissue specificity of regulatory elements, and their contribution to disease by integrating with genome-wide association studies (GWAS). We summarize a variety of molecular xQTL studies in human tissues and cells. In addition, using the Alzheimer's Disease Sequencing Project (ADSP) as an example, we describe the ADSP xQTL project, a collaborative effort across the ADSP Functional Genomics Consortium (ADSP-FGC). The project's ultimate goal is a reference map of Alzheimer's-related QTLs using existing datasets from multiple omics layers to help us study the consequences of genetic variants identified in the ADSP. xQTL studies enable the identification of the causal genes and pathways in GWAS loci, which will likely aid in the discovery of novel biomarkers and therapeutic targets for complex diseases. © 2022 Wiley Periodicals LLC.
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Affiliation(s)
- Oluwatosin A Olayinka
- Bioinformatics Program, Boston University, Boston, Massachusetts
- Department of Medicine (Biomedical Genetics), Boston University School of Medicine, Boston, Massachusetts
| | - Nicholas K O'Neill
- Bioinformatics Program, Boston University, Boston, Massachusetts
- Department of Medicine (Biomedical Genetics), Boston University School of Medicine, Boston, Massachusetts
| | - Lindsay A Farrer
- Bioinformatics Program, Boston University, Boston, Massachusetts
- Department of Medicine (Biomedical Genetics), Boston University School of Medicine, Boston, Massachusetts
- Department of Neurology, Boston University School of Medicine, Boston, Massachusetts
- Department of Ophthalmology, Boston University School of Medicine, Boston, Massachusetts
- Department of Biostatistics, Boston University School of Public Health, Boston, Massachusetts
- Department of Epidemiology, Boston University School of Public Health, Boston, Massachusetts
| | - Gao Wang
- Department of Neurology, Columbia University, New York, New York
- Gertrude H. Sergievsky Center, Columbia University, New York, New York
| | - Xiaoling Zhang
- Bioinformatics Program, Boston University, Boston, Massachusetts
- Department of Medicine (Biomedical Genetics), Boston University School of Medicine, Boston, Massachusetts
- Department of Biostatistics, Boston University School of Public Health, Boston, Massachusetts
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32
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Lopez S, Martinez-Perez A, Rodriguez-Rius A, Viñuela A, Brown AA, Martin-Fernandez L, Vilalta N, Arús M, Panousis NI, Buil A, Sabater-Lleal M, Souto JC, Dermitzakis ET, Soria JM. Integrated GWAS and Gene Expression Suggest ORM1 as a Potential Regulator of Plasma Levels of Cell-Free DNA and Thrombosis Risk. Thromb Haemost 2022; 122:1027-1039. [PMID: 35272364 PMCID: PMC9251712 DOI: 10.1055/s-0041-1742169] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
Abstract
Plasma cell-free DNA (cfDNA) is a surrogate marker of neutrophil extracellular traps (NETs) that contribute to immunothrombosis. There is growing interest about the mechanisms underlying NET formation and elevated cfDNA, but little is known about the factors involved. We aimed to identify genes involved in the regulation of cfDNA levels using data from the Genetic Analysis of Idiopathic Thrombophilia (GAIT-2) Project.
Imputed genotypes, whole blood RNA-Seq data, and plasma cfDNA quantification were available for 935 of the GAIT-2 participants from 35 families with idiopathic thrombophilia. We performed heritability and GWAS analysis for cfDNA. The heritability of cfDNA was 0.26 (
p
= 3.7 × 10
−6
), while the GWAS identified a significant association (rs1687391,
p
= 3.55 × 10
−10
) near the
ORM1
gene, on chromosome 9. An eQTL (expression quantitative trait loci) analysis revealed a significant association between the lead GWAS variant and the expression of
ORM1
in whole blood (
p
= 6.14 × 10
−9
). Additionally,
ORM1
expression correlated with levels of cfDNA (
p
= 4.38 × 10
−4
). Finally, genetic correlation analysis between cfDNA and thrombosis identified a suggestive association (
ρg
= 0.43,
p
= 0.089).
All in all, we show evidence of the role of
ORM1
in regulating cfDNA levels in plasma, which might contribute to the susceptibility to thrombosis through mechanisms of immunothrombosis.
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Affiliation(s)
- Sonia Lopez
- Genomics of Complex Diseases Unit, Research Institute Hospital de la Santa Creu i Sant Pau, IIB Sant Pau, Barcelona, Spain
| | - Angel Martinez-Perez
- Genomics of Complex Diseases Unit, Research Institute Hospital de la Santa Creu i Sant Pau, IIB Sant Pau, Barcelona, Spain
| | - Alba Rodriguez-Rius
- Genomics of Complex Diseases Unit, Research Institute Hospital de la Santa Creu i Sant Pau, IIB Sant Pau, Barcelona, Spain
| | - Ana Viñuela
- Biosciences Institute, Faculty of Medicine, Newcastle University, Newcastle Upon Tyne, United Kingdom
| | - Andrew A Brown
- Population Health and Genomics, University of Dundee, Dundee, Scotland, United Kingdom
| | - Laura Martin-Fernandez
- Genomics of Complex Diseases Unit, Research Institute Hospital de la Santa Creu i Sant Pau, IIB Sant Pau, Barcelona, Spain.,Fundación Española de Trombosis y Hemostasia (FETH), Madrid, Spain.,Congenital Coagulopathies Laboratory, Banc de Sang i Teixits, Barcelona, Spain.,Transfusional Medicine, Vall d'Hebron Research Institute, Universitat Autònoma de Barcelona (VHIR-UAB), Barcelona, Spain
| | - Noelia Vilalta
- Haemostasis and Thrombosis Unit, Department of Hematology, Hospital de la Santa Creu i Sant Pau, Barcelona, Spain
| | - Marc Arús
- Haemostasis and Thrombosis Unit, Department of Hematology, Hospital de la Santa Creu i Sant Pau, Barcelona, Spain
| | - Nikolaos I Panousis
- Wellcome Sanger Institute, Wellcome Genome Campus, Hinxton, South Cambridgeshire, United Kingdom.,Department of Genetic Medicine and Development, University of Geneva, Geneva, Switzerland
| | - Alfonso Buil
- Institute of Biological Psychiatry, Mental Health Sct. Hans Hospital, Roskilde, Denmark
| | - Maria Sabater-Lleal
- Genomics of Complex Diseases Unit, Research Institute Hospital de la Santa Creu i Sant Pau, IIB Sant Pau, Barcelona, Spain.,Cardiovascular Medicine Unit, Department of Medicine, Center for Molecular Medicine, Karolinska Institutet, Stockholm, Sweden
| | - Juan Carlos Souto
- Haemostasis and Thrombosis Unit, Department of Hematology, Hospital de la Santa Creu i Sant Pau, Barcelona, Spain
| | | | - Jose Manuel Soria
- Genomics of Complex Diseases Unit, Research Institute Hospital de la Santa Creu i Sant Pau, IIB Sant Pau, Barcelona, Spain
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33
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Yuan K, Zeng T, Chen L. Interpreting Functional Impact of Genetic Variations by Network QTL for Genotype–Phenotype Association Study. Front Cell Dev Biol 2022; 9:720321. [PMID: 35155440 PMCID: PMC8826544 DOI: 10.3389/fcell.2021.720321] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/04/2021] [Accepted: 12/13/2021] [Indexed: 12/18/2022] Open
Abstract
An enormous challenge in the post-genome era is to annotate and resolve the consequences of genetic variation on diverse phenotypes. The genome-wide association study (GWAS) is a well-known method to identify potential genetic loci for complex traits from huge genetic variations, following which it is crucial to identify expression quantitative trait loci (eQTL). However, the conventional eQTL methods usually disregard the systematical role of single-nucleotide polymorphisms (SNPs) or genes, thereby overlooking many network-associated phenotypic determinates. Such a problem motivates us to recognize the network-based quantitative trait loci (QTL), i.e., network QTL (nQTL), which is to detect the cascade association as genotype → network → phenotype rather than conventional genotype → expression → phenotype in eQTL. Specifically, we develop the nQTL framework on the theory and approach of single-sample networks, which can identify not only network traits (e.g., the gene subnetwork associated with genotype) for analyzing complex biological processes but also network signatures (e.g., the interactive gene biomarker candidates screened from network traits) for characterizing targeted phenotype and corresponding subtypes. Our results show that the nQTL framework can efficiently capture associations between SNPs and network traits (i.e., edge traits) in various simulated data scenarios, compared with traditional eQTL methods. Furthermore, we have carried out nQTL analysis on diverse biological and biomedical datasets. Our analysis is effective in detecting network traits for various biological problems and can discover many network signatures for discriminating phenotypes, which can help interpret the influence of nQTL on disease subtyping, disease prognosis, drug response, and pathogen factor association. Particularly, in contrast to the conventional approaches, the nQTL framework could also identify many network traits from human bulk expression data, validated by matched single-cell RNA-seq data in an independent or unsupervised manner. All these results strongly support that nQTL and its detection framework can simultaneously explore the global genotype–network–phenotype associations and the underlying network traits or network signatures with functional impact and importance.
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Affiliation(s)
- Kai Yuan
- Key Laboratory of Systems Biology, Shanghai Institute of Biochemistry and Cell Biology, Center for Excellence in Molecular Cell Science, Chinese Academy of Sciences, Shanghai, China
| | - Tao Zeng
- Key Laboratory of Systems Biology, Shanghai Institute of Biochemistry and Cell Biology, Center for Excellence in Molecular Cell Science, Chinese Academy of Sciences, Shanghai, China
- Bio-Med Big Data Center, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai, China
- Guangzhou Laboratory, Guangzhou, China
- *Correspondence: Tao Zeng, ; Luonan Chen,
| | - Luonan Chen
- Key Laboratory of Systems Biology, Shanghai Institute of Biochemistry and Cell Biology, Center for Excellence in Molecular Cell Science, Chinese Academy of Sciences, Shanghai, China
- Key Laboratory of Systems Health Science of Zhejiang Province, Hangzhou Institute for Advanced Study, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Hangzhou, China
- School of Life Science and Technology, ShanghaiTech University, Shanghai, China
- Center for Excellence in Animal Evolution and Genetics, Chinese Academy of Sciences, Kunming, China
- *Correspondence: Tao Zeng, ; Luonan Chen,
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34
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Wang P, Castellani CA, Yao J, Huan T, Bielak LF, Zhao W, Haessler J, Joehanes R, Sun X, Guo X, Longchamps RJ, Manson JE, Grove ML, Bressler J, Taylor KD, Lappalainen T, Kasela S, Van Den Berg DJ, Hou L, Reiner A, Liu Y, Boerwinkle E, Smith JA, Peyser PA, Fornage M, Rich SS, Rotter JI, Kooperberg C, Arking DE, Levy D, Liu C. Epigenome-wide association study of mitochondrial genome copy number. Hum Mol Genet 2021; 31:309-319. [PMID: 34415308 PMCID: PMC8742999 DOI: 10.1093/hmg/ddab240] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/16/2021] [Revised: 07/27/2021] [Accepted: 08/11/2021] [Indexed: 01/03/2023] Open
Abstract
We conducted cohort- and race-specific epigenome-wide association analyses of mitochondrial deoxyribonucleic acid (mtDNA) copy number (mtDNA CN) measured in whole blood from participants of African and European origins in five cohorts (n = 6182, mean age = 57-67 years, 65% women). In the meta-analysis of all the participants, we discovered 21 mtDNA CN-associated DNA methylation sites (CpG) (P < 1 × 10-7), with a 0.7-3.0 standard deviation increase (3 CpGs) or decrease (18 CpGs) in mtDNA CN corresponding to a 1% increase in DNA methylation. Several significant CpGs have been reported to be associated with at least two risk factors (e.g. chronological age or smoking) for cardiovascular disease (CVD). Five genes [PR/SET domain 16, nuclear receptor subfamily 1 group H member 3 (NR1H3), DNA repair protein, DNA polymerase kappa and decaprenyl-diphosphate synthase subunit 2], which harbor nine significant CpGs, are known to be involved in mitochondrial biosynthesis and functions. For example, NR1H3 encodes a transcription factor that is differentially expressed during an adipose tissue transition. The methylation level of cg09548275 in NR1H3 was negatively associated with mtDNA CN (effect size = -1.71, P = 4 × 10-8) and was positively associated with the NR1H3 expression level (effect size = 0.43, P = 0.0003), which indicates that the methylation level in NR1H3 may underlie the relationship between mtDNA CN, the NR1H3 transcription factor and energy expenditure. In summary, the study results suggest that mtDNA CN variation in whole blood is associated with DNA methylation levels in genes that are involved in a wide range of mitochondrial activities. These findings will help reveal molecular mechanisms between mtDNA CN and CVD.
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Affiliation(s)
- Penglong Wang
- Population Sciences Branch, National Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, MD 20892, USA
| | - Christina A Castellani
- McKusick-Nathans Department of Genetic Medicine, Johns Hopkins University School of Medicine, Baltimore, MD 21287, USA
- Department of Pathology and Laboratory Medicine, Western University, London, Ontario N6A 5C1, Canada
| | - Jie Yao
- Department of Pediatrics, The Institute for Translational Genomics and Population Sciences, The Lundquist Institute for Biomedical Innovation at Harbor-UCLA Medical Center, Torrance, CA 90502, USA
| | - Tianxiao Huan
- Population Sciences Branch, National Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, MD 20892, USA
| | - Lawrence F Bielak
- Department of Epidemiology, School of Public Health, University of Michigan, Ann Arbor, MI 48109, USA
| | - Wei Zhao
- Department of Epidemiology, School of Public Health, University of Michigan, Ann Arbor, MI 48109, USA
| | - Jeffrey Haessler
- Division of Public Health Science, Fred Hutchinson Cancer Research Center, Seattle, WA 98109, USA
| | - Roby Joehanes
- Population Sciences Branch, National Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, MD 20892, USA
| | - Xianbang Sun
- Department of Biostatistics, Boston University, Boston, MA 02118, USA
| | - Xiuqing Guo
- Department of Pediatrics, The Institute for Translational Genomics and Population Sciences, The Lundquist Institute for Biomedical Innovation at Harbor-UCLA Medical Center, Torrance, CA 90502, USA
| | - Ryan J Longchamps
- McKusick-Nathans Department of Genetic Medicine, Johns Hopkins University School of Medicine, Baltimore, MD 21287, USA
| | - JoAnn E Manson
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA 02115, USA
| | - Megan L Grove
- Human Genetics Center, Department of Epidemiology, Human Genetics and Environmental Sciences, School of Public Health, The University of Texas Health Science Center at Houston, Houston, TX 77030, USA
| | - Jan Bressler
- Human Genetics Center, Department of Epidemiology, Human Genetics and Environmental Sciences, School of Public Health, The University of Texas Health Science Center at Houston, Houston, TX 77030, USA
| | - Kent D Taylor
- Department of Pathology and Laboratory Medicine, Western University, London, Ontario N6A 5C1, Canada
| | - Tuuli Lappalainen
- New York Genome Center, New York, NY 10013, USA
- Department of Systems Biology, Columbia University, New York, NY 10034, USA
| | - Silva Kasela
- New York Genome Center, New York, NY 10013, USA
- Department of Systems Biology, Columbia University, New York, NY 10034, USA
| | - David J Van Den Berg
- Department of Population and Public Health Sciences, Center for Genetic Epidemiology, Keck School of Medicine of USC, University of Southern California, Los Angeles, CA 90033, USA
| | - Lifang Hou
- Feinberg School of Medicine, Northwestern University, Chicago, IL 60611, USA
| | - Alexander Reiner
- Division of Public Health Science, Fred Hutchinson Cancer Research Center, Seattle, WA 98109, USA
| | - Yongmei Liu
- Department of Medicine, Divisions of Cardiology and Neurology, Duke University Medical Center, Durham, NC 27704, USA
| | - Eric Boerwinkle
- Human Genetics Center, Department of Epidemiology, Human Genetics and Environmental Sciences, School of Public Health, The University of Texas Health Science Center at Houston, Houston, TX 77030, USA
- Human Genome Sequencing Center, Baylor College of Medicine, Houston, TX 77030, USA
| | - Jennifer A Smith
- Department of Epidemiology, School of Public Health, University of Michigan, Ann Arbor, MI 48109, USA
| | - Patricia A Peyser
- Department of Epidemiology, School of Public Health, University of Michigan, Ann Arbor, MI 48109, USA
| | - Myriam Fornage
- Human Genetics Center, Department of Epidemiology, Human Genetics and Environmental Sciences, School of Public Health, The University of Texas Health Science Center at Houston, Houston, TX 77030, USA
- Brown Foundation Institute of Molecular Medicine, McGovern Medical School, University of Texas Health Science Center at Houston, Houston, TX 77030, USA
| | - Stephen S Rich
- Center for Public Health Genomics, University of Virginia, Charlottesville, VA 22903, USA
| | - Jerome I Rotter
- Department of Pediatrics, The Institute for Translational Genomics and Population Sciences, The Lundquist Institute for Biomedical Innovation at Harbor-UCLA Medical Center, Torrance, CA 90502, USA
| | - Charles Kooperberg
- Division of Public Health Science, Fred Hutchinson Cancer Research Center, Seattle, WA 98109, USA
| | - Dan E Arking
- McKusick-Nathans Department of Genetic Medicine, Johns Hopkins University School of Medicine, Baltimore, MD 21287, USA
| | - Daniel Levy
- Population Sciences Branch, National Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, MD 20892, USA
- Framingham Heart Study, National Heart, Lung, and Blood Institute (NHLBI), Framingham, MA 01702, USA
| | - Chunyu Liu
- Department of Biostatistics, Boston University, Boston, MA 02118, USA
- Framingham Heart Study, National Heart, Lung, and Blood Institute (NHLBI), Framingham, MA 01702, USA
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35
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Ng B, Casazza W, Kim NH, Wang C, Farhadi F, Tasaki S, Bennett DA, De Jager PL, Gaiteri C, Mostafavi S. Cascading epigenomic analysis for identifying disease genes from the regulatory landscape of GWAS variants. PLoS Genet 2021; 17:e1009918. [PMID: 34807913 PMCID: PMC8648125 DOI: 10.1371/journal.pgen.1009918] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/23/2021] [Revised: 12/06/2021] [Accepted: 10/30/2021] [Indexed: 12/14/2022] Open
Abstract
The majority of genetic variants detected in genome wide association studies (GWAS) exert their effects on phenotypes through gene regulation. Motivated by this observation, we propose a multi-omic integration method that models the cascading effects of genetic variants from epigenome to transcriptome and eventually to the phenome in identifying target genes influenced by risk alleles. This cascading epigenomic analysis for GWAS, which we refer to as CEWAS, comprises two types of models: one for linking cis genetic effects to epigenomic variation and another for linking cis epigenomic variation to gene expression. Applying these models in cascade to GWAS summary statistics generates gene level statistics that reflect genetically-driven epigenomic effects. We show on sixteen brain-related GWAS that CEWAS provides higher gene detection rate than related methods, and finds disease relevant genes and gene sets that point toward less explored biological processes. CEWAS thus presents a novel means for exploring the regulatory landscape of GWAS variants in uncovering disease mechanisms. The majority of genetic variants detected in genome wide association studies (GWAS) exert their effects on phenotypes through gene regulation. Motivated by this observation, we propose a multi-omic integration method that models the cascading effects of genetic variants from epigenome to transcriptome and eventually to the phenome in identifying target genes influenced by risk alleles. This cascading epigenomic analysis for GWAS, which we refer to as CEWAS, combines the effect of genetic variants on DNA methylation as well as gene expression. We show on sixteen brain-related GWAS that CEWAS provides higher gene detection rate than related methods, and finds disease relevant genes and gene sets that point toward less explored biological processes.
