201
|
Roden DM. Phenome-wide association studies: a new method for functional genomics in humans. J Physiol 2017; 595:4109-4115. [PMID: 28229460 PMCID: PMC5471509 DOI: 10.1113/jp273122] [Citation(s) in RCA: 32] [Impact Index Per Article: 4.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/01/2016] [Accepted: 02/01/2017] [Indexed: 01/08/2023] Open
Abstract
In experimental physiological research, a common study design for examining the functional role of a gene or a genetic variant is to introduce that genetic variant into a model organism (such as yeast or mouse) and then to search for phenotypic consequences. The development of DNA biobanks linked to dense phenotypic information enables such an experiment to be applied to human subjects in the form of a phenome-wide association study (PheWAS). The PheWAS paradigm takes advantage of a curated medical phenome, often derived from electronic health records, to search for associations between 'input functions' and phenotypes in an unbiased fashion. The most commonly studied input function to date has been single nucleotide polymorphisms (SNPs), but other inputs, such as sets of SNPs or a disease or drug exposure, are now being explored to probe the genetic and phenotypic architecture of human traits. Potential outcomes of these approaches include defining subsets of complex diseases (that can then be targeted by specific therapies) and drug repurposing.
Collapse
Affiliation(s)
- Dan M. Roden
- Departments of Medicine, Pharmacology and Biomedical InformaticsVanderbilt University Medical CenterNashvilleTNUSA
| |
Collapse
|
202
|
Roden DM. Reprint of Editiorial Commentary: Genomics and drug discovery: The next frontier in precision medicine. Trends Cardiovasc Med 2017; 27:360-362. [PMID: 28601251 DOI: 10.1016/j.tcm.2017.05.008] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Affiliation(s)
- Dan M Roden
- Departments of Medicine, Pharmacology, and Biomedical Informatics, Vanderbilt University Medical Center, 2215B Garland Ave, 1285 MRBIV, Nashville, TN 37232-0575.
| |
Collapse
|
203
|
Merkel PA, Xie G, Monach PA, Ji X, Ciavatta DJ, Byun J, Pinder BD, Zhao A, Zhang J, Tadesse Y, Qian D, Weirauch M, Nair R, Tsoi A, Pagnoux C, Carette S, Chung S, Cuthbertson D, Davis JC, Dellaripa PF, Forbess L, Gewurz-Singer O, Hoffman GS, Khalidi N, Koening C, Langford CA, Mahr AD, McAlear C, Moreland L, Seo EP, Specks U, Spiera RF, Sreih A, St Clair EW, Stone JH, Ytterberg SR, Elder JT, Qu J, Ochi T, Hirano N, Edberg JC, Falk RJ, Amos CI, Siminovitch KA. Identification of Functional and Expression Polymorphisms Associated With Risk for Antineutrophil Cytoplasmic Autoantibody-Associated Vasculitis. Arthritis Rheumatol 2017; 69:1054-1066. [PMID: 28029757 PMCID: PMC5434905 DOI: 10.1002/art.40034] [Citation(s) in RCA: 103] [Impact Index Per Article: 14.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/08/2016] [Accepted: 12/20/2016] [Indexed: 01/28/2023]
Abstract
Objective To identify risk alleles relevant to the causal and biologic mechanisms of antineutrophil cytoplasmic antibody (ANCA)–associated vasculitis (AAV). Methods A genome‐wide association study and subsequent replication study were conducted in a total cohort of 1,986 cases of AAV (patients with granulomatosis with polyangiitis [Wegener's] [GPA] or microscopic polyangiitis [MPA]) and 4,723 healthy controls. Meta‐analysis of these data sets and functional annotation of identified risk loci were performed, and candidate disease variants with unknown functional effects were investigated for their impact on gene expression and/or protein function. Results Among the genome‐wide significant associations identified, the largest effect on risk of AAV came from the single‐nucleotide polymorphism variants rs141530233 and rs1042169 at the HLA–DPB1 locus (odds ratio [OR] 2.99 and OR 2.82, respectively) which, together with a third variant, rs386699872, constitute a triallelic risk haplotype associated with reduced expression of the HLA–DPB1 gene and HLA–DP protein in B cells and monocytes and with increased frequency of complementary proteinase 3 (PR3)–reactive T cells relative to that in carriers of the protective haplotype. Significant associations were also observed at the SERPINA1 and PTPN22 loci, the peak signals arising from functionally relevant missense variants, and at PRTN3, in which the top‐scoring variant correlated with increased PRTN3 expression in neutrophils. Effects of individual loci on AAV risk differed between patients with GPA and those with MPA or between patients with PR3‐ANCAs and those with myeloperoxidase‐ANCAs, but the collective population attributable fraction for these variants was substantive, at 77%. Conclusion This study reveals the association of susceptibility to GPA and MPA with functional gene variants that explain much of the genetic etiology of AAV, could influence and possibly be predictors of the clinical presentation, and appear to alter immune cell proteins and responses likely to be key factors in the pathogenesis of AAV.
Collapse
Affiliation(s)
| | - Gang Xie
- Mount Sinai Hospital, Lunenfeld-Tanenbaum Research Institute and Toronto General Research Institute, Toronto, Ontario, Canada
| | - Paul A Monach
- Boston University and VA Boston Healthcare System, Boston, Massachusetts
| | - Xuemei Ji
- Dartmouth College, Lebanon, New Hampshire
| | | | | | - Benjamin D Pinder
- Mount Sinai Hospital, Lunenfeld-Tanenbaum Research Institute and Toronto General Research Institute, Toronto, Ontario, Canada
| | - Ai Zhao
- Mount Sinai Hospital, Lunenfeld-Tanenbaum Research Institute and Toronto General Research Institute, Toronto, Ontario, Canada
| | - Jinyi Zhang
- Mount Sinai Hospital, Lunenfeld-Tanenbaum Research Institute, Toronto General Research Institute and University of Toronto, Toronto, Ontario, Canada
| | - Yohannes Tadesse
- Mount Sinai Hospital, Lunenfeld-Tanenbaum Research Institute and Toronto General Research Institute, Toronto, Ontario, Canada
| | - David Qian
- Dartmouth College, Lebanon, New Hampshire
| | | | | | | | - Christian Pagnoux
- Mount Sinai Hospital and University of Toronto, Toronto, Ontario, Canada
| | - Simon Carette
- Mount Sinai Hospital and University of Toronto, Toronto, Ontario, Canada
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | - James T Elder
- University of Michigan and Ann Arbor VA Hospital, Ann Arbor, Michigan
| | - Jia Qu
- Wenzhou Medical University, Wenzhou, China
| | - Toshiki Ochi
- University of Toronto and Princess Margaret Cancer Centre, Toronto, Ontario, Canada
| | - Naoto Hirano
- University of Toronto and Princess Margaret Cancer Centre, Toronto, Ontario, Canada
| | | | | | | | - Katherine A Siminovitch
- Mount Sinai Hospital, Lunenfeld-Tanenbaum Research Institute, Toronto General Research Institute and University of Toronto, Toronto, Ontario, Canada
| | | |
Collapse
|
204
|
Abstract
Indigenous Tibetan people have lived on the Tibetan Plateau for millennia. There is a long-standing question about the genetic basis of high-altitude adaptation in Tibetans. We conduct a genome-wide study of 7.3 million genotyped and imputed SNPs of 3,008 Tibetans and 7,287 non-Tibetan individuals of Eastern Asian ancestry. Using this large dataset, we detect signals of high-altitude adaptation at nine genomic loci, of which seven are unique. The alleles under natural selection at two of these loci [methylenetetrahydrofolate reductase (MTHFR) and EPAS1] are strongly associated with blood-related phenotypes, such as hemoglobin, homocysteine, and folate in Tibetans. The folate-increasing allele of rs1801133 at the MTHFR locus has an increased frequency in Tibetans more than expected under a drift model, which is probably a consequence of adaptation to high UV radiation. These findings provide important insights into understanding the genomic consequences of high-altitude adaptation in Tibetans.
Collapse
|
205
|
Lindström S, Loomis S, Turman C, Huang H, Huang J, Aschard H, Chan AT, Choi H, Cornelis M, Curhan G, De Vivo I, Eliassen AH, Fuchs C, Gaziano M, Hankinson SE, Hu F, Jensen M, Kang JH, Kabrhel C, Liang L, Pasquale LR, Rimm E, Stampfer MJ, Tamimi RM, Tworoger SS, Wiggs JL, Hunter DJ, Kraft P. A comprehensive survey of genetic variation in 20,691 subjects from four large cohorts. PLoS One 2017; 12:e0173997. [PMID: 28301549 PMCID: PMC5354293 DOI: 10.1371/journal.pone.0173997] [Citation(s) in RCA: 51] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/08/2016] [Accepted: 03/01/2017] [Indexed: 12/18/2022] Open
Abstract
The Nurses' Health Study (NHS), Nurses' Health Study II (NHSII), Health Professionals Follow Up Study (HPFS) and the Physicians Health Study (PHS) have collected detailed longitudinal data on multiple exposures and traits for approximately 310,000 study participants over the last 35 years. Over 160,000 study participants across the cohorts have donated a DNA sample and to date, 20,691 subjects have been genotyped as part of genome-wide association studies (GWAS) of twelve primary outcomes. However, these studies utilized six different GWAS arrays making it difficult to conduct analyses of secondary phenotypes or share controls across studies. To allow for secondary analyses of these data, we have created three new datasets merged by platform family and performed imputation using a common reference panel, the 1,000 Genomes Phase I release. Here, we describe the methodology behind the data merging and imputation and present imputation quality statistics and association results from two GWAS of secondary phenotypes (body mass index (BMI) and venous thromboembolism (VTE)). We observed the strongest BMI association for the FTO SNP rs55872725 (β = 0.45, p = 3.48x10-22), and using a significance level of p = 0.05, we replicated 19 out of 32 known BMI SNPs. For VTE, we observed the strongest association for the rs2040445 SNP (OR = 2.17, 95% CI: 1.79-2.63, p = 2.70x10-15), located downstream of F5 and also observed significant associations for the known ABO and F11 regions. This pooled resource can be used to maximize power in GWAS of phenotypes collected across the cohorts and for studying gene-environment interactions as well as rare phenotypes and genotypes.
