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Upadhyay VR, Ramesh V, Kumar H, Somagond YM, Priyadarsini S, Kuniyal A, Prakash V, Sahoo A. Phenomics in Livestock Research: Bottlenecks and Promises of Digital Phenotyping and Other Quantification Techniques on a Global Scale. OMICS : A JOURNAL OF INTEGRATIVE BIOLOGY 2024; 28:380-393. [PMID: 39012961 DOI: 10.1089/omi.2024.0109] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/18/2024]
Abstract
Bottlenecks in moving genomics to real-life applications also include phenomics. This is true not only for genomics medicine and public health genomics but also in ecology and livestock phenomics. This expert narrative review explores the intricate relationship between genetic makeup and observable phenotypic traits across various biological levels in the context of livestock research. We unpack and emphasize the significance of precise phenotypic data in selective breeding outcomes and examine the multifaceted applications of phenomics, ranging from improvement to assessing welfare, reproductive traits, and environmental adaptation in livestock. As phenotypic traits exhibit strong correlations, their measurement alongside specific biological outcomes provides insights into performance, overall health, and clinical endpoints like morbidity and disease. In addition, automated assessment of livestock holds potential for monitoring the dynamic phenotypic traits across various species, facilitating a deeper comprehension of how they adapt to their environment and attendant stressors. A key challenge in genetic improvement in livestock is predicting individuals with optimal fitness without direct measurement. Temporal predictions from unmanned aerial systems can surpass genomic predictions, offering in-depth data on livestock. In the near future, digital phenotyping and digital biomarkers may further unravel the genetic intricacies of stress tolerance, adaptation and welfare aspects of animals enabling the selection of climate-resilient and productive livestock. This expert review thus delves into challenges associated with phenotyping and discusses technological advancements shaping the future of biological research concerning livestock.
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Affiliation(s)
| | - Vikram Ramesh
- ICAR-National Research Centre on Mithun, Medziphema, Nagaland, India
| | - Harshit Kumar
- ICAR-National Research Centre on Mithun, Medziphema, Nagaland, India
| | - Y M Somagond
- ICAR-National Research Centre on Mithun, Medziphema, Nagaland, India
| | | | - Aruna Kuniyal
- ICAR-National Research Centre on Camel, Bikaner, Rajasthan, India
| | - Ved Prakash
- ICAR-National Research Centre on Camel, Bikaner, Rajasthan, India
| | - Artabandhu Sahoo
- ICAR-National Research Centre on Camel, Bikaner, Rajasthan, India
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Tao Y, Zhao R, Yang B, Han J, Li Y. Dissecting the shared genetic landscape of anxiety, depression, and schizophrenia. J Transl Med 2024; 22:373. [PMID: 38637810 PMCID: PMC11025255 DOI: 10.1186/s12967-024-05153-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/02/2024] [Accepted: 04/01/2024] [Indexed: 04/20/2024] Open
Abstract
BACKGROUND Numerous studies highlight the genetic underpinnings of mental disorders comorbidity, particularly in anxiety, depression, and schizophrenia. However, their shared genetic loci are not well understood. Our study employs Mendelian randomization (MR) and colocalization analyses, alongside multi-omics data, to uncover potential genetic targets for these conditions, thereby informing therapeutic and drug development strategies. METHODS We utilized the Consortium for Linkage Disequilibrium Score Regression (LDSC) and Mendelian Randomization (MR) analysis to investigate genetic correlations among anxiety, depression, and schizophrenia. Utilizing GTEx V8 eQTL and deCODE Genetics pQTL data, we performed a three-step summary-data-based Mendelian randomization (SMR) and protein-protein interaction analysis. This helped assess causal and comorbid loci for these disorders and determine if identified loci share coincidental variations with psychiatric diseases. Additionally, phenome-wide association studies, drug prediction, and molecular docking validated potential drug targets. RESULTS We found genetic correlations between anxiety, depression, and schizophrenia, and under a meta-analysis of MR from multiple databases, the causal relationships among these disorders are supported. Based on this, three-step SMR and colocalization analyses identified ITIH3 and CCS as being related to the risk of developing depression, while CTSS and DNPH1 are related to the onset of schizophrenia. BTN3A1, PSMB4, and TIMP4 were identified as comorbidity loci for both disorders. Molecules that could not be determined through colocalization analysis were also presented. Drug prediction and molecular docking showed that some drugs and proteins have good binding affinity and available structural data. CONCLUSIONS Our study indicates genetic correlations and shared risk loci between anxiety, depression, and schizophrenia. These findings offer insights into the underlying mechanisms of their comorbidities and aid in drug development.
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Affiliation(s)
- Yiming Tao
- Department of Intensive Care Medicine, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Hankou, Wuhan, 430030, China
- Department of Critical Care Medicine, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, 250101, Shandong, China
| | - Rui Zhao
- Department of Laboratory Medicine, The First Afliated Hospital of Chongqing Medical University, Chongqing, 400042, China
| | - Bin Yang
- Department of Intensive Care Medicine, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Hankou, Wuhan, 430030, China
| | - Jie Han
- Department of Emergency, School of Medicine, Qingdao Municipal Hospital, Qingdao University, Qingdao, 266071, China.
| | - Yongsheng Li
- Department of Intensive Care Medicine, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Hankou, Wuhan, 430030, China.
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Zeng C, Schlueter DJ, Tran TC, Babbar A, Cassini T, Bastarache LA, Denny JC. Comparison of phenomic profiles in the All of Us Research Program against the US general population and the UK Biobank. J Am Med Inform Assoc 2024; 31:846-854. [PMID: 38263490 PMCID: PMC10990551 DOI: 10.1093/jamia/ocad260] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2023] [Revised: 12/05/2023] [Accepted: 01/08/2024] [Indexed: 01/25/2024] Open
Abstract
IMPORTANCE Knowledge gained from cohort studies has dramatically advanced both public and precision health. The All of Us Research Program seeks to enroll 1 million diverse participants who share multiple sources of data, providing unique opportunities for research. It is important to understand the phenomic profiles of its participants to conduct research in this cohort. OBJECTIVES More than 280 000 participants have shared their electronic health records (EHRs) in the All of Us Research Program. We aim to understand the phenomic profiles of this cohort through comparisons with those in the US general population and a well-established nation-wide cohort, UK Biobank, and to test whether association results of selected commonly studied diseases in the All of Us cohort were comparable to those in UK Biobank. MATERIALS AND METHODS We included participants with EHRs in All of Us and participants with health records from UK Biobank. The estimates of prevalence of diseases in the US general population were obtained from the Global Burden of Diseases (GBD) study. We conducted phenome-wide association studies (PheWAS) of 9 commonly studied diseases in both cohorts. RESULTS This study included 287 012 participants from the All of Us EHR cohort and 502 477 participants from the UK Biobank. A total of 314 diseases curated by the GBD were evaluated in All of Us, 80.9% (N = 254) of which were more common in All of Us than in the US general population [prevalence ratio (PR) >1.1, P < 2 × 10-5]. Among 2515 diseases and phenotypes evaluated in both All of Us and UK Biobank, 85.6% (N = 2152) were more common in All of Us (PR >1.1, P < 2 × 10-5). The Pearson correlation coefficients of effect sizes from PheWAS between All of Us and UK Biobank were 0.61, 0.50, 0.60, 0.57, 0.40, 0.53, 0.46, 0.47, and 0.24 for ischemic heart diseases, lung cancer, chronic obstructive pulmonary disease, dementia, colorectal cancer, lower back pain, multiple sclerosis, lupus, and cystic fibrosis, respectively. DISCUSSION Despite the differences in prevalence of diseases in All of Us compared to the US general population or the UK Biobank, our study supports that All of Us can facilitate rapid investigation of a broad range of diseases. CONCLUSION Most diseases were more common in All of Us than in the general US population or the UK Biobank. Results of disease-disease association tests from All of Us are comparable to those estimated in another well-studied national cohort.
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Affiliation(s)
- Chenjie Zeng
- National Human Genome Research Institute, National Institutes of Health, Bethesda, MD, United States
| | - David J Schlueter
- National Human Genome Research Institute, National Institutes of Health, Bethesda, MD, United States
- Department of Health and Society, University of Toronto, Scarborough, Toronto, ON, Canada
| | - Tam C Tran
- National Human Genome Research Institute, National Institutes of Health, Bethesda, MD, United States
| | - Anav Babbar
- National Human Genome Research Institute, National Institutes of Health, Bethesda, MD, United States
| | - Thomas Cassini
- National Human Genome Research Institute, National Institutes of Health, Bethesda, MD, United States
- Department of Pediatrics, Vanderbilt University Medical Center, Nashville, TN 37232, United States
| | - Lisa A Bastarache
- Center for Precision Medicine, Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN, United States
| | - Josh C Denny
- National Human Genome Research Institute, National Institutes of Health, Bethesda, MD, United States
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Haan E, Krebs K, Võsa U, Brikell I, Larsson H, Lehto K. Associations between attention-deficit hyperactivity disorder genetic liability and ICD-10 medical conditions in adults: utilizing electronic health records in a Phenome-Wide Association Study. Psychol Med 2024:1-14. [PMID: 38563284 DOI: 10.1017/s0033291724000606] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 04/04/2024]
Abstract
BACKGROUND Attention-deficit hyperactivity disorder (ADHD) is often comorbid with other medical conditions in adult patients. However, ADHD is extremely underdiagnosed in adults and little is known about the medical comorbidities in undiagnosed adult individuals with high ADHD liability. In this study we investigated associations between ADHD genetic liability and electronic health record (EHR)-based ICD-10 diagnoses across all diagnostic categories, in individuals without ADHD diagnosis history. METHODS We used data from the Estonian Biobank cohort (N = 111 261) and generated polygenic risk scores (PRS) for ADHD (PRSADHD) based on the ADHD genome-wide association study. We performed a phenome-wide association study (PheWAS) to test for associations between standardized PRSADHD and 1515 EHR-based ICD-10 diagnoses in the full and sex-stratified sample. We compared the observed significant ICD-10 associations to associations with (1) ADHD diagnosis and (2) questionnaire-based high ADHD risk analyses. RESULTS After Bonferroni correction (p = 3.3 × 10-5) we identified 80 medical conditions associated with PRSADHD. The strongest evidence was seen with chronic obstructive pulmonary disease (OR 1.15, CI 1.11-1.18), obesity (OR 1.13, CI 1.11-1.15), and type 2 diabetes (OR 1.11, CI 1.09-1.14). Sex-stratified analysis generally showed similar associations in males and females. Out of all identified associations, 40% and 78% were also observed using ADHD diagnosis or questionnaire-based ADHD, respectively, as the predictor. CONCLUSIONS Overall our findings indicate that ADHD genetic liability is associated with an increased risk of a substantial number of medical conditions in undiagnosed individuals. These results highlight the need for timely detection and improved management of ADHD symptoms in adults.
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Affiliation(s)
- Elis Haan
- Estonian Genome Centre, Institute of Genomics, University of Tartu, Tartu, Estonia
- Viljandi Hospital, Psychiatric Clinic, Viljandi, Estonia
| | - Kristi Krebs
- Estonian Genome Centre, Institute of Genomics, University of Tartu, Tartu, Estonia
| | - Urmo Võsa
- Estonian Genome Centre, Institute of Genomics, University of Tartu, Tartu, Estonia
| | - Isabell Brikell
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
- Department of Global Public Health and Primary Care, University of Bergen, Bergen, Norway
- Deparment of Biomedicine, Aarhus University, Aarhus, Denmark
| | - Henrik Larsson
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
- School of Medical Sciences, Örebro University, Örebro, Sweden
| | - Kelli Lehto
- Estonian Genome Centre, Institute of Genomics, University of Tartu, Tartu, Estonia
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Mulugeta A, Lumsden AL, Madakkatel I, Stacey D, Lee SH, Mäenpää J, Oehler MK, Hyppönen E. Phenome-wide association study of ovarian cancer identifies common comorbidities and reveals shared genetics with complex diseases and biomarkers. Cancer Med 2024; 13:e7051. [PMID: 38457211 PMCID: PMC10923028 DOI: 10.1002/cam4.7051] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/05/2023] [Revised: 01/29/2024] [Accepted: 02/09/2024] [Indexed: 03/09/2024] Open
Abstract
BACKGROUND Ovarian cancer (OC) is commonly diagnosed among older women who have comorbidities. This hypothesis-free phenome-wide association study (PheWAS) aimed to identify comorbidities associated with OC, as well as traits that share a genetic architecture with OC. METHODS We used data from 181,203 white British female UK Biobank participants and analysed OC and OC subtype-specific genetic risk scores (OC-GRS) for an association with 889 diseases and 43 other traits. We conducted PheWAS and colocalization analyses for individual variants to identify evidence for shared genetic architecture. RESULTS The OC-GRS was associated with 10 diseases, and the clear cell OC-GRS was associated with five diseases at the FDR threshold (p = 5.6 × 10-4 ). Mendelian randomizaiton analysis (MR) provided robust evidence for the association of OC with higher risk of "secondary malignant neoplasm of digestive systems" (OR 1.64, 95% CI 1.33, 2.02), "ascites" (1.48, 95% CI 1.17, 1.86), "chronic airway obstruction" (1.17, 95% CI 1.07, 1.29), and "abnormal findings on examination of the lung" (1.51, 95% CI 1.22, 1.87). Analyses of lung spirometry measures provided further support for compromised respiratory function. PheWAS on individual OC variants identified five genetic variants associated with other diseases, and seven variants associated with biomarkers (all, p ≤ 4.5 × 10-8 ). Colocalization analysis identified rs4449583 (from TERT locus) as the shared causal variant for OC and seborrheic keratosis. CONCLUSIONS OC is associated with digestive and respiratory comorbidities. Several variants affecting OC risk were associated with other diseases and biomarkers, with this study identifying a novel genetic locus shared between OC and skin conditions.
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Affiliation(s)
- Anwar Mulugeta
- Australian Centre for Precision Health, Unit of Clinical and Health SciencesUniversity of South AustraliaAdelaideSouth AustraliaAustralia
- South Australian Health and Medical Research InstituteAdelaideSouth AustraliaAustralia
- Department of Pharmacology and Clinical Pharmacy, College of Health ScienceAddis Ababa UniversityAddis AbabaEthiopia
| | - Amanda L. Lumsden
- Australian Centre for Precision Health, Unit of Clinical and Health SciencesUniversity of South AustraliaAdelaideSouth AustraliaAustralia
- South Australian Health and Medical Research InstituteAdelaideSouth AustraliaAustralia
| | - Iqbal Madakkatel
- Australian Centre for Precision Health, Unit of Clinical and Health SciencesUniversity of South AustraliaAdelaideSouth AustraliaAustralia
- South Australian Health and Medical Research InstituteAdelaideSouth AustraliaAustralia
| | - David Stacey
- Australian Centre for Precision Health, Unit of Clinical and Health SciencesUniversity of South AustraliaAdelaideSouth AustraliaAustralia
- South Australian Health and Medical Research InstituteAdelaideSouth AustraliaAustralia
| | - S. Hong Lee
- Australian Centre for Precision Health, Unit of Clinical and Health SciencesUniversity of South AustraliaAdelaideSouth AustraliaAustralia
- South Australian Health and Medical Research InstituteAdelaideSouth AustraliaAustralia
- UniSA Allied Health & Human PerformanceUniversity of South AustraliaAdelaideSouth AustraliaAustralia
| | - Johanna Mäenpää
- Faculty of Medicine and Medical TechnologyTampere UniversityTampereFinland
- Cancer Centre, Tampere University and University HospitalTampereFinland
| | - Martin K. Oehler
- Department of Gynaecological OncologyRoyal Adelaide HospitalAdelaideSouth AustraliaAustralia
- Adelaide Medical School, Robinson Research Institute, University of AdelaideAdelaideSouth AustraliaAustralia
| | - Elina Hyppönen
- Australian Centre for Precision Health, Unit of Clinical and Health SciencesUniversity of South AustraliaAdelaideSouth AustraliaAustralia
- South Australian Health and Medical Research InstituteAdelaideSouth AustraliaAustralia
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Roy DG, Singh L, Chaturvedi HK, Chinnaswamy S. Gender-dependent multiple cross-phenotype association of interferon lambda genetic variants with peripheral blood profiles in healthy individuals. Mol Genet Genomic Med 2024; 12:e2292. [PMID: 37795763 PMCID: PMC10767428 DOI: 10.1002/mgg3.2292] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/02/2023] [Revised: 09/08/2023] [Accepted: 09/19/2023] [Indexed: 10/06/2023] Open
Abstract
BACKGROUND Type III interferons (IFN), also called as lambda IFNs (IFN-λs), are antiviral and immunomodulatory cytokines that are evolutionarily important in humans. Given their central roles in innate immunity, they could be influencing other aspects of human biology. This study aimed to examine the association of genetic variants that control the expression and/or activity of IFN-λ3 and IFN-λ4 with multiple phenotypes in blood profiles of healthy individuals. METHODS In a cohort of about 550 self-declared healthy individuals, after applying several exclusion criteria to determine their health status, we measured 30 blood parameters, including cellular, biochemical, and metabolic profiles. We genotyped them at rs12979860 and rs28416813 using competitive allele-specific PCR assays and tested their association with the blood profiles under dominant and recessive models for the minor allele. IFN-λ4 variants rs368234815 and rs117648444 were also genotyped or inferred. RESULTS We saw no association in the combined cohort under either of the models for any of the phenotypes. When we stratified the cohort based on gender, we saw a significant association only in males with monocyte (p = 1 × 10-3 ) and SGOT (p = 7 × 10-3 ) levels under the dominant model and with uric acid levels (p = 0.01) under the recessive model. When we tested the IFN-λ4 activity modifying variant within groupings based on absence or presence of one or two copies of IFN-λ4 and on different activity levels of IFN-λ4, we found significant (p < 0.05) association with several phenotypes like monocyte, triglyceride, VLDL, ALP, and uric acid levels, only in males. All the above significant associations did not show any confounding when we tested for the same with up to ten different demographic and lifestyle variables. CONCLUSIONS These results show that lambda interferons can have pleiotropic effects. However, gender seems to be an effect modifier, with males being more sensitive than females to the effect.
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Affiliation(s)
- Debarati Guha Roy
- Infectious Disease GeneticsNational Institute of Biomedical GenomicsKalyaniIndia
- Regional Centre for BiotechnologyFaridabadIndia
| | - Lucky Singh
- ICMR‐National Institute of Medical StatisticsNew DelhiIndia
| | | | - Sreedhar Chinnaswamy
- Infectious Disease GeneticsNational Institute of Biomedical GenomicsKalyaniIndia
- Regional Centre for BiotechnologyFaridabadIndia
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Topaloudi A, Jain P, Martinez MB, Bryant JK, Reynolds G, Zagoriti Z, Lagoumintzis G, Zamba-Papanicolaou E, Tzartos J, Poulas K, Kleopa KA, Tzartos S, Georgitsi M, Drineas P, Paschou P. PheWAS and cross-disorder analysis reveal genetic architecture, pleiotropic loci and phenotypic correlations across 11 autoimmune disorders. Front Immunol 2023; 14:1147573. [PMID: 37809097 PMCID: PMC10552152 DOI: 10.3389/fimmu.2023.1147573] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/18/2023] [Accepted: 09/04/2023] [Indexed: 10/10/2023] Open
Abstract
Introduction Autoimmune disorders (ADs) are a group of about 80 disorders that occur when self-attacking autoantibodies are produced due to failure in the self-tolerance mechanisms. ADs are polygenic disorders and associations with genes both in the human leukocyte antigen (HLA) region and outside of it have been described. Previous studies have shown that they are highly comorbid with shared genetic risk factors, while epidemiological studies revealed associations between various lifestyle and health-related phenotypes and ADs. Methods Here, for the first time, we performed a comparative polygenic risk score (PRS) - Phenome Wide Association Study (PheWAS) for 11 different ADs (Juvenile Idiopathic Arthritis, Primary Sclerosing Cholangitis, Celiac Disease, Multiple Sclerosis, Rheumatoid Arthritis, Psoriasis, Myasthenia Gravis, Type 1 Diabetes, Systemic Lupus Erythematosus, Vitiligo Late Onset, Vitiligo Early Onset) and 3,254 phenotypes available in the UK Biobank that include a wide range of socio-demographic, lifestyle and health-related outcomes. Additionally, we investigated the genetic relationships of the studied ADs, calculating their genetic correlation and conducting cross-disorder GWAS meta-analyses for the observed AD clusters. Results In total, we identified 508 phenotypes significantly associated with at least one AD PRS. 272 phenotypes were significantly associated after excluding variants in the HLA region from the PRS estimation. Through genetic correlation and genetic factor analyses, we identified four genetic factors that run across studied ADs. Cross-trait meta-analyses within each factor revealed pleiotropic genome-wide significant loci. Discussion Overall, our study confirms the association of different factors with genetic susceptibility for ADs and reveals novel observations that need to be further explored.
