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Sariya S, Lee JH, Mayeux R, Vardarajan BN, Reyes-Dumeyer D, Manly JJ, Brickman AM, Lantigua R, Medrano M, Jimenez-Velazquez IZ, Tosto G. Rare Variants Imputation in Admixed Populations: Comparison Across Reference Panels and Bioinformatics Tools. Front Genet 2019; 10:239. [PMID: 31001313 PMCID: PMC6456789 DOI: 10.3389/fgene.2019.00239] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/06/2018] [Accepted: 03/04/2019] [Indexed: 11/13/2022] Open
Abstract
Background Imputation has become a standard approach in genome-wide association studies (GWAS) to infer in silico untyped markers. Although feasibility for common variants imputation is well established, we aimed to assess rare and ultra-rare variants’ imputation in an admixed Caribbean Hispanic population (CH). Methods We evaluated imputation accuracy in CH (N = 1,000), focusing on rare (0.1% ≤ minor allele frequency (MAF) ≤ 1%) and ultra-rare (MAF < 0.1%) variants. We used two reference panels, the Haplotype Reference Consortium (HRC; N = 27,165) and 1000 Genome Project (1000G phase 3; N = 2,504) and multiple phasing (SHAPEIT, Eagle2) and imputation algorithms (IMPUTE2, MACH-Admix). To assess imputation quality, we reported: (a) high-quality variant counts according to imputation tools’ internal indexes (e.g., IMPUTE2 “Info” ≥ 80%). (b) Wilcoxon Signed-Rank Test comparing imputation quality for genotyped variants that were masked and imputed; (c) Cohen’s kappa coefficient to test agreement between imputed and whole-exome sequencing (WES) variants; (d) imputation of G206A mutation in the PSEN1 (ultra-rare in the general population an more frequent in CH) followed by confirmation genotyping. We also tested ancestry proportion (European, African and Native American) against WES-imputation mismatches in a Poisson regression fashion. Results SHAPEIT2 retrieved higher percentage of imputed high-quality variants than Eagle2 (rare: 51.02% vs. 48.60%; ultra-rare 0.66% vs. 0.65%, Wilcoxon p-value < 0.001). SHAPEIT-IMPUTE2 employing HRC outperformed 1000G (64.50% vs. 59.17%; 1.69% vs. 0.75% for high-quality rare and ultra-rare variants, respectively, Wilcoxon p-value < 0.001). SHAPEIT-IMPUTE2 outperformed MaCH-Admix. Compared to 1000G, HRC-imputation retrieved a higher number of high-quality rare and ultra-rare variants, despite showing lower agreement between imputed and WES variants (e.g., rare: 98.86% for HRC vs. 99.02% for 1000G). High Kappa (K = 0.99) was observed for both reference panels. Twelve G206A mutation carriers were imputed and all validated by confirmation genotyping. African ancestry was associated with higher imputation errors for uncommon and rare variants (p-value < 1e-05). Conclusion Reference panels with larger numbers of haplotypes can improve imputation quality for rare and ultra-rare variants in admixed populations such as CH. Ethnic composition is an important predictor of imputation accuracy, with higher African ancestry associated with poorer imputation accuracy.
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Affiliation(s)
- Sanjeev Sariya
- Taub Institute for Research on Alzheimer's Disease and the Aging Brain, Vagelos College of Physicians and Surgeons, Columbia University, New York, NY, United States.,The Gertrude H. Sergievsky Center, College of Physicians and Surgeons, Columbia University, New York, NY, United States
| | - Joseph H Lee
- Taub Institute for Research on Alzheimer's Disease and the Aging Brain, Vagelos College of Physicians and Surgeons, Columbia University, New York, NY, United States.,The Gertrude H. Sergievsky Center, College of Physicians and Surgeons, Columbia University, New York, NY, United States.,Department of Neurology, College of Physicians and Surgeons, New York-Presbyterian Hospital, Columbia University Medical Center, New York, NY, United States
| | - Richard Mayeux
- Taub Institute for Research on Alzheimer's Disease and the Aging Brain, Vagelos College of Physicians and Surgeons, Columbia University, New York, NY, United States.,The Gertrude H. Sergievsky Center, College of Physicians and Surgeons, Columbia University, New York, NY, United States.,Department of Neurology, College of Physicians and Surgeons, New York-Presbyterian Hospital, Columbia University Medical Center, New York, NY, United States
| | - Badri N Vardarajan
- Taub Institute for Research on Alzheimer's Disease and the Aging Brain, Vagelos College of Physicians and Surgeons, Columbia University, New York, NY, United States.,The Gertrude H. Sergievsky Center, College of Physicians and Surgeons, Columbia University, New York, NY, United States
| | - Dolly Reyes-Dumeyer
- Taub Institute for Research on Alzheimer's Disease and the Aging Brain, Vagelos College of Physicians and Surgeons, Columbia University, New York, NY, United States.,The Gertrude H. Sergievsky Center, College of Physicians and Surgeons, Columbia University, New York, NY, United States
| | - Jennifer J Manly
- Taub Institute for Research on Alzheimer's Disease and the Aging Brain, Vagelos College of Physicians and Surgeons, Columbia University, New York, NY, United States.,The Gertrude H. Sergievsky Center, College of Physicians and Surgeons, Columbia University, New York, NY, United States.,Department of Neurology, College of Physicians and Surgeons, New York-Presbyterian Hospital, Columbia University Medical Center, New York, NY, United States
| | - Adam M Brickman
- Taub Institute for Research on Alzheimer's Disease and the Aging Brain, Vagelos College of Physicians and Surgeons, Columbia University, New York, NY, United States.,The Gertrude H. Sergievsky Center, College of Physicians and Surgeons, Columbia University, New York, NY, United States.,Department of Neurology, College of Physicians and Surgeons, New York-Presbyterian Hospital, Columbia University Medical Center, New York, NY, United States
| | - Rafael Lantigua
- Medicine College of Physicians and Surgeons, and The Department of Epidemiology, School of Public Health, Columbia University, New York, NY, United States
| | - Martin Medrano
- School of Medicine, Pontificia Universidad Catolica Madre y Maestra, Santiago, Dominican Republic
| | - Ivonne Z Jimenez-Velazquez
- Department of Medicine, Geriatrics Program, University of Puerto Rico School of Medicine, San Juan, Puerto Rico
| | - Giuseppe Tosto
- Taub Institute for Research on Alzheimer's Disease and the Aging Brain, Vagelos College of Physicians and Surgeons, Columbia University, New York, NY, United States.,The Gertrude H. Sergievsky Center, College of Physicians and Surgeons, Columbia University, New York, NY, United States.,Department of Neurology, College of Physicians and Surgeons, New York-Presbyterian Hospital, Columbia University Medical Center, New York, NY, United States
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52
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Nazarian A, Arbeev KG, Yashkin AP, Kulminski AM. Genetic heterogeneity of Alzheimer's disease in subjects with and without hypertension. GeroScience 2019; 41:137-154. [PMID: 31055733 PMCID: PMC6544706 DOI: 10.1007/s11357-019-00071-5] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/03/2019] [Accepted: 04/25/2019] [Indexed: 01/01/2023] Open
Abstract
Alzheimer's disease (AD) is a progressive neurodegenerative disorder caused by the interplay of multiple genetic and non-genetic factors. Hypertension is one of the AD risk factors that has been linked to underlying pathological changes like senile plaques and neurofibrillary tangles formation as well as hippocampal atrophy. In this study, we investigated the differences in the genetic architecture of AD between hypertensive and non-hypertensive subjects in four independent cohorts. Our genome-wide association analyses revealed significant associations of 15 novel potentially AD-associated polymorphisms (P < 5E-06) that were located outside the chromosome 19q13 region and were significant either in hypertensive or non-hypertensive groups. The closest genes to 14 polymorphisms were not associated with AD at P < 5E-06 in previous genome-wide association studies (GWAS). Also, four of them were located within two chromosomal regions (i.e., 3q13.11 and 17q21.2) that were not associated with AD at P < 5E-06 before. In addition, 30 genes demonstrated evidence of group-specific associations with AD at the false discovery rates (FDR) < 0.05 in our gene-based and transcriptome-wide association analyses. The chromosomal regions corresponding to four genes (i.e., 2p13.1, 9p13.3, 17q12, and 18q21.1) were not associated with AD at P < 5E-06 in previous GWAS. These genes may serve as a list of prioritized candidates for future functional studies. Our pathway-enrichment analyses revealed the associations of 11 non-group-specific and four group-specific pathways with AD at FDR < 0.05. These findings provided novel insights into the potential genetic heterogeneity of AD among subjects with and without hypertension.
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Affiliation(s)
- Alireza Nazarian
- Biodemography of Aging Research Unit, Social Science Research Institute, Duke University, Erwin Mill Building, 2024 W. Main St., Durham, NC, 27705, USA.
| | - Konstantin G Arbeev
- Biodemography of Aging Research Unit, Social Science Research Institute, Duke University, Erwin Mill Building, 2024 W. Main St., Durham, NC, 27705, USA
| | - Arseniy P Yashkin
- Biodemography of Aging Research Unit, Social Science Research Institute, Duke University, Erwin Mill Building, 2024 W. Main St., Durham, NC, 27705, USA
| | - Alexander M Kulminski
- Biodemography of Aging Research Unit, Social Science Research Institute, Duke University, Erwin Mill Building, 2024 W. Main St., Durham, NC, 27705, USA.
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53
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Schurz H, Müller SJ, van Helden PD, Tromp G, Hoal EG, Kinnear CJ, Möller M. Evaluating the Accuracy of Imputation Methods in a Five-Way Admixed Population. Front Genet 2019; 10:34. [PMID: 30804980 PMCID: PMC6370942 DOI: 10.3389/fgene.2019.00034] [Citation(s) in RCA: 35] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/27/2018] [Accepted: 01/17/2019] [Indexed: 12/30/2022] Open
Abstract
Genotype imputation is a powerful tool for increasing statistical power in an association analysis. Meta-analysis of multiple study datasets also requires a substantial overlap of SNPs for a successful association analysis, which can be achieved by imputation. Quality of imputed datasets is largely dependent on the software used, as well as the reference populations chosen. The accuracy of imputation of available reference populations has not been tested for the five-way admixed South African Colored (SAC) population. In this study, imputation results obtained using three freely-accessible methods were evaluated for accuracy and quality. We show that the African Genome Resource is the best reference panel for imputation of missing genotypes in samples from the SAC population, implemented via the freely accessible Sanger Imputation Server.
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Affiliation(s)
- Haiko Schurz
- DST-NRF Centre of Excellence for Biomedical Tuberculosis Research, South African Medical Research Council Centre for Tuberculosis Research, Division of Molecular Biology and Human Genetics, Faculty of Medicine and Health Sciences, Stellenbosch University, Cape Town, South Africa.,South African Tuberculosis Bioinformatics Initiative (SATBBI), Faculty of Medicine and Health Sciences, Stellenbosch University, Cape Town, South Africa
| | - Stephanie J Müller
- DST-NRF Centre of Excellence for Biomedical Tuberculosis Research, South African Medical Research Council Centre for Tuberculosis Research, Division of Molecular Biology and Human Genetics, Faculty of Medicine and Health Sciences, Stellenbosch University, Cape Town, South Africa.,South African Tuberculosis Bioinformatics Initiative (SATBBI), Faculty of Medicine and Health Sciences, Stellenbosch University, Cape Town, South Africa
| | - Paul David van Helden
- DST-NRF Centre of Excellence for Biomedical Tuberculosis Research, South African Medical Research Council Centre for Tuberculosis Research, Division of Molecular Biology and Human Genetics, Faculty of Medicine and Health Sciences, Stellenbosch University, Cape Town, South Africa
| | - Gerard Tromp
- DST-NRF Centre of Excellence for Biomedical Tuberculosis Research, South African Medical Research Council Centre for Tuberculosis Research, Division of Molecular Biology and Human Genetics, Faculty of Medicine and Health Sciences, Stellenbosch University, Cape Town, South Africa.,South African Tuberculosis Bioinformatics Initiative (SATBBI), Faculty of Medicine and Health Sciences, Stellenbosch University, Cape Town, South Africa
| | - Eileen G Hoal
- DST-NRF Centre of Excellence for Biomedical Tuberculosis Research, South African Medical Research Council Centre for Tuberculosis Research, Division of Molecular Biology and Human Genetics, Faculty of Medicine and Health Sciences, Stellenbosch University, Cape Town, South Africa
| | - Craig J Kinnear
- DST-NRF Centre of Excellence for Biomedical Tuberculosis Research, South African Medical Research Council Centre for Tuberculosis Research, Division of Molecular Biology and Human Genetics, Faculty of Medicine and Health Sciences, Stellenbosch University, Cape Town, South Africa
| | - Marlo Möller
- DST-NRF Centre of Excellence for Biomedical Tuberculosis Research, South African Medical Research Council Centre for Tuberculosis Research, Division of Molecular Biology and Human Genetics, Faculty of Medicine and Health Sciences, Stellenbosch University, Cape Town, South Africa
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54
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Nazarian A, Yashin AI, Kulminski AM. Genome-wide analysis of genetic predisposition to Alzheimer's disease and related sex disparities. ALZHEIMERS RESEARCH & THERAPY 2019; 11:5. [PMID: 30636644 PMCID: PMC6330399 DOI: 10.1186/s13195-018-0458-8] [Citation(s) in RCA: 47] [Impact Index Per Article: 9.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/22/2018] [Accepted: 12/06/2018] [Indexed: 12/21/2022]
Abstract
BACKGROUND Alzheimer's disease (AD) is the most common cause of dementia in the elderly and the sixth leading cause of death in the United States. AD is mainly considered a complex disorder with polygenic inheritance. Despite discovering many susceptibility loci, a major proportion of AD genetic variance remains to be explained. METHODS We investigated the genetic architecture of AD in four publicly available independent datasets through genome-wide association, transcriptome-wide association, and gene-based and pathway-based analyses. To explore differences in the genetic basis of AD between males and females, analyses were performed on three samples in each dataset: males and females combined, only males, or only females. RESULTS Our genome-wide association analyses corroborated the associations of several previously detected AD loci and revealed novel significant associations of 35 single-nucleotide polymorphisms (SNPs) outside the chromosome 19q13 region at the suggestive significance level of p < 5E-06. These SNPs were mapped to 21 genes in 19 chromosomal regions. Of these, 17 genes were not associated with AD at genome-wide or suggestive levels of associations by previous genome-wide association studies. Also, the chromosomal regions corresponding to 8 genes did not contain any previously detected AD-associated SNPs with p < 5E-06. Our transcriptome-wide association and gene-based analyses revealed that 26 genes located in 20 chromosomal regions outside chromosome 19q13 had evidence of potential associations with AD at a false discovery rate of 0.05. Of these, 13 genes/regions did not contain any previously AD-associated SNPs at genome-wide or suggestive levels of associations. Most of the newly detected AD-associated SNPs and genes were sex specific, indicating sex disparities in the genetic basis of AD. Also, 7 of 26 pathways that showed evidence of associations with AD in our pathway-bases analyses were significant only in females. CONCLUSIONS Our findings, particularly the newly discovered sex-specific genetic contributors, provide novel insight into the genetic architecture of AD and can advance our understanding of its pathogenesis.