Collapse
Affiliation(s)
- Sara Lindström
- Program in Genetic Epidemiology and Statistical Genetics, Harvard T.H. Chan School of Public Health, Boston, MA, United States of America
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, United States of America
- Department of Epidemiology, University of Washington, Seattle, WA, United States of America
| | - Stephanie Loomis
- Department of Ophthalmology, Harvard Medical School, Massachusetts Eye and Ear Infirmary, Boston, MA, United States of America
| | - Constance Turman
- Program in Genetic Epidemiology and Statistical Genetics, Harvard T.H. Chan School of Public Health, Boston, MA, United States of America
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, United States of America
| | - Hongyan Huang
- Program in Genetic Epidemiology and Statistical Genetics, Harvard T.H. Chan School of Public Health, Boston, MA, United States of America
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, United States of America
| | - Jinyan Huang
- Program in Genetic Epidemiology and Statistical Genetics, Harvard T.H. Chan School of Public Health, Boston, MA, United States of America
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, United States of America
| | - Hugues Aschard
- Program in Genetic Epidemiology and Statistical Genetics, Harvard T.H. Chan School of Public Health, Boston, MA, United States of America
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, United States of America
| | - Andrew T. Chan
- Gastrointestinal Unit, Massachusetts General Hospital, Boston, MA, United States of America
| | - Hyon Choi
- Section of Rheumatology and Clinical Epidemiology Unit, Boston University School of Medicine, Boston, MA, United States of America
| | - Marilyn Cornelis
- Department of Preventive Medicine, Northwestern University Feinberg School of Medicine, Chicago, IL, United States of America
| | - Gary Curhan
- Channing Division of Network Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, United States of America
- Renal Division, Department of Medicine, Brigham and Women's Hospital, Boston, MA, United States of America
| | - Immaculata De Vivo
- Program in Genetic Epidemiology and Statistical Genetics, Harvard T.H. Chan School of Public Health, Boston, MA, United States of America
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, United States of America
- Channing Division of Network Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, United States of America
| | - A. Heather Eliassen
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, United States of America
- Channing Division of Network Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, United States of America
| | - Charles Fuchs
- Channing Division of Network Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, United States of America
- Department of Medical Oncology, Dana-Farber Cancer Institute and Harvard Medical School, Boston, MA, United States of America
| | - Michael Gaziano
- Division of Aging, Department of Medicine, Brigham and Women's Hospital, Boston, MA, United States of America
| | - Susan E. Hankinson
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, United States of America
- Channing Division of Network Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, United States of America
- Department of Biostatistics and Epidemiology, University of Massachusetts, Amherst, MA, United States of America
| | - Frank Hu
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, United States of America
- Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, MA, United States of America
| | - Majken Jensen
- Channing Division of Network Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, United States of America
- Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, MA, United States of America
| | - Jae H. Kang
- Channing Division of Network Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, United States of America
| | - Christopher Kabrhel
- Channing Division of Network Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, United States of America
- Department of Emergency Medicine, Center for Vascular Emergencies, Massachusetts General Hospital, Harvard Medical School, Boston, MA, United States of America
| | - Liming Liang
- Program in Genetic Epidemiology and Statistical Genetics, Harvard T.H. Chan School of Public Health, Boston, MA, United States of America
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, United States of America
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, United States of America
| | - Louis R. Pasquale
- Department of Ophthalmology, Harvard Medical School, Massachusetts Eye and Ear Infirmary, Boston, MA, United States of America
- Channing Division of Network Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, United States of America
| | - Eric Rimm
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, United States of America
- Channing Division of Network Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, United States of America
- Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, MA, United States of America
| | - Meir J. Stampfer
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, United States of America
- Channing Division of Network Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, United States of America
- Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, MA, United States of America
| | - Rulla M. Tamimi
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, United States of America
- Channing Division of Network Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, United States of America
| | - Shelley S. Tworoger
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, United States of America
- Channing Division of Network Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, United States of America
| | - Janey L. Wiggs
- Department of Ophthalmology, Harvard Medical School, Massachusetts Eye and Ear Infirmary, Boston, MA, United States of America
| | - David J. Hunter
- Program in Genetic Epidemiology and Statistical Genetics, Harvard T.H. Chan School of Public Health, Boston, MA, United States of America
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, United States of America
- Channing Division of Network Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, United States of America
- Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, MA, United States of America
| | - Peter Kraft
- Program in Genetic Epidemiology and Statistical Genetics, Harvard T.H. Chan School of Public Health, Boston, MA, United States of America
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, United States of America
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, United States of America
| |
Collapse
|
206
|
Dumitrescu L, Ritchie MD, Denny JC, El Rouby NM, McDonough CW, Bradford Y, Ramirez AH, Bielinski SJ, Basford MA, Chai HS, Peissig P, Carrell D, Pathak J, Rasmussen LV, Wang X, Pacheco JA, Kho AN, Hayes MG, Matsumoto M, Smith ME, Li R, Cooper-DeHoff RM, Kullo IJ, Chute CG, Chisholm RL, Jarvik GP, Larson EB, Carey D, McCarty CA, Williams MS, Roden DM, Bottinger E, Johnson JA, de Andrade M, Crawford DC. Genome-wide study of resistant hypertension identified from electronic health records. PLoS One 2017; 12:e0171745. [PMID: 28222112 PMCID: PMC5319785 DOI: 10.1371/journal.pone.0171745] [Citation(s) in RCA: 31] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/08/2016] [Accepted: 01/25/2017] [Indexed: 12/11/2022] Open
Abstract
Resistant hypertension is defined as high blood pressure that remains above treatment goals in spite of the concurrent use of three antihypertensive agents from different classes. Despite the important health consequences of resistant hypertension, few studies of resistant hypertension have been conducted. To perform a genome-wide association study for resistant hypertension, we defined and identified cases of resistant hypertension and hypertensives with treated, controlled hypertension among >47,500 adults residing in the US linked to electronic health records (EHRs) and genotyped as part of the electronic MEdical Records & GEnomics (eMERGE) Network. Electronic selection logic using billing codes, laboratory values, text queries, and medication records was used to identify resistant hypertension cases and controls at each site, and a total of 3,006 cases of resistant hypertension and 876 controlled hypertensives were identified among eMERGE Phase I and II sites. After imputation and quality control, a total of 2,530,150 SNPs were tested for an association among 2,830 multi-ethnic cases of resistant hypertension and 876 controlled hypertensives. No test of association was genome-wide significant in the full dataset or in the dataset limited to European American cases (n = 1,719) and controls (n = 708). The most significant finding was CLNK rs13144136 at p = 1.00x10-6 (odds ratio = 0.68; 95% CI = 0.58–0.80) in the full dataset with similar results in the European American only dataset. We also examined whether SNPs known to influence blood pressure or hypertension also influenced resistant hypertension. None was significant after correction for multiple testing. These data highlight both the difficulties and the potential utility of EHR-linked genomic data to study clinically-relevant traits such as resistant hypertension.
Collapse
Affiliation(s)
- Logan Dumitrescu
- Department of Molecular Physiology and Biophysics, Vanderbilt University School of Medicine, Nashville, Tennessee, United States of America
| | - Marylyn D. Ritchie
- Biomedical and Translational Informatics, Geisinger Health System, Danville, Pennsylvania, United States of America
| | - Joshua C. Denny
- Department of Biomedical Informatics, Vanderbilt University, Nashville, Tennessee, United States of America
- Department of Medicine, Vanderbilt University, Nashville, Tennessee, United States of America
| | - Nihal M. El Rouby
- Department of Pharmacotherapy and Translational Research and Center for Pharmacogenomics, College of Pharmacy, University of Florida, Gainesville, Florida, United States of America
| | - Caitrin W. McDonough
- Department of Pharmacotherapy and Translational Research and Center for Pharmacogenomics, College of Pharmacy, University of Florida, Gainesville, Florida, United States of America
| | - Yuki Bradford
- Biomedical and Translational Informatics, Geisinger Health System, Danville, Pennsylvania, United States of America
| | - Andrea H. Ramirez
- Department of Medicine, Vanderbilt University, Nashville, Tennessee, United States of America
| | - Suzette J. Bielinski
- Division of Epidemiology, Mayo Clinic, Rochester, Minnesota, United States of America
| | - Melissa A. Basford
- Office of Research, Vanderbilt University, Nashville, Tennessee, United States of America
| | - High Seng Chai
- Division of Biomedical Statistics and Informatics, Department of Health Sciences Research, Mayo Clinic, Rochester, Minnesota, United States of America
| | - Peggy Peissig
- Biomedical Informatics Research Center, Marshfield Clinic Research Foundation, Marshfield, Wisconsin, United States of America
| | - David Carrell
- Group Health Research Institute, Seattle, Washington, United States of America
| | - Jyotishman Pathak
- Division of Biomedical Statistics and Informatics, Department of Health Sciences Research, Mayo Clinic, Rochester, Minnesota, United States of America
| | - Luke V. Rasmussen
- Department of Preventive Medicine, Division of Health and Biomedical Informatics, Northwestern University, Chicago, Illinois, United States of America
| | - Xiaoming Wang
- Department of Biomedical Informatics, Vanderbilt University, Nashville, Tennessee, United States of America
| | - Jennifer A. Pacheco
- Center for Genetic Medicine, Northwestern University, Chicago, Illinois, United States of America
| | - Abel N. Kho
- Department Medicine, Feinberg School of Medicine, Northwestern University, Chicago, Illinois, United States of America
| | - M. Geoffrey Hayes
- Department Medicine, Feinberg School of Medicine, Northwestern University, Chicago, Illinois, United States of America
| | - Martha Matsumoto
- Division of Biomedical Statistics and Informatics, Department of Health Sciences Research, Mayo Clinic, Rochester, Minnesota, United States of America
| | - Maureen E. Smith
- Center for Genetic Medicine, Northwestern University, Chicago, Illinois, United States of America
| | - Rongling Li
- Division of Genomic Medicine, National Human Genome Research Institute, Bethesda, Maryland, United States of America
| | - Rhonda M. Cooper-DeHoff
- Department of Pharmacotherapy and Translational Research and Center for Pharmacogenomics, College of Pharmacy, University of Florida, Gainesville, Florida, United States of America
- Epidemiology and Biostatistics, Institute for Computational Biology, Case Western Reserve University, Cleveland, Ohio, United States of America
| | - Iftikhar J. Kullo
- Department of Cardiovascular Diseases, Mayo Clinic, Rochester, Minnesota, United States of America
| | - Christopher G. Chute
- Division of General Internal Medicine, Johns Hopkins University, Baltimore, Maryland, United States of America
| | - Rex L. Chisholm
- Center for Genetic Medicine, Northwestern University, Chicago, Illinois, United States of America
| | - Gail P. Jarvik
- Department of Medicine, University of Washington Medical Center, Seattle, Washington, United States of America
| | - Eric B. Larson
- Group Health Research Institute, Seattle, Washington, United States of America
| | - David Carey
- Weis Center for Research, Geisinger Health System, Danville, Pennsylvania, United States of America
| | | | - Marc S. Williams
- Genomic Medicine Institute, Geisinger Health System, Danville, Pennsylvania, United States of America
| | - Dan M. Roden
- Department of Medicine, Vanderbilt University, Nashville, Tennessee, United States of America
- Department of Pharmacology, Vanderbilt University, Nashville, Tennessee, United States of America
| | - Erwin Bottinger
- Charles R. Bronfman Institute for Personalized Medicine, Mount Sinai, New York, New York, United States of America
| | - Julie A. Johnson
- Department of Pharmacotherapy and Translational Research and Center for Pharmacogenomics, College of Pharmacy, University of Florida, Gainesville, Florida, United States of America
- Division of Cardiovascular Medicine, College of Medicine, University of Florida, Gainesville, Florida, United States of America
| | - Mariza de Andrade
- Division of Biomedical Statistics and Informatics, Department of Health Sciences Research, Mayo Clinic, Rochester, Minnesota, United States of America
| | - Dana C. Crawford
- Epidemiology and Biostatistics, Institute for Computational Biology, Case Western Reserve University, Cleveland, Ohio, United States of America
- * E-mail:
| |
Collapse
|
207
|
Lee SH, Weerasinghe WMSP, Wray NR, Goddard ME, van der Werf JHJ. Using information of relatives in genomic prediction to apply effective stratified medicine. Sci Rep 2017; 7:42091. [PMID: 28181587 PMCID: PMC5299615 DOI: 10.1038/srep42091] [Citation(s) in RCA: 35] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/30/2016] [Accepted: 01/05/2017] [Indexed: 01/14/2023] Open
Abstract
Genomic prediction shows promise for personalised medicine in which diagnosis and treatment are tailored to individuals based on their genetic profiles for complex diseases. We present a theoretical framework to demonstrate that prediction accuracy can be improved by targeting more informative individuals in the data set used to generate the predictors ("discovery sample") to include those with genetically close relationships with the subjects put forward for risk prediction. Increase of prediction accuracy from closer relationships is achieved under an additive model and does not rely on any family or interaction effects. Using theory, simulations and real data analyses, we show that the predictive accuracy or the area under the receiver operating characteristic curve (AUC) increased exponentially with decreasing effective size (Ne), i.e. when individuals are closely related. For example, with the sample size of discovery set N = 3000, heritability h2 = 0.5 and population prevalence K = 0.1, AUC value approached to 0.9 and the top percentile of the estimated genetic profile scores had 23 times higher proportion of cases than the general population. This suggests that there is considerable room to increase prediction accuracy by using a design that does not exclude closer relationships.
Collapse
Affiliation(s)
- S. Hong Lee
- School of Environmental and Rural Science, University of New England, NSW 2351, Australia
| | | | - Naomi R. Wray
- The Centre of Neurogenetics and Statistical Genomics, Queensland Brain Institute, The University of Queensland, QLD 4072, Australia
| | - Michael E. Goddard
- Faculty of Land and Food Resources, University of Melbourne, Melbourne, Australia
- Department of Primary Industries, Biosciences Research Division, Bundoora, Australia
| | | |
Collapse
|
208
|
Genome-wide association study of prostate-specific antigen levels identifies novel loci independent of prostate cancer. Nat Commun 2017; 8:14248. [PMID: 28139693 PMCID: PMC5290311 DOI: 10.1038/ncomms14248] [Citation(s) in RCA: 48] [Impact Index Per Article: 6.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/29/2016] [Accepted: 12/12/2016] [Indexed: 12/22/2022] Open
Abstract
Prostate-specific antigen (PSA) levels have been used for detection and surveillance of prostate cancer (PCa). However, factors other than PCa—such as genetics—can impact PSA. Here we present findings from a genome-wide association study (GWAS) of PSA in 28,503 Kaiser Permanente whites and 17,428 men from replication cohorts. We detect 40 genome-wide significant (P<5 × 10−8) single-nucleotide polymorphisms (SNPs): 19 novel, 15 previously identified for PSA (14 of which were also PCa-associated), and 6 previously identified for PCa only. Further analysis incorporating PCa cases suggests that at least half of the 40 SNPs are PSA-associated independent of PCa. The 40 SNPs explain 9.5% of PSA variation in non-Hispanic whites, and the remaining GWAS SNPs explain an additional 31.7%; this percentage is higher in younger men, supporting the genetic basis of PSA levels. These findings provide important information about genetic markers for PSA that may improve PCa screening, thereby reducing over-diagnosis and over-treatment. Prostate-specific antigen is used as a biomarker of prostate cancer, but levels can be affected by other factors not related to cancer. Here, the authors find genes associated with prostate specific antigen levels in healthy men, which could be used to reduce over-diagnosis and over-treatment.