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Affiliation(s)
- Apostolia Topaloudi
- Department of Biological Sciences, Purdue University, West Lafayette, IN, United States
| | - Pritesh Jain
- Department of Biological Sciences, Purdue University, West Lafayette, IN, United States
| | - Melanie B. Martinez
- Department of Biological Sciences, Purdue University, West Lafayette, IN, United States
| | - Josephine K. Bryant
- Department of Biological Sciences, Purdue University, West Lafayette, IN, United States
| | - Grace Reynolds
- Department of Biological Sciences, Purdue University, West Lafayette, IN, United States
- West Lafayette High School, West Lafayette, IN, United States
| | - Zoi Zagoriti
- Department of Pharmacy, University of Patras, Rio, Greece
| | | | - Eleni Zamba-Papanicolaou
- Department of Neuroepidemiology and Centre for Neuromuscular Disorders, The Cyprus Institute of Neurology and Genetics, Nicosia, Cyprus
| | - John Tzartos
- B’ Neurology Department, School of Medicine, National & Kapodistrian University of Athens, “Attikon” University Hospital., Athens, Greece
| | | | - Kleopas A. Kleopa
- Department of Neuroscience and Centre for Neuromuscular Disorders, The Cyprus Institute of Neurology and Genetics, Nicosia, Cyprus
| | - Socrates Tzartos
- Department of Pharmacy, University of Patras, Rio, Greece
- Tzartos NeuroDiagnostics, Athens, Greece
| | - Marianthi Georgitsi
- Department of Molecular Biology and Genetics, School of Health Sciences, Democritus University of Thrace, Alexandroupoli, Greece
| | - Petros Drineas
- Department of Computer Science, Purdue University, West Lafayette, IN, United States
| | - Peristera Paschou
- Department of Biological Sciences, Purdue University, West Lafayette, IN, United States
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Yang M, Zinkgraf M, Fitzgerald-Cook C, Harrison BR, Putzier A, Promislow DEL, Wang AM. Using Drosophila to identify naturally occurring genetic modifiers of amyloid beta 42- and tau-induced toxicity. G3 (BETHESDA, MD.) 2023; 13:jkad132. [PMID: 37311212 PMCID: PMC10468303 DOI: 10.1093/g3journal/jkad132] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/11/2023] [Revised: 04/15/2023] [Accepted: 05/15/2023] [Indexed: 06/15/2023]
Abstract
Alzheimer's disease is characterized by 2 pathological proteins, amyloid beta 42 and tau. The majority of Alzheimer's disease cases in the population are sporadic and late-onset Alzheimer's disease, which exhibits high levels of heritability. While several genetic risk factors for late-onset Alzheimer's disease have been identified and replicated in independent studies, including the ApoE ε4 allele, the great majority of the heritability of late-onset Alzheimer's disease remains unexplained, likely due to the aggregate effects of a very large number of genes with small effect size, as well as to biases in sample collection and statistical approaches. Here, we present an unbiased forward genetic screen in Drosophila looking for naturally occurring modifiers of amyloid beta 42- and tau-induced ommatidial degeneration. Our results identify 14 significant SNPs, which map to 12 potential genes in 8 unique genomic regions. Our hits that are significant after genome-wide correction identify genes involved in neuronal development, signal transduction, and organismal development. Looking more broadly at suggestive hits (P < 10-5), we see significant enrichment in genes associated with neurogenesis, development, and growth as well as significant enrichment in genes whose orthologs have been identified as significantly or suggestively associated with Alzheimer's disease in human GWAS studies. These latter genes include ones whose orthologs are in close proximity to regions in the human genome that are associated with Alzheimer's disease, but where a causal gene has not been identified. Together, our results illustrate the potential for complementary and convergent evidence provided through multitrait GWAS in Drosophila to supplement and inform human studies, helping to identify the remaining heritability and novel modifiers of complex diseases.
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Affiliation(s)
- Ming Yang
- Department of Laboratory Medicine and Pathology, University of Washington School of Medicine, Seattle, WA 98195, USA
| | - Matthew Zinkgraf
- Department of Biology, Western Washington University, Bellingham, WA 98225, USA
| | - Cecilia Fitzgerald-Cook
- Department of Laboratory Medicine and Pathology, University of Washington School of Medicine, Seattle, WA 98195, USA
| | - Benjamin R Harrison
- Department of Laboratory Medicine and Pathology, University of Washington School of Medicine, Seattle, WA 98195, USA
| | - Alexandra Putzier
- Department of Biology, Western Washington University, Bellingham, WA 98225, USA
| | - Daniel E L Promislow
- Department of Laboratory Medicine and Pathology, University of Washington School of Medicine, Seattle, WA 98195, USA
- Department of Biology, University of Washington, Seattle, WA 98195, USA
| | - Adrienne M Wang
- Department of Biology, Western Washington University, Bellingham, WA 98225, USA
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McCoy JCS, Spicer JI, Ibbini Z, Tills O. Phenomics as an approach to Comparative Developmental Physiology. Front Physiol 2023; 14:1229500. [PMID: 37645563 PMCID: PMC10461620 DOI: 10.3389/fphys.2023.1229500] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/26/2023] [Accepted: 07/24/2023] [Indexed: 08/31/2023] Open
Abstract
The dynamic nature of developing organisms and how they function presents both opportunity and challenge to researchers, with significant advances in understanding possible by adopting innovative approaches to their empirical study. The information content of the phenotype during organismal development is arguably greater than at any other life stage, incorporating change at a broad range of temporal, spatial and functional scales and is of broad relevance to a plethora of research questions. Yet, effectively measuring organismal development, and the ontogeny of physiological regulations and functions, and their responses to the environment, remains a significant challenge. "Phenomics", a global approach to the acquisition of phenotypic data at the scale of the whole organism, is uniquely suited as an approach. In this perspective, we explore the synergies between phenomics and Comparative Developmental Physiology (CDP), a discipline of increasing relevance to understanding sensitivity to drivers of global change. We then identify how organismal development itself provides an excellent model for pushing the boundaries of phenomics, given its inherent complexity, comparably smaller size, relative to adult stages, and the applicability of embryonic development to a broad suite of research questions using a diversity of species. Collection, analysis and interpretation of whole organismal phenotypic data are the largest obstacle to capitalising on phenomics for advancing our understanding of biological systems. We suggest that phenomics within the context of developing organismal form and function could provide an effective scaffold for addressing grand challenges in CDP and phenomics.
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Affiliation(s)
| | | | | | - Oliver Tills
- School of Biological and Marine Sciences, University of Plymouth, Plymouth, United Kingdom
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Chang JL, Goldberg AN, Alt JA, Alzoubaidi M, Ashbrook L, Auckley D, Ayappa I, Bakhtiar H, Barrera JE, Bartley BL, Billings ME, Boon MS, Bosschieter P, Braverman I, Brodie K, Cabrera-Muffly C, Caesar R, Cahali MB, Cai Y, Cao M, Capasso R, Caples SM, Chahine LM, Chang CP, Chang KW, Chaudhary N, Cheong CSJ, Chowdhuri S, Cistulli PA, Claman D, Collen J, Coughlin KC, Creamer J, Davis EM, Dupuy-McCauley KL, Durr ML, Dutt M, Ali ME, Elkassabany NM, Epstein LJ, Fiala JA, Freedman N, Gill K, Boyd Gillespie M, Golisch L, Gooneratne N, Gottlieb DJ, Green KK, Gulati A, Gurubhagavatula I, Hayward N, Hoff PT, Hoffmann OM, Holfinger SJ, Hsia J, Huntley C, Huoh KC, Huyett P, Inala S, Ishman SL, Jella TK, Jobanputra AM, Johnson AP, Junna MR, Kado JT, Kaffenberger TM, Kapur VK, Kezirian EJ, Khan M, Kirsch DB, Kominsky A, Kryger M, Krystal AD, Kushida CA, Kuzniar TJ, Lam DJ, Lettieri CJ, Lim DC, Lin HC, Liu SY, MacKay SG, Magalang UJ, Malhotra A, Mansukhani MP, Maurer JT, May AM, Mitchell RB, Mokhlesi B, Mullins AE, Nada EM, Naik S, Nokes B, Olson MD, Pack AI, Pang EB, Pang KP, Patil SP, Van de Perck E, Piccirillo JF, Pien GW, Piper AJ, Plawecki A, Quigg M, Ravesloot MJ, Redline S, Rotenberg BW, Ryden A, Sarmiento KF, Sbeih F, Schell AE, Schmickl CN, Schotland HM, Schwab RJ, Seo J, Shah N, Shelgikar AV, Shochat I, Soose RJ, Steele TO, Stephens E, Stepnowsky C, Strohl KP, Sutherland K, Suurna MV, Thaler E, Thapa S, Vanderveken OM, de Vries N, Weaver EM, Weir ID, Wolfe LF, Tucker Woodson B, Won CH, Xu J, Yalamanchi P, Yaremchuk K, Yeghiazarians Y, Yu JL, Zeidler M, Rosen IM. International Consensus Statement on Obstructive Sleep Apnea. Int Forum Allergy Rhinol 2023; 13:1061-1482. [PMID: 36068685 PMCID: PMC10359192 DOI: 10.1002/alr.23079] [Citation(s) in RCA: 62] [Impact Index Per Article: 62.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/12/2022] [Revised: 08/12/2022] [Accepted: 08/18/2022] [Indexed: 11/08/2022]
Abstract
BACKGROUND Evaluation and interpretation of the literature on obstructive sleep apnea (OSA) allows for consolidation and determination of the key factors important for clinical management of the adult OSA patient. Toward this goal, an international collaborative of multidisciplinary experts in sleep apnea evaluation and treatment have produced the International Consensus statement on Obstructive Sleep Apnea (ICS:OSA). METHODS Using previously defined methodology, focal topics in OSA were assigned as literature review (LR), evidence-based review (EBR), or evidence-based review with recommendations (EBR-R) formats. Each topic incorporated the available and relevant evidence which was summarized and graded on study quality. Each topic and section underwent iterative review and the ICS:OSA was created and reviewed by all authors for consensus. RESULTS The ICS:OSA addresses OSA syndrome definitions, pathophysiology, epidemiology, risk factors for disease, screening methods, diagnostic testing types, multiple treatment modalities, and effects of OSA treatment on multiple OSA-associated comorbidities. Specific focus on outcomes with positive airway pressure (PAP) and surgical treatments were evaluated. CONCLUSION This review of the literature consolidates the available knowledge and identifies the limitations of the current evidence on OSA. This effort aims to create a resource for OSA evidence-based practice and identify future research needs. Knowledge gaps and research opportunities include improving the metrics of OSA disease, determining the optimal OSA screening paradigms, developing strategies for PAP adherence and longitudinal care, enhancing selection of PAP alternatives and surgery, understanding health risk outcomes, and translating evidence into individualized approaches to therapy.
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Affiliation(s)
- Jolie L. Chang
- University of California, San Francisco, California, USA
| | | | | | | | - Liza Ashbrook
- University of California, San Francisco, California, USA
| | | | - Indu Ayappa
- Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | | | | | | | | | - Maurits S. Boon
- Sidney Kimmel Medical Center at Thomas Jefferson University, Philadelphia, Pennsylvania, USA
| | - Pien Bosschieter
- Academic Centre for Dentistry Amsterdam, Amsterdam, The Netherlands
| | - Itzhak Braverman
- Hillel Yaffe Medical Center, Hadera Technion, Faculty of Medicine, Hadera, Israel
| | - Kara Brodie
- University of California, San Francisco, California, USA
| | | | - Ray Caesar
- Stone Oak Orthodontics, San Antonio, Texas, USA
| | | | - Yi Cai
- University of California, San Francisco, California, USA
| | | | | | | | | | | | | | | | | | - Susmita Chowdhuri
- Wayne State University and John D. Dingell VA Medical Center, Detroit, Michigan, USA
| | - Peter A. Cistulli
- Faculty of Medicine and Health, University of Sydney, Sydney, Australia
| | - David Claman
- University of California, San Francisco, California, USA
| | - Jacob Collen
- Uniformed Services University, Bethesda, Maryland, USA
| | | | | | - Eric M. Davis
- University of Virginia, Charlottesville, Virginia, USA
| | | | | | - Mohan Dutt
- University of Michigan, Ann Arbor, Michigan, USA
| | - Mazen El Ali
- University of Pittsburgh, Pittsburgh, Pennsylvania, USA
| | | | | | | | | | - Kirat Gill
- Stanford University, Palo Alto, California, USA
| | | | - Lea Golisch
- University Hospital Mannheim, Ruprecht-Karls-University Heidelberg, Heidelberg, Germany
| | | | | | | | - Arushi Gulati
- University of California, San Francisco, California, USA
| | | | | | - Paul T. Hoff
- University of Michigan, Ann Arbor, Michigan, USA
| | - Oliver M.G. Hoffmann
- University Hospital Mannheim, Ruprecht-Karls-University Heidelberg, Heidelberg, Germany
| | | | - Jennifer Hsia
- University of Minnesota, Minneapolis, Minnesota, USA
| | - Colin Huntley
- Sidney Kimmel Medical Center at Thomas Jefferson University, Philadelphia, Pennsylvania, USA
| | | | | | - Sanjana Inala
- Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | | | | | | | | | | | | | | | | | | | - Meena Khan
- Ohio State University, Columbus, Ohio, USA
| | | | - Alan Kominsky
- Cleveland Clinic Head and Neck Institute, Cleveland, Ohio, USA
| | - Meir Kryger
- Yale School of Medicine, New Haven, Connecticut, USA
| | | | | | | | - Derek J. Lam
- Oregon Health and Science University, Portland, Oregon, USA
| | | | | | | | | | | | | | - Atul Malhotra
- University of California, San Diego, California, USA
| | | | - Joachim T. Maurer
- University Hospital Mannheim, Ruprecht-Karls-University Heidelberg, Heidelberg, Germany
| | - Anna M. May
- Case Western Reserve University, Cleveland, Ohio, USA
| | - Ron B. Mitchell
- University of Texas, Southwestern and Children’s Medical Center Dallas, Texas, USA
| | | | | | | | | | - Brandon Nokes
- University of California, San Diego, California, USA
| | | | - Allan I. Pack
- University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | | | | | | | | | | | | | | | | | - Mark Quigg
- University of Virginia, Charlottesville, Virginia, USA
| | | | - Susan Redline
- Brigham and Women’s Hospital, Harvard Medical School, Boston, Massachusetts, USA
| | | | - Armand Ryden
- Veterans Affairs Greater Los Angeles Healthcare System, Los Angeles, California, USA
| | | | - Firas Sbeih
- Cleveland Clinic Head and Neck Institute, Cleveland, Ohio, USA
| | | | | | | | | | - Jiyeon Seo
- University of California, Los Angeles, California, USA
| | - Neomi Shah
- Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | | | | | - Ryan J. Soose
- University of Pittsburgh, Pittsburgh, Pennsylvania, USA
| | | | - Erika Stephens
- University of California, San Francisco, California, USA
| | | | | | | | | | - Erica Thaler
- University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Sritika Thapa
- Yale School of Medicine, New Haven, Connecticut, USA
| | | | - Nico de Vries
- Academic Centre for Dentistry Amsterdam, Amsterdam, The Netherlands
| | | | - Ian D. Weir
- Yale School of Medicine, New Haven, Connecticut, USA
| | | | | | | | - Josie Xu
- University of Toronto, Ontario, Canada
| | | | | | | | | | | | - Ilene M. Rosen
- University of Pennsylvania, Philadelphia, Pennsylvania, USA
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11
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Ghoussaini M, Nelson MR, Dunham I. Future prospects for human genetics and genomics in drug discovery. Curr Opin Struct Biol 2023; 80:102568. [PMID: 36963162 PMCID: PMC7614359 DOI: 10.1016/j.sbi.2023.102568] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2022] [Revised: 01/27/2023] [Accepted: 02/13/2023] [Indexed: 03/26/2023]
Abstract
Evidence from human genetics supporting the therapeutic hypothesis increases the likelihood that a drug will succeed in clinical trials. Rare and common disease genetics yield a wide array of alleles with a range of effect sizes that can proxy for the effect of a drug in disease. Recent advances in large scale population collections and whole genome sequencing approaches have provided a rich resource of human genetic evidence to support drug target selection. As the range of phenotypes profiled increases and ever more alleles are discovered across world-wide populations, these approaches will increasingly influence multiple stages across the lifespan of a drug discovery programme.
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Affiliation(s)
- Maya Ghoussaini
- Wellcome Sanger Institute, Wellcome Genome Campus, United Kingdom; Open Targets, Wellcome Genome Campus, United Kingdom. https://twitter.com/MayaGhoussaini
| | | | - Ian Dunham
- Wellcome Sanger Institute, Wellcome Genome Campus, United Kingdom; Open Targets, Wellcome Genome Campus, United Kingdom; European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, United Kingdom.
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12
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Drouet DE, Liu S, Crawford DC. Assessment of multi-population polygenic risk scores for lipid traits in African Americans. PeerJ 2023; 11:e14910. [PMID: 37214096 PMCID: PMC10198155 DOI: 10.7717/peerj.14910] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/22/2022] [Accepted: 01/25/2023] [Indexed: 05/24/2023] Open
Abstract
Polygenic risk scores (PRS) based on genome-wide discoveries are promising predictors or classifiers of disease development, severity, and/or progression for common clinical outcomes. A major limitation of most risk scores is the paucity of genome-wide discoveries in diverse populations, prompting an emphasis to generate these needed data for trans-population and population-specific PRS construction. Given diverse genome-wide discoveries are just now being completed, there has been little opportunity for PRS to be evaluated in diverse populations independent from the discovery efforts. To fill this gap, we leverage here summary data from a recent genome-wide discovery study of lipid traits (HDL-C, LDL-C, triglycerides, and total cholesterol) conducted in diverse populations represented by African Americans, Hispanics, Asians, Native Hawaiians, Native Americans, and others by the Population Architecture using Genomics and Epidemiology (PAGE) Study. We constructed lipid trait PRS using PAGE Study published genetic variants and weights in an independent African American adult patient population linked to de-identified electronic health records and genotypes from the Illumina Metabochip (n = 3,254). Using multi-population lipid trait PRS, we assessed levels of association for their respective lipid traits, clinical outcomes (cardiovascular disease and type 2 diabetes), and common clinical labs. While none of the multi-population PRS were strongly associated with the tested trait or outcome, PRSLDL-Cwas nominally associated with cardiovascular disease. These data demonstrate the complexity in applying PRS to real-world clinical data even when data from multiple populations are available.
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Affiliation(s)
- Domenica E. Drouet
- Department of Medicine, Case Western Reserve University, Cleveland, OH, United States of America
| | - Shiying Liu
- Population and Quantitative Health Sciences, Case Western Reserve University, Cleveland, OH, United States of America
| | - Dana C. Crawford
- Population and Quantitative Health Sciences, Case Western Reserve University, Cleveland, OH, United States of America
- Cleveland Institute for Computational Biology, Case Western Reserve University, Cleveland, OH, United States of America
- Genetics and Genome Sciences, Case Western Reserve University, Cleveland, OH, United States of America
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13
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Johnson JS, Cote AC, Dobbyn A, Sloofman LG, Xu J, Cotter L, Charney AW, Birgegård A, Jordan J, Kennedy M, Landén M, Maguire SL, Martin NG, Mortensen PB, Thornton LM, Bulik CM, Huckins LM. Mapping anorexia nervosa genes to clinical phenotypes. Psychol Med 2023; 53:2619-2633. [PMID: 35379376 PMCID: PMC10123844 DOI: 10.1017/s0033291721004554] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/21/2021] [Revised: 09/23/2021] [Accepted: 10/20/2021] [Indexed: 12/21/2022]
Abstract
BACKGROUND Anorexia nervosa (AN) is a psychiatric disorder with complex etiology, with a significant portion of disease risk imparted by genetics. Traditional genome-wide association studies (GWAS) produce principal evidence for the association of genetic variants with disease. Transcriptomic imputation (TI) allows for the translation of those variants into regulatory mechanisms, which can then be used to assess the functional outcome of genetically regulated gene expression (GReX) in a broader setting through the use of phenome-wide association studies (pheWASs) in large and diverse clinical biobank populations with electronic health record phenotypes. METHODS Here, we applied TI using S-PrediXcan to translate the most recent PGC-ED AN GWAS findings into AN-GReX. For significant genes, we imputed AN-GReX in the Mount Sinai BioMe™ Biobank and performed pheWASs on over 2000 outcomes to test the clinical consequences of aberrant expression of these genes. We performed a secondary analysis to assess the impact of body mass index (BMI) and sex on AN-GReX clinical associations. RESULTS Our S-PrediXcan analysis identified 53 genes associated with AN, including what is, to our knowledge, the first-genetic association of AN with the major histocompatibility complex. AN-GReX was associated with autoimmune, metabolic, and gastrointestinal diagnoses in our biobank cohort, as well as measures of cholesterol, medications, substance use, and pain. Additionally, our analyses showed moderation of AN-GReX associations with measures of cholesterol and substance use by BMI, and moderation of AN-GReX associations with celiac disease by sex. CONCLUSIONS Our BMI-stratified results provide potential avenues of functional mechanism for AN-genes to investigate further.