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Affiliation(s)
- Alireza Nazarian
- Biodemography of Aging Research Unit, Social Science Research Institute, Duke University, Erwin Mill Building, 2024 W. Main St., Durham, NC, 27705, USA.
| | - Anatoliy I Yashin
- Biodemography of Aging Research Unit, Social Science Research Institute, Duke University, Erwin Mill Building, 2024 W. Main St., Durham, NC, 27705, USA
| | - Alexander M Kulminski
- Biodemography of Aging Research Unit, Social Science Research Institute, Duke University, Erwin Mill Building, 2024 W. Main St., Durham, NC, 27705, USA.
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55
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Zhang X, Veturi Y, Verma S, Bone W, Verma A, Lucas A, Hebbring S, Denny JC, Stanaway IB, Jarvik GP, Crosslin D, Larson EB, Rasmussen-Torvik L, Pendergrass SA, Smoller JW, Hakonarson H, Sleiman P, Weng C, Fasel D, Wei WQ, Kullo I, Schaid D, Chung WK, Ritchie MD. Detecting potential pleiotropy across cardiovascular and neurological diseases using univariate, bivariate, and multivariate methods on 43,870 individuals from the eMERGE network. PACIFIC SYMPOSIUM ON BIOCOMPUTING. PACIFIC SYMPOSIUM ON BIOCOMPUTING 2019; 24:272-283. [PMID: 30864329 PMCID: PMC6457436] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 11/05/2022]
Abstract
The link between cardiovascular diseases and neurological disorders has been widely observed in the aging population. Disease prevention and treatment rely on understanding the potential genetic nexus of multiple diseases in these categories. In this study, we were interested in detecting pleiotropy, or the phenomenon in which a genetic variant influences more than one phenotype. Marker-phenotype association approaches can be grouped into univariate, bivariate, and multivariate categories based on the number of phenotypes considered at one time. Here we applied one statistical method per category followed by an eQTL colocalization analysis to identify potential pleiotropic variants that contribute to the link between cardiovascular and neurological diseases. We performed our analyses on ~530,000 common SNPs coupled with 65 electronic health record (EHR)-based phenotypes in 43,870 unrelated European adults from the Electronic Medical Records and Genomics (eMERGE) network. There were 31 variants identified by all three methods that showed significant associations across late onset cardiac- and neurologic- diseases. We further investigated functional implications of gene expression on the detected "lead SNPs" via colocalization analysis, providing a deeper understanding of the discovered associations. In summary, we present the framework and landscape for detecting potential pleiotropy using univariate, bivariate, multivariate, and colocalization methods. Further exploration of these potentially pleiotropic genetic variants will work toward understanding disease causing mechanisms across cardiovascular and neurological diseases and may assist in considering disease prevention as well as drug repositioning in future research.
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Affiliation(s)
- Xinyuan Zhang
- Genomics and Computational Biology Graduate Group, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA*Authors contributed equally to this work
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56
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Speed D, Balding DJ. SumHer better estimates the SNP heritability of complex traits from summary statistics. Nat Genet 2018; 51:277-284. [PMID: 30510236 PMCID: PMC6485398 DOI: 10.1038/s41588-018-0279-5] [Citation(s) in RCA: 125] [Impact Index Per Article: 20.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/12/2018] [Accepted: 10/17/2018] [Indexed: 11/09/2022]
Abstract
We present SumHer, software for estimating confounding bias, SNP heritability, enrichments of heritability and genetic correlations using summary statistics from genome-wide association studies. The key difference between SumHer and the existing software LD Score Regression (LDSC) is that SumHer allows the user to specify the heritability model. We apply SumHer to results from 24 large-scale association studies (average sample size 121,000) using our recommended heritability model. We show that these studies tended to substantially over-correct for confounding, and as a result the number of genome-wide significant loci was under-reported by about a quarter. We also estimate enrichments for 24 categories of SNPs defined by functional annotations. A previous study using LDSC reported that conserved regions were 13-fold enriched, and found a further six categories with above threefold enrichment. By contrast, our analysis using SumHer finds that none of the categories have enrichment above twofold. SumHer provides an improved understanding of the genetic architecture of complex traits, which enables more efficient analysis of future genetic data.
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Affiliation(s)
- Doug Speed
- Aarhus Institute of Advanced Studies, Aarhus University, Aarhus, Denmark. .,Bioinformatics Research Centre, Aarhus University, Aarhus, Denmark. .,UCL Genetics Institute, University College London, London, UK.
| | - David J Balding
- UCL Genetics Institute, University College London, London, UK.,Melbourne Integrative Genomics, School of BioSciences and School of Mathematics & Statistics, University of Melbourne, Melbourne, Victoria, Australia
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57
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Krittanawong C, Johnson KW, Hershman SG, Tang WW. Big data, artificial intelligence, and cardiovascular precision medicine. EXPERT REVIEW OF PRECISION MEDICINE AND DRUG DEVELOPMENT 2018. [DOI: 10.1080/23808993.2018.1528871] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/23/2022]
Affiliation(s)
- Chayakrit Krittanawong
- Department of Internal Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Kipp W. Johnson
- Institute for Next Generation Healthcare, Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Steven G. Hershman
- Department of Medicine, Stanford University, Stanford, CA, USA
- Division of Cardiovascular Medicine, Department of Medicine, Stanford University, Stanford, CA, USA
| | - W.H. Wilson Tang
- Department of Cardiovascular Medicine, Heart and Vascular Institute, Cleveland Clinic, Cleveland, OH, USA
- Department of Cellular and Molecular Medicine, Lerner Research Institute, Cleveland, OH, USA
- Center for Clinical Genomics, Cleveland Clinic, Cleveland, OH, USA
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58
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Stanaway IB, Hall TO, Rosenthal EA, Palmer M, Naranbhai V, Knevel R, Namjou-Khales B, Carroll RJ, Kiryluk K, Gordon AS, Linder J, Howell KM, Mapes BM, Lin FTJ, Joo YY, Hayes MG, Gharavi AG, Pendergrass SA, Ritchie MD, de Andrade M, Croteau-Chonka DC, Raychaudhuri S, Weiss ST, Lebo M, Amr SS, Carrell D, Larson EB, Chute CG, Rasmussen-Torvik LJ, Roy-Puckelwartz MJ, Sleiman P, Hakonarson H, Li R, Karlson EW, Peterson JF, Kullo IJ, Chisholm R, Denny JC, Jarvik GP, Crosslin DR. The eMERGE genotype set of 83,717 subjects imputed to ~40 million variants genome wide and association with the herpes zoster medical record phenotype. Genet Epidemiol 2018; 43:63-81. [PMID: 30298529 PMCID: PMC6375696 DOI: 10.1002/gepi.22167] [Citation(s) in RCA: 33] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/12/2018] [Revised: 08/10/2018] [Accepted: 08/28/2018] [Indexed: 12/30/2022]
Abstract
The Electronic Medical Records and Genomics (eMERGE) network is a network of medical centers with electronic medical records linked to existing biorepository samples for genomic discovery and genomic medicine research. The network sought to unify the genetic results from 78 Illumina and Affymetrix genotype array batches from 12 contributing medical centers for joint association analysis of 83,717 human participants. In this report, we describe the imputation of eMERGE results and methods to create the unified imputed merged set of genome‐wide variant genotype data. We imputed the data using the Michigan Imputation Server, which provides a missing single‐nucleotide variant genotype imputation service using the minimac3 imputation algorithm with the Haplotype Reference Consortium genotype reference set. We describe the quality control and filtering steps used in the generation of this data set and suggest generalizable quality thresholds for imputation and phenotype association studies. To test the merged imputed genotype set, we replicated a previously reported chromosome 6 HLA‐B herpes zoster (shingles) association and discovered a novel zoster‐associated loci in an epigenetic binding site near the terminus of chromosome 3 (3p29).
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Affiliation(s)
- Ian B Stanaway
- Department of Biomedical Informatics Medical Education, School of Medicine, University of Washington, Seattle, Washington
| | - Taryn O Hall
- Department of Biomedical Informatics Medical Education, School of Medicine, University of Washington, Seattle, Washington
| | - Elisabeth A Rosenthal
- Division of Medical Genetics, School of Medicine, University of Washington, Seattle, Washington
| | - Melody Palmer
- Division of Medical Genetics, School of Medicine, University of Washington, Seattle, Washington
| | - Vivek Naranbhai
- Department of Biomedical Informatics Medical Education, School of Medicine, University of Washington, Seattle, Washington.,Harvard Medical School, Harvard University, Cambridge, Massachusetts
| | - Rachel Knevel
- Harvard Medical School, Harvard University, Cambridge, Massachusetts
| | - Bahram Namjou-Khales
- Department of Pediatrics, Cincinnati Children's Hospital Medical Center, Cincinnati, Ohio
| | - Robert J Carroll
- Departments of Biomedical Informatics and Medicine, Vanderbilt University, Nashville, Tennessee
| | - Krzysztof Kiryluk
- Department of Medicine, Columbia University, New York City, New York
| | - Adam S Gordon
- Division of Medical Genetics, School of Medicine, University of Washington, Seattle, Washington
| | - Jodell Linder
- Vanderbilt Institute for Clinical and Translational Research, School of Medicine, Vanderbilt University, Nashville, Tennessee
| | - Kayla Marie Howell
- Vanderbilt Institute for Clinical and Translational Research, School of Medicine, Vanderbilt University, Nashville, Tennessee
| | - Brandy M Mapes
- Vanderbilt Institute for Clinical and Translational Research, School of Medicine, Vanderbilt University, Nashville, Tennessee
| | - Frederick T J Lin
- Feinberg School of Medicine, Northwestern University, Chicago, Illinois
| | | | - M Geoffrey Hayes
- Feinberg School of Medicine, Northwestern University, Chicago, Illinois
| | - Ali G Gharavi
- Department of Medicine, Columbia University, New York City, New York
| | | | - Marylyn D Ritchie
- Department of Genetics, University of Pennsylvania, Philadelphia, Pennsylvania
| | | | | | - Soumya Raychaudhuri
- Harvard Medical School, Harvard University, Cambridge, Massachusetts.,Program in Medical and Population Genetics, Broad Institute of Massachusetts Technical Institute and Harvard University, Cambridge, Massachusetts
| | - Scott T Weiss
- Harvard Medical School, Harvard University, Cambridge, Massachusetts
| | - Matt Lebo
- Harvard Medical School, Harvard University, Cambridge, Massachusetts
| | - Sami S Amr
- Harvard Medical School, Harvard University, Cambridge, Massachusetts
| | - David Carrell
- Kaiser Permanente Washington Health Research Institute (Formerly Group Health Cooperative-Seattle), Kaiser Permanente, Seattle, Washington
| | - Eric B Larson
- Kaiser Permanente Washington Health Research Institute (Formerly Group Health Cooperative-Seattle), Kaiser Permanente, Seattle, Washington
| | - Christopher G Chute
- Schools of Medicine, Public Health, and Nursing, Johns Hopkins University, Baltimore, Maryland
| | | | | | - Patrick Sleiman
- Children's Hospital of Philadelphia, Philadelphia, Pennsylvania
| | | | - Rongling Li
- National Human Genome Research Institute, National Institutes of Health, Bethesda, Maryland
| | - Elizabeth W Karlson
- Department of Genetics, University of Pennsylvania, Philadelphia, Pennsylvania
| | - Josh F Peterson
- Departments of Biomedical Informatics and Medicine, Vanderbilt University, Nashville, Tennessee
| | | | - Rex Chisholm
- Feinberg School of Medicine, Northwestern University, Chicago, Illinois
| | - Joshua Charles Denny
- Departments of Biomedical Informatics and Medicine, Vanderbilt University, Nashville, Tennessee
| | - Gail P Jarvik
- Division of Medical Genetics, School of Medicine, University of Washington, Seattle, Washington
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- National Human Genome Research Institute, National Institutes of Health, Bethesda, Maryland
| | - David R Crosslin
- Department of Biomedical Informatics Medical Education, School of Medicine, University of Washington, Seattle, Washington
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59
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Wang S, Huo D, Ogundiran TO, Ojengbede O, Zheng W, Nathanson KL, Nemesure B, Ambs S, Olopade OI, Zheng Y. Genetic variation in the Hippo pathway and breast cancer risk in women of African ancestry. Mol Carcinog 2018; 57:1311-1318. [PMID: 29873413 PMCID: PMC6662580 DOI: 10.1002/mc.22845] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/09/2018] [Revised: 05/18/2018] [Accepted: 06/01/2018] [Indexed: 12/14/2022]
Abstract
Gene expression changes within the Hippo pathway were found to be associated with large tumor size and metastasis in breast cancer. The combined effect of genetic variants in genes of this pathway may have a causal role in breast cancer development. We examined 7086 SNPs that were not highly correlated (r2 < 0.8) in 35 Hippo pathway genes using data from the genome-wide association study of breast cancer from the Root Consortium, which includes 3686 participants of African ancestry from Nigeria, United States of America, and Barbados: 1657 cases (403 estrogen receptor-positive [ER+], 374 ER-) and 2029 controls. Gene-level analyses were conducted using improved AdaJoint test for large-scale genetic association studies adjusting for age, study site and the first four eigenvectors from the principal component analysis. SNP-level analyses were conducted with logistic regression. The Hippo pathway was significantly associated with risk of ER+ breast cancer (pathway-level P = 0.019), with WWC1 (Padj = 0.04) being the leading gene. The pathway-level significance was lost without WWC1 (P = 0.12). rs147106204 in the WWC1 gene was the most statistically significant SNP after gene-level adjustment for multiple comparisons (OR = 0.53, 95%CI = 0.41-0.70, Padj = 0.025). We found evidence of an association between genetic variations in the Hippo pathway and ER+ breast cancer. Moreover, WWC1 was identified as the most important genetic susceptibility locus highlighting the importance of genetic epidemiology studies of breast cancer in understudied populations.