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Affiliation(s)
- Bernard Ng
- Department of Statistics and Department of Medical Genetics, University of British Columbia, Vancouver, British Columbia, Canada
- Centre for Molecular Medicine and Therapeutics, Vancouver, British Columbia, Canada
| | - William Casazza
- Department of Statistics and Department of Medical Genetics, University of British Columbia, Vancouver, British Columbia, Canada
- Centre for Molecular Medicine and Therapeutics, Vancouver, British Columbia, Canada
| | - Nam Hee Kim
- Department of Computer Science, University of British Columbia, Vancouver, British Columbia, Canada
| | - Chendi Wang
- Department of Statistics and Department of Medical Genetics, University of British Columbia, Vancouver, British Columbia, Canada
- Centre for Molecular Medicine and Therapeutics, Vancouver, British Columbia, Canada
| | - Farnush Farhadi
- Centre for Molecular Medicine and Therapeutics, Vancouver, British Columbia, Canada
| | - Shinya Tasaki
- Rush Alzheimer’s Disease Center, Rush University Medical Center, Chicago, Illinois, United States of America
| | - David A. Bennett
- Rush Alzheimer’s Disease Center, Rush University Medical Center, Chicago, Illinois, United States of America
| | - Philip L. De Jager
- Center for Translational & Computational Neuroimmunology, Department of Neurology and the Taub Institute for Research on Alzheimer’s Disease and the Aging Brain, Columbia University Irving Medical Center, New York, New York, United States of America
| | - Christopher Gaiteri
- Rush Alzheimer’s Disease Center, Rush University Medical Center, Chicago, Illinois, United States of America
| | - Sara Mostafavi
- Department of Statistics and Department of Medical Genetics, University of British Columbia, Vancouver, British Columbia, Canada
- Paul G. Allen School for Computer Science and Engineering, University of Washington, Seattle, Washington, United States of America
- * E-mail:
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Pott J, Gadin J, Theusch E, Kleber ME, Delgado GE, Kirsten H, Hauck SM, Burkhardt R, Scharnagl H, Krauss RM, Loeffler M, März W, Thiery J, Silveira A, Vant Hooft FM, Scholz M. Meta-GWAS of PCSK9 levels detects two novel loci at APOB and TM6SF2. Hum Mol Genet 2021; 31:999-1011. [PMID: 34590679 PMCID: PMC8947322 DOI: 10.1093/hmg/ddab279] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/01/2021] [Revised: 09/13/2021] [Accepted: 09/14/2021] [Indexed: 11/15/2022] Open
Abstract
Background Proprotein convertase subtilisin/kexin type 9 (PCSK9) is a key player in lipid metabolism, as it degrades low-density lipoprotein (LDL) receptors from hepatic cell membranes. So far, only variants of the PCSK9 gene locus were found to be associated with PCSK9 levels. Here we aimed to identify novel genetic loci that regulate PCSK9 levels and how they relate to other lipid traits. Additionally, we investigated to what extend the causal effect of PCSK9 on coronary artery disease (CAD) is mediated by low-density lipoprotein–cholesterol (LDL–C). Methods and Results We performed a genome-wide association study meta-analysis of PCSK9 levels in up to 12 721 samples of European ancestry. The estimated heritability was 10.3%, which increased to 12.6% using only samples from patients without statin treatment. We successfully replicated the known PCSK9 hit consisting of three independent signals. Interestingly, in a study of 300 African Americans, we confirmed the locus with a different PCSK9 variant. Beyond PCSK9, our meta-analysis detected three novel loci with genome-wide significance. Co-localization analysis with cis-eQTLs and lipid traits revealed biologically plausible candidate genes at two of them: APOB and TM6SF2. In a bivariate Mendelian Randomization analysis, we detected a strong effect of PCSK9 on LDL-C, but not vice versa. LDL-C mediated 63% of the total causal effect of PCSK9 on CAD. Conclusion Our study identified novel genetic loci with plausible candidate genes affecting PCSK9 levels. Ethnic heterogeneity was observed at the PCSK9 locus itself. Although the causal effect of PCSK9 on CAD is mainly mediated by LDL-C, an independent direct effect also occurs.
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Affiliation(s)
- Janne Pott
- Institute for Medical Informatics, Statistics and Epidemiology, Medical Faculty, University of Leipzig, Leipzig, Germany.,LIFE Research Center for Civilization Diseases, Medical Faculty, University of Leipzig, Leipzig, Germany
| | - Jesper Gadin
- Division of Cardiovascular Medicine, Department of Medicine, Karolinska Institutet, Stockholm, Karolinska University Hospital Solna, Sweden
| | - Elizabeth Theusch
- Department of Pediatrics, University of California San Francisco, Oakland, CA, USA
| | - Marcus E Kleber
- Vth Department of Medicine (Nephrology, Hypertensiology, Rheumatology, Endocrinology, Diabetology), Medical Faculty Mannheim, University of Heidelberg, Mannheim, Germany.,SYNLAB MVZ Humangenetik Mannheim, Mannheim, Germany
| | - Graciela E Delgado
- Vth Department of Medicine (Nephrology, Hypertensiology, Rheumatology, Endocrinology, Diabetology), Medical Faculty Mannheim, University of Heidelberg, Mannheim, Germany
| | - Holger Kirsten
- Institute for Medical Informatics, Statistics and Epidemiology, Medical Faculty, University of Leipzig, Leipzig, Germany.,LIFE Research Center for Civilization Diseases, Medical Faculty, University of Leipzig, Leipzig, Germany
| | - Stefanie M Hauck
- Metabolomics and Proteomics Core and Research Unit Protein Science, Helmholtz Zentrum München, Neuherberg, Germany
| | - Ralph Burkhardt
- LIFE Research Center for Civilization Diseases, Medical Faculty, University of Leipzig, Leipzig, Germany.,Institute of Laboratory Medicine, Clinical Chemistry and Molecular Diagnostics, University Hospital Leipzig.,Institute of Clinical Chemistry and Laboratory Medicine, University Hospital Regensburg, Regensburg, Germany
| | - Hubert Scharnagl
- Clinical Institute of Medical and Chemical Laboratory Diagnostics, Medical University of Graz, Graz, Austria
| | - Ronald M Krauss
- Department of Pediatrics, University of California San Francisco, Oakland, CA, USA.,Department of Medicine, University of California San Francisco, Oakland, CA, USA
| | - Markus Loeffler
- Institute for Medical Informatics, Statistics and Epidemiology, Medical Faculty, University of Leipzig, Leipzig, Germany.,LIFE Research Center for Civilization Diseases, Medical Faculty, University of Leipzig, Leipzig, Germany
| | - Winfried März
- Vth Department of Medicine (Nephrology, Hypertensiology, Rheumatology, Endocrinology, Diabetology), Medical Faculty Mannheim, University of Heidelberg, Mannheim, Germany.,Clinical Institute of Medical and Chemical Laboratory Diagnostics, Medical University of Graz, Graz, Austria.,SYNLAB Academy, SYNALB Holding Deutschland GmbH, Mannheim, Germany
| | - Joachim Thiery
- LIFE Research Center for Civilization Diseases, Medical Faculty, University of Leipzig, Leipzig, Germany.,Institute of Laboratory Medicine, Clinical Chemistry and Molecular Diagnostics, University Hospital Leipzig.,Faculty of Medicine, Kiel University, Kiel, Germany
| | - Angela Silveira
- Division of Cardiovascular Medicine, Department of Medicine, Karolinska Institutet, Stockholm, Karolinska University Hospital Solna, Sweden
| | - Ferdinand M Vant Hooft
- Division of Cardiovascular Medicine, Department of Medicine, Karolinska Institutet, Stockholm, Karolinska University Hospital Solna, Sweden
| | - Markus Scholz
- Institute for Medical Informatics, Statistics and Epidemiology, Medical Faculty, University of Leipzig, Leipzig, Germany.,LIFE Research Center for Civilization Diseases, Medical Faculty, University of Leipzig, Leipzig, Germany
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Rare and low-frequency exonic variants and gene-by-smoking interactions in pulmonary function. Sci Rep 2021; 11:19365. [PMID: 34588469 PMCID: PMC8481467 DOI: 10.1038/s41598-021-98120-7] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/03/2020] [Accepted: 09/02/2021] [Indexed: 02/08/2023] Open
Abstract
Genome-wide association studies have identified numerous common genetic variants associated with spirometric measures of pulmonary function, including forced expiratory volume in one second (FEV1), forced vital capacity, and their ratio. However, variants with lower minor allele frequencies are less explored. We conducted a large-scale gene-smoking interaction meta-analysis on exonic rare and low-frequency variants involving 44,429 individuals of European ancestry in the discovery stage and sought replication in the UK BiLEVE study with 45,133 European ancestry samples and UK Biobank study with 59,478 samples. We leveraged data on cigarette smoking, the major environmental risk factor for reduced lung function, by testing gene-by-smoking interaction effects only and simultaneously testing the genetic main effects and interaction effects. The most statistically significant signal that replicated was a previously reported low-frequency signal in GPR126, distinct from common variant associations in this gene. Although only nominal replication was obtained for a top rare variant signal rs142935352 in one of the two studies, interaction and joint tests for current smoking and PDE3B were significantly associated with FEV1. This study investigates the utility of assessing gene-by-smoking interactions and underscores their effects on potential pulmonary function.
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38
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Han X, Liang X, Wu M, Zhang L, Jiang H. Association of Genetic Variants in miR-217 Gene with Risk of Coronary Artery Disease: A Case-Control Study. Pharmgenomics Pers Med 2021; 14:1081-1086. [PMID: 34483680 PMCID: PMC8409599 DOI: 10.2147/pgpm.s324767] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/14/2021] [Accepted: 08/15/2021] [Indexed: 11/23/2022] Open
Abstract
Objective To evaluate the associations of genetic variants of the miR-217 gene with coronary artery disease (CAD) risk, as well as plasma level of vascular endothelial growth factor (VEGF). Methods A case-control study with 498 CAD patients and 499 frequency-matched healthy controls was conducted to evaluate the associations of four tagSNPs of the miR-217 gene, including rs6724872, rs4999828, rs10206823, and rs41291177, with CAD risk and plasma level of VEGF. Results SNP rs6724872 and rs4999828 were significantly associated with increased risk of CAD (P value was smaller than 0.05 even after Bonferroni multiple adjustment). Compared with the G allele, C allele of rs6724872 was significantly associated with 1.73-fold increased risk of CAD (95% CI: 1.25-2.39; P = 0.001). While C allele of rs4999828 was significantly associated with 1.75-fold increased risk of CAD, compared with T allele (95% CI: 1.34-2.29; P = 4 × 10-5). Meanwhile, rs6724872 and rs4999828 were also significantly associated with higher level of VEGF (P < 0.001). Conclusion These findings highlighted the important role of genetic variants of the miR-217 gene in the pathogenesis of CAD and potential targets for intervention.
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Affiliation(s)
- Xia Han
- Department of Cardiology, Jinan People's Hospital Affiliated to Shandong First Medical University, Laiwu, 271199, People's Republic of China
| | - Xiaotang Liang
- Shandong Second Provincial General Hospital, Jinan, Shandong Province, 250000, People's Republic of China
| | - Menghai Wu
- Department of Cardiology, Jinan People's Hospital Affiliated to Shandong First Medical University, Laiwu, 271199, People's Republic of China
| | - Lijun Zhang
- Department of Cardiology, Jinan People's Hospital Affiliated to Shandong First Medical University, Laiwu, 271199, People's Republic of China
| | - Honglei Jiang
- Shandong Second Provincial General Hospital, Jinan, Shandong Province, 250000, People's Republic of China
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39
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Liu C, Fetterman JL, Qian Y, Sun X, Blackwell TW, Pitsillides A, Cade BE, Wang H, Raffield LM, Lange LA, Anugu P, Abecasis G, Adrienne Cupples L, Redline S, Correa A, Vasan RS, Wilson JG, Ding J, Levy D. Presence and transmission of mitochondrial heteroplasmic mutations in human populations of European and African ancestry. Mitochondrion 2021; 60:33-42. [PMID: 34303007 PMCID: PMC8464516 DOI: 10.1016/j.mito.2021.07.004] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2020] [Revised: 06/13/2021] [Accepted: 07/19/2021] [Indexed: 11/20/2022]
Abstract
We investigated the concordance of mitochondrial DNA heteroplasmic mutations (heteroplasmies) in 6745 maternal pairs of European (EA, n = 4718 pairs) and African (AA, n = 2027 pairs) Americans in whole blood. Mother-offspring pairs displayed the highest concordance rate, followed by sibling-sibling and more distantly-related maternal pairs. The allele fractions of concordant heteroplasmies exhibited high correlation (R2 = 0.8) between paired individuals. Discordant heteroplasmies were more likely to be in coding regions, be nonsynonymous or nonsynonymous-deleterious (p < 0.001). The number of deleterious heteroplasmies was significantly correlated with advancing age (20-44, 45-64, and ≥65 years, p-trend = 0.01). One standard deviation increase in heteroplasmic burden (i.e., the number of heteroplasmies carried by an individual) was associated with 0.17 to 0.26 (p < 1e - 23) standard deviation decrease in mtDNA copy number, independent of age. White blood cell count and differential count jointly explained 0.5% to 1.3% (p ≤ 0.001) variance in heteroplasmic burden. A genome-wide association and meta-analysis identified a region at 11p11.12 (top signal rs779031139, p = 2.0e - 18, minor allele frequency = 0.38) associated with the heteroplasmic burden. However, the 11p11.12 region is adjacent to a nuclear mitochondrial DNA (NUMT) corresponding to a 542 bp area of the D-loop. This region was no longer significant after excluding heteroplasmies within the 542 bp from the heteroplasmic burden. The discovery that blood mtDNA heteroplasmies were both inherited and somatic origins and that an increase in heteroplasmic burden was strongly associated with a decrease in average number of mtDNA copy number in blood are important findings to be considered in association studies of mtDNA with disease traits.
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Affiliation(s)
- Chunyu Liu
- Department of Biostatistics, School of Public Health, Boston University, Boston, MA 02118, USA.
| | - Jessica L Fetterman
- Evans Department of Medicine and Whitaker Cardiovascular Institute, School of Medicine, Boston University, Boston, MA 20118, USA
| | - Yong Qian
- Longitudinal Studies Section, Translational Gerontology Branch, NIA/NIH, Baltimore, MD 21224, USA
| | - Xianbang Sun
- Department of Biostatistics, School of Public Health, Boston University, Boston, MA 02118, USA
| | - Thomas W Blackwell
- TOPMed Informatics Research Center, University of Michigan, Ann Arbor, MI 48109, USA
| | - Achilleas Pitsillides
- Department of Biostatistics, School of Public Health, Boston University, Boston, MA 02118, USA
| | - Brian E Cade
- Division of Sleep and Circadian Disorders, Department of Medicine, Brigham and Women's Hospital, Boston, MA 02115, USA; Division of Sleep Medicine, Harvard Medical School, Boston, MA 02115, USA
| | - Heming Wang
- Division of Sleep and Circadian Disorders, Department of Medicine, Brigham and Women's Hospital, Boston, MA 02115, USA; Division of Sleep Medicine, Harvard Medical School, Boston, MA 02115, USA
| | - Laura M Raffield
- Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC 27514, USA
| | - Leslie A Lange
- School of Medicine, University of Colorado, Anschutz Medical Campus, Aurora, CO 80045, USA
| | - Pramod Anugu
- Coordinating Center, University of Mississippi of Medical Center, Jackson, MS 39216, USA
| | - Goncalo Abecasis
- TOPMed Informatics Research Center, University of Michigan, Ann Arbor, MI 48109, USA
| | - L Adrienne Cupples
- Department of Biostatistics, School of Public Health, Boston University, Boston, MA 02118, USA
| | - Susan Redline
- Division of Sleep and Circadian Disorders, Department of Medicine, Brigham and Women's Hospital, Boston, MA 02115, USA; Division of Sleep Medicine, Harvard Medical School, Boston, MA 02115, USA
| | - Adolfo Correa
- Department of Medicine, University of Mississippi Medical Center, Jackson, MS, 39216, USA
| | - Ramachandran S Vasan
- Framingham Heart Study, Framingham, MA 01702, USA; Sections of Preventive Medicine and Epidemiology, and Cardiovascular Medicine, Boston University School of Medicine, Boston, MA 02118, USA
| | - James G Wilson
- Department of Physiology and Biophysics, University of Mississippi Medical Center, Jackson, MS 39216, USA
| | - Jun Ding
- Longitudinal Studies Section, Translational Gerontology Branch, NIA/NIH, Baltimore, MD 21224, USA
| | - Daniel Levy
- Framingham Heart Study, Framingham, MA 01702, USA; Population Sciences Branch, NHLBI/NIH, Bethesda, MD 20892, USA
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40
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Võsa U, Claringbould A, Westra HJ, Bonder MJ, Deelen P, Zeng B, Kirsten H, Saha A, Kreuzhuber R, Yazar S, Brugge H, Oelen R, de Vries DH, van der Wijst MGP, Kasela S, Pervjakova N, Alves I, Favé MJ, Agbessi M, Christiansen MW, Jansen R, Seppälä I, Tong L, Teumer A, Schramm K, Hemani G, Verlouw J, Yaghootkar H, Sönmez Flitman R, Brown A, Kukushkina V, Kalnapenkis A, Rüeger S, Porcu E, Kronberg J, Kettunen J, Lee B, Zhang F, Qi T, Hernandez JA, Arindrarto W, Beutner F, Dmitrieva J, Elansary M, Fairfax BP, Georges M, Heijmans BT, Hewitt AW, Kähönen M, Kim Y, Knight JC, Kovacs P, Krohn K, Li S, Loeffler M, Marigorta UM, Mei H, Momozawa Y, Müller-Nurasyid M, Nauck M, Nivard MG, Penninx BWJH, Pritchard JK, Raitakari OT, Rotzschke O, Slagboom EP, Stehouwer CDA, Stumvoll M, Sullivan P, 't Hoen PAC, Thiery J, Tönjes A, van Dongen J, van Iterson M, Veldink JH, Völker U, Warmerdam R, Wijmenga C, Swertz M, Andiappan A, Montgomery GW, Ripatti S, Perola M, Kutalik Z, Dermitzakis E, Bergmann S, Frayling T, van Meurs J, Prokisch H, Ahsan H, Pierce BL, Lehtimäki T, Boomsma DI, Psaty BM, Gharib SA, Awadalla P, Milani L, Ouwehand WH, Downes K, Stegle O, Battle A, Visscher PM, Yang J, Scholz M, Powell J, Gibson G, Esko T, Franke L. Large-scale cis- and trans-eQTL analyses identify thousands of genetic loci and polygenic scores that regulate blood gene expression. Nat Genet 2021; 53:1300-1310. [PMID: 34475573 PMCID: PMC8432599 DOI: 10.1038/s41588-021-00913-z] [Citation(s) in RCA: 617] [Impact Index Per Article: 205.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/20/2020] [Accepted: 07/12/2021] [Indexed: 12/22/2022]
Abstract
Trait-associated genetic variants affect complex phenotypes primarily via regulatory mechanisms on the transcriptome. To investigate the genetics of gene expression, we performed cis- and trans-expression quantitative trait locus (eQTL) analyses using blood-derived expression from 31,684 individuals through the eQTLGen Consortium. We detected cis-eQTL for 88% of genes, and these were replicable in numerous tissues. Distal trans-eQTL (detected for 37% of 10,317 trait-associated variants tested) showed lower replication rates, partially due to low replication power and confounding by cell type composition. However, replication analyses in single-cell RNA-seq data prioritized intracellular trans-eQTL. Trans-eQTL exerted their effects via several mechanisms, primarily through regulation by transcription factors. Expression of 13% of the genes correlated with polygenic scores for 1,263 phenotypes, pinpointing potential drivers for those traits. In summary, this work represents a large eQTL resource, and its results serve as a starting point for in-depth interpretation of complex phenotypes.
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Affiliation(s)
- Urmo Võsa
- Department of Genetics, University of Groningen, University Medical Center Groningen, Groningen, the Netherlands.
- Estonian Genome Centre, Institute of Genomics, University of Tartu, Tartu, Estonia.
| | - Annique Claringbould
- Department of Genetics, University of Groningen, University Medical Center Groningen, Groningen, the Netherlands.
- Oncode Institute, Amsterdam, the Netherlands.