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Affiliation(s)
- Jessica S. Johnson
- Pamela Sklar Division of Psychiatric Genomics, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Department of Genetics and Genomics, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Alanna C. Cote
- Pamela Sklar Division of Psychiatric Genomics, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Department of Genetics and Genomics, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Amanda Dobbyn
- Pamela Sklar Division of Psychiatric Genomics, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Department of Genetics and Genomics, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Icahn Institute for Genomics and Multiscale Biology, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Laura G. Sloofman
- Seaver Autism Center for Research and Treatment, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Jiayi Xu
- Pamela Sklar Division of Psychiatric Genomics, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Department of Genetics and Genomics, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Liam Cotter
- Pamela Sklar Division of Psychiatric Genomics, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Department of Genetics and Genomics, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Alexander W. Charney
- Pamela Sklar Division of Psychiatric Genomics, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Department of Genetics and Genomics, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Icahn Institute for Genomics and Multiscale Biology, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- James J. Peters Department of Veterans Affairs Medical Center, Mental Illness Research, Education and Clinical Centers, Bronx, NY 14068, USA
| | | | - Andreas Birgegård
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
| | - Jennifer Jordan
- Department of Psychological Medicine, Christchurch School of Medicine & Health Sciences, University of Otago, 2 Riccarton Avenue, PO Box 4345, 8140 Christchurch, New Zealand
| | - Martin Kennedy
- Department of Psychological Medicine, Christchurch School of Medicine & Health Sciences, University of Otago, 2 Riccarton Avenue, PO Box 4345, 8140 Christchurch, New Zealand
| | - Mikaél Landén
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
- Institute of Neuroscience and Physiology, Sahlgrenska Academy at Gothenburg University, SE-413 45 Gothenburg, Sweden
| | - Sarah L. Maguire
- InsideOut Institute, University of Sydney, New South Wales 2006, Australia
| | - Nicholas G. Martin
- QIMR Berghofer Medical Research Institute, Locked Bag 2000, Royal Brisbane Hospital, Herston, QLD 4029, Australia
| | - Preben Bo Mortensen
- The Lundbeck Foundation Initiative for Integrative Psychiatric Research, Aarhus, Denmark
- National Centre for Register-Based Research, Aarhus University, Aarhus, Denmark
| | - Laura M. Thornton
- Department of Psychiatry, University of North Carolina at Chapel Hill, Chapel Hill, NC 27517, USA
| | - Cynthia M. Bulik
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
- Department of Psychiatry, University of North Carolina at Chapel Hill, Chapel Hill, NC 27517, USA
- Department of Nutrition, University of North Carolina at Chapel Hill, Chapel Hill, NC 27517, USA
| | - Laura M. Huckins
- Pamela Sklar Division of Psychiatric Genomics, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Department of Genetics and Genomics, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Icahn Institute for Genomics and Multiscale Biology, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Seaver Autism Center for Research and Treatment, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- James J. Peters Department of Veterans Affairs Medical Center, Mental Illness Research, Education and Clinical Centers, Bronx, NY 14068, USA
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14
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Kember RL, Hartwell EE, Xu H, Rotenberg J, Almasy L, Zhou H, Gelernter J, Kranzler HR. Phenome-wide Association Analysis of Substance Use Disorders in a Deeply Phenotyped Sample. Biol Psychiatry 2023; 93:536-545. [PMID: 36273948 PMCID: PMC9931661 DOI: 10.1016/j.biopsych.2022.08.010] [Citation(s) in RCA: 12] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/09/2022] [Revised: 06/17/2022] [Accepted: 08/05/2022] [Indexed: 12/01/2022]
Abstract
BACKGROUND Substance use disorders (SUDs) are associated with a variety of co-occurring psychiatric disorders and other SUDs, which partly reflects genetic pleiotropy. Polygenic risk scores (PRSs) and phenome-wide association studies are useful in evaluating pleiotropic effects. However, the comparatively low prevalence of SUDs in population samples and the lack of detailed information available in electronic health records limit these data sets' informativeness for such analyses. METHODS We used the deeply phenotyped Yale-Penn sample (n = 10,610 with genetic data; 46.3% African ancestry, 53.7% European ancestry) to examine pleiotropy for 4 major substance-related traits: alcohol use disorder, opioid use disorder, smoking initiation, and lifetime cannabis use. The sample includes both affected and control subjects interviewed using the Semi-Structured Assessment for Drug Dependence and Alcoholism, a comprehensive psychiatric interview. RESULTS In African ancestry individuals, PRS for alcohol use disorder, and in European individuals, PRS for alcohol use disorder, opioid use disorder, and smoking initiation were associated with their respective primary DSM diagnoses. These PRSs were also associated with additional phenotypes involving the same substance. Phenome-wide association study analyses of PRS in European individuals identified associations across multiple phenotypic domains, including phenotypes not commonly assessed in phenome-wide association study analyses, such as family environment and early childhood experiences. CONCLUSIONS Smaller, deeply phenotyped samples can complement large biobank genetic studies with limited phenotyping by providing greater phenotypic granularity. These efforts allow associations to be identified between specific features of disorders and genetic liability for SUDs, which help to inform our understanding of the pleiotropic pathways underlying them.
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Affiliation(s)
- Rachel L Kember
- Department of Psychiatry, University of Pennsylvania Perelman School of Medicine, Philadelphia, Pennsylvania; Mental Illness Research, Education and Clinical Center, Corporal Michael J. Crescenz VA Medical Center, Philadelphia, Pennsylvania.
| | - Emily E Hartwell
- Department of Psychiatry, University of Pennsylvania Perelman School of Medicine, Philadelphia, Pennsylvania; Mental Illness Research, Education and Clinical Center, Corporal Michael J. Crescenz VA Medical Center, Philadelphia, Pennsylvania
| | - Heng Xu
- Department of Psychiatry, University of Pennsylvania Perelman School of Medicine, Philadelphia, Pennsylvania
| | - James Rotenberg
- Mental Illness Research, Education and Clinical Center, Corporal Michael J. Crescenz VA Medical Center, Philadelphia, Pennsylvania
| | - Laura Almasy
- Department of Biomedical and Health Informatics, Children's Hospital of Philadelphia, Philadelphia, Pennsylvania; Department of Genetics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania
| | - Hang Zhou
- Department of Psychiatry, Yale University School of Medicine, New Haven, Connecticut; VA Connecticut Healthcare System, West Haven, Connecticut
| | - Joel Gelernter
- Department of Psychiatry, Yale University School of Medicine, New Haven, Connecticut; VA Connecticut Healthcare System, West Haven, Connecticut; Departments of Genetics and Neuroscience, Yale University School of Medicine, New Haven, Connecticut
| | - Henry R Kranzler
- Department of Psychiatry, University of Pennsylvania Perelman School of Medicine, Philadelphia, Pennsylvania; Mental Illness Research, Education and Clinical Center, Corporal Michael J. Crescenz VA Medical Center, Philadelphia, Pennsylvania
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15
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Albiñana C, Zhu Z, Borbye-Lorenzen N, Boelt SG, Cohen AS, Skogstrand K, Wray NR, Revez JA, Privé F, Petersen LV, Bulik CM, Plana-Ripoll O, Musliner KL, Agerbo E, Børglum AD, Hougaard DM, Nordentoft M, Werge T, Mortensen PB, Vilhjálmsson BJ, McGrath JJ. Genetic correlates of vitamin D-binding protein and 25-hydroxyvitamin D in neonatal dried blood spots. Nat Commun 2023; 14:852. [PMID: 36792583 PMCID: PMC9932173 DOI: 10.1038/s41467-023-36392-5] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/17/2022] [Accepted: 01/30/2023] [Indexed: 02/17/2023] Open
Abstract
The vitamin D binding protein (DBP), encoded by the group-specific component (GC) gene, is a component of the vitamin D system. In a genome-wide association study of DBP concentration in 65,589 neonates we identify 26 independent loci, 17 of which are in or close to the GC gene, with fine-mapping identifying 2 missense variants on chromosomes 12 and 17 (within SH2B3 and GSDMA, respectively). When adjusted for GC haplotypes, we find 15 independent loci distributed over 10 chromosomes. Mendelian randomization analyses identify a unidirectional effect of higher DBP concentration and (a) higher 25-hydroxyvitamin D concentration, and (b) a reduced risk of multiple sclerosis and rheumatoid arthritis. A phenome-wide association study confirms that higher DBP concentration is associated with a reduced risk of vitamin D deficiency. Our findings provide valuable insights into the influence of DBP on vitamin D status and a range of health outcomes.
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Affiliation(s)
- Clara Albiñana
- National Centre for Register-Based Research, Aarhus University, 8210, Aarhus V, Denmark
| | - Zhihong Zhu
- National Centre for Register-Based Research, Aarhus University, 8210, Aarhus V, Denmark
| | - Nis Borbye-Lorenzen
- Center for Neonatal Screening, Department of Congenital Disorders, Statens Serum Institut, Copenhagen, Denmark
| | - Sanne Grundvad Boelt
- Center for Neonatal Screening, Department of Congenital Disorders, Statens Serum Institut, Copenhagen, Denmark
- Clinical Mass Spectrometry, Statens Serum Institut, Artillerivej 5, DK-2300, Copenhagen S, Denmark
| | - Arieh S Cohen
- Testcenter Denmark, Statens Serum Institut, Artillerivej 5, DK-2300, Copenhagen S, Denmark
| | - Kristin Skogstrand
- Center for Neonatal Screening, Department of Congenital Disorders, Statens Serum Institut, Copenhagen, Denmark
| | - Naomi R Wray
- Institute for Molecular Bioscience, University of Queensland, Brisbane, QLD, Australia
- Queensland Brain Institute, University of Queensland, Brisbane, QLD, Australia
| | - Joana A Revez
- Institute for Molecular Bioscience, University of Queensland, Brisbane, QLD, Australia
| | - Florian Privé
- National Centre for Register-Based Research, Aarhus University, 8210, Aarhus V, Denmark
| | - Liselotte V Petersen
- National Centre for Register-Based Research, Aarhus University, 8210, Aarhus V, Denmark
- The Lundbeck Foundation Initiative for Integrative Psychiatric Research, iPSYCH, 8210, Aarhus V, Denmark
| | - Cynthia M Bulik
- Department of Psychiatry, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
- Department of Nutrition, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Oleguer Plana-Ripoll
- National Centre for Register-Based Research, Aarhus University, 8210, Aarhus V, Denmark
- Department of Clinical Epidemiology, Aarhus University and Aarhus University Hospital, 8200, Aarhus N, Denmark
| | - Katherine L Musliner
- Department of Affective Disorders, Aarhus University and Aarhus University Hospital-Psychiatry, Aarhus, Denmark
| | - Esben Agerbo
- National Centre for Register-Based Research, Aarhus University, 8210, Aarhus V, Denmark
- The Lundbeck Foundation Initiative for Integrative Psychiatric Research, iPSYCH, 8210, Aarhus V, Denmark
- CIRRAU - Centre for Integrated Register-based Research, Aarhus University, Aarhus, Denmark
| | - Anders D Børglum
- The Lundbeck Foundation Initiative for Integrative Psychiatric Research, iPSYCH, 8210, Aarhus V, Denmark
- Center for Genomics and Personalized Medicine, Aarhus University, Aarhus, Denmark
- Department of Biomedicine and the iSEQ Center, Aarhus University, Aarhus, Denmark
| | - David M Hougaard
- The Lundbeck Foundation Initiative for Integrative Psychiatric Research, iPSYCH, 8210, Aarhus V, Denmark
- Department for Congenital Disorders, Statens Serum Institut, 2300, Copenhagen S, Denmark
| | - Merete Nordentoft
- The Lundbeck Foundation Initiative for Integrative Psychiatric Research, iPSYCH, 8210, Aarhus V, Denmark
- Mental Health Services in the Capital Region of Denmark, Mental Health Center Copenhagen, University of Copenhagen, 2100, Copenhagen, Denmark
- Department of Clinical Medicine, University of Copenhagen, 2200, Copenhagen N, Denmark
| | - Thomas Werge
- The Lundbeck Foundation Initiative for Integrative Psychiatric Research, iPSYCH, 8210, Aarhus V, Denmark
- Institute of Biological Psychiatry, Mental Health Services, Copenhagen University Hospital, Copenhagen N, Denmark
- Department of Clinical Medicine, University of Copenhagen, Copenhagen, Denmark
- Lundbeck Center for Geogenetics, GLOBE Institute, University of Copenhagen, Copenhagen, Denmark
| | - Preben Bo Mortensen
- National Centre for Register-Based Research, Aarhus University, 8210, Aarhus V, Denmark
- The Lundbeck Foundation Initiative for Integrative Psychiatric Research, iPSYCH, 8210, Aarhus V, Denmark
- CIRRAU - Centre for Integrated Register-based Research, Aarhus University, Aarhus, Denmark
| | - Bjarni J Vilhjálmsson
- National Centre for Register-Based Research, Aarhus University, 8210, Aarhus V, Denmark
- The Lundbeck Foundation Initiative for Integrative Psychiatric Research, iPSYCH, 8210, Aarhus V, Denmark
- Bioinformatics Research Centre, Aarhus University, 8000, Aarhus C, Denmark
| | - John J McGrath
- National Centre for Register-Based Research, Aarhus University, 8210, Aarhus V, Denmark.
- Queensland Brain Institute, University of Queensland, Brisbane, QLD, Australia.
- Queensland Centre for Mental Health Research, The Park Centre for Mental Health, Brisbane, QLD, 4076, Australia.
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16
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Liang X, Cao X, Sha Q, Zhang S. HCLC-FC: A novel statistical method for phenome-wide association studies. PLoS One 2022; 17:e0276646. [PMID: 36350801 PMCID: PMC9645610 DOI: 10.1371/journal.pone.0276646] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/30/2022] [Accepted: 10/11/2022] [Indexed: 11/11/2022] Open
Abstract
The emergence of genetic data coupled to longitudinal electronic medical records (EMRs) offers the possibility of phenome-wide association studies (PheWAS). In PheWAS, the whole phenome can be divided into numerous phenotypic categories according to the genetic architecture across phenotypes. Currently, statistical analyses for PheWAS are mainly univariate analyses, which test the association between one genetic variant and one phenotype at a time. In this article, we derived a novel and powerful multivariate method for PheWAS. The proposed method involves three steps. In the first step, we apply the bottom-up hierarchical clustering method to partition a large number of phenotypes into disjoint clusters within each phenotypic category. In the second step, the clustering linear combination method is used to combine test statistics within each category based on the phenotypic clusters and obtain p-values from each phenotypic category. In the third step, we propose a new false discovery rate (FDR) control approach. We perform extensive simulation studies to compare the performance of our method with that of other existing methods. The results show that our proposed method controls FDR very well and outperforms other methods we compared with. We also apply the proposed approach to a set of EMR-based phenotypes across more than 300,000 samples from the UK Biobank. We find that the proposed approach not only can well-control FDR at a nominal level but also successfully identify 1,244 significant SNPs that are reported to be associated with some phenotypes in the GWAS catalog. Our open-access tools and instructions on how to implement HCLC-FC are available at https://github.com/XiaoyuLiang/HCLCFC.
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Affiliation(s)
- Xiaoyu Liang
- Department of Preventive Medicine, Division of Biostatistics, University of Tennessee Health Science Center, Memphis, Tennessee, United States of America
| | - Xuewei Cao
- Department of Mathematical Sciences, Michigan Technological University, Houghton, Michigan, United States of America
| | - Qiuying Sha
- Department of Mathematical Sciences, Michigan Technological University, Houghton, Michigan, United States of America
| | - Shuanglin Zhang
- Department of Mathematical Sciences, Michigan Technological University, Houghton, Michigan, United States of America
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17
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Fromme M, Schneider CV, Schlapbach C, Cazzaniga S, Trautwein C, Rader DJ, Borradori L, Strnad P. Comorbidities in lichen planus by phenome-wide association study in two biobank population cohorts. Br J Dermatol 2022; 187:722-729. [PMID: 35819183 DOI: 10.1111/bjd.21762] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/28/2022] [Revised: 06/05/2022] [Accepted: 07/09/2022] [Indexed: 12/30/2022]
Abstract
BACKGROUND Lichen planus (LP) is a relatively frequent mucocutaneous inflammatory disease affecting the skin, skin appendages and mucosae, including oral mucosae, and less frequently the anogenital area, conjunctivae, oesophagus or larynx. OBJECTIVES To estimate the association of LP, with emphasis on dermatological and gastrointestinal conditions, in two large independent population cohorts. MATERIALS AND METHODS We performed a phenome-wide association study (PheWAS) and examined conditions associated with LP in two unrelated cohorts, i.e. the multicentre, community-based UK Biobank (UKB: 501 381 controls; 1130 LP subjects) and the healthcare-associated Penn Medicine BioBank (PMBB; 42 702 controls; 764 LP subjects). The data were analysed in 2021. The 'PheWAS' R package was used to perform the PheWAS analyses and Bonferroni correction was used to adjust for multiple testing. Odds ratios (ORs) were adjusted for age, sex and body mass index. RESULTS In the UKB, PheWAS revealed 133 phenome codes (PheCodes) significantly associated with LP and most of them were confirmed in PMBB. Dermatological and digestive PheCodes were the most abundant: 29 and 34 of these disorders, respectively, were significantly overrepresented in LP individuals from both cohorts. The 29 dermatological and 12 oral disorders were often highly enriched, whereas hepatic, gastric, oesophageal and intestinal PheCodes displayed ORs in the range of 1·6-4·5. Several autoimmune disorders also exhibited OR > 5 in both cohorts. CONCLUSIONS PheWAS in two large unrelated cohorts identified previously unknown comorbidities and may support clinical counselling of patients with LP. What is already known about this topic? Lichen planus (LP) is known to affect the skin, skin appendages and mucosae, including oral mucosae, and less frequently the anogenital area, conjunctivae, oesophagus or larynx. What does this study add? Our data provide the most comprehensive collection of associated dermatological, digestive and autoimmune disorders to date. Our findings are expected to be useful for the evaluation and management of patients with LP.
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Affiliation(s)
- Malin Fromme
- Medical Clinic III, Gastroenterology, Metabolic Diseases and Intensive Care, University Hospital RWTH Aachen, Aachen, Germany
| | - Carolin V Schneider
- Medical Clinic III, Gastroenterology, Metabolic Diseases and Intensive Care, University Hospital RWTH Aachen, Aachen, Germany.,The Institute for Translational Medicine and Therapeutics, The Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Christoph Schlapbach
- Department of Dermatology, Inselspital, Bern University Hospital, University of Bern, Bern, Switzerland
| | - Simone Cazzaniga
- Department of Dermatology, Inselspital, Bern University Hospital, University of Bern, Bern, Switzerland.,Centro Studi GISED, Bergamo, Italy
| | - Christian Trautwein
- Medical Clinic III, Gastroenterology, Metabolic Diseases and Intensive Care, University Hospital RWTH Aachen, Aachen, Germany
| | - Dan J Rader
- The Institute for Translational Medicine and Therapeutics, The Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Luca Borradori
- Department of Dermatology, Inselspital, Bern University Hospital, University of Bern, Bern, Switzerland
| | - Pavel Strnad
- Medical Clinic III, Gastroenterology, Metabolic Diseases and Intensive Care, University Hospital RWTH Aachen, Aachen, Germany
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18
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Haleem A, Klees S, Schmitt AO, Gültas M. Deciphering Pleiotropic Signatures of Regulatory SNPs in Zea mays L. Using Multi-Omics Data and Machine Learning Algorithms. Int J Mol Sci 2022; 23:5121. [PMID: 35563516 PMCID: PMC9100765 DOI: 10.3390/ijms23095121] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/30/2022] [Revised: 04/28/2022] [Accepted: 05/02/2022] [Indexed: 01/25/2023] Open
Abstract
Maize is one of the most widely grown cereals in the world. However, to address the challenges in maize breeding arising from climatic anomalies, there is a need for developing novel strategies to harness the power of multi-omics technologies. In this regard, pleiotropy is an important genetic phenomenon that can be utilized to simultaneously enhance multiple agronomic phenotypes in maize. In addition to pleiotropy, another aspect is the consideration of the regulatory SNPs (rSNPs) that are likely to have causal effects in phenotypic development. By incorporating both aspects in our study, we performed a systematic analysis based on multi-omics data to reveal the novel pleiotropic signatures of rSNPs in a global maize population. For this purpose, we first applied Random Forests and then Markov clustering algorithms to decipher the pleiotropic signatures of rSNPs, based on which hierarchical network models are constructed to elucidate the complex interplay among transcription factors, rSNPs, and phenotypes. The results obtained in our study could help to understand the genetic programs orchestrating multiple phenotypes and thus could provide novel breeding targets for the simultaneous improvement of several agronomic traits.
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Affiliation(s)
- Ataul Haleem
- Breeding Informatics Group, Department of Animal Sciences, Georg-August University, Margarethe von Wrangell-Weg 7, 37075 Göttingen, Germany; (A.H.); (S.K.); (A.O.S.)
- Faculty of Agriculture, South Westphalia University of Applied Sciences, Lübecker Ring 2, 59494 Soest, Germany
| | - Selina Klees
- Breeding Informatics Group, Department of Animal Sciences, Georg-August University, Margarethe von Wrangell-Weg 7, 37075 Göttingen, Germany; (A.H.); (S.K.); (A.O.S.)
- Center for Integrated Breeding Research (CiBreed), Georg-August University, Carl-Sprengel-Weg 1, 37075 Göttingen, Germany
| | - Armin Otto Schmitt
- Breeding Informatics Group, Department of Animal Sciences, Georg-August University, Margarethe von Wrangell-Weg 7, 37075 Göttingen, Germany; (A.H.); (S.K.); (A.O.S.)