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Affiliation(s)
- Shengfeng Wang
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University Health Science Center, Beijing, China
- Center for Clinical Cancer Genetics and Global Health, Department of Medicine, University of Chicago, Chicago, Illinois; USA
| | - Dezheng Huo
- Department of Public Health Sciences, University of Chicago, Chicago, Illinois, USA
| | | | - Oladosu Ojengbede
- Center for Population and Reproductive Health, College of Medicine, University of Ibadan, Ibadan, Nigeria
| | - Wei Zheng
- Division of Epidemiology, Department of Medicine, Vanderbilt Epidemiology Center, Vanderbilt-Ingram Cancer Center, Vanderbilt University, Nashville, Tennessee, USA
| | | | - Barbara Nemesure
- Department of Preventive Medicine, State University of New York at Stony Brook, Stony Brook, New York, USA
| | - Stefan Ambs
- Laboratory of Human Carcinogenesis, National Cancer Institute, Bethesda, Maryland, USA
| | - Olufunmilayo I. Olopade
- Center for Clinical Cancer Genetics and Global Health, Department of Medicine, University of Chicago, Chicago, Illinois; USA
| | - Yonglan Zheng
- Center for Clinical Cancer Genetics and Global Health, Department of Medicine, University of Chicago, Chicago, Illinois; USA
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60
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Gregson CL, Newell F, Leo PJ, Clark GR, Paternoster L, Marshall M, Forgetta V, Morris JA, Ge B, Bao X, Duncan Bassett JH, Williams GR, Youlten SE, Croucher PI, Davey Smith G, Evans DM, Kemp JP, Brown MA, Tobias JH, Duncan EL. Genome-wide association study of extreme high bone mass: Contribution of common genetic variation to extreme BMD phenotypes and potential novel BMD-associated genes. Bone 2018; 114:62-71. [PMID: 29883787 PMCID: PMC6086337 DOI: 10.1016/j.bone.2018.06.001] [Citation(s) in RCA: 32] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/27/2018] [Revised: 05/13/2018] [Accepted: 06/02/2018] [Indexed: 12/29/2022]
Abstract
BACKGROUND Generalised high bone mass (HBM), associated with features of a mild skeletal dysplasia, has a prevalence of 0.18% in a UK DXA-scanned adult population. We hypothesized that the genetic component of extreme HBM includes contributions from common variants of small effect and rarer variants of large effect, both enriched in an extreme phenotype cohort. METHODS We performed a genome-wide association study (GWAS) of adults with either extreme high or low BMD. Adults included individuals with unexplained extreme HBM (n = 240) from the UK with BMD Z-scores ≥+3.2, high BMD females from the Anglo-Australasian Osteoporosis Genetics Consortium (AOGC) (n = 1055) with Z-scores +1.5 to +4.0 and low BMD females also part of AOGC (n = 900), with Z-scores -1.5 to -4.0. Following imputation, we tested association between 6,379,332 SNPs and total hip and lumbar spine BMD Z-scores. For potential target genes, we assessed expression in human osteoblasts and murine osteocytes. RESULTS We observed significant enrichment for associations with established BMD-associated loci, particularly those known to regulate endochondral ossification and Wnt signalling, suggesting that part of the genetic contribution to unexplained HBM is polygenic. Further, we identified associations exceeding genome-wide significance between BMD and four loci: two established BMD-associated loci (5q14.3 containing MEF2C and 1p36.12 containing WNT4) and two novel loci: 5p13.3 containing NPR3 (rs9292469; minor allele frequency [MAF] = 0.33%) associated with lumbar spine BMD and 11p15.2 containing SPON1 (rs2697825; MAF = 0.17%) associated with total hip BMD. Mouse models with mutations in either Npr3 or Spon1 have been reported, both have altered skeletal phenotypes, providing in vivo validation that these genes are physiologically important in bone. NRP3 regulates endochondral ossification and skeletal growth, whilst SPON1 modulates TGF-β regulated BMP-driven osteoblast differentiation. Rs9292469 (downstream of NPR3) also showed some evidence for association with forearm BMD in the independent GEFOS sample (n = 32,965). We found Spon1 was highly expressed in murine osteocytes from the tibiae, femora, humeri and calvaria, whereas Npr3 expression was more variable. CONCLUSION We report the most extreme-truncate GWAS of BMD performed to date. Our findings, suggest potentially new anabolic bone regulatory pathways that warrant further study.
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Affiliation(s)
- Celia L Gregson
- Musculoskeletal Research Unit, Translational Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK.
| | - Felicity Newell
- Translational Genomics Group, Institute of Health and Biomedical Innovation, Queensland University of Technology at Translational Research Institute, 37 Kent Street, Woolloongabba 4102, QLD, Australia
| | - Paul J Leo
- Translational Genomics Group, Institute of Health and Biomedical Innovation, Queensland University of Technology at Translational Research Institute, 37 Kent Street, Woolloongabba 4102, QLD, Australia
| | - Graeme R Clark
- Translational Genomics Group, Institute of Health and Biomedical Innovation, Queensland University of Technology at Translational Research Institute, 37 Kent Street, Woolloongabba 4102, QLD, Australia
| | | | - Mhairi Marshall
- Translational Genomics Group, Institute of Health and Biomedical Innovation, Queensland University of Technology at Translational Research Institute, 37 Kent Street, Woolloongabba 4102, QLD, Australia
| | - Vincenzo Forgetta
- Department of Human Genetics, McGill University and Genome Quebec Innovation Centre, Montreal, Quebec, Canada
| | - John A Morris
- Department of Human Genetics, McGill University and Genome Quebec Innovation Centre, Montreal, Quebec, Canada
| | - Bing Ge
- Department of Human Genetics, McGill University and Genome Quebec Innovation Centre, Montreal, Quebec, Canada; Lady Davis Institute, Jewish General Hospital, McGill University, Montreal, Quebec, Canada
| | - Xiao Bao
- Translational Genomics Group, Institute of Health and Biomedical Innovation, Queensland University of Technology at Translational Research Institute, 37 Kent Street, Woolloongabba 4102, QLD, Australia
| | - J H Duncan Bassett
- Molecular Endocrinology Laboratory, Department of Medicine, Imperial College London, Hammersmith Campus, London W12 0NN, UK
| | - Graham R Williams
- Molecular Endocrinology Laboratory, Department of Medicine, Imperial College London, Hammersmith Campus, London W12 0NN, UK
| | - Scott E Youlten
- The Garvan Institute of Medical Research, Sydney, New South Wales, Australia
| | - Peter I Croucher
- The Garvan Institute of Medical Research, Sydney, New South Wales, Australia; St Vincent's Clinical School, University of New South Wales Medicine, Sydney, New South Wales, Australia
| | | | - David M Evans
- MRC Integrative Epidemiology Unit, University of Bristol, Bristol, UK; University of Queensland Diamantina Institute, Translational Research Institute, Brisbane, Queensland, Australia
| | - John P Kemp
- MRC Integrative Epidemiology Unit, University of Bristol, Bristol, UK; University of Queensland Diamantina Institute, Translational Research Institute, Brisbane, Queensland, Australia
| | - Matthew A Brown
- Translational Genomics Group, Institute of Health and Biomedical Innovation, Queensland University of Technology at Translational Research Institute, 37 Kent Street, Woolloongabba 4102, QLD, Australia
| | - Jon H Tobias
- Musculoskeletal Research Unit, Translational Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
| | - Emma L Duncan
- Translational Genomics Group, Institute of Health and Biomedical Innovation, Queensland University of Technology at Translational Research Institute, 37 Kent Street, Woolloongabba 4102, QLD, Australia; Royal Brisbane and Women's Hospital, Brisbane, Queensland, Australia
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Haas DW, Bradford Y, Verma A, Verma SS, Eron JJ, Gulick RM, Riddler S, Sax PE, Daar ES, Morse GD, Acosta EP, Ritchie MD. Brain neurotransmitter transporter/receptor genomics and efavirenz central nervous system adverse events. Pharmacogenet Genomics 2018; 28:179-187. [PMID: 29847509 PMCID: PMC6010221 DOI: 10.1097/fpc.0000000000000341] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/11/2023]
Abstract
OBJECTIVE We characterized associations between central nervous system (CNS) adverse events and brain neurotransmitter transporter/receptor genomics among participants randomized to efavirenz-containing regimens in AIDS Clinical Trials Group studies in the USA. PARTICIPANTS AND METHODS Four clinical trials randomly assigned treatment-naive participants to efavirenz-containing regimens. Genome-wide genotype and PrediXcan were used to infer gene expression levels in tissues including 10 brain regions. Multivariable regression models stratified by race/ethnicity were adjusted for CYP2B6/CYP2A6 genotypes that predict plasma efavirenz exposure, age, and sex. Combined analyses also adjusted for genetic ancestry. RESULTS Analyses included 167 cases with grade 2 or greater efavirenz-consistent CNS adverse events within 48 weeks of study entry, and 653 efavirenz-tolerant controls. CYP2B6/CYP2A6 genotype level was independently associated with CNS adverse events (odds ratio: 1.07; P=0.044). Predicted expression of six genes postulated to mediate efavirenz CNS side effects (SLC6A2, SLC6A3, PGR, HTR2A, HTR2B, HTR6) were not associated with CNS adverse events after correcting for multiple testing, the lowest P value being for PGR in hippocampus (P=0.012), nor were polymorphisms in these genes or AR and HTR2C, the lowest P value being for rs12393326 in HTR2C (P=6.7×10(-4)). As a positive control, baseline plasma bilirubin concentration was associated with predicted liver UGT1A1 expression level (P=1.9×10(-27)). CONCLUSION Efavirenz-related CNS adverse events were not associated with predicted neurotransmitter transporter/receptor gene expression levels in brain or with polymorphisms in these genes. Variable susceptibility to efavirenz-related CNS adverse events may not be explained by brain neurotransmitter transporter/receptor genomics.
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Affiliation(s)
- David W. Haas
- Vanderbilt University School of Medicine, Nashville, TN
- Meharry Medical College, Nashville, TN
| | - Yuki Bradford
- Department of Genetics, University of Pennsylvania, Perelman School of Medicine, Philadelphia, PA
| | - Anurag Verma
- Department of Genetics, University of Pennsylvania, Perelman School of Medicine, Philadelphia, PA
- Institute for Biomedical Informatics, University of Pennsylvania, Perelman School of Medicine, Philadelphia, PA
| | - Shefali S. Verma
- Department of Genetics, University of Pennsylvania, Perelman School of Medicine, Philadelphia, PA
| | - Joseph J. Eron
- University of North Carolina at Chapel Hill, Department of Medicine, Chapel Hill, NC
| | - Roy M. Gulick
- Weill Cornell Medicine, Department of Medicine, New York, NY
| | | | - Paul E. Sax
- Brigham and Women's Hospital and Harvard Medical School, Department of Medicine, Boston, MA
| | - Eric S. Daar
- Los Angeles Biomedical Research Institute at Harbor–UCLA Medical Center, David Geffen School of Medicine at UCLA, Los Angeles, CA
| | | | | | - Marylyn D. Ritchie
- Department of Genetics, University of Pennsylvania, Perelman School of Medicine, Philadelphia, PA
- Institute for Biomedical Informatics, University of Pennsylvania, Perelman School of Medicine, Philadelphia, PA
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Harley JB, Chen X, Pujato M, Miller D, Maddox A, Forney C, Magnusen AF, Lynch A, Chetal K, Yukawa M, Barski A, Salomonis N, Kaufman KM, Kottyan LC, Weirauch MT. Transcription factors operate across disease loci, with EBNA2 implicated in autoimmunity. Nat Genet 2018; 50:699-707. [PMID: 29662164 PMCID: PMC6022759 DOI: 10.1038/s41588-018-0102-3] [Citation(s) in RCA: 222] [Impact Index Per Article: 37.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/17/2017] [Accepted: 01/31/2018] [Indexed: 01/10/2023]
Abstract
Explaining the genetics of many diseases is challenging because most associations localize to incompletely characterized regulatory regions. We show that transcription factors (TFs) occupy multiple loci of individual complex genetic disorders using novel computational methods. Application to 213 phenotypes and 1,544 TF binding datasets identifies 2,264 relationships between hundreds of TFs and 94 phenotypes, including AR in prostate cancer and GATA3 in breast cancer. Strikingly, nearly half of the systemic lupus erythematosus risk loci are occupied by the Epstein-Barr virus EBNA2 protein and many co-clustering human TFs, revealing gene-environment interaction. Similar EBNA2-anchored associations exist in multiple sclerosis, rheumatoid arthritis, inflammatory bowel disease, type 1 diabetes, juvenile idiopathic arthritis, and celiac disease. Instances of allele-dependent DNA binding and downstream effects on gene expression at plausibly causal variants support genetic mechanisms dependent upon EBNA2. Our results nominate mechanisms that operate across risk loci within disease phenotypes, suggesting new paradigms for disease origins.
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Affiliation(s)
- John B Harley
- Center for Autoimmune Genomics and Etiology, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, USA. .,Division of Immunobiology, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, USA. .,Division of Developmental Biology, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, USA. .,Department of Pediatrics, University of Cincinnati College of Medicine, Cincinnati, OH, USA. .,US Department of Veterans Affairs Medical Center, Cincinnati, OH, USA.
| | - Xiaoting Chen
- Center for Autoimmune Genomics and Etiology, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, USA
| | - Mario Pujato
- Center for Autoimmune Genomics and Etiology, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, USA
| | - Daniel Miller
- Center for Autoimmune Genomics and Etiology, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, USA
| | - Avery Maddox
- Center for Autoimmune Genomics and Etiology, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, USA
| | - Carmy Forney
- Center for Autoimmune Genomics and Etiology, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, USA
| | - Albert F Magnusen
- Center for Autoimmune Genomics and Etiology, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, USA
| | - Arthur Lynch
- Center for Autoimmune Genomics and Etiology, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, USA
| | - Kashish Chetal
- Division of Biomedical Informatics, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, USA
| | - Masashi Yukawa
- Division of Allergy & Immunology, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, USA
| | - Artem Barski
- Department of Pediatrics, University of Cincinnati College of Medicine, Cincinnati, OH, USA.,Division of Allergy & Immunology, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, USA.,Division of Human Genetics, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, USA
| | - Nathan Salomonis
- Department of Pediatrics, University of Cincinnati College of Medicine, Cincinnati, OH, USA.,Division of Biomedical Informatics, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, USA
| | - Kenneth M Kaufman
- Center for Autoimmune Genomics and Etiology, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, USA.,Division of Immunobiology, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, USA.,Department of Pediatrics, University of Cincinnati College of Medicine, Cincinnati, OH, USA.,US Department of Veterans Affairs Medical Center, Cincinnati, OH, USA
| | - Leah C Kottyan
- Center for Autoimmune Genomics and Etiology, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, USA. .,Department of Pediatrics, University of Cincinnati College of Medicine, Cincinnati, OH, USA.
| | - Matthew T Weirauch
- Center for Autoimmune Genomics and Etiology, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, USA. .,Division of Developmental Biology, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, USA. .,Department of Pediatrics, University of Cincinnati College of Medicine, Cincinnati, OH, USA. .,Division of Biomedical Informatics, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, USA.
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Vergara C, Parker MM, Franco L, Cho MH, Valencia-Duarte AV, Beaty TH, Duggal P. Genotype imputation performance of three reference panels using African ancestry individuals. Hum Genet 2018; 137:281-292. [PMID: 29637265 PMCID: PMC6209094 DOI: 10.1007/s00439-018-1881-4] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/10/2018] [Accepted: 03/31/2018] [Indexed: 12/22/2022]
Abstract
Genotype imputation estimates unobserved genotypes from genome-wide makers, to increase genome coverage and power for genome-wide association studies. Imputation has been successful for European ancestry populations in which very large reference panels are available. Smaller subsets of African descent populations are available in 1000 Genomes (1000G), the Consortium on Asthma among African ancestry Populations in the Americas (CAAPA) and the Haplotype Reference Consortium (HRC). We compared the performance of these reference panels when imputing variation in 3747 African Americans (AA) from two cohorts (HCV and COPDGene) genotyped using Illumina Omni microarrays. The haplotypes of 2504 (1000G), 883 (CAAPA) and 32,470 individuals (HRC) were used as reference. We compared the number of variants, imputation quality, imputation accuracy and coverage between panels. In both cohorts, 1000G imputed 1.5-1.6× more variants than CAAPA and 1.2× more than HRC. Similar findings were observed for variants with imputation R2 > 0.5 and for rare, low-frequency, and common variants. When merging imputed variants of the three panels, the total number was 62-63 M with 20 M overlapping variants imputed by all three panels, and a range of 5-15 M variants imputed exclusively with one of them. For overlapping variants, imputation quality was highest for HRC, followed by 1000G, then CAAPA, and improved as the minor allele frequency increased. 1000G, HRC and CAAPA provided high performance and accuracy for imputation of African American individuals, increasing the number of variants available for subsequent analyses. These panels are complementary and would benefit from the development of an integrated African reference panel.