Collapse
|
209
|
Robbins NM, Bernat JL. Minority Representation in Migraine Treatment Trials. Headache 2017; 57:525-533. [PMID: 28127754 DOI: 10.1111/head.13018] [Citation(s) in RCA: 26] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/09/2016] [Accepted: 11/10/2016] [Indexed: 02/06/2023]
Abstract
BACKGROUND Minorities have historically been underrepresented in clinical research trials despite having comparatively poor health indicators. Recognizing the dual inequalities of increased disease burden and decreased research participation, the National Institute of Health (NIH) Revitalization Act of 1993 mandated the inclusion and reporting of women and minorities in NIH-funded research. While progress has been made in the subsequent decades, this underrepresentation of minorities in research trials persists and has been documented in multiple disciplines. However, the extent of adequate representation and reporting of minority inclusion in clinical trials for migraine remains unknown. OBJECTIVES In this systematic review and study, we review the literature examining the representation of women and minorities in migraine clinical research trials METHODS: First we searched PubMed for pertinent articles examining the inclusion of women and minorities in migraine clinical research trials. Second, we identified controlled-trials for migraine published since 2011 in major neurology, headache, and general medicine journals using the terms "migraine randomized controlled trial." We then reviewed the results manually and excluded pilot studies and those with fewer than 50 participants. We next determined (a) how frequently representation of minorities and women were reported in these major trials; (b) what factors correlated with reporting; and (c) whether women and minority inclusion comprised their ratios in the general population. RESULTS We identified 128 relevant clinical trials, of which 36 met our inclusion criteria. All 36 trials (100%) reported gender frequency, and 25 of 36 (69.4%) reported ethnicity or race. Among all studies, women and Whites represented 84.2 and 82.9% of participants (mean), respectively. Studies conducted in the United States and funded by a private company were more likely to report race than studies conducted exclusively outside of the U.S. or with a public sponsor. No studies stratified efficacy or safety by ethnicity or gender. Men and non-Whites in the U.S. were statistically underrepresented. CONCLUSIONS Most recent headache studies comply with the NIH mandate to include women and minorities in research trials, particularly U.S.-based and industry-funded studies. Whites are overrepresented compared to both the general population and the population of migraineurs. Future studies should strive to increase minority participation and investigate race-based differences in migraine expression, treatment response, and medication toxicity.
Collapse
Affiliation(s)
- Nathaniel M Robbins
- Department of Neurology, Dartmouth-Hitchcock Medical Center, Lebanon, NH, USA
| | - James L Bernat
- Geisel School of Medicine at Dartmouth, Hanover, NH, USA
| |
Collapse
|
210
|
Bush WS, Crawford DC. Predicting Incident Coronary Heart Disease Many Markers at a Time. ACTA ACUST UNITED AC 2016; 9:472-473. [DOI: 10.1161/circgenetics.116.001645] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Affiliation(s)
- William S. Bush
- From the Department of Epidemiology and Biostatistics, Institute for Computational Biology, Case Western Reserve University, Cleveland, OH
| | - Dana C. Crawford
- From the Department of Epidemiology and Biostatistics, Institute for Computational Biology, Case Western Reserve University, Cleveland, OH
| |
Collapse
|
211
|
Genome-wide association analyses using electronic health records identify new loci influencing blood pressure variation. Nat Genet 2016; 49:54-64. [PMID: 27841878 PMCID: PMC5370207 DOI: 10.1038/ng.3715] [Citation(s) in RCA: 227] [Impact Index Per Article: 28.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/08/2016] [Accepted: 10/11/2016] [Indexed: 11/17/2022]
Abstract
Longitudinal electronic health records on 99,785 Genetic Epidemiology Research on Adult Health and Aging (GERA) cohort individuals provided 1,342,814 systolic and diastolic blood pressure measurements for a genome-wide association study on long-term average systolic, diastolic, and pulse pressure. We identified 39 novel among 75 significant loci (P≤5×10−8), most replicating in the combined International Consortium for Blood Pressure (ICBP, n=69,396) and UK Biobank (UKB, n=152,081) studies. Combining GERA with ICBP yielded 36 additional novel loci, most replicating in UKB. Combining all three studies (n=321,262) yielded 241 additional genome-wide significant loci, although for these no replication sample was available. All associated loci explained 2.9%/2.5%/3.1% of systolic/diastolic/pulse pressure variation in GERA non-Hispanic whites. Using multiple BP measurements in GERA doubled the variance explained. A normalized risk score was associated with time-to-onset of hypertension (hazards ratio=1.18, P=10−44). Expression quantitative trait locus analysis of BP loci showed enrichment in aorta and tibial artery.
Collapse
|
212
|
Galinsky KJ, Loh PR, Mallick S, Patterson NJ, Price AL. Population Structure of UK Biobank and Ancient Eurasians Reveals Adaptation at Genes Influencing Blood Pressure. Am J Hum Genet 2016; 99:1130-1139. [PMID: 27773431 PMCID: PMC5097941 DOI: 10.1016/j.ajhg.2016.09.014] [Citation(s) in RCA: 31] [Impact Index Per Article: 3.9] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/16/2016] [Accepted: 09/21/2016] [Indexed: 01/11/2023] Open
Abstract
Analyzing genetic differences between closely related populations can be a powerful way to detect recent adaptation. The very large sample size of the UK Biobank is ideal for using population differentiation to detect selection and enables an analysis of the UK population structure at fine resolution. In this study, analyses of 113,851 UK Biobank samples showed that population structure in the UK is dominated by five principal components (PCs) spanning six clusters: Northern Ireland, Scotland, northern England, southern England, and two Welsh clusters. Analyses of ancient Eurasians revealed that populations in the northern UK have higher levels of Steppe ancestry and that UK population structure cannot be explained as a simple mixture of Celts and Saxons. A scan for unusual population differentiation along the top PCs identified a genome-wide-significant signal of selection at the coding variant rs601338 in FUT2 (p = 9.16 × 10-9). In addition, by combining evidence of unusual differentiation within the UK with evidence from ancient Eurasians, we identified genome-wide-significant (p = 5 × 10-8) signals of recent selection at two additional loci: CYP1A2-CSK and F12. We detected strong associations between diastolic blood pressure in the UK Biobank and both the variants with selection signals at CYP1A2-CSK (p = 1.10 × 10-19) and the variants with ancient Eurasian selection signals at the ATXN2-SH2B3 locus (p = 8.00 × 10-33), implicating recent adaptation related to blood pressure.
Collapse
Affiliation(s)
- Kevin J Galinsky
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA 02115, USA; Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA.
| | - Po-Ru Loh
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA; Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA 02115, USA
| | - Swapan Mallick
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA; Department of Genetics, Harvard Medical School, Boston, MA 02115, USA
| | - Nick J Patterson
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
| | - Alkes L Price
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA 02115, USA; Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA; Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA 02115, USA.
| |
Collapse
|
213
|
Shen QK, Sulaiman X, Yao YG, Peng MS, Zhang YP. Was ADH1B under Selection in European Populations? Am J Hum Genet 2016; 99:1217-1219. [PMID: 27814524 DOI: 10.1016/j.ajhg.2016.09.017] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/17/2016] [Accepted: 09/20/2016] [Indexed: 11/18/2022] Open
Affiliation(s)
- Quan-Kuan Shen
- State Key Laboratory of Genetic Resources and Evolution, Kunming Institute of Zoology, Chinese Academy of Sciences, Kunming 650223, China; KIZ/CUHK Joint Laboratory of Bioresources and Molecular Research in Common Diseases, Kunming 650223, China; Kunming College of Life Science, University of Chinese Academy of Sciences, Kunming 650204, China
| | | | - Yong-Gang Yao
- KIZ/CUHK Joint Laboratory of Bioresources and Molecular Research in Common Diseases, Kunming 650223, China; Kunming College of Life Science, University of Chinese Academy of Sciences, Kunming 650204, China; Key Laboratory of Animal Models and Human Disease Mechanisms, Chinese Academy of Sciences and Yunnan Province, Kunming Institute of Zoology, Kunming 650223, China
| | - Min-Sheng Peng
- State Key Laboratory of Genetic Resources and Evolution, Kunming Institute of Zoology, Chinese Academy of Sciences, Kunming 650223, China; KIZ/CUHK Joint Laboratory of Bioresources and Molecular Research in Common Diseases, Kunming 650223, China; Kunming College of Life Science, University of Chinese Academy of Sciences, Kunming 650204, China.
| | - Ya-Ping Zhang
- State Key Laboratory of Genetic Resources and Evolution, Kunming Institute of Zoology, Chinese Academy of Sciences, Kunming 650223, China; KIZ/CUHK Joint Laboratory of Bioresources and Molecular Research in Common Diseases, Kunming 650223, China; Kunming College of Life Science, University of Chinese Academy of Sciences, Kunming 650204, China; State Key Laboratory for Conservation and Utilization of Bio-Resources, Yunnan University, Kunming 650091, China.
| |
Collapse
|
214
|
Loh PR, Danecek P, Palamara PF, Fuchsberger C, A Reshef Y, K Finucane H, Schoenherr S, Forer L, McCarthy S, Abecasis GR, Durbin R, L Price A. Reference-based phasing using the Haplotype Reference Consortium panel. Nat Genet 2016; 48:1443-1448. [PMID: 27694958 PMCID: PMC5096458 DOI: 10.1038/ng.3679] [Citation(s) in RCA: 1038] [Impact Index Per Article: 129.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/06/2016] [Accepted: 08/29/2016] [Indexed: 12/17/2022]
Abstract
Haplotype phasing is a fundamental problem in medical and population genetics. Phasing is generally performed via statistical phasing in a genotyped cohort, an approach that can yield high accuracy in very large cohorts but attains lower accuracy in smaller cohorts. Here we instead explore the paradigm of reference-based phasing. We introduce a new phasing algorithm, Eagle2, that attains high accuracy across a broad range of cohort sizes by efficiently leveraging information from large external reference panels (such as the Haplotype Reference Consortium; HRC) using a new data structure based on the positional Burrows-Wheeler transform. We demonstrate that Eagle2 attains a ∼20× speedup and ∼10% increase in accuracy compared to reference-based phasing using SHAPEIT2. On European-ancestry samples, Eagle2 with the HRC panel achieves >2× the accuracy of 1000 Genomes-based phasing. Eagle2 is open source and freely available for HRC-based phasing via the Sanger Imputation Service and the Michigan Imputation Server.