- Center for Integrated Breeding Research (CiBreed), Georg-August University, Carl-Sprengel-Weg 1, 37075 Göttingen, Germany
| | - Mehmet Gültas
- Faculty of Agriculture, South Westphalia University of Applied Sciences, Lübecker Ring 2, 59494 Soest, Germany
- Center for Integrated Breeding Research (CiBreed), Georg-August University, Carl-Sprengel-Weg 1, 37075 Göttingen, Germany
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19
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Lu C, Jin D, Palmer N, Fox K, Kohane IS, Smoller JW, Yu KH. Large-scale real-world data analysis identifies comorbidity patterns in schizophrenia. Transl Psychiatry 2022; 12:154. [PMID: 35410453 PMCID: PMC9001711 DOI: 10.1038/s41398-022-01916-y] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/27/2021] [Revised: 03/22/2022] [Accepted: 03/23/2022] [Indexed: 11/23/2022] Open
Abstract
Schizophrenia affects >3.2 million people in the USA. However, its comorbidity patterns have not been systematically characterized in real-world populations. To address this gap, we conducted an observational study using a cohort of 86 million patients in a nationwide health insurance dataset. We identified participants with schizophrenia and those without schizophrenia matched by age, sex, and the first three digits of zip code. For each phenotype encoded in phecodes, we compared their prevalence in schizophrenia patients and the matched non-schizophrenic participants, and we performed subgroup analyses stratified by age and sex. Results show that anxiety, posttraumatic stress disorder, and substance abuse commonly occur in adolescents and young adults prior to schizophrenia diagnoses. Patients aged 60 and above are at higher risks of developing delirium, alcoholism, dementia, pelvic fracture, and osteomyelitis than their matched controls. Type 2 diabetes, sleep apnea, and eating disorders were more prevalent in women prior to schizophrenia diagnosis, whereas acute renal failure, rhabdomyolysis, and developmental delays were found at higher rates in men. Anxiety and obesity are more commonly seen in patients with schizoaffective disorders compared to patients with other types of schizophrenia. Leveraging a large-scale insurance claims dataset, this study identified less-known comorbidity patterns of schizophrenia and confirmed known ones. These comorbidity profiles can guide clinicians and researchers to take heed of early signs of co-occurring diseases.
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Affiliation(s)
- Chenyue Lu
- grid.38142.3c000000041936754XDepartment of Biomedical Informatics, Harvard Medical School, Boston, MA USA
| | - Di Jin
- grid.116068.80000 0001 2341 2786Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, MA USA
| | - Nathan Palmer
- grid.38142.3c000000041936754XDepartment of Biomedical Informatics, Harvard Medical School, Boston, MA USA
| | - Kathe Fox
- grid.38142.3c000000041936754XDepartment of Biomedical Informatics, Harvard Medical School, Boston, MA USA
| | - Isaac S. Kohane
- grid.38142.3c000000041936754XDepartment of Biomedical Informatics, Harvard Medical School, Boston, MA USA
| | - Jordan W. Smoller
- grid.32224.350000 0004 0386 9924Department of Psychiatry, Massachusetts General Hospital, Boston, MA USA ,grid.38142.3c000000041936754XDepartment of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA USA
| | - Kun-Hsing Yu
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA.
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20
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Kerley CI, Chaganti S, Nguyen TQ, Bermudez C, Cutting LE, Beason-Held LL, Lasko T, Landman BA. pyPheWAS: A Phenome-Disease Association Tool for Electronic Medical Record Analysis. Neuroinformatics 2022; 20:483-505. [PMID: 34981404 PMCID: PMC9250547 DOI: 10.1007/s12021-021-09553-4] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 10/29/2021] [Indexed: 11/29/2022]
Abstract
Along with the increasing availability of electronic medical record (EMR) data, phenome-wide association studies (PheWAS) and phenome-disease association studies (PheDAS) have become a prominent, first-line method of analysis for uncovering the secrets of EMR. Despite this recent growth, there is a lack of approachable software tools for conducting these analyses on large-scale EMR cohorts. In this article, we introduce pyPheWAS, an open-source python package for conducting PheDAS and related analyses. This toolkit includes 1) data preparation, such as cohort censoring and age-matching; 2) traditional PheDAS analysis of ICD-9 and ICD-10 billing codes; 3) PheDAS analysis applied to a novel EMR phenotype mapping: current procedural terminology (CPT) codes; and 4) novelty analysis of significant disease-phenotype associations found through PheDAS. The pyPheWAS toolkit is approachable and comprehensive, encapsulating data prep through result visualization all within a simple command-line interface. The toolkit is designed for the ever-growing scale of available EMR data, with the ability to analyze cohorts of 100,000 + patients in less than 2 h. Through a case study of Down Syndrome and other intellectual developmental disabilities, we demonstrate the ability of pyPheWAS to discover both known and potentially novel disease-phenotype associations across different experiment designs and disease groups. The software and user documentation are available in open source at https://github.com/MASILab/pyPheWAS .
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Affiliation(s)
- Cailey I Kerley
- Department of Electrical Engineering, Vanderbilt University, Nashville, TN, USA.
| | - Shikha Chaganti
- Department of Computer Science, Vanderbilt University, Nashville, TN, USA
| | - Tin Q Nguyen
- Vanderbilt Brain Institute, Vanderbilt University, Nashville, TN, USA.,Department of Special Education, Peabody College of Education and Human Development, Nashville, TN, USA
| | - Camilo Bermudez
- Department of Biomedical Engineering, Vanderbilt University, Nashville, TN, USA
| | - Laurie E Cutting
- Vanderbilt Brain Institute, Vanderbilt University, Nashville, TN, USA.,Department of Special Education, Peabody College of Education and Human Development, Nashville, TN, USA.,Vanderbilt Kennedy Center, Vanderbilt University Medical Center, Nashville, TN, USA.,Vanderbilt University Institute of Imaging Science, Vanderbilt University, Nashville, TN, USA
| | - Lori L Beason-Held
- Laboratory of Behavioral Neuroscience, National Institute On Aging, NIH, Baltimore, MD, USA
| | - Thomas Lasko
- Department of Computer Science, Vanderbilt University, Nashville, TN, USA.,Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Bennett A Landman
- Department of Electrical Engineering, Vanderbilt University, Nashville, TN, USA.,Department of Computer Science, Vanderbilt University, Nashville, TN, USA.,Vanderbilt Brain Institute, Vanderbilt University, Nashville, TN, USA.,Department of Biomedical Engineering, Vanderbilt University, Nashville, TN, USA.,Vanderbilt Kennedy Center, Vanderbilt University Medical Center, Nashville, TN, USA.,Vanderbilt University Institute of Imaging Science, Vanderbilt University, Nashville, TN, USA.,Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN, USA
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21
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Maturation and application of phenome-wide association studies. Trends Genet 2022; 38:353-363. [PMID: 34991903 DOI: 10.1016/j.tig.2021.12.002] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/11/2021] [Revised: 11/12/2021] [Accepted: 12/02/2021] [Indexed: 12/12/2022]
Abstract
In the past 10 years since its introduction, phenome-wide association studies (PheWAS) have uncovered novel genotype-phenotype relationships. Along the way, PheWAS have evolved in many aspects as a study design with the expanded availability of large data repositories with genome-wide data linked to detailed phenotypic data. Advancement in methods, including algorithms, software, and publicly available integrated resources, makes it feasible to more fully realize the potential of PheWAS, overcoming the previous computational and analytical limitations. We review here the most recent improvements and notable applications of PheWAS since the second half of the decade from its inception. We also note the challenges that remain embedded along the entire PheWAS analytical pipeline that necessitate further development of tools and resources to further advance the understanding of the complex genetic architecture underlying human diseases and traits.
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22
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Abstract
Electronic health records (EHRs) are a rich source of data for researchers, but extracting meaningful information out of this highly complex data source is challenging. Phecodes represent one strategy for defining phenotypes for research using EHR data. They are a high-throughput phenotyping tool based on ICD (International Classification of Diseases) codes that can be used to rapidly define the case/control status of thousands of clinically meaningful diseases and conditions. Phecodes were originally developed to conduct phenome-wide association studies to scan for phenotypic associations with common genetic variants. Since then, phecodes have been used to support a wide range of EHR-based phenotyping methods, including the phenotype risk score. This review aims to comprehensively describe the development, validation, and applications of phecodes and suggest some future directions for phecodes and high-throughput phenotyping.
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Affiliation(s)
- Lisa Bastarache
- Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, Tennessee 37232, USA;
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23
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Andörfer L, Holtfreter B, Weiss S, Matthes R, Pitchika V, Schmidt CO, Samietz S, Kastenmüller G, Nauck M, Völker U, Völzke H, Csonka LN, Suhre K, Pietzner M, Kocher T. Salivary metabolites associated with a 5-year tooth loss identified in a population-based setting. BMC Med 2021; 19:161. [PMID: 34256740 PMCID: PMC8278731 DOI: 10.1186/s12916-021-02035-z] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/06/2021] [Accepted: 06/11/2021] [Indexed: 12/24/2022] Open
Abstract
BACKGROUND Periodontitis is among the most common chronic diseases worldwide, and it is one of the main reasons for tooth loss. Comprehensive profiling of the metabolite content of the saliva can enable the identification of novel pathways associated with periodontitis and highlight non-invasive markers to facilitate time and cost-effective screening efforts for the presence of periodontitis and the prediction of tooth loss. METHODS We first investigated cross-sectional associations of 13 oral health variables with saliva levels of 562 metabolites, measured by untargeted mass spectrometry among a sub-sample (n = 938) of the Study of Health in Pomerania (SHIP-2) using linear regression models adjusting for common confounders. We took forward any candidate metabolite associated with at least two oral variables, to test for an association with a 5-year tooth loss over and above baseline oral health status using negative binomial regression models. RESULTS We identified 84 saliva metabolites that were associated with at least one oral variable cross-sectionally, for a subset of which we observed robust replication in an independent study. Out of 34 metabolites associated with more than two oral variables, baseline saliva levels of nine metabolites were positively associated with a 5-year tooth loss. Across all analyses, the metabolites 2-pyrrolidineacetic acid and butyrylputrescine were the most consistent candidate metabolites, likely reflecting oral dysbiosis. Other candidate metabolites likely reflected tissue destruction and cell proliferation. CONCLUSIONS Untargeted metabolic profiling of saliva replicated metabolic signatures of periodontal status and revealed novel metabolites associated with periodontitis and future tooth loss.
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Affiliation(s)
- Leonie Andörfer
- Department of Restorative Dentistry, Periodontology, Endodontology, and Preventive and Pediatric Dentistry, University Medicine Greifswald, Fleischmannstr. 42, 17475, Greifswald, Germany
| | - Birte Holtfreter
- Department of Restorative Dentistry, Periodontology, Endodontology, and Preventive and Pediatric Dentistry, University Medicine Greifswald, Fleischmannstr. 42, 17475, Greifswald, Germany
| | - Stefan Weiss
- Interfaculty Institute for Genetics and Functional Genomics, University Medicine Greifswald, Greifswald, Germany
- DZHK (German Center for Cardiovascular Research), partner site Greifswald, Greifswald, Germany
| | - Rutger Matthes
- Department of Restorative Dentistry, Periodontology, Endodontology, and Preventive and Pediatric Dentistry, University Medicine Greifswald, Fleischmannstr. 42, 17475, Greifswald, Germany
| | - Vinay Pitchika
- Department of Restorative Dentistry, Periodontology, Endodontology, and Preventive and Pediatric Dentistry, University Medicine Greifswald, Fleischmannstr. 42, 17475, Greifswald, Germany
| | - Carsten Oliver Schmidt
- Institute for Community Medicine, SHIP/Clinical Epidemiology Research, University Medicine Greifswald, Greifswald, Germany
| | - Stefanie Samietz
- Department of Prosthetic Dentistry, Gerodontology and Biomaterials, University Medicine Greifswald, Greifswald, Germany
| | - Gabi Kastenmüller
- Institute of Bioinformatics and Systems Biology, Helmholtz Zentrum München, Neuherberg, Germany
| | - Matthias Nauck
- DZHK (German Center for Cardiovascular Research), partner site Greifswald, Greifswald, Germany
- Institute of Clinical Chemistry and Laboratory Medicine, University Medicine Greifswald, Greifswald, Germany
| | - Uwe Völker
- Interfaculty Institute for Genetics and Functional Genomics, University Medicine Greifswald, Greifswald, Germany
- DZHK (German Center for Cardiovascular Research), partner site Greifswald, Greifswald, Germany
| | - Henry Völzke
- DZHK (German Center for Cardiovascular Research), partner site Greifswald, Greifswald, Germany
- Institute for Community Medicine, SHIP/Clinical Epidemiology Research, University Medicine Greifswald, Greifswald, Germany
| | - Laszlo N Csonka
- Department of Biological Sciences, Purdue University, West Lafayette, USA
| | - Karsten Suhre
- Institute of Bioinformatics and Systems Biology, Helmholtz Zentrum München, Neuherberg, Germany
- Weill Cornell Medicine-Qatar, Education City, Qatar Foundation, Doha, Qatar
| | - Maik Pietzner
- Institute of Clinical Chemistry and Laboratory Medicine, University Medicine Greifswald, Greifswald, Germany
- Computational Medicine, Berlin Institute of Health (BIH), Charité-Universitätsmedizin Berlin, Berlin, Germany
| | - Thomas Kocher
- Department of Restorative Dentistry, Periodontology, Endodontology, and Preventive and Pediatric Dentistry, University Medicine Greifswald, Fleischmannstr. 42, 17475, Greifswald, Germany.
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24
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Holdsworth G, Staley JR, Hall P, van Koeverden I, Vangjeli C, Okoye R, Boyce RW, Turk JR, Armstrong M, Wolfreys A, Pasterkamp G. Sclerostin Downregulation Globally by Naturally Occurring Genetic Variants, or Locally in Atherosclerotic Plaques, Does Not Associate With Cardiovascular Events in Humans. J Bone Miner Res 2021; 36:1326-1339. [PMID: 33784435 PMCID: PMC8360163 DOI: 10.1002/jbmr.4287] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/02/2020] [Revised: 03/04/2021] [Accepted: 03/07/2021] [Indexed: 12/13/2022]
Abstract
Inhibition of sclerostin increases bone formation and decreases bone resorption, leading to increased bone mass, bone mineral density, and bone strength and reduced fracture risk. In a clinical study of the sclerostin antibody romosozumab versus alendronate in postmenopausal women (ARCH), an imbalance in adjudicated serious cardiovascular (CV) adverse events driven by an increase in myocardial infarction (MI) and stroke was observed. To explore whether there was a potential mechanistic plausibility that sclerostin expression, or its inhibition, in atherosclerotic (AS) plaques may have contributed to this imbalance, sclerostin was immunostained in human plaques to determine whether it was detected in regions relevant to plaque stability in 94 carotid and 50 femoral AS plaques surgically collected from older female patients (mean age 69.6 ± 10.4 years). Sclerostin staining was absent in most plaques (67%), and when detected, it was of reduced intensity compared with normal aorta and was located in deeper regions of the plaque/wall but was not observed in areas considered relevant to plaque stability (fibrous cap and endothelium). Additionally, genetic variants associated with lifelong reduced sclerostin expression were explored for associations with phenotypes including those related to bone physiology and CV risk factors/events in a population-based phenomewide association study (PheWAS). Natural genetic modulation of sclerostin by variants with a significant positive effect on bone physiology showed no association with lifetime risk of MI or stroke. These data do not support a causal association between the presence of sclerostin, or its inhibition, in the vasculature and increased risk of serious cardiovascular events. © 2021 The Authors. Journal of Bone and Mineral Research published by Wiley Periodicals LLC on behalf of American Society for Bone and Mineral Research (ASBMR).
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25
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Zhao L, Batta I, Matloff W, O'Driscoll C, Hobel S, Toga AW. Neuroimaging PheWAS (Phenome-Wide Association Study): A Free Cloud-Computing Platform for Big-Data, Brain-Wide Imaging Association Studies. Neuroinformatics 2021; 19:285-303. [PMID: 32822005 DOI: 10.1007/s12021-020-09486-4] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Abstract
Large-scale, case-control genome-wide association studies (GWASs) have revealed genetic variations associated with diverse neurological and psychiatric disorders. Recent advances in neuroimaging and genomic databases of large healthy and diseased cohorts have empowered studies to characterize effects of the discovered genetic factors on brain structure and function, implicating neural pathways and genetic mechanisms in the underlying biology. However, the unprecedented scale and complexity of the imaging and genomic data requires new advanced biomedical data science tools to manage, process and analyze the data. In this work, we introduce Neuroimaging PheWAS (phenome-wide association study): a web-based system for searching over a wide variety of brain-wide imaging phenotypes to discover true system-level gene-brain relationships using a unified genotype-to-phenotype strategy. This design features a user-friendly graphical user interface (GUI) for anonymous data uploading, study definition and management, and interactive result visualizations as well as a cloud-based computational infrastructure and multiple state-of-art methods for statistical association analysis and multiple comparison correction. We demonstrated the potential of Neuroimaging PheWAS with a case study analyzing the influences of the apolipoprotein E (APOE) gene on various brain morphological properties across the brain in the Alzheimer's Disease Neuroimaging Initiative (ADNI) cohort. Benchmark tests were performed to evaluate the system's performance using data from UK Biobank. The Neuroimaging PheWAS system is freely available. It simplifies the execution of PheWAS on neuroimaging data and provides an opportunity for imaging genetics studies to elucidate routes at play for specific genetic variants on diseases in the context of detailed imaging phenotypic data.
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Affiliation(s)
- Lu Zhao
- Laboratory of Neuro Imaging, USC Mark and Mary Stevens Neuroimaging and Informatics Institute, University of Southern California, Los Angeles, CA, USA
| | - Ishaan Batta
- Laboratory of Neuro Imaging, USC Mark and Mary Stevens Neuroimaging and Informatics Institute, University of Southern California, Los Angeles, CA, USA
| | - William Matloff
- Laboratory of Neuro Imaging, USC Mark and Mary Stevens Neuroimaging and Informatics Institute, University of Southern California, Los Angeles, CA, USA
| | - Caroline O'Driscoll
- Laboratory of Neuro Imaging, USC Mark and Mary Stevens Neuroimaging and Informatics Institute, University of Southern California, Los Angeles, CA, USA
| | - Samuel Hobel
- Laboratory of Neuro Imaging, USC Mark and Mary Stevens Neuroimaging and Informatics Institute, University of Southern California, Los Angeles, CA, USA
| | - Arthur W Toga
- Laboratory of Neuro Imaging, USC Mark and Mary Stevens Neuroimaging and Informatics Institute, University of Southern California, Los Angeles, CA, USA.
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26
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Ahluwalia TS, Prins BP, Abdollahi M, Armstrong NJ, Aslibekyan S, Bain L, Jefferis B, Baumert J, Beekman M, Ben-Shlomo Y, Bis JC, Mitchell BD, de Geus E, Delgado GE, Marek D, Eriksson J, Kajantie E, Kanoni S, Kemp JP, Lu C, Marioni RE, McLachlan S, Milaneschi Y, Nolte IM, Petrelis AM, Porcu E, Sabater-Lleal M, Naderi E, Seppälä I, Shah T, Singhal G, Standl M, Teumer A, Thalamuthu A, Thiering E, Trompet S, Ballantyne CM, Benjamin EJ, Casas JP, Toben C, Dedoussis G, Deelen J, Durda P, Engmann J, Feitosa MF, Grallert H, Hammarstedt A, Harris SE, Homuth G, Hottenga JJ, Jalkanen S, Jamshidi Y, Jawahar MC, Jess T, Kivimaki M, Kleber ME, Lahti J, Liu Y, Marques-Vidal P, Mellström D, Mooijaart SP, Müller-Nurasyid M, Penninx B, Revez JA, Rossing P, Räikkönen K, Sattar N, Scharnagl H, Sennblad B, Silveira A, Pourcain BS, Timpson NJ, Trollor J, van Dongen J, Van Heemst D, Visvikis-Siest S, Vollenweider P, Völker U, Waldenberger M, Willemsen G, Zabaneh D, Morris RW, Arnett DK, Baune BT, Boomsma DI, Chang YPC, Deary IJ, Deloukas P, Eriksson JG, Evans DM, Ferreira MA, Gaunt T, Gudnason V, Hamsten A, Heinrich J, Hingorani A, Humphries SE, Jukema JW, Koenig W, Kumari M, Kutalik Z, Lawlor DA, Lehtimäki T, März W, Mather KA, Naitza S, Nauck M, Ohlsson C, Price JF, Raitakari O, Rice K, Sachdev PS, Slagboom E, Sørensen TIA, Spector T, Stacey D, Stathopoulou MG, Tanaka T, Wannamethee SG, Whincup P, Rotter JI, Dehghan A, Boerwinkle E, Psaty BM, Snieder H, Alizadeh BZ. Genome-wide association study of circulating interleukin 6 levels identifies novel loci. Hum Mol Genet 2021; 30:393-409. [PMID: 33517400 PMCID: PMC8098112 DOI: 10.1093/hmg/ddab023] [Citation(s) in RCA: 28] [Impact Index Per Article: 9.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/02/2020] [Revised: 12/02/2020] [Accepted: 01/04/2021] [Indexed: 12/13/2022] Open
Abstract
Interleukin 6 (IL-6) is a multifunctional cytokine with both pro- and anti-inflammatory properties with a heritability estimate of up to 61%. The circulating levels of IL-6 in blood have been associated with an increased risk of complex disease pathogenesis. We conducted a two-staged, discovery and replication meta genome-wide association study (GWAS) of circulating serum IL-6 levels comprising up to 67 428 (ndiscovery = 52 654 and nreplication = 14 774) individuals of European ancestry. The inverse variance fixed effects based discovery meta-analysis, followed by replication led to the identification of two independent loci, IL1F10/IL1RN rs6734238 on chromosome (Chr) 2q14, (Pcombined = 1.8 × 10-11), HLA-DRB1/DRB5 rs660895 on Chr6p21 (Pcombined = 1.5 × 10-10) in the combined meta-analyses of all samples. We also replicated the IL6R rs4537545 locus on Chr1q21 (Pcombined = 1.2 × 10-122). Our study identifies novel loci for circulating IL-6 levels uncovering new immunological and inflammatory pathways that may influence IL-6 pathobiology.