- Structural & Computational Biology Unit, European Molecular Biology Laboratory, Heidelberg, Germany.
| | - Harm-Jan Westra
- Department of Genetics, University of Groningen, University Medical Center Groningen, Groningen, the Netherlands
- Oncode Institute, Amsterdam, the Netherlands
| | - Marc Jan Bonder
- Department of Genetics, University of Groningen, University Medical Center Groningen, Groningen, the Netherlands
- Genome Biology Unit, European Molecular Biology Laboratory, Heidelberg, Germany
| | - Patrick Deelen
- Department of Genetics, University of Groningen, University Medical Center Groningen, Groningen, the Netherlands
- Oncode Institute, Amsterdam, the Netherlands
- Genomics Coordination Center, University Medical Centre Groningen, Groningen, the Netherlands
- Department of Genetics, University Medical Centre Utrecht, Utrecht, the Netherlands
| | - Biao Zeng
- School of Biological Sciences, Georgia Tech, Atlanta, GA, USA
| | - Holger Kirsten
- Institute for Medical Informatics, Statistics and Epidemiology, University of Leipzig, Leipzig, Germany
- LIFE Research Center for Civilization Diseases, University of Leipzig, Leipzig, Germany
| | - Ashis Saha
- Department of Computer Science, Johns Hopkins University, Baltimore, MD, USA
| | - Roman Kreuzhuber
- Department of Haematology, University of Cambridge, Cambridge, United Kingdom
- NHS Blood and Transplant, Cambridge Biomedical Campus, Cambridge, United Kingdom
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, United Kingdom
| | - Seyhan Yazar
- Garvan Institute of Medical Research, Garvan-Weizmann Centre for Cellular Genomics, Sydney, New South Wales, Australia
| | - Harm Brugge
- Department of Genetics, University of Groningen, University Medical Center Groningen, Groningen, the Netherlands
- Oncode Institute, Amsterdam, the Netherlands
| | - Roy Oelen
- Department of Genetics, University of Groningen, University Medical Center Groningen, Groningen, the Netherlands
- Oncode Institute, Amsterdam, the Netherlands
| | - Dylan H de Vries
- Department of Genetics, University of Groningen, University Medical Center Groningen, Groningen, the Netherlands
- Oncode Institute, Amsterdam, the Netherlands
| | - Monique G P van der Wijst
- Department of Genetics, University of Groningen, University Medical Center Groningen, Groningen, the Netherlands
- Oncode Institute, Amsterdam, the Netherlands
| | - Silva Kasela
- Estonian Genome Centre, Institute of Genomics, University of Tartu, Tartu, Estonia
| | - Natalia Pervjakova
- Estonian Genome Centre, Institute of Genomics, University of Tartu, Tartu, Estonia
| | - Isabel Alves
- Computational Biology, Ontario Institute for Cancer Research, Toronto, Ontario, Canada
- L'institut du thorax, Université de Nantes, CHU Nantes, INSERM, CNRS, Nantes, France
| | - Marie-Julie Favé
- Computational Biology, Ontario Institute for Cancer Research, Toronto, Ontario, Canada
| | - Mawussé Agbessi
- Computational Biology, Ontario Institute for Cancer Research, Toronto, Ontario, Canada
| | - Mark W Christiansen
- Cardiovascular Health Research Unit, University of Washington, Seattle, WA, USA
| | - Rick Jansen
- Department of Psychiatry, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam Public Health Research Institute and Amsterdam Neuroscience, Amsterdam, the Netherlands
| | - Ilkka Seppälä
- Department of Clinical Chemistry, Fimlab Laboratories and Finnish Cardiovascular Research Center-Tampere, Faculty of Medicine and Health Technology, Tampere University, Tampere, Finland
| | - Lin Tong
- Department of Public Health Sciences, University of Chicago, Chicago, IL, USA
| | - Alexander Teumer
- Institute for Community Medicine, University Medicine Greifswald, Greifswald, Germany
- DZHK (German Center for Cardiovascular Research), Partner Site Greifswald, Greifswald, Germany
| | - Katharina Schramm
- Institute of Genetic Epidemiology, Helmholtz Zentrum München-German Research Center for Environmental Health, Neuherberg, Germany
- Department of Medicine I, University Hospital Munich, Ludwig Maximilian's University, Munich, Germany
| | - Gibran Hemani
- MRC Integrative Epidemiology Unit, University of Bristol, Bristol, United Kingdom
| | - Joost Verlouw
- Department of Internal Medicine, Erasmus Medical Center, Rotterdam, the Netherlands
| | - Hanieh Yaghootkar
- Genetics of Complex Traits, University of Exeter Medical School, Royal Devon & Exeter Hospital, Exeter, United Kingdom
- School of Life Sciences, College of Liberal Arts and Science, University of Westminster, London, United Kingdom
- Division of Medical Sciences, Department of Health Sciences, Luleå University of Technology, Luleå, Sweden
| | - Reyhan Sönmez Flitman
- Department of Computational Biology, University of Lausanne, Lausanne, Switzerland
- Swiss Institute of Bioinformatics, Lausanne, Switzerland
| | - Andrew Brown
- Department of Genetic Medicine and Development, University of Geneva Medical School, Geneva, Switzerland
- Population Health and Genomics, University of Dundee, Dundee, United Kingdom
| | - Viktorija Kukushkina
- Estonian Genome Centre, Institute of Genomics, University of Tartu, Tartu, Estonia
| | - Anette Kalnapenkis
- Estonian Genome Centre, Institute of Genomics, University of Tartu, Tartu, Estonia
| | - Sina Rüeger
- Lausanne University Hospital, Lausanne, Switzerland
| | | | - Jaanika Kronberg
- Estonian Genome Centre, Institute of Genomics, University of Tartu, Tartu, Estonia
| | - Johannes Kettunen
- Computational Medicine, Faculty of Medicine, University of Oulu, Oulu, Finland
- Center for Life Course Health Research, Faculty of Medicine, University of Oulu, Oulu, Finland
- Biocenter Oulu, University of Oulu, Oulu, Finland
- Finnish Institute for Health and Welfare, Helsinki, Finland
| | - Bernett Lee
- Singapore Immunology Network, Agency for Science, Technology and Research, Singapore, Singapore
| | - Futao Zhang
- Institute for Molecular Bioscience, University of Queensland, Brisbane, Queensland, Australia
| | - Ting Qi
- Institute for Molecular Bioscience, University of Queensland, Brisbane, Queensland, Australia
| | - Jose Alquicira Hernandez
- Garvan Institute of Medical Research, Garvan-Weizmann Centre for Cellular Genomics, Sydney, New South Wales, Australia
| | | | - Frank Beutner
- Heart Center Leipzig, Universität Leipzig, Leipzig, Germany
| | - Julia Dmitrieva
- Unit of Animal Genomics, WELBIO, GIGA-R & Faculty of Veterinary Medicine, University of Liège, Liège, Belgium
| | - Mahmoud Elansary
- Unit of Animal Genomics, WELBIO, GIGA-R & Faculty of Veterinary Medicine, University of Liège, Liège, Belgium
| | - Benjamin P Fairfax
- Wellcome Centre for Human Genetics, University of Oxford, Oxford, United Kingdom
| | - Michel Georges
- Unit of Animal Genomics, WELBIO, GIGA-R & Faculty of Veterinary Medicine, University of Liège, Liège, Belgium
| | | | - Alex W Hewitt
- Menzies Institute for Medical Research, School of Medicine, University of Tasmania, Hobart, Tasmania, Australia
- Centre for Eye Research Australia, Department of Surgery, University of Melbourne, Melbourne, Victoria, Australia
| | - Mika Kähönen
- Department of Clinical Physiology, Tampere University Hospital and Faculty of Medicine and Health Technology, Tampere University, Tampere, Finland
| | - Yungil Kim
- Department of Computer Science, Johns Hopkins University, Baltimore, MD, USA
- Genetics and Genomic Science Department, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Julian C Knight
- Wellcome Centre for Human Genetics, University of Oxford, Oxford, United Kingdom
| | - Peter Kovacs
- IFB Adiposity Diseases, Universität Leipzig, Leipzig, Germany
| | - Knut Krohn
- Interdisciplinary Center for Clinical Research, Faculty of Medicine, Universität Leipzig, Leipzig, Germany
| | - Shuang Li
- Department of Genetics, University of Groningen, University Medical Center Groningen, Groningen, the Netherlands
- Genomics Coordination Center, University Medical Centre Groningen, Groningen, the Netherlands
| | - Markus Loeffler
- Institute for Medical Informatics, Statistics and Epidemiology, University of Leipzig, Leipzig, Germany
- LIFE Research Center for Civilization Diseases, University of Leipzig, Leipzig, Germany
| | - Urko M Marigorta
- School of Biological Sciences, Georgia Tech, Atlanta, GA, USA
- Integrative Genomics Lab, CIC bioGUNE, Basque Research and Technology Alliance (BRTA), Bizkaia Science and Technology Park, Derio, Spain
- IKERBASQUE, Basque Foundation for Science, Bilbao, Spain
| | - Hailang Mei
- Department of Medical Statistics and Bioinformatics, Leiden University Medical Center, Leiden, the Netherlands
| | - Yukihide Momozawa
- Unit of Animal Genomics, WELBIO, GIGA-R & Faculty of Veterinary Medicine, University of Liège, Liège, Belgium
- Laboratory for Genotyping Development, RIKEN Center for Integrative Medical Sciences, Kanagawa, Japan
| | - Martina Müller-Nurasyid
- Institute of Genetic Epidemiology, Helmholtz Zentrum München-German Research Center for Environmental Health, Neuherberg, Germany
- Department of Medicine I, University Hospital Munich, Ludwig Maximilian's University, Munich, Germany
- IBE, Faculty of Medicine, LMU Munich, Munich, Germany
| | - Matthias Nauck
- DZHK (German Center for Cardiovascular Research), Partner Site Greifswald, Greifswald, Germany
- Institute of Clinical Chemistry and Laboratory Medicine, University Medicine Greifswald, Greifswald, Germany
| | - Michel G Nivard
- Department of Biological Psychology, Faculty of Behaviour and Movement Sciences, Vrije Universiteit, Amsterdam, the Netherlands
| | - Brenda W J H Penninx
- Department of Psychiatry, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam Public Health Research Institute and Amsterdam Neuroscience, Amsterdam, the Netherlands
| | - Jonathan K Pritchard
- Department of Biology, Stanford University, Stanford, CA, USA
- Department of Genetics, Stanford University, Stanford, CA, USA
| | - Olli T Raitakari
- Research Centre of Applied and Preventive Cardiovascular Medicine, University of Turku, Turku, Finland
- Department of Clinical Physiology and Nuclear Medicine, Turku University Hospital, Turku, Finland
- Centre for Population Health Research, University of Turku and Turku University Hospital, Turku, Finland
| | - Olaf Rotzschke
- Singapore Immunology Network, Agency for Science, Technology and Research, Singapore, Singapore
| | | | - Coen D A Stehouwer
- Department of Internal Medicine and School for Cardiovascular Diseases (CARIM), Maastricht University Medical Center, Maastricht, the Netherlands
| | | | - Patrick Sullivan
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
| | - Peter A C 't Hoen
- Center for Molecular and Biomolecular Informatics, Radboud Institute for Molecular Life Sciences, Radboud University Medical Center Nijmegen, Nijmegen, the Netherlands
| | - Joachim Thiery
- LIFE Research Center for Civilization Diseases, University of Leipzig, Leipzig, Germany
- Institute for Laboratory Medicine, LIFE-Leipzig Research Center for Civilization Diseases, Universität Leipzig, Leipzig, Germany
| | - Anke Tönjes
- Department of Medicine, Universität Leipzig, Leipzig, Germany
| | - Jenny van Dongen
- Netherlands Twin Register, Department of Biological Psychology, Vrije Universiteit Amsterdam, Amsterdam Public Health Research Institute and Amsterdam Neuroscience, Amsterdam, the Netherlands
| | | | - Jan H Veldink
- UMC Utrecht Brain Center, University Medical Center Utrecht, Department of Neurology, Utrecht University, Utrecht, the Netherlands
| | - Uwe Völker
- Interfaculty Institute for Genetics and Functional Genomics, University Medicine Greifswald, Greifswald, Germany
| | - Robert Warmerdam
- Department of Genetics, University of Groningen, University Medical Center Groningen, Groningen, the Netherlands
- Oncode Institute, Amsterdam, the Netherlands
| | - Cisca Wijmenga
- Department of Genetics, University of Groningen, University Medical Center Groningen, Groningen, the Netherlands
| | - Morris Swertz
- Genomics Coordination Center, University Medical Centre Groningen, Groningen, the Netherlands
| | - Anand Andiappan
- Singapore Immunology Network, Agency for Science, Technology and Research, Singapore, Singapore
| | - Grant W Montgomery
- Institute for Molecular Bioscience, University of Queensland, Brisbane, Queensland, Australia
| | - Samuli Ripatti
- Institute for Molecular Medicine Finland (FIMM), HiLIFE, University of Helsinki, Helsinki, Finland
- Public Health, Faculty of Medicine, University of Helsinki, Helsinki, Finland
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Markus Perola
- National Institute for Health and Welfare, University of Helsinki, Helsinki, Finland
| | - Zoltan Kutalik
- Center for Primary Care and Public Health, University of Lausanne, Lausanne, Switzerland
| | - Emmanouil Dermitzakis
- Swiss Institute of Bioinformatics, Lausanne, Switzerland
- Department of Genetic Medicine and Development, University of Geneva Medical School, Geneva, Switzerland
- Institute of Genetics and Genomics in Geneva (iGE3), University of Geneva, Geneva, Switzerland
| | - Sven Bergmann
- Department of Computational Biology, University of Lausanne, Lausanne, Switzerland
- Swiss Institute of Bioinformatics, Lausanne, Switzerland
| | - Timothy Frayling
- Genetics of Complex Traits, University of Exeter Medical School, Royal Devon & Exeter Hospital, Exeter, United Kingdom
| | - Joyce van Meurs
- Department of Internal Medicine, Erasmus Medical Center, Rotterdam, the Netherlands
| | - Holger Prokisch
- Institute of Neurogenomics, Helmholtz Zentrum München, Neuherberg, Germany
- Institute of Human Genetics, Technical University Munich, Munich, Germany
| | - Habibul Ahsan
- Department of Public Health Sciences, University of Chicago, Chicago, IL, USA
| | - Brandon L Pierce
- Department of Public Health Sciences, University of Chicago, Chicago, IL, USA
| | - Terho Lehtimäki
- Department of Clinical Chemistry, Fimlab Laboratories and Finnish Cardiovascular Research Center-Tampere, Faculty of Medicine and Health Technology, Tampere University, Tampere, Finland
| | - Dorret I Boomsma
- Netherlands Twin Register, Department of Biological Psychology, Vrije Universiteit Amsterdam, Amsterdam Public Health Research Institute and Amsterdam Neuroscience, Amsterdam, the Netherlands
| | - Bruce M Psaty
- Cardiovascular Health Research Unit, Departments of Medicine, Epidemiology, and Health Services, University of Washington, Seattle, WA, USA
| | - Sina A Gharib
- Cardiovascular Health Research Unit, University of Washington, Seattle, WA, USA
- Department of Medicine, University of Washington, Seattle, WA, USA
| | - Philip Awadalla
- Computational Biology, Ontario Institute for Cancer Research, Toronto, Ontario, Canada
| | - Lili Milani
- Estonian Genome Centre, Institute of Genomics, University of Tartu, Tartu, Estonia
| | - Willem H Ouwehand
- Department of Haematology, University of Cambridge, Cambridge, United Kingdom
- NHS Blood and Transplant, Cambridge Biomedical Campus, Cambridge, United Kingdom
- Human Genetics, Wellcome Sanger Institute, Wellcome Genome Campus, Hinxton, United Kingdom
| | - Kate Downes
- Department of Haematology, University of Cambridge, Cambridge, United Kingdom
- NHS Blood and Transplant, Cambridge Biomedical Campus, Cambridge, United Kingdom
| | - Oliver Stegle
- Genome Biology Unit, European Molecular Biology Laboratory, Heidelberg, Germany
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, United Kingdom
- Division of Computational Genomics and Systems Genetics, German Cancer Research Center, Heidelberg, Germany
| | - Alexis Battle
- Department of Computer Science, Johns Hopkins University, Baltimore, MD, USA
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, USA
| | - Peter M Visscher
- Institute for Molecular Bioscience, University of Queensland, Brisbane, Queensland, Australia
| | - Jian Yang
- Institute for Molecular Bioscience, University of Queensland, Brisbane, Queensland, Australia
- School of Life Sciences, Westlake University, Hangzhou, China
- Westlake Laboratory of Life Sciences and Biomedicine, Hangzhou, China
| | - Markus Scholz
- Institute for Medical Informatics, Statistics and Epidemiology, University of Leipzig, Leipzig, Germany
- LIFE Research Center for Civilization Diseases, University of Leipzig, Leipzig, Germany
| | - Joseph Powell
- Garvan Institute of Medical Research, Garvan-Weizmann Centre for Cellular Genomics, Sydney, New South Wales, Australia
- UNSW Cellular Genomics Futures Institute, University of New South Wales, Sydney, New South Wales, Australia
| | - Greg Gibson
- School of Biological Sciences, Georgia Tech, Atlanta, GA, USA
| | - Tõnu Esko
- Estonian Genome Centre, Institute of Genomics, University of Tartu, Tartu, Estonia
| | - Lude Franke
- Department of Genetics, University of Groningen, University Medical Center Groningen, Groningen, the Netherlands.
- Oncode Institute, Amsterdam, the Netherlands.
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Jiang W, Wang X, Gao P, Li F, Lu K, Tan X, Zheng S, Pei W, An M, Li X, Hu R, Zhong Y, Zhu J, Du J, Wang Y. Association of IL1R1 Coding Variant With Plasma-Level Soluble ST2 and Risk of Aortic Dissection. Front Cardiovasc Med 2021; 8:710425. [PMID: 34409081 PMCID: PMC8365023 DOI: 10.3389/fcvm.2021.710425] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/16/2021] [Accepted: 06/28/2021] [Indexed: 01/16/2023] Open
Abstract
Objective: Aortic dissection (AD) is characterized by an acute onset, rapid progress, and high mortality. Levels of soluble ST2 (sST2) on presentation are elevated in patients with acute AD, which can be used to discriminate AD patients from patients with chest pain. sST2 concentrations were found to be highly heritable in the general population. The aim of this study was to investigate the associations of variations in ST2-related gene expression with sST2 concentrations and AD risk. Methods: This case-control study involving a total of 2,277 participants were conducted, including 435 AD patients and age- and sex-matched 435 controls in the discovery stage, and 464 patients and 943 controls in the validation stage. Eight ST2-related genes were selected by systematic review. Tag single-nucleotide polymorphisms (SNPs) were screened out from the Chinese population of the 1,000 Genomes Database. Twenty-one ST2-related SNPs were genotyped, and plasma sST2 concentrations were measured. Results: In the discovery stage, rs13019803 located in IL1R1 was significantly associated with AD after Bonferroni correction (p = 0.0009) and was correlated with circulating sST2 levels in patients with type A AD(AAD) [log-sST2 per C allele increased by 0.180 (95%) CI: 0.002 - 0.357] but not in type B. Combining the two stages together, rs13019803C was associated with plasma sST2 level in AAD patients [log-sST2 increased by 0.141 (95% CI: 0.055-0.227) for per C allele]. Odds ratio of rs13019803 on the risk of AAD is 1.67 (95% CI: 1.33-2.09). Conclusions: The IL1R1 SNP rs13019803C is associated with higher sST2 levels and increased risk of AAD.
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Affiliation(s)
- Wenxi Jiang
- Key Laboratory of Remodeling-Related Cardiovascular Diseases, Ministry of Education, The Collaborative Innovation Center for Cardiovascular Disorders, Beijing Anzhen Hospital, Capital Medical University, Beijing, China.,Department of Vascular Biology, Beijing Institute of Heart, Lung and Blood Vessel Disease, Beijing, China
| | - Xue Wang
- Key Laboratory of Remodeling-Related Cardiovascular Diseases, Ministry of Education, The Collaborative Innovation Center for Cardiovascular Disorders, Beijing Anzhen Hospital, Capital Medical University, Beijing, China.,Department of Vascular Biology, Beijing Institute of Heart, Lung and Blood Vessel Disease, Beijing, China
| | - Pei Gao
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing, China
| | - Fengjuan Li
- Key Laboratory of Remodeling-Related Cardiovascular Diseases, Ministry of Education, The Collaborative Innovation Center for Cardiovascular Disorders, Beijing Anzhen Hospital, Capital Medical University, Beijing, China.,Department of Vascular Biology, Beijing Institute of Heart, Lung and Blood Vessel Disease, Beijing, China
| | - Ke Lu
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing, China
| | - Xin Tan
- Key Laboratory of Remodeling-Related Cardiovascular Diseases, Ministry of Education, The Collaborative Innovation Center for Cardiovascular Disorders, Beijing Anzhen Hospital, Capital Medical University, Beijing, China.,Department of Vascular Biology, Beijing Institute of Heart, Lung and Blood Vessel Disease, Beijing, China
| | - Shuai Zheng
- Key Laboratory of Remodeling-Related Cardiovascular Diseases, Ministry of Education, The Collaborative Innovation Center for Cardiovascular Disorders, Beijing Anzhen Hospital, Capital Medical University, Beijing, China.,Department of Vascular Biology, Beijing Institute of Heart, Lung and Blood Vessel Disease, Beijing, China
| | - Wang Pei
- Key Laboratory of Remodeling-Related Cardiovascular Diseases, Ministry of Education, The Collaborative Innovation Center for Cardiovascular Disorders, Beijing Anzhen Hospital, Capital Medical University, Beijing, China.,Department of Vascular Biology, Beijing Institute of Heart, Lung and Blood Vessel Disease, Beijing, China
| | - Meiyu An
- Key Laboratory of Remodeling-Related Cardiovascular Diseases, Ministry of Education, The Collaborative Innovation Center for Cardiovascular Disorders, Beijing Anzhen Hospital, Capital Medical University, Beijing, China.,Department of Vascular Biology, Beijing Institute of Heart, Lung and Blood Vessel Disease, Beijing, China
| | - Xi Li
- Key Laboratory of Remodeling-Related Cardiovascular Diseases, Ministry of Education, The Collaborative Innovation Center for Cardiovascular Disorders, Beijing Anzhen Hospital, Capital Medical University, Beijing, China
| | - Rong Hu
- Key Laboratory of Remodeling-Related Cardiovascular Diseases, Ministry of Education, The Collaborative Innovation Center for Cardiovascular Disorders, Beijing Anzhen Hospital, Capital Medical University, Beijing, China
| | - Yongliang Zhong
- Key Laboratory of Remodeling-Related Cardiovascular Diseases, Ministry of Education, The Collaborative Innovation Center for Cardiovascular Disorders, Beijing Anzhen Hospital, Capital Medical University, Beijing, China
| | - Junming Zhu
- Key Laboratory of Remodeling-Related Cardiovascular Diseases, Ministry of Education, The Collaborative Innovation Center for Cardiovascular Disorders, Beijing Anzhen Hospital, Capital Medical University, Beijing, China
| | - Jie Du
- Key Laboratory of Remodeling-Related Cardiovascular Diseases, Ministry of Education, The Collaborative Innovation Center for Cardiovascular Disorders, Beijing Anzhen Hospital, Capital Medical University, Beijing, China.,Department of Vascular Biology, Beijing Institute of Heart, Lung and Blood Vessel Disease, Beijing, China
| | - Yuan Wang
- Key Laboratory of Remodeling-Related Cardiovascular Diseases, Ministry of Education, The Collaborative Innovation Center for Cardiovascular Disorders, Beijing Anzhen Hospital, Capital Medical University, Beijing, China.,Department of Vascular Biology, Beijing Institute of Heart, Lung and Blood Vessel Disease, Beijing, China
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42
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Yang T, Wei P, Pan W. Integrative analysis of multi-omics data for discovering low-frequency variants associated with low-density lipoprotein cholesterol levels. Bioinformatics 2021; 36:5223-5228. [PMID: 33070182 DOI: 10.1093/bioinformatics/btaa898] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/30/2020] [Revised: 09/26/2020] [Accepted: 10/06/2020] [Indexed: 11/13/2022] Open
Abstract
MOTIVATION The abundance of omics data has facilitated integrative analyses of single and multiple molecular layers with genome-wide association studies focusing on common variants. Built on its successes, we propose a general analysis framework to leverage multi-omics data with sequencing data to improve the statistical power of discovering new associations and understanding of the disease susceptibility due to low-frequency variants. The proposed test features its robustness to model misspecification, high power across a wide range of scenarios and the potential of offering insights into the underlying genetic architecture and disease mechanisms. RESULTS Using the Framingham Heart Study data, we show that low-frequency variants are predictive of DNA methylation, even after conditioning on the nearby common variants. In addition, DNA methylation and gene expression provide complementary information to functional genomics. In the Avon Longitudinal Study of Parents and Children with a sample size of 1497, one gene CLPTM1 is identified to be associated with low-density lipoprotein cholesterol levels by the proposed powerful adaptive gene-based test integrating information from gene expression, methylation and enhancer-promoter interactions. It is further replicated in the TwinsUK study with 1706 samples. The signal is driven by both low-frequency and common variants. AVAILABILITY AND IMPLEMENTATION Models are available at https://github.com/ytzhong/DNAm. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Tianzhong Yang
- Department of Biostatistics, School of Public Health, University of Minnesota, Minneapolis, MN 55455, USA
| | - Peng Wei
- Department of Biostatistics, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
| | - Wei Pan
- Department of Biostatistics, School of Public Health, University of Minnesota, Minneapolis, MN 55455, USA
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43
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Jhun MA, Mendelson M, Wilson R, Gondalia R, Joehanes R, Salfati E, Zhao X, Braun KVE, Do AN, Hedman ÅK, Zhang T, Carnero-Montoro E, Shen J, Bartz TM, Brody JA, Montasser ME, O'Connell JR, Yao C, Xia R, Boerwinkle E, Grove M, Guan W, Liliane P, Singmann P, Müller-Nurasyid M, Meitinger T, Gieger C, Peters A, Zhao W, Ware EB, Smith JA, Dhana K, van Meurs J, Uitterlinden A, Ikram MA, Ghanbari M, Zhi D, Gustafsson S, Lind L, Li S, Sun D, Spector TD, Chen YDI, Damcott C, Shuldiner AR, Absher DM, Horvath S, Tsao PS, Kardia S, Psaty BM, Sotoodehnia N, Bell JT, Ingelsson E, Chen W, Dehghan A, Arnett DK, Waldenberger M, Hou L, Whitsel EA, Baccarelli A, Levy D, Fornage M, Irvin MR, Assimes TL. A multi-ethnic epigenome-wide association study of leukocyte DNA methylation and blood lipids. Nat Commun 2021; 12:3987. [PMID: 34183656 PMCID: PMC8238961 DOI: 10.1038/s41467-021-23899-y] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/11/2019] [Accepted: 05/25/2021] [Indexed: 02/06/2023] Open
Abstract
Here we examine the association between DNA methylation in circulating leukocytes and blood lipids in a multi-ethnic sample of 16,265 subjects. We identify 148, 35, and 4 novel associations among Europeans, African Americans, and Hispanics, respectively, and an additional 186 novel associations through a trans-ethnic meta-analysis. We observe a high concordance in the direction of effects across racial/ethnic groups, a high correlation of effect sizes between high-density lipoprotein and triglycerides, a modest overlap of associations with epigenome-wide association studies of other cardio-metabolic traits, and a largely non-overlap with lipid loci identified to date through genome-wide association studies. Thirty CpGs reached significance in at least 2 racial/ethnic groups including 7 that showed association with the expression of an annotated gene. CpGs annotated to CPT1A showed evidence of being influenced by triglycerides levels. DNA methylation levels of circulating leukocytes show robust and consistent association with blood lipid levels across multiple racial/ethnic groups.
