1
|
Kazemi Naeini M, Akbarzadeh M, Kazemi I, Speed D, Hosseini SM. Using the Bayesian variational spike and slab model in a genome-wide association study for finding associated loci with bipolar disorder. Ann Hum Genet 2024; 88:212-246. [PMID: 38161273 DOI: 10.1111/ahg.12538] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/24/2023] [Revised: 10/17/2023] [Accepted: 10/23/2023] [Indexed: 01/03/2024]
Abstract
OBJECTIVE The genome-wide association studies (GWAS) analysis, the most successful technique for discovering disease-related genetic variation, has some statistical concerns, including multiple testing, the correlation among variants (single-nucleotide polymorphisms) based on linkage disequilibrium and omitting the important variants when fitting the model with just one variant. To eliminate these problems in a small sample-size study, we used a sparse Bayesian learning model for finding bipolar disorder (BD) genetic variants. METHODS This study used the Wellcome Trust Case Control Consortium data set, including 1998 BD cases and 1500 control samples, and after quality control, 380,628 variants were analysed. In this GWAS, a Bayesian logistic model with hierarchical shrinkage spike and slab priors was used, with all variants considered simultaneously in one model. In order to decrease the computational burden, an alternative inferential method, Bayesian variational inference, has been used. RESULTS Thirteen variants were selected as associated with BD. The three of them (rs7572953, rs1378850 and rs4148944) were reported in previous GWAS. Eight of which were related to hemogram parameters, such as lymphocyte percentage, plateletcrit and haemoglobin concentration. Among selected related genes, GABPA, ELF3 and JAM2 were enriched in the platelet-derived growth factor pathway. These three genes, along with APP, ARL8A, CDH23 and GPR37L1, could be differential diagnostic variants for BD. CONCLUSIONS By reducing the statistical restrictions of GWAS analysis, the application of the Bayesian variational spike and slab models can offer insight into the genetic link with BD even with a small sample size. To uncover related variations with other traits, this model needs to be further examined.
Collapse
Affiliation(s)
- Maryam Kazemi Naeini
- Department of Biostatistics and Epidemiology, School of Health, Isfahan University of Medical Sciences, Isfahan, Iran
| | - Mahdi Akbarzadeh
- Cellular and Molecular Endocrine Research Center, Research Institute for Endocrine Sciences, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Iraj Kazemi
- Department of Statistics, Faculty of Mathematics & Statistics, University of Isfahan, Isfahan, Iran
| | - Doug Speed
- Bioinformatics Research Centre, Institute of Advanced Studies, Quantitative Genetics and Genomics, Aarhus University, Aarhus, Denmark
| | - Sayed Mohsen Hosseini
- Department of Biostatistics and Epidemiology, School of Health, Isfahan University of Medical Sciences, Isfahan, Iran
| |
Collapse
|
2
|
Turgut AO, Koca D. The effects of case/control ratio and sample size on genome-wide association studies: A simulation study. Vet Med Sci 2024; 10:e1444. [PMID: 38581306 PMCID: PMC10998454 DOI: 10.1002/vms3.1444] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/23/2023] [Revised: 02/23/2024] [Accepted: 03/24/2024] [Indexed: 04/08/2024] Open
Abstract
BACKGROUND Genome-wide association studies (GWAS) is a useful tool for the detection of disease or quantitative trait-related genetic variations in the veterinary field. For a binary trait, a case/control experiment is designed in GWAS. However, there is limited information on the optimal case/control and sample size in GWAS. OBJECTIVES In this study, it was aimed to detect the effects of case/control ratio and sample size for GWAS using computer simulation under certain assumptions. METHOD Using the PLINK software, we simulated three different disease scenarios. In scenario 1, we simulated 10 different case/control ratios with increasing ratio of cases to controls. In scenario 2, we did versa of scenario 1 with the increasing ratio of controls to cases. In scenarios 1 and 2, sample size gradually was increased with the change case/control ratios. In scenario 3, the total sample size was fixed to 2000 to see real effects of case/control ratio on the number of disease-related single nucleotide polymorphisms (SNPs). RESULTS The results showed that the number of disease-related SNPs were the highest when the case/control ratio is close to 1:1 in scenarios 1 and 2 and did not change with an increase in sample size. Similarly, the number of disease-related SNPs was the highest in case/control ratios 1:1 in scenario 3. However, unbalanced case/control ratio caused the detection of lower number of disease-related SNPs in scenario 3. The estimated average power of SNPs was highest when case/control ratio is 1:1 in all scenarios. CONCLUSIONS All findings led to the conclusion that an increase in sample size may enhance the statistical power of GWAS when the number of cases is small. In addition, case/control ratio 1:1 may be the optimal ratio for GWAS. These findings may be valuable not only for veterinary field but also for human clinical experiments.
Collapse
Affiliation(s)
- Ali Osman Turgut
- Faculty of Veterinary MedicineDepartment of Animal ScienceSiirt UniversitySiirtTurkey
| | - Davut Koca
- Faculty of Veterinary MedicineDepartment of Obstetrics and GynecologyVan Yüzüncü Yıl UniversityVanTurkey
| |
Collapse
|
3
|
Zhang X, Wang S, Liu S, Du Z, Wu G, Liang Y, Huang Y, Shang X, Hu Y, Zhu Z, Sun W, Zhang X, Yu H. Epidemiologic association and shared genetic architecture between cataract and hearing difficulties among middle-aged and older adults. Hum Genomics 2024; 18:39. [PMID: 38632618 DOI: 10.1186/s40246-024-00601-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/06/2023] [Accepted: 03/20/2024] [Indexed: 04/19/2024] Open
Abstract
Age-related cataract and hearing difficulties are major sensory disorders that often co-exist in the global-wide elderly and have a tangible influence on the quality of life. However, the epidemiologic association between cataract and hearing difficulties remains unexplored, while little is known about whether the two share their genetic etiology. We first investigated the clinical association between cataract and hearing difficulties using the UK Biobank covering 502,543 individuals. Both unmatched analysis (adjusted for confounders) and a matched analysis (one control matched for each patient with cataract according to confounding factors) were undertaken and confirmed that cataract was associated with hearing difficulties (OR, 2.12; 95% CI, 1.98-2.27; OR, 2.03; 95% CI, 1.86-2.23, respectively). Furthermore, we explored and quantified the shared genetic architecture of these two complex sensory disorders at the common variant level using the bivariate causal mixture model (MiXeR) and conditional/conjunctional false discovery rate method based on the largest available genome-wide association studies of cataract (N = 585,243) and hearing difficulties (N = 323,978). Despite detecting only a negligible genetic correlation, we observe polygenic overlap between cataract and hearing difficulties and identify 6 shared loci with mixed directions of effects. Follow-up analysis of the shared loci implicates candidate genes QKI, STK17A, TYR, NSF, and TCF4 likely contribute to the pathophysiology of cataracts and hearing difficulties. In conclusion, this study demonstrates the presence of epidemiologic association between cataract and hearing difficulties and provides new insights into the shared genetic architecture of these two disorders at the common variant level.
Collapse
Affiliation(s)
- Xiayin Zhang
- Department of Ophthalmology, Guangdong Eye Institute, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Southern Medical University, Guangzhou, China
- Guangdong Cardiovascular Institute, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou, China
| | - Shan Wang
- Department of Ophthalmology, Guangdong Eye Institute, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Southern Medical University, Guangzhou, China
| | - Shunming Liu
- Department of Ophthalmology, Guangdong Eye Institute, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Southern Medical University, Guangzhou, China
| | - Zijing Du
- Department of Ophthalmology, Guangdong Eye Institute, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Southern Medical University, Guangzhou, China
| | - Guanrong Wu
- Department of Ophthalmology, Guangdong Eye Institute, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Southern Medical University, Guangzhou, China
| | - Yingying Liang
- Department of Ophthalmology, Guangzhou First people's Hospital, Guangzhou, China
| | - Yu Huang
- Department of Ophthalmology, Guangdong Eye Institute, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Southern Medical University, Guangzhou, China
- Guangdong Cardiovascular Institute, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou, China
| | - Xianwen Shang
- Department of Ophthalmology, Guangdong Eye Institute, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Southern Medical University, Guangzhou, China
- Guangdong Cardiovascular Institute, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou, China
| | - Yijun Hu
- Department of Ophthalmology, Guangdong Eye Institute, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Southern Medical University, Guangzhou, China
| | - Zhuoting Zhu
- Centre for Eye Research Australia, Royal Victorian Eye and Ear Hospital, VIC, East Melbourne, Australia
| | - Wei Sun
- Department of Ophthalmology, Guangdong Eye Institute, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Southern Medical University, Guangzhou, China.
| | - Xueli Zhang
- Department of Ophthalmology, Guangdong Eye Institute, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Southern Medical University, Guangzhou, China.
| | - Honghua Yu
- Department of Ophthalmology, Guangdong Eye Institute, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Southern Medical University, Guangzhou, China.
- Guangdong Provincial Key Laboratory of Artificial Intelligence in Medical Image Analysis and Application, Guangzhou, China.
| |
Collapse
|
4
|
Troubat L, Fettahoglu D, Henches L, Aschard H, Julienne H. Multi-trait GWAS for diverse ancestries: mapping the knowledge gap. BMC Genomics 2024; 25:375. [PMID: 38627641 PMCID: PMC11022331 DOI: 10.1186/s12864-024-10293-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/13/2023] [Accepted: 04/09/2024] [Indexed: 04/19/2024] Open
Abstract
BACKGROUND Approximately 95% of samples analyzed in univariate genome-wide association studies (GWAS) are of European ancestry. This bias toward European ancestry populations in association screening also exists for other analyses and methods that are often developed and tested on European ancestry only. However, existing data in non-European populations, which are often of modest sample size, could benefit from innovative approaches as recently illustrated in the context of polygenic risk scores. METHODS Here, we extend and assess the potential limitations and gains of our multi-trait GWAS pipeline, JASS (Joint Analysis of Summary Statistics), for the analysis of non-European ancestries. To this end, we conducted the joint GWAS of 19 hematological traits and glycemic traits across five ancestries (European (EUR), admixed American (AMR), African (AFR), East Asian (EAS), and South-East Asian (SAS)). RESULTS We detected 367 new genome-wide significant associations in non-European populations (15 in Admixed American (AMR), 72 in African (AFR) and 280 in East Asian (EAS)). New associations detected represent 5%, 17% and 13% of associations in the AFR, AMR and EAS populations, respectively. Overall, multi-trait testing increases the replication of European associated loci in non-European ancestry by 15%. Pleiotropic effects were highly similar at significant loci across ancestries (e.g. the mean correlation between multi-trait genetic effects of EUR and EAS ancestries was 0.88). For hematological traits, strong discrepancies in multi-trait genetic effects are tied to known evolutionary divergences: the ARKC1 loci, which is adaptive to overcome p.vivax induced malaria. CONCLUSIONS Multi-trait GWAS can be a valuable tool to narrow the genetic knowledge gap between European and non-European populations.
Collapse
Affiliation(s)
- Lucie Troubat
- Department of Computational Biology, Institut Pasteur, Université Paris Cité, Paris, F-75015, France
| | - Deniz Fettahoglu
- Department of Computational Biology, Institut Pasteur, Université Paris Cité, Paris, F-75015, France
| | - Léo Henches
- Department of Computational Biology, Institut Pasteur, Université Paris Cité, Paris, F-75015, France
| | - Hugues Aschard
- Department of Computational Biology, Institut Pasteur, Université Paris Cité, Paris, F-75015, France
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, 02115, USA
| | - Hanna Julienne
- Department of Computational Biology, Institut Pasteur, Université Paris Cité, Paris, F-75015, France.
- Institut Pasteur, Université Paris Cité, Bioinformatics and Biostatistics Hub, Paris, F-75015, France.
| |
Collapse
|
5
|
Alireza Z, Maleeha M, Kaikkonen M, Fortino V. Enhancing prediction accuracy of coronary artery disease through machine learning-driven genomic variant selection. J Transl Med 2024; 22:356. [PMID: 38627847 PMCID: PMC11020205 DOI: 10.1186/s12967-024-05090-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/12/2023] [Accepted: 03/14/2024] [Indexed: 04/19/2024] Open
Abstract
Machine learning (ML) methods are increasingly becoming crucial in genome-wide association studies for identifying key genetic variants or SNPs that statistical methods might overlook. Statistical methods predominantly identify SNPs with notable effect sizes by conducting association tests on individual genetic variants, one at a time, to determine their relationship with the target phenotype. These genetic variants are then used to create polygenic risk scores (PRSs), estimating an individual's genetic risk for complex diseases like cancer or cardiovascular disorders. Unlike traditional methods, ML algorithms can identify groups of low-risk genetic variants that improve prediction accuracy when combined in a mathematical model. However, the application of ML strategies requires addressing the feature selection challenge to prevent overfitting. Moreover, ensuring the ML model depends on a concise set of genomic variants enhances its clinical applicability, where testing is feasible for only a limited number of SNPs. In this study, we introduce a robust pipeline that applies ML algorithms in combination with feature selection (ML-FS algorithms), aimed at identifying the most significant genomic variants associated with the coronary artery disease (CAD) phenotype. The proposed computational approach was tested on individuals from the UK Biobank, differentiating between CAD and non-CAD individuals within this extensive cohort, and benchmarked against standard PRS-based methodologies like LDpred2 and Lassosum. Our strategy incorporates cross-validation to ensure a more robust evaluation of genomic variant-based prediction models. This method is commonly applied in machine learning strategies but has often been neglected in previous studies assessing the predictive performance of polygenic risk scores. Our results demonstrate that the ML-FS algorithm can identify panels with as few as 50 genetic markers that can achieve approximately 80% accuracy when used in combination with known risk factors. The modest increase in accuracy over PRS performances is noteworthy, especially considering that PRS models incorporate a substantially larger number of genetic variants. This extensive variant selection can pose practical challenges in clinical settings. Additionally, the proposed approach revealed novel CAD-genetic variant associations.
Collapse
Affiliation(s)
- Z Alireza
- Institute of Biomedicine, University of Eastern Finland, 70210, Kuopio, Finland
| | - M Maleeha
- Institute of Biomedicine, University of Eastern Finland, 70210, Kuopio, Finland
| | - M Kaikkonen
- A.I.Virtanen Institute, University of Eastern Finland, 70210, Kuopio, Finland
| | - V Fortino
- Institute of Biomedicine, University of Eastern Finland, 70210, Kuopio, Finland.
| |
Collapse
|
6
|
Behr M, Kumbier K, Cordova-Palomera A, Aguirre M, Ronen O, Ye C, Ashley E, Butte AJ, Arnaout R, Brown B, Priest J, Yu B. Learning epistatic polygenic phenotypes with Boolean interactions. PLoS One 2024; 19:e0298906. [PMID: 38625909 PMCID: PMC11020961 DOI: 10.1371/journal.pone.0298906] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/26/2023] [Accepted: 01/31/2024] [Indexed: 04/18/2024] Open
Abstract
Detecting epistatic drivers of human phenotypes is a considerable challenge. Traditional approaches use regression to sequentially test multiplicative interaction terms involving pairs of genetic variants. For higher-order interactions and genome-wide large-scale data, this strategy is computationally intractable. Moreover, multiplicative terms used in regression modeling may not capture the form of biological interactions. Building on the Predictability, Computability, Stability (PCS) framework, we introduce the epiTree pipeline to extract higher-order interactions from genomic data using tree-based models. The epiTree pipeline first selects a set of variants derived from tissue-specific estimates of gene expression. Next, it uses iterative random forests (iRF) to search training data for candidate Boolean interactions (pairwise and higher-order). We derive significance tests for interactions, based on a stabilized likelihood ratio test, by simulating Boolean tree-structured null (no epistasis) and alternative (epistasis) distributions on hold-out test data. Finally, our pipeline computes PCS epistasis p-values that probabilisticly quantify improvement in prediction accuracy via bootstrap sampling on the test set. We validate the epiTree pipeline in two case studies using data from the UK Biobank: predicting red hair and multiple sclerosis (MS). In the case of predicting red hair, epiTree recovers known epistatic interactions surrounding MC1R and novel interactions, representing non-linearities not captured by logistic regression models. In the case of predicting MS, a more complex phenotype than red hair, epiTree rankings prioritize novel interactions surrounding HLA-DRB1, a variant previously associated with MS in several populations. Taken together, these results highlight the potential for epiTree rankings to help reduce the design space for follow up experiments.
Collapse
Affiliation(s)
- Merle Behr
- Faculty of Informatics and Data Science, University of Regensburg, Regensburg, Germany
| | - Karl Kumbier
- Department of Pharmaceutical Chemistry, University of California, San Francisco, San Francisco, CA, United States of America
| | | | - Matthew Aguirre
- Department of Pediatrics, Stanford Medicine, Stanford, CA, United States of America
- Department of Biomedical Data Science, Stanford Medicine, Stanford, CA, United States of America
| | - Omer Ronen
- Department of Statistics, University of California at Berkeley, Berkeley, CA, United States of America
| | - Chengzhong Ye
- Department of Statistics, University of California at Berkeley, Berkeley, CA, United States of America
| | - Euan Ashley
- Division of Cardiovascular Medicine, Stanford Medicine, Stanford, CA, United States of America
| | - Atul J. Butte
- Bakar Computational Health Sciences Institute, University of California, San Francisco, San Francisco, CA, United States of America
| | - Rima Arnaout
- Bakar Computational Health Sciences Institute, University of California, San Francisco, San Francisco, CA, United States of America
- Division of Cardiology, Department of Medicine, University of California, San Francisco, San Francisco, CA, United States of America
| | - Ben Brown
- Department of Statistics, University of California at Berkeley, Berkeley, CA, United States of America
- Biosciences Area, Lawrence Berkeley National Laboratory, Berkeley, CA, United States of America
| | - James Priest
- Department of Pediatrics, Stanford Medicine, Stanford, CA, United States of America
| | - Bin Yu
- Department of Statistics, University of California at Berkeley, Berkeley, CA, United States of America
- Department of Electrical Engineering and Computer Sciences and Center for Computational Biology, University of California at Berkeley, Berkeley, CA, United States of America
| |
Collapse
|
7
|
He R, Liu M, Lin Z, Zhuang Z, Shen X, Pan W. DeLIVR: a deep learning approach to IV regression for testing nonlinear causal effects in transcriptome-wide association studies. Biostatistics 2024; 25:468-485. [PMID: 36610078 PMCID: PMC11017120 DOI: 10.1093/biostatistics/kxac051] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/24/2022] [Revised: 12/08/2022] [Accepted: 12/14/2022] [Indexed: 01/09/2023] Open
Abstract
Transcriptome-wide association studies (TWAS) have been increasingly applied to identify (putative) causal genes for complex traits and diseases. TWAS can be regarded as a two-sample two-stage least squares method for instrumental variable (IV) regression for causal inference. The standard TWAS (called TWAS-L) only considers a linear relationship between a gene's expression and a trait in stage 2, which may lose statistical power when not true. Recently, an extension of TWAS (called TWAS-LQ) considers both the linear and quadratic effects of a gene on a trait, which however is not flexible enough due to its parametric nature and may be low powered for nonquadratic nonlinear effects. On the other hand, a deep learning (DL) approach, called DeepIV, has been proposed to nonparametrically model a nonlinear effect in IV regression. However, it is both slow and unstable due to the ill-posed inverse problem of solving an integral equation with Monte Carlo approximations. Furthermore, in the original DeepIV approach, statistical inference, that is, hypothesis testing, was not studied. Here, we propose a novel DL approach, called DeLIVR, to overcome the major drawbacks of DeepIV, by estimating a related but different target function and including a hypothesis testing framework. We show through simulations that DeLIVR was both faster and more stable than DeepIV. We applied both parametric and DL approaches to the GTEx and UK Biobank data, showcasing that DeLIVR detected additional 8 and 7 genes nonlinearly associated with high-density lipoprotein (HDL) cholesterol and low-density lipoprotein (LDL) cholesterol, respectively, all of which would be missed by TWAS-L, TWAS-LQ, and DeepIV; these genes include BUD13 associated with HDL, SLC44A2 and GMIP with LDL, all supported by previous studies.
Collapse
Affiliation(s)
- Ruoyu He
- Division of Biostatistics, School of Public Health, University of Minnesota, A460 Mayo Building, 420 Delaware Street SE, Minneapolis, MN 55455 and School of Statistics, University of Minnesota, 313 Ford Hall, 224 Church St SE, Minneapolis, MN 55455
| | - Mingyang Liu
- Division of Biostatistics, School of Public Health, University of Minnesota, A460 Mayo Building, 420 Delaware Street SE, Minneapolis, MN 55455 and School of Statistics, University of Minnesota, 313 Ford Hall, 224 Church St SE, Minneapolis, MN 55455
| | - Zhaotong Lin
- Division of Biostatistics, School of Public Health, University of Minnesota, A460 Mayo Building, 420 Delaware Street SE, Minneapolis, MN 55455 and School of Statistics, University of Minnesota, 313 Ford Hall, 224 Church St SE, Minneapolis, MN 55455
| | - Zhong Zhuang
- Division of Biostatistics, School of Public Health, University of Minnesota, A460 Mayo Building, 420 Delaware Street SE, Minneapolis, MN 55455 and School of Statistics, University of Minnesota, 313 Ford Hall, 224 Church St SE, Minneapolis, MN 55455
| | - Xiaotong Shen
- Division of Biostatistics, School of Public Health, University of Minnesota, A460 Mayo Building, 420 Delaware Street SE, Minneapolis, MN 55455 and School of Statistics, University of Minnesota, 313 Ford Hall, 224 Church St SE, Minneapolis, MN 55455
| | - Wei Pan
- Division of Biostatistics, School of Public Health, University of Minnesota, A460 Mayo Building, 420 Delaware Street SE, Minneapolis, MN 55455 and School of Statistics, University of Minnesota, 313 Ford Hall, 224 Church St SE, Minneapolis, MN 55455
| |
Collapse
|
8
|
Johnston KJA, Cote AC, Hicks E, Johnson J, Huckins LM. Genetically Regulated Gene Expression in the Brain Associated With Chronic Pain: Relationships With Clinical Traits and Potential for Drug Repurposing. Biol Psychiatry 2024; 95:745-761. [PMID: 37678542 PMCID: PMC10924073 DOI: 10.1016/j.biopsych.2023.08.023] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/28/2022] [Revised: 07/20/2023] [Accepted: 08/28/2023] [Indexed: 09/09/2023]
Abstract
BACKGROUND Chronic pain is a common, poorly understood condition. Genetic studies including genome-wide association studies have identified many relevant variants, which have yet to be translated into full understanding of chronic pain. Transcriptome-wide association studies using transcriptomic imputation methods such as S-PrediXcan can help bridge this genotype-phenotype gap. METHODS We carried out transcriptomic imputation using S-PrediXcan to identify genetically regulated gene expression associated with multisite chronic pain in 13 brain tissues and whole blood. Then, we imputed genetically regulated gene expression for over 31,000 Mount Sinai BioMe participants and performed a phenome-wide association study to investigate clinical relationships in chronic pain-associated gene expression changes. RESULTS We identified 95 experiment-wide significant gene-tissue associations (p < 7.97 × 10-7), including 36 unique genes and an additional 134 gene-tissue associations reaching within-tissue significance, including 53 additional unique genes. Of the 89 unique genes in total, 59 were novel for multisite chronic pain and 18 are established drug targets. Chronic pain genetically regulated gene expression for 10 unique genes was significantly associated with cardiac dysrhythmia, metabolic syndrome, disc disorders/dorsopathies, joint/ligament sprain, anemias, and neurologic disorder phecodes. Phenome-wide association study analyses adjusting for mean pain score showed that associations were not driven by mean pain score. CONCLUSIONS We carried out the largest transcriptomic imputation study of any chronic pain trait to date. Results highlight potential causal genes in chronic pain development and tissue and direction of effect. Several gene results were also drug targets. Phenome-wide association study results showed significant associations for phecodes including cardiac dysrhythmia and metabolic syndrome, thereby indicating potential shared mechanisms.