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Affiliation(s)
- Tarunveer S Ahluwalia
- Steno Diabetes Center Copenhagen, Gentofte DK2820, Denmark.,Department of Biology, The Bioinformatics Center, University of Copenhagen, Copenhagen DK2200, Denmark
| | - Bram P Prins
- Department of Epidemiology, University of Groningen, University Medical Center Groningen, Groningen 9700 RB, The Netherlands
| | - Mohammadreza Abdollahi
- Department of Epidemiology, University of Groningen, University Medical Center Groningen, Groningen 9700 RB, The Netherlands
| | | | - Stella Aslibekyan
- Department of Epidemiology, University of Alabama at Birmingham School of Public Health, Birmingham, Alabama 35233, USA
| | - Lisa Bain
- QIMR Berghofer Medical Research Institute, Brisbane 4006, Australia
| | - Barbara Jefferis
- Department of Primary Care & Population Health, UCL Institute of Epidemiology & Health Care, University College London, London NW3 2PF, UK
| | - Jens Baumert
- Institute of Epidemiology, Helmholtz Zentrum München-German Research Center for Environmental Health, Neuherberg 85764, Germany
| | - Marian Beekman
- Department of Biomedical Data Sciences, Section of Molecular Epidemiology, Leiden University Medical Center, Leiden 2300 RC, The Netherlands
| | - Yoav Ben-Shlomo
- Population Health Sciences, University of Bristol, Bristol BS8 2PS, UK
| | - Joshua C Bis
- Cardiovascular Health Research Unit, Department of Medicine, University of Washington, Seattle, WA 98101, USA
| | - Braxton D Mitchell
- Department of Medicine, University of Maryland School of Medicine, Baltimore, MD 21202, USA
| | - Eco de Geus
- Department of Biological Psychology, Behaviour and Movement Sciences, Vrije Universiteit Amsterdam, Amsterdam 1081 BT, The Netherlands.,Amsterdam Public Health Research Institute, Amsterdam University Medical Center, Amsterdam 1105 AZ, The Netherlands
| | - Graciela E Delgado
- Vth Department of Medicine (Nephrology, Hypertensiology, Rheumatology, Endocrinology, Diabetology), Medical Faculty Mannheim, University of Heidelberg, Mannheim 68167, Germany
| | - Diana Marek
- SIB Swiss Institute of Bioinformatics, Lausanne 1015, Switzerland
| | - Joel Eriksson
- Department of Internal Medicine and Clinical Nutrition, Sahlgrenska Academy, Centre for Bone and Arthritis Research (CBAR), University of Gothenburg, Gothenburg 41345, Sweden
| | - Eero Kajantie
- Chronic Disease Prevention Unit, National Institute for Health and Welfare, PO Box 30, Helsinki 00271, Finland.,Hospital for Children and Adolescents, Helsinki University Central Hospital and University of Helsinki, Helsinki 00014, Finland
| | - Stavroula Kanoni
- William Harvey Research Institute, Barts & the London Medical School, Queen Mary University of London, London EC1M 6BQ, UK
| | - John P Kemp
- The University of Queensland Diamantina Institute, The University of Queensland, Woolloongabba, Queensland 4102, Australia.,MRC Integrative Epidemiology Unit, Population Health Sciences, University of Bristol, Bristol BS8 2BN, UK
| | - Chen Lu
- Department of Biostatistics, Boston University School of Public Health, Boston, MA 02118, USA
| | - Riccardo E Marioni
- Centre for Genomic and Experimental Medicine, Institute of Genetics and Molecular Medicine, University of Edinburgh, Edinburgh EH4 2XU, UK.,Centre for Cognitive Ageing and Cognitive Epidemiology, University of Edinburgh, Edinburgh EH8 9JZ, UK
| | - Stela McLachlan
- Usher Institute, University of Edinburgh, Edinburgh EH8 9AG, UK
| | - Yuri Milaneschi
- Department of Psychiatry, Amsterdam UMC, Vrije Universiteit, Amsterdam 1081 HJ, The Netherlands
| | - Ilja M Nolte
- Department of Epidemiology, University of Groningen, University Medical Center Groningen, Groningen 9700 RB, The Netherlands
| | | | - Eleonora Porcu
- Istituto di Ricerca Genetica e Biomedica, Consiglio Nazionale delle Ricerche, Monserrato (CA) 09042, Italy
| | - Maria Sabater-Lleal
- Cardiovascular Medicine, Department of Medicine Solna, Center for Molecular Medicine, Karolinska Institutet, Stockholm 17176, Sweden.,Unit of Genomics of Complex Diseases, Institut d'Investigació Biomèdica Sant Pau (IIB-Sant Pau), Barcelona 08041, Spain
| | - Elnaz Naderi
- Department of Epidemiology, University of Groningen, University Medical Center Groningen, Groningen 9700 RB, The Netherlands
| | - Ilkka Seppälä
- Department of Clinical Chemistry, Fimlab Laboratories, and Finnish Cardiovascular Research Center-Tampere, Faculty of Medicine and Health Technology, Tampere University, Tampere 33520, Finland
| | - Tina Shah
- Institute of Cardiovascular Science, University College London, London WC1E 6BT, UK
| | - Gaurav Singhal
- Discipline of Psychiatry, Adelaide Medical School, University of Adelaide, Adelaide 5005, Australia
| | - Marie Standl
- Institute of Epidemiology, Helmholtz Zentrum München-German Research Center for Environmental Health, Neuherberg 85764, Germany
| | - Alexander Teumer
- Institute for Community Medicine, University Medicine Greifswald, Greifswald 17475, Germany
| | - Anbupalam Thalamuthu
- Centre for Healthy Brain Ageing, School of Psychiatry, University of New South Wales, Sydney 2052, Australia
| | - Elisabeth Thiering
- Institute of Epidemiology, Helmholtz Zentrum München-German Research Center for Environmental Health, Neuherberg 85764, Germany.,Division of Metabolic Diseases and Nutritional Medicine, Ludwig-Maximilians-University of Munich, Dr. von Hauner Children's Hospital, Munich 80337, Germany
| | - Stella Trompet
- Department of Cardiology, Leiden University Medical Center, Leiden 2300 RC, The Netherlands.,Section of Gerontology and Geriatrics, Department of Internal Medicine, Leiden University Medical Center, Leiden 2333 ZA, The Netherlands
| | | | - Emelia J Benjamin
- National Heart, Lung, and Blood Institute's and Boston University's Framingham Heart Study, Framingham, MA 01702, USA.,Section of Cardiovascular Medicine and Preventive Medicine, Department of Medicine, Boston University School of Medicine, Boston, MA 02118, USA
| | - Juan P Casas
- Massachusetts Veterans Epidemiology Research and Information Center (MAVERIC), VA Boston Healthcare System, Boston, MA 02130, USA
| | - Catherine Toben
- Discipline of Psychiatry, Adelaide Medical School, University of Adelaide, Adelaide 5005, Australia
| | - George Dedoussis
- 44Department of Nutrition-Dietetics, Harokopio University, Athens 17671, Greece
| | - Joris Deelen
- Department of Biomedical Data Sciences, Section of Molecular Epidemiology, Leiden University Medical Center, Leiden 2300 RC, The Netherlands.,Max Planck Institute for Biology of Ageing, Cologne 50931, Germany
| | - Peter Durda
- Department of Pathology and Laboratory Medicine, Larner College of Medicine, University of Vermont, Burlington, VT 05405, USA
| | - Jorgen Engmann
- Institute of Cardiovascular Science, University College London, London WC1E 6BT, UK
| | - Mary F Feitosa
- Division of Statistical Genomics, Department of Genetics, Washington University School of Medicine, St. Louis, MO 63110-1093, USA
| | - Harald Grallert
- Institute of Epidemiology, Helmholtz Zentrum München-German Research Center for Environmental Health, Neuherberg 85764, Germany.,German Center for Diabetes Research (DZD), Neuherberg 85764, Germany
| | - Ann Hammarstedt
- The Lundberg Laboratory for Diabetes Research, Department of Molecular and Clinical Medicine, Sahlgrenska Academy at the University of Gothenburg, Gothenburg SE-41345, Sweden
| | - Sarah E Harris
- Centre for Cognitive Ageing and Cognitive Epidemiology, University of Edinburgh, Edinburgh EH8 9JZ, UK.,Department of Psychology, University of Edinburgh, Edinburgh EH8 9JZ, UK
| | - Georg Homuth
- Interfaculty Institute for Genetics and Functional Genomics, University Medicine Greifswald, Greifswald 17475, Germany
| | - Jouke-Jan Hottenga
- Department of Biological Psychology, Behaviour and Movement Sciences, Vrije Universiteit Amsterdam, Amsterdam 1081 BT, The Netherlands.,Amsterdam Public Health Research Institute, Amsterdam University Medical Center, Amsterdam 1105 AZ, The Netherlands
| | - Sirpa Jalkanen
- MediCity Research Laboratory, University of Turku, Turku 20520, Finland.,Department of Medical Microbiology and Immunology, University of Turku, Turku 20520, Finland
| | - Yalda Jamshidi
- Genetics Research Centre, Molecular and Clinical Sciences Institute, St George's University of London, London SW17 0RE, UK
| | - Magdalene C Jawahar
- Discipline of Psychiatry, Adelaide Medical School, University of Adelaide, Adelaide 5005, Australia
| | - Tine Jess
- 55Department of Epidemiology Research, Statens Serum Institute, Copenhagen DK2300, Denmark
| | - Mika Kivimaki
- Department of Epidemiology & Public Health, UCL Institute of Epidemiology & Health Care, University College London, London WC1E 7HB, UK
| | - Marcus E Kleber
- Vth Department of Medicine (Nephrology, Hypertensiology, Rheumatology, Endocrinology, Diabetology), Medical Faculty Mannheim, University of Heidelberg, Mannheim 68167, Germany
| | - Jari Lahti
- Turku Institute for Advanced Studies, University of Turku, Turku 20014, Finland.,Department of Psychology and Logopedics, University of Helsinki, Helsinki 00014, Finland
| | - Yongmei Liu
- Department of Epidemiology and Prevention, Wake Forest School of Medicine, Winston-Salem, NC 27157, USA
| | - Pedro Marques-Vidal
- Department of Internal Medicine, Lausanne University Hospital (CHUV), Lausanne 1011, Switzerland.,University of Lausanne, Lausanne 1011, Switzerland
| | - Dan Mellström
- Department of Internal Medicine and Clinical Nutrition, Sahlgrenska Academy, Centre for Bone and Arthritis Research (CBAR), University of Gothenburg, Gothenburg 41345, Sweden
| | - Simon P Mooijaart
- Section of Gerontology and Geriatrics, Department of Internal Medicine, Leiden University Medical Center, Leiden 2333 ZA, The Netherlands
| | - Martina Müller-Nurasyid
- IBE, Faculty of Medicine, Ludwig Maximilians University (LMU) Munich, Munich 81377, Germany.,Institute of Medical Biostatistics, Epidemiology and Informatics (IMBEI), University Medical Center, Johhanes Gutenberg University, Mainz 55101, Germany
| | - Brenda Penninx
- Department of Psychiatry, Amsterdam UMC, Vrije Universiteit, Amsterdam 1081 HJ, The Netherlands
| | - Joana A Revez
- QIMR Berghofer Medical Research Institute, Brisbane 4006, Australia
| | - Peter Rossing
- Steno Diabetes Center Copenhagen, Gentofte DK2820, Denmark.,Department of Clinical Medicine, University of Copenhagen, Copenhagen DK2200, Denmark
| | - Katri Räikkönen
- Department of Psychology and Logopedics, University of Helsinki, Helsinki 00014, Finland
| | - Naveed Sattar
- BHF Glasgow Cardiovascular Research Centre, Faculty of Medicine, Glasgow G12 8TA, UK
| | - Hubert Scharnagl
- 66Clinical Institute of Medical and Chemical Laboratory Diagnostics, Medical University of Graz, Graz 8036, Austria
| | - Bengt Sennblad
- Cardiovascular Medicine, Department of Medicine Solna, Center for Molecular Medicine, Karolinska Institutet, Stockholm 17176, Sweden.,Department of Cell and Molecular Biology, National Bioinformatics Infrastructure Sweden, Science for Life Laboratory, Uppsala University, Uppsala 75124, Sweden
| | - Angela Silveira
- Cardiovascular Medicine, Department of Medicine Solna, Center for Molecular Medicine, Karolinska Institutet, Stockholm 17176, Sweden
| | - Beate St Pourcain
- MRC Integrative Epidemiology Unit, Population Health Sciences, University of Bristol, Bristol BS8 2BN, UK.,Max Planck Institute for Psycholinguistics, Nijmegen XD 6525, The Netherlands.,Donders Institute for Brain, Cognition and Behaviour, Radboud University, Nijmegen 6525 AJ, The Netherlands
| | - Nicholas J Timpson
- MRC Integrative Epidemiology Unit, Population Health Sciences, University of Bristol, Bristol BS8 2BN, UK
| | - Julian Trollor
- Centre for Healthy Brain Ageing, School of Psychiatry, University of New South Wales, Sydney 2052, Australia.,Department of Developmental Disability Neuropsychiatry, School of Psychiatry, University of New South Wales, Sydney 2031, Australia
| | | | - Jenny van Dongen
- Department of Biological Psychology, Behaviour and Movement Sciences, Vrije Universiteit Amsterdam, Amsterdam 1081 BT, The Netherlands.,Amsterdam Public Health Research Institute, Amsterdam University Medical Center, Amsterdam 1105 AZ, The Netherlands
| | | | | | - Peter Vollenweider
- Department of Internal Medicine, Lausanne University Hospital (CHUV), Lausanne 1011, Switzerland.,University of Lausanne, Lausanne 1011, Switzerland
| | - Uwe Völker
- MediCity Research Laboratory, University of Turku, Turku 20520, Finland
| | - Melanie Waldenberger
- Institute of Epidemiology, Helmholtz Zentrum München-German Research Center for Environmental Health, Neuherberg 85764, Germany
| | - Gonneke Willemsen
- Department of Biological Psychology, Behaviour and Movement Sciences, Vrije Universiteit Amsterdam, Amsterdam 1081 BT, The Netherlands.,Amsterdam Public Health Research Institute, Amsterdam University Medical Center, Amsterdam 1105 AZ, The Netherlands
| | - Delilah Zabaneh
- Department of Genetics, Environment and Evolution, University College London Genetics Institute, London WC1E 6BT, UK
| | - Richard W Morris
- Department of Population Health Sciences, Bristol Medical School, University of Bristol, Bristol BS8 1UD, UK
| | - Donna K Arnett
- Dean's Office, College of Public Health, University of Kentucky, Lexington, KY 40536, USA
| | - Bernhard T Baune
- Department of Psychiatry, Melbourne Medical School, The University of Melbourne, Parkville 3000, Australia.,Department of Psychiatry and Psychotherapy, University of Muenster, Muenster 48149, Germany.,The Florey Institute of Neuroscience and Mental Health, The University of Melbourne, Parkville 3000, Australia
| | - Dorret I Boomsma
- Department of Biological Psychology, Behaviour and Movement Sciences, Vrije Universiteit Amsterdam, Amsterdam 1081 BT, The Netherlands.,Amsterdam Public Health Research Institute, Amsterdam University Medical Center, Amsterdam 1105 AZ, The Netherlands
| | - Yen-Pei C Chang
- Department of Medicine, University of Maryland School of Medicine, Baltimore, MD 21202, USA
| | - Ian J Deary
- Centre for Cognitive Ageing and Cognitive Epidemiology, University of Edinburgh, Edinburgh EH8 9JZ, UK.,Department of Psychology, University of Edinburgh, Edinburgh EH8 9JZ, UK
| | - Panos Deloukas
- William Harvey Research Institute, Barts & the London Medical School, Queen Mary University of London, London EC1M 6BQ, UK.,77Centre for Genomic Health, Queen Mary University of London, London EC1M 6BQ, UK
| | - Johan G Eriksson
- National Institute for Health and Welfare, University of Helsinki, Helsinki 00014, Finland.,Department of General Practice and Primary Health Care, University of Helsinki, Helsinki 00014, Finland
| | - David M Evans
- The University of Queensland Diamantina Institute, The University of Queensland, Woolloongabba, Queensland 4102, Australia.,MRC Integrative Epidemiology Unit, Population Health Sciences, University of Bristol, Bristol BS8 2BN, UK
| | | | - Tom Gaunt
- MRC Integrative Epidemiology Unit at the University of Bristol, Bristol BS6 2BN, UK.,Population Health Science, Bristol Medical School, University of Bristol, Bristol BS8 2BN, UK
| | - Vilmundur Gudnason
- Icelandic Heart Association, Kópavogur 201, Iceland.,Faculty of Medicine, University of Iceland, Reykjavik 101, Iceland
| | - Anders Hamsten
- Cardiovascular Medicine, Department of Medicine Solna, Center for Molecular Medicine, Karolinska Institutet, Stockholm 17176, Sweden
| | - Joachim Heinrich
- Institute of Epidemiology, Helmholtz Zentrum München-German Research Center for Environmental Health, Neuherberg 85764, Germany.,Institute and Clinic for Occupational, Social and Environmental Medicine, University Hospital, LMU Munich, Munich 81377, Germany.,Allergy and Lung Health Unit, Melbourne School of Population and Global Health, The University of Melbourne, Melbourne 3010, Australia
| | - Aroon Hingorani
- Institute of Cardiovascular Science, University College London, London WC1E 6BT, UK
| | - Steve E Humphries
- Institute of Cardiovascular Science, University College London, London WC1E 6BT, UK
| | - J Wouter Jukema
- Section of Gerontology and Geriatrics, Department of Internal Medicine, Leiden University Medical Center, Leiden 2333 ZA, The Netherlands.,Durrer Center for Cardiogenetic Research, Amsterdam 1105 AZ, The Netherlands
| | - Wolfgang Koenig
- Deutsches Herzzentrum München, Technische Universität München, Munich 80636, Germany.,88DZHK (German Centre for Cardiovascular Research), partner site Munich Heart Alliance, Munich 80336, Germany.,Institute of Epidemiology and Medical Biometry, University of Ulm, Ulm 89081, Germany
| | - Meena Kumari
- Department of Epidemiology & Public Health, UCL Institute of Epidemiology & Health Care, University College London, London WC1E 7HB, UK.,Institute for Social and Economic Research, University of Essex, Colchester CO4 3SQ, Germany
| | - Zoltan Kutalik
- SIB Swiss Institute of Bioinformatics, Lausanne 1015, Switzerland.,University Center for Primary Care and Public Health, University of Lausanne, Lausanne 1010, Switzerland
| | - Deborah A Lawlor
- MRC Integrative Epidemiology Unit at the University of Bristol, Bristol BS6 2BN, UK.,Population Health Science, Bristol Medical School, University of Bristol, Bristol BS8 2BN, UK
| | - Terho Lehtimäki
- Department of Clinical Chemistry, Fimlab Laboratories, and Finnish Cardiovascular Research Center-Tampere, Faculty of Medicine and Health Technology, Tampere University, Tampere 33520, Finland
| | - Winfried März
- Vth Department of Medicine (Nephrology, Hypertensiology, Rheumatology, Endocrinology, Diabetology), Medical Faculty Mannheim, University of Heidelberg, Mannheim 68167, Germany.,66Clinical Institute of Medical and Chemical Laboratory Diagnostics, Medical University of Graz, Graz 8036, Austria.,SYNLAB Academy, SYNALB Holding Deutschland GmbH, Mannheim 68163, Germany
| | - Karen A Mather
- Centre for Healthy Brain Ageing, School of Psychiatry, University of New South Wales, Sydney 2052, Australia.,Neuroscience Research Australia, Sydney 2031, Australia
| | - Silvia Naitza
- Istituto di Ricerca Genetica e Biomedica, Consiglio Nazionale delle Ricerche, Monserrato (CA) 09042, Italy
| | - Matthias Nauck
- Institute of Clinical Chemistry and Laboratory Medicine, University Medicine Greifswald, Greifswald 17475, Germany.,DZHK (German Center for Cardiovascular Research), partner site Greifswald, Greifswald 17475, Germany
| | - Claes Ohlsson
- Department of Internal Medicine and Clinical Nutrition, Sahlgrenska Academy, Centre for Bone and Arthritis Research (CBAR), University of Gothenburg, Gothenburg 41345, Sweden
| | - Jackie F Price
- Usher Institute, University of Edinburgh, Edinburgh EH8 9AG, UK
| | - Olli Raitakari
- Centre for Population Health Research, University of Turku, Turku University Hospital, Turku 20520, Finland.,Research Centre of Applied and Preventive Cardiovascular Medicine, University of Turku, Turku 20520, Finland.,Department of Clinical Physiology and Nuclear Medicine, Turku University Hospital, Turku 20014, Finland
| | - Ken Rice
- Department of Biostatistics, University of Washington, Seattle, WA 98195, USA
| | - Perminder S Sachdev
- Centre for Healthy Brain Ageing, School of Psychiatry, University of New South Wales, Sydney 2052, Australia.,Neuropsychiatric Institute, Prince of Wales Hospital, Sydney 2031, Australia
| | - Eline Slagboom
- Department of Biomedical Data Sciences, Section of Molecular Epidemiology, Leiden University Medical Center, Leiden 2300 RC, The Netherlands.,Max Planck Institute for Biology of Ageing, Cologne 50931, Germany
| | - Thorkild I A Sørensen
- Novo Nordisk Foundation Center For Basic Metabolic Research, Section of Metabolic Genetics, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen DK2200, Denmark.,Department of Public Health, Section on Epidemiology, University of Copenhagen, Copenhagen DK1014, Denmark
| | - Tim Spector
- Department of Twin Research and Genetic Epidemiology, King's College London, London SE1 7EH, UK
| | - David Stacey
- MRC/BHF Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge CB1 8RN, UK
| | | | - Toshiko Tanaka
- Longitudinal Study Section, Translational Gerontology Branch, National Institute on Aging, Baltimore, MD 21224, USA
| | - S Goya Wannamethee
- Department of Primary Care & Population Health, UCL Institute of Epidemiology & Health Care, University College London, London NW3 2PF, UK
| | - Peter Whincup
- Population Health Research Institute, St George's, University of London, London SW17 0RE, UK
| | - Jerome I Rotter
- Department of Pediatrics, The Institute for Translational Genomics and Population Sciences, The Lundquist Institute at Harbor-UCLA Medical Center, Torrance, CA 90502, USA
| | - Abbas Dehghan
- Department of Epidemiology, Erasmus MC, Rotterdam 3000 CA, The Netherlands
| | - Eric Boerwinkle
- Human Genetics Center, School of Public Health, University of Texas Health Science Center at Houston, Houston, TX 77030, USA.,Human Genome Sequencing Center, Baylor College of Medicine, Houston, TX 77030, USA
| | - Bruce M Psaty
- Cardiovascular Health Research Unit, Department of Medicine, University of Washington, Seattle, WA 98101, USA.,Departments of Epidemiology and Health Services, University of Washington, Seattle, WA 98101, USA
| | - Harold Snieder
- Department of Epidemiology, University of Groningen, University Medical Center Groningen, Groningen 9700 RB, The Netherlands
| | - Behrooz Z Alizadeh
- Department of Epidemiology, University of Groningen, University Medical Center Groningen, Groningen 9700 RB, The Netherlands
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Fawcett KA, Song K, Qian G, Farmaki AE, Packer R, John C, Shrine N, Granell R, Ring S, Timpson NJ, Yerges-Armstrong LM, Eastell R, Wain LV, Scott RA, Tobin MD, Hall IP. Pleiotropic associations of heterozygosity for the SERPINA1 Z allele in the UK Biobank. ERJ Open Res 2021; 7:00049-2021. [PMID: 33981765 PMCID: PMC8107350 DOI: 10.1183/23120541.00049-2021] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/22/2021] [Accepted: 02/20/2021] [Indexed: 11/20/2022] Open
Abstract
Homozygosity for the SERPINA1 Z allele causes α1-antitrypsin deficiency, a rare condition that can cause lung and liver disease. However, the effects of Z allele heterozygosity on nonrespiratory phenotypes, and on lung function in the general population, remain unclear. We conducted a large, population-based study to determine Z allele effects on >2400 phenotypes in the UK Biobank (N=303 353). Z allele heterozygosity was strongly associated with increased height (β=1.02 cm, p=3.91×10-68), and with other nonrespiratory phenotypes including increased risk of gall bladder disease, reduced risk of heart disease and lower blood pressure, reduced risk of osteoarthritis and reduced bone mineral density, increased risk of headache and enlarged prostate, as well as with blood biomarkers of liver function. Heterozygosity was associated with higher height-adjusted forced expiratory volume in 1 s (FEV1) (β=19.36 mL, p=9.21×10-4) and FEV1/forced vital capacity (β=0.0031, p=1.22×10-5) in nonsmokers, whereas in smokers, this protective effect was abolished. Furthermore, we show for the first time that sex modifies the association of the Z allele on lung function. We conclude that Z allele heterozygosity and homozygosity exhibit opposing effects on lung function in the UK population, and that these associations are modified by smoking and sex. In exploratory analyses, heterozygosity for the Z allele also showed pleiotropic associations with nonrespiratory health-related traits and disease risk.