Collapse
Affiliation(s)
- Po-Ru Loh
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, Massachusetts, USA
- Program in Medical and Population Genetics, Broad Institute of Harvard and MIT, Cambridge, Massachusetts, USA
| | - Petr Danecek
- Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Hinxton, UK
| | - Pier Francesco Palamara
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, Massachusetts, USA
- Program in Medical and Population Genetics, Broad Institute of Harvard and MIT, Cambridge, Massachusetts, USA
| | - Christian Fuchsberger
- Center for Biomedicine, European Academy of Bozen/Bolzano (EURAC), affiliated with the University of Lübeck, Bolzano, Italy
- Center for Statistical Genetics, Department of Biostatistics, University of Michigan, Ann Arbor, Michigan, USA
| | - Yakir A Reshef
- Department of Computer Science, Harvard University, Cambridge, Massachusetts, USA
| | - Hilary K Finucane
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, Massachusetts, USA
- Department of Mathematics, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA
| | - Sebastian Schoenherr
- Division of Genetic Epidemiology, Department of Medical Genetics, Molecular and Clinical Pharmacology, Medical University of Innsbruck, Innsbruck, Austria
| | - Lukas Forer
- Division of Genetic Epidemiology, Department of Medical Genetics, Molecular and Clinical Pharmacology, Medical University of Innsbruck, Innsbruck, Austria
| | - Shane McCarthy
- Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Hinxton, UK
| | - Goncalo R Abecasis
- Center for Statistical Genetics, Department of Biostatistics, University of Michigan, Ann Arbor, Michigan, USA
| | - Richard Durbin
- Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Hinxton, UK
| | - Alkes L Price
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, Massachusetts, USA
- Program in Medical and Population Genetics, Broad Institute of Harvard and MIT, Cambridge, Massachusetts, USA
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, Massachusetts, USA
| |
Collapse
|
215
|
Roden DM. Editiorial Commentary: Genomics and drug discovery: The next frontier in precision medicine. Trends Cardiovasc Med 2016; 27:203-206. [PMID: 27771237 DOI: 10.1016/j.tcm.2016.09.004] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/10/2016] [Accepted: 09/10/2016] [Indexed: 11/28/2022]
Affiliation(s)
- Dan M Roden
- Departments of Medicine, Pharmacology, and Biomedical Informatics, Vanderbilt University Medical Center, 2215B Garland Ave, 1285 MRBIV, Nashville, TN 37232-0575.
| |
Collapse
|
216
|
Iribarren C, Lu M, Jorgenson E, Martínez M, Lluis-Ganella C, Subirana I, Salas E, Elosua R. Clinical Utility of Multimarker Genetic Risk Scores for Prediction of Incident Coronary Heart Disease: A Cohort Study Among Over 51 000 Individuals of European Ancestry. ACTA ACUST UNITED AC 2016; 9:531-540. [PMID: 27780846 DOI: 10.1161/circgenetics.116.001522] [Citation(s) in RCA: 43] [Impact Index Per Article: 5.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/25/2016] [Accepted: 09/28/2016] [Indexed: 12/28/2022]
Abstract
BACKGROUND We evaluated whether including multilocus genetic risk scores (GRSs) into the Framingham Risk Equation improves the predictive capacity, discrimination, and reclassification of asymptomatic individuals with respect to coronary heart disease (CHD) risk. METHODS AND RESULTS We performed a cohort study among 51 954 European-ancestry members of a Northern California integrated healthcare system (67% female; mean age 59) free of CHD at baseline (2007-2008). Four GRSs were constructed using between 8 and 51 previously identified genetic variants. After a mean (±SD) follow-up of 5.9 (±1.5) years, 1864 incident CHD events were documented. All GRSs were linearly associated with CHD in a model adjusted by individual risk factors: hazard ratio (95% confidence interval) per SD unit: 1.21 (1.15-1.26) for GRS_8, 1.20 (1.15-1.26) for GRS_12, 1.23 (1.17-1.28) for GRS_36, and 1.23 (1.17-1.28) for GRS_51. Inclusion of the GRSs improved the C statistic (ΔC statistic =0.008 for GRS_8 and GRS_36; 0.007 for GRS_12; and 0.009 for GRS_51; all P<0.001). The net reclassification improvement was 5% for GRS_8, GRS_12, and GRS_36 and 4% for GRS_51 in the entire cohort and was (after correcting for bias) 9% for GRS_8 and GRS_12 and 7% for GRS_36 and GRS_51 when analyzing those classified as intermediate Framingham risk (10%-20%). The number required to treat to prevent 1 CHD after selectively treating with statins up-reclassified subjects on the basis of genetic information was 36 for GRS_8 and GRS_12, 41 for GRS_36, and 43 for GRS_51. CONCLUSIONS Our results demonstrate significant and clinically relevant incremental discriminative/predictive capability of 4 multilocus GRSs for incident CHD among subjects of European ancestry.
Collapse
Affiliation(s)
- Carlos Iribarren
- From the Kaiser Permanente Northern California Division of Research, Oakland, CA (C.I., M.L., E.J.); Gendiag, Inc/Ferrer inCode, Inc, Barcelona, Spain (M.M., C.L.-G., E.S.); CIBER of Epidemiology and Public Health, Barcelona, Spain (I.S.); and Cardiovascular Epidemiology & Genetics, IMIM, Barcelona, Spain (I.S., R.E.).
| | - Meng Lu
- From the Kaiser Permanente Northern California Division of Research, Oakland, CA (C.I., M.L., E.J.); Gendiag, Inc/Ferrer inCode, Inc, Barcelona, Spain (M.M., C.L.-G., E.S.); CIBER of Epidemiology and Public Health, Barcelona, Spain (I.S.); and Cardiovascular Epidemiology & Genetics, IMIM, Barcelona, Spain (I.S., R.E.)
| | - Eric Jorgenson
- From the Kaiser Permanente Northern California Division of Research, Oakland, CA (C.I., M.L., E.J.); Gendiag, Inc/Ferrer inCode, Inc, Barcelona, Spain (M.M., C.L.-G., E.S.); CIBER of Epidemiology and Public Health, Barcelona, Spain (I.S.); and Cardiovascular Epidemiology & Genetics, IMIM, Barcelona, Spain (I.S., R.E.)
| | - Manuel Martínez
- From the Kaiser Permanente Northern California Division of Research, Oakland, CA (C.I., M.L., E.J.); Gendiag, Inc/Ferrer inCode, Inc, Barcelona, Spain (M.M., C.L.-G., E.S.); CIBER of Epidemiology and Public Health, Barcelona, Spain (I.S.); and Cardiovascular Epidemiology & Genetics, IMIM, Barcelona, Spain (I.S., R.E.)
| | - Carla Lluis-Ganella
- From the Kaiser Permanente Northern California Division of Research, Oakland, CA (C.I., M.L., E.J.); Gendiag, Inc/Ferrer inCode, Inc, Barcelona, Spain (M.M., C.L.-G., E.S.); CIBER of Epidemiology and Public Health, Barcelona, Spain (I.S.); and Cardiovascular Epidemiology & Genetics, IMIM, Barcelona, Spain (I.S., R.E.)
| | - Isaac Subirana
- From the Kaiser Permanente Northern California Division of Research, Oakland, CA (C.I., M.L., E.J.); Gendiag, Inc/Ferrer inCode, Inc, Barcelona, Spain (M.M., C.L.-G., E.S.); CIBER of Epidemiology and Public Health, Barcelona, Spain (I.S.); and Cardiovascular Epidemiology & Genetics, IMIM, Barcelona, Spain (I.S., R.E.)
| | - Eduardo Salas
- From the Kaiser Permanente Northern California Division of Research, Oakland, CA (C.I., M.L., E.J.); Gendiag, Inc/Ferrer inCode, Inc, Barcelona, Spain (M.M., C.L.-G., E.S.); CIBER of Epidemiology and Public Health, Barcelona, Spain (I.S.); and Cardiovascular Epidemiology & Genetics, IMIM, Barcelona, Spain (I.S., R.E.)
| | - Roberto Elosua
- From the Kaiser Permanente Northern California Division of Research, Oakland, CA (C.I., M.L., E.J.); Gendiag, Inc/Ferrer inCode, Inc, Barcelona, Spain (M.M., C.L.-G., E.S.); CIBER of Epidemiology and Public Health, Barcelona, Spain (I.S.); and Cardiovascular Epidemiology & Genetics, IMIM, Barcelona, Spain (I.S., R.E.)
| |
Collapse
|
217
|
Hoffmann TJ, Keats BJ, Yoshikawa N, Schaefer C, Risch N, Lustig LR. A Large Genome-Wide Association Study of Age-Related Hearing Impairment Using Electronic Health Records. PLoS Genet 2016; 12:e1006371. [PMID: 27764096 PMCID: PMC5072625 DOI: 10.1371/journal.pgen.1006371] [Citation(s) in RCA: 63] [Impact Index Per Article: 7.9] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/27/2016] [Accepted: 09/16/2016] [Indexed: 01/22/2023] Open
Abstract
Age-related hearing impairment (ARHI), one of the most common sensory disorders, can be mitigated, but not cured or eliminated. To identify genetic influences underlying ARHI, we conducted a genome-wide association study of ARHI in 6,527 cases and 45,882 controls among the non-Hispanic whites from the Genetic Epidemiology Research on Adult Health and Aging (GERA) cohort. We identified two novel genome-wide significant SNPs: rs4932196 (odds ratio = 1.185, p = 4.0x10-11), 52Kb 3’ of ISG20, which replicated in a meta-analysis of the other GERA race/ethnicity groups (1,025 cases, 12,388 controls, p = 0.00094) and in a UK Biobank case-control analysis (30,802 self-reported cases, 78,586 controls, p = 0.015); and rs58389158 (odds ratio = 1.132, p = 1.8x10-9), which replicated in the UK Biobank (p = 0.00021). The latter SNP lies just outside exon 8 and is highly correlated (r2 = 0.96) with the missense SNP rs5756795 in exon 7 of TRIOBP, a gene previously associated with prelingual nonsyndromic hearing loss. We further tested these SNPs in phenotypes from audiologist notes available on a subset of GERA (4,903 individuals), stratified by case/control status, to construct an independent replication test, and found a significant effect of rs58389158 on speech reception threshold (SRT; overall GERA meta-analysis p = 1.9x10-6). We also tested variants within exons of 132 other previously-identified hearing loss genes, and identified two common additional significant SNPs: rs2877561 (synonymous change in ILDR1, p = 6.2x10-5), which replicated in the UK Biobank (p = 0.00057), and had a significant GERA SRT (p = 0.00019) and speech discrimination score (SDS; p = 0.0019); and rs9493627 (missense change in EYA4, p = 0.00011) which replicated in the UK Biobank (p = 0.0095), other GERA groups (p = 0.0080), and had a consistent significant result for SRT (p = 0.041) and suggestive result for SDS (p = 0.081). Large cohorts with GWAS data and electronic health records may be a useful method to characterize the genetic architecture of ARHI. Age-related hearing impairment (ARHI) is one of the most common sensory disorders. While ARHI effects can be mitigated with current technologies, it cannot be cured or eliminated. It is thus hoped that identification of genetic influences on ARHI may one day lead to curative therapies. Towards this goal, the current study utilized electronic health record data from non-Hispanic whites in the Genetic Epidemiology Research on Adult Health and Aging (GERA) cohort to conduct a genome-wide association study of ARHI, and tested the significant variants for replication in other GERA race/ethnicity groups, independent GERA phenotypes, and self-reported ARHI from the UK Biobank. We discovered two genome-wide significant SNPs. The first was novel and near ISG20. The second was in TRIOBP, a gene previously associated with prelingual nonsyndromic hearing loss. Motivated by our TRIOBP results, we also looked at exons in known hearing loss genes, and identified two additional SNPs, rs2877561 in ILDR1 and rs9493672 in EYA4 (at a significance threshold adjusted for number of SNPs in those regions). These results suggest that large cohorts with GWAS data and electronic health records may be a useful method to characterize the genetic architecture of ARHI.
Collapse
Affiliation(s)
- Thomas J. Hoffmann
- Department of Epidemiology and Biostatistics, and Institute for Human Genetics, University of California San Francisco, San Francisco, United States of America
- * E-mail:
| | - Bronya J. Keats
- Department of Genetics, Louisiana State University Health Sciences Center, New Orleans, United States of America
| | - Noriko Yoshikawa
- Department of Head and Neck Surgery, Kaiser Permanente Medical Center, Oakland, United States of America
| | - Catherine Schaefer
- Kaiser Permanente Northern California Division of Research, Oakland, United States of America
| | - Neil Risch
- Department of Epidemiology and Biostatistics, and Institute for Human Genetics, University of California San Francisco, San Francisco, United States of America
- Kaiser Permanente Northern California Division of Research, Oakland, United States of America
| | - Lawrence R. Lustig
- Department of Otolaryngology—Head and Neck Surgery, Columbia University Medical Center, Columbia, United States of America
- New York Presbyterian Hospital, New York, United States of America
| |
Collapse
|
218
|
Comparison of body mass index, waist circumference, and waist to height ratio in the prediction of hypertension and diabetes mellitus: Filipino-American women cardiovascular study. Prev Med Rep 2016; 4:608-613. [PMID: 27882291 PMCID: PMC5118592 DOI: 10.1016/j.pmedr.2016.10.003] [Citation(s) in RCA: 19] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/18/2016] [Revised: 10/09/2016] [Accepted: 10/18/2016] [Indexed: 01/18/2023] Open
Abstract
The relative ability of three obesity indices to predict hypertension (HTN) and diabetes (DM) and the validity of using Asian-specific thresholds of these indices were examined in Filipino-American women (FAW). Filipino-American women (n = 382), 40–65 years of age were screened for hypertension (HTN) and diabetes (DM) in four major US cities. Body mass index (BMI), waist circumference (WC) and waist circumference to height ratio (WHtR) were measured. ROC analyses determined that the three obesity measurements were similar in predicting HTN and DM (AUC: 0.6–0.7). The universal WC threshold of ≥ 35 in. missed 13% of the hypertensive patients and 12% of the diabetic patients. The Asian WC threshold of ≥ 31.5 in. increased detection of HTN and DM but with a high rate of false positives. The traditional BMI ≥ 25 kg/m2 threshold missed 35% of those with hypertension and 24% of those with diabetes. The Asian BMI threshold improved detection but resulted in a high rate of false positives. The suggested WHtR cut-off of ≥ 0.5 missed only 1% of those with HTN and 0% of those with DM. The three obesity measurements had similar but modest ability to predict HTN and DM in FAW. Using Asian-specific thresholds increased accuracy but with a high rate of false positives. Whether FAW, especially at older ages, should be encouraged to reach these lower thresholds needs further investigation because of the high false positive rates. WC, BMI and WHtR measurements cutoff points were higher in middle aged FAW. WC, BMI and WHtR measurements had similar ability to predict HTN and DM in FAW. Need to tailor thresholds of obesity measurements for specific Asian subgroups.