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Affiliation(s)
- Min-A Jhun
- Department of Epidemiology, University of Michigan School of Public Health, Ann Arbor, MI, USA.
- Department of Medicine, Stanford University School of Medicine, Stanford, CA, USA.
| | - Michael Mendelson
- Population Sciences Branch, National Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, MD, USA
- Department of Cardiology, Boston Children's Hospital, Boston, MA, USA
| | - Rory Wilson
- Research Unit Molecular Epidemiology, Institute of Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health, Munich, Germany
- Institute of Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health, Munich, Germany
| | - Rahul Gondalia
- Department of Epidemiology, University of North Carolina, Chapel Hill, NC, USA
| | - Roby Joehanes
- Hebrew SeniorLife, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, USA
| | - Elias Salfati
- Department of Medicine, Stanford University School of Medicine, Stanford, CA, USA
| | - Xiaoping Zhao
- The Brown Foundation Institute of Molecular Medicine, McGovern Medical School, The University of Texas Health Science Center at Houston, Houston, TX, USA
| | | | - Anh Nguyet Do
- Department of Epidemiology, School of Public Health, University of Alabama at Birmingham, Birmingham, AL, USA
| | - Åsa K Hedman
- Department of Medical Sciences, Molecular Epidemiology and Science for Life Laboratory, Uppsala University, Uppsala, Sweden
| | - Tao Zhang
- Department of Epidemiology, Tulane University School of Public Health and Tropical Medicine, New Orleans, LA, USA
| | - Elena Carnero-Montoro
- Department of Twin Research and Genetic Epidemiology, School of Life Course Sciences, King's College London, London, UK
- GENYO, Center for Genomics and Oncological Research Pfizer/University of Granada/Andalusian Regional Government, Granada, Spain
| | - Jincheng Shen
- Department of Population Health Sciences, University of Utah, Salt Lake City, UT, USA
| | - Traci M Bartz
- Cardiovascular Health Research Unit, Departments of Medicine and Biostatistics, University of Washington, Seattle, WA, USA
| | - Jennifer A Brody
- Cardiovascular Health Research Unit, Department of Medicine, University of Washington, Seattle, WA, USA
| | - May E Montasser
- Division of Endocrinology, Diabetes, and Nutrition, University of Maryland School of Medicine, Baltimore, MD, USA
- Program for Personalized and Genomic Medicine, University of Maryland School of Medicine, Baltimore, MD, USA
| | - Jeff R O'Connell
- Division of Endocrinology, Diabetes, and Nutrition, University of Maryland School of Medicine, Baltimore, MD, USA
- Program for Personalized and Genomic Medicine, University of Maryland School of Medicine, Baltimore, MD, USA
| | - Chen Yao
- Population Sciences Branch, National Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, MD, USA
| | - Rui Xia
- The Brown Foundation Institute of Molecular Medicine, McGovern Medical School, The University of Texas Health Science Center at Houston, Houston, TX, USA
| | - Eric Boerwinkle
- School of Public Health, University of Texas Health Science Center at Houston, Huston, TX, USA
| | - Megan Grove
- School of Public Health, University of Texas Health Science Center at Houston, Huston, TX, USA
| | - Weihua Guan
- Division of Biostatistics, School of Public Health, University of Minnesota, Minneapolis, MN, USA
| | - Pfeiffer Liliane
- Research Unit Molecular Epidemiology, Institute of Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health, Munich, Germany
- Institute of Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health, Munich, Germany
| | - Paula Singmann
- Research Unit Molecular Epidemiology, Institute of Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health, Munich, Germany
- Institute of Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health, Munich, Germany
| | - Martina Müller-Nurasyid
- Institute of Genetic Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany
- IBE, Faculty of Medicine, LMU Munich, Munich, Germany
- Institute of Medical Biostatistics, Epidemiology and Informatics (IMBEI), University Medical Center, Johannes Gutenberg University, Mainz, Germany
| | - Thomas Meitinger
- Institute of Medical Biostatistics, Epidemiology and Informatics (IMBEI), University Medical Center, Johannes Gutenberg University, Mainz, Germany
- Institute of Human Genetics, Helmholtz Zentrum München, German Research Center for Environmental Health, Munich, Germany
- Institute of Human Genetics, Technical University Munich, Munich, Germany
| | - Christian Gieger
- Research Unit Molecular Epidemiology, Institute of Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health, Munich, Germany
- Institute of Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health, Munich, Germany
| | - Annette Peters
- Institute of Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health, Munich, Germany
- Institute of Medical Biostatistics, Epidemiology and Informatics (IMBEI), University Medical Center, Johannes Gutenberg University, Mainz, Germany
| | - Wei Zhao
- Department of Epidemiology, University of Michigan School of Public Health, Ann Arbor, MI, USA
| | - Erin B Ware
- Department of Epidemiology, University of Michigan School of Public Health, Ann Arbor, MI, USA
- Survey Research Center, Institute for Social Research, Ann Arbor, MI, USA
| | - Jennifer A Smith
- Department of Epidemiology, University of Michigan School of Public Health, Ann Arbor, MI, USA
- Survey Research Center, Institute for Social Research, Ann Arbor, MI, USA
| | - Klodian Dhana
- Department of Internal Medicine, Rush University Medical Center, Chicago, IL, USA
| | - Joyce van Meurs
- Department of Internal Medicine, Erasmus MC University Medical Center, Rotterdam, The Netherlands
| | - Andre Uitterlinden
- Department of Internal Medicine, Erasmus MC University Medical Center, Rotterdam, The Netherlands
| | - Mohammad Arfan Ikram
- Department of Epidemiology, Erasmus MC University Medical Center, Rotterdam, The Netherlands
| | - Mohsen Ghanbari
- Department of Epidemiology, Erasmus MC University Medical Center, Rotterdam, The Netherlands
| | - Deugi Zhi
- School of Biomedical Informatics, University of Texas Health Science Center at Houston, Houston, TX, USA
| | - Stefan Gustafsson
- Department of Medical Sciences, Molecular Epidemiology and Science for Life Laboratory, Uppsala University, Uppsala, Sweden
| | - Lars Lind
- Department of Medical Sciences, Molecular Epidemiology and Science for Life Laboratory, Uppsala University, Uppsala, Sweden
| | - Shengxu Li
- Department of Epidemiology, Tulane University School of Public Health and Tropical Medicine, New Orleans, LA, USA
| | - Dianjianyi Sun
- Department of Epidemiology, Tulane University School of Public Health and Tropical Medicine, New Orleans, LA, USA
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University Health Science Center, Beijing, China
| | - Tim D Spector
- Department of Twin Research and Genetic Epidemiology, School of Life Course Sciences, King's College London, London, UK
| | - Yii-der Ida Chen
- Institute for Translational Genomics and Population Sciences, Los Angeles Biomedical Research Institute, and Department of Pediatrics, Harbor-UCLA Medical Center, Torrance, CA, USA
| | - Coleen Damcott
- Division of Endocrinology, Diabetes, and Nutrition, University of Maryland School of Medicine, Baltimore, MD, USA
- Program for Personalized and Genomic Medicine, University of Maryland School of Medicine, Baltimore, MD, USA
| | - Alan R Shuldiner
- Division of Endocrinology, Diabetes, and Nutrition, University of Maryland School of Medicine, Baltimore, MD, USA
- Program for Personalized and Genomic Medicine, University of Maryland School of Medicine, Baltimore, MD, USA
| | - Devin M Absher
- HudsonAlpha Institute for Biotechnology, Huntsville, AL, USA
| | - Steve Horvath
- Department of Human Genetics, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA
- Department of Biostatistics, Fielding School of Public Health, University of California Los Angeles, Los Angeles, CA, USA
| | - Philip S Tsao
- Department of Medicine, Stanford University School of Medicine, Stanford, CA, USA
- VA Palo Alto Healthcare System, Palo Alto, CA, USA
- Stanford Cardiovascular Institute, Stanford University, Stanford, CA, USA
| | - Sharon Kardia
- Department of Epidemiology, University of Michigan School of Public Health, Ann Arbor, MI, USA
| | - Bruce M Psaty
- Cardiovascular Health Research Unit, Departments of Epidemiology, Medicine, and Health Services, University of Washington, Seattle, WA, USA
- Kaiser Permanente Washington Health Research Institute, Seattle, WA, USA
| | - Nona Sotoodehnia
- Cardiovascular Health Research Unit, Division of Cardiology, University of Washington, Seattle, WA, USA
| | - Jordana T Bell
- Department of Twin Research and Genetic Epidemiology, School of Life Course Sciences, King's College London, London, UK
| | - Erik Ingelsson
- Department of Medicine, Stanford University School of Medicine, Stanford, CA, USA
- Department of Medical Sciences, Molecular Epidemiology and Science for Life Laboratory, Uppsala University, Uppsala, Sweden
- Stanford Cardiovascular Institute, Stanford University, Stanford, CA, USA
- Stanford Diabetes Research Center, Stanford University, Stanford, CA, USA
| | - Wei Chen
- Department of Epidemiology, Tulane University School of Public Health and Tropical Medicine, New Orleans, LA, USA
| | - Abbas Dehghan
- Department of Epidemiology, Erasmus MC University Medical Center, Rotterdam, The Netherlands
- Department of Biostatistics and Epidemiology, MRC Centre for Environment and Health, School of Public Health, Imperial College, London, UK
| | - Donna K Arnett
- Department of Epidemiology, School of Public Health, University of Alabama at Birmingham, Birmingham, AL, USA
| | - Melanie Waldenberger
- Research Unit Molecular Epidemiology, Institute of Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health, Munich, Germany
- Institute of Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health, Munich, Germany
| | - Lifang Hou
- Department of Preventive Medicine, Feinberg School of Medicine, Northwestern University, Chicago, IL, USA
| | - Eric A Whitsel
- Department of Epidemiology, University of North Carolina, Chapel Hill, NC, USA
- Department of Medicine, University of North Carolina, Chapel Hill, NC, USA
| | - Andrea Baccarelli
- Department of Environmental Health Sciences, Harvard T.H. Chan School of Public Health, Boston, MA, USA
- Department of Environmental Health Sciences, Columbia University Mailman School of Public Health, New York, NY, USA
| | - Daniel Levy
- Population Sciences Branch, National Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, MD, USA
- Framingham Heart Study, Framingham, MA, USA
| | - Myriam Fornage
- The Brown Foundation Institute of Molecular Medicine, McGovern Medical School, The University of Texas Health Science Center at Houston, Houston, TX, USA
| | - Marguerite R Irvin
- Department of Epidemiology, School of Public Health, University of Alabama at Birmingham, Birmingham, AL, USA
| | - Themistocles L Assimes
- Department of Medicine, Stanford University School of Medicine, Stanford, CA, USA.
- VA Palo Alto Healthcare System, Palo Alto, CA, USA.
- Stanford Cardiovascular Institute, Stanford University, Stanford, CA, USA.
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44
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Vigorito E, Lin WY, Starr C, Kirk PDW, White SR, Wallace C. Detection of quantitative trait loci from RNA-seq data with or without genotypes using BaseQTL. NATURE COMPUTATIONAL SCIENCE 2021; 1:421-432. [PMID: 34993494 PMCID: PMC7612174 DOI: 10.1038/s43588-021-00087-y] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/22/2020] [Accepted: 05/19/2021] [Indexed: 05/03/2023]
Abstract
Detecting genetic variants associated with traits (quantitative trait loci, QTL) requires genotyped study individuals. Here we describe BaseQTL, a Bayesian method that exploits allele-specific expression to map molecular QTL from sequencing reads (eQTL for gene expression) even when no genotypes are available. When used with genotypes to map eQTL, BaseQTL has lower error rates and increased power compared with existing QTL mapping methods. Running without genotypes limits how many tests can be performed, but due to the proximity of QTL variants to gene bodies, the 2.8% of variants within a 100 kB window that could be tested contained 26% of eQTL detectable with genotypes. eQTL effect estimates were invariably consistent between analyses performed with and without genotypes. Often, sequencing data may be generated in the absence of genotypes on patients and controls in differential expression studies, and we identified an apparent psoriasis-specific eQTL for GSTP1 in one such dataset, providing new insights into disease-dependent gene regulation.
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Affiliation(s)
- Elena Vigorito
- MRC Biostatistics Unit, University of Cambridge, Cambridge, UK
| | - Wei-Yu Lin
- MRC Biostatistics Unit, University of Cambridge, Cambridge, UK
| | - Colin Starr
- MRC Biostatistics Unit, University of Cambridge, Cambridge, UK
| | - Paul D W Kirk
- MRC Biostatistics Unit, University of Cambridge, Cambridge, UK
- Cambridge Institute of Therapeutic Immunology and Infectious Disease (CITIID), Jeffrey Cheah Biomedical Centre, Cambridge Biomedical Campus, University of Cambridge, Cambridge, UK
| | - Simon R White
- MRC Biostatistics Unit, University of Cambridge, Cambridge, UK
- Department of Psychiatry, University of Cambridge, Cambridge, UK
| | - Chris Wallace
- MRC Biostatistics Unit, University of Cambridge, Cambridge, UK
- Cambridge Institute of Therapeutic Immunology and Infectious Disease (CITIID), Jeffrey Cheah Biomedical Centre, Cambridge Biomedical Campus, University of Cambridge, Cambridge, UK
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Chen J, Spracklen CN, Marenne G, Varshney A, Corbin LJ, Luan J, Willems SM, Wu Y, Zhang X, Horikoshi M, Boutin TS, Mägi R, Waage J, Li-Gao R, Chan KHK, Yao J, Anasanti MD, Chu AY, Claringbould A, Heikkinen J, Hong J, Hottenga JJ, Huo S, Kaakinen MA, Louie T, März W, Moreno-Macias H, Ndungu A, Nelson SC, Nolte IM, North KE, Raulerson CK, Ray D, Rohde R, Rybin D, Schurmann C, Sim X, Southam L, Stewart ID, Wang CA, Wang Y, Wu P, Zhang W, Ahluwalia TS, Appel EVR, Bielak LF, Brody JA, Burtt NP, Cabrera CP, Cade BE, Chai JF, Chai X, Chang LC, Chen CH, Chen BH, Chitrala KN, Chiu YF, de Haan HG, Delgado GE, Demirkan A, Duan Q, Engmann J, Fatumo SA, Gayán J, Giulianini F, Gong JH, Gustafsson S, Hai Y, Hartwig FP, He J, Heianza Y, Huang T, Huerta-Chagoya A, Hwang MY, Jensen RA, Kawaguchi T, Kentistou KA, Kim YJ, Kleber ME, Kooner IK, Lai S, Lange LA, Langefeld CD, Lauzon M, Li M, Ligthart S, Liu J, Loh M, Long J, Lyssenko V, Mangino M, Marzi C, Montasser ME, Nag A, Nakatochi M, Noce D, Noordam R, Pistis G, Preuss M, Raffield L, Rasmussen-Torvik LJ, Rich SS, Robertson NR, Rueedi R, Ryan K, Sanna S, Saxena R, Schraut KE, Sennblad B, Setoh K, Smith AV, Sparsø T, Strawbridge RJ, Takeuchi F, Tan J, Trompet S, van den Akker E, van der Most PJ, Verweij N, Vogel M, Wang H, Wang C, Wang N, Warren HR, Wen W, Wilsgaard T, Wong A, Wood AR, Xie T, Zafarmand MH, Zhao JH, Zhao W, Amin N, Arzumanyan Z, Astrup A, Bakker SJL, Baldassarre D, Beekman M, Bergman RN, Bertoni A, Blüher M, Bonnycastle LL, Bornstein SR, Bowden DW, Cai Q, Campbell A, Campbell H, Chang YC, de Geus EJC, Dehghan A, Du S, Eiriksdottir G, Farmaki AE, Frånberg M, Fuchsberger C, Gao Y, Gjesing AP, Goel A, Han S, Hartman CA, Herder C, Hicks AA, Hsieh CH, Hsueh WA, Ichihara S, Igase M, Ikram MA, Johnson WC, Jørgensen ME, Joshi PK, Kalyani RR, Kandeel FR, Katsuya T, Khor CC, Kiess W, Kolcic I, Kuulasmaa T, Kuusisto J, Läll K, Lam K, Lawlor DA, Lee NR, Lemaitre RN, Li H, Lin SY, Lindström J, Linneberg A, Liu J, Lorenzo C, Matsubara T, Matsuda F, Mingrone G, Mooijaart S, Moon S, Nabika T, Nadkarni GN, Nadler JL, Nelis M, Neville MJ, Norris JM, Ohyagi Y, Peters A, Peyser PA, Polasek O, Qi Q, Raven D, Reilly DF, Reiner A, Rivideneira F, Roll K, Rudan I, Sabanayagam C, Sandow K, Sattar N, Schürmann A, Shi J, Stringham HM, Taylor KD, Teslovich TM, Thuesen B, Timmers PRHJ, Tremoli E, Tsai MY, Uitterlinden A, van Dam RM, van Heemst D, van Hylckama Vlieg A, van Vliet-Ostaptchouk JV, Vangipurapu J, Vestergaard H, Wang T, Willems van Dijk K, Zemunik T, Abecasis GR, Adair LS, Aguilar-Salinas CA, Alarcón-Riquelme ME, An P, Aviles-Santa L, Becker DM, Beilin LJ, Bergmann S, Bisgaard H, Black C, Boehnke M, Boerwinkle E, Böhm BO, Bønnelykke K, Boomsma DI, Bottinger EP, Buchanan TA, Canouil M, Caulfield MJ, Chambers JC, Chasman DI, Chen YDI, Cheng CY, Collins FS, Correa A, Cucca F, de Silva HJ, Dedoussis G, Elmståhl S, Evans MK, Ferrannini E, Ferrucci L, Florez JC, Franks PW, Frayling TM, Froguel P, Gigante B, Goodarzi MO, Gordon-Larsen P, Grallert H, Grarup N, Grimsgaard S, Groop L, Gudnason V, Guo X, Hamsten A, Hansen T, Hayward C, Heckbert SR, Horta BL, Huang W, Ingelsson E, James PS, Jarvelin MR, Jonas JB, Jukema JW, Kaleebu P, Kaplan R, Kardia SLR, Kato N, Keinanen-Kiukaanniemi SM, Kim BJ, Kivimaki M, Koistinen HA, Kooner JS, Körner A, Kovacs P, Kuh D, Kumari M, Kutalik Z, Laakso M, Lakka TA, Launer LJ, Leander K, Li H, Lin X, Lind L, Lindgren C, Liu S, Loos RJF, Magnusson PKE, Mahajan A, Metspalu A, Mook-Kanamori DO, Mori TA, Munroe PB, Njølstad I, O'Connell JR, Oldehinkel AJ, Ong KK, Padmanabhan S, Palmer CNA, Palmer ND, Pedersen O, Pennell CE, Porteous DJ, Pramstaller PP, Province MA, Psaty BM, Qi L, Raffel LJ, Rauramaa R, Redline S, Ridker PM, Rosendaal FR, Saaristo TE, Sandhu M, Saramies J, Schneiderman N, Schwarz P, Scott LJ, Selvin E, Sever P, Shu XO, Slagboom PE, Small KS, Smith BH, Snieder H, Sofer T, Sørensen TIA, Spector TD, Stanton A, Steves CJ, Stumvoll M, Sun L, Tabara Y, Tai ES, Timpson NJ, Tönjes A, Tuomilehto J, Tusie T, Uusitupa M, van der Harst P, van Duijn C, Vitart V, Vollenweider P, Vrijkotte TGM, Wagenknecht LE, Walker M, Wang YX, Wareham NJ, Watanabe RM, Watkins H, Wei WB, Wickremasinghe AR, Willemsen G, Wilson JF, Wong TY, Wu JY, Xiang AH, Yanek LR, Yengo L, Yokota M, Zeggini E, Zheng W, Zonderman AB, Rotter JI, Gloyn AL, McCarthy MI, Dupuis J, Meigs JB, Scott RA, Prokopenko I, Leong A, Liu CT, Parker SCJ, Mohlke KL, Langenberg C, Wheeler E, Morris AP, Barroso I. The trans-ancestral genomic architecture of glycemic traits. Nat Genet 2021; 53:840-860. [PMID: 34059833 PMCID: PMC7610958 DOI: 10.1038/s41588-021-00852-9] [Citation(s) in RCA: 314] [Impact Index Per Article: 104.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/07/2020] [Accepted: 03/22/2021] [Indexed: 02/02/2023]
Abstract
Glycemic traits are used to diagnose and monitor type 2 diabetes and cardiometabolic health. To date, most genetic studies of glycemic traits have focused on individuals of European ancestry. Here we aggregated genome-wide association studies comprising up to 281,416 individuals without diabetes (30% non-European ancestry) for whom fasting glucose, 2-h glucose after an oral glucose challenge, glycated hemoglobin and fasting insulin data were available. Trans-ancestry and single-ancestry meta-analyses identified 242 loci (99 novel; P < 5 × 10-8), 80% of which had no significant evidence of between-ancestry heterogeneity. Analyses restricted to individuals of European ancestry with equivalent sample size would have led to 24 fewer new loci. Compared with single-ancestry analyses, equivalent-sized trans-ancestry fine-mapping reduced the number of estimated variants in 99% credible sets by a median of 37.5%. Genomic-feature, gene-expression and gene-set analyses revealed distinct biological signatures for each trait, highlighting different underlying biological pathways. Our results increase our understanding of diabetes pathophysiology by using trans-ancestry studies for improved power and resolution.