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Affiliation(s)
| | - Margaret M Parker
- Channing Division of Network Medicine, Brigham and Women's Hospital, Boston, MA, USA
| | - Liliana Franco
- National School of Public Health, Universidad de Antioquia, Medellín, Colombia
- School of Medicine, Universidad Pontificia Bolivariana, Medellín, Colombia
| | - Michael H Cho
- Channing Division of Network Medicine, Brigham and Women's Hospital, Boston, MA, USA
- Division of Pulmonary and Critical Care Medicine, Brigham and Women's Hospital, Boston, MA, USA
| | | | - Terri H Beaty
- Johns Hopkins University, Bloomberg School of Public Health, Baltimore, MD, USA
| | - Priya Duggal
- Johns Hopkins University, Bloomberg School of Public Health, Baltimore, MD, USA.
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Bergmeijer TO, Reny JL, Pakyz RE, Gong L, Lewis JP, Kim EY, Aradi D, Fernandez-Cadenas I, Horenstein RB, Lee MTM, Whaley RM, Montaner J, Gensini GF, Cleator JH, Chang K, Holmvang L, Hochholzer W, Roden DM, Winter S, Altman RB, Alexopoulos D, Kim HS, Déry JP, Gawaz M, Bliden K, Valgimigli M, Marcucci R, Campo G, Schaeffeler E, Dridi NP, Wen MS, Shin JG, Simon T, Fontana P, Giusti B, Geisler T, Kubo M, Trenk D, Siller-Matula JM, Ten Berg JM, Gurbel PA, Hulot JS, Mitchell BD, Schwab M, Ritchie MD, Klein TE, Shuldiner AR. Genome-wide and candidate gene approaches of clopidogrel efficacy using pharmacodynamic and clinical end points-Rationale and design of the International Clopidogrel Pharmacogenomics Consortium (ICPC). Am Heart J 2018; 198:152-159. [PMID: 29653637 PMCID: PMC5903579 DOI: 10.1016/j.ahj.2017.12.010] [Citation(s) in RCA: 21] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/14/2017] [Accepted: 12/10/2017] [Indexed: 02/07/2023]
Abstract
RATIONALE The P2Y12 receptor inhibitor clopidogrel is widely used in patients with acute coronary syndrome, percutaneous coronary intervention, or ischemic stroke. Platelet inhibition by clopidogrel shows wide interpatient variability, and high on-treatment platelet reactivity is a risk factor for atherothrombotic events, particularly in high-risk populations. CYP2C19 polymorphism plays an important role in this variability, but heritability estimates suggest that additional genetic variants remain unidentified. The aim of the International Clopidogrel Pharmacogenomics Consortium (ICPC) is to identify genetic determinants of clopidogrel pharmacodynamics and clinical response. STUDY DESIGN Based on the data published on www.clinicaltrials.gov, clopidogrel intervention studies containing genetic and platelet function data were identified for participation. Lead investigators were invited to share DNA samples, platelet function test results, patient characteristics, and cardiovascular outcomes to perform candidate gene and genome-wide studies. RESULTS In total, 17 study sites from 13 countries participate in the ICPC, contributing individual patient data from 8,829 patients. Available adenosine diphosphate-stimulated platelet function tests included vasodilator-stimulated phosphoprotein assay, light transmittance aggregometry, and the VerifyNow P2Y12 assay. A proof-of-principle analysis based on genotype data provided by each group showed a strong and consistent association between CYP2C19*2 and platelet reactivity (P value=5.1 × 10-40). CONCLUSION The ICPC aims to identify new loci influencing clopidogrel efficacy by using state-of-the-art genetic approaches in a large cohort of clopidogrel-treated patients to better understand the genetic basis of on-treatment response variability.
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Affiliation(s)
- Thomas O Bergmeijer
- St Antonius Center for Platelet Function Research, Department of Cardiology, St Antonius Hospital Nieuwegein, the Netherlands
| | - Jean-Luc Reny
- Internal Medicine, Béziers Hospital, France, Geneva Platelet Group, University of Geneva School of Medicine, Department of Internal Medicine, Rehabilitation and Geriatrics, University Hospitals of Geneva, Geneva, Switzerland
| | - Ruth E Pakyz
- Department of Medicine, Program for Personalized and Genomic Medicine, University of Maryland School of Medicine, Baltimore, MD, USA
| | - Li Gong
- Department of Biomedical Data Science, Stanford University, Stanford, CA, USA
| | - Joshua P Lewis
- Department of Medicine, Program for Personalized and Genomic Medicine, University of Maryland School of Medicine, Baltimore, MD, USA
| | - Eun-Young Kim
- Department of Clinical Pharmacology, Inje University Busan Paik Hospital, Busan, South Korea
| | - Daniel Aradi
- Heart Center Balatonfüred and Heart and Vascular Center, Semmelweis University, Budapest, Hungary
| | - Israel Fernandez-Cadenas
- Stroke Pharmacogenomics and Genetics, Fundació Docència i Recerca Mútua Terrassa, Neurovascular Research Laboratory, Valle d'Hebron Hebron Institute of Research, Barcelona, Spain
| | - Richard B Horenstein
- Department of Medicine, Program for Personalized and Genomic Medicine, University of Maryland School of Medicine, Baltimore, MD, USA
| | | | - Ryan M Whaley
- Department of Biomedical Data Science, Stanford University, Stanford, CA, USA
| | - Joan Montaner
- Neurovascular Research Laboratory, Vall d'Hebron Institute of Research, Barcelona, Spain
| | - Gian Franco Gensini
- Department of Experimental and Clinical Medicine, University of Florence, Florence, Italy
| | - John H Cleator
- Departments of Medicine and Pharmacology, Vanderbilt University School of Medicine, Nashville, TN, USA
| | - Kiyuk Chang
- Cardiovascular Center and Cardiology Division, Seoul St. Mary's Hospital, College of Medicine, The Catholic University of Korea, Seoul, South Korea
| | - Lene Holmvang
- Department of Cardiology and Cardiac Catheterization Laboratory, Rigshospitalet, University of Copenhagen, Copenhagen, Denmark
| | - Willibald Hochholzer
- University Heart Center Freiburg, Bad Krozingen, Department of Cardiology and Angiology II, Bad Krozingen, Germany
| | - Dan M Roden
- Departments of Medicine, Pharmacology, and Biomedical Informatics, Vanderbilt University School of Medicine, Nashville, TN, USA
| | - Stefan Winter
- Dr Margarete Fischer-Bosch Institute of Clinical Pharmacology, Stuttgart and University of Tübingen, Tübingen, Germany
| | - Russ B Altman
- Department of Biomedical Data Science, Stanford University, Stanford, CA, USA; Departments of Bioengineering and Genetics, Stanford University, Stanford, CA, USA; Department of Medicine, Stanford University, Stanford, CA, USA
| | | | - Ho-Sook Kim
- Department of Pharmacology and Pharmacogenomics Research Center, College of Medicine, Inje University, Busan, South Korea
| | | | - Meinrad Gawaz
- Department of Cardiology and Cardiovascular Medicine, University Hospital Tübingen, Tübingen, Germany
| | - Kevin Bliden
- Inova Center for Thrombosis Research and Drug Development. Inova Heart and Vascular Institute, Falls Church, VA, USA
| | - Marco Valgimigli
- Department of Cardiology, Swiss Cardiovascular Center Bern, Bern University Hospital, Bern, Switzerland
| | - Rossella Marcucci
- Department of Experimental and Clinical Medicine, University of Florence, Atherothrombotic Diseases Center, Careggi Hospital, Florence, Italy
| | - Gianluca Campo
- Cardiology Unit, Azienda Ospedaliera Universitria di Ferrara, Cona (FE) and Maria Cecilia Hospital, GVM Care and Research, Cotignola, (RA), Italy
| | - Elke Schaeffeler
- Dr Margarete Fischer-Bosch Institute of Clinical Pharmacology, Stuttgart and University of Tübingen, Tübingen, Germany
| | - Nadia P Dridi
- Department of Cardiology and Cardiac Catheterization Laboratory, Rigshospitalet, University of Copenhagen, Copenhagen, Denmark
| | - Ming-Shien Wen
- Division of Cardiology, Department of Internal Medicine, Chang Gung Memorial Hospital, Linkou and School of Medicine, Chang Gung University, Taoyuan City, Taiwan
| | - Jae Gook Shin
- Department of Pharmacology and Pharmacogenomics Research Center, College of Medicine, Inje University, Busan, South Korea
| | | | - Pierre Fontana
- Geneva Platelet Group, University of Geneva School of Medicine, Division of Angiology and Haemostasis, University Hospitals of Geneva, Geneva, Switzerland
| | - Betti Giusti
- Department of Experimental and Clinical Medicine, University of Florence, Atherothrombotic Diseases Center, Careggi Hospital, Florence, Italy
| | - Tobias Geisler
- Department of Cardiology and Cardiovascular Medicine, University Hospital Tübingen, Tübingen, Germany
| | - Michiaki Kubo
- Laboratory for Genotyping Development, RIKEN Center for Integrative Medical Sciences, Tokyo, Japan
| | - Dietmar Trenk
- Department of Cardiology and Cardiovascular Medicine, University Hospital Tübingen, Tübingen, Germany
| | | | - Jurriën M Ten Berg
- St Antonius Center for Platelet Function Research, Department of Cardiology, St Antonius Hospital Nieuwegein, the Netherlands
| | - Paul A Gurbel
- Inova Center for Thrombosis Research and Drug Development. Inova Heart and Vascular Institute, Falls Church, VA, USA
| | - Jean-Sebastien Hulot
- Sorbonne Universités, UPMC Univ Paris 06, Institute of Cardiometabolism and Nutrition (ICAN), Pitié-Salpêtrière Hospital, F-75013 Paris, France
| | - Braxton D Mitchell
- Department of Medicine, University of Maryland, Baltimore, MD, USA; Geriatric Research, Education and Clinical Center, Veterans Affairs Medical Center, Baltimore, MD
| | - Matthias Schwab
- Dr Margarete Fischer-Bosch Institute of Clinical Pharmacology, Stuttgart and University of Tübingen, Tübingen, Germany; Department of Clinical Pharmacology, University Hospital, Tübingen, Germany
| | - Marylyn DeRiggi Ritchie
- Department of Biomedical and Translational Informatics, Geisinger Health System, Danville, PA, USA
| | - Teri E Klein
- Department of Biomedical Data Science, Stanford University, Stanford, CA, USA; Department of Medicine, Stanford University, Stanford, CA, USA
| | - Alan R Shuldiner
- Department of Medicine, Program for Personalized and Genomic Medicine, University of Maryland School of Medicine, Baltimore, MD, USA.
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Spencer M, Takahashi N, Chakraborty S, Miles J, Shyu CR. Heritable genotype contrast mining reveals novel gene associations specific to autism subgroups. J Biomed Inform 2018; 77:50-61. [PMID: 29197649 PMCID: PMC5788310 DOI: 10.1016/j.jbi.2017.11.016] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/01/2017] [Revised: 11/15/2017] [Accepted: 11/28/2017] [Indexed: 12/11/2022]
Abstract
Though the genetic etiology of autism is complex, our understanding can be improved by identifying genes and gene-gene interactions that contribute to the development of specific autism subtypes. Identifying such gene groupings will allow individuals to be diagnosed and treated according to their precise characteristics. To this end, we developed a method to associate gene combinations with groups with shared autism traits, targeting genetic elements that distinguish patient populations with opposing phenotypes. Our computational method prioritizes genetic variants for genome-wide association, then utilizes Frequent Pattern Mining to highlight potential interactions between variants. We introduce a novel genotype assessment metric, the Unique Inherited Combination support, which accounts for inheritance patterns observed in the nuclear family while estimating the impact of genetic variation on phenotype manifestation at the individual level. High-contrast variant combinations are tested for significant subgroup associations. We apply this method by contrasting autism subgroups defined by severe or mild manifestations of a phenotype. Significant associations connected 286 genes to the subgroups, including 193 novel autism candidates. 71 pairs of genes have joint associations with subgroups, presenting opportunities to investigate interacting functions. This study analyzed 12 autism subgroups, but our informatics method can explore other meaningful divisions of autism patients, and can further be applied to reveal precise genetic associations within other phenotypically heterogeneous disorders, such as Alzheimer's disease.
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Affiliation(s)
- Matt Spencer
- Informatics Institute, University of Missouri, 241 Naka Hall, Columbia, MO 65211, USA.
| | - Nicole Takahashi
- Thompson Center for Autism & Neurodevelopmental Disorders, University of Missouri, 205 Portland St, Columbia, MO 65211, USA.
| | - Sounak Chakraborty
- Department of Statistics, University of Missouri, 146 Middlebush Hall, Columbia, MO 65211, USA.
| | - Judith Miles
- Thompson Center for Autism & Neurodevelopmental Disorders, University of Missouri, 205 Portland St, Columbia, MO 65211, USA; Department of Child Health, School of Medicine, MA204 Medical Sciences Building, University of Missouri, Columbia, MO 65212, USA.
| | - Chi-Ren Shyu
- Informatics Institute, University of Missouri, 241 Naka Hall, Columbia, MO 65211, USA; Department of Electrical Engineering and Computer Science, University of Missouri, 201 Naka Hall, Columbia, MO 65211, USA; School of Medicine, University of Missouri, MA204 Medical Sciences Building, Columbia, MO 65212, USA.
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Abadi A, Alyass A, Robiou du Pont S, Bolker B, Singh P, Mohan V, Diaz R, Engert JC, Yusuf S, Gerstein HC, Anand SS, Meyre D. Penetrance of Polygenic Obesity Susceptibility Loci across the Body Mass Index Distribution. Am J Hum Genet 2017; 101:925-938. [PMID: 29220676 PMCID: PMC5812888 DOI: 10.1016/j.ajhg.2017.10.007] [Citation(s) in RCA: 52] [Impact Index Per Article: 7.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/16/2017] [Accepted: 10/12/2017] [Indexed: 12/17/2022] Open
Abstract
A growing number of single-nucleotide polymorphisms (SNPs) have been associated with body mass index (BMI) and obesity, but whether the effects of these obesity-susceptibility loci are uniform across the BMI distribution remains unclear. We studied the effects of 37 BMI-associated SNPs in 75,230 adults of European ancestry across BMI percentiles by using conditional quantile regression (CQR) and meta-regression (MR) models. The effects of nine SNPs (24%)-rs1421085 (FTO; p = 8.69 × 10-15), rs6235 (PCSK1; p = 7.11 × 10-6), rs7903146 (TCF7L2; p = 9.60 × 10-6), rs11873305 (MC4R; p = 5.08 × 10-5), rs12617233 (FANCL; p = 5.30 × 10-5), rs11672660 (GIPR; p = 1.64 × 10-4), rs997295 (MAP2K5; p = 3.25 × 10-4), rs6499653 (FTO; p = 6.23 × 10-4), and rs3824755 (NT5C2; p = 7.90 × 10-4)-increased significantly across the sample BMI distribution. We showed that such increases stemmed from unadjusted gene interactions that enhanced the effects of SNPs in persons with a high BMI. When 125 height-associated SNPs were analyzed for comparison, only one (<1%), rs6219 (IGF1, p = 1.80 × 10-4), showed effects that varied significantly across height percentiles. Cumulative gene scores of these SNPs (GS-BMI and GS-height) showed that only GS-BMI had effects that increased significantly across the sample distribution (BMI: p = 7.03 × 10-37; height: p = 0.499). Overall, these findings underscore the importance of gene-gene and gene-environment interactions in shaping the genetic architecture of BMI and advance a method for detecting such interactions by using only the sample outcome distribution.