Collapse
|
219
|
Rhead B, Bäärnhielm M, Gianfrancesco M, Mok A, Shao X, Quach H, Shen L, Schaefer C, Link J, Gyllenberg A, Hedström AK, Olsson T, Hillert J, Kockum I, Glymour MM, Alfredsson L, Barcellos LF. Mendelian randomization shows a causal effect of low vitamin D on multiple sclerosis risk. NEUROLOGY-GENETICS 2016; 2:e97. [PMID: 27652346 PMCID: PMC5022843 DOI: 10.1212/nxg.0000000000000097] [Citation(s) in RCA: 141] [Impact Index Per Article: 17.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/08/2016] [Accepted: 07/12/2016] [Indexed: 01/02/2023]
Abstract
Objective: We sought to estimate the causal effect of low serum 25(OH)D on multiple sclerosis (MS) susceptibility that is not confounded by environmental or lifestyle factors or subject to reverse causality. Methods: We conducted mendelian randomization (MR) analyses using an instrumental variable (IV) comprising 3 single nucleotide polymorphisms found to be associated with serum 25(OH)D levels at genome-wide significance. We analyzed the effect of the IV on MS risk and both age at onset and disease severity in 2 separate populations using logistic regression models that controlled for sex, year of birth, smoking, education, genetic ancestry, body mass index at age 18–20 years or in 20s, a weighted genetic risk score for 110 known MS-associated variants, and the presence of one or more HLA-DRB1*15:01 alleles. Results: Findings from MR analyses using the IV showed increasing levels of 25(OH)D are associated with a decreased risk of MS in both populations. In white, non-Hispanic members of Kaiser Permanente Northern California (1,056 MS cases and 9,015 controls), the odds ratio (OR) was 0.79 (p = 0.04, 95% confidence interval (CI): 0.64–0.99). In members of a Swedish population from the Epidemiological Investigation of Multiple Sclerosis and Genes and Environment in Multiple Sclerosis MS case-control studies (6,335 cases and 5,762 controls), the OR was 0.86 (p = 0.03, 95% CI: 0.76–0.98). A meta-analysis of the 2 populations gave a combined OR of 0.85 (p = 0.003, 95% CI: 0.76–0.94). No association was observed for age at onset or disease severity. Conclusions: These results provide strong evidence that low serum 25(OH)D concentration is a cause of MS, independent of established risk factors.
Collapse
Affiliation(s)
- Brooke Rhead
- Computational Biology Graduate Group (B.R.), Division of Epidemiology (M.G., A.M., X.S., H.Q., L.F.B.), School of Public Health, University of California, Berkeley; Institute of Environmental Medicine (M.B., A.K.H., L.A.), Karolinska Institutet, Stockholm, Sweden; Kaiser Permanente Division of Research (L.S., C.S., L.F.B.), Research Program on Genes, Environment, and Health (C.S.), Kaiser Permanente, Oakland, CA; Department of Clinical Neuroscience and Center for Molecular Medicine (J.L., A.G., T.O., J.H., I.K.), Karolinska Institutet at Karolinska University Hospital, Stockholm, Sweden; Department of Epidemiology and Biostatistics (M.M.G.), University of California, San Francisco; and Centre for Occupational and Environmental Medicine (L.A.), Stockholm County Council, Sweden
| | - Maria Bäärnhielm
- Computational Biology Graduate Group (B.R.), Division of Epidemiology (M.G., A.M., X.S., H.Q., L.F.B.), School of Public Health, University of California, Berkeley; Institute of Environmental Medicine (M.B., A.K.H., L.A.), Karolinska Institutet, Stockholm, Sweden; Kaiser Permanente Division of Research (L.S., C.S., L.F.B.), Research Program on Genes, Environment, and Health (C.S.), Kaiser Permanente, Oakland, CA; Department of Clinical Neuroscience and Center for Molecular Medicine (J.L., A.G., T.O., J.H., I.K.), Karolinska Institutet at Karolinska University Hospital, Stockholm, Sweden; Department of Epidemiology and Biostatistics (M.M.G.), University of California, San Francisco; and Centre for Occupational and Environmental Medicine (L.A.), Stockholm County Council, Sweden
| | - Milena Gianfrancesco
- Computational Biology Graduate Group (B.R.), Division of Epidemiology (M.G., A.M., X.S., H.Q., L.F.B.), School of Public Health, University of California, Berkeley; Institute of Environmental Medicine (M.B., A.K.H., L.A.), Karolinska Institutet, Stockholm, Sweden; Kaiser Permanente Division of Research (L.S., C.S., L.F.B.), Research Program on Genes, Environment, and Health (C.S.), Kaiser Permanente, Oakland, CA; Department of Clinical Neuroscience and Center for Molecular Medicine (J.L., A.G., T.O., J.H., I.K.), Karolinska Institutet at Karolinska University Hospital, Stockholm, Sweden; Department of Epidemiology and Biostatistics (M.M.G.), University of California, San Francisco; and Centre for Occupational and Environmental Medicine (L.A.), Stockholm County Council, Sweden
| | - Amanda Mok
- Computational Biology Graduate Group (B.R.), Division of Epidemiology (M.G., A.M., X.S., H.Q., L.F.B.), School of Public Health, University of California, Berkeley; Institute of Environmental Medicine (M.B., A.K.H., L.A.), Karolinska Institutet, Stockholm, Sweden; Kaiser Permanente Division of Research (L.S., C.S., L.F.B.), Research Program on Genes, Environment, and Health (C.S.), Kaiser Permanente, Oakland, CA; Department of Clinical Neuroscience and Center for Molecular Medicine (J.L., A.G., T.O., J.H., I.K.), Karolinska Institutet at Karolinska University Hospital, Stockholm, Sweden; Department of Epidemiology and Biostatistics (M.M.G.), University of California, San Francisco; and Centre for Occupational and Environmental Medicine (L.A.), Stockholm County Council, Sweden
| | - Xiaorong Shao
- Computational Biology Graduate Group (B.R.), Division of Epidemiology (M.G., A.M., X.S., H.Q., L.F.B.), School of Public Health, University of California, Berkeley; Institute of Environmental Medicine (M.B., A.K.H., L.A.), Karolinska Institutet, Stockholm, Sweden; Kaiser Permanente Division of Research (L.S., C.S., L.F.B.), Research Program on Genes, Environment, and Health (C.S.), Kaiser Permanente, Oakland, CA; Department of Clinical Neuroscience and Center for Molecular Medicine (J.L., A.G., T.O., J.H., I.K.), Karolinska Institutet at Karolinska University Hospital, Stockholm, Sweden; Department of Epidemiology and Biostatistics (M.M.G.), University of California, San Francisco; and Centre for Occupational and Environmental Medicine (L.A.), Stockholm County Council, Sweden
| | - Hong Quach
- Computational Biology Graduate Group (B.R.), Division of Epidemiology (M.G., A.M., X.S., H.Q., L.F.B.), School of Public Health, University of California, Berkeley; Institute of Environmental Medicine (M.B., A.K.H., L.A.), Karolinska Institutet, Stockholm, Sweden; Kaiser Permanente Division of Research (L.S., C.S., L.F.B.), Research Program on Genes, Environment, and Health (C.S.), Kaiser Permanente, Oakland, CA; Department of Clinical Neuroscience and Center for Molecular Medicine (J.L., A.G., T.O., J.H., I.K.), Karolinska Institutet at Karolinska University Hospital, Stockholm, Sweden; Department of Epidemiology and Biostatistics (M.M.G.), University of California, San Francisco; and Centre for Occupational and Environmental Medicine (L.A.), Stockholm County Council, Sweden
| | - Ling Shen
- Computational Biology Graduate Group (B.R.), Division of Epidemiology (M.G., A.M., X.S., H.Q., L.F.B.), School of Public Health, University of California, Berkeley; Institute of Environmental Medicine (M.B., A.K.H., L.A.), Karolinska Institutet, Stockholm, Sweden; Kaiser Permanente Division of Research (L.S., C.S., L.F.B.), Research Program on Genes, Environment, and Health (C.S.), Kaiser Permanente, Oakland, CA; Department of Clinical Neuroscience and Center for Molecular Medicine (J.L., A.G., T.O., J.H., I.K.), Karolinska Institutet at Karolinska University Hospital, Stockholm, Sweden; Department of Epidemiology and Biostatistics (M.M.G.), University of California, San Francisco; and Centre for Occupational and Environmental Medicine (L.A.), Stockholm County Council, Sweden
| | - Catherine Schaefer
- Computational Biology Graduate Group (B.R.), Division of Epidemiology (M.G., A.M., X.S., H.Q., L.F.B.), School of Public Health, University of California, Berkeley; Institute of Environmental Medicine (M.B., A.K.H., L.A.), Karolinska Institutet, Stockholm, Sweden; Kaiser Permanente Division of Research (L.S., C.S., L.F.B.), Research Program on Genes, Environment, and Health (C.S.), Kaiser Permanente, Oakland, CA; Department of Clinical Neuroscience and Center for Molecular Medicine (J.L., A.G., T.O., J.H., I.K.), Karolinska Institutet at Karolinska University Hospital, Stockholm, Sweden; Department of Epidemiology and Biostatistics (M.M.G.), University of California, San Francisco; and Centre for Occupational and Environmental Medicine (L.A.), Stockholm County Council, Sweden
| | - Jenny Link
- Computational Biology Graduate Group (B.R.), Division of Epidemiology (M.G., A.M., X.S., H.Q., L.F.B.), School of Public Health, University of California, Berkeley; Institute of Environmental Medicine (M.B., A.K.H., L.A.), Karolinska Institutet, Stockholm, Sweden; Kaiser Permanente Division of Research (L.S., C.S., L.F.B.), Research Program on Genes, Environment, and Health (C.S.), Kaiser Permanente, Oakland, CA; Department of Clinical Neuroscience and Center for Molecular Medicine (J.L., A.G., T.O., J.H., I.K.), Karolinska Institutet at Karolinska University Hospital, Stockholm, Sweden; Department of Epidemiology and Biostatistics (M.M.G.), University of California, San Francisco; and Centre for Occupational and Environmental Medicine (L.A.), Stockholm County Council, Sweden
| | - Alexandra Gyllenberg
- Computational Biology Graduate Group (B.R.), Division of Epidemiology (M.G., A.M., X.S., H.Q., L.F.B.), School of Public Health, University of California, Berkeley; Institute of Environmental Medicine (M.B., A.K.H., L.A.), Karolinska Institutet, Stockholm, Sweden; Kaiser Permanente Division of Research (L.S., C.S., L.F.B.), Research Program on Genes, Environment, and Health (C.S.), Kaiser Permanente, Oakland, CA; Department of Clinical Neuroscience and Center for Molecular Medicine (J.L., A.G., T.O., J.H., I.K.), Karolinska Institutet at Karolinska University Hospital, Stockholm, Sweden; Department of Epidemiology and Biostatistics (M.M.G.), University of California, San Francisco; and Centre for Occupational and Environmental Medicine (L.A.), Stockholm County Council, Sweden
| | - Anna Karin Hedström
- Computational Biology Graduate Group (B.R.), Division of Epidemiology (M.G., A.M., X.S., H.Q., L.F.B.), School of Public Health, University of California, Berkeley; Institute of Environmental Medicine (M.B., A.K.H., L.A.), Karolinska Institutet, Stockholm, Sweden; Kaiser Permanente Division of Research (L.S., C.S., L.F.B.), Research Program on Genes, Environment, and Health (C.S.), Kaiser Permanente, Oakland, CA; Department of Clinical Neuroscience and Center for Molecular Medicine (J.L., A.G., T.O., J.H., I.K.), Karolinska Institutet at Karolinska University Hospital, Stockholm, Sweden; Department of Epidemiology and Biostatistics (M.M.G.), University of California, San Francisco; and Centre for Occupational and Environmental Medicine (L.A.), Stockholm County Council, Sweden
| | - Tomas Olsson
- Computational Biology Graduate Group (B.R.), Division of Epidemiology (M.G., A.M., X.S., H.Q., L.F.B.), School of Public Health, University of California, Berkeley; Institute of Environmental Medicine (M.B., A.K.H., L.A.), Karolinska Institutet, Stockholm, Sweden; Kaiser Permanente Division of Research (L.S., C.S., L.F.B.), Research Program on Genes, Environment, and Health (C.S.), Kaiser Permanente, Oakland, CA; Department of Clinical Neuroscience and Center for Molecular Medicine (J.L., A.G., T.O., J.H., I.K.), Karolinska Institutet at Karolinska University Hospital, Stockholm, Sweden; Department of Epidemiology and Biostatistics (M.M.G.), University of California, San Francisco; and Centre for Occupational and Environmental Medicine (L.A.), Stockholm County Council, Sweden