Collapse
Affiliation(s)
- Keira J A Johnston
- Department of Psychiatry, Yale University School of Medicine, New Haven, Connecticut.
| | - Alanna C Cote
- Pamela Sklar Division of Psychiatric Genetics, Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, New York
| | - Emily Hicks
- Pamela Sklar Division of Psychiatric Genetics, Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, New York
| | - Jessica Johnson
- School of Medicine, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina
| | - Laura M Huckins
- Department of Psychiatry, Yale University School of Medicine, New Haven, Connecticut.
| |
Collapse
|
9
|
Mandal S, Kim DH, Hua X, Li S, Shi J. Estimating the overall fraction of phenotypic variance attributed to high-dimensional predictors measured with error. Biostatistics 2024; 25:486-503. [PMID: 36797830 PMCID: PMC11017132 DOI: 10.1093/biostatistics/kxad001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/25/2022] [Revised: 01/24/2023] [Accepted: 01/31/2023] [Indexed: 02/18/2023] Open
Abstract
In prospective genomic studies (e.g., DNA methylation, metagenomics, and transcriptomics), it is crucial to estimate the overall fraction of phenotypic variance (OFPV) attributed to the high-dimensional genomic variables, a concept similar to heritability analyses in genome-wide association studies (GWAS). Unlike genetic variants in GWAS, these genomic variables are typically measured with error due to technical limitation and temporal instability. While the existing methods developed for GWAS can be used, ignoring measurement error may severely underestimate OFPV and mislead the design of future studies. Assuming that measurement error variances are distributed similarly between causal and noncausal variables, we show that the asymptotic attenuation factor equals to the average intraclass correlation coefficients of all genomic variables, which can be estimated based on a pilot study with repeated measurements. We illustrate the method by estimating the contribution of microbiome taxa to body mass index and multiple allergy traits in the American Gut Project. Finally, we show that measurement error does not cause meaningful bias when estimating the correlation of effect sizes for two traits.
Collapse
Affiliation(s)
- Soutrik Mandal
- Biostatistics Branch, Division of Cancer Epidemiology and Genetics, National Cancer Institute, 9609 Medical Center Drive, Bethesda, MD 20892, USA
| | - Do Hyun Kim
- Department of Biostatistics, Fielding School of Public Health, University of California at Los Angeles (UCLA), Los Angeles, CA 90095, USA
| | - Xing Hua
- Public Health Sciences Division, Fred Hutchinson Cancer Research Center, 1100 Fairview Avenue North, Seattle, WA 98109, USA
| | - Shilan Li
- Biostatistics Branch, Division of Cancer Epidemiology and Genetics, National Cancer Institute, 9609 Medical Center Drive, Bethesda, MD 20892, USA
| | - Jianxin Shi
- Biostatistics Branch, Division of Cancer Epidemiology and Genetics, National Cancer Institute, 9609 Medical Center Drive, Bethesda, MD 20892, USA
| |
Collapse
|
10
|
Xiong Z, Thach TQ, Zhang YD, Sham PC. Improved estimation of functional enrichment in SNP heritability using feasible generalized least squares. HGG Adv 2024; 5:100272. [PMID: 38327050 PMCID: PMC10901842 DOI: 10.1016/j.xhgg.2024.100272] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/06/2023] [Revised: 01/23/2024] [Accepted: 01/23/2024] [Indexed: 02/09/2024] Open
Abstract
Functional enrichment results typically implicate tissue or cell-type-specific biological pathways in disease pathogenesis and as therapeutic targets. We propose generalized linkage disequilibrium score regression (g-LDSC) that requires only genome-wide association studies (GWASs) summary-level data to estimate functional enrichment. The method adopts the same assumptions and regression model formulation as stratified linkage disequilibrium score regression (s-LDSC). Although s-LDSC only partially uses LD information, our method uses the whole LD matrix, which accounts for possible correlated error structure via a feasible generalized least-squares estimation. We demonstrate through simulation studies under various scenarios that g-LDSC provides more precise estimates of functional enrichment than s-LDSC, regardless of model misspecification. In an application to GWAS summary statistics of 15 traits from the UK Biobank, estimates of functional enrichment using g-LDSC were lower and more realistic than those obtained from s-LDSC. In addition, g-LDSC detected more significantly enriched functional annotations among 24 functional annotations for the 15 traits than s-LDSC (118 vs. 51).
Collapse
Affiliation(s)
- Zewei Xiong
- Department of Psychiatry, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong SAR, China
| | - Thuan-Quoc Thach
- Department of Psychiatry, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong SAR, China
| | - Yan Dora Zhang
- Department of Statistics and Actuarial Science, The University of Hong Kong, Hong Kong SAR, China; Centre for PanorOmic Sciences, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong SAR, 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, Li Ka Shing Faculty of Medicine, 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.
| |
Collapse
|
11
|
Guo X, Chatterjee N, Dutta D. Subset-based method for cross-tissue transcriptome-wide association studies improves power and interpretability. HGG Adv 2024; 5:100283. [PMID: 38491773 PMCID: PMC10999697 DOI: 10.1016/j.xhgg.2024.100283] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/12/2023] [Revised: 03/09/2024] [Accepted: 03/09/2024] [Indexed: 03/18/2024] Open
Abstract
Integrating results from genome-wide association studies (GWASs) and studies of molecular phenotypes such as gene expressions can improve our understanding of the biological functions of trait-associated variants and can help prioritize candidate genes for downstream analysis. Using reference expression quantitative trait locus (eQTL) studies, several methods have been proposed to identify gene-trait associations, primarily based on gene expression imputation. To increase the statistical power by leveraging substantial eQTL sharing across tissues, meta-analysis methods aggregating such gene-based test results across multiple tissues or contexts have been developed as well. However, most existing meta-analysis methods have limited power to identify associations when the gene has weaker associations in only a few tissues and cannot identify the subset of tissues in which the gene is "activated." For this, we developed a cross-tissue subset-based transcriptome-wide association study (CSTWAS) meta-analysis method that improves power under such scenarios and can extract the set of potentially associated tissues. To improve applicability, CSTWAS uses only GWAS summary statistics and pre-computed correlation matrices to identify a subset of tissues that have the maximal evidence of gene-trait association. Through numerical simulations, we found that CSTWAS can maintain a well-calibrated type-I error rate, improves power especially when there is a small number of associated tissues for a gene-trait association, and identifies an accurate associated tissue set. By analyzing GWAS summary statistics of three complex traits and diseases, we demonstrate that CSTWAS could identify biological meaningful signals while providing an interpretation of disease etiology by extracting a set of potentially associated tissues.
Collapse
Affiliation(s)
- Xinyu Guo
- Department of Quantitative and Computational Biology, University of Southern California, Los Angeles, CA 90007, USA
| | - Nilanjan Chatterjee
- Department of Biostatistics, Bloomberg School of Public Health, Johns Hopkins University, Baltimore, MD 21218, USA; Department of Oncology, School of Medicine, Johns Hopkins University, Baltimore, MD 21218, USA
| | - Diptavo Dutta
- Integrative Tumor Epidemiology Branch, Division of Cancer Epidemiology & Genetics, National Cancer Institute, Rockville, MD 20850, USA.
| |
Collapse
|
12
|
Jin J, Zhan J, Zhang J, Zhao R, O'Connell J, Jiang Y, Buyske S, Gignoux C, Haiman C, Kenny EE, Kooperberg C, North K, Koelsch BL, Wojcik G, Zhang H, Chatterjee N. MUSSEL: Enhanced Bayesian polygenic risk prediction leveraging information across multiple ancestry groups. Cell Genom 2024; 4:100539. [PMID: 38604127 PMCID: PMC11019365 DOI: 10.1016/j.xgen.2024.100539] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/23/2023] [Revised: 09/07/2023] [Accepted: 03/14/2024] [Indexed: 04/13/2024]
Abstract
Polygenic risk scores (PRSs) are now showing promising predictive performance on a wide variety of complex traits and diseases, but there exists a substantial performance gap across populations. We propose MUSSEL, a method for ancestry-specific polygenic prediction that borrows information in summary statistics from genome-wide association studies (GWASs) across multiple ancestry groups via Bayesian hierarchical modeling and ensemble learning. In our simulation studies and data analyses across four distinct studies, totaling 5.7 million participants with a substantial ancestral diversity, MUSSEL shows promising performance compared to alternatives. For example, MUSSEL has an average gain in prediction R2 across 11 continuous traits of 40.2% and 49.3% compared to PRS-CSx and CT-SLEB, respectively, in the African ancestry population. The best-performing method, however, varies by GWAS sample size, target ancestry, trait architecture, and linkage disequilibrium reference samples; thus, ultimately a combination of methods may be needed to generate the most robust PRSs across diverse populations.
Collapse
Affiliation(s)
- Jin Jin
- Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD 21205, USA; Department of Biostatistics, Epidemiology and Informatics, University of Pennsylvania, Philadelphia, PA 19103, USA.
| | | | - Jingning Zhang
- Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD 21205, USA
| | - Ruzhang Zhao
- Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD 21205, USA
| | | | | | - Steven Buyske
- Department of Statistics, Rutgers University, New Brunswick, NJ 08854, USA
| | - Christopher Gignoux
- Colorado Center for Personalized Medicine, University of Colorado Anschutz Medical Campus, Aurora, CO 80045, USA
| | - Christopher Haiman
- Department of Preventive Medicine, Keck School of Medicine, University of Southern California, Los Angeles, CA 90032, USA
| | - Eimear E Kenny
- Icahn Institute for Genomic Health, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Charles Kooperberg
- Division of Public Health Sciences, Fred Hutchinson Cancer Center, Seattle, WA 98109, USA
| | - Kari North
- Department of Epidemiology, University of North Carolina at Chapel Hill, Chapel Hill, NC 27516, USA
| | | | - Genevieve Wojcik
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD 21205, USA
| | - Haoyu Zhang
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA 02115, USA; Division of Cancer Epidemiology and Genetics, National Cancer Institute, Bethesda, MD 20892, USA
| | - Nilanjan Chatterjee
- Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD 21205, USA; Department of Oncology, School of Medicine, Johns Hopkins University, Baltimore, MD 21205, USA.
| |
Collapse
|
13
|
Rich AL, Lin P, Gamazon ER, Zinkel SS. The broad impact of cell death genes on the human disease phenome. Cell Death Dis 2024; 15:251. [PMID: 38589365 PMCID: PMC11002008 DOI: 10.1038/s41419-024-06632-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/13/2023] [Revised: 03/09/2024] [Accepted: 03/22/2024] [Indexed: 04/10/2024]
Abstract
Cell death mediated by genetically defined signaling pathways influences the health and dynamics of all tissues, however the tissue specificity of cell death pathways and the relationships between these pathways and human disease are not well understood. We analyzed the expression profiles of an array of 44 cell death genes involved in apoptosis, necroptosis, and pyroptosis cell death pathways across 49 human tissues from GTEx, to elucidate the landscape of cell death gene expression across human tissues, and the relationship between tissue-specific genetically determined expression and the human phenome. We uncovered unique cell death gene expression profiles across tissue types, suggesting there are physiologically distinct cell death programs in different tissues. Using summary statistics-based transcriptome wide association studies (TWAS) on human traits in the UK Biobank (n ~ 500,000), we evaluated 513 traits encompassing ICD-10 defined diagnoses and laboratory-derived traits. Our analysis revealed hundreds of significant (FDR < 0.05) associations between genetically regulated cell death gene expression and an array of human phenotypes encompassing both clinical diagnoses and hematologic parameters, which were independently validated in another large-scale DNA biobank (BioVU) at Vanderbilt University Medical Center (n = 94,474) with matching phenotypes. Cell death genes were highly enriched for significant associations with blood traits versus non-cell-death genes, with apoptosis-associated genes enriched for leukocyte and platelet traits. Our findings are also concordant with independently published studies (e.g. associations between BCL2L11/BIM expression and platelet & lymphocyte counts). Overall, these results suggest that cell death genes play distinct roles in their contribution to human phenotypes, and that cell death genes influence a diverse array of human traits.
Collapse
Affiliation(s)
- Abigail L Rich
- Department of Pathology, Microbiology & Immunology, Vanderbilt University Medical Center, Nashville, TN, USA
- Division of Hematology/Oncology, Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Phillip Lin
- Division of Genetic Medicine, Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, USA
- Vanderbilt Genetics Institute, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Eric R Gamazon
- Division of Genetic Medicine, Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, USA.
- Vanderbilt Genetics Institute, Vanderbilt University Medical Center, Nashville, TN, USA.
| | - Sandra S Zinkel
- Department of Pathology, Microbiology & Immunology, Vanderbilt University Medical Center, Nashville, TN, USA.
- Division of Hematology/Oncology, Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, USA.
- Department of Cell and Developmental Biology, Vanderbilt University Medical Center, Nashville, TN, USA.
| |
Collapse
|
14
|
Sun X, Qian Y, Cheng W, Ye D, Liu B, Zhou D, Wen C, Andreassen OA, Mao Y. Characterizing the polygenic overlap and shared loci between rheumatoid arthritis and cardiovascular diseases. BMC Med 2024; 22:152. [PMID: 38589871 PMCID: PMC11003061 DOI: 10.1186/s12916-024-03376-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/05/2023] [Accepted: 03/26/2024] [Indexed: 04/10/2024] Open
Abstract
BACKGROUND Despite substantial research revealing that patients with rheumatoid arthritis (RA) have excessive morbidity and mortality of cardiovascular disease (CVD), the mechanism underlying this association has not been fully known. This study aims to systematically investigate the phenotypic and genetic correlation between RA and CVD. METHODS Based on UK Biobank, we conducted two cohort studies to evaluate the phenotypic relationships between RA and CVD, including atrial fibrillation (AF), coronary artery disease (CAD), heart failure (HF), and stroke. Next, we used linkage disequilibrium score regression, Local Analysis of [co]Variant Association, and bivariate causal mixture model (MiXeR) methods to examine the genetic correlation and polygenic overlap between RA and CVD, using genome-wide association summary statistics. Furthermore, we explored specific shared genetic loci by conjunctional false discovery rate analysis and association analysis based on subsets. RESULTS Compared with the general population, RA patients showed a higher incidence of CVD (hazard ratio [HR] = 1.21, 95% confidence interval [CI]: 1.15-1.28). We observed positive genetic correlations of RA with AF and stroke, and a mixture of negative and positive local genetic correlations underlying the global genetic correlation for CAD and HF, with 13 ~ 33% of shared genetic variants for these trait pairs. We further identified 23 pleiotropic loci associated with RA and at least one CVD, including one novel locus (rs7098414, TSPAN14, 10q23.1). Genes mapped to these shared loci were enriched in immune and inflammatory-related pathways, and modifiable risk factors, such as high diastolic blood pressure. CONCLUSIONS This study revealed the shared genetic architecture of RA and CVD, which may facilitate drug target identification and improved clinical management.
Collapse
Affiliation(s)
- Xiaohui Sun
- Department of Epidemiology, School of Public Health, Zhejiang Chinese Medical University, Hangzhou, 310053, China
| | - Yu Qian
- Department of Epidemiology, School of Public Health, Zhejiang Chinese Medical University, Hangzhou, 310053, China
- School of Life Sciences, Westlake University, Hangzhou, 310024, China
| | - Weiqiu Cheng
- NORMENT, Institute of Clinical Medicine, University of Oslo, Oslo, 0407, Norway
| | - Ding Ye
- Department of Epidemiology, School of Public Health, Zhejiang Chinese Medical University, Hangzhou, 310053, China
| | - Bin Liu
- Department of Epidemiology, School of Public Health, Zhejiang Chinese Medical University, Hangzhou, 310053, China
| | - Dan Zhou
- School of Public Health and the Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Chengping Wen
- College of Basic Medical Sciences, Zhejiang Chinese Medical University, Hangzhou, 310053, China
| | - Ole A Andreassen
- NORMENT, Institute of Clinical Medicine, University of Oslo, Oslo, 0407, Norway.
| | - Yingying Mao
- Department of Epidemiology, School of Public Health, Zhejiang Chinese Medical University, Hangzhou, 310053, China.
| |
Collapse
|
15
|
Tang J, Xu H, Xin Z, Mei Q, Gao M, Yang T, Zhang X, Levy D, Liu CT. Identifying BMI-associated genes via a genome-wide multi-omics integrative approach using summary data. Hum Mol Genet 2024; 33:733-738. [PMID: 38215789 PMCID: PMC11000658 DOI: 10.1093/hmg/ddad212] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/05/2023] [Revised: 11/30/2023] [Accepted: 12/19/2023] [Indexed: 01/14/2024] Open
Abstract
OBJECTIVE This study aims to identify BMI-associated genes by integrating aggregated summary information from different omics data. METHODS We conducted a meta-analysis to leverage information from a genome-wide association study (n = 339 224), a transcriptome-wide association study (n = 5619), and an epigenome-wide association study (n = 3743). We prioritized the significant genes with a machine learning-based method, netWAS, which borrows information from adipose tissue-specific interaction networks. We also used the brain-specific network in netWAS to investigate genes potentially involved in brain-adipose interaction. RESULTS We identified 195 genes that were significantly associated with BMI through meta-analysis. The netWAS analysis narrowed down the list to 21 genes in adipose tissue. Among these 21 genes, six genes, including FUS, STX4, CCNT2, FUBP1, NDUFS3, and RAPSN, were not reported to be BMI-associated in PubMed or GWAS Catalog. We also identified 11 genes that were significantly associated with BMI in both adipose and whole brain tissues. CONCLUSION This study integrated three types of omics data and identified a group of genes that have not previously been reported to be associated with BMI. This strategy could provide new insights for future studies to identify molecular mechanisms contributing to BMI regulation.
Collapse
Affiliation(s)
- Jingxian Tang
- Department of Biostatistics, Boston University School of Public Health, 801 Massachusetts Ave, Boston, MA 02118, United States
| | - Hanfei Xu
- Department of Biostatistics, Boston University School of Public Health, 801 Massachusetts Ave, Boston, MA 02118, United States
| | - Zihao Xin
- Department of Biostatistics, Boston University School of Public Health, 801 Massachusetts Ave, Boston, MA 02118, United States
| | - Quanshun Mei
- Department of Biostatistics, Boston University School of Public Health, 801 Massachusetts Ave, Boston, MA 02118, United States
| | - Musong Gao
- Department of Biostatistics, Boston University School of Public Health, 801 Massachusetts Ave, Boston, MA 02118, United States
| | - Tiantian Yang
- Department of Biostatistics, Boston University School of Public Health, 801 Massachusetts Ave, Boston, MA 02118, United States
| | - Xiaoyu Zhang
- Department of Biostatistics, Boston University School of Public Health, 801 Massachusetts Ave, Boston, MA 02118, United States
| | - Daniel Levy
- Framingham Heart Study, National Heart, Lung, and Blood Institute’s Framingham Heart Study, 73 Mt Wayte Ave, Framingham, MA, United States
- Population Sciences Branch, National Heart, Lung, and Blood Institute, National Institutes of Health, 9000 Rockville Pike, Bethesda, MD, United States
| | - Ching-Ti Liu
- Department of Biostatistics, Boston University School of Public Health, 801 Massachusetts Ave, Boston, MA 02118, United States
| |
Collapse
|
16
|
Hu D, Zhao Y, Zhu L, Li X, Zhang J, Cui X, Li W, Hao D, Yang Z, Wu F, Dong S, Su X, Huang F, Yu D. Genetic dissection of ten photosynthesis-related traits based on InDel- and SNP-GWAS in soybean. Theor Appl Genet 2024; 137:96. [PMID: 38589730 DOI: 10.1007/s00122-024-04607-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/29/2023] [Accepted: 03/21/2024] [Indexed: 04/10/2024]
Abstract
KEY MESSAGE A total of 416 InDels and 112 SNPs were significantly associated with soybean photosynthesis-related traits. GmIWS1 and GmCDC48 might be related to chlorophyll fluorescence and gas-exchange parameters, respectively. Photosynthesis is one of the main factors determining crop yield. A better understanding of the genetic architecture for photosynthesis is of great significance for soybean yield improvement. Our previous studies identified 5,410,112 single nucleotide polymorphisms (SNPs) from the resequencing data of 219 natural soybean accessions. Here, we identified 634,106 insertions and deletions (InDels) from these 219 accessions and used these InDel variations to perform principal component and linkage disequilibrium analysis of this population. The genome-wide association study (GWAS) were conducted on six chlorophyll fluorescence parameters (chlorophyll content, light energy absorbed per reaction center, quantum yield for electron transport, probability that a trapped exciton moves an electron into the electron transport chain beyond primary quinone acceptor, maximum quantum yield of photosystem II primary photochemistry in the dark-adapted state, performance index on absorption basis) and four gas-exchange parameters (intercellular carbon dioxide concentration, stomatal conductance, net photosynthesis rate, transpiration rate) and revealed 416 significant InDels and 112 significant SNPs. Based on GWAS results, GmIWS1 (encoding a transcription elongation factor) and GmCDC48 (encoding a cell division cycle protein) with the highest expression in the mapping region were determined as the candidate genes responsible for chlorophyll fluorescence and gas-exchange parameters, respectively. Further identification of favorable haplotypes with higher photosynthesis, seed weight and seed yield were carried out for GmIWS1 and GmCDC48. Overall, this study revealed the natural variations and candidate genes underlying the photosynthesis-related traits based on abundant phenotypic and genetic data, providing valuable insights into the genetic mechanisms controlling photosynthesis and yield in soybean.