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Affiliation(s)
- Arkan Abadi
- Department of Health Research Methods, Evidence, and Impact, McMaster University, Hamilton, ON L8S 4L8, Canada
| | - Akram Alyass
- Department of Health Research Methods, Evidence, and Impact, McMaster University, Hamilton, ON L8S 4L8, Canada
| | - Sebastien Robiou du Pont
- Department of Health Research Methods, Evidence, and Impact, McMaster University, Hamilton, ON L8S 4L8, Canada
| | - Ben Bolker
- Department of Mathematics and Statistics, McMaster University, Hamilton, ON L8S 4L8, Canada
| | - Pardeep Singh
- Department of Pathology and Molecular Medicine, McMaster University, Hamilton, ON L8S 4L8, Canada
| | - Viswanathan Mohan
- Madras Diabetes Research Foundation, Gopalapuram, Chennai 600086, India
| | - Rafael Diaz
- Estudios Clínicos Latino America, Paraguay 160, S2000CVD Rosario, Santa Fe, Argentina
| | | | - Salim Yusuf
- Department of Health Research Methods, Evidence, and Impact, McMaster University, Hamilton, ON L8S 4L8, Canada; Population Health Research Institute, McMaster University and Hamilton Health Sciences, Hamilton General Hospital, Hamilton, ON L8S 4L8, Canada; Department of Medicine, McMaster University, Hamilton, ON L8S 4L8, Canada
| | - Hertzel C Gerstein
- Department of Health Research Methods, Evidence, and Impact, McMaster University, Hamilton, ON L8S 4L8, Canada; Population Health Research Institute, McMaster University and Hamilton Health Sciences, Hamilton General Hospital, Hamilton, ON L8S 4L8, Canada; Department of Medicine, McMaster University, Hamilton, ON L8S 4L8, Canada
| | - Sonia S Anand
- Department of Health Research Methods, Evidence, and Impact, McMaster University, Hamilton, ON L8S 4L8, Canada; Population Health Research Institute, McMaster University and Hamilton Health Sciences, Hamilton General Hospital, Hamilton, ON L8S 4L8, Canada; Department of Medicine, McMaster University, Hamilton, ON L8S 4L8, Canada
| | - David Meyre
- Department of Health Research Methods, Evidence, and Impact, McMaster University, Hamilton, ON L8S 4L8, Canada; Department of Pathology and Molecular Medicine, McMaster University, Hamilton, ON L8S 4L8, Canada.
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Multiphenotype association study of patients randomized to initiate antiretroviral regimens in AIDS Clinical Trials Group protocol A5202. Pharmacogenet Genomics 2017; 27:101-111. [PMID: 28099408 PMCID: PMC5285297 DOI: 10.1097/fpc.0000000000000263] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/29/2022]
Abstract
Supplemental Digital Content is available in the text. Background High-throughput approaches are increasingly being used to identify genetic associations across multiple phenotypes simultaneously. Here, we describe a pilot analysis that considered multiple on-treatment laboratory phenotypes from antiretroviral therapy-naive patients who were randomized to initiate antiretroviral regimens in a prospective clinical trial, AIDS Clinical Trials Group protocol A5202. Participants and methods From among 5 9545 294 polymorphisms imputed genome-wide, we analyzed 2544, including 2124 annotated in the PharmGKB, and 420 previously associated with traits in the GWAS Catalog. We derived 774 phenotypes on the basis of context from six variables: plasma atazanavir (ATV) pharmacokinetics, plasma efavirenz (EFV) pharmacokinetics, change in the CD4+ T-cell count, HIV-1 RNA suppression, fasting low-density lipoprotein-cholesterol, and fasting triglycerides. Permutation testing assessed the likelihood of associations being by chance alone. Pleiotropy was assessed for polymorphisms with the lowest P-values. Results This analysis included 1181 patients. At P less than 1.5×10−4, most associations were not by chance alone. Polymorphisms with the lowest P-values for EFV pharmacokinetics (CYPB26 rs3745274), low-density lipoprotein -cholesterol (APOE rs7412), and triglyceride (APOA5 rs651821) phenotypes had been associated previously with those traits in previous studies. The association between triglycerides and rs651821 was present with ATV-containing regimens, but not with EFV-containing regimens. Polymorphisms with the lowest P-values for ATV pharmacokinetics, CD4 T-cell count, and HIV-1 RNA phenotypes had not been reported previously to be associated with that trait. Conclusion Using data from a prospective HIV clinical trial, we identified expected genetic associations, potentially novel associations, and at least one context-dependent association. This study supports high-throughput strategies that simultaneously explore multiple phenotypes from clinical trials’ datasets for genetic associations.
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Wang S, Huo D, Ogundiran TO, Ojengbede O, Zheng W, Nathanson KL, Nemesure B, Ambs S, Olopade OI, Zheng Y. Association of breast cancer risk and the mTOR pathway in women of African ancestry in 'The Root' Consortium. Carcinogenesis 2017; 38:789-796. [PMID: 28582508 DOI: 10.1093/carcin/bgx055] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/20/2017] [Accepted: 06/02/2017] [Indexed: 12/16/2022] Open
Abstract
Functional studies have elucidated the role of the mammalian target of rapamycin (mTOR) pathway in breast carcinogenesis, but to date, there is a paucity of data on its contribution to breast cancer risk in women of African ancestry. We examined 47628 SNPs in 61 mTOR pathway genes in the genome wide association study of breast cancer in the African Diaspora study (The Root consortium), which included 3686 participants (1657 cases). Pathway- and gene-level analyses were conducted using the adaptive rank truncated product (ARTP) test for 10994 SNPs that were not highly correlated (r2 < 0.8). Odds ratio (OR) and 95% confidence interval (CI) were estimated with logistic regression for each single-nucleotide polymorphism. The mTOR pathway was significantly associated with overall and estrogen receptor-negative (ER-) breast cancer risk (P = 0.003 and 0.03, respectively). PRKAG3 (Padj = 0.0018) and RPS6KA3 (Padj = 0.061) were the leading genes for the associations with overall breast cancer risk and ER- breast cancer risk, respectively. rs190843378 in PRKAG3 was statistically significant after gene-level adjustment for multiple comparisons (OR = 0.50 for each T allele, 95% CI = 0.38-0.66, Padj = 3.6E-05), with a statistical power of 0.914. These results provide new insights on the biological relevance of the mTOR pathway in breast cancer progression and underscore the need for more genetic epidemiology studies of breast cancer in the African Diaspora.
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Affiliation(s)
- Shengfeng Wang
- Department of Medicine, Center for Clinical Cancer Genetics and Global Health, University of Chicago, Chicago, IL, USA
| | - Dezheng Huo
- Department of Public Health Sciences, University of Chicago, Chicago, IL, USA
| | - Temidayo O Ogundiran
- Department of Surgery, College of Medicine, University of Ibadan, Ibadan, Nigeria
| | - Oladosu Ojengbede
- Center for Population and Reproductive Health, College of Medicine, University of Ibadan, Ibadan, Nigeria
| | - Wei Zheng
- Division of Epidemiology, Department of Medicine, Vanderbilt Epidemiology Center, Vanderbilt-Ingram Cancer Center, Vanderbilt University, Nashville, TN, USA
| | | | - Barbara Nemesure
- Department of Preventive Medicine, State University of New York at Stony Brook, Stony Brook, NY, USA
| | - Stefan Ambs
- Laboratory of Human Carcinogenesis, National Cancer Institute, Bethesda, MD, USA
| | | | - Yonglan Zheng
- To whom correspondence should be addressed. Tel: +1 773 702 1632; Fax: +1 773 834 1659;
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Wang S, Huo D, Kupfer S, Alleyne D, Ogundiran TO, Ojengbede O, Zheng W, Nathanson KL, Nemesure B, Ambs S, Olopade OI, Zheng Y. Genetic variation in the vitamin D related pathway and breast cancer risk in women of African ancestry in the root consortium. Int J Cancer 2017; 142:36-43. [PMID: 28891071 DOI: 10.1002/ijc.31038] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/20/2017] [Revised: 07/07/2017] [Accepted: 07/12/2017] [Indexed: 01/08/2023]
Abstract
The vitamin D related pathway has been evaluated in carcinogenesis but its genetic contribution remains poorly understood. We examined single-nucleotide polymorphisms (SNPs) in the vitamin D related pathway genes using data from a genome-wide association study (GWAS) of breast cancer in the African Diaspora that included 3,686 participants (1,657 cases). Pathway- and gene-level analyses were conducted using the adaptive rank truncated product test. Odds ratios (OR) and 95% confidence intervals (CI) were estimated at SNP-level. After stringent Bonferroni corrections, we observed no significant association between variants in the vitamin D pathway and breast cancer risk at the pathway-, gene-, or SNP-level. In addition, no association was found for either the reported signals from GWASs of vitamin D related traits, or the SNPs within vitamin D receptor (VDR) binding regions. Furthermore, a decrease in genetically predicted 25(OH)D levels by Mendelian randomization was not associated with breast cancer (p = 0.23). However, an association for breast cancer with the pigment synthesis/metabolism pathway almost approached significance (pathway-level p = 0.08), driven primarily by a nonsense SNP rs41302073 in TYRP1, with an OR of 1.54 (95% CI = 1.24-1.91, padj = 0.007). In conclusion, we found no evidence to support an association between vitamin D status and breast cancer risk in women of African ancestry, suggesting that vitamin D is unlikely to have significant effect on breast carcinogenesis. Interestingly, TYRP1 might be related to breast cancer through a non-vitamin D relevant mechanism but further studies are needed.
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Affiliation(s)
- Shengfeng Wang
- Center for Clinical Cancer Genetics & Global Health, Department of Medicine, University of Chicago, Chicago, IL
| | - Dezheng Huo
- Department of Public Health Sciences, University of Chicago, Chicago, IL
| | - Sonia Kupfer
- Department of Medicine, University of Chicago, Chicago, Illinois
| | - Dereck Alleyne
- Department of Medicine, University of Chicago, Chicago, Illinois
| | - Temidayo O Ogundiran
- Department of Surgery, College of Medicine, University of Ibadan, Ibadan, Nigeria
| | - Oladosu Ojengbede
- Center for Population and Reproductive Health, College of Medicine, University of Ibadan, Ibadan, Nigeria
| | - Wei Zheng
- Division of Epidemiology, Department of Medicine, Vanderbilt Epidemiology Center, Vanderbilt-Ingram Cancer Center, Vanderbilt University, Nashville, TN
| | | | - Barbara Nemesure
- Department of Preventive Medicine, State University of New York at Stony Brook, Stony Brook, NY
| | - Stefan Ambs
- Laboratory of Human Carcinogenesis, National Cancer Institute, Bethesda, MD
| | - Olufunmilayo I Olopade
- Center for Clinical Cancer Genetics & Global Health, Department of Medicine, University of Chicago, Chicago, IL
| | - Yonglan Zheng
- Center for Clinical Cancer Genetics & Global Health, Department of Medicine, University of Chicago, Chicago, IL
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Almoguera B, Vazquez L, Mentch F, Connolly J, Pacheco JA, Sundaresan AS, Peissig PL, Linneman JG, McCarty CA, Crosslin D, Carrell DS, Lingren T, Namjou-Khales B, Harley JB, Larson E, Jarvik GP, Brilliant M, Williams MS, Kullo IJ, Hysinger EB, Sleiman PMA, Hakonarson H. Identification of Four Novel Loci in Asthma in European American and African American Populations. Am J Respir Crit Care Med 2017; 195:456-463. [PMID: 27611488 DOI: 10.1164/rccm.201604-0861oc] [Citation(s) in RCA: 64] [Impact Index Per Article: 9.1] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022] Open
Abstract
RATIONALE Despite significant advances in knowledge of the genetic architecture of asthma, specific contributors to the variability in the burden between populations remain uncovered. OBJECTIVES To identify additional genetic susceptibility factors of asthma in European American and African American populations. METHODS A phenotyping algorithm mining electronic medical records was developed and validated to recruit cases with asthma and control subjects from the Electronic Medical Records and Genomics network. Genome-wide association analyses were performed in pediatric and adult asthma cases and control subjects with European American and African American ancestry followed by metaanalysis. Nominally significant results were reanalyzed conditioning on allergy status. MEASUREMENTS AND MAIN RESULTS The validation of the algorithm yielded an average of 95.8% positive predictive values for both cases and control subjects. The algorithm accrued 21,644 subjects (65.83% European American and 34.17% African American). We identified four novel population-specific associations with asthma after metaanalyses: loci 6p21.31, 9p21.2, and 10q21.3 in the European American population, and the PTGES gene in African Americans. TEK at 9p21.2, which encodes TIE2, has been shown to be involved in remodeling the airway wall in asthma, and the association remained significant after conditioning by allergy. PTGES, which encodes the prostaglandin E synthase, has also been linked to asthma, where deficient prostaglandin E2 synthesis has been associated with airway remodeling. CONCLUSIONS This study adds to understanding of the genetic architecture of asthma in European Americans and African Americans and reinforces the need to study populations of diverse ethnic backgrounds to identify shared and unique genetic predictors of asthma.