| | - Jan Hillert
- Computational Biology Graduate Group (B.R.), Division of Epidemiology (M.G., A.M., X.S., H.Q., L.F.B.), School of Public Health, University of California, Berkeley; Institute of Environmental Medicine (M.B., A.K.H., L.A.), Karolinska Institutet, Stockholm, Sweden; Kaiser Permanente Division of Research (L.S., C.S., L.F.B.), Research Program on Genes, Environment, and Health (C.S.), Kaiser Permanente, Oakland, CA; Department of Clinical Neuroscience and Center for Molecular Medicine (J.L., A.G., T.O., J.H., I.K.), Karolinska Institutet at Karolinska University Hospital, Stockholm, Sweden; Department of Epidemiology and Biostatistics (M.M.G.), University of California, San Francisco; and Centre for Occupational and Environmental Medicine (L.A.), Stockholm County Council, Sweden
| | - Ingrid Kockum
- Computational Biology Graduate Group (B.R.), Division of Epidemiology (M.G., A.M., X.S., H.Q., L.F.B.), School of Public Health, University of California, Berkeley; Institute of Environmental Medicine (M.B., A.K.H., L.A.), Karolinska Institutet, Stockholm, Sweden; Kaiser Permanente Division of Research (L.S., C.S., L.F.B.), Research Program on Genes, Environment, and Health (C.S.), Kaiser Permanente, Oakland, CA; Department of Clinical Neuroscience and Center for Molecular Medicine (J.L., A.G., T.O., J.H., I.K.), Karolinska Institutet at Karolinska University Hospital, Stockholm, Sweden; Department of Epidemiology and Biostatistics (M.M.G.), University of California, San Francisco; and Centre for Occupational and Environmental Medicine (L.A.), Stockholm County Council, Sweden
| | - M Maria Glymour
- Computational Biology Graduate Group (B.R.), Division of Epidemiology (M.G., A.M., X.S., H.Q., L.F.B.), School of Public Health, University of California, Berkeley; Institute of Environmental Medicine (M.B., A.K.H., L.A.), Karolinska Institutet, Stockholm, Sweden; Kaiser Permanente Division of Research (L.S., C.S., L.F.B.), Research Program on Genes, Environment, and Health (C.S.), Kaiser Permanente, Oakland, CA; Department of Clinical Neuroscience and Center for Molecular Medicine (J.L., A.G., T.O., J.H., I.K.), Karolinska Institutet at Karolinska University Hospital, Stockholm, Sweden; Department of Epidemiology and Biostatistics (M.M.G.), University of California, San Francisco; and Centre for Occupational and Environmental Medicine (L.A.), Stockholm County Council, Sweden
| | - Lars Alfredsson
- Computational Biology Graduate Group (B.R.), Division of Epidemiology (M.G., A.M., X.S., H.Q., L.F.B.), School of Public Health, University of California, Berkeley; Institute of Environmental Medicine (M.B., A.K.H., L.A.), Karolinska Institutet, Stockholm, Sweden; Kaiser Permanente Division of Research (L.S., C.S., L.F.B.), Research Program on Genes, Environment, and Health (C.S.), Kaiser Permanente, Oakland, CA; Department of Clinical Neuroscience and Center for Molecular Medicine (J.L., A.G., T.O., J.H., I.K.), Karolinska Institutet at Karolinska University Hospital, Stockholm, Sweden; Department of Epidemiology and Biostatistics (M.M.G.), University of California, San Francisco; and Centre for Occupational and Environmental Medicine (L.A.), Stockholm County Council, Sweden
| | - Lisa F Barcellos
- Computational Biology Graduate Group (B.R.), Division of Epidemiology (M.G., A.M., X.S., H.Q., L.F.B.), School of Public Health, University of California, Berkeley; Institute of Environmental Medicine (M.B., A.K.H., L.A.), Karolinska Institutet, Stockholm, Sweden; Kaiser Permanente Division of Research (L.S., C.S., L.F.B.), Research Program on Genes, Environment, and Health (C.S.), Kaiser Permanente, Oakland, CA; Department of Clinical Neuroscience and Center for Molecular Medicine (J.L., A.G., T.O., J.H., I.K.), Karolinska Institutet at Karolinska University Hospital, Stockholm, Sweden; Department of Epidemiology and Biostatistics (M.M.G.), University of California, San Francisco; and Centre for Occupational and Environmental Medicine (L.A.), Stockholm County Council, Sweden
| |
Collapse
|
220
|
Zhou K, Yee SW, Seiser EL, van Leeuwen N, Tavendale R, Bennett AJ, Groves CJ, Coleman RL, van der Heijden AA, Beulens JW, de Keyser CE, Zaharenko L, Rotroff DM, Out M, Jablonski KA, Chen L, Javorský M, Židzik J, Levin AM, Williams LK, Dujic T, Semiz S, Kubo M, Chien HC, Maeda S, Witte JS, Wu L, Tkáč I, Kooy A, van Schaik RHN, Stehouwer CDA, Logie L, Sutherland C, Klovins J, Pirags V, Hofman A, Stricker BH, Motsinger-Reif AA, Wagner MJ, Innocenti F, 't Hart LM, Holman RR, McCarthy MI, Hedderson MM, Palmer CNA, Florez JC, Giacomini KM, Pearson ER. Variation in the glucose transporter gene SLC2A2 is associated with glycemic response to metformin. Nat Genet 2016; 48:1055-1059. [PMID: 27500523 PMCID: PMC5007158 DOI: 10.1038/ng.3632] [Citation(s) in RCA: 141] [Impact Index Per Article: 17.6] [Reference Citation Analysis] [Abstract] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/22/2016] [Accepted: 06/30/2016] [Indexed: 02/06/2023]
Abstract
Metformin is the first-line antidiabetic drug with over 100 million users worldwide, yet its mechanism of action remains unclear1. Here the Metformin Genetics (MetGen) Consortium reports a three-stage genome wide association study (GWAS), consisting of 13,123 participants of different ancestries. The C-allele of rs8192675 in the intron of SLC2A2, which encodes the facilitated glucose transporter GLUT2, was associated with a 0.17% (p=6.6x10-14) greater metformin induced HbA1c reduction in 10,577 participants of European ancestry. rs8192675 is the top cis-eQTL for SLC2A2 in 1,226 human liver samples, suggesting a key role for hepatic GLUT2 in regulation of metformin action. In obese individuals C-allele homozygotes at rs8192675 had a 0.33% (3.6mmol/mol) greater absolute HbA1c reduction than T-allele homozygotes.This is about half the effect seen with the addition of a DPP-4 inhibitor, and equates to a dose difference of 550mg of metformin, suggesting rs8192675 as a potential biomarker for stratified medicine.
Collapse
Affiliation(s)
- Kaixin Zhou
- School of Medicine, University of Dundee, Dundee, UK
| | - Sook Wah Yee
- Department of Bioengineering and Therapeutic Sciences, University of California, San Francisco, San Francisco, California, USA
| | - Eric L Seiser
- Division of Pharmacotherapy and Experimental Therapeutics, Center for Pharmacogenomics and Individualized Therapy, Eshelman School of Pharmacy, University of North Carolina, Chapel Hill, North Carolina, USA
| | - Nienke van Leeuwen
- Department of Molecular Cell Biology, Leiden University Medical Center, Leiden, the Netherlands
| | | | - Amanda J Bennett
- Oxford Centre for Diabetes, Endocrinology and Metabolism, University of Oxford, Oxford, UK
| | - Christopher J Groves
- Oxford Centre for Diabetes, Endocrinology and Metabolism, University of Oxford, Oxford, UK
| | - Ruth L Coleman
- Diabetes Trials Unit, Oxford Centre for Diabetes, Endocrinology and Metabolism, University of Oxford, Oxford, UK
| | - Amber A van der Heijden
- Department of General Practice, EMGO+ Institute for Health and Care Research, VU University Medical Center, Amsterdam, the Netherlands
| | - Joline W Beulens
- Department of Epidemiology and Biostatistics, EMGO+ Institute for Health and Care Research, VU University Medical Center, Amsterdam, the Netherlands.,Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht, the Netherlands
| | | | - Linda Zaharenko
- Latvian Genome Data Base (LGDB), Riga, Latvia.,Latvian Biomedical Research and Study Centre, Riga, Latvia
| | - Daniel M Rotroff
- Bioinformatics Research Center, North Carolina State University, Raleigh, North Carolina, USA.,Department of Statistics, North Carolina State University, Raleigh, North Carolina, USA
| | - Mattijs Out
- Treant Zorggroep, Location Bethesda, Hoogeveen, the Netherlands.,Bethesda Diabetes Research Centre, Hoogeveen, the Netherlands
| | | | - Ling Chen
- Diabetes Unit and Center for Human Genetic Research, Massachusetts General Hospital, Boston, Massachusetts, USA
| | | | - Jozef Židzik
- Faculty of Medicine, Šafárik University, Košice, Slovakia
| | - Albert M Levin
- Department of Public Health Sciences, Henry Ford Health System, Detroit, Michigan, USA
| | - L Keoki Williams
- Center for Health Policy and Health Services Research, Henry Ford Health System, Detroit, Michigan, USA.,Department of Internal Medicine, Henry Ford Health System, Detroit, Michigan, USA
| | - Tanja Dujic
- School of Medicine, University of Dundee, Dundee, UK.,Faculty of Pharmacy, University of Sarajevo, Sarajevo, Bosnia and Herzegovina
| | - Sabina Semiz
- Faculty of Pharmacy, University of Sarajevo, Sarajevo, Bosnia and Herzegovina.,Faculty of Engineering and Natural Sciences, International University of Sarajevo, Sarajevo, Bosnia and Herzegovina
| | - Michiaki Kubo
- RIKEN Center for Integrative Medical Sciences (IMS), Yokohama, Japan
| | - Huan-Chieh Chien
- Department of Bioengineering and Therapeutic Sciences, University of California, San Francisco, San Francisco, California, USA
| | - Shiro Maeda
- Department of Advanced Genomic and Laboratory Medicine, Graduate School of Medicine, University of the Ryukyus, Nishihara, Japan.,Division of Clinical Laboratory and Blood Transfusion, University of the Ryukyus Hospital, Nishihara, Japan
| | - John S Witte
- Department of Epidemiology and Biostatistics, University of California, San Francisco, San Francisco, California, USA.,Institute for Human Genetics, University of California, San Francisco, San Francisco, California, USA.,Department of Urology, University of California, San Francisco, San Francisco, California, USA.,UCSF Helen Diller Family Comprehensive Cancer Center, University of California, San Francisco, San Francisco, California, USA
| | - Longyang Wu
- Department of Epidemiology and Biostatistics, University of California, San Francisco, San Francisco, California, USA
| | - Ivan Tkáč
- Faculty of Medicine, Šafárik University, Košice, Slovakia
| | - Adriaan Kooy
- Treant Zorggroep, Location Bethesda, Hoogeveen, the Netherlands.,Bethesda Diabetes Research Centre, Hoogeveen, the Netherlands
| | - Ron H N van Schaik
- Department of Clinical Chemistry, Erasmus University Medical Center, Rotterdam, the Netherlands
| | - Coen D A Stehouwer
- Department of Internal Medicine and Cardiovascular Research Institute Maastricht, Maastricht University Medical Center, Maastricht, the Netherlands
| | - Lisa Logie
- School of Medicine, University of Dundee, Dundee, UK
| | | | | | | | | | - Janis Klovins
- Latvian Genome Data Base (LGDB), Riga, Latvia.,Latvian Biomedical Research and Study Centre, Riga, Latvia
| | - Valdis Pirags
- Latvian Biomedical Research and Study Centre, Riga, Latvia.,Faculty of Medicine, University of Latvia, Riga, Latvia.,Department of Endocrinology, Pauls Stradins Clinical University Hospital, Riga, Latvia
| | - Albert Hofman
- Department of Epidemiology, Erasmus Medical Center, Rotterdam, the Netherlands
| | - Bruno H Stricker
- Department of Epidemiology, Erasmus Medical Center, Rotterdam, the Netherlands.,Inspectorate of Healthcare, Heerlen, the Netherlands
| | - Alison A Motsinger-Reif
- Bioinformatics Research Center, North Carolina State University, Raleigh, North Carolina, USA
| | - Michael J Wagner