Collapse
Affiliation(s)
- Dezhou Hu
- National Center for Soybean Improvement, State Key Laboratory of Crop Genetics & Germplasm Enhancement and Utilization, Nanjing Agricultural University, Nanjing, 210095, China
- College of Life Sciences, Nanjing Agricultural University, Nanjing, 210095, China
| | - Yajun Zhao
- National Center for Soybean Improvement, State Key Laboratory of Crop Genetics & Germplasm Enhancement and Utilization, Nanjing Agricultural University, Nanjing, 210095, China
| | - Lixun Zhu
- National Center for Soybean Improvement, State Key Laboratory of Crop Genetics & Germplasm Enhancement and Utilization, Nanjing Agricultural University, Nanjing, 210095, China
| | - Xiao Li
- National Center for Soybean Improvement, State Key Laboratory of Crop Genetics & Germplasm Enhancement and Utilization, Nanjing Agricultural University, Nanjing, 210095, China
| | - Jinyu Zhang
- Henan Collaborative Innovation Center of Modern Biological Breeding, School of Agriculture, Henan Institute of Science and Technology, Xinxiang, 453003, China
| | - Xuan Cui
- National Center for Soybean Improvement, State Key Laboratory of Crop Genetics & Germplasm Enhancement and Utilization, Nanjing Agricultural University, Nanjing, 210095, China
| | - Wenlong Li
- National Center for Soybean Improvement, State Key Laboratory of Crop Genetics & Germplasm Enhancement and Utilization, Nanjing Agricultural University, Nanjing, 210095, China
| | - Derong Hao
- Jiangsu Yanjiang Institute of Agricultural Sciences, Nantong, 226012, China
| | - Zhongyi Yang
- National Center for Soybean Improvement, State Key Laboratory of Crop Genetics & Germplasm Enhancement and Utilization, Nanjing Agricultural University, Nanjing, 210095, China
| | - Fei Wu
- National Center for Soybean Improvement, State Key Laboratory of Crop Genetics & Germplasm Enhancement and Utilization, Nanjing Agricultural University, Nanjing, 210095, China
| | - Shupeng Dong
- National Center for Soybean Improvement, State Key Laboratory of Crop Genetics & Germplasm Enhancement and Utilization, Nanjing Agricultural University, Nanjing, 210095, China
| | - Xiaoyue Su
- National Center for Soybean Improvement, State Key Laboratory of Crop Genetics & Germplasm Enhancement and Utilization, Nanjing Agricultural University, Nanjing, 210095, China
| | - Fang Huang
- National Center for Soybean Improvement, State Key Laboratory of Crop Genetics & Germplasm Enhancement and Utilization, Nanjing Agricultural University, Nanjing, 210095, China.
| | - Deyue Yu
- National Center for Soybean Improvement, State Key Laboratory of Crop Genetics & Germplasm Enhancement and Utilization, Nanjing Agricultural University, Nanjing, 210095, China.
| |
Collapse
|
17
|
Patel K, Xie Z, Yuan H, Islam SMS, Xie Y, He W, Zhang W, Gottlieb A, Chen H, Giancardo L, Knaack A, Fletcher E, Fornage M, Ji S, Zhi D. Unsupervised deep representation learning enables phenotype discovery for genetic association studies of brain imaging. Commun Biol 2024; 7:414. [PMID: 38580839 PMCID: PMC10997628 DOI: 10.1038/s42003-024-06096-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2024] [Accepted: 03/22/2024] [Indexed: 04/07/2024] Open
Abstract
Understanding the genetic architecture of brain structure is challenging, partly due to difficulties in designing robust, non-biased descriptors of brain morphology. Until recently, brain measures for genome-wide association studies (GWAS) consisted of traditionally expert-defined or software-derived image-derived phenotypes (IDPs) that are often based on theoretical preconceptions or computed from limited amounts of data. Here, we present an approach to derive brain imaging phenotypes using unsupervised deep representation learning. We train a 3-D convolutional autoencoder model with reconstruction loss on 6130 UK Biobank (UKBB) participants' T1 or T2-FLAIR (T2) brain MRIs to create a 128-dimensional representation known as Unsupervised Deep learning derived Imaging Phenotypes (UDIPs). GWAS of these UDIPs in held-out UKBB subjects (n = 22,880 discovery and n = 12,359/11,265 replication cohorts for T1/T2) identified 9457 significant SNPs organized into 97 independent genetic loci of which 60 loci were replicated. Twenty-six loci were not reported in earlier T1 and T2 IDP-based UK Biobank GWAS. We developed a perturbation-based decoder interpretation approach to show that these loci are associated with UDIPs mapped to multiple relevant brain regions. Our results established unsupervised deep learning can derive robust, unbiased, heritable, and interpretable brain imaging phenotypes.
Collapse
Affiliation(s)
- Khush Patel
- McWilliams School of Biomedical Informatics, University of Texas Health Science Center, Houston, TX, 77030, USA
| | - Ziqian Xie
- McWilliams School of Biomedical Informatics, University of Texas Health Science Center, Houston, TX, 77030, USA
| | - Hao Yuan
- Department of Computer Science and Engineering, Texas A&M University, College Station, TX, 77843, USA
| | | | - Yaochen Xie
- Department of Computer Science and Engineering, Texas A&M University, College Station, TX, 77843, USA
| | - Wei He
- McWilliams School of Biomedical Informatics, University of Texas Health Science Center, Houston, TX, 77030, USA
| | - Wanheng Zhang
- School of Public Health, University of Texas Health Science Center, Houston, TX, 77030, USA
| | - Assaf Gottlieb
- McWilliams School of Biomedical Informatics, University of Texas Health Science Center, Houston, TX, 77030, USA
| | - Han Chen
- McWilliams School of Biomedical Informatics, University of Texas Health Science Center, Houston, TX, 77030, USA
- School of Public Health, University of Texas Health Science Center, Houston, TX, 77030, USA
| | - Luca Giancardo
- McWilliams School of Biomedical Informatics, University of Texas Health Science Center, Houston, TX, 77030, USA
| | - Alexander Knaack
- Department of Neurology and Imaging of Dementia and Aging (IDeA) Laboratory, University of California at Davis, Davis, CA, 95618, USA
| | - Evan Fletcher
- Department of Neurology and Imaging of Dementia and Aging (IDeA) Laboratory, University of California at Davis, Davis, CA, 95618, USA
| | - Myriam Fornage
- School of Public Health, University of Texas Health Science Center, Houston, TX, 77030, USA
- McGovern Medical School, University of Texas Health Science Center, Houston, TX, 77030, USA
| | - Shuiwang Ji
- Department of Computer Science and Engineering, Texas A&M University, College Station, TX, 77843, USA
| | - Degui Zhi
- McWilliams School of Biomedical Informatics, University of Texas Health Science Center, Houston, TX, 77030, USA.
| |
Collapse
|
18
|
Sun W, Jon K, Zhu W. Multiple phenotype association tests based on sliced inverse regression. BMC Bioinformatics 2024; 25:144. [PMID: 38575890 PMCID: PMC10996256 DOI: 10.1186/s12859-024-05731-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/30/2023] [Accepted: 03/05/2024] [Indexed: 04/06/2024] Open
Abstract
BACKGROUND Joint analysis of multiple phenotypes in studies of biological systems such as Genome-Wide Association Studies is critical to revealing the functional interactions between various traits and genetic variants, but growth of data in dimensionality has become a very challenging problem in the widespread use of joint analysis. To handle the excessiveness of variables, we consider the sliced inverse regression (SIR) method. Specifically, we propose a novel SIR-based association test that is robust and powerful in testing the association between multiple predictors and multiple outcomes. RESULTS We conduct simulation studies in both low- and high-dimensional settings with various numbers of Single-Nucleotide Polymorphisms and consider the correlation structure of traits. Simulation results show that the proposed method outperforms the existing methods. We also successfully apply our method to the genetic association study of ADNI dataset. Both the simulation studies and real data analysis show that the SIR-based association test is valid and achieves a higher efficiency compared with its competitors. CONCLUSION Several scenarios with low- and high-dimensional responses and genotypes are considered in this paper. Our SIR-based method controls the estimated type I error at the pre-specified level α .
Collapse
Affiliation(s)
- Wenyuan Sun
- Key Laboratory for Applied Statistics of MOE, School of Mathematics and Statistics, Northeast Normal University, Changchun, 130024, Jilin, China
- Department of Mathematics, College of Science, Yanbian University, Yanji, 133002, Jilin, China
| | - Kyongson Jon
- Key Laboratory for Applied Statistics of MOE, School of Mathematics and Statistics, Northeast Normal University, Changchun, 130024, Jilin, China
- Faculty of Mathematics, Kim Il Sung University, Pyongyan , 999093, Democratic People's Republic of Korea
| | - Wensheng Zhu
- Key Laboratory for Applied Statistics of MOE, School of Mathematics and Statistics, Northeast Normal University, Changchun, 130024, Jilin, China.
- School of Mathematical Sciences, Harbin Normal University, Harbin, 150025, Heilongjiang, China.
| |
Collapse
|
19
|
Wang P, Xu X, Li M, Lou XY, Xu S, Wu B, Gao G, Yin P, Liu N. Gene-based association tests in family samples using GWAS summary statistics. Genet Epidemiol 2024; 48:103-113. [PMID: 38317324 DOI: 10.1002/gepi.22548] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/12/2023] [Revised: 11/18/2023] [Accepted: 01/08/2024] [Indexed: 02/07/2024]
Abstract
Genome-wide association studies (GWAS) have led to rapid growth in detecting genetic variants associated with various phenotypes. Owing to a great number of publicly accessible GWAS summary statistics, and the difficulty in obtaining individual-level genotype data, many existing gene-based association tests have been adapted to require only GWAS summary statistics rather than individual-level data. However, these association tests are restricted to unrelated individuals and thus do not apply to family samples directly. Moreover, due to its flexibility and effectiveness, the linear mixed model has been increasingly utilized in GWAS to handle correlated data, such as family samples. However, it remains unknown how to perform gene-based association tests in family samples using the GWAS summary statistics estimated from the linear mixed model. In this study, we show that, when family size is negligible compared to the total sample size, the diagonal block structure of the kinship matrix makes it possible to approximate the correlation matrix of marginal Z scores by linkage disequilibrium matrix. Based on this result, current methods utilizing summary statistics for unrelated individuals can be directly applied to family data without any modifications. Our simulation results demonstrate that this proposed strategy controls the type 1 error rate well in various situations. Finally, we exemplify the usefulness of the proposed approach with a dental caries GWAS data set.
Collapse
Affiliation(s)
- Peng Wang
- Department of Epidemiology and Biostatistics, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Hubei, People's Republic of China
| | - Xiao Xu
- Department of Epidemiology and Biostatistics, Indiana University School of Public Health-Bloomington, Bloomington, Indiana, USA
| | - Ming Li
- Department of Epidemiology and Biostatistics, Indiana University School of Public Health-Bloomington, Bloomington, Indiana, USA
| | - Xiang-Yang Lou
- Department of Biostatistics, College of Public Health and Health Professions and College of Medicine, University of Florida, Gainesville, Florida, USA
| | - Siqi Xu
- Department of Statistics and Actuarial Science, The University of Hong Kong, Hong Kong, Hong Kong
| | - Baolin Wu
- Division of Biostatistics, School of Public Health, University of Minnesota, Minneapolis, Minnesota, USA
| | - Guimin Gao
- Department of Public Health Sciences, University of Chicago, Chicago, Illinois, USA
| | - Ping Yin
- Department of Epidemiology and Biostatistics, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Hubei, People's Republic of China
| | - Nianjun Liu
- Department of Epidemiology and Biostatistics, Indiana University School of Public Health-Bloomington, Bloomington, Indiana, USA
| |
Collapse
|
20
|
Karjalainen MK, Karthikeyan S, Oliver-Williams C, Sliz E, Allara E, Fung WT, Surendran P, Zhang W, Jousilahti P, Kristiansson K, Salomaa V, Goodwin M, Hughes DA, Boehnke M, Fernandes Silva L, Yin X, Mahajan A, Neville MJ, van Zuydam NR, de Mutsert R, Li-Gao R, Mook-Kanamori DO, Demirkan A, Liu J, Noordam R, Trompet S, Chen Z, Kartsonaki C, Li L, Lin K, Hagenbeek FA, Hottenga JJ, Pool R, Ikram MA, van Meurs J, Haller T, Milaneschi Y, Kähönen M, Mishra PP, Joshi PK, Macdonald-Dunlop E, Mangino M, Zierer J, Acar IE, Hoyng CB, Lechanteur YTE, Franke L, Kurilshikov A, Zhernakova A, Beekman M, van den Akker EB, Kolcic I, Polasek O, Rudan I, Gieger C, Waldenberger M, Asselbergs FW, Hayward C, Fu J, den Hollander AI, Menni C, Spector TD, Wilson JF, Lehtimäki T, Raitakari OT, Penninx BWJH, Esko T, Walters RG, Jukema JW, Sattar N, Ghanbari M, Willems van Dijk K, Karpe F, McCarthy MI, Laakso M, Järvelin MR, Timpson NJ, Perola M, Kooner JS, Chambers JC, van Duijn C, Slagboom PE, Boomsma DI, Danesh J, Ala-Korpela M, Butterworth AS, Kettunen J. Genome-wide characterization of circulating metabolic biomarkers. Nature 2024; 628:130-138. [PMID: 38448586 PMCID: PMC10990933 DOI: 10.1038/s41586-024-07148-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/07/2022] [Accepted: 02/01/2024] [Indexed: 03/08/2024]
Abstract
Genome-wide association analyses using high-throughput metabolomics platforms have led to novel insights into the biology of human metabolism1-7. This detailed knowledge of the genetic determinants of systemic metabolism has been pivotal for uncovering how genetic pathways influence biological mechanisms and complex diseases8-11. Here we present a genome-wide association study for 233 circulating metabolic traits quantified by nuclear magnetic resonance spectroscopy in up to 136,016 participants from 33 cohorts. We identify more than 400 independent loci and assign probable causal genes at two-thirds of these using manual curation of plausible biological candidates. We highlight the importance of sample and participant characteristics that can have significant effects on genetic associations. We use detailed metabolic profiling of lipoprotein- and lipid-associated variants to better characterize how known lipid loci and novel loci affect lipoprotein metabolism at a granular level. We demonstrate the translational utility of comprehensively phenotyped molecular data, characterizing the metabolic associations of intrahepatic cholestasis of pregnancy. Finally, we observe substantial genetic pleiotropy for multiple metabolic pathways and illustrate the importance of careful instrument selection in Mendelian randomization analysis, revealing a putative causal relationship between acetone and hypertension. Our publicly available results provide a foundational resource for the community to examine the role of metabolism across diverse diseases.
Collapse
Affiliation(s)
- Minna K Karjalainen
- Systems Epidemiology, Faculty of Medicine, University of Oulu and Biocenter Oulu, Oulu, Finland.
- Research Unit of Population Health, Faculty of Medicine, University of Oulu, Oulu, Finland.
- Northern Finland Birth Cohorts, Arctic Biobank, Infrastructure for Population Studies, Faculty of Medicine, University of Oulu, Oulu, Finland.