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Affiliation(s)
- Berta Almoguera
- 1 Center for Applied Genomics, The Children's Hospital of Philadelphia, Philadelphia, Pennsylvania
| | - Lyam Vazquez
- 1 Center for Applied Genomics, The Children's Hospital of Philadelphia, Philadelphia, Pennsylvania
| | - Frank Mentch
- 1 Center for Applied Genomics, The Children's Hospital of Philadelphia, Philadelphia, Pennsylvania
| | - John Connolly
- 1 Center for Applied Genomics, The Children's Hospital of Philadelphia, Philadelphia, Pennsylvania
| | - Jennifer A Pacheco
- 2 Northwestern University Feinberg School of Medicine, Chicago, Illinois
| | | | - Peggy L Peissig
- 4 Marshfield Clinic Research Foundation, Marshfield, Wisconsin
| | | | | | - David Crosslin
- 6 University of Washington Medical Center, Seattle, Washington
| | | | - Todd Lingren
- 8 Cincinnati Children's Hospital Medical Center, Cincinnati, Ohio
| | | | - John B Harley
- 8 Cincinnati Children's Hospital Medical Center, Cincinnati, Ohio.,9 U.S. Department of Veterans Affairs Medical Center, Cincinnati, Ohio
| | - Eric Larson
- 7 Group Health Research Institute, Seattle, Washington
| | - Gail P Jarvik
- 6 University of Washington Medical Center, Seattle, Washington
| | | | | | | | - Erik B Hysinger
- 1 Center for Applied Genomics, The Children's Hospital of Philadelphia, Philadelphia, Pennsylvania
| | - Patrick M A Sleiman
- 1 Center for Applied Genomics, The Children's Hospital of Philadelphia, Philadelphia, Pennsylvania.,11 Department of Pediatrics, The Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania
| | - Hakon Hakonarson
- 1 Center for Applied Genomics, The Children's Hospital of Philadelphia, Philadelphia, Pennsylvania.,11 Department of Pediatrics, The Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania
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Holzinger ER, Verma SS, Moore CB, Hall M, De R, Gilbert-Diamond D, Lanktree MB, Pankratz N, Amuzu A, Burt A, Dale C, Dudek S, Furlong CE, Gaunt TR, Kim DS, Riess H, Sivapalaratnam S, Tragante V, van Iperen EP, Brautbar A, Carrell DS, Crosslin DR, Jarvik GP, Kuivaniemi H, Kullo IJ, Larson EB, Rasmussen-Torvik LJ, Tromp G, Baumert J, Cruickshanks KJ, Farrall M, Hingorani AD, Hovingh GK, Kleber ME, Klein BE, Klein R, Koenig W, Lange LA, Mӓrz W, North KE, Charlotte Onland-Moret N, Reiner AP, Talmud PJ, van der Schouw YT, Wilson JG, Kivimaki M, Kumari M, Moore JH, Drenos F, Asselbergs FW, Keating BJ, Ritchie MD. Discovery and replication of SNP-SNP interactions for quantitative lipid traits in over 60,000 individuals. BioData Min 2017; 10:25. [PMID: 28770004 PMCID: PMC5525436 DOI: 10.1186/s13040-017-0145-5] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/02/2017] [Accepted: 07/12/2017] [Indexed: 12/01/2022] Open
Abstract
BACKGROUND The genetic etiology of human lipid quantitative traits is not fully elucidated, and interactions between variants may play a role. We performed a gene-centric interaction study for four different lipid traits: low-density lipoprotein cholesterol (LDL-C), high-density lipoprotein cholesterol (HDL-C), total cholesterol (TC), and triglycerides (TG). RESULTS Our analysis consisted of a discovery phase using a merged dataset of five different cohorts (n = 12,853 to n = 16,849 depending on lipid phenotype) and a replication phase with ten independent cohorts totaling up to 36,938 additional samples. Filters are often applied before interaction testing to correct for the burden of testing all pairwise interactions. We used two different filters: 1. A filter that tested only single nucleotide polymorphisms (SNPs) with a main effect of p < 0.001 in a previous association study. 2. A filter that only tested interactions identified by Biofilter 2.0. Pairwise models that reached an interaction significance level of p < 0.001 in the discovery dataset were tested for replication. We identified thirteen SNP-SNP models that were significant in more than one replication cohort after accounting for multiple testing. CONCLUSIONS These results may reveal novel insights into the genetic etiology of lipid levels. Furthermore, we developed a pipeline to perform a computationally efficient interaction analysis with multi-cohort replication.
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Affiliation(s)
- Emily R. Holzinger
- Computational and Statistical Genomics Branch, National Human Genome Research Institute, National Institute for General Medical Sciences, National Institutes of Health, Baltimore, MD USA
| | - Shefali S. Verma
- The Center for Systems Genomics, The Pennsylvania State University, University Park, State College, PA USA
| | | | - Molly Hall
- The Center for Systems Genomics, The Pennsylvania State University, University Park, State College, PA USA
| | - Rishika De
- Department of Genetics, Geisel School of Medicine at Dartmouth, Hanover, NH USA
| | | | | | - Nathan Pankratz
- Department of Lab Medicine and Pathology, University of Minnesota, Minneapolis, MN USA
| | | | - Amber Burt
- Division of Medical Genetics, Department of Medicine, University of Washington, Seattle, WA USA
| | - Caroline Dale
- London School of Hygiene and Tropical Medicine, London, UK
| | - Scott Dudek
- The Center for Systems Genomics, The Pennsylvania State University, University Park, State College, PA USA
| | - Clement E. Furlong
- Division of Medical Genetics, Department of Medicine, University of Washington, Seattle, WA USA
| | - Tom R. Gaunt
- MRC Integrative Epidemiology Unit, University of Bristol, Oakfield House, Oakfield Grove, Bristol, UK
| | - Daniel Seung Kim
- Division of Medical Genetics, Department of Medicine, University of Washington, Seattle, WA USA
| | - Helene Riess
- Institute of Epidemiology II, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany
| | | | - Vinicius Tragante
- Department of Cardiology, Division Heart and Lungs, University Medical Center Utrecht, Utrecht, The Netherlands
- Department of Medical Genetics, Biomedical Genetics, University Medical Center Utrecht, Utrecht, The Netherlands
| | - Erik P.A. van Iperen
- Durrer Center for Cardiogenetic Research, ICIN-Netherlands Heart Institute, Utrecht, The Netherlands
- Department of Clinical Epidemiology, Biostatistics and Bioinformatics, Academic Medical Center, Amsterdam, The Netherlands
| | - Ariel Brautbar
- Department of Medical Genetics, Marshfield Clinic, Marshfield, WI USA
| | - David S. Carrell
- Group Health Research Institute, Group Health Cooperative, Seattle, WA USA
| | - David R. Crosslin
- Division of Medical Genetics, Department of Medicine, University of Washington, Seattle, WA USA
| | - Gail P. Jarvik
- Division of Medical Genetics, Department of Medicine, University of Washington, Seattle, WA USA
| | - Helena Kuivaniemi
- Division of Molecular Biology and Human Genetics, Department of Biomedical Sciences, Stellenbosch University, Tygerberg, South Africa
| | | | - Eric B. Larson
- Group Health Research Institute, Group Health Cooperative, Seattle, WA USA
| | - Laura J. Rasmussen-Torvik
- Department of Preventive Medicine, Northwestern University Feinberg School of Medicine, Chicago, IL USA
| | - Gerard Tromp
- Division of Molecular Biology and Human Genetics, Department of Biomedical Sciences, Stellenbosch University, Tygerberg, South Africa
| | - Jens Baumert
- Institute of Epidemiology II, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany
| | - Karen J. Cruickshanks
- Department of Population Health Sciences, Department of Ophthalmology and Visual Sciences, University of Wisconsin-Madison, Madison, WI USA
| | - Martin Farrall
- Department of Cardiovascular Medicine, The Wellcome Trust Centre for Human Genetics, University of Oxford, Oxford, UK
| | - Aroon D. Hingorani
- Department of Epidemiology and Public Health, UCL Institute of Epidemiology & Health Care, University College London, London, UK
| | - G. K. Hovingh
- Department of Vascular Medicine, Academic Medical Center, Amsterdam, The Netherlands
| | - Marcus E. Kleber
- Vth Department of Medicine, Medical Faculty Mannheim, Heidelberg University, Heidelberg, Germany
| | - Barbara E. Klein
- Department of Population Health Sciences, Department of Ophthalmology and Visual Sciences, University of Wisconsin-Madison, Madison, WI USA
| | - Ronald Klein
- Department of Population Health Sciences, Department of Ophthalmology and Visual Sciences, University of Wisconsin-Madison, Madison, WI USA
| | - Wolfgang Koenig
- Department of Internal Medicine II – Cardiology, University of Ulm Medical Centre, Ulm, Germany
| | - Leslie A. Lange
- Department of Genetics, University of North Carolina School of Medicine at Chapel Hill, Chapel Hill, NC USA
| | - Winfried Mӓrz
- Vth Department of Medicine, Medical Faculty Mannheim, Heidelberg University, Heidelberg, Germany
- Synlab Academy, Synlab Services GmbH, Mannheim, Germany
| | - Kari E. North
- Department of Epidemiology, School of Public Health, University of North Carolina at Chapel Hill, Chapel Hill, NC USA
| | - N. Charlotte Onland-Moret
- Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht, The Netherlands
| | - Alex P. Reiner
- Division of Public Health Sciences, Fred Hutchinson Cancer Research Center, Seattle, WA USA
| | - Philippa J. Talmud
- MRC Integrative Epidemiology Unit, School of Social and Community Medicine, University of Bristol, Bristol, UK
| | - Yvonne T. van der Schouw
- Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht, The Netherlands
| | - James G. Wilson
- Department of Physiology and Biophysics, University of Mississippi Medical Center, Jackson, MS USA
| | - Mika Kivimaki
- Department of Epidemiology and Public Health, UCL Institute of Epidemiology & Health Care, University College London, London, UK
| | - Meena Kumari
- Department of Epidemiology and Public Health, UCL Institute of Epidemiology & Health Care, University College London, London, UK
- ISER, University of Essex, Essex, UK
| | - Jason H. Moore
- Institute for Biomedical Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA USA
| | - Fotios Drenos
- MRC Integrative Epidemiology Unit, School of Social and Community Medicine, University of Bristol, Bristol, UK
- Centre of Cardiovascular Genetics, Institute of Cardiovascular Science, Faculty of Population Health Sciences, University College London, London, UK
| | - Folkert W. Asselbergs
- Department of Cardiology, Division Heart and Lungs, University Medical Center Utrecht, Utrecht, The Netherlands
- Durrer Center for Cardiogenetic Research, ICIN-Netherlands Heart Institute, Utrecht, The Netherlands
- Centre of Cardiovascular Genetics, Institute of Cardiovascular Science, Faculty of Population Health Sciences, University College London, London, UK
| | - Brendan J. Keating
- Division of Genetics, The Children’s Hospital of Philadelphia, Philadelphia, PA USA
- Division of Transplantation, Department of Surgery, University of Pennsylvania, Philadelphia, PA USA
| | - Marylyn D. Ritchie
- Biomedical and Translational Informatics, Geisinger Clinic, Danville, PA USA
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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.
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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:
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Shan Y, Tromp G, Kuivaniemi H, Smelser DT, Verma SS, Ritchie MD, Elmore JR, Carey DJ, Conley YP, Gorin MB, Weeks DE. Genetic risk models: Influence of model size on risk estimates and precision. Genet Epidemiol 2017; 41:282-296. [PMID: 28198095 DOI: 10.1002/gepi.22035] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/14/2016] [Revised: 11/08/2016] [Accepted: 12/01/2016] [Indexed: 12/11/2022]
Abstract
Disease risk estimation plays an important role in disease prevention. Many studies have found that the ability to predict risk improves as the number of risk single-nucleotide polymorphisms (SNPs) in the risk model increases. However, the width of the confidence interval of the risk estimate is often not considered in the evaluation of the risk model. Here, we explore how the risk and the confidence interval width change as more SNPs are added to the model in the order of decreasing effect size, using both simulated data and real data from studies of abdominal aortic aneurysms and age-related macular degeneration. Our results show that confidence interval width is positively correlated with model size and the majority of the bigger models have wider confidence interval widths than smaller models. Once the model size is bigger than a certain level, the risk does not shift markedly, as 100% of the risk estimates of the one-SNP-bigger models lie inside the confidence interval of the one-SNP-smaller models. We also created a confidence interval-augmented reclassification table. It shows that both more effective SNPs with larger odds ratios and less effective SNPs with smaller odds ratios contribute to the correct decision of whom to screen. The best screening strategy is selected and evaluated by the net benefit quantity and the reclassification rate. We suggest that individuals whose upper bound of their risk confidence interval is above the screening threshold, which corresponds to the population prevalence of the disease, should be screened.
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Affiliation(s)
- Ying Shan
- Department of Biostatistics, Graduate School of Public Health, University of Pittsburgh, Pittsburgh, Pennsylvania, United States of America
| | - Gerard Tromp
- The Sigfried and Janet Weis Center for Research, Geisinger Health System, Danville, Pennsylvania, United States of America.,Division of Molecular Biology and Human Genetics, Department of Biomedical Sciences, Faculty of Medicine and Health Sciences, Stellenbosch University, Tygerberg, South Africa
| | - Helena Kuivaniemi
- The Sigfried and Janet Weis Center for Research, Geisinger Health System, Danville, Pennsylvania, United States of America.,Division of Molecular Biology and Human Genetics, Department of Biomedical Sciences, Faculty of Medicine and Health Sciences, Stellenbosch University, Tygerberg, South Africa
| | - Diane T Smelser
- The Sigfried and Janet Weis Center for Research, Geisinger Health System, Danville, Pennsylvania, United States of America
| | - Shefali S Verma
- Department of Biomedical and Translational Informatics, Geisinger Health System, Danville, Pennsylvania, United States of America
| | - Marylyn D Ritchie
- Department of Biomedical and Translational Informatics, Geisinger Health System, Danville, Pennsylvania, United States of America
| | - James R Elmore
- Department of Vascular and Endovascular Surgery, Geisinger Health System, Danville, PA
| | - David J Carey
- The Sigfried and Janet Weis Center for Research, Geisinger Health System, Danville, Pennsylvania, United States of America
| | - Yvette P Conley
- Department of Health Promotion and Development, School of Nursing, University of Pittsburgh, Pittsburgh, Pennsylvania, United States of America.,Department of Human Genetics, Graduate School of Public Health, University of Pittsburgh, Pittsburgh, Pennsylvania, United States of America
| | - Michael B Gorin
- Departments of Ophthalmology and Human Genetics, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, California, United States of America.,Stein Eye Institute, Los Angeles, California, United States of America
| | - Daniel E Weeks
- Department of Biostatistics, Graduate School of Public Health, University of Pittsburgh, Pittsburgh, Pennsylvania, United States of America.,Department of Human Genetics, Graduate School of Public Health, University of Pittsburgh, Pittsburgh, Pennsylvania, United States of America
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74
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Identifying gene-gene interactions that are highly associated with four quantitative lipid traits across multiple cohorts. Hum Genet 2016; 136:165-178. [PMID: 27848076 DOI: 10.1007/s00439-016-1738-7] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/25/2016] [Accepted: 10/07/2016] [Indexed: 10/20/2022]
Abstract
Genetic loci explain only 25-30 % of the heritability observed in plasma lipid traits. Epistasis, or gene-gene interactions may contribute to a portion of this missing heritability. Using the genetic data from five NHLBI cohorts of 24,837 individuals, we combined the use of the quantitative multifactor dimensionality reduction (QMDR) algorithm with two SNP-filtering methods to exhaustively search for SNP-SNP interactions that are associated with HDL cholesterol (HDL-C), LDL cholesterol (LDL-C), total cholesterol (TC) and triglycerides (TG). SNPs were filtered either on the strength of their independent effects (main effect filter) or the prior knowledge supporting a given interaction (Biofilter). After the main effect filter, QMDR identified 20 SNP-SNP models associated with HDL-C, 6 associated with LDL-C, 3 associated with TC, and 10 associated with TG (permutation P value <0.05). With the use of Biofilter, we identified 2 SNP-SNP models associated with HDL-C, 3 associated with LDL-C, 1 associated with TC and 8 associated with TG (permutation P value <0.05). In an independent dataset of 7502 individuals from the eMERGE network, we replicated 14 of the interactions identified after main effect filtering: 11 for HDL-C, 1 for LDL-C and 2 for TG. We also replicated 23 of the interactions found to be associated with TG after applying Biofilter. Prior knowledge supports the possible role of these interactions in the genetic etiology of lipid traits. This study also presents a computationally efficient pipeline for analyzing data from large genotyping arrays and detecting SNP-SNP interactions that are not primarily driven by strong main effects.