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Affiliation(s)
- Ji Chen
- Exeter Centre of Excellence for Diabetes Research (EXCEED), Genetics of Complex Traits, University of Exeter Medical School, University of Exeter, Exeter, UK
- Department of Human Genetics, Wellcome Sanger Institute, Cambridge, UK
| | - Cassandra N Spracklen
- Department of Genetics, University of North Carolina, Chapel Hill, NC, USA
- Department of Biostatistics and Epidemiology, University of Massachusetts, Amherst, MA, USA
| | - Gaëlle Marenne
- Department of Human Genetics, Wellcome Sanger Institute, Cambridge, UK
- Inserm, Univ Brest, EFS, UMR 1078, GGB, Brest, France
| | - Arushi Varshney
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, USA
| | - Laura J Corbin
- MRC Integrative Epidemiology Unit, University of Bristol, Bristol, UK
- Department of Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
| | - Jian'an Luan
- MRC Epidemiology Unit, Institute of Metabolic Science, University of Cambridge, Cambridge, UK
| | - Sara M Willems
- MRC Epidemiology Unit, Institute of Metabolic Science, University of Cambridge, Cambridge, UK
| | - Ying Wu
- Department of Genetics, University of North Carolina, Chapel Hill, NC, USA
| | - Xiaoshuai Zhang
- MRC Epidemiology Unit, Institute of Metabolic Science, University of Cambridge, Cambridge, UK
- Department of Biostatistics, School of Public Health, Shandong University, Jinan, China
| | - Momoko Horikoshi
- Oxford Centre for Diabetes, Endocrinology and Metabolism, Radcliffe Department of Medicine, University of Oxford, Oxford, UK
- Wellcome Centre for Human Genetics, University of Oxford, Oxford, UK
- Laboratory for Genomics of Diabetes and Metabolism, RIKEN Centre for Integrative Medical Sciences, Yokohama, Japan
| | - Thibaud S Boutin
- Medical Research Council Human Genetics Unit, Institute for Genetics and Molecular Medicine, Edinburgh, UK
| | - Reedik Mägi
- Estonian Genome Center, Institute of Genomics, University of Tartu, Tartu, Estonia
| | - Johannes Waage
- COPSAC, Copenhagen Prospective Studies on Asthma in Childhood, Herlev and Gentofte Hospital, University of Copenhagen, Copenhagen, Denmark
| | - Ruifang Li-Gao
- Department of Clinical Epidemiology, Leiden University Medical Center, Leiden, the Netherlands
| | - Kei Hang Katie Chan
- Department of Epidemiology, Brown University School of Public Health, Brown University, Providence, RI, USA
- Department of Biomedical Sciences, City University of Hong Kong, Hong Kong SAR, China
- Department of Electrical Engineering, City University of Hong Kong, Hong Kong SAR, China
| | - Jie Yao
- The Institute for Translational Genomics and Population Sciences, Department of Pediatrics, The Lundquist Institute for Biomedical Innovation at Harbor-UCLA Medical Center, Torrance, CA, USA
| | - Mila D Anasanti
- Department of Metabolism, Digestion and Reproduction, Imperial College London, London, UK
| | - Audrey Y Chu
- Division of Preventive Medicine, Brigham and Women's Hospital, Boston, MA, USA
| | - Annique Claringbould
- Department of Genetics, University of Groningen, University Medical Center Groningen, Groningen, the Netherlands
| | - Jani Heikkinen
- Department of Metabolism, Digestion and Reproduction, Imperial College London, London, UK
| | - Jaeyoung Hong
- Department of Biostatistics, Boston University School of Public Health, Boston, MA, USA
| | - Jouke-Jan Hottenga
- Department of Biological Psychology, Faculty of Behaviour and Movement Sciences, Vrije Universiteit Amsterdam, Amsterdam, the Netherlands
- Amsterdam Public Health Research Institute, Amsterdam University Medical Center, Amsterdam, the Netherlands
| | - Shaofeng Huo
- CAS Key Laboratory of Nutrition, Metabolism and Food Safety, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai, China
| | - Marika A Kaakinen
- Department of Metabolism, Digestion and Reproduction, Imperial College London, London, UK
- Section of Statistical Multi-omics, Department of Clinical and Experimental Research, University of Surrey, Guildford, UK
| | - Tin Louie
- Department of Biostatistics, University of Washington, Seattle, WA, USA
| | - Winfried März
- SYNLAB Academy, SYNLAB Holding Deutschland GmbH, Mannheim, Germany
- Clinical Institute of Medical and Chemical Laboratory Diagnostics, Medical University Graz, Graz, Austria
- Vth Department of Medicine (Nephrology, Hypertensiology, Rheumatology, Endocrinology, Diabetology), Medical Faculty Mannheim, Heidelberg University, Mannheim, Baden-Württemberg, Germany
| | | | - Anne Ndungu
- Wellcome Centre for Human Genetics, University of Oxford, Oxford, UK
| | - Sarah C Nelson
- Department of Biostatistics, University of Washington, Seattle, WA, USA
| | - Ilja M Nolte
- Department of Epidemiology, University of Groningen, University Medical Center Groningen, Groningen, the Netherlands
| | - Kari E North
- CVD Genetic Epidemiology Computational Laboratory, Gillings School of Global Public Health, University of North Carolina, Chapel Hill, NC, USA
| | | | - Debashree Ray
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
| | - Rebecca Rohde
- CVD Genetic Epidemiology Computational Laboratory, Gillings School of Global Public Health, University of North Carolina, Chapel Hill, NC, USA
| | - Denis Rybin
- Department of Biostatistics, Boston University School of Public Health, Boston, MA, USA
| | - Claudia Schurmann
- The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- HPI Digital Health Center, Digital Health and Personalized Medicine, Hasso Plattner Institute, Potsdam, Germany
| | - Xueling Sim
- Saw Swee Hock School of Public Health, National Univeristy of Singapore and National University Health System, Singapore, Singapore
- Center for Statistical Genetics, University of Michigan, Ann Arbor, MI, USA
- Department of Biostatistics, School of Public Health, University of Michigan, Ann Arbor, MI, USA
| | - Lorraine Southam
- Department of Human Genetics, Wellcome Sanger Institute, Cambridge, UK
- Institute of Translational Genomics, Helmholtz Zentrum München-German Research Center for Environmental Health, Neuherberg, Germany
| | - Isobel D Stewart
- MRC Epidemiology Unit, Institute of Metabolic Science, University of Cambridge, Cambridge, UK
| | - Carol A Wang
- School of Medicine and Public Health, College of Health, Medicine and Wellbeing, The University of Newcastle, Newcastle, New South Wales, Australia
| | - Yujie Wang
- CVD Genetic Epidemiology Computational Laboratory, Gillings School of Global Public Health, University of North Carolina, Chapel Hill, NC, USA
| | - Peitao Wu
- Department of Biostatistics, Boston University School of Public Health, Boston, MA, USA
| | - Weihua Zhang
- Department of Epidemiology and Biostatistics, Imperial College London, London, UK
- Department of Cardiology, Ealing Hospital, London North West Healthcare NHS Trust, London, UK
| | - Tarunveer S Ahluwalia
- COPSAC, Copenhagen Prospective Studies on Asthma in Childhood, Herlev and Gentofte Hospital, University of Copenhagen, Copenhagen, Denmark
- Steno Diabetes Center Copenhagen, Gentofte, Denmark
- The Bioinformatics Centre, Department of Biology, University of Copenhagen, Copenhagen, Denmark
| | - Emil V R Appel
- Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Lawrence F Bielak
- Department of Epidemiology, School of Public Health, University of Michigan, Ann Arbor, MI, USA
| | - Jennifer A Brody
- Department of Medicine, Cardiovascular Health Research Unit, University of Washington, Seattle, WA, USA
| | - Noël P Burtt
- Metabolism Program, Program in Medical and Population Genetics, Broad Institute, Cambridge, MA, USA
| | - Claudia P Cabrera
- Department of Clinical Pharmacology, William Harvey Research Institute, Barts and The London School of Medicine and Dentistry, Queen Mary University of London, London, UK
- NIHR Barts Cardiovascular Biomedical Research Centre, Queen Mary University of London, London, UK
| | - Brian E Cade
- Department of Medicine, Sleep and Circadian Disorders, Brigham and Women's Hospital, Boston, MA, USA
- Department of Medicine, Sleep Medicine, Harvard Medical School, Boston, MA, USA
| | - Jin Fang Chai
- Saw Swee Hock School of Public Health, National Univeristy of Singapore and National University Health System, Singapore, Singapore
| | - Xiaoran Chai
- Ocular Epidemiology, Singapore Eye Research Institute, Singapore National Eye Centre, Singapore, Singapore
- Department of Ophthalmology, National University of Singapore and National University Health System, Singapore, Singapore
| | - Li-Ching Chang
- Institute of Biomedical Sciences, Academia Sinica, Taipei, Taiwan
| | - Chien-Hsiun Chen
- Institute of Biomedical Sciences, Academia Sinica, Taipei, Taiwan
| | - Brian H Chen
- Department of Epidemiology, The Herbert Wertheim School of Public Health and Human Longevity Science, University of California San Diego, La Jolla, CA, USA
| | - Kumaraswamy Naidu Chitrala
- Laboratory of Epidemiology and Population Sciences, National Institute on Aging, National Institutes of Health, Baltimore, MD, USA
| | - Yen-Feng Chiu
- Institute of Population Health Sciences, National Health Research Institutes, Miaoli, Taiwan
| | - Hugoline G de Haan
- Department of Clinical Epidemiology, Leiden University Medical Center, Leiden, the Netherlands
| | - Graciela E Delgado
- Vth Department of Medicine (Nephrology, Hypertensiology, Rheumatology, Endocrinology, Diabetology), Medical Faculty Mannheim, Heidelberg University, Mannheim, Baden-Württemberg, Germany
| | - Ayse Demirkan
- Section of Statistical Multi-omics, Department of Clinical and Experimental Research, University of Surrey, Guildford, UK
- Department of Epidemiology, Erasmus Medical Center, Rotterdam, the Netherlands
| | - Qing Duan
- Department of Genetics, University of North Carolina, Chapel Hill, NC, USA
- Department of Statistics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Jorgen Engmann
- Institute of Cardiovascular Science, University College London, London, UK
| | - Segun A Fatumo
- Uganda Medical Informatics Centre (UMIC), MRC/UVRI and London School of Hygiene & Tropical Medicine (Uganda Research Unit), Entebbe, Uganda
- London School of Hygiene & Tropical Medicine, London, UK
- H3Africa Bioinformatics Network (H3ABioNet) Node, Centre for Genomics Research and Innovation, NABDA/FMST, Abuja, Nigeria
| | | | - Franco Giulianini
- Division of Preventive Medicine, Brigham and Women's Hospital, Boston, MA, USA
| | - Jung Ho Gong
- Department of Epidemiology, Brown University School of Public Health, Brown University, Providence, RI, USA
| | - Stefan Gustafsson
- Molecular Epidemiology and Science for Life Laboratory, Department of Medical Sciences, Uppsala University, Uppsala, Sweden
| | - Yang Hai
- Department of Statistics, The University of Auckland, Science Center, Auckland, New Zealand
| | - Fernando P Hartwig
- MRC Integrative Epidemiology Unit, University of Bristol, Bristol, UK
- Postgraduate Program in Epidemiology, Federal University of Pelotas, Pelotas, Brazil
| | - Jing He
- Department of Medicine, Epidemiology, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Yoriko Heianza
- Department of Epidemiology, Tulane University Obesity Research Center, Tulane University, New Orleans, LA, USA
| | - Tao Huang
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing, China
| | - Alicia Huerta-Chagoya
- Molecular Biology and Genomic Medicine Unit, National Council for Science and Technology, Mexico City, Mexico
- Molecular Biology and Genomic Medicine Unit, National Institute of Medical Sciences and Nutrition, Mexico City, Mexico
| | - Mi Yeong Hwang
- Division of Genome Science, Department of Precision Medicine, National Institute of Health, Cheongju, South Korea
| | - Richard A Jensen
- Department of Medicine, Cardiovascular Health Research Unit, University of Washington, Seattle, WA, USA
| | - Takahisa Kawaguchi
- Center for Genomic Medicine, Kyoto University Graduate School of Medicine, Kyoto, Japan
| | - Katherine A Kentistou
- Centre for Global Health Research, Usher Institute, University of Edinburgh, Edinburgh, UK
- Centre for Cardiovascular Sciences, Queen's Medical Research Institute, University of Edinburgh, Edinburgh, UK
| | - Young Jin Kim
- Division of Genome Science, Department of Precision Medicine, National Institute of Health, Cheongju, South Korea
| | - Marcus E Kleber
- Vth Department of Medicine (Nephrology, Hypertensiology, Rheumatology, Endocrinology, Diabetology), Medical Faculty Mannheim, Heidelberg University, Mannheim, Baden-Württemberg, Germany
| | - Ishminder K Kooner
- Department of Cardiology, Ealing Hospital, London North West Healthcare NHS Trust, London, UK
| | - Shuiqing Lai
- Department of Epidemiology, Brown University School of Public Health, Brown University, Providence, RI, USA
| | - Leslie A Lange
- Department of Medicine, Divison of Biomedical Informatics and Personalized Medicine, University of Colorado Anschutz Medical Campus, Denver, CO, USA
| | - Carl D Langefeld
- Department of Biostatistics and Data Science, Wake Forest School of Medicine, Winston-Salem, NC, USA
| | - Marie Lauzon
- The Institute for Translational Genomics and Population Sciences, Department of Pediatrics, The Lundquist Institute for Biomedical Innovation at Harbor-UCLA Medical Center, Torrance, CA, USA
| | - Man Li
- Department of Medicine, Division of Nephrology and Hypertension, University of Utah, Salt Lake City, UT, USA
| | - Symen Ligthart
- Department of Epidemiology, Erasmus Medical Center, Rotterdam, the Netherlands
| | - Jun Liu
- Department of Epidemiology, Erasmus Medical Center, Rotterdam, the Netherlands
- Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | - Marie Loh
- Department of Epidemiology and Biostatistics, Imperial College London, London, UK
- Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore, Singapore
| | - Jirong Long
- Division of Epidemiology, Department of Medicine, Vanderbilt Epidemiology Center, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Valeriya Lyssenko
- Department of Clinical Science, Center for Diabetes Research, University of Bergen, Bergen, Norway
- Department of Clinical Sciences, Lund University Diabetes Centre, Lund University, Malmo, Sweden
| | - Massimo Mangino
- Department of Twin Research and Genetic Epidemiology, School of Life Course Sciences, King's College London, London, UK
- NIHR Biomedical Research Centre, Guy's and St Thomas' NHS Foundation Trust, London, UK
| | - Carola Marzi
- Institute of Epidemiology, Research Unit of Molecular Epidemiology, Helmholtz Zentrum München Research Center for Environmental Health, Neuherberg, Germany
- German Center for Diabetes Research (DZD), Neuherberg, Germany
| | - May E Montasser
- Department of Medicine, Division of Endocrinology, Diabetes and Nutrition, University of Maryland School of Medicine, Baltimore, MD, USA
| | - Abhishek Nag
- Wellcome Centre for Human Genetics, University of Oxford, Oxford, UK
| | - Masahiro Nakatochi
- Public Health Informatics Unit, Department of Integrated Sciences, Nagoya University Graduate School of Medicine, Nagoya, Japan
| | - Damia Noce
- Institute for Biomedicine, Eurac Research, Bolzano, Italy
| | - Raymond Noordam
- Department of Internal Medicine, Section of Gerontology and Geriatrics, Leiden University Medical Center, Leiden, the Netherlands
| | - Giorgio Pistis
- Istituto di Ricerca Genetica e Biomedica (IRGB), Consiglio Nazionale delle Ricerche (CNR), Monserrato, Italy
| | - Michael Preuss
- The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- The Mindich Child Health and Development Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Laura Raffield
- Department of Genetics, University of North Carolina, Chapel Hill, NC, USA
| | - Laura J Rasmussen-Torvik
- Department of Preventive Medicine, Northwestern University Feinberg School of Medicine, Chicago, IL, USA
| | - Stephen S Rich
- Center for Public Health Genomics, University of Virginia, Charlottesville, VA, USA
- Department of Public Health Sciences, University of Virginia, Charlottesville, VA, USA
| | - Neil R Robertson
- Oxford Centre for Diabetes, Endocrinology and Metabolism, Radcliffe Department of Medicine, University of Oxford, Oxford, UK
- Wellcome Centre for Human Genetics, University of Oxford, Oxford, UK
| | - Rico Rueedi
- Department of Computational Biology, University of Lausanne, Lausanne, Switzerland
- Swiss Institute of Bioinformatics, Lausanne, Switzerland
| | - Kathleen Ryan
- Department of Medicine, Division of Endocrinology, Diabetes and Nutrition, University of Maryland School of Medicine, Baltimore, MD, USA
| | - Serena Sanna
- Department of Genetics, University of Groningen, University Medical Center Groningen, Groningen, the Netherlands
- Istituto di Ricerca Genetica e Biomedica (IRGB), Consiglio Nazionale delle Ricerche (CNR), Monserrato, Italy
| | - Richa Saxena
- Center for Genomic Medicine, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
- Department of Anesthesia, Critical Care and Pain Medicine, Massachusetts General Hospital, Boston, MA, USA
- Program in Medical and Population Genetics, Broad Institute, Cambridge, MA, USA
| | - Katharina E Schraut
- Centre for Global Health Research, Usher Institute, University of Edinburgh, Edinburgh, UK
- Centre for Cardiovascular Sciences, Queen's Medical Research Institute, University of Edinburgh, Edinburgh, UK
| | - Bengt Sennblad
- Department of Cell and Molecular Biology, National Bioinformatics Infrastructure Sweden, Science for Life Laboratory, Uppsala University, Uppsala, Sweden
| | - Kazuya Setoh
- Center for Genomic Medicine, Kyoto University Graduate School of Medicine, Kyoto, Japan
| | - Albert V Smith
- Department of Biostatistics, University of Michigan, Ann Arbor, MI, USA
- Icelandic Heart Association, Kopavogur, Iceland
| | - Thomas Sparsø
- Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Rona J Strawbridge
- Institute of Health and Wellbeing, University of Glasgow, Glasgow, UK
- Department of Medicine Solna, Cardiovascular Medicine, Karolinska Institutet, Stockholm, Sweden
| | | | - Jingyi Tan
- The Institute for Translational Genomics and Population Sciences, Department of Pediatrics, The Lundquist Institute for Biomedical Innovation at Harbor-UCLA Medical Center, Torrance, CA, USA
| | - Stella Trompet
- Department of Internal Medicine, Section of Gerontology and Geriatrics, Leiden University Medical Center, Leiden, the Netherlands
- Department of Cardiology, Leiden University Medical Center, Leiden, the Netherlands
| | - Erik van den Akker
- Department of Biomedical Data Sciences, Molecular Epidemiology, Leiden University Medical Center, Leiden, the Netherlands
- Department of Pattern Recognition and Bioinformatics, Delft University of Technology, Delft, the Netherlands
- Department of Biomedical Data Sciences, Leiden Computational Biology Center, Leiden University Medical Center, Leiden, the Netherlands
| | - Peter J van der Most
- Department of Epidemiology, University of Groningen, University Medical Center Groningen, Groningen, the Netherlands
| | - Niek Verweij
- Department of Cardiology, University of Groningen, University Medical Center Groningen, Groningen, the Netherlands
- Genomics PLC, Oxford, UK
| | - Mandy Vogel
- Center of Pediatric Research, University Children's Hospital Leipzig, University of Leipzig Medical Center, Leipzig, Germany
| | - Heming Wang
- Department of Medicine, Sleep and Circadian Disorders, Brigham and Women's Hospital, Boston, MA, USA
- Department of Medicine, Sleep Medicine, Harvard Medical School, Boston, MA, USA
| | - Chaolong Wang
- Department of Epidemiology and Biostatistics, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
- Genome Institute of Singapore, Agency for Science, Technology and Research, Singapore, Singapore
| | - Nan Wang
- Department of Preventive Medicine, Keck School of Medicine of University of Southern California, Los Angeles, CA, USA
- University of Southern California Diabetes and Obesity Research Institute, Keck School of Medicine of University of Southern California, Los Angeles, CA, USA
| | - Helen R Warren
- Department of Clinical Pharmacology, William Harvey Research Institute, Barts and The London School of Medicine and Dentistry, Queen Mary University of London, London, UK
- NIHR Barts Cardiovascular Biomedical Research Centre, Queen Mary University of London, London, UK
| | - Wanqing Wen
- Division of Epidemiology, Department of Medicine, Vanderbilt Epidemiology Center, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Tom Wilsgaard
- Department of Community Medicine, Faculty of Health Sciences, UIT the Arctic University of Norway, Tromsø, Norway
| | - Andrew Wong
- MRC Unit for Lifelong Health and Ageing at University College London, London, UK
| | - Andrew R Wood
- Exeter Centre of Excellence for Diabetes Research (EXCEED), Genetics of Complex Traits, University of Exeter Medical School, University of Exeter, Exeter, UK
| | - Tian Xie