| | - Savita Karthikeyan
- British Heart Foundation Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
| | - Clare Oliver-Williams
- British Heart Foundation Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
- Public Health Specialty Training Programme, Cambridge, UK
| | - Eeva Sliz
- Systems Epidemiology, Faculty of Medicine, University of Oulu and Biocenter Oulu, Oulu, Finland
- Research Unit of Population Health, Faculty of Medicine, University of Oulu, Oulu, Finland
| | - Elias Allara
- British Heart Foundation Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
- National Institute for Health and Care Research Blood and Transplant Research Unit in Donor Health and Behaviour, University of Cambridge, Cambridge, UK
- Victor Phillip Dahdaleh Heart and Lung Research Institute, University of Cambridge, Cambridge, UK
| | - Wing Tung Fung
- British Heart Foundation Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
- Victor Phillip Dahdaleh Heart and Lung Research Institute, University of Cambridge, Cambridge, UK
| | - Praveen Surendran
- British Heart Foundation Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
- Rutherford Fund Fellow, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
- British Heart Foundation Centre of Research Excellence, University of Cambridge, Cambridge, UK
- Health Data Research UK Cambridge, Wellcome Genome Campus and University of Cambridge, Cambridge, UK
| | - Weihua Zhang
- Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London, London, UK
- Department of Cardiology, Ealing Hospital, London North West University Healthcare NHS Trust, London, UK
| | - Pekka Jousilahti
- Department of Public Health and Welfare, Finnish Institute for Health and Welfare, Helsinki, Finland
| | - Kati Kristiansson
- Department of Public Health and Welfare, Finnish Institute for Health and Welfare, Helsinki, Finland
| | - Veikko Salomaa
- Department of Public Health and Welfare, Finnish Institute for Health and Welfare, Helsinki, Finland
| | - Matt Goodwin
- MRC Integrative Epidemiology Unit, University of Bristol, Bristol, UK
- Population Health Science, Bristol Medical School, University of Bristol, Bristol, UK
| | - David A Hughes
- MRC Integrative Epidemiology Unit, University of Bristol, Bristol, UK
- Population Health Science, Bristol Medical School, University of Bristol, Bristol, UK
| | - Michael Boehnke
- Department of Biostatistics and Center for Statistical Genetics, University of Michigan, Ann Arbor, MI, USA
| | - Lilian Fernandes Silva
- Institute of Clinical Medicine, Internal Medicine, University of Eastern Finland, Kuopio, Finland
| | - Xianyong Yin
- Department of Biostatistics and Center for Statistical Genetics, University of Michigan, Ann Arbor, MI, USA
- Department of Epidemiology, School of Public Health, Nanjing Medical University, Jiangsu, China
| | - Anubha Mahajan
- Wellcome Centre for Human Genetics, Nuffield Department of Medicine, University of Oxford, Oxford, UK
- Genentech, South San Francisco, CA, USA
| | - Matt J Neville
- NIHR Oxford Biomedical Research Centre, OUHFT Oxford, Oxford, UK
- Oxford Centre for Diabetes, Endocrinology and Metabolism, Radcliffe Department of Medicine, University of Oxford, Oxford, UK
| | - Natalie R van Zuydam
- Wellcome Centre for Human Genetics, Nuffield Department of Medicine, University of Oxford, Oxford, UK
- Oxford Centre for Diabetes, Endocrinology and Metabolism, Radcliffe Department of Medicine, University of Oxford, Oxford, UK
| | - Renée de Mutsert
- Department of Clinical Epidemiology, Leiden University Medical Center, Leiden, The Netherlands
| | - Ruifang Li-Gao
- Department of Clinical Epidemiology, Leiden University Medical Center, Leiden, The Netherlands
| | - 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
| | - Ayse Demirkan
- Surrey Institute for People-Centred AI, University of Surrey, Guildford, UK
- Section of Statistical Multi-Omics, Department of Clinical and Experimental Medicine, University of Surrey, Guildford, UK
| | - Jun Liu
- Nuffield Department of Population Health, University of Oxford, Oxford, UK
- Department of Epidemiology, Erasmus MC, University Medical Center Rotterdam, Rotterdam, The Netherlands
| | - Raymond Noordam
- Department of Internal Medicine, Section of Gerontology and Geriatrics, Leiden University Medical Center, Leiden, The Netherlands
| | - 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
| | - Zhengming Chen
- Nuffield Department of Population Health, University of Oxford, Oxford, UK
- MRC Population Health Research Unit, University of Oxford, Oxford, UK
| | - Christiana Kartsonaki
- Nuffield Department of Population Health, University of Oxford, Oxford, UK
- MRC Population Health Research Unit, University of Oxford, Oxford, UK
| | - Liming Li
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing, China
- Peking University Center for Public Health and Epidemic Preparedness and Response, Beijing, China
- Key Laboratory of Epidemiology of Major Diseases, Peking University, Ministry of Education, Beijing, China
| | - Kuang Lin
- Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | - Fiona A Hagenbeek
- Department of Biological Psychology, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
- Amsterdam Public Health Research Institute, Amsterdam, The Netherlands
- Institute for Molecular Medicine Finland (FIMM), HiLIFE, University of Helsinki, Helsinki, Finland
| | - Jouke Jan Hottenga
- Department of Biological Psychology, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
- Amsterdam Public Health Research Institute, Amsterdam, The Netherlands
| | - René Pool
- Department of Biological Psychology, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
- Amsterdam Public Health Research Institute, Amsterdam, The Netherlands
| | - M Arfan Ikram
- Department of Epidemiology, Erasmus MC, University Medical Center Rotterdam, Rotterdam, The Netherlands
| | - Joyce van Meurs
- Department of Internal Medicine, Erasmus MC, University Medical Center Rotterdam, Rotterdam, The Netherlands
| | - Toomas Haller
- Institute of Genomics, University of Tartu, Tartu, Estonia
| | - Yuri Milaneschi
- Department of Psychiatry, Amsterdam Neuroscience and Amsterdam Public Health, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
| | - Mika Kähönen
- Finnish Cardiovascular Research Center Tampere, Faculty of Medicine and Health Technology, Tampere University, Tampere, Finland
- Department of Clinical Physiology, Tampere University Hospital, Tampere, Finland
| | - Pashupati P Mishra
- Finnish Cardiovascular Research Center Tampere, Faculty of Medicine and Health Technology, Tampere University, Tampere, Finland
- Department of Clinical Chemistry, Faculty of Medicine and Health Technology, Tampere University, Tampere, Finland
- Department of Clinical Chemistry, Fimlab Laboratories, Tampere, Finland
| | - Peter K Joshi
- Centre for Global Health, Usher Institute, University of Edinburgh, Edinburgh, Scotland
| | - Erin Macdonald-Dunlop
- Centre for Global Health, Usher Institute, University of Edinburgh, Edinburgh, Scotland
| | - Massimo Mangino
- Department of Twin Research and Genetic Epidemiology, King's College London, London, UK
- NIHR Biomedical Research Centre at Guy's and St Thomas' Foundation Trust, London, UK
| | - Jonas Zierer
- Department of Twin Research and Genetic Epidemiology, King's College London, London, UK
| | - Ilhan E Acar
- Department of Biosystems Science and Engineering, ETH Zurich, Basel, Switzerland
- Department of Ophthalmology, Radboud University Medical Center, Nijmegen, The Netherlands
| | - Carel B Hoyng
- Department of Ophthalmology, Radboud University Medical Center, Nijmegen, The Netherlands
| | - Yara T E Lechanteur
- Department of Ophthalmology, Radboud University Medical Center, Nijmegen, The Netherlands
| | - Lude Franke
- Department of Genetics, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands
| | - Alexander Kurilshikov
- Department of Genetics, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands
| | - Alexandra Zhernakova
- Department of Genetics, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands
| | - Marian Beekman
- Section of Molecular Epidemiology, Department of Biomedical Data Sciences, Leiden University Medical Center, Leiden, The Netherlands
| | - Erik B van den Akker
- Section of Molecular Epidemiology, Department of Biomedical Data Sciences, Leiden University Medical Center, Leiden, The Netherlands
- Center for Computational Biology, Leiden University Medical Center, Leiden, The Netherlands
- The Delft Bioinformatics Lab, Delft University of Technology, Delft, The Netherlands
| | - Ivana Kolcic
- Department of Public Health, School of Medicine, University of Split, Split, Croatia
| | - Ozren Polasek
- Department of Public Health, School of Medicine, University of Split, Split, Croatia
| | - Igor Rudan
- Centre for Global Health, Usher Institute, University of Edinburgh, Edinburgh, Scotland
| | - Christian Gieger
- Research Unit Molecular Epidemiology, Institute of Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany
- German Center for Cardiovascular Research (DZHK), Partner Site Munich Heart Alliance, Munich, Germany
| | - Melanie Waldenberger
- Research Unit Molecular Epidemiology, Institute of Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany
- German Center for Cardiovascular Research (DZHK), Partner Site Munich Heart Alliance, Munich, Germany
| | - Folkert W Asselbergs
- Amsterdam University Medical Centers, Department of Cardiology, University of Amsterdam, Amsterdam, The Netherlands
- Health Data Research UK and Institute of Health Informatics, University College London, London, UK
| | - Caroline Hayward
- Medical Research Council Human Genetics Unit, Institute of Genetics and Cancer, University of Edinburgh, Edinburgh, UK
| | - Jingyuan Fu
- Department of Genetics, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands
- Department of Pediatrics, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands
| | - Anneke I den Hollander
- Department of Ophthalmology, Radboud University Medical Center, Nijmegen, The Netherlands
- Genomics Research Center, Abbvie, Cambridge, MA, USA
| | - Cristina Menni
- Department of Twin Research and Genetic Epidemiology, King's College London, London, UK
| | - Tim D Spector
- Department of Twin Research and Genetic Epidemiology, King's College London, London, UK
| | - James F Wilson
- Centre for Global Health, Usher Institute, University of Edinburgh, Edinburgh, Scotland
- Medical Research Council Human Genetics Unit, Institute of Genetics and Cancer, University of Edinburgh, Edinburgh, UK
| | - Terho Lehtimäki
- Finnish Cardiovascular Research Center Tampere, Faculty of Medicine and Health Technology, Tampere University, Tampere, Finland
- Department of Clinical Chemistry, Faculty of Medicine and Health Technology, Tampere University, Tampere, Finland
- Department of Clinical Chemistry, Fimlab Laboratories, Tampere, Finland
| | - 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
- InFLAMES Research Flagship, University of Turku, Turku, Finland
| | - Brenda W J H Penninx
- Department of Psychiatry, Amsterdam Neuroscience and Amsterdam Public Health, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
| | - Tonu Esko
- Institute of Genomics, University of Tartu, Tartu, Estonia
| | - Robin G Walters
- Nuffield Department of Population Health, University of Oxford, Oxford, UK
- MRC Population Health Research Unit, University of Oxford, Oxford, UK
| | - J Wouter Jukema
- Department of Cardiology, Leiden University Medical Center, Leiden, The Netherlands
- Netherlands Heart Institute, Utrecht, The Netherlands
| | - Naveed Sattar
- School of Cardiovascular and Metabolic Health, University of Glasgow, Glasgow, UK
| | - Mohsen Ghanbari
- Department of Epidemiology, Erasmus MC, University Medical Center Rotterdam, Rotterdam, The Netherlands
| | - Ko Willems van Dijk
- Department of Human Genetics, Leiden University Medical Center, Leiden, The Netherlands
- Department of Internal Medicine, Division Endocrinology, Leiden University Medical Center, Leiden, The Netherlands
- Leiden Laboratory for Experimental Vascular Medicine, Leiden University Medical Center, Leiden, The Netherlands
| | - Fredrik Karpe
- NIHR Oxford Biomedical Research Centre, OUHFT Oxford, Oxford, UK
- Oxford Centre for Diabetes, Endocrinology and Metabolism, Radcliffe Department of Medicine, University of Oxford, Oxford, UK
| | - Mark I McCarthy
- Wellcome Centre for Human Genetics, Nuffield Department of Medicine, University of Oxford, Oxford, UK
- Oxford Centre for Diabetes, Endocrinology and Metabolism, Radcliffe Department of Medicine, University of Oxford, Oxford, UK
- Genentech, South San Francisco, CA, USA
| | - Markku Laakso
- Institute of Clinical Medicine, Internal Medicine, University of Eastern Finland, Kuopio, Finland
- Kuopio University Hospital, Kuopio, Finland
| | - Marjo-Riitta Järvelin
- Research Unit of Population Health, Faculty of Medicine, University of Oulu, Oulu, Finland
- Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London, London, UK
- Department of Life Sciences, College of Health and Life Sciences, Brunel University London, Uxbridge, UK
- Unit of Primary Health Care, Oulu University Hospital, OYS, Oulu, Finland
| | - Nicholas J Timpson
- MRC Integrative Epidemiology Unit, University of Bristol, Bristol, UK
- Population Health Science, Bristol Medical School, University of Bristol, Bristol, UK
| | - Markus Perola
- Department of Public Health and Welfare, Finnish Institute for Health and Welfare, Helsinki, Finland
- Diabetes and Obesity Research Program, University of Helsinki, Helsinki, Finland
- Estonian Genome Center, University of Tartu, Tartu, Estonia
| | - Jaspal S Kooner
- Department of Cardiology, Ealing Hospital, London North West University 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
| | - John C Chambers
- Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London, London, UK
- Department of Cardiology, Ealing Hospital, London North West University 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
- Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore, Singapore
| | - Cornelia van Duijn
- Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | - P Eline Slagboom
- Section of Molecular Epidemiology, Department of Biomedical Data Sciences, Leiden University Medical Center, Leiden, The Netherlands
| | - Dorret I Boomsma
- Department of Biological Psychology, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
- Amsterdam Public Health Research Institute, Amsterdam, The Netherlands
- Amsterdam Reproduction and Development (AR&D) Research Institute, Amsterdam, The Netherlands
| | - John Danesh
- British Heart Foundation Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
- National Institute for Health and Care Research Blood and Transplant Research Unit in Donor Health and Behaviour, University of Cambridge, Cambridge, UK
- Victor Phillip Dahdaleh Heart and Lung Research Institute, University of Cambridge, Cambridge, UK
- British Heart Foundation Centre of Research Excellence, University of Cambridge, Cambridge, UK
- Health Data Research UK Cambridge, Wellcome Genome Campus and University of Cambridge, Cambridge, UK
- Department of Human Genetics, Wellcome Sanger Institute, Hinxton, UK
| | - Mika Ala-Korpela
- Systems Epidemiology, Faculty of Medicine, University of Oulu and Biocenter Oulu, Oulu, Finland
- Research Unit of Population Health, Faculty of Medicine, University of Oulu, Oulu, Finland
- NMR Metabolomics Laboratory, School of Pharmacy, University of Eastern Finland, Kuopio, Finland
| | - Adam S Butterworth
- British Heart Foundation Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
- National Institute for Health and Care Research Blood and Transplant Research Unit in Donor Health and Behaviour, University of Cambridge, Cambridge, UK
- Victor Phillip Dahdaleh Heart and Lung Research Institute, University of Cambridge, Cambridge, UK
- British Heart Foundation Centre of Research Excellence, University of Cambridge, Cambridge, UK
- Health Data Research UK Cambridge, Wellcome Genome Campus and University of Cambridge, Cambridge, UK
| | - Johannes Kettunen
- Systems Epidemiology, Faculty of Medicine, University of Oulu and Biocenter Oulu, Oulu, Finland
- Research Unit of Population Health, Faculty of Medicine, University of Oulu, Oulu, Finland
- Department of Public Health and Welfare, Finnish Institute for Health and Welfare, Helsinki, Finland
| |
Collapse
|
21
|
Sakaue S, Weinand K, Isaac S, Dey KK, Jagadeesh K, Kanai M, Watts GFM, Zhu Z, Brenner MB, McDavid A, Donlin LT, Wei K, Price AL, Raychaudhuri S. Tissue-specific enhancer-gene maps from multimodal single-cell data identify causal disease alleles. Nat Genet 2024; 56:615-626. [PMID: 38594305 DOI: 10.1038/s41588-024-01682-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/07/2023] [Accepted: 02/07/2024] [Indexed: 04/11/2024]
Abstract
Translating genome-wide association study (GWAS) loci into causal variants and genes requires accurate cell-type-specific enhancer-gene maps from disease-relevant tissues. Building enhancer-gene maps is essential but challenging with current experimental methods in primary human tissues. Here we developed a nonparametric statistical method, SCENT (single-cell enhancer target gene mapping), that models association between enhancer chromatin accessibility and gene expression in single-cell or nucleus multimodal RNA sequencing and ATAC sequencing data. We applied SCENT to 9 multimodal datasets including >120,000 single cells or nuclei and created 23 cell-type-specific enhancer-gene maps. These maps were highly enriched for causal variants in expression quantitative loci and GWAS for 1,143 diseases and traits. We identified likely causal genes for both common and rare diseases and linked somatic mutation hotspots to target genes. We demonstrate that application of SCENT to multimodal data from disease-relevant human tissue enables the scalable construction of accurate cell-type-specific enhancer-gene maps, essential for defining noncoding variant function.
Collapse
Affiliation(s)
- Saori Sakaue
- Center for Data Sciences, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
- Divisions of Genetics and Rheumatology, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Kathryn Weinand
- Center for Data Sciences, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
- Divisions of Genetics and Rheumatology, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
| | - Shakson Isaac
- Center for Data Sciences, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
- Divisions of Genetics and Rheumatology, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
| | - Kushal K Dey
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Department of Epidemiology, Harvard T. H. Chan School of Public Health, Boston, MA, USA
| | - Karthik Jagadeesh
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Department of Epidemiology, Harvard T. H. Chan School of Public Health, Boston, MA, USA
| | - Masahiro Kanai
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA, USA
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Center for Computational and Integrative Biology, Massachusetts General Hospital, Boston, MA, USA
| | - Gerald F M Watts
- Division of Rheumatology, Inflammation, and Immunity, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
| | - Zhu Zhu
- Division of Rheumatology, Inflammation, and Immunity, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
| | - Michael B Brenner
- Division of Rheumatology, Inflammation, and Immunity, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
| | - Andrew McDavid
- Department of Biostatistics and Computational Biology, University of Rochester Medical Center, Rochester, NY, USA
| | - Laura T Donlin
- Hospital for Special Surgery, New York, NY, USA
- Weill Cornell Medicine, New York, NY, USA
| | - Kevin Wei
- Division of Rheumatology, Inflammation, and Immunity, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
| | - Alkes L Price
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Department of Epidemiology, Harvard T. H. Chan School of Public Health, Boston, MA, USA
- Department of Biostatistics, Harvard T. H. Chan School of Public Health, Boston, MA, USA
| | - Soumya Raychaudhuri
- Center for Data Sciences, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA.
- Divisions of Genetics and Rheumatology, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA.
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA.
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA.
| |
Collapse
|
22
|
Nicolas G. Recent advances in Alzheimer disease genetics. Curr Opin Neurol 2024; 37:154-165. [PMID: 38235704 DOI: 10.1097/wco.0000000000001242] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/19/2024]
Abstract
PURPOSE OF REVIEW Genetics studies provide important insights into Alzheimer disease (AD) etiology and mechanisms. Critical advances have been made recently, mainly thanks to the access to novel techniques and larger studies. RECENT FINDINGS In monogenic AD, progress has been made with a better understanding of the mechanisms associated with pathogenic variants and the input of clinical studies in presymptomatic individuals. In complex AD, increasing sample sizes in both DNA chip-based (genome-wide association studies, GWAS) and exome/genome sequencing case-control studies unveiled novel common and rare risk factors, while the understanding of their combined effect starts to suggest the existence of rare families with oligogenic inheritance of early-onset, nonmonogenic, AD. SUMMARY Most genetic risk factors with a known consequence designate the aggregation of the Aβ peptide as a core etiological factor in complex AD thus confirming that the research based on monogenic AD - where the amyloid cascade seems more straightforward - is relevant to complex AD as well. Novel mechanistic insights and risk factor studies unveiling novel factors and attempting to combine the effect of common and rare variants will offer promising perspectives for future AD prevention, at least regarding early-onset AD, and probably in case of later onset as well.
Collapse
Affiliation(s)
- Gaël Nicolas
- Univ Rouen Normandie, Normandie Univ, Inserm U1245 and CHU Rouen, Department of Genetics and CNRMAJ, F-76000 Rouen, France
| |
Collapse
|
23
|
Natri HM, Del Azodi CB, Peter L, Taylor CJ, Chugh S, Kendle R, Chung MI, Flaherty DK, Matlock BK, Calvi CL, Blackwell TS, Ware LB, Bacchetta M, Walia R, Shaver CM, Kropski JA, McCarthy DJ, Banovich NE. Cell-type-specific and disease-associated expression quantitative trait loci in the human lung. Nat Genet 2024; 56:595-604. [PMID: 38548990 PMCID: PMC11018522 DOI: 10.1038/s41588-024-01702-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/21/2023] [Accepted: 02/28/2024] [Indexed: 04/04/2024]
Abstract
Common genetic variants confer substantial risk for chronic lung diseases, including pulmonary fibrosis. Defining the genetic control of gene expression in a cell-type-specific and context-dependent manner is critical for understanding the mechanisms through which genetic variation influences complex traits and disease pathobiology. To this end, we performed single-cell RNA sequencing of lung tissue from 66 individuals with pulmonary fibrosis and 48 unaffected donors. Using a pseudobulk approach, we mapped expression quantitative trait loci (eQTLs) across 38 cell types, observing both shared and cell-type-specific regulatory effects. Furthermore, we identified disease interaction eQTLs and demonstrated that this class of associations is more likely to be cell-type-specific and linked to cellular dysregulation in pulmonary fibrosis. Finally, we connected lung disease risk variants to their regulatory targets in disease-relevant cell types. These results indicate that cellular context determines the impact of genetic variation on gene expression and implicates context-specific eQTLs as key regulators of lung homeostasis and disease.
Collapse
Affiliation(s)
- Heini M Natri
- Translational Genomics Research Institute, Phoenix, AZ, USA
| | - Christina B Del Azodi
- St. Vincent's Institute of Medical Research, Melbourne, Victoria, Australia
- Melbourne Integrative Genomics, University of Melbourne, Melbourne, Victoria, Australia
| | - Lance Peter
- Translational Genomics Research Institute, Phoenix, AZ, USA
| | - Chase J Taylor
- Division of Allergy, Pulmonary and Critical Care Medicine, Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Sagrika Chugh
- St. Vincent's Institute of Medical Research, Melbourne, Victoria, Australia
- Melbourne Integrative Genomics, University of Melbourne, Melbourne, Victoria, Australia
- School of Mathematics and Statistics, Faculty of Science, University of Melbourne, Melbourne, Victoria, Australia
| | - Robert Kendle
- Translational Genomics Research Institute, Phoenix, AZ, USA
| | - Mei-I Chung
- Translational Genomics Research Institute, Phoenix, AZ, USA
| | - David K Flaherty
- Flow Cytometry Shared Resource, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Brittany K Matlock
- Flow Cytometry Shared Resource, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Carla L Calvi
- Division of Allergy, Pulmonary and Critical Care Medicine, Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Timothy S Blackwell
- Division of Allergy, Pulmonary and Critical Care Medicine, Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, USA
- Department of Cell and Developmental Biology, Vanderbilt University, Nashville, TN, USA
- Department of Veterans Affairs Medical Center, Nashville, TN, USA
| | - Lorraine B Ware
- Division of Allergy, Pulmonary and Critical Care Medicine, Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, USA
- Department of Pathology, Microbiology and Immunology, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Matthew Bacchetta
- Department of Cardiac Surgery, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Rajat Walia
- Department of Thoracic Disease and Transplantation, Norton Thoracic Institute, Phoenix, AZ, USA
| | - Ciara M Shaver
- Division of Allergy, Pulmonary and Critical Care Medicine, Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Jonathan A Kropski
- Division of Allergy, Pulmonary and Critical Care Medicine, Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, USA
- Department of Cell and Developmental Biology, Vanderbilt University, Nashville, TN, USA
- Department of Veterans Affairs Medical Center, Nashville, TN, USA
| | - Davis J McCarthy
- St. Vincent's Institute of Medical Research, Melbourne, Victoria, Australia
- Melbourne Integrative Genomics, University of Melbourne, Melbourne, Victoria, Australia
- School of Mathematics and Statistics, Faculty of Science, University of Melbourne, Melbourne, Victoria, Australia
| | | |
Collapse
|
24
|
Fujita M, Gao Z, Zeng L, McCabe C, White CC, Ng B, Green GS, Rozenblatt-Rosen O, Phillips D, Amir-Zilberstein L, Lee H, Pearse RV, Khan A, Vardarajan BN, Kiryluk K, Ye CJ, Klein HU, Wang G, Regev A, Habib N, Schneider JA, Wang Y, Young-Pearse T, Mostafavi S, Bennett DA, Menon V, De Jager PL. Cell subtype-specific effects of genetic variation in the Alzheimer's disease brain. Nat Genet 2024; 56:605-614. [PMID: 38514782 DOI: 10.1038/s41588-024-01685-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/16/2022] [Accepted: 02/08/2024] [Indexed: 03/23/2024]
Abstract
The relationship between genetic variation and gene expression in brain cell types and subtypes remains understudied. Here, we generated single-nucleus RNA sequencing data from the neocortex of 424 individuals of advanced age; we assessed the effect of genetic variants on RNA expression in cis (cis-expression quantitative trait loci) for seven cell types and 64 cell subtypes using 1.5 million transcriptomes. This effort identified 10,004 eGenes at the cell type level and 8,099 eGenes at the cell subtype level. Many eGenes are only detected within cell subtypes. A new variant influences APOE expression only in microglia and is associated with greater cerebral amyloid angiopathy but not Alzheimer's disease pathology, after adjusting for APOEε4, providing mechanistic insights into both pathologies. Furthermore, only a TMEM106B variant affects the proportion of cell subtypes. Integration of these results with genome-wide association studies highlighted the targeted cell type and probable causal gene within Alzheimer's disease, schizophrenia, educational attainment and Parkinson's disease loci.