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75
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Abstract
Most analyses of genome-wide association data consider each variant independently without considering or adjusting for the genetic background present in the rest of the genome. New approaches to genome analysis use representations of genomic sharing to better account for confounding factors like population stratification or to directly approximate heritability through the estimated sharing of individuals in a dataset. These approaches use mixed linear models, which relate genotypic sharing to phenotypic sharing, and rely on the efficient computation of genetic sharing among individuals in a dataset. This unit describes the principles and practical application of mixed models for the analysis of genome-wide association study data. © 2016 by John Wiley & Sons, Inc.
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Affiliation(s)
- Jacob B Hall
- Institute for Computational Biology, Case Western Reserve University, Cleveland, Ohio
| | - William S Bush
- Institute for Computational Biology, Case Western Reserve University, Cleveland, Ohio
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Verma SS, Cooke Bailey JN, Lucas A, Bradford Y, Linneman JG, Hauser MA, Pasquale LR, Peissig PL, Brilliant MH, McCarty CA, Haines JL, Wiggs JL, Vrabec TR, Tromp G, Ritchie MD. Epistatic Gene-Based Interaction Analyses for Glaucoma in eMERGE and NEIGHBOR Consortium. PLoS Genet 2016; 12:e1006186. [PMID: 27623284 PMCID: PMC5021356 DOI: 10.1371/journal.pgen.1006186] [Citation(s) in RCA: 26] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/26/2016] [Accepted: 06/22/2016] [Indexed: 12/22/2022] Open
Abstract
Primary open angle glaucoma (POAG) is a complex disease and is one of the major leading causes of blindness worldwide. Genome-wide association studies have successfully identified several common variants associated with glaucoma; however, most of these variants only explain a small proportion of the genetic risk. Apart from the standard approach to identify main effects of variants across the genome, it is believed that gene-gene interactions can help elucidate part of the missing heritability by allowing for the test of interactions between genetic variants to mimic the complex nature of biology. To explain the etiology of glaucoma, we first performed a genome-wide association study (GWAS) on glaucoma case-control samples obtained from electronic medical records (EMR) to establish the utility of EMR data in detecting non-spurious and relevant associations; this analysis was aimed at confirming already known associations with glaucoma and validating the EMR derived glaucoma phenotype. Our findings from GWAS suggest consistent evidence of several known associations in POAG. We then performed an interaction analysis for variants found to be marginally associated with glaucoma (SNPs with main effect p-value <0.01) and observed interesting findings in the electronic MEdical Records and GEnomics Network (eMERGE) network dataset. Genes from the top epistatic interactions from eMERGE data (Likelihood Ratio Test i.e. LRT p-value <1e-05) were then tested for replication in the NEIGHBOR consortium dataset. To replicate our findings, we performed a gene-based SNP-SNP interaction analysis in NEIGHBOR and observed significant gene-gene interactions (p-value <0.001) among the top 17 gene-gene models identified in the discovery phase. Variants from gene-gene interaction analysis that we found to be associated with POAG explain 3.5% of additional genetic variance in eMERGE dataset above what is explained by the SNPs in genes that are replicated from previous GWAS studies (which was only 2.1% variance explained in eMERGE dataset); in the NEIGHBOR dataset, adding replicated SNPs from gene-gene interaction analysis explain 3.4% of total variance whereas GWAS SNPs alone explain only 2.8% of variance. Exploring gene-gene interactions may provide additional insights into many complex traits when explored in properly designed and powered association studies. The complex nature of primary-open angle glaucoma (POAG) has left researchers exploring the genetic architecture and searching for the missing heritability using a number of different study designs. Over the past decade, many studies have been conducted to explain the etiology of POAG; however, a high proportion of estimated heritability still remains unexplained. GWA studies for POAG have identified significant associations but these associations have only explained a small proportion of the genetic risk (odds ratios range between 1–3). In this paper, we sought to confirm the primary genome-wide significant associations that have been discovered so far for glaucoma in phenotypes developed from EMR data in an effort to show that EMR data can be a powerful resource for finding genetic variants influencing POAG susceptibility. Next, we tested for statistical interactions, which can be presented as an important tool in an attempt to explain POAG heritability. We used a reduced list of variants filtered by marginal main effect analysis to look for epistatic interactions. We present our results from replication of gene-based interaction analyses performed in eMERGE and the NEIGHBOR consortium data. Using expression data and annotations from various publicly available databases, the most significant genes that replicated in our analyses show expression in the eye and trabecular meshwork. Analysis for estimation of genetic variance explained by significant associations from previous GWAS and replicated variants from gene-based interactions suggest that these explain 5.6% of variance in eMERGE dataset and also explain 3.4% variance in NEIGHBOR dataset.
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Affiliation(s)
- Shefali Setia Verma
- Department of Biomedical and Translational Informatics, Geisinger Health System, Danville, Pennsylvania, United States of America
- The Huck Institute of Life Sciences, The Pennsylvania State University, University Park, Pennsylvania, United States of America
| | - Jessica N. Cooke Bailey
- Department of Epidemiology and Biostatistics, Institute for Computational Biology, Case Western Reserve University School of Medicine, Cleveland, Ohio, United States of America
| | - Anastasia Lucas
- Department of Biomedical and Translational Informatics, Geisinger Health System, Danville, Pennsylvania, United States of America
| | - Yuki Bradford
- Department of Biomedical and Translational Informatics, Geisinger Health System, Danville, Pennsylvania, United States of America
| | - James G. Linneman
- Center for Human Genetics, Marshfield Clinic Research Foundation, Marshfield, Wisconsin, United States of America
| | - Michael A. Hauser
- Department of Ophthalmology, Duke University Medical Center, Durham, North Carolina, United States of America
| | - Louis R. Pasquale
- Department of Ophthalmology, Harvard Medical School, Massachusetts Eye and Ear Infirmary, Boston, Massachusetts, United States of America
- Channing Division of Network Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts, United States of America
| | - Peggy L. Peissig
- Center for Human Genetics, Marshfield Clinic Research Foundation, Marshfield, Wisconsin, United States of America
| | - Murray H. Brilliant
- Center for Human Genetics, Marshfield Clinic Research Foundation, Marshfield, Wisconsin, United States of America
| | | | - Jonathan L. Haines
- Department of Epidemiology and Biostatistics, Institute for Computational Biology, Case Western Reserve University School of Medicine, Cleveland, Ohio, United States of America
| | - Janey L. Wiggs
- Department of Ophthalmology, Harvard Medical School, Massachusetts Eye and Ear Infirmary, Boston, Massachusetts, United States of America
| | - Tamara R. Vrabec
- Department of Ophthalmology, Geisinger Health System, Danville, Pennsylvania, United States of America
| | - Gerard Tromp
- Division of Molecular Biology and Human Genetics, Department of Biomedical Sciences, Faculty of Medicine and Health Sciences, Stellenbosch University, Tygerberg, South Africa
| | - Marylyn D. Ritchie
- Department of Biomedical and Translational Informatics, Geisinger Health System, Danville, Pennsylvania, United States of America
- Department of Biochemistry and Molecular Biology, The Pennsylvania State University, University Park, Pennsylvania, United States of America
- * E-mail:
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eMERGE Phenome-Wide Association Study (PheWAS) identifies clinical associations and pleiotropy for stop-gain variants. BMC Med Genomics 2016; 9 Suppl 1:32. [PMID: 27535653 PMCID: PMC4989894 DOI: 10.1186/s12920-016-0191-8] [Citation(s) in RCA: 24] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/21/2022] Open
Abstract
BACKGROUND We explored premature stop-gain variants to test the hypothesis that variants, which are likely to have a consequence on protein structure and function, will reveal important insights with respect to the phenotypes associated with them. We performed a phenome-wide association study (PheWAS) exploring the association between a selected list of functional stop-gain genetic variants (variation resulting in truncated proteins or in nonsense-mediated decay) and an extensive group of diagnoses to identify novel associations and uncover potential pleiotropy. RESULTS In this study, we selected 25 stop-gain variants: 5 stop-gain variants with previously reported phenotypic associations, and a set of 20 putative stop-gain variants identified using dbSNP. For the PheWAS, we used data from the electronic MEdical Records and GEnomics (eMERGE) Network across 9 sites with a total of 41,057 unrelated patients. We divided all these samples into two datasets by equal proportion of eMERGE site, sex, race, and genotyping platform. We calculated single effect associations between these 25 stop-gain variants and ICD-9 defined case-control diagnoses. We also performed stratified analyses for samples of European and African ancestry. Associations were adjusted for sex, site, genotyping platform and the first three principal components to account for global ancestry. We identified previously known associations, such as variants in LPL associated with hyperglyceridemia indicating that our approach was robust. We also found a total of three significant associations with p < 0.01 in both datasets, with the most significant replicating result being LPL SNP rs328 and ICD-9 code 272.1 "Disorder of Lipoid metabolism" (pdiscovery = 2.59x10-6, preplicating = 2.7x10-4). The other two significant replicated associations identified by this study are: variant rs1137617 in KCNH2 gene associated with ICD-9 code category 244 "Acquired Hypothyroidism" (pdiscovery = 5.31x103, preplicating = 1.15x10-3) and variant rs12060879 in DPT gene associated with ICD-9 code category 996 "Complications peculiar to certain specified procedures" (pdiscovery = 8.65x103, preplicating = 4.16x10-3). CONCLUSION In conclusion, this PheWAS revealed novel associations of stop-gained variants with interesting phenotypes (ICD-9 codes) along with pleiotropic effects.
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Phenome-Wide Association Study to Explore Relationships between Immune System Related Genetic Loci and Complex Traits and Diseases. PLoS One 2016; 11:e0160573. [PMID: 27508393 PMCID: PMC4980020 DOI: 10.1371/journal.pone.0160573] [Citation(s) in RCA: 20] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/19/2016] [Accepted: 07/16/2016] [Indexed: 12/21/2022] Open
Abstract
We performed a Phenome-Wide Association Study (PheWAS) to identify interrelationships between the immune system genetic architecture and a wide array of phenotypes from two de-identified electronic health record (EHR) biorepositories. We selected variants within genes encoding critical factors in the immune system and variants with known associations with autoimmunity. To define case/control status for EHR diagnoses, we used International Classification of Diseases, Ninth Revision (ICD-9) diagnosis codes from 3,024 Geisinger Clinic MyCode® subjects (470 diagnoses) and 2,899 Vanderbilt University Medical Center BioVU biorepository subjects (380 diagnoses). A pooled-analysis was also carried out for the replicating results of the two data sets. We identified new associations with potential biological relevance including SNPs in tumor necrosis factor (TNF) and ankyrin-related genes associated with acute and chronic sinusitis and acute respiratory tract infection. The two most significant associations identified were for the C6orf10 SNP rs6910071 and “rheumatoid arthritis” (ICD-9 code category 714) (pMETAL = 2.58 x 10−9) and the ATN1 SNP rs2239167 and “diabetes mellitus, type 2” (ICD-9 code category 250) (pMETAL = 6.39 x 10−9). This study highlights the utility of using PheWAS in conjunction with EHRs to discover new genotypic-phenotypic associations for immune-system related genetic loci.
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79
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Khan RJ, Riestra P, Gebreab SY, Wilson JG, Gaye A, Xu R, Davis SK. Vitamin D Receptor Gene Polymorphisms Are Associated with Abdominal Visceral Adipose Tissue Volume and Serum Adipokine Concentrations but Not with Body Mass Index or Waist Circumference in African Americans: The Jackson Heart Study. J Nutr 2016; 146:1476-82. [PMID: 27358421 PMCID: PMC4958289 DOI: 10.3945/jn.116.229963] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/12/2016] [Accepted: 05/19/2016] [Indexed: 12/19/2022] Open
Abstract
BACKGROUND The biological actions of vitamin D are mediated through the vitamin D receptor (VDR). Single-nucleotide polymorphisms (SNPs) in the VDR gene have been previously associated with adiposity traits. However, to our knowledge, few studies have included direct measures of adiposity and adipokine concentrations. OBJECTIVE We examined the association of tagging SNPs in the VDR gene with multiple adiposity measures, including waist circumference (WC), body mass index (BMI), body fat percentage, subcutaneous and visceral adipose tissue (VAT) volume, and serum adipokine (adiponectin and leptin) concentrations in adult African Americans (AAs). METHODS Data from 3020 participants (61.9% women; mean age, 54.6 y) from the Jackson Heart Study were used for this analysis. Forty-five tag SNPs were chosen with the use of genotype data from the International HapMap project. We used linear regression to test the associations of imputed VDR SNPs with each of the traits, adjusted for age, sex, educational status, physical activity, smoking, alcohol intake, serum vitamin D concentration, European ancestry, and multiple testing. RESULTS The G allele of the SNP rs4328262 remained associated with increased VAT volume after multiple testing correction (β = 45.7; P < 0.001). The A allele of another SNP (rs11574070) was nominally associated with body fat percentage (β = 0.96; P = 0.002). None of the VDR SNPs analyzed showed any link with WC or BMI. The A allele of rs2228570 (β = 0.08; P = 0.001) for men and the T allele of rs2853563 (β = 0.04; P < 0.001) for women remained positively associated with serum adiponectin concentrations after multiple testing correction. CONCLUSION Although we did not find any association for anthropometric measures, we did observe associations of VDR variants with serum adipokines and with the more metabolically active fat, VAT. Therefore, our findings demonstrate a possible role of VDR variants in regulating adipose tissue activity and adiposity among AAs.