- Department of Epidemiology, University of Groningen, University Medical Center Groningen, Groningen, the Netherlands
| | - Mohammad Hadi Zafarmand
- Department of Public Health, Amsterdam Public Health Research Institute, Amsterdam University Medical Center, Amsterdam, the Netherlands
- Department of Clinical Epidemiology, Biostatistics, and Bioinformatics, Amsterdam Public Health Research Institute, Amsterdam University Medical Center, Amsterdam, the Netherlands
| | - Jing-Hua Zhao
- Department of Public Health and Primary Care, School of Clinical Medicine, University of Cambridge, Cambridge, UK
| | - Wei Zhao
- Department of Epidemiology, School of Public Health, University of Michigan, Ann Arbor, MI, USA
| | - Najaf Amin
- Department of Epidemiology, Erasmus Medical Center, Rotterdam, the Netherlands
- Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | - Zorayr Arzumanyan
- The Institute for Translational Genomics and Population Sciences, Department of Pediatrics, The Lundquist Institute for Biomedical Innovation at Harbor-UCLA Medical Center, Torrance, CA, USA
| | - Arne Astrup
- Department of Nutrition, Exercise, and Sports, Faculty of Science, University of Copenhagen, Copenhagen, Denmark
| | - Stephan J L Bakker
- Department of Internal Medicine, University of Groningen, University Medical Center Groningen, Groningen, the Netherlands
| | - Damiano Baldassarre
- Department of Medical Biotechnology and Translational Medicine, University of Milan, Milan, Italy
- Centro Cardiologico Monzino, IRCCS, Milan, Italy
| | - Marian Beekman
- Department of Biomedical Data Sciences, Molecular Epidemiology, Leiden University Medical Center, Leiden, the Netherlands
| | - Richard N Bergman
- Diabetes and Obesity Research Institute, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Alain Bertoni
- Department of Epidemiology and Prevention, Division of Public Health Sciences, Wake Forest School of Medicine, Winston-Salem, NC, USA
| | - Matthias Blüher
- Medical Department III-Endocrinology, Nephrology, Rheumatology, University of Leipzig Medical Center, Leipzig, Germany
| | - Lori L Bonnycastle
- Medical Genomics and Metabolic Genetics Branch, National Human Genome Research Institute, National Institues of Health, Bethesda, MD, USA
| | - Stefan R Bornstein
- Department for Prevention and Care of Diabetes, Faculty of Medicine Carl Gustav Carus, Technische Universität Dresden, Dresden, Germany
| | - Donald W Bowden
- Department of Biochemistry, Wake Forest School of Medicine, Winston-Salem, NC, USA
| | - Qiuyin Cai
- Department of Medicine, Epidemiology, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Archie Campbell
- Centre for Genomic and Experimental Medicine, Institute of Genetics and Molecular Medicine, University of Edinburgh, Western General Hospital, Edinburgh, UK
- Usher Institute, University of Edinburgh, Edinburgh, UK
| | - Harry Campbell
- Centre for Global Health Research, Usher Institute, University of Edinburgh, Edinburgh, UK
| | - Yi Cheng Chang
- Institute of Biomedical Sciences, Academia Sinica, Taipei, Taiwan
- Department of Internal Medicine, National Taiwan University Hospital, Taipei, Taiwan
- Graduate Institute of Medical Genomics and Proteomics, National Taiwan University, Taipei, Taiwan
| | - Eco J C de Geus
- Department of Biological Psychology, Faculty of Behaviour and Movement Sciences, Vrije Universiteit Amsterdam, Amsterdam, the Netherlands
- Amsterdam Public Health Research Institute, Amsterdam University Medical Center, Amsterdam, the Netherlands
| | - Abbas Dehghan
- Department of Epidemiology, Erasmus Medical Center, Rotterdam, the Netherlands
| | - Shufa Du
- Department of Nutrition, Gillings School of Global Public Health, University of North Carolina, Chapel Hill, NC, USA
| | | | - Aliki Eleni Farmaki
- Department of Population Science and Experimental Medicine, Institute of Cardiovascular Science, University College London, London, UK
- Department of Nutrition and Dietetics, School of Health Science and Education, Harokopio University of Athens, Athens, Greece
| | - Mattias Frånberg
- Department of Medicine Solna, Cardiovascular Medicine, Karolinska Institutet, Stockholm, Sweden
| | | | - Yutang Gao
- Department of Epidemiology, Shanghai Cancer Institute, Shanghai, China
| | - Anette P Gjesing
- Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Anuj Goel
- Wellcome Centre for Human Genetics, University of Oxford, Oxford, UK
- Division of Cardiovascular Medicine, Radcliffe Department of Medicine, University of Oxford, Oxford, UK
| | - Sohee Han
- Division of Genome Science, Department of Precision Medicine, National Institute of Health, Cheongju, South Korea
| | - Catharina A Hartman
- Department of Psychiatry, Interdisciplinary Center Psychopathy and Emotion Regulation, University of Groningen, University Medical Center Groningen, Groningen, the Netherlands
| | - Christian Herder
- Institute for Clinical Diabetology, German Diabetes Center, Leibniz Center for Diabetes Research at Heinrich Heine University Düsseldorf, Düsseldorf, Germany
- Division of Endocrinology and Diabetology, Medical Faculty, Heinrich Heine University Düsseldorf, Düsseldorf, Germany
- German Center for Diabetes Research (DZD), Düsseldorf, Germany
| | - Andrew A Hicks
- Institute for Biomedicine, Eurac Research, Bolzano, Italy
| | - Chang-Hsun Hsieh
- Internal Medicine, Endocrine and Metabolism, Tri-Service General Hospital, Taipei, Taiwan
- School of Medicine, National Defense Medical Center, Taipei, Taiwan
| | - Willa A Hsueh
- Internal Medicine, Endocrinology, Diabetes and Metabolism, Diabetes and Metabolism Research Center, The Ohio State University Wexner Medical Center, Columbus, OH, USA
| | - Sahoko Ichihara
- Department of Environmental and Preventive Medicine, Jichi Medical University School of Medicine, Shimotsuke, Japan
| | - Michiya Igase
- Department of Anti-aging Medicine, Ehime University Graduate School of Medicine, Toon, Japan
| | - M Arfan Ikram
- Department of Epidemiology, Erasmus Medical Center, Rotterdam, the Netherlands
| | - W Craig Johnson
- Department of Biostatistics, University of Washington, Seattle, WA, USA
| | - Marit E Jørgensen
- Steno Diabetes Center Copenhagen, Gentofte, Denmark
- National Institute of Public Health, University of Southern Denmark, Odense, Denmark
| | - Peter K Joshi
- Centre for Global Health Research, Usher Institute, University of Edinburgh, Edinburgh, UK
| | - Rita R Kalyani
- Department of Medicine, Endocrinology, Diabetes and Metabolism, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Fouad R Kandeel
- Clinical Diabetes, Endocrinology and Metabolism, Translational Research and Cellular Therapeutics, Beckman Research Institute of the City of Hope, Duarte, CA, USA
| | - Tomohiro Katsuya
- Department of Clinical Gene Therapy, Osaka University Graduate School of Medicine, Suita, Japan
- Department of Geriatric and General Medicine, Osaka University Graduate School of Medicine, Suita, Japan
| | - Chiea Chuen Khor
- Genome Institute of Singapore, Agency for Science, Technology and Research, Singapore, Singapore
| | - Wieland Kiess
- Center of Pediatric Research, University Children's Hospital Leipzig, University of Leipzig Medical Center, Leipzig, Germany
| | - Ivana Kolcic
- Department of Public Health, University of Split School of Medicine, Split, Croatia
| | - Teemu Kuulasmaa
- Institute of Biomedicine, Bioinformatics Center, Univeristy of Eastern Finland, Kuopio, Finland
| | - Johanna Kuusisto
- Department of Medicine, University of Eastern Finland and Kuopio University Hospital, Kuopio, Finland
| | - Kristi Läll
- Estonian Genome Center, Institute of Genomics, University of Tartu, Tartu, Estonia
| | - Kelvin Lam
- The Institute for Translational Genomics and Population Sciences, Department of Pediatrics, The Lundquist Institute for Biomedical Innovation at Harbor-UCLA Medical Center, Torrance, CA, USA
| | - Deborah A Lawlor
- MRC Integrative Epidemiology Unit, University of Bristol, Bristol, UK
- Department of Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
| | - Nanette R Lee
- USC-Office of Population Studies Foundation, University of San Carlos, Cebu City, the Philippines
- Department of Anthropology, Sociology and History, University of San Carlos, Cebu City, the Philippines
| | - Rozenn N Lemaitre
- Department of Medicine, Cardiovascular Health Research Unit, University of Washington, Seattle, WA, USA
| | - Honglan Li
- State Key Laboratory of Oncogene and Related Genes and Department of Epidemiology, Shanghai Cancer Institute, Renji Hospital, Shanghai Jiaotong University School of Medicine, Shanghai, China
| | - Shih-Yi Lin
- Center for Geriatrics and Gerontology, Taichung Veterans General Hospital, Taichung, Taiwan
- National Defense Medical Center, National Yang-Ming University, Taipei, Taiwan
| | - Jaana Lindström
- Diabetes Prevention Unit, National Institute for Health and Welfare, Helsinki, Finland
| | - Allan Linneberg
- Center for Clinical Research and Prevention, Bispebjerg and Frederiksberg Hospital, Copenhagen, Denmark
- Department of Clinical Medicine, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Jianjun Liu
- Genome Institute of Singapore, Agency for Science, Technology and Research, Singapore, Singapore
- Yong Loo Lin School of Medicine, National University of Singapore and National University Health System, Singapore, Singapore
| | - Carlos Lorenzo
- Department of Medicine, University of Texas Health Sciences Center, San Antonio, TX, USA
| | - Tatsuaki Matsubara
- Department of Internal Medicine, Aichi Gakuin University School of Dentistry, Nagoya, Japan
| | - Fumihiko Matsuda
- Center for Genomic Medicine, Kyoto University Graduate School of Medicine, Kyoto, Japan
| | - Geltrude Mingrone
- Department of Diabetes, Diabetes, and Nutritional Sciences, James Black Centre, King's College London, London, UK
| | - Simon Mooijaart
- Department of Internal Medicine, Section of Gerontology and Geriatrics, Leiden University Medical Center, Leiden, the Netherlands
| | - Sanghoon Moon
- Division of Genome Science, Department of Precision Medicine, National Institute of Health, Cheongju, South Korea
| | - Toru Nabika
- Department of Functional Pathology, Shimane University School of Medicine, Izumo, Japan
| | - Girish N Nadkarni
- The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Jerry L Nadler
- Department of Medicine and Pharmacology, New York Medical College School of Medicine, Valhalla, NY, USA
| | - Mari Nelis
- Estonian Genome Center, Institute of Genomics, University of Tartu, Tartu, Estonia
| | - Matt J Neville
- Oxford Centre for Diabetes, Endocrinology and Metabolism, Radcliffe Department of Medicine, University of Oxford, Oxford, UK
- Oxford NIHR Biomedical Research Centre, Oxford University Hospitals NHS Foundation Trust, Oxford, UK
| | - Jill M Norris
- Colorado School of Public Health, University of Colorado Anschutz Medical Campus, Aurora, CO, USA
| | - Yasumasa Ohyagi
- Department of Geriatric Medicine and Neurology, Ehime University Graduate School of Medicine, Toon, Japan
| | - Annette Peters
- German Center for Diabetes Research (DZD), Neuherberg, Germany
- Institute of Epidemiology, Helmholtz Zentrum München Research Center for Environmental Health, Neuherberg, Germany
- Institute for Medical Information Processing, Biometry and Epidemiology, Ludwig-Maximilians University Munich, Munich, Germany
| | - Patricia A Peyser
- Department of Epidemiology, School of Public Health, University of Michigan, Ann Arbor, MI, USA
| | - Ozren Polasek
- Department of Public Health, University of Split School of Medicine, Split, Croatia
- Gen-Info, Zagreb, Croatia
| | - Qibin Qi
- Department of Epidemiology and Population Health, Albert Einstein College of Medicine, New York, NY, USA
| | - Dennis Raven
- Department of Psychiatry, Interdisciplinary Center Psychopathy and Emotion Regulation, University of Groningen, University Medical Center Groningen, Groningen, the Netherlands
| | - Dermot F Reilly
- Genetics and Pharmacogenomics, Merck Sharp & Dohme, Kenilworth, NJ, USA
| | - Alex Reiner
- Department of Public Health Sciences, Fred Hutchinson Cancer Research Center, Seattle, WA, USA
| | - Fernando Rivideneira
- Department of Internal Medicine, Erasmus Medical Center, Rotterdam, the Netherlands
| | - Kathryn Roll
- The Institute for Translational Genomics and Population Sciences, Department of Pediatrics, The Lundquist Institute for Biomedical Innovation at Harbor-UCLA Medical Center, Torrance, CA, USA
| | - Igor Rudan
- Centre for Global Health, The Usher Institute, University of Edinburgh, Edinburgh, UK
| | - Charumathi Sabanayagam
- Ocular Epidemiology, Singapore Eye Research Institute, Singapore National Eye Centre, Singapore, Singapore
- Ophthalmology & Visual Sciences Academic Clinical Program (Eye ACP), Duke-NUS Medical School, Singapore, Singapore
| | - Kevin Sandow
- The Institute for Translational Genomics and Population Sciences, Department of Pediatrics, The Lundquist Institute for Biomedical Innovation at Harbor-UCLA Medical Center, Torrance, CA, USA
| | - Naveed Sattar
- BHF Glasgow Cardiovascular Research Centre, Institute of Cardiovascular and Medical Sciences, University of Glasgow, Glasgow, UK
| | - Annette Schürmann
- German Center for Diabetes Research (DZD), Neuherberg, Germany
- Department of Experimental Diabetology, German Institute of Human Nutrition Potsdam-Rehbruecke, Nuthetal, Germany
| | - Jinxiu Shi
- Department of Genetics, Shanghai-MOST Key Laboratory of Health and Disease Genomics, Chinese National Human Genome Center at Shanghai (CHGC) and Shanghai Academy of Science & Technology (SAST), Shanghai, China
| | - Heather M Stringham
- Center for Statistical Genetics, University of Michigan, Ann Arbor, MI, USA
- Department of Biostatistics, School of Public Health, University of Michigan, Ann Arbor, MI, USA
| | - Kent D Taylor
- The Institute for Translational Genomics and Population Sciences, Department of Pediatrics, The Lundquist Institute for Biomedical Innovation at Harbor-UCLA Medical Center, Torrance, CA, USA
| | | | - Betina Thuesen
- Center for Clinical Research and Prevention, Bispebjerg and Frederiksberg Hospital, Copenhagen, Denmark
| | - Paul R H J Timmers
- Centre for Global Health Research, Usher Institute, University of Edinburgh, Edinburgh, UK
- Medical Research Council Human Genetics Unit, Institute for Genetics and Cancer, University of Edinburgh, Edinburgh, UK
| | | | - Michael Y Tsai
- Department of Laboratory Medicine and Pathology, University of Minnesota, Minneapolis, MN, USA
| | - Andre Uitterlinden
- Department of Internal Medicine, Erasmus Medical Center, Rotterdam, the Netherlands
| | - Rob M van Dam
- Saw Swee Hock School of Public Health, National Univeristy of Singapore and National University Health System, Singapore, Singapore
- Yong Loo Lin School of Medicine, National University of Singapore and National University Health System, Singapore, Singapore
- Department of Nutrition, Harvard T. H. Chan School of Public Health, Boston, MA, USA
| | - Diana van Heemst
- Department of Internal Medicine, Section of Gerontology and Geriatrics, Leiden University Medical Center, Leiden, the Netherlands
| | | | - Jana V van Vliet-Ostaptchouk
- Department of Epidemiology, University of Groningen, University Medical Center Groningen, Groningen, the Netherlands
| | - Jagadish Vangipurapu
- Institute of Clinical Medicine, Internal Medicine, University of Eastern Finland, Kuopio, Finland
| | - Henrik Vestergaard
- Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
- Department of Medicine, Bornholms Hospital, Rønne, Denmark
| | - Tao Wang
- Department of Epidemiology and Population Health, Albert Einstein College of Medicine, New York, NY, USA
| | - Ko Willems van Dijk
- Department of Internal Medicine, Division of Endocrinology, Leiden University Medical Center, Leiden, the Netherlands
- Laboratory for Experimental Vascular Medicine, Leiden University Medical Center, Leiden, the Netherlands
- Department of Human Genetics, Leiden University Medical Center, Leiden, the Netherlands
| | - Tatijana Zemunik
- Department of Human Biology, University of Split School of Medicine, Split, Croatia
| | - Gonçalo R Abecasis
- Department of Biostatistics, School of Public Health, University of Michigan, Ann Arbor, MI, USA
| | - Linda S Adair
- Department of Nutrition, Gillings School of Global Public Health, University of North Carolina, Chapel Hill, NC, USA
- Carolina Population Center, University of North Carolina, Chapel Hill, NC, USA
| | - Carlos Alberto Aguilar-Salinas
- Department of Endocrinology and Metabolism, Instituto Nacional de Ciencias Medicas y Nutricion, Mexico City, Mexico
- Unidad de Investigación de Enfermedades Metabólicas, Instituto Nacional de Ciencias Médicas y Nutrición and Tec Salud, Mexico City, Mexico
- Instituto Tecnológico y de Estudios Superiores de Monterrey Tec Salud, Monterrey, Mexico
| | - Marta E Alarcón-Riquelme
- Department of Medical Genomics, Pfizer/University of Granada/Andalusian Government Center for Genomics and Oncological Research (GENYO), Granada, Spain
- Institute for Environmental Medicine, Chronic Inflammatory Diseases, Karolinska Institutet, Solna, Sweden
| | - Ping An
- Department of Genetics, Division of Statistical Genomics, Washington University School of Medicine, St Louis, MO, USA
| | - Larissa Aviles-Santa
- Clinical and Health Services Research, National Institute on Minority Health and Health Disparities, Bethesda, MD, USA
| | - Diane M Becker
- Department of Medicine, General Internal Medicine, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Lawrence J Beilin
- Medical School, Royal Perth Hospital Unit, University of Western Australia, Perth, Western Australia, Australia
| | - Sven Bergmann
- Department of Computational Biology, University of Lausanne, Lausanne, Switzerland
- Swiss Institute of Bioinformatics, Lausanne, Switzerland
- Department of Integrative Biomedical Sciences, University of Cape Town, Cape Town, South Africa
| | - Hans Bisgaard
- COPSAC, Copenhagen Prospective Studies on Asthma in Childhood, Herlev and Gentofte Hospital, University of Copenhagen, Copenhagen, Denmark
| | - Corri Black
- Aberdeen Centre for Health Data Science, School of Medicine, Medical Sciences and Nutrition, University of Aberdeen, Aberdeen, UK
| | - Michael Boehnke
- Center for Statistical Genetics, University of Michigan, Ann Arbor, MI, USA
- Department of Biostatistics, School of Public Health, University of Michigan, Ann Arbor, MI, USA
| | - Eric Boerwinkle
- Human Genetics Center, School of Public Health, The University of Texas Health Science Center at Houston, Houston, TX, USA
- Human Genome Sequencing Center, Baylor College of Medicine, Houston, TX, USA
| | - Bernhard O Böhm
- Division of Endocrinology and Diabetes, Graduate School of Molecular Endocrinology and Diabetes, University of Ulm, Ulm, Germany
- LKC School of Medicine, Nanyang Technological University, Singapore and Imperial College London, UK, Singapore, Singapore
| | - Klaus Bønnelykke
- COPSAC, Copenhagen Prospective Studies on Asthma in Childhood, Herlev and Gentofte Hospital, University of Copenhagen, Copenhagen, Denmark
| | - D I Boomsma
- Department of Biological Psychology, Faculty of Behaviour and Movement Sciences, Vrije Universiteit Amsterdam, Amsterdam, the Netherlands
- Amsterdam Public Health Research Institute, Amsterdam University Medical Center, Amsterdam, the Netherlands
| | - Erwin P Bottinger
- The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Hasso Plattner Institute for Digital Health at Mount Sinai, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Digital Health Center, Hasso Plattner Institut, University Potsdam, Potsdam, Germany
| | - Thomas A Buchanan
- University of Southern California Diabetes and Obesity Research Institute, Keck School of Medicine of University of Southern California, Los Angeles, CA, USA
- Department of Medicine, Keck School of Medicine of University of Southern California, Los Angeles, CA, USA
- Department of Physiology and Neuroscience, Keck School of Medicine of University of Southern California, Los Angeles, CA, USA
| | - Mickaël Canouil
- INSERM UMR 1283/CNRS UMR 8199, European Institute for Diabetes (EGID), Université de Lille, Lille, France
- INSERM UMR 1283/CNRS UMR 8199, European Institute for Diabetes (EGID), Institut Pasteur de Lille, Lille, France
| | - Mark J Caulfield
- Department of Clinical Pharmacology, William Harvey Research Institute, Barts and The London School of Medicine and Dentistry, Queen Mary University of London, London, UK
- NIHR Barts Cardiovascular Biomedical Research Centre, Queen Mary University of London, London, UK
| | - John C Chambers
- Department of Epidemiology and Biostatistics, Imperial College London, London, UK
- Department of Cardiology, Ealing Hospital, London North West Healthcare NHS Trust, London, UK
- Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore, Singapore
- Imperial College Healthcare NHS Trust, Imperial College London, London, UK
- MRC-PHE Centre for Environment and Health, Imperial College London, London, UK
| | - Daniel I Chasman
- Division of Preventive Medicine, Brigham and Women's Hospital, Boston, MA, USA
- Harvard Medical School, Boston, MA, USA
| | - Yii-Der Ida Chen
- The Institute for Translational Genomics and Population Sciences, Department of Pediatrics, The Lundquist Institute for Biomedical Innovation at Harbor-UCLA Medical Center, Torrance, CA, USA
| | - Ching-Yu Cheng
- Ocular Epidemiology, Singapore Eye Research Institute, Singapore National Eye Centre, Singapore, Singapore
- Ophthalmology & Visual Sciences Academic Clinical Program (Eye ACP), Duke-NUS Medical School, Singapore, Singapore
| | - Francis S Collins
- Medical Genomics and Metabolic Genetics Branch, National Human Genome Research Institute, National Institues of Health, Bethesda, MD, USA
| | - Adolfo Correa
- Department of Medicine, Jackson Heart Study, University of Mississippi Medical Center, Jackson, MS, USA
| | - Francesco Cucca
- Istituto di Ricerca Genetica e Biomedica (IRGB), Consiglio Nazionale delle Ricerche (CNR), Monserrato, Italy
| | - H Janaka de Silva
- Department of Medicine, Faculty of Medicine, University of Kelaniya, Ragama, Sri Lanka
| | - George Dedoussis
- Department of Nutrition and Dietetics, School of Health Science and Education, Harokopio University of Athens, Kallithea, Greece
| | - Sölve Elmståhl
- Department of Clinical Sciences, Lund University, Malmö, Sweden
| | - Michele K Evans
- Laboratory of Epidemiology and Population Sciences, National Institute on Aging Intramural Research Program, National Institutes of Health, Baltimore, MD, USA
| | | | - Luigi Ferrucci
- Intramural Research Program, National Institute of Aging, Baltimore, MD, USA
| | - Jose C Florez
- Program in Medical and Population Genetics, Broad Institute, Cambridge, MA, USA
- Diabetes Unit and Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA
- Department of Medicine, Harvard Medical School, Boston, MA, USA
| | - Paul W Franks
- Department of Clinical Sciences, Lund University Diabetes Centre, Lund University, Malmo, Sweden
- Department of Public Health and Clinical Medicine, Umeå University, Umeå, Sweden
| | - Timothy M Frayling
- Exeter Centre of Excellence for Diabetes Research (EXCEED), Genetics of Complex Traits, University of Exeter Medical School, University of Exeter, Exeter, UK
| | - Philippe Froguel
- INSERM UMR 1283/CNRS UMR 8199, European Institute for Diabetes (EGID), Université de Lille, Lille, France
- INSERM UMR 1283/CNRS UMR 8199, European Institute for Diabetes (EGID), Institut Pasteur de Lille, Lille, France
- Department of Genomics of Common Disease, Imperial College London, London, UK
| | - Bruna Gigante
- Department of Medicine, Cardiovascular Medicine, Karolinska Institutet, Stockholm, Sweden
| | - Mark O Goodarzi
- Department of Medicine, Division of Endocrinology, Diabetes and Metabolism, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Penny Gordon-Larsen
- Department of Nutrition, Gillings School of Global Public Health, University of North Carolina, Chapel Hill, NC, USA
- Carolina Population Center, University of North Carolina, Chapel Hill, NC, USA
| | - Harald Grallert
- Institute of Epidemiology, Research Unit of Molecular Epidemiology, Helmholtz Zentrum München Research Center for Environmental Health, Neuherberg, Germany
- German Center for Diabetes Research (DZD), Neuherberg, Germany
| | - Niels Grarup
- Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Sameline Grimsgaard
- Department of Community Medicine, Faculty of Health Sciences, UIT the Arctic University of Norway, Tromsø, Norway
| | - Leif Groop
- Diabetes Centre, Lund University, Lund, Sweden
- Finnish Institute of Molecular Medicine, Helsinki University, Helsinki, Finland
| | - Vilmundur Gudnason
- Icelandic Heart Association, Kopavogur, Iceland
- Faculty of Medicine, School of Health Sciences, University of Iceland, Reykjavik, Iceland
| | - Xiuqing Guo
- The Institute for Translational Genomics and Population Sciences, Department of Pediatrics, The Lundquist Institute for Biomedical Innovation at Harbor-UCLA Medical Center, Torrance, CA, USA
| | - Anders Hamsten
- Department of Medicine Solna, Cardiovascular Medicine, Karolinska Institutet, Stockholm, Sweden
| | - Torben Hansen
- Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Caroline Hayward
- Medical Research Council Human Genetics Unit, Institute for Genetics and Cancer, University of Edinburgh, Edinburgh, UK
| | - Susan R Heckbert
- Department of Epidemiology, Cardiovascular Health Research Unit, University of Washington, Seattle, WA, USA
| | - Bernardo L Horta
- Postgraduate Program in Epidemiology, Federal University of Pelotas, Pelotas, Brazil
| | - Wei Huang
- Department of Genetics, Shanghai-MOST Key Laboratory of Health and Disease Genomics, Chinese National Human Genome Center at Shanghai (CHGC) and Shanghai Academy of Science & Technology (SAST), Shanghai, China
| | - Erik Ingelsson
- Department of Medicine, Division of Cardiovascular Medicine, Stanford University School of Medicine, Stanford University, Stanford, CA, USA
| | - Pankow S James
- Division of Epidemiology and Community Health, University of Minnesota, Minneapolis, MN, USA
| | - Marjo-Ritta Jarvelin
- Department of Epidemiology and Biostatistics, MRC-PHE Centre for Environment and Health, School of Public Health, Imperial College London, London, UK
- Center for Life Course Health Research, Faculty of Medicine, University of Oulu, Oulu, Finland
- Unit of Primary Health Care, Oulu Univerisity Hospital, OYS, Oulu, Finland
- Department of Life Sciences, College of Health and Life Sciences, Brunel University London, London, UK
| | - Jost B Jonas
- Department of Ophthalmology, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany
- Beijing Institute of Ophthalmology, Beijing Ophthalmology and Visual Science Key Lab, Beijing Tongren Eye Center, Beijing Tongren Hospital, Capital Medical University, Beijing, China
- Institute of Molecular and Clinical Ophthalmology Basel IOB, Basel, Switzerland
| | - J Wouter Jukema
- Department of Cardiology, Leiden University Medical Center, Leiden, the Netherlands
- Netherlands Heart Institute, Utrecht, the Netherlands
| | | | - Robert Kaplan
- Department of Epidemiology and Population Health, Albert Einstein College of Medicine, New York, NY, USA
- Department of Public Health Sciences, Fred Hutchinson Cancer Research Center, Seattle, WA, USA
| | - Sharon L R Kardia
- Department of Epidemiology, School of Public Health, University of Michigan, Ann Arbor, MI, USA
| | - Norihiro Kato
- National Center for Global Health and Medicine, Tokyo, Japan
| | - Sirkka M Keinanen-Kiukaanniemi
- Faculty of Medicine, Institute of Health Sciences, University of Oulu, Oulu, Finland
- Unit of General Practice, Oulu University Hospital, Oulu, Finland
| | - Bong-Jo Kim
- Division of Genome Science, Department of Precision Medicine, National Institute of Health, Cheongju, South Korea
| | - Mika Kivimaki
- Department of Epidemiology and Public Health, University College London, London, UK
| | - Heikki A Koistinen
- Department of Public Health Solutions, Finnish Institute for Health and Welfare, Helsinki, Finland
- Department of Medicine, University of Helsinki and Helsinki University Central Hospital, Helsinki, Finland
- Minerva Foundation Institute for Medical Research, Helsinki, Finland
| | - Jaspal S Kooner
- Department of Cardiology, Ealing Hospital, London North West Healthcare NHS Trust, London, UK
- Imperial College Healthcare NHS Trust, Imperial College London, London, UK
- MRC-PHE Centre for Environment and Health, Imperial College London, London, UK
- National Heart and Lung Institute, Imperial College London, London, UK
| | - Antje Körner
- Center of Pediatric Research, University Children's Hospital Leipzig, University of Leipzig Medical Center, Leipzig, Germany
| | - Peter Kovacs
- Medical Department III-Endocrinology, Nephrology, Rheumatology, University of Leipzig Medical Center, Leipzig, Germany
- IFB Adiposity Diseases, University of Leipzig Medical Center, Leipzig, Germany
| | - Diana Kuh
- MRC Unit for Lifelong Health and Ageing at University College London, London, UK
| | - Meena Kumari
- Institute for Social and Economic Research, University of Essex, Colchester, UK
| | - Zoltan Kutalik
- Swiss Institute of Bioinformatics, Lausanne, Switzerland
- University Institute of Primary Care and Public Health, Division of Biostatistics, University of Lausanne, Lausanne, Switzerland
| | - Markku Laakso
- Department of Medicine, University of Eastern Finland and Kuopio University Hospital, Kuopio, Finland
| | - Timo A Lakka
- Institute of Biomedicine, School of Medicine, University of Eastern Finland, Kuopio, Finland
- Department of Clinical Physiology and Nuclear Medicine, Kuopio University Hospital, Kuopio, Finland
- Foundation for Research in Health Exercise and Nutrition, Kuopio Research Institute of Exercise Medicine, Kuopio, Finland
| | - Lenore J Launer
- Laboratory of Epidemiology and Population Sciences, National Institute on Aging, National Institutes of Health, Baltimore, MD, USA
| | - Karin Leander
- Institute of Environmental Medicine, Cardiovascular and Nutritional Epidemiology, Karolinska Institutet, Stockholm, Sweden
| | - Huaixing Li
- CAS Key Laboratory of Nutrition, Metabolism and Food Safety, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai, China
| | - Xu Lin
- CAS Key Laboratory of Nutrition, Metabolism and Food Safety, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai, China
| | - Lars Lind
- Department of Medical Sciences, University of Uppsala, Uppsala, Sweden
| | - Cecilia Lindgren
- Wellcome Centre for Human Genetics, University of Oxford, Oxford, UK
- Big Data Institute, Nuffield Department of Medicine, University of Oxford, Oxford, UK
- Nuffield Department of Women's and Reproductive Health, University of Oxford, Oxford, UK
| | - Simin Liu
- Department of Epidemiology, Brown University School of Public Health, Brown University, Providence, RI, USA
| | - Ruth J F Loos
- The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- The Mindich Child Health and Development Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Patrik K E Magnusson
- Department of Medical Epidemiology and Biostatistics and the Swedish Twin Registry, Karolinska Institutet, Stockholm, Sweden
| | - Anubha Mahajan
- Wellcome Centre for Human Genetics, University of Oxford, Oxford, UK
- Genentech, South San Francisco, CA, USA
| | - Andres Metspalu
- Estonian Genome Center, Institute of Genomics, University of Tartu, Tartu, Estonia
| | - Dennis O Mook-Kanamori
- Department of Clinical Epidemiology, Leiden University Medical Center, Leiden, the Netherlands
- Department of Public Health and Primary Care, Leiden University Medical Center, Leiden, the Netherlands
| | - Trevor A Mori
- Medical School, Royal Perth Hospital Unit, University of Western Australia, Perth, Western Australia, Australia
| | - Patricia B Munroe
- Department of Clinical Pharmacology, William Harvey Research Institute, Barts and The London School of Medicine and Dentistry, Queen Mary University of London, London, UK
- NIHR Barts Cardiovascular Biomedical Research Centre, Queen Mary University of London, London, UK
| | - Inger Njølstad
- Department of Community Medicine, Faculty of Health Sciences, UIT the Arctic University of Norway, Tromsø, Norway
| | - Jeffrey R O'Connell
- Department of Medicine, Division of Endocrinology, Diabetes and Nutrition, University of Maryland School of Medicine, Baltimore, MD, USA
| | - Albertine J Oldehinkel
- Department of Psychiatry, Interdisciplinary Center Psychopathy and Emotion Regulation, University of Groningen, University Medical Center Groningen, Groningen, the Netherlands
| | - Ken K Ong
- MRC Epidemiology Unit, Institute of Metabolic Science, University of Cambridge, Cambridge, UK
| | - Sandosh Padmanabhan
- Institute of Cardiovascular and Medical Sciences, University of Glasgow, Glasgow, UK
| | - Colin N A Palmer
- Division of Population Health and Genomics, School of Medicine, University of Dundee, Ninewells Hospital and Medical School, Dundee, UK
| | - Nicholette D Palmer
- Department of Biochemistry, Wake Forest School of Medicine, Winston-Salem, NC, USA
| | - Oluf Pedersen
- Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Craig E Pennell
- School of Medicine and Public Health, College of Health, Medicine and Wellbeing, The University of Newcastle, Newcastle, New South Wales, Australia
| | - David J Porteous
- Centre for Genomic and Experimental Medicine, Institute of Genetics and Molecular Medicine, University of Edinburgh, Western General Hospital, Edinburgh, UK
- Centre for Cognitive Ageing and Cognitive Epidemiology, University of Edinburgh, Edinburgh, UK
| | | | - Michael A Province
- Department of Genetics, Division of Statistical Genomics, Washington University School of Medicine, St Louis, MO, USA
| | - Bruce M Psaty
- Department of Medicine, Cardiovascular Health Research Unit, University of Washington, Seattle, WA, USA
- Department of Epidemiology, Cardiovascular Health Research Unit, University of Washington, Seattle, WA, USA
- Department of Health Services, Cardiovascular Health Research Unit, University of Washington, Seattle, WA, USA
| | - Lu Qi
- Department of Epidemiology, Tulane University School of Public Health and Tropical Medicine, New Orleans, LA, USA
| | - Leslie J Raffel
- Department of Pediatrics, Genetic and Genomic Medicine, University of California, Irvine, Irvine, CA, USA
| | - Rainer Rauramaa
- Foundation for Research in Health Exercise and Nutrition, Kuopio Research Institute of Exercise Medicine, Kuopio, Finland
| | - Susan Redline
- Department of Medicine, Sleep and Circadian Disorders, Brigham and Women's Hospital, Boston, MA, USA
- Department of Medicine, Sleep Medicine, Harvard Medical School, Boston, MA, USA
| | - Paul M Ridker
- Division of Preventive Medicine, Brigham and Women's Hospital, Boston, MA, USA
- Havard Medical School, Boston, MA, USA
| | - Frits R Rosendaal
- Department of Clinical Epidemiology, Leiden University Medical Center, Leiden, the Netherlands
| | - Timo E Saaristo
- Tampere, Finnish Diabetes Association, Tampere, Finland
- Pirkanmaa Hospital District, Tampere, Finland
| | | | | | | | - Peter Schwarz
- German Center for Diabetes Research (DZD), Neuherberg, Germany
- Department for Prevention and Care of Diabetes, Faculty of Medicine Carl Gustav Carus, Technische Universität Dresden, Dresden, Germany
- Paul Langerhans Institute Dresden of the Helmholtz Center Munich, University Hospital and Faculty of Medicine, Dresden, Germany
| | - Laura J Scott
- Center for Statistical Genetics, University of Michigan, Ann Arbor, MI, USA
- Department of Biostatistics, School of Public Health, University of Michigan, Ann Arbor, MI, USA
| | - Elizabeth Selvin
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
| | - Peter Sever
- National Heart and Lung Institute, Imperial College London, London, UK
| | - Xiao-Ou Shu
- Division of Epidemiology, Department of Medicine, Vanderbilt Epidemiology Center, Vanderbilt University Medical Center, Nashville, TN, USA
| | - P Eline Slagboom
- Department of Biomedical Data Sciences, Molecular Epidemiology, Leiden University Medical Center, Leiden, the Netherlands
| | - Kerrin S Small
- Department of Twin Research and Genetic Epidemiology, School of Life Course Sciences, King's College London, London, UK
| | - Blair H Smith
- Division of Population Health and Genomics, Ninewells Hospital and Medical School, University of Dundee, Dundee, UK
| | - Harold Snieder
- Department of Epidemiology, University of Groningen, University Medical Center Groningen, Groningen, the Netherlands
| | - Tamar Sofer
- Department of Medicine, Harvard Medical School, Boston, MA, USA
- Division of Sleep and Circadian Disorders, Brigham and Women's Hospital, Boston, MA, USA
| | - Thorkild I A Sørensen
- MRC Integrative Epidemiology Unit, University of Bristol, Bristol, UK
- Department of Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
- Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
- Department of Public Health, Section of Epidemiology, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Tim D Spector
- Department of Twin Research and Genetic Epidemiology, School of Life Course Sciences, King's College London, London, UK
| | - Alice Stanton
- Department of Molecular and Cellular Therapeutics, Royal College of Surgeons in Ireland, Dublin, Ireland
| | - Claire J Steves
- Department of Twin Research and Genetic Epidemiology, School of Life Course Sciences, King's College London, London, UK
- Department of Ageing and Health, Guy's and St Thomas' NHS Foundation Trust, London, UK
| | - Michael Stumvoll
- Medical Department III-Endocrinology, Nephrology, Rheumatology, University of Leipzig Medical Center, Leipzig, Germany
| | - Liang Sun
- CAS Key Laboratory of Nutrition, Metabolism and Food Safety, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai, China
| | - Yasuharu Tabara
- Center for Genomic Medicine, Kyoto University Graduate School of Medicine, Kyoto, Japan
| | - E Shyong Tai
- Saw Swee Hock School of Public Health, National Univeristy of Singapore and National University Health System, Singapore, Singapore
- Yong Loo Lin School of Medicine, National University of Singapore and National University Health System, Singapore, Singapore
- Cardiovascular and Metabolic Disease Signature Research Program, Duke-NUS Medical School, Singapore, Singapore
| | - Nicholas J Timpson
- MRC Integrative Epidemiology Unit, University of Bristol, Bristol, UK
- Department of Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
| | - Anke Tönjes
- Medical Department III-Endocrinology, Nephrology, Rheumatology, University of Leipzig Medical Center, Leipzig, Germany
| | - Jaakko Tuomilehto
- Department of Public Health Solutions, National Institute for Health and Welfare, Helsinki, Finland
- Department of Public Health, University of Helsinki, Helsinki, Finland
- Saudi Diabetes Research Group, King Abdulaziz University, Jeddah, Saudi Arabia
| | - Teresa Tusie
- Molecular Biology and Genomic Medicine Unit, National Institute of Medical Sciences and Nutrition, Mexico City, Mexico
- Department of Genomic Medicine and Environmental Toxicology, Instituto de Investigaciones Biomedicas, Universidad Nacional Autonoma de Mexico, Mexico City, Mexico
| | - Matti Uusitupa
- Department of Public Health and Clinical Nutrition, University of Eastern Finland, Kuopio, Finland
| | - Pim van der Harst
- Department of Genetics, University of Groningen, University Medical Center Groningen, Groningen, the Netherlands
- Department of Cardiology, University of Groningen, University Medical Center Groningen, Groningen, the Netherlands
| | - Cornelia van Duijn
- Department of Epidemiology, Erasmus Medical Center, Rotterdam, the Netherlands
- Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | - Veronique Vitart
- Medical Research Council Human Genetics Unit, Institute for Genetics and Cancer, University of Edinburgh, Edinburgh, UK
| | - Peter Vollenweider
- Department of Medicine, Internal Medicine, Lausanne University Hospital (CHUV), Lausanne, Switzerland
| | - Tanja G M Vrijkotte
- Department of Public Health, Amsterdam Public Health Research Institute, Amsterdam University Medical Center, Amsterdam, the Netherlands
| | - Lynne E Wagenknecht
- Department of Public Health Sciences, Wake Forest School of Medicine, Winston-Salem, NC, USA
| | - Mark Walker
- Faculty of Medical Sciences, Newcastle University, Newcastle upon Tyne, UK
| | - Ya X Wang
- Beijing Institute of Ophthalmology, Beijing Ophthalmology and Visual Science Key Lab, Beijing Tongren Eye Center, Beijing Tongren Hospital, Capital Medical University, Beijing, China
| | - Nick J Wareham
- MRC Epidemiology Unit, Institute of Metabolic Science, University of Cambridge, Cambridge, UK
| | - Richard M Watanabe
- Department of Preventive Medicine, Keck School of Medicine of University of Southern California, Los Angeles, CA, USA
- University of Southern California Diabetes and Obesity Research Institute, Keck School of Medicine of University of Southern California, Los Angeles, CA, USA
- Department of Physiology and Neuroscience, Keck School of Medicine of University of Southern California, Los Angeles, CA, USA
| | - Hugh Watkins
- Wellcome Centre for Human Genetics, University of Oxford, Oxford, UK
- Division of Cardiovascular Medicine, Radcliffe Department of Medicine, University of Oxford, Oxford, UK
| | - Wen B Wei
- Beijing Tongren Eye Center, Beijing Key Laboratory of Intraocular Tumor Diagnosis and Treatment, Beijing Ophthalmology & Visual Sciences Key Lab, Beijing Tongren Hospital, Capital Medical University, Beijing, China
| | | | - Gonneke Willemsen