Collapse
Affiliation(s)
- Masashi Fujita
- Center for Translational and Computational Neuroimmunology, Department of Neurology, Columbia University Irving Medical Center, New York, NY, USA
| | - Zongmei Gao
- Center for Translational and Computational Neuroimmunology, Department of Neurology, Columbia University Irving Medical Center, New York, NY, USA
| | - Lu Zeng
- Center for Translational and Computational Neuroimmunology, Department of Neurology, Columbia University Irving Medical Center, New York, NY, USA
| | - Cristin McCabe
- Klarman Cell Observatory, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Charles C White
- Center for Translational and Computational Neuroimmunology, Department of Neurology, Columbia University Irving Medical Center, New York, NY, USA
| | - Bernard Ng
- Rush Alzheimer's Disease Center, Rush University Medical Center, Chicago, IL, USA
| | - Gilad Sahar Green
- Edmond & Lily Safra Center for Brain Sciences, The Hebrew University of Jerusalem, Jerusalem, Israel
| | - Orit Rozenblatt-Rosen
- Klarman Cell Observatory, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Genentech, South San Francisco, CA, USA
| | - Devan Phillips
- Klarman Cell Observatory, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Genentech, South San Francisco, CA, USA
| | | | - Hyo Lee
- Ann Romney Center for Neurologic Diseases, Brigham and Women's Hospital, Boston, MA, USA
- Harvard Medical School, Boston, MA, USA
| | - Richard V Pearse
- Ann Romney Center for Neurologic Diseases, Brigham and Women's Hospital, Boston, MA, USA
- Harvard Medical School, Boston, MA, USA
| | - Atlas Khan
- Department of Medicine, Vagelos College of Physicians & Surgeons, Columbia University, New York, NY, USA
| | - Badri N Vardarajan
- Taub Institute for Research on Alzheimer's Disease and the Aging Brain, College of Physicians and Surgeons, Columbia University, New York, NY, USA
- Department of Neurology, College of Physicians and Surgeons, Columbia University and the New York Presbyterian Hospital, New York, NY, USA
- The Gertrude H. Sergievsky Center, College of Physicians and Surgeons, Columbia University, New York, NY, USA
| | - Krzysztof Kiryluk
- Department of Medicine, Vagelos College of Physicians & Surgeons, Columbia University, New York, NY, USA
| | - Chun Jimmie Ye
- Institute for Human Genetics, University of California, San Francisco, CA, USA
- Department of Epidemiology and Biostatistics, University of California, San Francisco, CA, USA
- Parker Institute for Cancer Immunotherapy, San Francisco, CA, USA
- Chan Zuckerberg Biohub, San Francisco, CA, USA
| | - Hans-Ulrich Klein
- Center for Translational and Computational Neuroimmunology, Department of Neurology, Columbia University Irving Medical Center, New York, NY, USA
| | - Gao Wang
- Department of Neurology, College of Physicians and Surgeons, Columbia University and the New York Presbyterian Hospital, New York, NY, USA
| | - Aviv Regev
- Klarman Cell Observatory, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Department of Biology, Massachusetts Institute of Technology, Cambridge, MA, USA
- Genentech, South San Francisco, CA, USA
| | - Naomi Habib
- Edmond & Lily Safra Center for Brain Sciences, The Hebrew University of Jerusalem, Jerusalem, Israel
| | - Julie A Schneider
- Rush Alzheimer's Disease Center, Rush University Medical Center, Chicago, IL, USA
| | - Yanling Wang
- Rush Alzheimer's Disease Center, Rush University Medical Center, Chicago, IL, USA
| | - Tracy Young-Pearse
- Ann Romney Center for Neurologic Diseases, Brigham and Women's Hospital, Boston, MA, USA
- Harvard Medical School, Boston, MA, USA
| | - Sara Mostafavi
- Department of Statistics, Centre for Molecular Medicine and Therapeutics, British Columbia Children's Hospital, University of British Columbia, Vancouver, British Columbia, Canada
- Paul G. Allen School of Computer Science and Engineering, University of Washington, Seattle, WA, USA
| | - David A Bennett
- Rush Alzheimer's Disease Center, Rush University Medical Center, Chicago, IL, USA
| | - Vilas Menon
- Center for Translational and Computational Neuroimmunology, Department of Neurology, Columbia University Irving Medical Center, New York, NY, USA
| | - Philip L De Jager
- Center for Translational and Computational Neuroimmunology, Department of Neurology, Columbia University Irving Medical Center, New York, NY, USA.
| |
Collapse
|
25
|
Kumar S, Bhushan B, Kumar A, Panigrahi M, Bharati J, Kumari S, Kaiho K, Banik S, Karthikeyan A, Chaudhary R, Gaur GK, Dutt T. Elucidation of novel SNPs affecting immune response to classical swine fever vaccination in pigs using immunogenomics approach. Vet Res Commun 2024; 48:941-953. [PMID: 38017322 DOI: 10.1007/s11259-023-10262-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/11/2023] [Accepted: 11/19/2023] [Indexed: 11/30/2023]
Abstract
The host genetic makeup plays a significant role in causing the within-breed variation among individuals after vaccination. The present study was undertaken to elucidate the genetic basis of differential immune response between high and low responder Landlly (Landrace X Ghurrah) piglets vis-à-vis CSF vaccination. For the purpose, E2 antibody response against CSF vaccination was estimated in sampled animals on the day of vaccination and 21-day post-vaccination as a measure of humoral immune response. Double-digestion restriction associated DNA (ddRAD) sequencing was undertaken on 96 randomly chosen Landlly piglets using Illumina HiSeq platform. SNP markers were called using standard methodology. Genome-wide association study (GWAS) was undertaken in PLINK program to identify the informative SNP markers significantly associated with differential immune response. The results revealed significant SNPs associated with E2 antibody response against CSF vaccination. The genome-wide informative SNPs for the humoral immune response against CSF vaccination were located on SSC10, SSC17, SSC9, SSC2, SSC3 and SSC6. The overlapping and flanking genes (500Kb upstream and downstream) of significant SNPs were CYB5R1, PCMTD2, WT1, IL9R, CD101, TMEM64, TLR6, PIGG, ADIPOR1, PRSS37, EIF3M, and DNAJC24. Functional enrichment and annotation analysis were undertaken for these genes in order to gain maximum insights into the association of these genes with immune system functionality in pigs. The genetic makeup was associated with differential immune response against CSF vaccination in Landlly piglets while the identified informative SNPs may be used as suitable markers for determining variation in host immune response against CSF vaccination in pigs.
Collapse
Affiliation(s)
- Satish Kumar
- Division of Animal Genetics, ICAR-Indian Veterinary Research Institute, Izatnagar, Bareilly, U.P, 243122, India.
- ICAR-National Research Centre on Pig, Rani, Guwahati, Assam, 781131, India.
| | - Bharat Bhushan
- Division of Animal Genetics, ICAR-Indian Veterinary Research Institute, Izatnagar, Bareilly, U.P, 243122, India.
| | - Amit Kumar
- Division of Animal Genetics, ICAR-Indian Veterinary Research Institute, Izatnagar, Bareilly, U.P, 243122, India.
| | - Manjit Panigrahi
- Division of Animal Genetics, ICAR-Indian Veterinary Research Institute, Izatnagar, Bareilly, U.P, 243122, India
| | - Jaya Bharati
- ICAR-National Research Centre on Pig, Rani, Guwahati, Assam, 781131, India
| | - Soni Kumari
- Division of Animal Genetics, ICAR-Indian Veterinary Research Institute, Izatnagar, Bareilly, U.P, 243122, India
| | - Kaisa Kaiho
- Division of Animal Genetics, ICAR-Indian Veterinary Research Institute, Izatnagar, Bareilly, U.P, 243122, India
| | - Santanu Banik
- ICAR-National Research Centre on Pig, Rani, Guwahati, Assam, 781131, India
| | - A Karthikeyan
- Division of Animal Genetics, ICAR-Indian Veterinary Research Institute, Izatnagar, Bareilly, U.P, 243122, India
| | - Rajni Chaudhary
- Division of Animal Genetics, ICAR-Indian Veterinary Research Institute, Izatnagar, Bareilly, U.P, 243122, India
| | - G K Gaur
- Livestock Production and Management Section, ICAR-Indian Veterinary Research Institute, Izatnagar, Bareilly, U.P, 243122, India
| | - Triveni Dutt
- Livestock Production and Management Section, ICAR-Indian Veterinary Research Institute, Izatnagar, Bareilly, U.P, 243122, India
| |
Collapse
|
26
|
Berkowitz E, Falik Zaccai TC, Irge D, Gur I, Tiosano B, Kesler A. A genetic survey of patients with familial idiopathic intracranial hypertension residing in a Middle Eastern village: genetic association study. Eur J Med Res 2024; 29:194. [PMID: 38528581 DOI: 10.1186/s40001-024-01800-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/07/2022] [Accepted: 03/18/2024] [Indexed: 03/27/2024] Open
Abstract
BACKGROUND The aim of this study was to determine whether genetic variants are associated with idiopathic intracranial hypertension (IIH) in a unique village where many of the IIH patients have familial ties, a homogenous population and a high prevalence of consanguinity. Several autosomal recessive disorders are common in this village and its population is considered at a high risk for genetic disorders. METHODS The samples were genotyped by the Ilumina OmniExpress-24 Kit, and analyzed by the Eagle V2.4 and DASH software package to cluster haplotypes shared between our cohort. Subsequently, we searched for specific haplotypes that were significantly associated with the patient groups. RESULTS Fourteen patients and 30 controls were included. Samples from 22 female participants (11 patients and 11 controls) were evaluated for haplotype clustering and genome-wide association studies (GWAS). A total of 710,000 single nucleotide polymorphisms (SNPs) were evaluated. Candidate areas positively associated with IIH included genes located on chromosomes 16, 8 (including the CA5A and BANP genes, p < 0.01), and negatively associated with genes located on chromosomes 1 and 6 (including PBX1, LMX1A, ESR1 genes, p < 0.01). CONCLUSIONS We discovered new loci possibly associated with IIH by employing a GWAS technique to estimate the associations with haplotypes instead of specific SNPs. This method can in all probability be used in cases where there is a limited amount of samples but strong familial connections. Several loci were identified that might be strong candidates for follow-up studies in other well-phenotypes cohorts.
Collapse
Affiliation(s)
- Eran Berkowitz
- Department of Ophthalmology, Hillel Yaffe Medical Center, 1 Ha-Shalom Street, 38100, Hadera, Israel.
- The Adelson School of Medicine, Ariel University, Ariel, Israel.
- The Ruth and Bruce Rappaport Faculty of Medicine, Technion, Haifa, Israel.
| | | | - Dana Irge
- Genetic Institue, Meir Medical center, Kfar Saba, Israel
| | - Inbar Gur
- Department of Ophthalmology, Hillel Yaffe Medical Center, 1 Ha-Shalom Street, 38100, Hadera, Israel
| | - Beatrice Tiosano
- Department of Ophthalmology, Hillel Yaffe Medical Center, 1 Ha-Shalom Street, 38100, Hadera, Israel
| | - Anat Kesler
- Department of Ophthalmology, Hillel Yaffe Medical Center, 1 Ha-Shalom Street, 38100, Hadera, Israel
- Sackler Faculty of Medicine, Tel Aviv University, Tel Aviv, Israel
- The Adelson School of Medicine, Ariel University, Ariel, Israel
| |
Collapse
|
27
|
Carré C, Carluer JB, Chaux C, Estoup-Streiff C, Roche N, Hosy E, Mas A, Krouk G. Next-Gen GWAS: full 2D epistatic interaction maps retrieve part of missing heritability and improve phenotypic prediction. Genome Biol 2024; 25:76. [PMID: 38523316 PMCID: PMC10962106 DOI: 10.1186/s13059-024-03202-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/13/2023] [Accepted: 02/19/2024] [Indexed: 03/26/2024] Open
Abstract
The problem of missing heritability requires the consideration of genetic interactions among different loci, called epistasis. Current GWAS statistical models require years to assess the entire combinatorial epistatic space for a single phenotype. We propose Next-Gen GWAS (NGG) that evaluates over 60 billion single nucleotide polymorphism combinatorial first-order interactions within hours. We apply NGG to Arabidopsis thaliana providing two-dimensional epistatic maps at gene resolution. We demonstrate on several phenotypes that a large proportion of the missing heritability can be retrieved, that it indeed lies in epistatic interactions, and that it can be used to improve phenotype prediction.
Collapse
Affiliation(s)
- Clément Carré
- BionomeeX, Montpellier, France.
- IMAG, Univ. Montpellier, CNRS, Montpellier, France.
- IPSiM, Univ. Montpellier, CNRS, INRAE, Montpellier, France.
| | - Jean Baptiste Carluer
- IMAG, Univ. Montpellier, CNRS, Montpellier, France
- IPSiM, Univ. Montpellier, CNRS, INRAE, Montpellier, France
| | | | | | | | - Eric Hosy
- Interdisciplinary Institute for Neuroscience, University of Bordeaux, CNRS, Bordeaux, France
| | - André Mas
- BionomeeX, Montpellier, France.
- IMAG, Univ. Montpellier, CNRS, Montpellier, France.
| | - Gabriel Krouk
- BionomeeX, Montpellier, France.
- IPSiM, Univ. Montpellier, CNRS, INRAE, Montpellier, France.
| |
Collapse
|
28
|
Melton HJ, Zhang Z, Wu C. SUMMIT-FA: a new resource for improved transcriptome imputation using functional annotations. Hum Mol Genet 2024; 33:624-635. [PMID: 38129112 PMCID: PMC10954367 DOI: 10.1093/hmg/ddad205] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/16/2023] [Revised: 10/24/2023] [Accepted: 11/30/2023] [Indexed: 12/23/2023] Open
Abstract
Transcriptome-wide association studies (TWAS) integrate gene expression prediction models and genome-wide association studies (GWAS) to identify gene-trait associations. The power of TWAS is determined by the sample size of GWAS and the accuracy of the expression prediction model. Here, we present a new method, the Summary-level Unified Method for Modeling Integrated Transcriptome using Functional Annotations (SUMMIT-FA), which improves gene expression prediction accuracy by leveraging functional annotation resources and a large expression quantitative trait loci (eQTL) summary-level dataset. We build gene expression prediction models in whole blood using SUMMIT-FA with the comprehensive functional database MACIE and eQTL summary-level data from the eQTLGen consortium. We apply these models to GWAS for 24 complex traits and show that SUMMIT-FA identifies significantly more gene-trait associations and improves predictive power for identifying "silver standard" genes compared to several benchmark methods. We further conduct a simulation study to demonstrate the effectiveness of SUMMIT-FA.
Collapse
Affiliation(s)
- Hunter J Melton
- Department of Statistics, Florida State University, 214 Rogers Building, 117 N. Woodward Avenue, Tallahassee, FL 32306, United States
| | - Zichen Zhang
- Department of Biostatistics, The University of Texas MD Anderson Cancer Center, 7007 Bertner Avenue, Unit 1689, Houston, TX 77030, United States
| | - Chong Wu
- Department of Biostatistics, The University of Texas MD Anderson Cancer Center, 7007 Bertner Avenue, Unit 1689, Houston, TX 77030, United States
| |
Collapse
|
29
|
Liu Y, Clarke R, Bennett DA, Zong G, Gan W. Iron Status and Risk of Heart Disease, Stroke, and Diabetes: A Mendelian Randomization Study in European Adults. J Am Heart Assoc 2024; 13:e031732. [PMID: 38497484 PMCID: PMC11010009 DOI: 10.1161/jaha.123.031732] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/10/2023] [Accepted: 02/28/2024] [Indexed: 03/19/2024]
Abstract
BACKGROUND The relevance of iron status biomarkers for coronary artery disease (CAD), heart failure (HF), ischemic stroke (IS), and type 2 diabetes (T2D) is uncertain. We compared the observational and Mendelian randomization (MR) analyses of iron status biomarkers and hemoglobin with these diseases. METHODS AND RESULTS Observational analyses of hemoglobin were compared with genetically predicted hemoglobin with cardiovascular diseases and diabetes in the UK Biobank. Iron biomarkers included transferrin saturation, serum iron, ferritin, and total iron binding capacity. MR analyses assessed associations with CAD (CARDIOGRAMplusC4D [Coronary Artery Disease Genome Wide Replication and Meta-Analysis Plus The Coronary Artery Disease Genetics], n=181 522 cases), HF (HERMES [Heart Failure Molecular Epidemiology for Therapeutic Targets), n=115 150 cases), IS (GIGASTROKE, n=62 100 cases), and T2D (DIAMANTE [Diabetes Meta-Analysis of Trans-Ethnic Association Studies], n=80 154 cases) genome-wide consortia. Observational analyses demonstrated J-shaped associations of hemoglobin with CAD, HF, IS, and T2D. In contrast, MR analyses demonstrated linear positive associations of higher genetically predicted hemoglobin levels with 8% higher risk per 1 SD higher hemoglobin for CAD, 10% to 13% for diabetes, but not with IS or HF in UK Biobank. Bidirectional MR analyses confirmed the causal relevance of iron biomarkers for hemoglobin. Further MR analyses in global consortia demonstrated modest protective effects of iron biomarkers for CAD (7%-14% lower risk for 1 SD higher levels of iron biomarkers), adverse effects for T2D, but no associations with IS or HF. CONCLUSIONS Higher levels of iron biomarkers were protective for CAD, had adverse effects on T2D, but had no effects on IS or HF. Randomized trials are now required to assess effects of iron supplements on risk of CAD in high-risk older people.
Collapse
Affiliation(s)
- Yunan Liu
- 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
| | - Robert Clarke
- Nuffield Department of Population Health University of Oxford Oxford United Kingdom
| | - Derrick A Bennett
- Nuffield Department of Population Health University of Oxford Oxford United Kingdom
- Medical Research Council Population Health Research Unit at the University of Oxford Oxford United Kingdom
| | - Geng Zong
- 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
| | - Wei Gan
- Human Genetics Centre of Excellence, Novo Nordisk Research Centre Oxford, Innovation Building, Old Road Campus Oxford United Kingdom
| |
Collapse
|
30
|
Chen Y, Liu P, Zhang Z, Ye Y, Yi S, Fan C, Zhao W, Liu J. Genetic overlap and causality between COVID-19 and multi-site chronic pain: the importance of immunity. Front Immunol 2024; 15:1277720. [PMID: 38633255 PMCID: PMC11022998 DOI: 10.3389/fimmu.2024.1277720] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/16/2023] [Accepted: 02/13/2024] [Indexed: 04/19/2024] Open
Abstract
Background The existence of chronic pain increases susceptibility to virus and is now widely acknowledged as a prominent feature recognized as a major manifestation of long-term coronavirus disease 2019 (COVID-19) infection. Given the ongoing COVID-19 pandemic, it is imperative to explore the genetic associations between chronic pain and predisposition to COVID-19. Methods We conducted genetic analysis at the single nucleotide polymorphism (SNP), gene, and molecular levels using summary statistics of genome-wide association study (GWAS) and analyzed the drug targets by summary data-based Mendelian randomization analysis (SMR) to alleviate the multi-site chronic pain in COVID-19. Additionally, we performed a latent causal variable (LCV) method to investigate the causal relationship between chronic pain and susceptibility to COVID-19. Results The cross-trait meta-analysis identified 19 significant SNPs shared between COVID-19 and chronic pain. Coloc analysis indicated that the posterior probability of association (PPH4) for three loci was above 70% in both critical COVID-19 and COVID-19, with the corresponding top three SNPs being rs13135092, rs7588831, and rs13135092. A total of 482 significant overlapped genes were detected from MAGMA and CPASSOC results. Additionally, the gene ANAPC4 was identified as a potential drug target for treating chronic pain (P=7.66E-05) in COVID-19 (P=8.23E-03). Tissue enrichment analysis highlighted that the amygdala (P=7.81E-04) and prefrontal cortex (P=8.19E-05) as pivotal in regulating chronic pain of critical COVID-19. KEGG pathway enrichment further revealed the enrichment of pleiotropic genes in both COVID-19 (P=3.20E-03,Padjust=4.77E-02,hsa05171) and neurotrophic pathways (P=9.03E-04,Padjust =2.55E-02,hsa04621). Finally, the latent causal variable (LCV) model was applied to find the genetic component of critical COVID-19 was causal for multi-site chronic pain (P=0.015), with a genetic causality proportion (GCP) of was 0.60. Conclusions In this study, we identified several functional genes and underscored the pivotal role of the inflammatory system in the correlation between the paired traits. Notably, heat shock proteins emerged as potential objective biomarkers for chronic pain symptoms in individuals with COVID-19. Additionally, the ubiquitin system might play a role in mediating the impact of COVID-19 on chronic pain. These findings contribute to a more comprehensive understanding of the pleiotropy between COVID-19 and chronic pain, offering insights for therapeutic trials.
Collapse
Affiliation(s)
- Yanjing Chen
- Department of Radiology, Second Xiangya Hospital, South University, Changsha, China
| | - Ping Liu
- Department of Radiology, Second Xiangya Hospital, South University, Changsha, China
| | - Zhiyi Zhang
- Fujian University of Traditional Chinese Medicine, Fuzhou, China
| | - Yingling Ye
- Fujian University of Traditional Chinese Medicine, Fuzhou, China
| | - Sijie Yi
- Department of Radiology, Second Xiangya Hospital, South University, Changsha, China
| | - Chunhua Fan
- Department of Radiology, Second Xiangya Hospital, South University, Changsha, China
| | - Wei Zhao
- Clinical Research Center for Medical Imaging in Hunan Province, Changsha, Hunan, China
| | - Jun Liu
- Clinical Research Center for Medical Imaging in Hunan Province, Changsha, Hunan, China
| |
Collapse
|
31
|
Wu Y, Kong XJ, Ji YY, Fan J, Ji CC, Chen XM, Ma YD, Tang AL, Cheng YJ, Wu SH. Serum electrolyte concentrations and risk of atrial fibrillation: an observational and mendelian randomization study. BMC Genomics 2024; 25:280. [PMID: 38493091 PMCID: PMC10944597 DOI: 10.1186/s12864-024-10197-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/05/2023] [Accepted: 03/06/2024] [Indexed: 03/18/2024] Open
Abstract
BACKGROUND Atrial fibrillation (AF) is a prevalent arrhythmic condition resulting in increased stroke risk and is associated with high mortality. Electrolyte imbalance can increase the risk of AF, where the relationship between AF and serum electrolytes remains unclear. METHODS A total of 15,792 individuals were included in the observational study, with incident AF ascertainment in the Atherosclerosis Risk in Communities (ARIC) study. The Cox regression models were applied to calculate the hazard ratio (HR) and 95% confidence interval (CI) for AF based on different serum electrolyte levels. Mendelian randomization (MR) analyses were performed to examine the causal association. RESULTS In observational study, after a median 19.7 years of follow-up, a total of 2551 developed AF. After full adjustment, participants with serum potassium below the 5th percentile had a higher risk of AF relative to participants in the middle quintile. Serum magnesium was also inversely associated with the risk of AF. An increased incidence of AF was identified in individuals with higher serum phosphate percentiles. Serum calcium levels were not related to AF risk. Moreover, MR analysis indicated that genetically predicted serum electrolyte levels were not causally associated with AF risk. The odds ratio for AF were 0.999 for potassium, 1.044 for magnesium, 0.728 for phosphate, and 0.979 for calcium, respectively. CONCLUSIONS Serum electrolyte disorders such as hypokalemia, hypomagnesemia and hyperphosphatemia were associated with an increased risk of AF and may also serve to be prognostic factors. However, the present study did not support serum electrolytes as causal mediators for AF development.