- Center for Pharmacogenomics and Individualized Therapy, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA
| | - Federico Innocenti
- Division of Pharmacotherapy and Experimental Therapeutics, Center for Pharmacogenomics and Individualized Therapy, Eshelman School of Pharmacy, University of North Carolina, Chapel Hill, North Carolina, USA
| | - Leen M 't Hart
- Department of Molecular Cell Biology, Leiden University Medical Center, Leiden, the Netherlands.,Department of Epidemiology and Biostatistics, EMGO+ Institute for Health and Care Research, VU University Medical Center, Amsterdam, the Netherlands.,Department of Molecular Epidemiology, Leiden University Medical Center, Leiden, the Netherlands
| | - Rury R Holman
- Diabetes Trials Unit, Oxford Centre for Diabetes, Endocrinology and Metabolism, University of Oxford, Oxford, UK
| | - Mark I McCarthy
- Oxford Centre for Diabetes, Endocrinology and Metabolism, University of Oxford, Oxford, UK.,Wellcome Trust Centre for Human Genetics, University of Oxford, Oxford, UK.,Oxford NIHR Biomedical Research Centre, Churchill Hospital, Oxford, UK
| | - Monique M Hedderson
- Division of Research, Kaiser Permanente Northern California, Oakland, California, USA
| | | | - Jose C Florez
- Diabetes Unit and Center for Human Genetic Research, Massachusetts General Hospital, Boston, Massachusetts, USA.,Program in Metabolism, Broad Institute, Cambridge, Massachusetts, USA.,Program in Medical and Population Genetics, Broad Institute, Cambridge, Massachusetts, USA.,Department of Medicine, Harvard Medical School, Boston, Massachusetts, USA
| | - Kathleen M Giacomini
- Department of Bioengineering and Therapeutic Sciences, University of California, San Francisco, San Francisco, California, USA.,Institute for Human Genetics, University of California, San Francisco, San Francisco, California, USA
| | | |
Collapse
|
221
|
Loh PR, Palamara PF, Price AL. Fast and accurate long-range phasing in a UK Biobank cohort. Nat Genet 2016; 48:811-6. [PMID: 27270109 PMCID: PMC4925291 DOI: 10.1038/ng.3571] [Citation(s) in RCA: 221] [Impact Index Per Article: 27.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/03/2015] [Accepted: 04/22/2016] [Indexed: 01/01/2023]
Abstract
Recent work has leveraged the extensive genotyping of the Icelandic population to perform long-range phasing (LRP), enabling accurate imputation and association analysis of rare variants in target samples typed on genotyping arrays. Here we develop a fast and accurate LRP method, Eagle, that extends this paradigm to populations with much smaller proportions of genotyped samples by harnessing long (>4-cM) identical-by-descent (IBD) tracts shared among distantly related individuals. We applied Eagle to N ≈ 150,000 samples (0.2% of the British population) from the UK Biobank, and we determined that it is 1-2 orders of magnitude faster than existing methods while achieving similar or better phasing accuracy (switch error rate ≈ 0.3%, corresponding to perfect phase in a majority of 10-Mb segments). We also observed that, when used within an imputation pipeline, Eagle prephasing improved downstream imputation accuracy in comparison to prephasing in batches using existing methods, as necessary to achieve comparable computational cost.
Collapse
Affiliation(s)
- Po-Ru Loh
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, Massachusetts, USA
- Program in Medical and Population Genetics, Broad Institute of Harvard and MIT, Cambridge, Massachusetts, USA
| | - Pier Francesco Palamara
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, Massachusetts, USA
- Program in Medical and Population Genetics, Broad Institute of Harvard and MIT, Cambridge, Massachusetts, USA
| | - Alkes L Price
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, Massachusetts, USA
- Program in Medical and Population Genetics, Broad Institute of Harvard and MIT, Cambridge, Massachusetts, USA
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, Massachusetts, USA
| |
Collapse
|
222
|
Oetjens MT, Brown-Gentry K, Goodloe R, Dilks HH, Crawford DC. Population Stratification in the Context of Diverse Epidemiologic Surveys Sans Genome-Wide Data. Front Genet 2016; 7:76. [PMID: 27200085 PMCID: PMC4858524 DOI: 10.3389/fgene.2016.00076] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/28/2015] [Accepted: 04/18/2016] [Indexed: 01/01/2023] Open
Abstract
Population stratification or confounding by genetic ancestry is a potential cause of false associations in genetic association studies. Estimation of and adjustment for genetic ancestry has become common practice thanks in part to the availability of ancestry informative markers on genome-wide association study (GWAS) arrays. While array data is now widespread, these data are not ubiquitous as several large epidemiologic and clinic-based studies lack genome-wide data. One such large epidemiologic-based study lacking genome-wide data accessible to investigators is the National Health and Nutrition Examination Surveys (NHANES), population-based cross-sectional surveys of Americans linked to demographic, health, and lifestyle data conducted by the Centers for Disease Control and Prevention. DNA samples (n = 14,998) were extracted from biospecimens from consented NHANES participants between 1991-1994 (NHANES III, phase 2) and 1999-2002 and represent three major self-identified racial/ethnic groups: non-Hispanic whites (n = 6,634), non-Hispanic blacks (n = 3,458), and Mexican Americans (n = 3,950). We as the Epidemiologic Architecture for Genes Linked to Environment study genotyped candidate gene and GWAS-identified index variants in NHANES as part of the larger Population Architecture using Genomics and Epidemiology I study for collaborative genetic association studies. To enable basic quality control such as estimation of genetic ancestry to control for population stratification in NHANES san genome-wide data, we outline here strategies that use limited genetic data to identify the markers optimal for characterizing genetic ancestry. From among 411 and 295 autosomal SNPs available in NHANES III and NHANES 1999-2002, we demonstrate that markers with ancestry information can be identified to estimate global ancestry. Despite limited resolution, global genetic ancestry is highly correlated with self-identified race for the majority of participants, although less so for ethnicity. Overall, the strategies outlined here for a large epidemiologic study can be applied to other datasets accessible for genotype-phenotype studies but are sans genome-wide data.
Collapse
Affiliation(s)
- Matthew T. Oetjens
- Center for Human Genetics Research Vanderbilt University, NashvilleTN, USA
| | | | - Robert Goodloe
- Center for Human Genetics Research Vanderbilt University, NashvilleTN, USA
| | | | - Dana C. Crawford
- Department of Epidemiology and Biostatistics, Institute for Computational Biology, Case Western Reserve University, ClevelandOH, USA
| |
Collapse
|
223
|
Yudell M, Roberts D, DeSalle R, Tishkoff S. SCIENCE AND SOCIETY. Taking race out of human genetics. Science 2016; 351:564-5. [PMID: 26912690 DOI: 10.1126/science.aac4951] [Citation(s) in RCA: 302] [Impact Index Per Article: 37.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/21/2022]
Affiliation(s)
| | | | - Rob DeSalle
- American Museum of Natural History, New York, NY 10024, USA
| | | |
Collapse
|
224
|
Galinsky KJ, Bhatia G, Loh PR, Georgiev S, Mukherjee S, Patterson NJ, Price AL. Fast Principal-Component Analysis Reveals Convergent Evolution of ADH1B in Europe and East Asia. Am J Hum Genet 2016; 98:456-472. [PMID: 26924531 PMCID: PMC4827102 DOI: 10.1016/j.ajhg.2015.12.022] [Citation(s) in RCA: 226] [Impact Index Per Article: 28.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/31/2015] [Accepted: 12/31/2015] [Indexed: 01/13/2023] Open
Abstract
Searching for genetic variants with unusual differentiation between subpopulations is an established approach for identifying signals of natural selection. However, existing methods generally require discrete subpopulations. We introduce a method that infers selection using principal components (PCs) by identifying variants whose differentiation along top PCs is significantly greater than the null distribution of genetic drift. To enable the application of this method to large datasets, we developed the FastPCA software, which employs recent advances in random matrix theory to accurately approximate top PCs while reducing time and memory cost from quadratic to linear in the number of individuals, a computational improvement of many orders of magnitude. We apply FastPCA to a cohort of 54,734 European Americans, identifying 5 distinct subpopulations spanning the top 4 PCs. Using the PC-based test for natural selection, we replicate previously known selected loci and identify three new genome-wide significant signals of selection, including selection in Europeans at ADH1B. The coding variant rs1229984(∗)T has previously been associated to a decreased risk of alcoholism and shown to be under selection in East Asians; we show that it is a rare example of independent evolution on two continents. We also detect selection signals at IGFBP3 and IGH, which have also previously been associated to human disease.
Collapse
Affiliation(s)
- Kevin J Galinsky
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA 02115, USA; Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA.
| | - Gaurav Bhatia
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA; Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA 02115, USA
| | - Po-Ru Loh
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA; Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA 02115, USA
| | | | - Sayan Mukherjee
- Departments of Statistical Science, Computer Science, and Mathematics, Duke University, Durham, NC 27708, USA
| | - Nick J Patterson
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
| | - Alkes L Price
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA 02115, USA; Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA; Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA 02115, USA.
| |
Collapse
|
225
|
Risch N. Presidential Address: All in the Family, or "Gee, Our Old LaSalle Ran Great". Am J Hum Genet 2016; 98:407-416. [PMID: 26942276 PMCID: PMC4800054 DOI: 10.1016/j.ajhg.2016.02.009] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/10/2016] [Indexed: 10/22/2022] Open
Affiliation(s)
- Neil Risch
- Institute for Human Genetics, University of California, San Francisco, San Francisco, CA 94143 USA.
| |
Collapse
|
226
|
Roden DM, Denny JC. Integrating electronic health record genotype and phenotype datasets to transform patient care. Clin Pharmacol Ther 2016; 99:298-305. [PMID: 26667791 PMCID: PMC4760864 DOI: 10.1002/cpt.321] [Citation(s) in RCA: 22] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/24/2015] [Revised: 12/11/2015] [Accepted: 12/11/2015] [Indexed: 12/16/2022]
Abstract
The Health Information Technology for Economic and Clinical Health (HITECH) Act of 2009 mandates the development and implementation of electronic health record (EHR) systems across the country. While a primary goal is to improve the care of individual patients, EHRs are also key enabling resources for a vision of individualized (or personalized or precision) medicine: the aggregation of multiple EHRs within or across healthcare systems should allow discovery of patient subsets that have unusual and definable clinical trajectories that deviate importantly from the expected response in a "typical" patient. The spectrum of such personalized care can then extend from prevention to choice of medication to intensity or nature of follow-up.