- Department of Biological Psychology, Faculty of Behaviour and Movement Sciences, Vrije Universiteit Amsterdam, Amsterdam, the Netherlands
- Amsterdam Public Health Research Institute, Amsterdam University Medical Center, Amsterdam, the Netherlands
| | - James F Wilson
- Centre for Global Health Research, Usher Institute, University of Edinburgh, Edinburgh, UK
- Medical Research Council Human Genetics Unit, Institute for Genetics and Cancer, University of Edinburgh, Edinburgh, UK
| | - Tien-Yin Wong
- Ocular Epidemiology, Singapore Eye Research Institute, Singapore National Eye Centre, Singapore, Singapore
- Ophthalmology & Visual Sciences Academic Clinical Program (Eye ACP), Duke-NUS Medical School, Singapore, Singapore
| | - Jer-Yuarn Wu
- Institute of Biomedical Sciences, Academia Sinica, Taipei, Taiwan
| | - Anny H Xiang
- Department of Research and Evaluation, Kaiser Permanente of Southern California, Pasadena, CA, USA
| | - Lisa R Yanek
- Department of Medicine, General Internal Medicine, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Loïc Yengo
- Institute for Molecular Bioscience, The University of Queensland, St Lucia, Queensland, Australia
| | | | - Eleftheria Zeggini
- Department of Human Genetics, Wellcome Sanger Institute, Cambridge, UK
- Institute of Translational Genomics, Helmholtz Zentrum München-German Research Center for Environmental Health, Neuherberg, Germany
- TUM School of Medicine, Technical University of Munich and Klinikum Rechts der Isar, Munich, Germany
| | - Wei Zheng
- Division of Epidemiology, Department of Medicine, Vanderbilt Epidemiology Center, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Alan B Zonderman
- Laboratory of Epidemiology and Population Sciences, National Institute on Aging, National Institutes of Health, Baltimore, MD, USA
| | - Jerome I Rotter
- The Institute for Translational Genomics and Population Sciences, Department of Pediatrics, The Lundquist Institute for Biomedical Innovation at Harbor-UCLA Medical Center, Torrance, CA, USA
| | - Anna L Gloyn
- Oxford Centre for Diabetes, Endocrinology and Metabolism, Radcliffe Department of Medicine, University of Oxford, Oxford, UK
- Wellcome Centre for Human Genetics, University of Oxford, Oxford, UK
- Oxford NIHR Biomedical Research Centre, Oxford University Hospitals NHS Foundation Trust, Oxford, UK
- Department of Pediatrics, Division of Endocrinology, Stanford School of Medicine, Stanford, CA, USA
| | - Mark I McCarthy
- Oxford Centre for Diabetes, Endocrinology and Metabolism, Radcliffe Department of Medicine, University of Oxford, Oxford, UK
- Wellcome Centre for Human Genetics, University of Oxford, Oxford, UK
- Oxford NIHR Biomedical Research Centre, Oxford University Hospitals NHS Foundation Trust, Oxford, UK
- Wellcome Centre for Human Genetics, Nuffield Department of Medicine, University of Oxford, Oxford, UK
- Genentech, South San Francisco, CA, USA
| | - Josée Dupuis
- Department of Biostatistics, Boston University School of Public Health, Boston, MA, USA
| | - James B Meigs
- Program in Medical and Population Genetics, Broad Institute, Cambridge, MA, USA
- Department of Medicine, Harvard Medical School, Boston, MA, USA
- Department of Medicine, Division of General Internal Medicine, Massachusetts General Hospital, Boston, MA, USA
| | - Robert A Scott
- MRC Epidemiology Unit, Institute of Metabolic Science, University of Cambridge, Cambridge, UK
| | - Inga Prokopenko
- Department of Metabolism, Digestion and Reproduction, Imperial College London, London, UK
- Section of Statistical Multi-omics, Department of Clinical and Experimental Research, University of Surrey, Guildford, UK
| | - Aaron Leong
- Harvard Medical School, Boston, MA, USA
- Department of Medicine, General Internal Medicine, Massachusetts General Hospital, Boston, MA, USA
- Department of Medicine, Diabetes Unit and Endocrine Unit, Massachusetts General Hospital, Boston, MA, USA
| | - Ching-Ti Liu
- Department of Biostatistics, Boston University School of Public Health, Boston, MA, USA
| | - Stephen C J Parker
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, USA
- Department of Human Genetics, University of Michigan, Ann Arbor, MI, USA
| | - Karen L Mohlke
- Department of Genetics, University of North Carolina, Chapel Hill, NC, USA
| | - Claudia Langenberg
- MRC Epidemiology Unit, Institute of Metabolic Science, University of Cambridge, Cambridge, UK
| | - Eleanor Wheeler
- Department of Human Genetics, Wellcome Sanger Institute, Cambridge, UK
- MRC Epidemiology Unit, Institute of Metabolic Science, University of Cambridge, Cambridge, UK
| | - Andrew P Morris
- Wellcome Centre for Human Genetics, University of Oxford, Oxford, UK
- Centre for Genetics and Genomics Versus Arthritis, Division of Musculoskeletal and Dermatological Sciences, The University of Manchester, Manchester, UK
- Centre for Musculoskeletal Research, Division of Musculoskeletal and Dermatological Sciences, The University of Manchester, Manchester, UK
- Department of Biostatistics, University of Liverpool, Liverpool, UK
| | - Inês Barroso
- Exeter Centre of Excellence for Diabetes Research (EXCEED), Genetics of Complex Traits, University of Exeter Medical School, University of Exeter, Exeter, UK.
- Department of Human Genetics, Wellcome Sanger Institute, Cambridge, UK.
- MRC Epidemiology Unit, Institute of Metabolic Science, University of Cambridge, Cambridge, UK.
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Banerjee S, Simonetti FL, Detrois KE, Kaphle A, Mitra R, Nagial R, Söding J. Tejaas: reverse regression increases power for detecting trans-eQTLs. Genome Biol 2021; 22:142. [PMID: 33957961 PMCID: PMC8101255 DOI: 10.1186/s13059-021-02361-8] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/09/2020] [Accepted: 04/22/2021] [Indexed: 12/18/2022] Open
Abstract
Trans-acting expression quantitative trait loci (trans-eQTLs) account for ≥70% expression heritability and could therefore facilitate uncovering mechanisms underlying the origination of complex diseases. Identifying trans-eQTLs is challenging because of small effect sizes, tissue specificity, and a severe multiple-testing burden. Tejaas predicts trans-eQTLs by performing L2-regularized “reverse” multiple regression of each SNP on all genes, aggregating evidence from many small trans-effects while being unaffected by the strong expression correlations. Combined with a novel unsupervised k-nearest neighbor method to remove confounders, Tejaas predicts 18851 unique trans-eQTLs across 49 tissues from GTEx. They are enriched in open chromatin, enhancers, and other regulatory regions. Many overlap with disease-associated SNPs, pointing to tissue-specific transcriptional regulation mechanisms.
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Affiliation(s)
- Saikat Banerjee
- Quantitative and Computational Biology, Max-Planck Institute for Biophysical Chemistry, Göttingen, 37077, Germany.
| | - Franco L Simonetti
- Quantitative and Computational Biology, Max-Planck Institute for Biophysical Chemistry, Göttingen, 37077, Germany
| | - Kira E Detrois
- Quantitative and Computational Biology, Max-Planck Institute for Biophysical Chemistry, Göttingen, 37077, Germany.,Georg-August University, Göttingen, 37075, Germany
| | - Anubhav Kaphle
- Quantitative and Computational Biology, Max-Planck Institute for Biophysical Chemistry, Göttingen, 37077, Germany.,Georg-August University, Göttingen, 37075, Germany
| | | | | | - Johannes Söding
- Quantitative and Computational Biology, Max-Planck Institute for Biophysical Chemistry, Göttingen, 37077, Germany. .,Campus-Institut Data Science (CIDAS), University of Göttingen, Göttingen, 37073, Germany. .,Cluster of Excellence "Multiscale Bioimaging" (MBExC), University of Göttingen, Göttingen, 37075, Germany.
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Hormozdiari F, Jung J, Eskin E, J. Joo JW. MARS: leveraging allelic heterogeneity to increase power of association testing. Genome Biol 2021; 22:128. [PMID: 33931127 PMCID: PMC8086090 DOI: 10.1186/s13059-021-02353-8] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/07/2020] [Accepted: 04/15/2021] [Indexed: 11/10/2022] Open
Abstract
In standard genome-wide association studies (GWAS), the standard association test is underpowered to detect associations between loci with multiple causal variants with small effect sizes. We propose a statistical method, Model-based Association test Reflecting causal Status (MARS), that finds associations between variants in risk loci and a phenotype, considering the causal status of variants, only requiring the existing summary statistics to detect associated risk loci. Utilizing extensive simulated data and real data, we show that MARS increases the power of detecting true associated risk loci compared to previous approaches that consider multiple variants, while controlling the type I error.
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Affiliation(s)
- Farhad Hormozdiari
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, 02115 MA USA
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA USA
| | - Junghyun Jung
- Department of Life Science, Dongguk University-Seoul, Seoul, 04620 South Korea
| | - Eleazar Eskin
- Department of Computer Science, University of California, Los Angeles, Los Angeles, 90095 CA USA
- Department of Human Genetics, University of California, Los Angeles, Los Angeles, 90095 CA USA
| | - Jong Wha J. Joo
- Department of Computer Science and Engineering, Dongguk University-Seoul, Seoul, 04620 South Korea
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Abstract
It has recently been shown that epidermal growth factor receptor (EGFR) contributes to the pathogenesis of pain. We scanned genetic markers within genes coding for receptors of the EGFR family (EGFR, ERBB2, ERBB3, and ERBB4) and their ligands (AREG, BTC, EGF, EPGN, EREG, HBEGF, MUC4, NRG1, NRG2, NRG3, NRG4, and TGFA) for association with self-reported pain intensity in patients with chronic facial pain who participated in the Orofacial Pain: Prospective Evaluation and Risk Assessment (OPPERA) cohort. We found that only epiregulin (EREG) was associated with pain. The strongest effect was observed for a minor allele at rs6836436 in EREG, which was associated with lower chronic pain intensity. However, the same allele was associated with higher facial pain intensity among cases with recent onset of facial pain. Similar trends were observed in an independent cohort of UK Biobank (UKB) where the minor allele at rs6836436 was associated with a higher number of acute pain sites but a lower number of chronic pain sites. Expression quantitative trait loci analyses established rs6836436 as a loss-of-function variant of EREG. Finally, we investigated the functional role of EREG using mouse models of chronic and acute pain. Injecting mice with an EREG monoclonal antibody reversed established mechanosensitivity in the complete Freund's adjuvant and spared nerve injury models of chronic pain. However, the EREG monoclonal antibody prolonged allodynia when administered during the development of complete Freund's adjuvant-induced mechanosensitivity and enhanced pain behavior in the capsaicin model of acute pain.
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Patro CPK, Nousome D, Lai RK. Meta-Analyses of Splicing and Expression Quantitative Trait Loci Identified Susceptibility Genes of Glioma. Front Genet 2021; 12:609657. [PMID: 33936159 PMCID: PMC8081720 DOI: 10.3389/fgene.2021.609657] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/24/2020] [Accepted: 03/09/2021] [Indexed: 12/13/2022] Open
Abstract
Background The functions of most glioma risk alleles are unknown. Very few studies had evaluated expression quantitative trait loci (eQTL), and insights of susceptibility genes were limited due to scarcity of available brain tissues. Moreover, no prior study had examined the effect of glioma risk alleles on alternative RNA splicing. Objective This study explored splicing quantitative trait loci (sQTL) as molecular QTL and improved the power of QTL mapping through meta-analyses of both cis eQTL and sQTL. Methods We first evaluated eQTLs and sQTLs of the CommonMind Consortium (CMC) and Genotype-Tissue Expression Project (GTEx) using genotyping, or whole-genome sequencing and RNA-seq data. Alternative splicing events were characterized using an annotation-free method that detected intron excision events. Then, we conducted meta-analyses by pooling the eQTL and sQTL results of CMC and GTEx using the inverse variance-weighted model. Afterward, we integrated QTL meta-analysis results (Q < 0.05) with the Glioma International Case Control Study (GICC) GWAS meta-analysis (case:12,496, control:18,190), using a summary statistics-based mendelian randomization (SMR) method. Results Between CMC and GTEx, we combined the QTL data of 354 unique individuals of European ancestry. SMR analyses revealed 15 eQTLs in 11 loci and 32 sQTLs in 9 loci relevant to glioma risk. Two loci only harbored sQTLs (1q44 and 16p13.3). In seven loci, both eQTL and sQTL coexisted (2q33.3, 7p11.2, 11q23.3 15q24.2, 16p12.1, 20q13.33, and 22q13.1), but the target genes were different for five of these seven loci. Three eQTL loci (9p21.3, 20q13.33, and 22q13.1) and 4 sQTL loci (11q23.3, 16p13.3, 16q12.1, and 20q13.33) harbored multiple target genes. Eight target genes of sQTLs (C2orf80, SEC61G, TMEM25, PHLDB1, RP11-161M6.2, HEATR3, RTEL1-TNFRSF6B, and LIME1) had multiple alternatively spliced transcripts. Conclusion Our study revealed that the regulation of transcriptome by glioma risk alleles is complex, with the potential for eQTL and sQTL jointly affecting gliomagenesis in risk loci. QTLs of many loci involved multiple target genes, some of which were specific to alternative splicing. Therefore, quantitative trait loci that evaluate only total gene expression will miss many important target genes.
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Affiliation(s)
- C Pawan K Patro
- Department of Neurology and Preventive Medicine, Keck School of Medicine, University of Southern California, Los Angeles, Los Angeles, CA, United States
| | - Darryl Nousome
- Center for Prostate Disease Research, Department of Surgery, Uniformed Services University of the Health Sciences, Rockville, MD, United States
| | | | - Rose K Lai
- Department of Neurology and Preventive Medicine, Keck School of Medicine, University of Southern California, Los Angeles, Los Angeles, CA, United States
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50
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Yao C, Joehanes R, Wilson R, Tanaka T, Ferrucci L, Kretschmer A, Prokisch H, Schramm K, Gieger C, Peters A, Waldenberger M, Marzi C, Herder C, Levy D. Epigenome-wide association study of whole blood gene expression in Framingham Heart Study participants provides molecular insight into the potential role of CHRNA5 in cigarette smoking-related lung diseases. Clin Epigenetics 2021; 13:60. [PMID: 33752734 PMCID: PMC7986283 DOI: 10.1186/s13148-021-01041-5] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/12/2020] [Accepted: 02/28/2021] [Indexed: 11/28/2022] Open
Abstract
Background DNA methylation is a key epigenetic modification that can directly affect gene regulation. DNA methylation is highly influenced by environmental factors such as cigarette smoking, which is causally related to chronic obstructive pulmonary disease (COPD) and lung cancer. To date, there have been few large-scale, combined analyses of DNA methylation and gene expression and their interrelations with lung diseases. Results We performed an epigenome-wide association study of whole blood gene expression in ~ 6000 individuals from four cohorts. We discovered and replicated numerous CpGs associated with the expression of cis genes within 500 kb of each CpG, with 148 to 1,741 cis CpG-transcript pairs identified across cohorts. We found that the closer a CpG resided to a transcription start site, the larger its effect size, and that 36% of cis CpG-transcript pairs share the same causal genetic variant. Mendelian randomization analyses revealed that hypomethylation and lower expression of CHRNA5, which encodes a smoking-related nicotinic receptor, are causally linked to increased risk of COPD and lung cancer. This putatively causal relationship was further validated in lung tissue data. Conclusions Our results provide a large and comprehensive association study of whole blood DNA methylation with gene expression. Expression platform differences rather than population differences are critical to the replication of cis CpG-transcript pairs. The low reproducibility of trans CpG-transcript pairs suggests that DNA methylation regulates nearby rather than remote gene expression. The putatively causal roles of methylation and expression of CHRNA5 in relation to COPD and lung cancer provide evidence for a mechanistic link between patterns of smoking-related epigenetic variation and lung diseases, and highlight potential therapeutic targets for lung diseases and smoking cessation. Supplementary Information The online version contains supplementary material available at 10.1186/s13148-021-01041-5.
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Affiliation(s)
- Chen Yao
- The Framingham Heart Study, 73 Mt. Wayte Avenue, Framingham, MA, 01702, USA.,The Population Sciences Branch, Division of Intramural Research, National Heart, Lung, and Blood Institute, Bethesda, MD, USA
| | - Roby Joehanes
- The Framingham Heart Study, 73 Mt. Wayte Avenue, Framingham, MA, 01702, USA.,The Population Sciences Branch, Division of Intramural Research, National Heart, Lung, and Blood Institute, Bethesda, MD, USA
| | - Rory Wilson
- Research Unit Molecular Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health, 85764, Bavaria, Neuherberg, Germany.,Institute of Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health, 85764, Bavaria, Neuherberg, Germany
| | - Toshiko Tanaka
- Longitudinal Study Section, National Institute On Aging, Baltimore, MD, USA
| | - Luigi Ferrucci
- Longitudinal Study Section, National Institute On Aging, Baltimore, MD, USA
| | - Anja Kretschmer
- Research Unit Molecular Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health, 85764, Bavaria, Neuherberg, Germany.,Institute of Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health, 85764, Bavaria, Neuherberg, Germany
| | - Holger Prokisch
- Institute of Human Genetics, Helmholtz Center Munich, German Research Center for Environmental Health, Neuherberg, Germany.,Institute of Human Genetics, Technical University Munich, München, Germany.,Institute for Neurogenomics, Helmholtz Center Munich, German Research Center for Environmental Health, Neuherberg, Germany
| | - Katharina Schramm
- Institute of Genetic Epidemiology, Helmholtz Zentrum München - German Research Center for Environmental Health, 85764, Neuherberg, Germany.,Chair of Genetic Epidemiology, IBE, Faculty of Medicine, LMU Munich, 81377, Munich, Germany.,Department of Internal Medicine I (Cardiology), Hospital of the Ludwig-Maximilians-University (LMU) Munich, 81377, Munich, Germany
| | - Christian Gieger
- Research Unit Molecular Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health, 85764, Bavaria, Neuherberg, Germany.,Institute of Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health, 85764, Bavaria, Neuherberg, Germany
| | - Annette Peters
- Research Unit Molecular Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health, 85764, Bavaria, Neuherberg, Germany.,Institute of Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health, 85764, Bavaria, Neuherberg, Germany.,German Center for Cardiovascular Research (DZHK), Partner Site Munich Heart Alliance, Munich, Germany
| | - Melanie Waldenberger
- Research Unit Molecular Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health, 85764, Bavaria, Neuherberg, Germany.,Institute of Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health, 85764, Bavaria, Neuherberg, Germany.,German Center for Cardiovascular Research (DZHK), Partner Site Munich Heart Alliance, Munich, Germany
| | - Carola Marzi
- Research Unit of Molecular Epidemiology, Institute of Epidemiology II, German Research Center for Environmental Health, Neuherberg, Germany
| | - Christian Herder
- Institute for Clinical Diabetology, German Diabetes Center, Leibniz Center for Diabetes Research At Heinrich Heine University Düsseldorf, Düsseldorf, Germany.,German Center for Diabetes Research (DZD), Partner Düsseldorf, Germany.,Division of Endocrinology and Diabetology, Medical Faculty, Heinrich Heine University, Düsseldorf, Germany
| | - Daniel Levy
- The Framingham Heart Study, 73 Mt. Wayte Avenue, Framingham, MA, 01702, USA. .,The Population Sciences Branch, Division of Intramural Research, National Heart, Lung, and Blood Institute, Bethesda, MD, USA.
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