Collapse
Affiliation(s)
- Yang Wu
- Department of Cardiology, the First Affiliated Hospital, Sun Yat-Sen University, Guangzhou, China
- NHC Key Laboratory of Assisted Circulation, Sun Yat-Sen University, Guangzhou, China
| | - Xiang-Jun Kong
- Department of Cardiology, the First Affiliated Hospital, Sun Yat-Sen University, Guangzhou, China
- NHC Key Laboratory of Assisted Circulation, Sun Yat-Sen University, Guangzhou, China
| | - Ying-Ying Ji
- Department of Cardiology, the First Affiliated Hospital, Sun Yat-Sen University, Guangzhou, China
- NHC Key Laboratory of Assisted Circulation, Sun Yat-Sen University, Guangzhou, China
| | - Jun Fan
- Department of Cardiology, Guangzhou First People's Hospital, School of Medicine, South China University of Technology, Guangzhou, China
| | - Cheng-Cheng Ji
- Department of Cardiology, the First Affiliated Hospital, Sun Yat-Sen University, Guangzhou, China
- NHC Key Laboratory of Assisted Circulation, Sun Yat-Sen University, Guangzhou, China
| | - Xu-Miao Chen
- Department of Cardiology, the First Affiliated Hospital, Sun Yat-Sen University, Guangzhou, China
- NHC Key Laboratory of Assisted Circulation, Sun Yat-Sen University, Guangzhou, China
| | - Yue-Dong Ma
- Department of Cardiology, the First Affiliated Hospital, Sun Yat-Sen University, Guangzhou, China
- NHC Key Laboratory of Assisted Circulation, Sun Yat-Sen University, Guangzhou, China
| | - An-Li Tang
- Department of Cardiology, the First Affiliated Hospital, Sun Yat-Sen University, Guangzhou, China
- NHC Key Laboratory of Assisted Circulation, Sun Yat-Sen University, Guangzhou, China
| | - Yun-Jiu Cheng
- Department of Cardiology, Guangdong Provincial People's Hospital, Guangdong Cardiovascular Institute, Guangdong Academy of Medical Sciences, Guangzhou, China.
| | - Su-Hua Wu
- Department of Cardiology, the First Affiliated Hospital, Sun Yat-Sen University, Guangzhou, China.
- NHC Key Laboratory of Assisted Circulation, Sun Yat-Sen University, Guangzhou, China.
| |
Collapse
|
32
|
Wang B, Dron JS, Wang Y, Choi SH, Huffman JE, Cho K, Wilson PWF, Natarajan P, Peloso GM. Lipid levels and risk of acute pancreatitis using bidirectional Mendelian randomization. Sci Rep 2024; 14:6267. [PMID: 38491158 PMCID: PMC10942988 DOI: 10.1038/s41598-024-56946-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/16/2023] [Accepted: 03/12/2024] [Indexed: 03/18/2024] Open
Abstract
Previous studies found lipid levels, especially triglycerides (TG), are associated with acute pancreatitis, but their causalities and bi-directions were not fully examined. We determined whether abnormal levels of TG, high-density lipoprotein cholesterol (HDL-C), and low-density lipoprotein cholesterol (LDL-C) are precursors and/or consequences of acute pancreatitis using bidirectional two-sample Mendelian randomization (MR) with two non-overlapping genome-wide association study (GWAS) summary statistics for lipid levels and acute pancreatitis. We found phenotypic associations that both higher TG levels and lower HDL-C levels contributed to increased risk of acute pancreatitis. Our GWAS meta-analysis of acute pancreatitis identified seven independent signals. Genetically predicted TG was positively associated with acute pancreatitis when using the variants specifically associated with TG using univariable MR [Odds ratio (OR), 95% CI 2.02, 1.22-3.31], but the reversed direction from acute pancreatitis to TG was not observed (mean difference = 0.003, SE = 0.002, P-value = 0.138). However, a bidirectional relationship of HDL-C and acute pancreatitis was observed: A 1-SD increment of genetically predicted HDL-C was associated with lower risk of acute pancreatitis (OR, 95% CI 0.84, 0.76-0.92) and genetically predisposed individuals with acute pancreatitis have, on average, 0.005 SD lower HDL-C (mean difference = - 0.005, SE = 0.002, P-value = 0.004). Our MR analysis confirms the evidence of TG as a risk factor of acute pancreatitis but not a consequence. A potential bidirectional relationship of HDL-C and acute pancreatitis occurs and raises the prospect of HDL-C modulation in the acute pancreatitis prevention and treatment.
Collapse
Affiliation(s)
- Biqi Wang
- Department of Biostatistics, Boston University School of Public Health, Boston, MA, USA
- Department of Medicine, University of Massachusetts Chan Medical School, Worcester, MA, USA
| | - Jacqueline S Dron
- 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
| | - Yuxuan Wang
- Department of Biostatistics, Boston University School of Public Health, Boston, MA, USA
| | - Seung Hoan Choi
- Department of Biostatistics, Boston University School of Public Health, Boston, MA, USA
| | | | - Kelly Cho
- Veterans Affairs Healthcare System, Boston, MA, USA
- Division of Aging, Department of Medicine, Mass General Brigham, Harvard Medical School, Boston, MA, USA
| | - Peter W F Wilson
- Atlanta Veterans Affairs Medical Center, Decatur, GA, USA
- Division of Cardiology, Department of Medicine, Emory University School of Medicine, Atlanta, GA, USA
- Department of Epidemiology, Rollins School of Public Health, Emory University, Atlanta, GA, USA
| | - Pradeep Natarajan
- 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
- Cardiovascular Research Center, Massachusetts General Hospital, Boston, 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.
- Veterans Affairs Healthcare System, Boston, MA, USA.
| |
Collapse
|
33
|
Constantinescu AE, Hughes DA, Bull CJ, Fleming K, Mitchell RE, Zheng J, Kar S, Timpson NJ, Amulic B, Vincent EE. A genome-wide association study of neutrophil count in individuals associated to an African continental ancestry group facilitates studies of malaria pathogenesis. Hum Genomics 2024; 18:26. [PMID: 38491524 PMCID: PMC10941368 DOI: 10.1186/s40246-024-00585-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/19/2023] [Accepted: 02/12/2024] [Indexed: 03/18/2024] Open
Abstract
BACKGROUND 'Benign ethnic neutropenia' (BEN) is a heritable condition characterized by lower neutrophil counts, predominantly observed in individuals of African ancestry, and the genetic basis of BEN remains a subject of extensive research. In this study, we aimed to dissect the genetic architecture underlying neutrophil count variation through a linear-mixed model genome-wide association study (GWAS) in a population of African ancestry (N = 5976). Malaria caused by P. falciparum imposes a tremendous public health burden on people living in sub-Saharan Africa. Individuals living in malaria endemic regions often have a reduced circulating neutrophil count due to BEN, raising the possibility that reduced neutrophil counts modulate severity of malaria in susceptible populations. As a follow-up, we tested this hypothesis by conducting a Mendelian randomization (MR) analysis of neutrophil counts on severe malaria (MalariaGEN, N = 17,056). RESULTS We carried out a GWAS of neutrophil count in individuals associated to an African continental ancestry group within UK Biobank, identifying 73 loci (r2 = 0.1) and 10 index SNPs (GCTA-COJO loci) associated with neutrophil count, including previously unknown rare loci regulating neutrophil count in a non-European population. BOLT-LMM was reliable when conducted in a non-European population, and additional covariates added to the model did not largely alter the results of the top loci or index SNPs. The two-sample bi-directional MR analysis between neutrophil count and severe malaria showed the greatest evidence for an effect between neutrophil count and severe anaemia, although the confidence intervals crossed the null. CONCLUSION Our GWAS of neutrophil count revealed unique loci present in individuals of African ancestry. We note that a small sample-size reduced our power to identify variants with low allele frequencies and/or low effect sizes in our GWAS. Our work highlights the need for conducting large-scale biobank studies in Africa and for further exploring the link between neutrophils and severe malaria.
Collapse
Affiliation(s)
- Andrei-Emil Constantinescu
- MRC Integrative Epidemiology Unit, University of Bristol, Bristol, UK
- Bristol Medical School, Population Health Sciences, University of Bristol, Bristol, UK
- School of Translational Health Sciences, University of Bristol, Bristol, UK
| | - David A Hughes
- MRC Integrative Epidemiology Unit, University of Bristol, Bristol, UK
- Bristol Medical School, Population Health Sciences, University of Bristol, Bristol, UK
- Louisiana State University, Louisiana, USA
| | - Caroline J Bull
- MRC Integrative Epidemiology Unit, University of Bristol, Bristol, UK
- Bristol Medical School, Population Health Sciences, University of Bristol, Bristol, UK
- School of Translational Health Sciences, University of Bristol, Bristol, UK
- Health Data Research UK, London, UK
| | - Kathryn Fleming
- School of Cellular and Molecular Medicine, University of Bristol, Bristol, UK
| | - Ruth E Mitchell
- MRC Integrative Epidemiology Unit, University of Bristol, Bristol, UK
- Bristol Medical School, Population Health Sciences, University of Bristol, Bristol, UK
| | - Jie Zheng
- Department of Endocrine and Metabolic Diseases, Shanghai Institute of Endocrine and Metabolic Diseases, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, People's Republic of China
- Shanghai National Clinical Research Center for Metabolic Diseases, Key Laboratory for Endocrine and Metabolic Diseases, National Health Commission, Shanghai, People's Republic of China
- Shanghai National Center for Translational Medicine, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, People's Republic of China
| | - Siddhartha Kar
- MRC Integrative Epidemiology Unit, University of Bristol, Bristol, UK
- Bristol Medical School, Population Health Sciences, University of Bristol, Bristol, UK
- Early Cancer Insitute, University of Cambridge, Cambridge, UK
| | - Nicholas J Timpson
- MRC Integrative Epidemiology Unit, University of Bristol, Bristol, UK
- Bristol Medical School, Population Health Sciences, University of Bristol, Bristol, UK
| | - Borko Amulic
- School of Cellular and Molecular Medicine, University of Bristol, Bristol, UK.
| | - Emma E Vincent
- MRC Integrative Epidemiology Unit, University of Bristol, Bristol, UK.
- Bristol Medical School, Population Health Sciences, University of Bristol, Bristol, UK.
- School of Translational Health Sciences, University of Bristol, Bristol, UK.
| |
Collapse
|
34
|
Lee YC, Jung SH, Shivakumar M, Cha S, Park WY, Won HH, Eun YG, Biobank PM, Kim D. Polygenic risk score-based phenome-wide association study of head and neck cancer across two large biobanks. BMC Med 2024; 22:120. [PMID: 38486201 PMCID: PMC10941505 DOI: 10.1186/s12916-024-03305-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/28/2023] [Accepted: 02/15/2024] [Indexed: 03/17/2024] Open
Abstract
BACKGROUND Numerous observational studies have highlighted associations of genetic predisposition of head and neck squamous cell carcinoma (HNSCC) with diverse risk factors, but these findings are constrained by design limitations of observational studies. In this study, we utilized a phenome-wide association study (PheWAS) approach, incorporating a polygenic risk score (PRS) derived from a wide array of genomic variants, to systematically investigate phenotypes associated with genetic predisposition to HNSCC. Furthermore, we validated our findings across heterogeneous cohorts, enhancing the robustness and generalizability of our results. METHODS We derived PRSs for HNSCC and its subgroups, oropharyngeal cancer and oral cancer, using large-scale genome-wide association study summary statistics from the Genetic Associations and Mechanisms in Oncology Network. We conducted a comprehensive investigation, leveraging genotyping data and electronic health records from 308,492 individuals in the UK Biobank and 38,401 individuals in the Penn Medicine Biobank (PMBB), and subsequently performed PheWAS to elucidate the associations between PRS and a wide spectrum of phenotypes. RESULTS We revealed the HNSCC PRS showed significant association with phenotypes related to tobacco use disorder (OR, 1.06; 95% CI, 1.05-1.08; P = 3.50 × 10-15), alcoholism (OR, 1.06; 95% CI, 1.04-1.09; P = 6.14 × 10-9), alcohol-related disorders (OR, 1.08; 95% CI, 1.05-1.11; P = 1.09 × 10-8), emphysema (OR, 1.11; 95% CI, 1.06-1.16; P = 5.48 × 10-6), chronic airway obstruction (OR, 1.05; 95% CI, 1.03-1.07; P = 2.64 × 10-5), and cancer of bronchus (OR, 1.08; 95% CI, 1.04-1.13; P = 4.68 × 10-5). These findings were replicated in the PMBB cohort, and sensitivity analyses, including the exclusion of HNSCC cases and the major histocompatibility complex locus, confirmed the robustness of these associations. Additionally, we identified significant associations between HNSCC PRS and lifestyle factors related to smoking and alcohol consumption. CONCLUSIONS The study demonstrated the potential of PRS-based PheWAS in revealing associations between genetic risk factors for HNSCC and various phenotypic traits. The findings emphasized the importance of considering genetic susceptibility in understanding HNSCC and highlighted shared genetic bases between HNSCC and other health conditions and lifestyles.
Collapse
Affiliation(s)
- Young Chan Lee
- Department of Biostatistics, Epidemiology and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Department of Otolaryngology-Head and Neck Surgery, School of Medicine, Kyung Hee University, Seoul, Republic of Korea
| | - Sang-Hyuk Jung
- Department of Biostatistics, Epidemiology and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Manu Shivakumar
- Department of Biostatistics, Epidemiology and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Soojin Cha
- Hanyang University Institute for Rheumatology Research, Seoul, Republic of Korea
| | - Woong-Yang Park
- Samsung Genome Institute, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea
| | - Hong-Hee Won
- Samsung Genome Institute, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea
- Samsung Medical Center, Samsung Advanced Institute for Health Sciences and Technology, Sungkyunkwan University, Seoul, Republic of Korea
| | - Young-Gyu Eun
- Department of Otolaryngology-Head and Neck Surgery, School of Medicine, Kyung Hee University, Seoul, Republic of Korea
| | - Penn Medicine Biobank
- Institute for Translational Medicine and Therapeutics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Dokyoon Kim
- Department of Biostatistics, Epidemiology and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.
- Institute for Biomedical Informatics, University of Pennsylvania, Philadelphia, PA, USA.
| |
Collapse
|
35
|
Garrido Marques A, Rubinacci S, Malaspinas AS, Delaneau O, Sousa da Mota B. Assessing the impact of post-mortem damage and contamination on imputation performance in ancient DNA. Sci Rep 2024; 14:6227. [PMID: 38486065 PMCID: PMC10940295 DOI: 10.1038/s41598-024-56584-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/19/2023] [Accepted: 03/08/2024] [Indexed: 03/18/2024] Open
Abstract
Low-coverage imputation is becoming ever more present in ancient DNA (aDNA) studies. Imputation pipelines commonly used for present-day genomes have been shown to yield accurate results when applied to ancient genomes. However, post-mortem damage (PMD), in the form of C-to-T substitutions at the reads termini, and contamination with DNA from closely related species can potentially affect imputation performance in aDNA. In this study, we evaluated imputation performance (i) when using a genotype caller designed for aDNA, ATLAS, compared to bcftools, and (ii) when contamination is present. We evaluated imputation performance with principal component analyses and by calculating imputation error rates. With a particular focus on differently imputed sites, we found that using ATLAS prior to imputation substantially improved imputed genotypes for a very damaged ancient genome (42% PMD). Trimming the ends of the sequencing reads led to similar improvements in imputation accuracy. For the remaining genomes, ATLAS brought limited gains. Finally, to examine the effect of contamination on imputation, we added various amounts of reads from two present-day genomes to a previously downsampled high-coverage ancient genome. We observed that imputation accuracy drastically decreased for contamination rates above 5%. In conclusion, we recommend (i) accounting for PMD by either trimming sequencing reads or using a genotype caller such as ATLAS before imputing highly damaged genomes and (ii) only imputing genomes containing up to 5% of contamination.
Collapse
Affiliation(s)
| | - Simone Rubinacci
- Division of Genetics, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Anna-Sapfo Malaspinas
- Department of Computational Biology, University of Lausanne, Lausanne, Switzerland
- Swiss Institute of Bioinformatics, University of Lausanne, Lausanne, Switzerland
| | | | - Bárbara Sousa da Mota
- Department of Computational Biology, University of Lausanne, Lausanne, Switzerland.
- Swiss Institute of Bioinformatics, University of Lausanne, Lausanne, Switzerland.
| |
Collapse
|
36
|
Lin W, Wall JD, Li G, Newman D, Yang Y, Abney M, VandeBerg JL, Olivier M, Gilad Y, Cox LA. Genetic regulatory effects in response to a high-cholesterol, high-fat diet in baboons. Cell Genom 2024; 4:100509. [PMID: 38430910 PMCID: PMC10943580 DOI: 10.1016/j.xgen.2024.100509] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/01/2023] [Revised: 11/20/2023] [Accepted: 02/05/2024] [Indexed: 03/05/2024]
Abstract
Steady-state expression quantitative trait loci (eQTLs) explain only a fraction of disease-associated loci identified through genome-wide association studies (GWASs), while eQTLs involved in gene-by-environment (GxE) interactions have rarely been characterized in humans due to experimental challenges. Using a baboon model, we found hundreds of eQTLs that emerge in adipose, liver, and muscle after prolonged exposure to high dietary fat and cholesterol. Diet-responsive eQTLs exhibit genomic localization and genic features that are distinct from steady-state eQTLs. Furthermore, the human orthologs associated with diet-responsive eQTLs are enriched for GWAS genes associated with human metabolic traits, suggesting that context-responsive eQTLs with more complex regulatory effects are likely to explain GWAS hits that do not seem to overlap with standard eQTLs. Our results highlight the complexity of genetic regulatory effects and the potential of eQTLs with disease-relevant GxE interactions in enhancing the understanding of GWAS signals for human complex disease using non-human primate models.
Collapse
Affiliation(s)
- Wenhe Lin
- Department of Human Genetics, The University of Chicago, Chicago, IL 60637, USA.
| | - Jeffrey D Wall
- Institute for Human Genetics, University of California, San Francisco, San Francisco, CA 94143, USA
| | - Ge Li
- Center for Precision Medicine, Wake Forest University School of Medicine, Winston-Salem, NC 27157, USA
| | - Deborah Newman
- Southwest National Primate Research Center, Texas Biomedical Research Institute, San Antonio, TX 78229, USA
| | - Yunqi Yang
- Committee on Genetics, Genomics and System Biology, The University of Chicago, Chicago, IL 60637, USA
| | - Mark Abney
- Department of Human Genetics, The University of Chicago, Chicago, IL 60637, USA
| | - John L VandeBerg
- Department of Human Genetics, South Texas Diabetes and Obesity Institute, University of Texas Rio Grande Valley, Brownsville, TX 78520, USA
| | - Michael Olivier
- Center for Precision Medicine, Wake Forest University School of Medicine, Winston-Salem, NC 27157, USA
| | - Yoav Gilad
- Department of Human Genetics, The University of Chicago, Chicago, IL 60637, USA; Department of Medicine, Section of Genetic Medicine, The University of Chicago, Chicago, IL 60637, USA.
| | - Laura A Cox
- Center for Precision Medicine, Wake Forest University School of Medicine, Winston-Salem, NC 27157, USA; Southwest National Primate Research Center, Texas Biomedical Research Institute, San Antonio, TX 78229, USA.
| |
Collapse
|
37
|
Quinlan CM, Chang X, March M, Mentch FD, Qu HQ, Liu Y, Glessner J, Sleiman PMA, Hakonarson H. Identification of novel loci in obstructive sleep apnea in European American and African American children. Sleep 2024; 47:zsac182. [PMID: 35902206 DOI: 10.1093/sleep/zsac182] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/07/2022] [Revised: 06/24/2022] [Indexed: 02/18/2024] Open
Abstract
STUDY OBJECTIVES To identify genetic susceptibility variants in pediatric obstructive sleep apnea in European American and African American children. METHODS A phenotyping algorithm using electronic medical records was developed to recruit cases with OSA and control subjects from the Center for Applied Genomics at Children's Hospital of Philadelphia (CHOP). Genome-wide association studies (GWAS) were performed in pediatric OSA cases and control subjects with European American (EA) and African American (AA) ancestry followed by meta-analysis and sex stratification. RESULTS The algorithm accrued 1486 subjects (46.3% European American, 53.7% African American). We identified genomic loci at 1p36.22 and 15q26.1 that associated with OSA risk in EA and AA, respectively. We also revealed a shared risk locus at 18p11.32 (rs114124196, p = 1.72 × 10-8) across EA and AA populations. Additionally, association at 1q43 (rs12754698) and 2p25.1 (rs72775219) was identified in the male-only analysis of EA children with OSA, while association at 8q21.11 (rs6472959), 11q24.3 (rs4370952) and 15q21.1 (rs149936782) was detected in the female-only analysis of EA children and association at 18p11.23 (rs9964029) was identified in the female-only analysis of African-American children. Moreover, the 18p11.32 locus was replicated in an EA cohort (rs114124196, p = 8.8 × 10-3). CONCLUSIONS We report the first GWAS for pediatric OSA in European Americans and African Americans. Our results provide novel insights to the genetic underpins of pediatric OSA.