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Affiliation(s)
| | - Kijoung Song
- Human Genetics, GlaxoSmithKline, Collegeville, PA, USA
| | - Guoqing Qian
- Dept of General Internal Medicine, Ningbo First Hospital, Ningbo City, Zhejiang Province, China
- Division of Respiratory Medicine, University of Nottingham, and NIHR Nottingham BRC, NUH NHS Trust, Nottingham, UK
| | - Aliki-Eleni Farmaki
- MRC Unit for Lifelong Health and Ageing, Institute of Cardiovascular Science, University College London, London, UK
| | - Richard Packer
- Dept of Health Sciences, University of Leicester, Leicester, UK
| | - Catherine John
- Dept of Health Sciences, University of Leicester, Leicester, UK
| | - Nick Shrine
- Dept of Health Sciences, University of Leicester, Leicester, UK
| | - Raquel Granell
- Medical Research Council Integrative Epidemiology Unit, Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
| | - Sue Ring
- Medical Research Council Integrative Epidemiology Unit, Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
| | - Nicholas J. Timpson
- Medical Research Council Integrative Epidemiology Unit, Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
| | | | - Richard Eastell
- Dept of Oncology and Metabolism, University of Sheffield, Sheffield, UK
| | - Louise V. Wain
- Dept of Health Sciences, University of Leicester, Leicester, UK
- National Institute for Health Research, Leicester Respiratory Biomedical Research Centre, Glenfield Hospital, Leicester, UK
| | - Robert A. Scott
- Human Genetics – R&D, GSK Medicines Research Centre, Stevenage, UK
| | - Martin D. Tobin
- Dept of Health Sciences, University of Leicester, Leicester, UK
- National Institute for Health Research, Leicester Respiratory Biomedical Research Centre, Glenfield Hospital, Leicester, UK
- These authors contributed equally
| | - Ian P. Hall
- Division of Respiratory Medicine, University of Nottingham, and NIHR Nottingham BRC, NUH NHS Trust, Nottingham, UK
- These authors contributed equally
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28
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Hermann ER, Chambers E, Davis DN, Montgomery MR, Lin D, Chowanadisai W. Brain Magnetic Resonance Imaging Phenome-Wide Association Study With Metal Transporter Gene SLC39A8. Front Genet 2021; 12:647946. [PMID: 33790950 PMCID: PMC8005600 DOI: 10.3389/fgene.2021.647946] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/11/2021] [Accepted: 02/22/2021] [Indexed: 12/16/2022] Open
Abstract
The SLC39A8 gene encodes a divalent metal transporter, ZIP8. SLC39A8 is associated with pleiotropic effects across multiple tissues, including the brain. We determine the different brain magnetic resonance imaging (MRI) phenotypes associated with SLC39A8. We used a phenome-wide association study approach followed by joint and conditional association analysis. Using the summary statistics datasets from a brain MRI genome-wide association study on adult United Kingdom (UK) Biobank participants, we systematically selected all brain MRI phenotypes associated with single-nucleotide polymorphisms (SNPs) within 500 kb of the SLC39A8 genetic locus. For all significant brain MRI phenotypes, we used GCTA-COJO to determine the number of independent association signals and identify index SNPs for each brain MRI phenotype. Linkage equilibrium for brain phenotypes with multiple independent signals was confirmed by LDpair. We identified 24 brain MRI phenotypes that vary due to MRI type and brain region and contain a SNP associated with the SLC39A8 locus. Missense ZIP8 polymorphism rs13107325 was associated with 22 brain MRI phenotypes. Rare ZIP8 variants present in a published UK Biobank dataset are associated with 6 brain MRI phenotypes also linked to rs13107325. Among the 24 datasets, an additional 4 association signals were identified by GCTA-COJO and confirmed to be in linkage equilibrium with rs13107325 using LDpair. These additional association signals represent new probable causative SNPs in addition to rs13107325. This study provides leads into how genetic variation in SLC39A8, a trace mineral transport gene, is linked to brain structure differences and may affect brain development and nervous system function.
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Affiliation(s)
- Evan R Hermann
- Department of Nutritional Sciences, Oklahoma State University, Stillwater, OK, United States
| | - Emily Chambers
- Department of Nutritional Sciences, Oklahoma State University, Stillwater, OK, United States
| | - Danielle N Davis
- Department of Nutritional Sciences, Oklahoma State University, Stillwater, OK, United States
| | - McKale R Montgomery
- Department of Nutritional Sciences, Oklahoma State University, Stillwater, OK, United States
| | - Dingbo Lin
- Department of Nutritional Sciences, Oklahoma State University, Stillwater, OK, United States
| | - Winyoo Chowanadisai
- Department of Nutritional Sciences, Oklahoma State University, Stillwater, OK, United States
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29
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Gill D, Georgakis MK, Walker VM, Schmidt AF, Gkatzionis A, Freitag DF, Finan C, Hingorani AD, Howson JM, Burgess S, Swerdlow DI, Davey Smith G, Holmes MV, Dichgans M, Scott RA, Zheng J, Psaty BM, Davies NM. Mendelian randomization for studying the effects of perturbing drug targets. Wellcome Open Res 2021; 6:16. [PMID: 33644404 PMCID: PMC7903200 DOI: 10.12688/wellcomeopenres.16544.2] [Citation(s) in RCA: 27] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 02/01/2021] [Indexed: 12/11/2022] Open
Abstract
Drugs whose targets have genetic evidence to support efficacy and safety are more likely to be approved after clinical development. In this paper, we provide an overview of how natural sequence variation in the genes that encode drug targets can be used in Mendelian randomization analyses to offer insight into mechanism-based efficacy and adverse effects. Large databases of summary level genetic association data are increasingly available and can be leveraged to identify and validate variants that serve as proxies for drug target perturbation. As with all empirical research, Mendelian randomization has limitations including genetic confounding, its consideration of lifelong effects, and issues related to heterogeneity across different tissues and populations. When appropriately applied, Mendelian randomization provides a useful empirical framework for using population level data to improve the success rates of the drug development pipeline.
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Affiliation(s)
- Dipender Gill
- Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London, London, UK
- Centre for Pharmacology and Therapeutics, Department of Medicine, Imperial College London, London, UK
- Novo Nordisk Research Centre, Oxford, UK
- Clinical Pharmacology and Therapeutics Section, Institute of Medical and Biomedical Education and Institute for Infection and Immunity, St George’s, University of London, London, UK
- Clinical Pharmacology Group, Pharmacy and Medicines Directorate, St George’s University Hospitals NHS Foundation Trust, London, UK
| | - Marios K. Georgakis
- Institute for Stroke and Dementia Research (ISD), University Hospital of Ludwig-Maximilians-University (LMU), Munich, Germany
| | - Venexia M. Walker
- Medical Research Council Integrative Epidemiology Unit, University of Bristol, Bristol, UK
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
- Department of Surgery, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - A. Floriaan Schmidt
- Institute of Cardiovascular Science, Faculty of Population Health, University College London, London, UK
- Department of Cardiology, Division Heart and Lungs, University Medical Center Utrecht, Utrecht, The Netherlands
| | - Apostolos Gkatzionis
- Medical Research Council Biostatistics Unit, University of Cambridge, Cambridge, UK
| | - Daniel F. Freitag
- Bayer Pharmaceuticals, Open Innovation & Digital Technologies, Wuppertal, Germany
| | - Chris Finan
- Institute of Cardiovascular Science, Faculty of Population Health, University College London, London, UK
- UCL British Heart Foundation Research Acceleratorversity College London, London, UK
- UCL Hospitals, NIHR Biomedical Research Centre, London, UK
| | - Aroon D. Hingorani
- Institute of Cardiovascular Science, Faculty of Population Health, University College London, London, UK
- UCL British Heart Foundation Research Acceleratorversity College London, London, UK
- UCL Hospitals, NIHR Biomedical Research Centre, London, UK
| | | | - Stephen Burgess
- Medical Research Council Biostatistics Unit, University of Cambridge, Cambridge, UK
- Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
| | - Daniel I. Swerdlow
- Institute of Cardiovascular Science, Faculty of Population Health, University College London, London, UK
| | - George Davey Smith
- Medical Research Council Integrative Epidemiology Unit, University of Bristol, Bristol, UK
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
- NIHR Bristol Biomedical Research Centre, University of Bristol, Bristol, UK
| | - Michael V. Holmes
- Medical Research Council Population Health Research Unit, University of Oxford, Oxford, UK
| | - Martin Dichgans
- Institute for Stroke and Dementia Research (ISD), University Hospital of Ludwig-Maximilians-University (LMU), Munich, Germany
- Munich Cluster for Systems Neurology (SyNergy), Munich, Germany
- German Centre for Neurodegenerative Diseases (DZNE), Munich, Germany
| | | | - Jie Zheng
- Medical Research Council Integrative Epidemiology Unit, University of Bristol, Bristol, UK
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
| | - Bruce M. Psaty
- Cardiovascular Health Research Unit, Departments of Medicine, Epidemiology and Health Services, University of Washington, Seattle, WA, USA
- Kaiser Permanente Washington Health Research Institute, Seattle, WA, USA
| | - Neil M. Davies
- Medical Research Council Integrative Epidemiology Unit, University of Bristol, Bristol, UK
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
- K.G. Jebsen Center for Genetic Epidemiology, Department of Public Health and Nursing, NTNU, Norwegian University of Science and Technology, Trondheim, Norway
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30
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Gill D, Georgakis MK, Walker VM, Schmidt AF, Gkatzionis A, Freitag DF, Finan C, Hingorani AD, Howson JM, Burgess S, Swerdlow DI, Davey Smith G, Holmes MV, Dichgans M, Scott RA, Zheng J, Psaty BM, Davies NM. Mendelian randomization for studying the effects of perturbing drug targets. Wellcome Open Res 2021; 6:16. [PMID: 33644404 PMCID: PMC7903200 DOI: 10.12688/wellcomeopenres.16544.1] [Citation(s) in RCA: 72] [Impact Index Per Article: 24.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 01/21/2021] [Indexed: 08/17/2023] Open
Abstract
Drugs whose targets have genetic evidence to support efficacy and safety are more likely to be approved after clinical development. In this paper, we provide an overview of how natural sequence variation in the genes that encode drug targets can be used in Mendelian randomization analyses to offer insight into mechanism-based efficacy and adverse effects. Large databases of summary level genetic association data are increasingly available and can be leveraged to identify and validate variants that serve as proxies for drug target perturbation. As with all empirical research, Mendelian randomization has limitations including genetic confounding, its consideration of lifelong effects, and issues related to heterogeneity across different tissues and populations. When appropriately applied, Mendelian randomization provides a useful empirical framework for using population level data to improve the success rates of the drug development pipeline.
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Affiliation(s)
- Dipender Gill
- Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London, London, UK
- Centre for Pharmacology and Therapeutics, Department of Medicine, Imperial College London, London, UK
- Novo Nordisk Research Centre, Oxford, UK
- Clinical Pharmacology and Therapeutics Section, Institute of Medical and Biomedical Education and Institute for Infection and Immunity, St George’s, University of London, London, UK
- Clinical Pharmacology Group, Pharmacy and Medicines Directorate, St George’s University Hospitals NHS Foundation Trust, London, UK
| | - Marios K. Georgakis
- Institute for Stroke and Dementia Research (ISD), University Hospital of Ludwig-Maximilians-University (LMU), Munich, Germany
| | - Venexia M. Walker
- Medical Research Council Integrative Epidemiology Unit, University of Bristol, Bristol, UK
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
- Department of Surgery, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - A. Floriaan Schmidt
- Institute of Cardiovascular Science, Faculty of Population Health, University College London, London, UK
- Department of Cardiology, Division Heart and Lungs, University Medical Center Utrecht, Utrecht, The Netherlands
| | - Apostolos Gkatzionis
- Medical Research Council Biostatistics Unit, University of Cambridge, Cambridge, UK
| | - Daniel F. Freitag
- Bayer Pharmaceuticals, Open Innovation & Digital Technologies, Wuppertal, Germany
| | - Chris Finan
- Institute of Cardiovascular Science, Faculty of Population Health, University College London, London, UK
- UCL British Heart Foundation Research Acceleratorversity College London, London, UK
- UCL Hospitals, NIHR Biomedical Research Centre, London, UK
| | - Aroon D. Hingorani
- Institute of Cardiovascular Science, Faculty of Population Health, University College London, London, UK
- UCL British Heart Foundation Research Acceleratorversity College London, London, UK
- UCL Hospitals, NIHR Biomedical Research Centre, London, UK
| | | | - Stephen Burgess
- Medical Research Council Biostatistics Unit, University of Cambridge, Cambridge, UK
- Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
| | - Daniel I. Swerdlow
- Institute of Cardiovascular Science, Faculty of Population Health, University College London, London, UK
| | - George Davey Smith
- Medical Research Council Integrative Epidemiology Unit, University of Bristol, Bristol, UK
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
- NIHR Bristol Biomedical Research Centre, University of Bristol, Bristol, UK
| | - Michael V. Holmes
- Medical Research Council Population Health Research Unit, University of Oxford, Oxford, UK
| | - Martin Dichgans
- Institute for Stroke and Dementia Research (ISD), University Hospital of Ludwig-Maximilians-University (LMU), Munich, Germany
- Munich Cluster for Systems Neurology (SyNergy), Munich, Germany
- German Centre for Neurodegenerative Diseases (DZNE), Munich, Germany
| | | | - Jie Zheng
- Medical Research Council Integrative Epidemiology Unit, University of Bristol, Bristol, UK
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
| | - Bruce M. Psaty
- Cardiovascular Health Research Unit, Departments of Medicine, Epidemiology and Health Services, University of Washington, Seattle, WA, USA
- Kaiser Permanente Washington Health Research Institute, Seattle, WA, USA
| | - Neil M. Davies
- Medical Research Council Integrative Epidemiology Unit, University of Bristol, Bristol, UK
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
- K.G. Jebsen Center for Genetic Epidemiology, Department of Public Health and Nursing, NTNU, Norwegian University of Science and Technology, Trondheim, Norway
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Abstract
The past few years have seen major advances in genome-wide association studies (GWAS) of CKD and kidney function-related traits in several areas: increases in sample size from >100,000 to >1 million, enabling the discovery of >250 associated genetic loci that are highly reproducible; the inclusion of participants not only of European but also of non-European ancestries; and the use of advanced computational methods to integrate additional genomic and other unbiased, high-dimensional data to characterize the underlying genetic architecture and prioritize potentially causal genes and variants. Together with other large-scale biobank and genetic association studies of complex traits, these GWAS of kidney function-related traits have also provided novel insight into the relationship of kidney function to other diseases with respect to their genetic associations, genetic correlation, and directional relationships. A number of studies also included functional experiments using model organisms or cell lines to validate prioritized potentially causal genes and/or variants. In this review article, we will summarize these recent GWAS of CKD and kidney function-related traits, explain approaches for downstream characterization of associated genetic loci and the value of such computational follow-up analyses, and discuss related challenges along with potential solutions to ultimately enable improved treatment and prevention of kidney diseases through genetics.
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Affiliation(s)
- Adrienne Tin
- Division of Nephrology, University of Mississippi Medical Center, Jackson, Mississippi
- MIND Center, University of Mississippi Medical Center, Jackson, Mississippi
| | - Anna Köttgen
- MIND Center, University of Mississippi Medical Center, Jackson, Mississippi
- Institute of Genetic Epidemiology, Faculty of Medicine and Medical Center–University of Freiburg, Freiburg, Germany
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32
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Dueñas HR, Seah C, Johnson JS, Huckins LM. Implicit bias of encoded variables: frameworks for addressing structured bias in EHR-GWAS data. Hum Mol Genet 2020; 29:R33-R41. [PMID: 32879975 PMCID: PMC7530523 DOI: 10.1093/hmg/ddaa192] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/01/2020] [Revised: 08/17/2020] [Accepted: 08/18/2020] [Indexed: 12/20/2022] Open
Abstract
The 'discovery' stage of genome-wide association studies required amassing large, homogeneous cohorts. In order to attain clinically useful insights, we must now consider the presentation of disease within our clinics and, by extension, within our medical records. Large-scale use of electronic health record (EHR) data can help to understand phenotypes in a scalable manner, incorporating lifelong and whole-phenome context. However, extending analyses to incorporate EHR and biobank-based analyses will require careful consideration of phenotype definition. Judgements and clinical decisions that occur 'outside' the system inevitably contain some degree of bias and become encoded in EHR data. Any algorithmic approach to phenotypic characterization that assumes non-biased variables will generate compounded biased conclusions. Here, we discuss and illustrate potential biases inherent within EHR analyses, how these may be compounded across time and suggest frameworks for large-scale phenotypic analysis to minimize and uncover encoded bias.