Collapse
Affiliation(s)
- D M Roden
- Department of Medicine, Vanderbilt University School of Medicine, Nashville, Tennessee, USA
- Department of Biomedical Informatics, Vanderbilt University School of Medicine, Nashville, Tennessee, USA
- Department of Pharmacology, Vanderbilt University School of Medicine, Nashville, Tennessee, USA
| | - J C Denny
- Department of Medicine, Vanderbilt University School of Medicine, Nashville, Tennessee, USA
- Department of Biomedical Informatics, Vanderbilt University School of Medicine, Nashville, Tennessee, USA
| |
Collapse
|
227
|
DeLorenze GN, Nelson CL, Scott WK, Allen AS, Ray GT, Tsai AL, Quesenberry CP, Fowler VG. Polymorphisms in HLA Class II Genes Are Associated With Susceptibility to Staphylococcus aureus Infection in a White Population. J Infect Dis 2016; 213:816-23. [PMID: 26450422 PMCID: PMC4747615 DOI: 10.1093/infdis/jiv483] [Citation(s) in RCA: 35] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/21/2015] [Accepted: 09/30/2015] [Indexed: 12/31/2022] Open
Abstract
BACKGROUND Staphylococcus aureus can cause life-threatening infections. Human susceptibility to S. aureus infection may be influenced by host genetic variation. METHODS A genome-wide association study (GWAS) in a large health plan-based cohort included biologic specimens from 4701 culture-confirmed S. aureus cases and 45 344 matched controls; 584 535 single-nucleotide polymorphisms (SNPs) were genotyped on an array specific to individuals of European ancestry. Coverage was increased by imputation of >25 million common SNPs, using the 1000 Genomes Reference panel. In addition, human leukocyte antigen (HLA) serotypes were also imputed. RESULTS Logistic regression analysis, performed under the assumption of an additive genetic model, revealed several imputed SNPs (eg, rs115231074: odds ratio [OR], 1.22 [P = 1.3 × 10(-10)]; rs35079132: OR, 1.24 [P = 3.8 × 10(-8)]) achieving genome-wide significance on chromosome 6 in the HLA class II region. One adjacent genotyped SNP was nearly genome-wide significant (rs4321864: OR, 1.13; P = 8.8 × 10(-8)). These polymorphisms are located near the genes encoding HLA-DRA and HLA-DRB1. Results of further logistic regression analysis, in which the most significant GWAS SNPs were conditioned on HLA-DRB1*04 serotype, showed additional support for the strength of association between HLA class II genetic variants and S. aureus infection. CONCLUSIONS Our study results are the first reported evidence of human genetic susceptibility to S. aureus infection.
Collapse
Affiliation(s)
| | | | - William K Scott
- John P. Hussman Institute for Human Genomics Dr. John T. Macdonald Foundation Department of Human Genetics, University of Miami Miller School of Medicine, Florida
| | - Andrew S Allen
- Department of Biostatistics and Bioinformatics, Duke University School of Medicine, Durham, North Carolina
| | - G Thomas Ray
- Division of Research, Kaiser Permanente Northern California, Oakland
| | - Ai-Lin Tsai
- Division of Research, Kaiser Permanente Northern California, Oakland
| | | | - Vance G Fowler
- Duke Clinical Research Institute Division of Infectious Diseases, Duke University Medical Center
| |
Collapse
|
228
|
Unravelling the human genome-phenome relationship using phenome-wide association studies. Nat Rev Genet 2016; 17:129-45. [PMID: 26875678 DOI: 10.1038/nrg.2015.36] [Citation(s) in RCA: 168] [Impact Index Per Article: 21.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/24/2022]
Abstract
Advances in genotyping technology have, over the past decade, enabled the focused search for common genetic variation associated with human diseases and traits. With the recently increased availability of detailed phenotypic data from electronic health records and epidemiological studies, the impact of one or more genetic variants on the phenome is starting to be characterized both in clinical and population-based settings using phenome-wide association studies (PheWAS). These studies reveal a number of challenges that will need to be overcome to unlock the full potential of PheWAS for the characterization of the complex human genome-phenome relationship.
Collapse
|
229
|
Asgari MM, Wang W, Ioannidis NM, Itnyre J, Hoffmann T, Jorgenson E, Whittemore AS. Identification of Susceptibility Loci for Cutaneous Squamous Cell Carcinoma. J Invest Dermatol 2016; 136:930-937. [PMID: 26829030 DOI: 10.1016/j.jid.2016.01.013] [Citation(s) in RCA: 78] [Impact Index Per Article: 9.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/16/2015] [Revised: 01/04/2016] [Accepted: 01/06/2016] [Indexed: 12/20/2022]
Abstract
We report a genome-wide association study of cutaneous squamous cell carcinoma conducted among non-Hispanic white members of the Kaiser Permanente Northern California health care system. The study includes a genome-wide screen of 61,457 members (6,891 cases and 54,566 controls) genotyped on the Affymetrix Axiom European array and a replication phase involving an independent set of 6,410 additional members (810 cases and 5,600 controls). Combined analysis of screening and replication phases identified 10 loci containing single-nucleotide polymorphisms (SNPs) with P-values < 5 × 10(-8). Six loci contain genes in the pigmentation pathway; SNPs at these loci appear to modulate squamous cell carcinoma risk independently of the pigmentation phenotypes. Another locus contains HLA class II genes studied in relation to elevated squamous cell carcinoma risk following immunosuppression. SNPs at the remaining three loci include an intronic SNP in FOXP1 at locus 3p13, an intergenic SNP at 3q28 near TP63, and an intergenic SNP at 9p22 near BNC2. These findings provide insights into the genetic factors accounting for inherited squamous cell carcinoma susceptibility.
Collapse
Affiliation(s)
- Maryam M Asgari
- Department of Dermatology, Massachusetts General Hospital, Boston, Massachusetts, USA; Division of Research, Kaiser Permanente Northern California, Oakland, California, USA
| | - Wei Wang
- Department of Health Research and Policy, Stanford University School of Medicine, Stanford, California, USA
| | - Nilah M Ioannidis
- Department of Health Research and Policy, Stanford University School of Medicine, Stanford, California, USA; Department of Genetics, Stanford University School of Medicine, Stanford, California, USA
| | - Jacqueline Itnyre
- Department of Health Research and Policy, Stanford University School of Medicine, Stanford, California, USA
| | - Thomas Hoffmann
- Department of Epidemiology and Biostatistics and Institute for Human Genetics, University of California, San Francisco, California, USA
| | - Eric Jorgenson
- Division of Research, Kaiser Permanente Northern California, Oakland, California, USA
| | - Alice S Whittemore
- Department of Health Research and Policy, Stanford University School of Medicine, Stanford, California, USA.
| |
Collapse
|
230
|
Shen L, Walter S, Melles RB, Glymour MM, Jorgenson E. Diabetes Pathology and Risk of Primary Open-Angle Glaucoma: Evaluating Causal Mechanisms by Using Genetic Information. Am J Epidemiol 2016; 183:147-55. [PMID: 26608880 DOI: 10.1093/aje/kwv204] [Citation(s) in RCA: 22] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/20/2015] [Accepted: 07/29/2015] [Indexed: 12/13/2022] Open
Abstract
Although type 2 diabetes (T2D) predicts glaucoma, the potential for unmeasured confounding has hampered causal conclusions. We performed separate sample genetic instrumental variable analyses using the Genetic Epidemiology Research Study on Adult Health and Aging cohort (n = 69,685; 1995-2013) to estimate effects of T2D on primary open-angle glaucoma (POAG; 3,554 cases). Genetic instrumental variables for overall and mechanism-specific (i.e., linked to T2D via influences on adiposity, β-cell function, insulin regulation, or other metabolic processes) T2D risk were constructed by using 39 genetic polymorphisms established to predict T2D in other samples. Instrumental variable estimates indicated that T2D increased POAG risk (odds ratio = 2.53, 95% confidence interval: 1.04, 6.11). The instrumental variable for β-cell dysregulation also significantly predicted POAG (odds ratioβ-cell = 5.26, 95% confidence interval: 1.75, 15.85), even among individuals without diagnosed T2D, suggesting that metabolic dysregulation may increase POAG risk prior to T2D diagnosis. The T2D risk variant in the melatonin receptor 1B gene (MTNR1B) predicted risk of POAG independently of T2D status, indicating possible pleiotropic physiological functions of melatonin, but instrumental variable effect estimates were significant even excluding MTNR1B variants. To our knowledge, this is the first genetic instrumental variable study of T2D and glaucoma, providing a novel approach to evaluating this hypothesized relationship. Our findings substantially bolster observational evidence that T2D increases POAG risk.
Collapse
|
231
|
Common coding variants in the HLA-DQB1 region confer susceptibility to age-related macular degeneration. Eur J Hum Genet 2016; 24:1049-55. [PMID: 26733291 DOI: 10.1038/ejhg.2015.247] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/25/2015] [Revised: 09/21/2015] [Accepted: 10/15/2015] [Indexed: 11/08/2022] Open
Abstract
Age-related macular degeneration (AMD) risk variants in the complement system point to the important role of immune response and inflammation in the pathogenesis of AMD. Although the human leukocyte antigen (HLA) region has a central role in regulating immune response, previous studies of genetic variation in HLA genes and AMD have been limited by sample size or incomplete coverage of the HLA region by first-generation genotyping arrays and imputation panels. Here, we conducted a large-scale HLA fine-mapping study with 4841 AMD cases and 23 790 controls of non-Hispanic white ancestry from the Kaiser Permanente Genetic Epidemiology Research on Adult Health and Aging cohort. Genotyping was conducted using custom Affymetrix Axiom arrays, with dense coverage of the HLA region. Classic HLA polymorphisms were imputed using SNP2HLA, which utilizes a large reference panel to provide improved imputation accuracy of variants in this region. We examined a total of 6937 SNPs and 172 classical HLA alleles, conditioning on established AMD risk variants, which revealed novel associations with two non-synonymous SNPs in perfect linkage disequilibrium, rs9274390 and rs41563814 (odds ratio (OR)=1.21; P=1.4 × 10(-11)) corresponding to amino-acid changes at position 66 and 67 in HLA-DQB1, respectively, and the DQB1*02 classical HLA allele (OR=1.22; P=3.9 × 10(-10)) with the risk of AMD. We confirmed these association signals, again conditioning on established risk variants, in the MMAP data set of subjects with advanced AMD (rs9274390/rs41563814: OR=1.28; P=1.30 × 10(-3), DQB1*02: OR=1.32; P=9.00 × 10(-4)). These findings support a role of HLA class II alleles in the risk of AMD.
Collapse
|
232
|
Jorgenson E, Makki N, Shen L, Chen DC, Tian C, Eckalbar WL, Hinds D, Ahituv N, Avins A. A genome-wide association study identifies four novel susceptibility loci underlying inguinal hernia. Nat Commun 2015; 6:10130. [PMID: 26686553 PMCID: PMC4703831 DOI: 10.1038/ncomms10130] [Citation(s) in RCA: 56] [Impact Index Per Article: 6.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/24/2015] [Accepted: 11/06/2015] [Indexed: 12/12/2022] Open
Abstract
Inguinal hernia repair is one of the most commonly performed operations in the world, yet little is known about the genetic mechanisms that predispose individuals to develop inguinal hernias. We perform a genome-wide association analysis of surgically confirmed inguinal hernias in 72,805 subjects (5,295 cases and 67,510 controls) and confirm top associations in an independent cohort of 92,444 subjects with self-reported hernia repair surgeries (9,701 cases and 82,743 controls). We identify four novel inguinal hernia susceptibility loci in the regions of EFEMP1, WT1, EBF2 and ADAMTS6. Moreover, we observe expression of all four genes in mouse connective tissue and network analyses show an important role for two of these genes (EFEMP1 and WT1) in connective tissue maintenance/homoeostasis. Our findings provide insight into the aetiology of hernia development and highlight genetic pathways for studies of hernia development and its treatment. Inguinal hernia has high lifetime prevalence, especially in men. This genome-wide association study identifies 4 loci to be associated with inguinal hernia, and shows expression of nearby genes in mouse connective tissues.
Collapse
Affiliation(s)
- Eric Jorgenson
- Kaiser Permanente Northern California, Division of Research, Oakland, California 94612, USA
| | - Nadja Makki
- Department of Bioengineering and Therapeutic Sciences, UCSF, San Francisco, California 94158, USA.,Institute for Human Genetics, UCSF, San Francisco, California 94158, USA
| | - Ling Shen
- Kaiser Permanente Northern California, Division of Research, Oakland, California 94612, USA
| | - David C Chen
- Lichtenstein Amid Hernia Clinic, David Geffen School of Medicine at University of California Los Angeles, Los Angeles, California 90095, USA
| | - Chao Tian
- 23andMe Inc. 899 W. Evelyn Avenue, Mountain View, California 94041, USA
| | - Walter L Eckalbar
- Department of Bioengineering and Therapeutic Sciences, UCSF, San Francisco, California 94158, USA.,Institute for Human Genetics, UCSF, San Francisco, California 94158, USA
| | - David Hinds
- 23andMe Inc. 899 W. Evelyn Avenue, Mountain View, California 94041, USA
| | - Nadav Ahituv
- Department of Bioengineering and Therapeutic Sciences, UCSF, San Francisco, California 94158, USA.,Institute for Human Genetics, UCSF, San Francisco, California 94158, USA
| | - Andrew Avins
- Kaiser Permanente Northern California, Division of Research, Oakland, California 94612, USA
| |
Collapse
|
233
|
Human genotype–phenotype databases: aims, challenges and opportunities. Nat Rev Genet 2015; 16:702-15. [DOI: 10.1038/nrg3932] [Citation(s) in RCA: 77] [Impact Index Per Article: 8.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/18/2022]
|