Collapse
Affiliation(s)
- Courtney M Quinlan
- The Center for Applied Genomics, Children's Hospital of Philadelphia, Philadelphia, PA, 19104, USA
- Pulmonary Medicine, Children's Hospital of Philadelphia, Philadelphia, PA, 19104, USA
| | - Xiao Chang
- The Center for Applied Genomics, Children's Hospital of Philadelphia, Philadelphia, PA, 19104, USA
| | - Michael March
- The Center for Applied Genomics, Children's Hospital of Philadelphia, Philadelphia, PA, 19104, USA
| | - Frank D Mentch
- The Center for Applied Genomics, Children's Hospital of Philadelphia, Philadelphia, PA, 19104, USA
| | - Hui-Qi Qu
- The Center for Applied Genomics, Children's Hospital of Philadelphia, Philadelphia, PA, 19104, USA
| | - Yichuan Liu
- The Center for Applied Genomics, Children's Hospital of Philadelphia, Philadelphia, PA, 19104, USA
| | - Joseph Glessner
- The Center for Applied Genomics, Children's Hospital of Philadelphia, Philadelphia, PA, 19104, USA
| | - Patrick M A Sleiman
- The Center for Applied Genomics, Children's Hospital of Philadelphia, Philadelphia, PA, 19104, USA
- Department of Pediatrics, University of Pennsylvania School of Medicine, Philadelphia, PA, 19104, USA
- Divisions of Human Genetics, Children's Hospital of Philadelphia, Philadelphia, PA, 19104, USA
| | - Hakon Hakonarson
- The Center for Applied Genomics, Children's Hospital of Philadelphia, Philadelphia, PA, 19104, USA
- Pulmonary Medicine, Children's Hospital of Philadelphia, Philadelphia, PA, 19104, USA
- Department of Pediatrics, University of Pennsylvania School of Medicine, Philadelphia, PA, 19104, USA
- Divisions of Human Genetics, Children's Hospital of Philadelphia, Philadelphia, PA, 19104, USA
| |
Collapse
|
38
|
Osborne AJ, Bierzynska A, Colby E, Andag U, Kalra PA, Radresa O, Skroblin P, Taal MW, Welsh GI, Saleem MA, Campbell C. Multivariate canonical correlation analysis identifies additional genetic variants for chronic kidney disease. NPJ Syst Biol Appl 2024; 10:28. [PMID: 38459044 PMCID: PMC10924093 DOI: 10.1038/s41540-024-00350-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/04/2023] [Accepted: 02/20/2024] [Indexed: 03/10/2024] Open
Abstract
Chronic kidney diseases (CKD) have genetic associations with kidney function. Univariate genome-wide association studies (GWAS) have identified single nucleotide polymorphisms (SNPs) associated with estimated glomerular filtration rate (eGFR) and blood urea nitrogen (BUN), two complementary kidney function markers. However, it is unknown whether additional SNPs for kidney function can be identified by multivariate statistical analysis. To address this, we applied canonical correlation analysis (CCA), a multivariate method, to two individual-level CKD genotype datasets, and metaCCA to two published GWAS summary statistics datasets. We identified SNPs previously associated with kidney function by published univariate GWASs with high replication rates, validating the metaCCA method. We then extended discovery and identified previously unreported lead SNPs for both kidney function markers, jointly. These showed expression quantitative trait loci (eQTL) colocalisation with genes having significant differential expression between CKD and healthy individuals. Several of these identified lead missense SNPs were predicted to have a functional impact, including in SLC14A2. We also identified previously unreported lead SNPs that showed significant correlation with both kidney function markers, jointly, in the European ancestry CKDGen, National Unified Renal Translational Research Enterprise (NURTuRE)-CKD and Salford Kidney Study (SKS) datasets. Of these, rs3094060 colocalised with FLOT1 gene expression and was significantly more common in CKD cases in both NURTURE-CKD and SKS, than in the general population. Overall, by using multivariate analysis by CCA, we identified additional SNPs and genes for both kidney function and CKD, that can be prioritised for further CKD analyses.
Collapse
Affiliation(s)
- Amy J Osborne
- Intelligent Systems Laboratory, University of Bristol, Bristol, BS8 1TW, UK.
| | - Agnieszka Bierzynska
- Bristol Renal, University of Bristol and Bristol Royal Hospital for Children, Bristol, BS1 3NY, UK
| | - Elizabeth Colby
- Bristol Renal, University of Bristol and Bristol Royal Hospital for Children, Bristol, BS1 3NY, UK
| | - Uwe Andag
- Department of Metabolic and Renal Diseases, Evotec International GmbH, Marie-Curie-Strasse 7, 37079, Göttingen, Germany
| | - Philip A Kalra
- Department of Renal Medicine, Salford Royal Hospital, Northern Care Alliance NHS Foundation Trust, Stott Lane, Salford, M6 8HD, UK
| | - Olivier Radresa
- Department of Metabolic and Renal Diseases, Evotec International GmbH, Marie-Curie-Strasse 7, 37079, Göttingen, Germany
| | - Philipp Skroblin
- Department of Metabolic and Renal Diseases, Evotec International GmbH, Marie-Curie-Strasse 7, 37079, Göttingen, Germany
| | - Maarten W Taal
- Centre for Kidney Research and Innovation, University of Nottingham, Derby, UK
| | - Gavin I Welsh
- Bristol Renal, University of Bristol and Bristol Royal Hospital for Children, Bristol, BS1 3NY, UK
| | - Moin A Saleem
- Bristol Renal, University of Bristol and Bristol Royal Hospital for Children, Bristol, BS1 3NY, UK
| | - Colin Campbell
- Intelligent Systems Laboratory, University of Bristol, Bristol, BS8 1TW, UK.
| |
Collapse
|
39
|
Zhang S, Jiang Z, Zeng P. Incorporating genetic similarity of auxiliary samples into eGene identification under the transfer learning framework. J Transl Med 2024; 22:258. [PMID: 38461317 PMCID: PMC10924384 DOI: 10.1186/s12967-024-05053-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/27/2023] [Accepted: 03/01/2024] [Indexed: 03/11/2024] Open
Abstract
BACKGROUND The term eGene has been applied to define a gene whose expression level is affected by at least one independent expression quantitative trait locus (eQTL). It is both theoretically and empirically important to identify eQTLs and eGenes in genomic studies. However, standard eGene detection methods generally focus on individual cis-variants and cannot efficiently leverage useful knowledge acquired from auxiliary samples into target studies. METHODS We propose a multilocus-based eGene identification method called TLegene by integrating shared genetic similarity information available from auxiliary studies under the statistical framework of transfer learning. We apply TLegene to eGene identification in ten TCGA cancers which have an explicit relevant tissue in the GTEx project, and learn genetic effect of variant in TCGA from GTEx. We also adopt TLegene to the Geuvadis project to evaluate its usefulness in non-cancer studies. RESULTS We observed substantial genetic effect correlation of cis-variants between TCGA and GTEx for a larger number of genes. Furthermore, consistent with the results of our simulations, we found that TLegene was more powerful than existing methods and thus identified 169 distinct candidate eGenes, which was much larger than the approach that did not consider knowledge transfer across target and auxiliary studies. Previous studies and functional enrichment analyses provided empirical evidence supporting the associations of discovered eGenes, and it also showed evidence of allelic heterogeneity of gene expression. Furthermore, TLegene identified more eGenes in Geuvadis and revealed that these eGenes were mainly enriched in cells EBV transformed lymphocytes tissue. CONCLUSION Overall, TLegene represents a flexible and powerful statistical method for eGene identification through transfer learning of genetic similarity shared across auxiliary and target studies.
Collapse
Affiliation(s)
- Shuo Zhang
- Department of Biostatistics, School of Public Health, Xuzhou Medical University, Xuzhou, 221004, Jiangsu, China
| | - Zhou Jiang
- Department of Biostatistics, School of Public Health, Xuzhou Medical University, Xuzhou, 221004, Jiangsu, China
| | - Ping Zeng
- Department of Biostatistics, School of Public Health, Xuzhou Medical University, Xuzhou, 221004, Jiangsu, China.
- Center for Medical Statistics and Data Analysis, Xuzhou Medical University, Xuzhou, 221004, Jiangsu, China.
- Key Laboratory of Human Genetics and Environmental Medicine, Xuzhou Medical University, Xuzhou, 221004, Jiangsu, China.
- Key Laboratory of Environment and Health, Xuzhou Medical University, Xuzhou, 221004, Jiangsu, China.
- Xuzhou Engineering Research Innovation Center of Biological Data Mining and Healthcare Transformation, Xuzhou Medical University, Xuzhou, 221004, Jiangsu, China.
- Jiangsu Engineering Research Center of Biological Data Mining and Healthcare Transformation, Xuzhou Medical University, Xuzhou, 221004, Jiangsu, China.
| |
Collapse
|
40
|
Zhao T, Wang F, Mott R, Dekkers J, Cheng H. Using encrypted genotypes and phenotypes for collaborative genomic analyses to maintain data confidentiality. Genetics 2024; 226:iyad210. [PMID: 38085098 DOI: 10.1093/genetics/iyad210] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/09/2023] [Accepted: 11/13/2023] [Indexed: 03/08/2024] Open
Abstract
To adhere to and capitalize on the benefits of the FAIR (findable, accessible, interoperable, and reusable) principles in agricultural genome-to-phenome studies, it is crucial to address privacy and intellectual property issues that prevent sharing and reuse of data in research and industry. Direct sharing of genotype and phenotype data is often prohibited due to intellectual property and privacy concerns. Thus, there is a pressing need for encryption methods that obscure confidential aspects of the data, without affecting the outcomes of certain statistical analyses. A homomorphic encryption method for genotypes and phenotypes (HEGP) has been proposed for single-marker regression in genome-wide association studies (GWAS) using linear mixed models with Gaussian errors. This methodology permits frequentist likelihood-based parameter estimation and inference. In this paper, we extend HEGP to broader applications in genome-to-phenome analyses. We show that HEGP is suited to commonly used linear mixed models for genetic analyses of quantitative traits including genomic best linear unbiased prediction (GBLUP) and ridge-regression best linear unbiased prediction (RR-BLUP), as well as Bayesian variable selection methods (e.g. those in Bayesian Alphabet), for genetic parameter estimation, genomic prediction, and GWAS. By advancing the capabilities of HEGP, we offer researchers and industry professionals a secure and efficient approach for collaborative genomic analyses while preserving data confidentiality.
Collapse
Affiliation(s)
- Tianjing Zhao
- Department of Animal Science, University of California, Davis, CA 95616, USA
- Department of Animal Science, University of Nebraska-Lincoln, Lincoln, NE 68583, USA
| | - Fangyi Wang
- Department of Plant Sciences, University of California, Davis, CA 95616, USA
| | - Richard Mott
- Genetics Institute, University College London, London, WC1E 6BT, UK
| | - Jack Dekkers
- Department of Animal Science, Iowa State University, Ames, IA 50011, USA
| | - Hao Cheng
- Department of Animal Science, University of California, Davis, CA 95616, USA
| |
Collapse
|
41
|
Pugsley K, Namipashaki A, Bellgrove MA, Hawi Z. Evaluating the regulatory function of non-coding autism-associated single nucleotide polymorphisms on gene expression in human brain tissue. Autism Res 2024; 17:467-481. [PMID: 38323502 DOI: 10.1002/aur.3101] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/11/2023] [Accepted: 01/18/2024] [Indexed: 02/08/2024]
Abstract
Common variants account for most of the estimated heritability associated with autism spectrum disorder (autism). Although several replicable single nucleotide polymorphisms (SNPs) for the condition have been detected using genome-wide association study (GWAS) methodologies, their pathophysiological relevance remains elusive. Examining this is complicated, however, as all detected loci are situated within non-coding regions of the genome. It is therefore likely that they possess roles of regulatory function as opposed to directly affecting gene coding sequences. To bridge the gap between SNP discovery and mechanistic insight, we applied a comprehensive bioinformatic pipeline to functionally annotate autism-associated polymorphisms and their non-coding linkage disequilibrium (i.e., non-randomly associated) partners. We identified 82 DNA variants of probable regulatory function that may contribute to autism pathogenesis. To validate these predictions, we measured the impact of 11 high-confidence candidates and their GWAS linkage disequilibrium partners on gene expression in human brain tissue from Autistic and non-Autistic donors. Although a small number of the surveyed variants exhibited measurable influence on gene expression as determined via quantitative polymerase chain reaction, these did not survive correction for multiple comparisons. Additionally, no significant genotype-by-diagnosis effects were observed for any of the SNP-gene associations. We contend that this may reflect an inability to effectively capture the modest, neurodevelopmental-specific impact of individual variants on biological dysregulation in available post-mortem tissue samples, as well as limitations in the existing autism GWAS data.
Collapse
Affiliation(s)
- Kealan Pugsley
- Turner Institute for Brain and Mental Health and School of Psychological Sciences, Monash University, Melbourne, Victoria, Australia
| | - Atefeh Namipashaki
- Turner Institute for Brain and Mental Health and School of Psychological Sciences, Monash University, Melbourne, Victoria, Australia
| | - Mark A Bellgrove
- Turner Institute for Brain and Mental Health and School of Psychological Sciences, Monash University, Melbourne, Victoria, Australia
| | - Ziarih Hawi
- Turner Institute for Brain and Mental Health and School of Psychological Sciences, Monash University, Melbourne, Victoria, Australia
| |
Collapse
|
42
|
Gangwar M, Kumar S, Ahmad SF, Singh A, Agrawal S, Anitta PL, Kumar A. Identification of genetic variants affecting reproduction traits in Vrindavani cattle. Mamm Genome 2024; 35:99-111. [PMID: 37924370 DOI: 10.1007/s00335-023-10023-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/23/2023] [Accepted: 10/08/2023] [Indexed: 11/06/2023]
Abstract
Genome-wide association studies (GWAS) are one of the best ways to look into the connection between single-nucleotide polymorphisms (SNPs) and the phenotypic performance. This study aimed to identify the genetic variants that significantly affect the important reproduction traits in Vrindavani cattle using genome-wide SNP chip array data. In this study, 96 randomly chosen Vrindavani cows were genotyped using the Illumina Bovine50K BeadChip platform. A linear regression model of the genome-wide association study was fitted in the PLINK program between genome-wide SNP markers and reproduction traits, including age at first calving (AFC), inter-calving period (ICP), dry days (DD), and service period (SP) across the first three lactations. Information on different QTLs and genes, overlapping or adjacent to genomic coordinates of significant SNPs, was also mined from relevant databases in order to identify the biological pathways associated with reproductive traits in bovine. The Bonferroni correction resulted in total 39 SNP markers present on different chromosomes being identified that significantly affected the variation in AFC (6 SNPs), ICP (7 SNPs), DD (9 SNPs), and SP (17 SNPs). Novel potential candidate genes associated with reproductive traits that were identified using the GWAS methodology included UMPS, ITGB5, ADAM2, UPK1B, TEX55, bta-mir-708, TMPO, TDRD5, MAPRE2, PTER, AP3B1, DPP8, PLAT, TXN2, NDUFAF1, TGFA, DTNA, RSU1, KCNQ1, ADAM32, and CHST8. The significant SNPs and genes associated with the reproductive traits and the enriched genes may be exploited as candidate biomarkers in animal improvement programs, especially for improved reproduction performance in bovines.
Collapse
Affiliation(s)
- Munish Gangwar
- Animal Genetics Division, ICAR-Indian Veterinary Research Institute, Izatnangar, Bareilly, 243122, India
| | - Subodh Kumar
- Animal Genetics Division, ICAR-Indian Veterinary Research Institute, Izatnangar, Bareilly, 243122, India.
| | - Sheikh Firdous Ahmad
- Animal Genetics Division, ICAR-Indian Veterinary Research Institute, Izatnangar, Bareilly, 243122, India
| | - Akansha Singh
- Animal Genetics Division, ICAR-Indian Veterinary Research Institute, Izatnangar, Bareilly, 243122, India
| | - Swati Agrawal
- Animal Genetics Division, ICAR-Indian Veterinary Research Institute, Izatnangar, Bareilly, 243122, India
| | - P L Anitta
- Animal Genetics Division, ICAR-Indian Veterinary Research Institute, Izatnangar, Bareilly, 243122, India
| | - Amit Kumar
- Animal Genetics Division, ICAR-Indian Veterinary Research Institute, Izatnangar, Bareilly, 243122, India
| |
Collapse
|
43
|
Zhang X, Hu Y, Vandenhoudt RE, Yan C, Marconi VC, Cohen MH, Wang Z, Justice AC, Aouizerat BE, Xu K. Computationally inferred cell-type specific epigenome-wide DNA methylation analysis unveils distinct methylation patterns among immune cells for HIV infection in three cohorts. PLoS Pathog 2024; 20:e1012063. [PMID: 38466776 PMCID: PMC10957090 DOI: 10.1371/journal.ppat.1012063] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/11/2023] [Revised: 03/21/2024] [Accepted: 02/20/2024] [Indexed: 03/13/2024] Open
Abstract
BACKGROUND Epigenome-wide association studies (EWAS) have identified CpG sites associated with HIV infection in blood cells in bulk, which offer limited knowledge of cell-type specific methylation patterns associated with HIV infection. In this study, we aim to identify differentially methylated CpG sites for HIV infection in immune cell types: CD4+ T-cells, CD8+ T-cells, B cells, Natural Killer (NK) cells, and monocytes. METHODS Applying a computational deconvolution method, we performed a cell-type based EWAS for HIV infection in three independent cohorts (Ntotal = 1,382). DNA methylation in blood or in peripheral blood mononuclear cells (PBMCs) was profiled by an array-based method and then deconvoluted by Tensor Composition Analysis (TCA). The TCA-computed CpG methylation in each cell type was first benchmarked by bisulfite DNA methylation capture sequencing in a subset of the samples. Cell-type EWAS of HIV infection was performed in each cohort separately and a meta-EWAS was conducted followed by gene set enrichment analysis. RESULTS The meta-analysis unveiled a total of 2,021 cell-type unique significant CpG sites for five inferred cell types. Among these inferred cell-type unique CpG sites, the concordance rate in the three cohorts ranged from 96% to 100% in each cell type. Cell-type level meta-EWAS unveiled distinct patterns of HIV-associated differential CpG methylation, where 74% of CpG sites were unique to individual cell types (false discovery rate, FDR <0.05). CD4+ T-cells had the largest number of unique HIV-associated CpG sites (N = 1,624) compared to any other cell type. Genes harboring significant CpG sites are involved in immunity and HIV pathogenesis (e.g. CD4+ T-cells: NLRC5, CX3CR1, B cells: IFI44L, NK cells: IL12R, monocytes: IRF7), and in oncogenesis (e.g. CD4+ T-cells: BCL family, PRDM16, monocytes: PRDM16, PDCD1LG2). HIV-associated CpG sites were enriched among genes involved in HIV pathogenesis and oncogenesis that were enriched among interferon-α and -γ, TNF-α, inflammatory response, and apoptotic pathways. CONCLUSION Our findings uncovered computationally inferred cell-type specific modifications in the host epigenome for people with HIV that contribute to the growing body of evidence regarding HIV pathogenesis.
Collapse
Affiliation(s)
- Xinyu Zhang
- Department of Psychiatry, Yale School of Medicine, New Haven, Connecticut, United States of America
- VA Connecticut Healthcare System, West Haven, Connecticut, United States of America
| | - Ying Hu
- Center for Biomedical Information and Information Technology, National Cancer Institute, Rockville, Maryland, United States of America
| | - Ral E. Vandenhoudt
- Department of Psychiatry, Yale School of Medicine, New Haven, Connecticut, United States of America
- VA Connecticut Healthcare System, West Haven, Connecticut, United States of America
| | - Chunhua Yan
- Center for Biomedical Information and Information Technology, National Cancer Institute, Rockville, Maryland, United States of America
| | - Vincent C. Marconi
- Division of Infectious Diseases, Emory University School of Medicine and Department of Global Health, Rollins School of Public Health, Emory University, Georgia, United States of America
- Atlanta Veterans Affairs Healthcare System, Decatur, Georgia, United States of America
| | - Mardge H. Cohen
- Department of Medicine, Stroger Hospital of Cook County, Chicago, Illinois, United States of America
| | - Zuoheng Wang
- Department of Biostatistics, Yale School of Public Health, New Haven, Connecticut, United States of America
| | - Amy C. Justice
- VA Connecticut Healthcare System, West Haven, Connecticut, United States of America
- Department of Biostatistics, Yale School of Public Health, New Haven, Connecticut, United States of America
- Department of Internal Medicine, Yale School of Medicine, New Haven, Connecticut, United States of America
| | - Bradley E. Aouizerat
- Translational Research Center, College of Dentistry, New York University, New York, New York, United States of America
- Department of Oral and Maxillofacial Surgery, College of Dentistry, New York University, New York, New York, United States of America
| | - Ke Xu
- Department of Psychiatry, Yale School of Medicine, New Haven, Connecticut, United States of America
- VA Connecticut Healthcare System, West Haven, Connecticut, United States of America
- Biomedical Informatics and Data Science, Yale School of Medicine, New Haven, Connecticut, United States of America
| |
Collapse
|
44
|
Wang T, Yan Z, Zhang Y, Lou Z, Zheng X, Mai D, Wang Y, Shang X, Xiao B, Peng J, Chen J. postGWAS: A web server for deciphering the causality post the genome-wide association studies. Comput Biol Med 2024; 171:108108. [PMID: 38359659 DOI: 10.1016/j.compbiomed.2024.108108] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/01/2023] [Revised: 01/23/2024] [Accepted: 02/04/2024] [Indexed: 02/17/2024]
Abstract
While genome-wide association studies (GWAS) have unequivocally identified vast disease susceptibility variants, a majority of them are situated in non-coding regions and are in high linkage disequilibrium (LD). To pave the way of translating GWAS signals to clinical drug targets, it is essential to identify the underlying causal variants and further causal genes. To this end, a myriad of post-GWAS methods have been devised, each grounded in distinct principles including fine-mapping, co-localization, and transcriptome-wide association study (TWAS) techniques. Yet, no platform currently exists that seamlessly integrates these diverse post-GWAS methodologies. In this work, we present a user-friendly web server for post-GWAS analysis, that seamlessly integrates 9 distinct methods with 12 models, categorized by fine-mapping, colocalization, and TWAS. The server mainly helps users decipher the causality hindered by complex GWAS signals, including casual variants and casual genes, without the burden of computational skills and complex environment configuration, and provides a convenient platform for post-GWAS analysis, result visualization, facilitating the understanding and interpretation of the genome-wide association studies. The postGWAS server is available at http://g2g.biographml.com/.