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Affiliation(s)
- Hillary R Dueñas
- Pamela Sklar Division of Psychiatric Genomics, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Carina Seah
- Pamela Sklar Division of Psychiatric Genomics, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Jessica S Johnson
- Pamela Sklar Division of Psychiatric Genomics, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Laura M Huckins
- Pamela Sklar Division of Psychiatric Genomics, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Department of Genetics and Genomics, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Icahn Institute for Genomics and Multiscale Biology, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Seaver Autism Center for Research and Treatment, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Mental Illness Research, Education and Clinical Centers, James J. Peters Department of Veterans Affairs Medical Center, Bronx, NY 10468, USA
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33
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Quan Y, Zhang QY, Lv BM, Xu RF, Zhang HY. Genome-wide pathogenesis interpretation using a heat diffusion-based systems genetics method and implications for gene function annotation. Mol Genet Genomic Med 2020; 8:e1456. [PMID: 32869547 PMCID: PMC7549611 DOI: 10.1002/mgg3.1456] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/19/2020] [Revised: 07/08/2020] [Accepted: 07/27/2020] [Indexed: 12/27/2022] Open
Abstract
Background Genetics is best dedicated to interpreting pathogenesis and revealing gene functions. The past decade has witnessed unprecedented progress in genetics, particularly in genome‐wide identification of disorder variants through Genome‐Wide Association Studies (GWAS) and Phenome‐Wide Association Studies (PheWAS). However, it is still a great challenge to use GWAS/PheWAS‐derived data to elucidate pathogenesis. Methods In this study, we used HotNet2, a heat diffusion‐based systems genetics algorithm, to calculate the networks for disease genes obtained from GWAS and PheWAS, with an attempt to get deeper insights into disease pathogenesis at a molecular level. Results Through HotNet2 calculation, significant networks for 202 (for GWAS) and 167 (for PheWAS) types of diseases were identified and evaluated, respectively. The GWAS‐derived disease networks exhibit a stronger biomedical relevance than PheWAS counterparts. Therefore, the GWAS‐derived networks were used for pathogenesis interpretation by integrating the accumulated biomedical information. As a result, the pathogenesis for 64 diseases was elucidated in terms of mutation‐caused abnormal transcriptional regulation, and 47 diseases were preliminarily interpreted in terms of mutation‐caused varied protein‐protein interactions. In addition, 3,802 genes (including 46 function‐unknown genes) were assigned with new functions by disease network information, some of which were validated through mice gene knockout experiments. Conclusions Systems genetics algorithm HotNet2 can efficiently establish genotype‐phenotype links at the level of biological networks. Compared with original GWAS/PheWAS results, HotNet2‐calculated disease‐gene associations have stronger biomedical significance, hence provide better interpretations for the pathogenesis of genome‐wide variants, and offer new insights into gene functions as well. These results are also helpful in drug development.
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Affiliation(s)
- Yuan Quan
- School of Computer Science and Technology, Harbin Institute of Technology, Shenzhen Graduate School, Shenzhen, China.,Hubei Key Laboratory of Agricultural Bioinformatics, College of Informatics, Huazhong Agricultural University, Wuhan, China
| | - Qing-Ye Zhang
- Hubei Key Laboratory of Agricultural Bioinformatics, College of Informatics, Huazhong Agricultural University, Wuhan, China
| | - Bo-Min Lv
- Hubei Key Laboratory of Agricultural Bioinformatics, College of Informatics, Huazhong Agricultural University, Wuhan, China
| | - Rui-Feng Xu
- School of Computer Science and Technology, Harbin Institute of Technology, Shenzhen Graduate School, Shenzhen, China
| | - Hong-Yu Zhang
- Hubei Key Laboratory of Agricultural Bioinformatics, College of Informatics, Huazhong Agricultural University, Wuhan, China
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34
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Rositch AF, Loffredo C, Bourlon MT, Pearlman PC, Adebamowo C. Creative Approaches to Global Cancer Research and Control. JCO Glob Oncol 2020; 6:4-7. [PMID: 32716656 PMCID: PMC7846070 DOI: 10.1200/go.20.00237] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/20/2022] Open
Affiliation(s)
- Anne F Rositch
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD
| | - Christopher Loffredo
- Department of Oncology, Lombardi Comprehensive Cancer Center, Georgetown University, Washington, DC
| | - Maria T Bourlon
- Hemato-Oncology Department, Instituto Nacional de Ciencias Médicas y Nutrición Salvador Zubirán, Mexico City, Mexico
| | - Paul C Pearlman
- National Cancer Institute Center for Global Health, Rockville, MD
| | - Clement Adebamowo
- Institute of Human Virology, Department of Epidemiology and Public Health, Marlene and Stewart Greenebaum Comprehensive Cancer Center, University of Maryland School of Medicine, Baltimore, MD.,Institute of Human Virology, Abuja, Nigeria.,Center for Bioethics and Research, Ibadan, Nigeria
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35
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Veatch OJ, Bauer CR, Keenan BT, Josyula NS, Mazzotti DR, Bagai K, Malow BA, Robishaw JD, Pack AI, Pendergrass SA. Characterization of genetic and phenotypic heterogeneity of obstructive sleep apnea using electronic health records. BMC Med Genomics 2020; 13:105. [PMID: 32711518 PMCID: PMC7382070 DOI: 10.1186/s12920-020-00755-4] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/12/2019] [Accepted: 07/13/2020] [Indexed: 12/22/2022] Open
Abstract
Background Obstructive sleep apnea (OSA) is defined by frequent episodes of reduced or complete cessation of airflow during sleep and is linked to negative health outcomes. Understanding the genetic factors influencing expression of OSA may lead to new treatment strategies. Electronic health records (EHRs) can be leveraged to both validate previously reported OSA-associated genomic variation and detect novel relationships between these variants and comorbidities. Methods We identified candidate single nucleotide polymorphisms (SNPs) via systematic literature review of existing research. Using datasets available at Geisinger (n = 39,407) and Vanderbilt University Medical Center (n = 24,084), we evaluated associations between 40 previously implicated SNPs and OSA diagnosis, defined using clinical codes. We also evaluated associations between these SNPs and OSA severity measures obtained from sleep reports at Geisinger (n = 6571). Finally, we used a phenome-wide association study approach to help reveal pleiotropic genetic effects between OSA candidate SNPs and other clinical codes and laboratory values available in the EHR. Results Most previously reported OSA candidate SNPs showed minimal to no evidence for associations with OSA diagnosis or severity in the EHR-derived datasets. Three SNPs in LEPR, MMP-9, and GABBR1 validated for an association with OSA diagnosis in European Americans; the SNP in GABBR1 was associated following meta-analysis of results from both clinical populations. The GABBR1 and LEPR SNPs, and one additional SNP, were associated with OSA severity measures in European Americans from Geisinger. Three additional candidate OSA SNPs were not associated with OSA-related traits but instead with hyperlipidemia and autoimmune diseases of the thyroid. Conclusions To our knowledge, this is one of the largest candidate gene studies and one of the first phenome-wide association studies of OSA genomic variation. Results validate genetic associates with OSA in the LEPR, MMP-9 and GABBR1 genes, but suggest that the majority of previously identified genetic associations with OSA may be false positives. Phenome-wide analyses provide evidence of mediated pleiotropy. Future well-powered genome-wide association analyses of OSA risk and severity across populations with diverse ancestral backgrounds are needed. The comprehensive nature of the analyses represents a platform for informing future work focused on understanding how genetic data can be useful to informing treatment of OSA and related comorbidities.
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Affiliation(s)
- Olivia J Veatch
- Division of Sleep Medicine/Department of Medicine, Perelman School of Medicine at the University of Pennsylvania, 125 S. 31st St, Office 2123, Philadelphia, PA, 19104, USA. .,Sleep Disorders Division/Department of Neurology, Vanderbilt University Medical Center, Nashville, TN, 37232, USA. .,Department of Psychiatry & Behavioral Sciences, University of Kansas Medical Center, Mail-Stop 4015, 3901 Rainbow Blvd., Kansas City, KS, 66160, USA.
| | | | - Brendan T Keenan
- Division of Sleep Medicine/Department of Medicine, Perelman School of Medicine at the University of Pennsylvania, 125 S. 31st St, Office 2123, Philadelphia, PA, 19104, USA
| | | | - Diego R Mazzotti
- Division of Sleep Medicine/Department of Medicine, Perelman School of Medicine at the University of Pennsylvania, 125 S. 31st St, Office 2123, Philadelphia, PA, 19104, USA
| | - Kanika Bagai
- Sleep Disorders Division/Department of Neurology, Vanderbilt University Medical Center, Nashville, TN, 37232, USA
| | - Beth A Malow
- Sleep Disorders Division/Department of Neurology, Vanderbilt University Medical Center, Nashville, TN, 37232, USA
| | - Janet D Robishaw
- Department of Biomedical Science, Charles E. Schmidt College of Medicine, Florida Atlantic University, Boca Raton, FL, 33431, USA
| | - Allan I Pack
- Division of Sleep Medicine/Department of Medicine, Perelman School of Medicine at the University of Pennsylvania, 125 S. 31st St, Office 2123, Philadelphia, PA, 19104, USA
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36
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Rao S, Lau A, So HC. Exploring Diseases/Traits and Blood Proteins Causally Related to Expression of ACE2, the Putative Receptor of SARS-CoV-2: A Mendelian Randomization Analysis Highlights Tentative Relevance of Diabetes-Related Traits. Diabetes Care 2020; 43:1416-1426. [PMID: 32430459 DOI: 10.2337/dc20-0643] [Citation(s) in RCA: 156] [Impact Index Per Article: 39.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/26/2020] [Accepted: 05/10/2020] [Indexed: 02/03/2023]
Abstract
OBJECTIVE COVID-19 has become a major public health problem. There is good evidence that ACE2 is a receptor for SARS-CoV-2, and high expression of ACE2 may increase susceptibility to infection. We aimed to explore risk factors affecting susceptibility to infection and prioritize drug repositioning candidates, based on Mendelian randomization (MR) studies on ACE2 lung expression. RESEARCH DESIGN AND METHODS We conducted a phenome-wide MR study to prioritize diseases/traits and blood proteins causally linked to ACE2 lung expression in GTEx. We also explored drug candidates whose targets overlapped with the top-ranked proteins in MR, as these drugs may alter ACE2 expression and may be clinically relevant. RESULTS The most consistent finding was tentative evidence of an association between diabetes-related traits and increased ACE2 expression. Based on one of the largest genome-wide association studies on type 2 diabetes mellitus (T2DM) to date (N = 898,130), T2DM was causally linked to raised ACE2 expression (P = 2.91E-03; MR-IVW). Significant associations (at nominal level; P < 0.05) with ACE2 expression were observed across multiple diabetes data sets and analytic methods for T1DM, T2DM, and related traits including early start of insulin. Other diseases/traits having nominal significant associations with increased expression included inflammatory bowel disease, (estrogen receptor-positive) breast cancer, lung cancer, asthma, smoking, and elevated alanine aminotransferase. We also identified drugs that may target the top-ranked proteins in MR, such as fostamatinib and zinc. CONCLUSIONS Our analysis suggested that diabetes and related traits may increase ACE2 expression, which may influence susceptibility to infection (or more severe infection). However, none of these findings withstood rigorous multiple testing corrections (at false discovery rate <0.05). Proteome-wide MR analyses might help uncover mechanisms underlying ACE2 expression and guide drug repositioning. Further studies are required to verify our findings.
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Affiliation(s)
- Shitao Rao
- School of Biomedical Sciences, The Chinese University of Hong Kong, Shatin, Hong Kong
| | - Alexandria Lau
- School of Biomedical Sciences, The Chinese University of Hong Kong, Shatin, Hong Kong
| | - Hon-Cheong So
- School of Biomedical Sciences, The Chinese University of Hong Kong, Shatin, Hong Kong .,Shenzhen Research Institute, The Chinese University of Hong Kong, Shenzhen, China.,KIZ-CUHK Joint Laboratory of Bioresources and Molecular Research of Common Diseases, Kunming Institute of Zoology and The Chinese University of Hong Kong, Shatin, Hong Kong.,Department of Psychiatry, The Chinese University of Hong Kong, Shatin, Hong Kong.,Margaret K.L. Cheung Research Centre for Management of Parkinsonism, The Chinese University of Hong Kong, Shatin, Hong Kong.,Brain and Mind Institute, The Chinese University of Hong Kong, Shatin, Hong Kong
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37
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Zong N, Sharma DK, Yu Y, Egan JB, Davila JI, Wang C, Jiang G. Developing a FHIR-based Framework for Phenome Wide Association Studies: A Case Study with A Pan-Cancer Cohort. AMIA JOINT SUMMITS ON TRANSLATIONAL SCIENCE PROCEEDINGS. AMIA JOINT SUMMITS ON TRANSLATIONAL SCIENCE 2020; 2020:750-759. [PMID: 32477698 PMCID: PMC7233075] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Subscribe] [Scholar Register] [Indexed: 06/11/2023]
Abstract
Phenome Wide Association Studies (PheWAS) enables phenome-wide scans to discover novel associations between genotype and clinical phenotypes via linking available genomic reports and large-scale Electronic Health Record (EHR). Data heterogeneity from different EHR systems and genetic reports has been a critical challenge that hinders meaningful validation. To address this, we propose an FHIR-based framework to model the PheWAS study in a standard manner. We developed an FHIR-based data model profile to enable the standard representation of data elements from genetic reports and EHR data that are used in the PheWAS study. As a proof-of-concept, we implemented the proposed method using a cohort of 1,595 pan-cancer patients with genetic reports from Foundation Medicine as well as the corresponding lab tests and diagnosis from Mayo EHRs. A PheWAS study is conducted and 81 significant genotype-phenotype associations are identified, in which 36 significant associations for cancers are validated based on a literature review.
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Affiliation(s)
- Nansu Zong
- Department of Health Sciences Research, Mayo Clinic, Rochester, MN, USA
| | - Deepak K Sharma
- Department of Health Sciences Research, Mayo Clinic, Rochester, MN, USA
| | - Yue Yu
- Department of Health Sciences Research, Mayo Clinic, Rochester, MN, USA
| | - Jan B Egan
- Center for Individualized Medicine, Mayo Clinic, Rochester, MN
| | - Jaime I Davila
- Department of Health Sciences Research, Mayo Clinic, Rochester, MN, USA
| | - Chen Wang
- Department of Health Sciences Research, Mayo Clinic, Rochester, MN, USA
| | - Guoqian Jiang
- Department of Health Sciences Research, Mayo Clinic, Rochester, MN, USA
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38
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Gysi DM, Nowick K. Construction, comparison and evolution of networks in life sciences and other disciplines. J R Soc Interface 2020; 17:20190610. [PMID: 32370689 PMCID: PMC7276545 DOI: 10.1098/rsif.2019.0610] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/03/2019] [Accepted: 04/09/2020] [Indexed: 12/12/2022] Open
Abstract
Network approaches have become pervasive in many research fields. They allow for a more comprehensive understanding of complex relationships between entities as well as their group-level properties and dynamics. Many networks change over time, be it within seconds or millions of years, depending on the nature of the network. Our focus will be on comparative network analyses in life sciences, where deciphering temporal network changes is a core interest of molecular, ecological, neuropsychological and evolutionary biologists. Further, we will take a journey through different disciplines, such as social sciences, finance and computational gastronomy, to present commonalities and differences in how networks change and can be analysed. Finally, we envision how borrowing ideas from these disciplines could enrich the future of life science research.
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Affiliation(s)
- Deisy Morselli Gysi
- Department of Computer Science, Interdisciplinary Center of Bioinformatics, University of Leipzig, 04109 Leipzig, Germany
- Swarm Intelligence and Complex Systems Group, Faculty of Mathematics and Computer Science, University of Leipzig, 04109 Leipzig, Germany
- Center for Complex Networks Research, Northeastern University, 177 Huntington Avenue, Boston, MA 02115, USA
| | - Katja Nowick
- Human Biology Group, Institute for Biology, Faculty of Biology, Chemistry, Pharmacy, Freie Universität Berlin, Königin-Luise-Straβe 1-3, 14195 Berlin, Germany
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39
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An atlas of evidence-based phenotypic associations across the mouse phenome. Sci Rep 2020; 10:3957. [PMID: 32127602 PMCID: PMC7054260 DOI: 10.1038/s41598-020-60891-w] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/28/2019] [Accepted: 02/17/2020] [Indexed: 01/18/2023] Open
Abstract
To date, reliable relationships between mammalian phenotypes, based on diagnostic test measurements, have not been reported on a large scale. The purpose of this study was to present a large mouse phenotype-phenotype relationships dataset as a reference resource, alongside detailed evaluation of the resource. We used bias-minimized comprehensive mouse phenotype data and applied association rule mining to a dataset consisting of only binary (normal and abnormal phenotypes) data to determine relationships among phenotypes. We present 3,686 evidence-based significant associations, comprising 345 phenotypes covering 60 biological systems (functions), and evaluate their characteristics in detail. To evaluate the relationships, we defined a set of phenotype-phenotype association pairs (PPAPs) as a module of phenotypic expression for each of the 345 phenotypes. By analyzing each PPAP, we identified phenotype sub-networks consisting of the largest numbers of phenotypes and distinct biological systems. Furthermore, using hierarchical clustering based on phenotype similarities among the 345 PPAPs, we identified seven community types within a putative phenome-wide association network. Moreover, to promote leverage of these data, we developed and published web-application tools. These mouse phenome-wide phenotype-phenotype association data reveal general principles of relationships among mammalian phenotypes and provide a reference resource for biomedical analyses.
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Pendergrass SA, Buyske S, Jeff JM, Frase A, Dudek S, Bradford Y, Ambite JL, Avery CL, Buzkova P, Deelman E, Fesinmeyer MD, Haiman C, Heiss G, Hindorff LA, Hsu CN, Jackson RD, Lin Y, Le Marchand L, Matise TC, Monroe KR, Moreland L, North KE, Park SL, Reiner A, Wallace R, Wilkens LR, Kooperberg C, Ritchie MD, Crawford DC. A phenome-wide association study (PheWAS) in the Population Architecture using Genomics and Epidemiology (PAGE) study reveals potential pleiotropy in African Americans. PLoS One 2019; 14:e0226771. [PMID: 31891604 PMCID: PMC6938343 DOI: 10.1371/journal.pone.0226771] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/02/2019] [Accepted: 12/03/2019] [Indexed: 12/11/2022] Open
Abstract
We performed a hypothesis-generating phenome-wide association study (PheWAS) to identify and characterize cross-phenotype associations, where one SNP is associated with two or more phenotypes, between thousands of genetic variants assayed on the Metabochip and hundreds of phenotypes in 5,897 African Americans as part of the Population Architecture using Genomics and Epidemiology (PAGE) I study. The PAGE I study was a National Human Genome Research Institute-funded collaboration of four study sites accessing diverse epidemiologic studies genotyped on the Metabochip, a custom genotyping chip that has dense coverage of regions in the genome previously associated with cardio-metabolic traits and outcomes in mostly European-descent populations. Here we focus on identifying novel phenome-genome relationships, where SNPs are associated with more than one phenotype. To do this, we performed a PheWAS, testing each SNP on the Metabochip for an association with up to 273 phenotypes in the participating PAGE I study sites. We identified 133 putative pleiotropic variants, defined as SNPs associated at an empirically derived p-value threshold of p<0.01 in two or more PAGE study sites for two or more phenotype classes. We further annotated these PheWAS-identified variants using publicly available functional data and local genetic ancestry. Amongst our novel findings is SPARC rs4958487, associated with increased glucose levels and hypertension. SPARC has been implicated in the pathogenesis of diabetes and is also known to have a potential role in fibrosis, a common consequence of multiple conditions including hypertension. The SPARC example and others highlight the potential that PheWAS approaches have in improving our understanding of complex disease architecture by identifying novel relationships between genetic variants and an array of common human phenotypes.
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Affiliation(s)
| | - Steven Buyske
- Department of Statistics, Rutgers University, Piscataway, New Jersey, United States of America
- Department of Genetics, Rutgers University, Piscataway, New Jersey, United States of America
| | - Janina M. Jeff
- Illumina, Inc., San Diego, California, United States of America
| | - Alex Frase
- Department of Genetics, Institute for Biomedical Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, United States of America
| | - Scott Dudek
- Department of Genetics, Institute for Biomedical Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, United States of America
| | - Yuki Bradford
- Department of Genetics, Institute for Biomedical Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, United States of America
| | - Jose-Luis Ambite
- Information Sciences Institute; University of Southern California, Marina del Rey, California, United States of America
| | - Christy L. Avery
- Department of Epidemiology, University of North Carolina, Chapel Hill, North Carolina, United States of America
| | - Petra Buzkova
- Department of Biostatistics, University of Washington, Seattle, Washington, United States of America
| | - Ewa Deelman
- Information Sciences Institute; University of Southern California, Marina del Rey, California, United States of America
| | | | - Christopher Haiman
- Department of Preventive Medicine, Keck School of Medicine, University of Southern California/Norris Comprehensive Cancer Center, Los Angeles, California, United States of America
| | - Gerardo Heiss
- Department of Epidemiology, University of North Carolina, Chapel Hill, North Carolina, United States of America
- Carolina Center for Genome Sciences, University of North Carolina, Chapel Hill, North Carolina, United States of America
| | - Lucia A. Hindorff
- National Human Genome Research Institute, National Institutes of Health, Bethesda, Maryland, United States of America
| | - Chun-Nan Hsu
- Center for Research in Biological Systems, Department of Neurosciences, University of California, San Diego, La Jolla, California, United States of America
| | | | - Yi Lin
- Division of Public Health Sciences, Fred Hutchinson Cancer Research Center, Seattle, Washington, United States of America
| | - Loic Le Marchand
- Epidemiology Program, University of Hawaii Cancer Center, Honolulu, Hawaii, United States of America
| | - Tara C. Matise
- Department of Genetics, Rutgers University, Piscataway, New Jersey, United States of America
| | - Kristine R. Monroe
- Department of Preventive Medicine, Keck School of Medicine, University of Southern California/Norris Comprehensive Cancer Center, Los Angeles, California, United States of America
| | - Larry Moreland
- University of Pittsburgh, Pittsburgh, Pennsylvania, United States of America
| | - Kari E. North
- Department of Epidemiology, University of North Carolina, Chapel Hill, North Carolina, United States of America
- Carolina Center for Genome Sciences, University of North Carolina, Chapel Hill, North Carolina, United States of America
| | - Sungshim L. Park
- Department of Preventive Medicine, Keck School of Medicine, University of Southern California/Norris Comprehensive Cancer Center, Los Angeles, California, United States of America
| | - Alex Reiner
- Department of Epidemiology, University of Washington, Seattle, Washington, United States of America
| | - Robert Wallace
- Departments of Epidemiology and Internal Medicine, University of Iowa, Iowa City, Iowa, United States of America
| | - Lynne R. Wilkens
- Epidemiology Program, University of Hawaii Cancer Center, Honolulu, Hawaii, United States of America
| | - Charles Kooperberg
- Division of Public Health Sciences, Fred Hutchinson Cancer Research Center, Seattle, Washington, United States of America
| | - Marylyn D. Ritchie
- Department of Genetics, Institute for Biomedical Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, United States of America
| | - Dana C. Crawford
- Cleveland Institute for Computational Biology, Cleveland, Ohio, United States of America
- Departments of Population and Quantitative Health Sciences and Genetics and Genome Sciences, Case Western Reserve University, Cleveland, Ohio, United States of America
- * E-mail:
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41
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Phenotype Algorithm based Big Data Analytics for Cancer Diagnose. J Med Syst 2019; 43:264. [PMID: 31270694 DOI: 10.1007/s10916-019-1409-z] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2019] [Accepted: 06/26/2019] [Indexed: 10/26/2022]
Abstract
Nowadays, Cancer diagnosis is one of the major challenging characteristics for treating cancer. The reality of cancer patients rely on the diagnosis of cancer at the early stages (either in stage 1 or stage 2). If the cancer is diagnosed in stage 3 or later stages means the changes of survival of the patient will become more critical. Normally, single patient records will generate a huge amount of data if the data could be manage and analyze means to solve many problems for identifying the patterns it will leads to diagnose the cancer. Recent work several machine learning algorithms are introduced for the classification of cancer. However still the classification accuracy of machine learning algorithms are reduced because of huge number of samples. So the proposed work introduces a new Hadoop Distributed File System (HDFS) is focused in this work. In this paper, the proposed phenotype techniques are used which handle and classifies the raw EHR (Electronic Health Record) and EMR (Electronic Medical Record). It is based on the HDFS and Two-Phase Map Reduce. Phenotype algorithm uses NLP (National Language Processing) tool which will analyze and classify the cancer patient data like gene mapping, age related data, image and ultrasonic frequency processing, identification and analysis of irregularities, disease and personal histories. In this paper, the three factorized model is used which calculates the mean score values. The values are calculated by disease stage, pain status, etc. This paper focuses big data analytics for cancer diagnosis and the simulation results shows the proposed system produces the highest performance.