Collapse
Affiliation(s)
- Tao Wang
- School of Computer Science, Northwestern Polytechnical University, Xi'an, 710072, China; Key Laboratory of Big Data Storage and Management, Northwestern Polytechnical University, Ministry of Industry and Information Technology, Xi'an, 710072, China
| | - Zhihao Yan
- School of Computer Science, Northwestern Polytechnical University, Xi'an, 710072, China; Key Laboratory of Big Data Storage and Management, Northwestern Polytechnical University, Ministry of Industry and Information Technology, Xi'an, 710072, China
| | - Yiming Zhang
- School of Computer Science, Northwestern Polytechnical University, Xi'an, 710072, China
| | - Zhuofei Lou
- School of Computer Science, Northwestern Polytechnical University, Xi'an, 710072, China
| | - Xiaozhu Zheng
- Department of Anesthesiology, The People's Hospital of Yubei District, Chongqing, 401120, China
| | - DuoDuo Mai
- School of Computer Science, Northwestern Polytechnical University, Xi'an, 710072, China; Key Laboratory of Big Data Storage and Management, Northwestern Polytechnical University, Ministry of Industry and Information Technology, Xi'an, 710072, China
| | - Yongtian Wang
- School of Computer Science, Northwestern Polytechnical University, Xi'an, 710072, China; Key Laboratory of Big Data Storage and Management, Northwestern Polytechnical University, Ministry of Industry and Information Technology, Xi'an, 710072, China
| | - Xuequn Shang
- School of Computer Science, Northwestern Polytechnical University, Xi'an, 710072, China; Key Laboratory of Big Data Storage and Management, Northwestern Polytechnical University, Ministry of Industry and Information Technology, Xi'an, 710072, China
| | - Bing Xiao
- School of Automation, Northwestern Polytechnical University, Xi'an, 710072, China
| | - Jiajie Peng
- School of Computer Science, Northwestern Polytechnical University, Xi'an, 710072, China; Key Laboratory of Big Data Storage and Management, Northwestern Polytechnical University, Ministry of Industry and Information Technology, Xi'an, 710072, China
| | - Jing Chen
- School of Computer Science and Engineering, Xi'an University of Technology, Xi'an, 710048, China.
| |
Collapse
|
45
|
Meuwissen T, Eikje LS, Gjuvsland AB. GWABLUP: genome-wide association assisted best linear unbiased prediction of genetic values. Genet Sel Evol 2024; 56:17. [PMID: 38429665 DOI: 10.1186/s12711-024-00881-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/28/2023] [Accepted: 01/31/2024] [Indexed: 03/03/2024] Open
Abstract
BACKGROUND Since the very beginning of genomic selection, researchers investigated methods that improved upon SNP-BLUP (single nucleotide polymorphism best linear unbiased prediction). SNP-BLUP gives equal weight to all SNPs, whereas it is expected that many SNPs are not near causal variants and thus do not have substantial effects. A recent approach to remedy this is to use genome-wide association study (GWAS) findings and increase the weights of GWAS-top-SNPs in genomic predictions. Here, we employ a genome-wide approach to integrate GWAS results into genomic prediction, called GWABLUP. RESULTS GWABLUP consists of the following steps: (1) performing a GWAS in the training data which results in likelihood ratios; (2) smoothing the likelihood ratios over the SNPs; (3) combining the smoothed likelihood ratio with the prior probability of SNPs having non-zero effects, which yields the posterior probability of the SNPs; (4) calculating a weighted genomic relationship matrix using the posterior probabilities as weights; and (5) performing genomic prediction using the weighted genomic relationship matrix. Using high-density genotypes and milk, fat, protein and somatic cell count phenotypes on dairy cows, GWABLUP was compared to GBLUP, GBLUP (topSNPs) with extra weights for GWAS top-SNPs, and BayesGC, i.e. a Bayesian variable selection model. The GWAS resulted in six, five, four, and three genome-wide significant peaks for milk, fat and protein yield and somatic cell count, respectively. GWABLUP genomic predictions were 10, 6, 7 and 1% more reliable than those of GBLUP for milk, fat and protein yield and somatic cell count, respectively. It was also more reliable than GBLUP (topSNPs) for all four traits, and more reliable than BayesGC for three of the traits. Although GWABLUP showed a tendency towards inflation bias for three of the traits, this was not statistically significant. In a multitrait analysis, GWABLUP yielded the highest accuracy for two of the traits. However, for SCC, which was relatively unrelated to the yield traits, including yield trait GWAS-results reduced the reliability compared to a single trait analysis. CONCLUSIONS GWABLUP uses GWAS results to differentially weigh all the SNPs in a weighted GBLUP genomic prediction analysis. GWABLUP yielded up to 10% and 13% more reliable genomic predictions than GBLUP for single and multitrait analyses, respectively. Extension of GWABLUP to single-step analyses is straightforward.
Collapse
Affiliation(s)
- Theo Meuwissen
- Faculty of Life Sciences, Norwegian University of Life Sciences, 1432, Ås, Norway.
| | | | | |
Collapse
|
46
|
Pagoni P, Higgins JPT, Lawlor DA, Stergiakouli E, Warrington NM, Morris TT, Tilling K. Meta-regression of genome-wide association studies to estimate age-varying genetic effects. Eur J Epidemiol 2024; 39:257-270. [PMID: 38183607 PMCID: PMC10995067 DOI: 10.1007/s10654-023-01086-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/30/2023] [Accepted: 11/15/2023] [Indexed: 01/08/2024]
Abstract
Fixed-effect meta-analysis has been used to summarize genetic effects on a phenotype across multiple Genome-Wide Association Studies (GWAS) assuming a common underlying genetic effect. Genetic effects may vary with age (or other characteristics), and not allowing for this in a GWAS might lead to bias. Meta-regression models between study heterogeneity and allows effect modification of the genetic effects to be explored. The aim of this study was to explore the use of meta-analysis and meta-regression for estimating age-varying genetic effects on phenotypes. With simulations we compared the performance of meta-regression to fixed-effect and random -effects meta-analyses in estimating (i) main genetic effects and (ii) age-varying genetic effects (SNP by age interactions) from multiple GWAS studies under a range of scenarios. We applied meta-regression on publicly available summary data to estimate the main and age-varying genetic effects of the FTO SNP rs9939609 on Body Mass Index (BMI). Fixed-effect and random-effects meta-analyses accurately estimated genetic effects when these did not change with age. Meta-regression accurately estimated both main genetic effects and age-varying genetic effects. When the number of studies or the age-diversity between studies was low, meta-regression had limited power. In the applied example, each additional minor allele (A) of rs9939609 was inversely associated with BMI at ages 0 to 3, and positively associated at ages 5.5 to 13. Our findings challenge the assumption that genetic effects are consistent across all ages and provide a method for exploring this. GWAS consortia should be encouraged to use meta-regression to explore age-varying genetic effects.
Collapse
Affiliation(s)
- Panagiota Pagoni
- MRC Integrative Epidemiology Unit at the University of Bristol, Oakfield House, Oakfield Grove, Bristol, BS8 2BN, UK.
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK.
| | - Julian P T Higgins
- MRC Integrative Epidemiology Unit at the University of Bristol, Oakfield House, Oakfield Grove, Bristol, BS8 2BN, UK
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
| | - Deborah A Lawlor
- MRC Integrative Epidemiology Unit at the University of Bristol, Oakfield House, Oakfield Grove, Bristol, BS8 2BN, UK
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
| | - Evie Stergiakouli
- MRC Integrative Epidemiology Unit at the University of Bristol, Oakfield House, Oakfield Grove, Bristol, BS8 2BN, UK
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
| | - Nicole M Warrington
- MRC Integrative Epidemiology Unit at the University of Bristol, Oakfield House, Oakfield Grove, Bristol, BS8 2BN, UK
- Institute for Molecular Bioscience, University of Queensland, Brisbane, QLD, Australia
- Frazer Institute, University of Queensland, Brisbane, QLD, Australia
- K.G. Jebsen Center for Genetic Epidemiology, Department of Public Health and Nursing, NTNU, Norwegian University of Science and Technology, Trondheim, Norway
| | - Tim T Morris
- Centre for Longitudinal Studies, Social Research Institute, University College London, London, UK
| | - Kate Tilling
- MRC Integrative Epidemiology Unit at the University of Bristol, Oakfield House, Oakfield Grove, Bristol, BS8 2BN, UK
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
| |
Collapse
|
47
|
Gnanapragasam N, Prasanth VV, Sundaram KT, Kumar A, Pahi B, Gurjar A, Venkateshwarlu C, Kalia S, Kumar A, Dixit S, Kohli A, Singh UM, Singh VK, Sinha P. Extreme trait GWAS (Et-GWAS): Unraveling rare variants in the 3,000 rice genome. Life Sci Alliance 2024; 7:e202302352. [PMID: 38148113 PMCID: PMC10751245 DOI: 10.26508/lsa.202302352] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/01/2023] [Revised: 12/12/2023] [Accepted: 12/13/2023] [Indexed: 12/28/2023] Open
Abstract
Identifying high-impact, rare genetic variants associated with specific traits is crucial for crop improvement. The 3,010 rice genome (3K RG) dataset offers a valuable resource for discovering genomic regions with potential applications in crop breeding. We used Extreme Trait GWAS (Et-GWAS), employing bulk pooling and allele frequency measurement to efficiently extract rare variants from the 3K RG. This innovative approach facilitates the detection of associations between genetic variants and target traits, concentrating and quantifying rare alleles. In our study, on grain yield under drought stress, Et-GWAS successfully identified five key genes (OsPP2C11, OsK5.2, OsIRO2, OsPEX1, and OsPWA1) known for enhancing yield under drought. In addition, we examined the overlap of our results with previously reported qDTY-QTLs and observed that OsUCH1 and OsUCH2 genes were located within qDTY2.2 We compared Et-GWAS with conventional GWAS, finding it effectively capturing most candidate genes associated with the target trait. Validation with resistant starch showed similar results. To enhance user-friendliness, we developed a GUI for Et-GWAS; https://et-gwas.shinyapps.io/Et-GWAS/.
Collapse
Affiliation(s)
| | | | | | - Ajay Kumar
- International Rice Research Institute, South Asia Hub, Patancheru, India
| | - Bandana Pahi
- International Rice Research Institute, South Asia Hub, Patancheru, India
| | - Anoop Gurjar
- International Rice Research Institute, South-Asia Regional Centre, Varanasi, India
| | | | - Sanjay Kalia
- Department of Biotechnology, CGO Complex, New Delhi, India
| | - Arvind Kumar
- International Rice Research Institute, South-Asia Regional Centre, Varanasi, India
| | - Shalabh Dixit
- International Rice Research Institute, Los Banos, Philippines
| | - Ajay Kohli
- International Rice Research Institute, Los Banos, Philippines
| | - Uma Maheshwer Singh
- International Rice Research Institute, South-Asia Regional Centre, Varanasi, India
| | - Vikas Kumar Singh
- International Rice Research Institute, South Asia Hub, Patancheru, India
| | - Pallavi Sinha
- International Rice Research Institute, South Asia Hub, Patancheru, India
| |
Collapse
|
48
|
Dubath C, Porcu E, Delacrétaz A, Grosu C, Laaboub N, Piras M, von Gunten A, Conus P, Plessen KJ, Kutalik Z, Eap CB. DNA methylation may partly explain psychotropic drug-induced metabolic side effects: results from a prospective 1-month observational study. Clin Epigenetics 2024; 16:36. [PMID: 38419113 PMCID: PMC10903022 DOI: 10.1186/s13148-024-01648-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/12/2023] [Accepted: 02/19/2024] [Indexed: 03/02/2024] Open
Abstract
BACKGROUND Metabolic side effects of psychotropic medications are a major drawback to patients' successful treatment. Using an epigenome-wide approach, we aimed to investigate DNA methylation changes occurring secondary to psychotropic treatment and evaluate associations between 1-month metabolic changes and both baseline and 1-month changes in DNA methylation levels. Seventy-nine patients starting a weight gain inducing psychotropic treatment were selected from the PsyMetab study cohort. Epigenome-wide DNA methylation was measured at baseline and after 1 month of treatment, using the Illumina Methylation EPIC BeadChip. RESULTS A global methylation increase was noted after the first month of treatment, which was more pronounced (p < 2.2 × 10-16) in patients whose weight remained stable (< 2.5% weight increase). Epigenome-wide significant methylation changes (p < 9 × 10-8) were observed at 52 loci in the whole cohort. When restricting the analysis to patients who underwent important early weight gain (≥ 5% weight increase), one locus (cg12209987) showed a significant increase in methylation levels (p = 3.8 × 10-8), which was also associated with increased weight gain in the whole cohort (p = 0.004). Epigenome-wide association analyses failed to identify a significant link between metabolic changes and methylation data. Nevertheless, among the strongest associations, a potential causal effect of the baseline methylation level of cg11622362 on glycemia was revealed by a two-sample Mendelian randomization analysis (n = 3841 for instrument-exposure association; n = 314,916 for instrument-outcome association). CONCLUSION These findings provide new insights into the mechanisms of psychotropic drug-induced weight gain, revealing important epigenetic alterations upon treatment, some of which may play a mediatory role.
Collapse
Affiliation(s)
- Céline Dubath
- Unit of Pharmacogenetics and Clinical Psychopharmacology, Center for Psychiatric Neuroscience, Department of Psychiatry, Lausanne University Hospital, University of Lausanne, Hôpital de Cery, 1008, Prilly, Lausanne, Switzerland.
| | - Eleonora Porcu
- Swiss Institute of Bioinformatics, Lausanne, Switzerland
- Center for Integrative Genomics, University of Lausanne, Lausanne, Switzerland
- Department of Computational Biology, University of Lausanne, Lausanne, Switzerland
| | - Aurélie Delacrétaz
- Unit of Pharmacogenetics and Clinical Psychopharmacology, Center for Psychiatric Neuroscience, Department of Psychiatry, Lausanne University Hospital, University of Lausanne, Hôpital de Cery, 1008, Prilly, Lausanne, Switzerland
| | - Claire Grosu
- Unit of Pharmacogenetics and Clinical Psychopharmacology, Center for Psychiatric Neuroscience, Department of Psychiatry, Lausanne University Hospital, University of Lausanne, Hôpital de Cery, 1008, Prilly, Lausanne, Switzerland
| | - Nermine Laaboub
- Unit of Pharmacogenetics and Clinical Psychopharmacology, Center for Psychiatric Neuroscience, Department of Psychiatry, Lausanne University Hospital, University of Lausanne, Hôpital de Cery, 1008, Prilly, Lausanne, Switzerland
| | - Marianna Piras
- Unit of Pharmacogenetics and Clinical Psychopharmacology, Center for Psychiatric Neuroscience, Department of Psychiatry, Lausanne University Hospital, University of Lausanne, Hôpital de Cery, 1008, Prilly, Lausanne, Switzerland
| | - Armin von Gunten
- Service of Old Age Psychiatry, Department of Psychiatry, Lausanne University Hospital, University of Lausanne, Prilly, Switzerland
| | - Philippe Conus
- Service of General Psychiatry, Department of Psychiatry, Lausanne University Hospital, University of Lausanne, Prilly, Switzerland
| | - Kerstin Jessica Plessen
- Service of Child and Adolescent Psychiatry, Department of Psychiatry, Lausanne University Hospital, University of Lausanne, Lausanne, Switzerland
| | - Zoltán Kutalik
- Swiss Institute of Bioinformatics, Lausanne, Switzerland
- Department of Computational Biology, University of Lausanne, Lausanne, Switzerland
- University Center for Primary Care and Public Health, University of Lausanne, Lausanne, Switzerland
| | - Chin Bin Eap
- Unit of Pharmacogenetics and Clinical Psychopharmacology, Center for Psychiatric Neuroscience, Department of Psychiatry, Lausanne University Hospital, University of Lausanne, Hôpital de Cery, 1008, Prilly, Lausanne, Switzerland.
- School of Pharmaceutical Sciences, University of Geneva, University of Lausanne, Geneva, Switzerland.
- Center for Research and Innovation in Clinical Pharmaceutical Sciences, University of Lausanne, Lausanne, Switzerland.
- Institute of Pharmaceutical Sciences of Western Switzerland, University of Geneva, University of Lausanne, Geneva, Switzerland.
| |
Collapse
|
49
|
Ren J, Pan W. Statistical inference with large-scale trait imputation. Stat Med 2024; 43:625-641. [PMID: 38038193 PMCID: PMC10848238 DOI: 10.1002/sim.9975] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/01/2023] [Revised: 09/26/2023] [Accepted: 11/17/2023] [Indexed: 12/02/2023]
Abstract
Recently a nonparametric method called LS-imputation has been proposed for large-scale trait imputation based on a GWAS summary dataset and a large set of genotyped individuals. The imputed trait values, along with the genotypes, can be treated as an individual-level dataset for downstream genetic analyses, including those that cannot be done with GWAS summary data. However, since the covariance matrix of the imputed trait values is often too large to calculate, the current method imposes a working assumption that the imputed trait values are identically and independently distributed, which is incorrect in truth. Here we propose a "divide and conquer/combine" strategy to estimate and account for the covariance matrix of the imputed trait values via batches, thus relaxing the incorrect working assumption. Applications of the methods to the UK Biobank data for marginal association analysis showed some improvement by the new method in some cases, but overall the original method performed well, which was explained by nearly constant variances of and mostly weak correlations among imputed trait values.
Collapse
Affiliation(s)
- Jingchen Ren
- School of Statistics, University of Minnesota, Minneapolis, MN, 55455
- Division of Biostatistics, School of Public Health, University of Minnesota, Minneapolis, MN, 55455
| | - Wei Pan
- Division of Biostatistics, School of Public Health, University of Minnesota, Minneapolis, MN, 55455
| |
Collapse
|
50
|
Jiang F, Hu SY, Tian W, Wang NN, Yang N, Dong SS, Song HM, Zhang DJ, Gao HW, Wang C, Wu H, He CY, Zhu DL, Chen XF, Guo Y, Yang Z, Yang TL. A landscape of gene expression regulation for synovium in arthritis. Nat Commun 2024; 15:1409. [PMID: 38360850 PMCID: PMC10869817 DOI: 10.1038/s41467-024-45652-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/09/2023] [Accepted: 01/29/2024] [Indexed: 02/17/2024] Open
Abstract
The synovium is an important component of any synovial joint and is the major target tissue of inflammatory arthritis. However, the multi-omics landscape of synovium required for functional inference is absent from large-scale resources. Here we integrate genomics with transcriptomics and chromatin accessibility features of human synovium in up to 245 arthritic patients, to characterize the landscape of genetic regulation on gene expression and the regulatory mechanisms mediating arthritic diseases predisposition. We identify 4765 independent primary and 616 secondary cis-expression quantitative trait loci (cis-eQTLs) in the synovium and find that the eQTLs with multiple independent signals have stronger effects and heritability than single independent eQTLs. Integration of genome-wide association studies (GWASs) and eQTLs identifies 84 arthritis related genes, revealing 38 novel genes which have not been reported by previous studies using eQTL data from the GTEx project or immune cells. We further develop a method called eQTac to identify variants that could affect gene expression by affecting chromatin accessibility and identify 1517 regions with potential regulatory function of chromatin accessibility. Altogether, our study provides a comprehensive synovium multi-omics resource for arthritic diseases and gains new insights into the regulation of gene expression.
Collapse
Affiliation(s)
- Feng Jiang
- Key Laboratory of Biomedical Information Engineering of Ministry of Education, Biomedical Informatics & Genomics Center, School of Life Science and Technology, Xi'an Jiaotong University, Xi'an, 710049, P.R. China
| | - Shou-Ye Hu
- Department of Joint Surgery, Honghui Hospital, Xi'an Jiaotong University, Xi'an, Shaanxi, 710054, P.R. China
| | - Wen Tian
- Key Laboratory of Biomedical Information Engineering of Ministry of Education, Biomedical Informatics & Genomics Center, School of Life Science and Technology, Xi'an Jiaotong University, Xi'an, 710049, P.R. China
| | - Nai-Ning Wang
- Key Laboratory of Biomedical Information Engineering of Ministry of Education, Biomedical Informatics & Genomics Center, School of Life Science and Technology, Xi'an Jiaotong University, Xi'an, 710049, P.R. China
| | - Ning Yang
- Key Laboratory of Biomedical Information Engineering of Ministry of Education, Biomedical Informatics & Genomics Center, School of Life Science and Technology, Xi'an Jiaotong University, Xi'an, 710049, P.R. China
| | - Shan-Shan Dong
- Key Laboratory of Biomedical Information Engineering of Ministry of Education, Biomedical Informatics & Genomics Center, School of Life Science and Technology, Xi'an Jiaotong University, Xi'an, 710049, P.R. China
| | - Hui-Miao Song
- Key Laboratory of Biomedical Information Engineering of Ministry of Education, Biomedical Informatics & Genomics Center, School of Life Science and Technology, Xi'an Jiaotong University, Xi'an, 710049, P.R. China
| | - Da-Jin Zhang
- Key Laboratory of Biomedical Information Engineering of Ministry of Education, Biomedical Informatics & Genomics Center, School of Life Science and Technology, Xi'an Jiaotong University, Xi'an, 710049, P.R. China
| | - Hui-Wu Gao
- Key Laboratory of Biomedical Information Engineering of Ministry of Education, Biomedical Informatics & Genomics Center, School of Life Science and Technology, Xi'an Jiaotong University, Xi'an, 710049, P.R. China
| | - Chen Wang
- Key Laboratory of Biomedical Information Engineering of Ministry of Education, Biomedical Informatics & Genomics Center, School of Life Science and Technology, Xi'an Jiaotong University, Xi'an, 710049, P.R. China
| | - Hao Wu
- Key Laboratory of Biomedical Information Engineering of Ministry of Education, Biomedical Informatics & Genomics Center, School of Life Science and Technology, Xi'an Jiaotong University, Xi'an, 710049, P.R. China
| | - Chang-Yi He
- Key Laboratory of Biomedical Information Engineering of Ministry of Education, Biomedical Informatics & Genomics Center, School of Life Science and Technology, Xi'an Jiaotong University, Xi'an, 710049, P.R. China
| | - Dong-Li Zhu
- Key Laboratory of Biomedical Information Engineering of Ministry of Education, Biomedical Informatics & Genomics Center, School of Life Science and Technology, Xi'an Jiaotong University, Xi'an, 710049, P.R. China
| | - Xiao-Feng Chen
- Key Laboratory of Biomedical Information Engineering of Ministry of Education, Biomedical Informatics & Genomics Center, School of Life Science and Technology, Xi'an Jiaotong University, Xi'an, 710049, P.R. China
| | - Yan Guo
- Key Laboratory of Biomedical Information Engineering of Ministry of Education, Biomedical Informatics & Genomics Center, School of Life Science and Technology, Xi'an Jiaotong University, Xi'an, 710049, P.R. China
| | - Zhi Yang
- Department of Joint Surgery, Honghui Hospital, Xi'an Jiaotong University, Xi'an, Shaanxi, 710054, P.R. China.
| | - Tie-Lin Yang
- Key Laboratory of Biomedical Information Engineering of Ministry of Education, Biomedical Informatics & Genomics Center, School of Life Science and Technology, Xi'an Jiaotong University, Xi'an, 710049, P.R. China.
| |
Collapse
|