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42
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Systematic analysis of genes and diseases using PheWAS-Associated networks. Comput Biol Med 2019; 109:311-321. [DOI: 10.1016/j.compbiomed.2019.04.037] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/04/2019] [Revised: 04/28/2019] [Accepted: 04/28/2019] [Indexed: 02/08/2023]
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43
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Weighill D, Jones P, Bleker C, Ranjan P, Shah M, Zhao N, Martin M, DiFazio S, Macaya-Sanz D, Schmutz J, Sreedasyam A, Tschaplinski T, Tuskan G, Jacobson D. Multi-Phenotype Association Decomposition: Unraveling Complex Gene-Phenotype Relationships. Front Genet 2019; 10:417. [PMID: 31134130 PMCID: PMC6522845 DOI: 10.3389/fgene.2019.00417] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/19/2018] [Accepted: 04/16/2019] [Indexed: 01/18/2023] Open
Abstract
Various patterns of multi-phenotype associations (MPAs) exist in the results of Genome Wide Association Studies (GWAS) involving different topologies of single nucleotide polymorphism (SNP)-phenotype associations. These can provide interesting information about the different impacts of a gene on closely related phenotypes or disparate phenotypes (pleiotropy). In this work we present MPA Decomposition, a new network-based approach which decomposes the results of a multi-phenotype GWAS study into three bipartite networks, which, when used together, unravel the multi-phenotype signatures of genes on a genome-wide scale. The decomposition involves the construction of a phenotype powerset space, and subsequent mapping of genes into this new space. Clustering of genes in this powerset space groups genes based on their detailed MPA signatures. We show that this method allows us to find multiple different MPA and pleiotropic signatures within individual genes and to classify and cluster genes based on these SNP-phenotype association topologies. We demonstrate the use of this approach on a GWAS analysis of a large population of 882 Populus trichocarpa genotypes using untargeted metabolomics phenotypes. This method should prove invaluable in the interpretation of large GWAS datasets and aid in future synthetic biology efforts designed to optimize phenotypes of interest.
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Affiliation(s)
- Deborah Weighill
- The Bredesen Center for Interdisciplinary Research and Graduate Education, University of Tennessee, Knoxville, TN, United States.,Biosciences Division, Oak Ridge National Laboratory, Oak Ridge, TN, United States
| | - Piet Jones
- The Bredesen Center for Interdisciplinary Research and Graduate Education, University of Tennessee, Knoxville, TN, United States.,Biosciences Division, Oak Ridge National Laboratory, Oak Ridge, TN, United States
| | - Carissa Bleker
- The Bredesen Center for Interdisciplinary Research and Graduate Education, University of Tennessee, Knoxville, TN, United States.,Biosciences Division, Oak Ridge National Laboratory, Oak Ridge, TN, United States
| | - Priya Ranjan
- Biosciences Division, Oak Ridge National Laboratory, Oak Ridge, TN, United States.,Department of Plant Sciences, The University of Tennessee Institute of Agriculture, University of Tennessee, Knoxville, TN, United States
| | - Manesh Shah
- Biosciences Division, Oak Ridge National Laboratory, Oak Ridge, TN, United States
| | - Nan Zhao
- Department of Plant Sciences, The University of Tennessee Institute of Agriculture, University of Tennessee, Knoxville, TN, United States
| | - Madhavi Martin
- Biosciences Division, Oak Ridge National Laboratory, Oak Ridge, TN, United States
| | - Stephen DiFazio
- Department of Biology, West Virginia University, Morgantown, WV, United States
| | - David Macaya-Sanz
- Department of Biology, West Virginia University, Morgantown, WV, United States
| | - Jeremy Schmutz
- Department of Energy Joint Genome Institute, Walnut Creek, CA, United States.,HudsonAlpha Institute for Biotechnology, Huntsville, AL, United States
| | | | - Timothy Tschaplinski
- Biosciences Division, Oak Ridge National Laboratory, Oak Ridge, TN, United States
| | - Gerald Tuskan
- Biosciences Division, Oak Ridge National Laboratory, Oak Ridge, TN, United States
| | - Daniel Jacobson
- The Bredesen Center for Interdisciplinary Research and Graduate Education, University of Tennessee, Knoxville, TN, United States.,Biosciences Division, Oak Ridge National Laboratory, Oak Ridge, TN, United States
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44
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Mir RR, Reynolds M, Pinto F, Khan MA, Bhat MA. High-throughput phenotyping for crop improvement in the genomics era. PLANT SCIENCE : AN INTERNATIONAL JOURNAL OF EXPERIMENTAL PLANT BIOLOGY 2019; 282:60-72. [PMID: 31003612 DOI: 10.1016/j.plantsci.2019.01.007] [Citation(s) in RCA: 94] [Impact Index Per Article: 18.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/18/2018] [Revised: 12/10/2018] [Accepted: 01/09/2019] [Indexed: 05/24/2023]
Abstract
Tremendous progress has been made with continually expanding genomics technologies to unravel and understand crop genomes. However, the impact of genomics data on crop improvement is still far from satisfactory, in large part due to a lack of effective phenotypic data; our capacity to collect useful high quality phenotypic data lags behind the current capacity to generate high-throughput genomics data. Thus, the research bottleneck in plant sciences is shifting from genotyping to phenotyping. This article review the current status of efforts made in the last decade to systematically collect phenotypic data to alleviate this 'phenomics bottlenecks' by recording trait data through sophisticated non-invasive imaging, spectroscopy, image analysis, robotics, high-performance computing facilities and phenomics databases. These modern phenomics platforms and tools aim to record data on traits like plant development, architecture, plant photosynthesis, growth or biomass productivity, on hundreds to thousands of plants in a single day, as a phenomics revolution. It is believed that this revolution will provide plant scientists with the knowledge and tools necessary for unlocking information coded in plant genomes. Efforts have been also made to present the advances made in the last 10 years in phenomics platforms and their use in generating phenotypic data on different traits in several major crops including rice, wheat, barley, and maize. The article also highlights the need for phenomics databases and phenotypic data sharing for crop improvement. The phenomics data generated has been used to identify genes/QTL through QTL mapping, association mapping and genome-wide association studies (GWAS) for genomics-assisted breeding (GAB) for crop improvement.
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Affiliation(s)
- Reyazul Rouf Mir
- Division of Genetics & Plant Breeding, Faculty of Agriculture (FoA), Sher-e-Kashmir University of Agricultural Sciences and Technology of Kashmir (SKUAST-K), Wadura Campus, Sopore-193201, Kashmir, India.
| | - Mathew Reynolds
- Global Wheat Program, International Maize and Wheat Improvement Centre (CIMMYT), Mexico, D.F., Mexico
| | - Francisco Pinto
- Global Wheat Program, International Maize and Wheat Improvement Centre (CIMMYT), Mexico, D.F., Mexico
| | - Mohd Anwar Khan
- Division of Genetics & Plant Breeding, Faculty of Agriculture (FoA), Sher-e-Kashmir University of Agricultural Sciences and Technology of Kashmir (SKUAST-K), Wadura Campus, Sopore-193201, Kashmir, India
| | - Mohd Ashraf Bhat
- Division of Genetics & Plant Breeding, Faculty of Agriculture (FoA), Sher-e-Kashmir University of Agricultural Sciences and Technology of Kashmir (SKUAST-K), Wadura Campus, Sopore-193201, Kashmir, India
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45
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Trochet H, Pirinen M, Band G, Jostins L, McVean G, Spencer CCA. Bayesian meta-analysis across genome-wide association studies of diverse phenotypes. Genet Epidemiol 2019; 43:532-547. [PMID: 30920090 DOI: 10.1002/gepi.22202] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/27/2018] [Revised: 03/01/2019] [Accepted: 03/05/2019] [Indexed: 11/09/2022]
Abstract
Genome-wide association studies (GWAS) are a powerful tool for understanding the genetic basis of diseases and traits, but most studies have been conducted in isolation, with a focus on either a single or a set of closely related phenotypes. We describe MetABF, a simple Bayesian framework for performing integrative meta-analysis across multiple GWAS using summary statistics. The approach is applicable across a wide range of study designs and can increase the power by 50% compared with standard frequentist tests when only a subset of studies have a true effect. We demonstrate its utility in a meta-analysis of 20 diverse GWAS which were part of the Wellcome Trust Case Control Consortium 2. The novelty of the approach is its ability to explore, and assess the evidence for a range of possible true patterns of association across studies in a computationally efficient framework.
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Affiliation(s)
- Holly Trochet
- Wellcome Trust Centre for Human Genetics, University of Oxford, Oxford, UK.,Institut de Cardiologie de Montréal (Centre de Recherche), Université de Montréal, Montréal, Québec, Canada
| | - Matti Pirinen
- Institute for Molecular Medicine Finland (FIMM), University of Helsinki, Helsinki, Finland.,Department of Mathematics and Statistics, Helsinki Institute for Information Technology HIIT, University of Helsinki, Helsinki, Finland.,Department of Public Health, University of Helsinki, Helsinki, Finland
| | - Gavin Band
- Wellcome Trust Centre for Human Genetics, University of Oxford, Oxford, UK
| | - Luke Jostins
- Wellcome Trust Centre for Human Genetics, University of Oxford, Oxford, UK.,Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, University of Oxford, Oxford, UK.,Kennedy Institute of Rheumatology, University of Oxford, Oxford, UK.,Christ Church, University of Oxford, Oxford, UK
| | - Gilean McVean
- Wellcome Trust Centre for Human Genetics, University of Oxford, Oxford, UK.,Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, University of Oxford, Oxford, UK
| | - Chris C A Spencer
- Wellcome Trust Centre for Human Genetics, University of Oxford, Oxford, UK
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46
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Zhao X, Geng X, Srinivasasainagendra V, Chaudhary N, Judd S, Wadley V, Gutiérrez OM, Wang H, Lange EM, Lange LA, Woo D, Unverzagt FW, Safford M, Cushman M, Limdi N, Quarells R, Arnett DK, Irvin MR, Zhi D. A PheWAS study of a large observational epidemiological cohort of African Americans from the REGARDS study. BMC Med Genomics 2019; 12:26. [PMID: 30704471 PMCID: PMC6357353 DOI: 10.1186/s12920-018-0462-7] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/05/2023] Open
Abstract
BACKGROUND Cardiovascular disease, diabetes, and kidney disease are among the leading causes of death and disability worldwide. However, knowledge of genetic determinants of those diseases in African Americans remains limited. RESULTS In our study, associations between 4956 GWAS catalog reported SNPs and 67 traits were examined among 7726 African Americans from the REasons for Geographic and Racial Differences in Stroke (REGARDS) study, which is focused on identifying factors that increase stroke risk. The prevalent and incident phenotypes studied included inflammation, kidney traits, cardiovascular traits and cognition. Our results validated 29 known associations, of which eight associations were reported for the first time in African Americans. CONCLUSION Our cross-racial validation of GWAS findings provide additional evidence for the important roles of these loci in the disease process and may help identify genes especially important for future functional validation.
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Affiliation(s)
- Xueyan Zhao
- Department of Biostatistics, University of Alabama at Birmingham, Birmingham, AL 35233 USA
| | - Xin Geng
- BGI-Shenzhen, Shenzhen, 518083 China
- School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, TX 77030 USA
| | | | - Ninad Chaudhary
- Department of Epidemiology, University of Alabama at Birmingham, Birmingham, AL 35233 USA
| | - Suzanne Judd
- Department of Biostatistics, University of Alabama at Birmingham, Birmingham, AL 35233 USA
| | - Virginia Wadley
- Department of Medicine, University of Alabama at Birmingham, Birmingham, AL 35233 USA
| | - Orlando M. Gutiérrez
- Department of Epidemiology, University of Alabama at Birmingham, Birmingham, AL 35233 USA
- Department of Medicine, University of Alabama at Birmingham, Birmingham, AL 35233 USA
| | - Henry Wang
- Department of Emergency Medicine, University of Alabama at Birmingham, Birmingham, AL 35233 USA
| | - Ethan M. Lange
- Division of Biomedical Informatics and Personalized Medicine, Department of Medicine, University of Colorado Anschutz Medical Campus, Aurora, CO 80045 USA
| | - Leslie A. Lange
- Division of Biomedical Informatics and Personalized Medicine, Department of Medicine, University of Colorado Anschutz Medical Campus, Aurora, CO 80045 USA
| | - Daniel Woo
- Department of Neurology and Rehabilitation Medicine, University of Cincinnati College of Medicine, Cincinnati, OH 45267 USA
| | - Frederick W. Unverzagt
- Department of Psychiatry, Indiana University School of Medicine, Indianapolis, IN 46202 USA
| | - Monika Safford
- Division of General Internal Medicine, Weill Cornell Medical College, Cornell University, New York, NY 10065 USA
| | - Mary Cushman
- Department of Medicine and Pathology, Larner College of Medicine at the University of Vermont, Burlington, VT 05405 USA
| | - Nita Limdi
- Department of Neurology, University of Alabama at Birmingham, Birmingham, AL 35294 USA
| | - Rakale Quarells
- Cardiovascular Research Institute, Department of Community Health and Preventive Medicine, Morehouse School of Medicine, Atlanta, GA 30310 USA
| | - Donna K. Arnett
- College of Public Health, University of Kentucky, Lexington, KY 40506 USA
| | - Marguerite R. Irvin
- Department of Epidemiology, University of Alabama at Birmingham, Birmingham, AL 35233 USA
| | - Degui Zhi
- School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, TX 77030 USA
- School of Public Health, The University of Texas Health Science Center at Houston, Houston, TX 77030 USA
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48
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Slavkin HC. From high definition precision healthcare to precision public oral health: opportunities and challenges. J Public Health Dent 2018; 80 Suppl 1:S23-S30. [PMID: 30516837 DOI: 10.1111/jphd.12296] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/24/2018] [Revised: 10/08/2018] [Accepted: 10/10/2018] [Indexed: 01/05/2023]
Abstract
In anticipation of a major transformation in healthcare, this review provides highlights that anticipate the near future for oral public health (and beyond). Personalized or precision healthcare reflects the expectation that advances in genomics, imaging, and other domains will extend our risk assessment, diagnostic, and prognostic capabilities, and enables more effective prevention and therapeutic options for all Americans. Meanwhile, the current healthcare system does not meet cost, access, or quality criteria for all Americans. It is now an imperative that the success of "smart," quality, and cost-effective high definition precision healthcare requires a public health perspective for several reasons: a) to enhance generalizability, b) to assess methods of implementation, and c) to focus on both risk and prevention in large and small populations, thereby providing a balance between the generation of long-term knowledge and short-term health gains. Sensitivity and resolution, reasonable cost, access to all Americans, coordinated comprehensive care, and advances in whole genome sequencing (WGS) and big data analyses, coupled to other advances in biotechnology and digital/artificial intelligence/machine learning devices, and the behavioral, social, and environmental sciences, offer remarkable opportunities to improve the health and wellness of the American people [genotype + phenotype + environment + behavior = high definition healthcare]. The opportunity is to significantly improve the well-being and life expectancy of all people across the lifespan including the least-advantaged people in our society and potentially increase access, reduce the national costs, and improve health outcomes.
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Affiliation(s)
- Harold C Slavkin
- Center for Craniofacial Molecular Biology, Division of Biomedical Sciences, Ostrow School of Dentistry, University of Southern California, CA, Los Angeles, USA.,Previous Director of the National Institute of Dental and Craniofacial Research (NIDCR), National Institutes of Health (NIH), MD, Bethesda, USA
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49
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Genomic and Phenomic Research in the 21st Century. Trends Genet 2018; 35:29-41. [PMID: 30342790 DOI: 10.1016/j.tig.2018.09.007] [Citation(s) in RCA: 15] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/23/2018] [Revised: 09/24/2018] [Accepted: 09/25/2018] [Indexed: 02/06/2023]
Abstract
The field of human genomics has changed dramatically over time. Initial genomic studies were predominantly restricted to rare disorders in small families. Over the past decade, researchers changed course from family-based studies and instead focused on common diseases and traits in populations of unrelated individuals. With further advancements in biobanking, computer science, electronic health record (EHR) data, and more affordable high-throughput genomics, we are experiencing a new paradigm in human genomic research. Rapidly changing technologies and resources now make it possible to study thousands of diseases simultaneously at the genomic level. This review will focus on these advancements as scientists begin to incorporate phenome-wide strategies in human genomic research to understand the etiology of human diseases and develop new drugs to treat them.
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50
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Prokop JW, May T, Strong K, Bilinovich SM, Bupp C, Rajasekaran S, Worthey EA, Lazar J. Genome sequencing in the clinic: the past, present, and future of genomic medicine. Physiol Genomics 2018; 50:563-579. [PMID: 29727589 PMCID: PMC6139636 DOI: 10.1152/physiolgenomics.00046.2018] [Citation(s) in RCA: 49] [Impact Index Per Article: 8.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022] Open
Abstract
Genomic sequencing has undergone massive expansion in the past 10 yr, from a rarely used research tool into an approach that has broad applications in a clinical setting. From rare disease to cancer, genomics is transforming our knowledge of biology. The transition from targeted gene sequencing, to whole exome sequencing, to whole genome sequencing has only been made possible due to rapid advancements in technologies and informatics that have plummeted the cost per base of DNA sequencing and analysis. The tools of genomics have resolved the etiology of disease for previously undiagnosable conditions, identified cancer driver gene variants, and have impacted the understanding of pathophysiology for many diseases. However, this expansion of use has also highlighted research's current voids in knowledge. The lack of precise animal models for gene-to-function association, lack of tools for analysis of genomic structural changes, skew in populations used for genetic studies, publication biases, and the "Unknown Proteome" all contribute to voids needing filled for genomics to work in a fast-paced clinical setting. The future will hold the tools to fill in these voids, with new data sets and the continual development of new technologies allowing for expansion of genomic medicine, ushering in the days to come for precision medicine. In this review we highlight these and other points in hopes of advancing and guiding precision medicine into the future for optimal success.
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Affiliation(s)
- Jeremy W Prokop
- HudsonAlpha Institute for Biotechnology , Huntsville, Alabama
- Department of Pediatrics and Human Development, Michigan State University , East Lansing, Michigan
- Department of Pharmacology and Toxicology, Michigan State University , East Lansing, Michigan
| | - Thomas May
- HudsonAlpha Institute for Biotechnology , Huntsville, Alabama
- Institute for Health and Aging, University of California San Francisco , San Francisco, California
- Elson S. Floyd College of Medicine, Washington State University , Spokane, Washington
| | - Kim Strong
- HudsonAlpha Institute for Biotechnology , Huntsville, Alabama
| | - Stephanie M Bilinovich
- Department of Pediatrics and Human Development, Michigan State University , East Lansing, Michigan
| | - Caleb Bupp
- Department of Pediatrics and Human Development, Michigan State University , East Lansing, Michigan
- Department of Genetics, Helen DeVos Children's Hospital, Spectrum Health, Grand Rapids, Michigan
| | - Surender Rajasekaran
- Department of Pediatrics and Human Development, Michigan State University , East Lansing, Michigan
- Department of Pediatric Critical Care Medicine, Helen DeVos Children's Hospital, Spectrum Health, Grand Rapids, Michigan
| | | | - Jozef Lazar
- HudsonAlpha Institute for Biotechnology , Huntsville, Alabama
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