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Affiliation(s)
- Rumana J Khan
- Cardiovascular Section, Metabolic, Cardiovascular and Inflammatory Disease Genomics Branch, National Human Genome Research Institute, NIH, Bethesda, MD; and
| | - Pia Riestra
- Cardiovascular Section, Metabolic, Cardiovascular and Inflammatory Disease Genomics Branch, National Human Genome Research Institute, NIH, Bethesda, MD; and
| | - Samson Y Gebreab
- Cardiovascular Section, Metabolic, Cardiovascular and Inflammatory Disease Genomics Branch, National Human Genome Research Institute, NIH, Bethesda, MD; and
| | - James G Wilson
- Department of Physiology and Biophysics, University of Mississippi Medical Center, Jackson, MS
| | - Amadou Gaye
- Cardiovascular Section, Metabolic, Cardiovascular and Inflammatory Disease Genomics Branch, National Human Genome Research Institute, NIH, Bethesda, MD; and
| | - Ruihua Xu
- Cardiovascular Section, Metabolic, Cardiovascular and Inflammatory Disease Genomics Branch, National Human Genome Research Institute, NIH, Bethesda, MD; and
| | - Sharon K Davis
- Cardiovascular Section, Metabolic, Cardiovascular and Inflammatory Disease Genomics Branch, National Human Genome Research Institute, NIH, Bethesda, MD; and
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80
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van 't Hof FNG, Ruigrok YM, Lee CH, Ripke S, Anderson G, de Andrade M, Baas AF, Blankensteijn JD, Böttinger EP, Bown MJ, Broderick J, Bijlenga P, Carrell DS, Crawford DC, Crosslin DR, Ebeling C, Eriksson JG, Fornage M, Foroud T, von Und Zu Fraunberg M, Friedrich CM, Gaál EI, Gottesman O, Guo DC, Harrison SC, Hernesniemi J, Hofman A, Inoue I, Jääskeläinen JE, Jones GT, Kiemeney LALM, Kivisaari R, Ko N, Koskinen S, Kubo M, Kullo IJ, Kuivaniemi H, Kurki MI, Laakso A, Lai D, Leal SM, Lehto H, LeMaire SA, Low SK, Malinowski J, McCarty CA, Milewicz DM, Mosley TH, Nakamura Y, Nakaoka H, Niemelä M, Pacheco J, Peissig PL, Pera J, Rasmussen-Torvik L, Ritchie MD, Rivadeneira F, van Rij AM, Santos-Cortez RLP, Saratzis A, Slowik A, Takahashi A, Tromp G, Uitterlinden AG, Verma SS, Vermeulen SH, Wang GT, Han B, Rinkel GJE, de Bakker PIW. Shared Genetic Risk Factors of Intracranial, Abdominal, and Thoracic Aneurysms. J Am Heart Assoc 2016; 5:e002603. [PMID: 27418160 PMCID: PMC5015357 DOI: 10.1161/jaha.115.002603] [Citation(s) in RCA: 40] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/23/2015] [Accepted: 03/16/2016] [Indexed: 01/22/2023]
Abstract
BACKGROUND Intracranial aneurysms (IAs), abdominal aortic aneurysms (AAAs), and thoracic aortic aneurysms (TAAs) all have a familial predisposition. Given that aneurysm types are known to co-occur, we hypothesized that there may be shared genetic risk factors for IAs, AAAs, and TAAs. METHODS AND RESULTS We performed a mega-analysis of 1000 Genomes Project-imputed genome-wide association study (GWAS) data of 4 previously published aneurysm cohorts: 2 IA cohorts (in total 1516 cases, 4305 controls), 1 AAA cohort (818 cases, 3004 controls), and 1 TAA cohort (760 cases, 2212 controls), and observed associations of 4 known IA, AAA, and/or TAA risk loci (9p21, 18q11, 15q21, and 2q33) with consistent effect directions in all 4 cohorts. We calculated polygenic scores based on IA-, AAA-, and TAA-associated SNPs and tested these scores for association to case-control status in the other aneurysm cohorts; this revealed no shared polygenic effects. Similarly, linkage disequilibrium-score regression analyses did not show significant correlations between any pair of aneurysm subtypes. Last, we evaluated the evidence for 14 previously published aneurysm risk single-nucleotide polymorphisms through collaboration in extended aneurysm cohorts, with a total of 6548 cases and 16 843 controls (IA) and 4391 cases and 37 904 controls (AAA), and found nominally significant associations for IA risk locus 18q11 near RBBP8 to AAA (odds ratio [OR]=1.11; P=4.1×10(-5)) and for TAA risk locus 15q21 near FBN1 to AAA (OR=1.07; P=1.1×10(-3)). CONCLUSIONS Although there was no evidence for polygenic overlap between IAs, AAAs, and TAAs, we found nominally significant effects of two established risk loci for IAs and TAAs in AAAs. These two loci will require further replication.
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Affiliation(s)
- Femke N G van 't Hof
- Utrecht Stroke Center, Department of Neurology and Neurosurgery, Rudolf Magnus Institute of Neuroscience, University Medical Center Utrecht, Utrecht, The Netherlands
| | - Ynte M Ruigrok
- Utrecht Stroke Center, Department of Neurology and Neurosurgery, Rudolf Magnus Institute of Neuroscience, University Medical Center Utrecht, Utrecht, The Netherlands
| | - Cue Hyunkyu Lee
- Department of Convergence Medicine, University of Ulsan College of Medicine and Asan Institute for Life Sciences Asan Medical Center, Seoul, Korea Department of Neurosurgery, Helsinki University Central Hospital, Helsinki, Finland
| | - Stephan Ripke
- Analytic and Translational Genetics Unit, Massachusetts General Hospital and Harvard Medical School, Boston, MA Department of Psychiatry and Psychotherapy, Charité, Universitätsmedizin Berlin, Berlin, Germany
| | - Graig Anderson
- The George Institute for International Health, University of Sydney, Australia
| | | | - Annette F Baas
- Department of Medical Genetics, Center for Molecular Medicine, University Medical Center Utrecht, Utrecht, The Netherlands
| | - Jan D Blankensteijn
- Department of Vascular Surgery, VU Medical Center, Amsterdam, The Netherlands
| | - Erwin P Böttinger
- Icahn School of Medicine Mount Sinai, The Charles Bronfman Institute for Personalized Medicine, New York, NY
| | - Matthew J Bown
- Department of Cardiovascular Sciences and the NIHR Leicester Cardiovascular Biomedical Research Unit, University of Leicester, United Kingdom
| | - Joseph Broderick
- Department of Neurology, University of Cincinnati School of Medicine, Cincinnati, OH
| | - Philippe Bijlenga
- Hôpitaux Universitaire de Genève et Faculté de médecine de Genève, Geneva, Switzerland
| | | | - Dana C Crawford
- Department of Epidemiology and Biostatistics, Institute for Computational Biology, Case Western Reserve University, Cleveland, OH Center for Human Genetics Research, Vanderbilt University, Nashville, TN
| | - David R Crosslin
- Division of Medical Genetics, Department of Medicine, University of Washington, Seattle, WA
| | - Christian Ebeling
- Fraunhofer Institut Algorithmen und Wissenschaftliches Rechnen, Sankt Augustin, Germany
| | - Johan G Eriksson
- Department of Chronic Disease Prevention, National Institute for Health and Welfare, Helsinki, Finland Folkhälsan Research Center, Helsinki, Finland Department of General Practice and Primary Health Care, and Helsinki University Hospital, University of Helsinki, Finland
| | - Myriam Fornage
- Human Genetics Center and Institute of Molecular Medicine, University of Texas Health Science Center, Houston, TX
| | - Tatiana Foroud
- Department of Medical and Molecular Genetics, Indiana University School of Medicine, Indianapolis, IN
| | | | - Christoph M Friedrich
- Department of Computer Science, University of Applied Science and Arts, Dortmund, Germany
| | - Emília I Gaál
- Institute for Molecular Medicine Finland (FIMM), University of Helsinki, Finland Public Health Genomics Unit, Department of Chronic Disease Prevention, National Institute for Health and Welfare, Helsinki, Finland Department of Neurosurgery, Helsinki University Central Hospital, Helsinki, Finland
| | - Omri Gottesman
- Icahn School of Medicine Mount Sinai, The Charles Bronfman Institute for Personalized Medicine, New York, NY
| | - Dong-Chuan Guo
- Department of Internal Medicine, The University of Texas Medical School at Houston, TX
| | - Seamus C Harrison
- Department of Cardiovascular Science, University of Leicester, United Kingdom
| | - Juha Hernesniemi
- Department of Neurosurgery, Helsinki University Central Hospital, Helsinki, Finland
| | - Albert Hofman
- Department of Epidemiology, Erasmus Medical Center, Rotterdam, The Netherlands
| | - Ituro Inoue
- Division of Human Genetics, National Institute of Genetics, Mishima, Japan
| | | | - Gregory T Jones
- Surgery Department, University of Otago, Dunedin, New Zealand
| | - Lambertus A L M Kiemeney
- Radboud University Medical Center, Radboud Institute for Health Sciences, Nijmegen, The Netherlands
| | - Riku Kivisaari
- Department of Neurosurgery, Helsinki University Central Hospital, Helsinki, Finland
| | - Nerissa Ko
- Department of Neurology, University of California, San Francisco, CA
| | - Seppo Koskinen
- Department of Health, Functional Capacity and Welfare, National Institute for Health and Welfare, Helsinki, Finland
| | - Michiaki Kubo
- Center for Integrative Medical Sciences, RIKEN, Kanagawa, Japan
| | | | - Helena Kuivaniemi
- Radboud University Medical Center, Radboud Institute for Health Sciences, Nijmegen, The Netherlands The Sigfried and Janet Weis Center for Research, Geisinger Health System, Danville, PA Department of Surgery, Temple University School of Medicine, Philadelphia, PA Department of Biomedical Sciences, Stellenbosch University, Tygerberg, South Africa
| | - Mitja I Kurki
- Neurosurgery of NeuroCenter, Kuopio University Hospital, Kuopio, Finland Center for Human Genetics Research, Massachusetts General Hospital, Boston, MA Medical and Population Genetics Program, Broad Institute, Boston, MA
| | - Aki Laakso
- Public Health Genomics Unit, Department of Chronic Disease Prevention, National Institute for Health and Welfare, Helsinki, Finland
| | - Dongbing Lai
- Department of Medical and Molecular Genetics, Indiana University School of Medicine, Indianapolis, IN
| | - Suzanne M Leal
- Center for Statistical Genetics, Baylor College of Medicine, Houston, TX
| | - Hanna Lehto
- Department of Neurosurgery, Helsinki University Central Hospital, Helsinki, Finland
| | - Scott A LeMaire
- Michael E. DeBakey Department of Surgery, Baylor College of Medicine and the Texas Heart Institute, Houston, TX
| | - Siew-Kee Low
- Center for Integrative Medical Sciences, RIKEN, Kanagawa, Japan
| | - Jennifer Malinowski
- Center for Human Genetics Research, Vanderbilt University, Nashville, TN Department of Surgery, Yale School of Medicine, New Haven, CT
| | | | - Dianna M Milewicz
- Department of Internal Medicine, The University of Texas Medical School at Houston, TX
| | - Thomas H Mosley
- Department of Medicine, University of Mississippi Medical Center, Jackson, MS
| | - Yusuke Nakamura
- Section of Hematology and Oncology, Department of Medicine, University of Chicago, IL
| | - Hirofumi Nakaoka
- Division of Human Genetics, National Institute of Genetics, Mishima, Japan
| | - Mika Niemelä
- Department of Neurosurgery, Helsinki University Central Hospital, Helsinki, Finland
| | - Jennifer Pacheco
- Center for Genetic Medicine, Northwestern University Feinberg School of Medicine, Chicago, IL
| | - Peggy L Peissig
- Center for Human Genetics, Marshfield Clinic Research Foundation, Marshfield, WI
| | - Joanna Pera
- Department of Neurology, Jagiellonian University, Krakow, Poland
| | - Laura Rasmussen-Torvik
- Department of Preventive Medicine, Northwestern University Feinberg School of Medicine, Chicago, IL
| | - Marylyn D Ritchie
- Center for Systems Genomics, The Pennsylvania State University, Pennsylvania, PA
| | - Fernando Rivadeneira
- Department of Internal Medicine, Erasmus Medical Center, Rotterdam, The Netherlands
| | - Andre M van Rij
- Surgery Department, University of Otago, Dunedin, New Zealand
| | | | - Athanasios Saratzis
- Department of Cardiovascular Sciences and the NIHR Leicester Cardiovascular Biomedical Research Unit, University of Leicester, United Kingdom
| | - Agnieszka Slowik
- Department of Neurology, Jagiellonian University, Krakow, Poland
| | | | - Gerard Tromp
- The Sigfried and Janet Weis Center for Research, Geisinger Health System, Danville, PA Department of Biomedical Sciences, Stellenbosch University, Tygerberg, South Africa
| | - André G Uitterlinden
- Department of Epidemiology, Erasmus Medical Center, Rotterdam, The Netherlands Department of Internal Medicine, Erasmus Medical Center, Rotterdam, The Netherlands
| | - Shefali S Verma
- Center for Systems Genomics, The Pennsylvania State University, Pennsylvania, PA
| | - Sita H Vermeulen
- Radboud University Medical Center, Radboud Institute for Health Sciences, Nijmegen, The Netherlands
| | - Gao T Wang
- Center for Statistical Genetics, Baylor College of Medicine, Houston, TX
| | - Buhm Han
- Department of Convergence Medicine, University of Ulsan College of Medicine and Asan Institute for Life Sciences Asan Medical Center, Seoul, Korea
| | - Gabriël J E Rinkel
- Utrecht Stroke Center, Department of Neurology and Neurosurgery, Rudolf Magnus Institute of Neuroscience, University Medical Center Utrecht, Utrecht, The Netherlands
| | - Paul I W de Bakker
- Department of Epidemiology, Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht, The Netherlands
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Ayers KL, Mirshahi UL, Wardeh AH, Murray MF, Hao K, Glicksberg BS, Li S, Carey DJ, Chen R. A loss of function variant in CASP7 protects against Alzheimer's disease in homozygous APOE ε4 allele carriers. BMC Genomics 2016; 17 Suppl 2:445. [PMID: 27358062 PMCID: PMC4928152 DOI: 10.1186/s12864-016-2725-z] [Citation(s) in RCA: 20] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/21/2022] Open
Abstract
Background Alzheimer’s disease (AD) represents the most common form of dementia in elder populations with approximately 30 million cases worldwide. Genome wide genotyping and sequencing studies have identified many genetic variants associated with late-onset Alzheimer’s disease (LOAD). While most of these variants are associated with increased risk of developing LOAD, only limited number of reports focused on variants that are protective against the disease. Methods Here we applied a novel approach to uncover protective alleles against AD by analyzing genetic and phenotypic data in Mount Sinai Biobank and Electronic Medical Record (EMR) databases. Results We discovered a likely loss-of-function small deletion variant in the caspase 7 (CASP7) gene associated with significantly reduced incidence of LOAD in carriers of the high-risk APOE ε4 allele. Further investigation of four independent cohorts of European ancestry revealed the protective effect of the CASP7 variant against AD is most significant in homozygous APOE ε4 allele carriers. Meta analysis of multiple datasets shows overall odds ratio = 0.45 (p = 0.004). Analysis of RNA sequencing derived gene expression data indicated the variant correlates with reduced caspase 7 expression in multiple brain tissues we examined. Conclusions Taken together, these results are consistent with the notion that caspase 7 plays a key role in microglial activation driving neuro-degeneration during AD pathogenesis, and may explain the underlying genetic mechanisms that anti-inflammatory interventions in AD show greater benefit in APOE ε4 carriers than non-carriers. Our findings inform potential novel therapeutic opportunities for AD and warrant further investigations. Electronic supplementary material The online version of this article (doi:10.1186/s12864-016-2725-z) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Kristin L Ayers
- Department of Genetics and Genomic Sciences, Icahn Institute for Genomics and Multiscale Biology, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
| | | | | | | | - Ke Hao
- Department of Genetics and Genomic Sciences, Icahn Institute for Genomics and Multiscale Biology, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
| | - Benjamin S Glicksberg
- Department of Genetics and Genomic Sciences, Icahn Institute for Genomics and Multiscale Biology, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
| | - Shuyu Li
- Department of Genetics and Genomic Sciences, Icahn Institute for Genomics and Multiscale Biology, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
| | | | - Rong Chen
- Department of Genetics and Genomic Sciences, Icahn Institute for Genomics and Multiscale Biology, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA.
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82
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Friedenberg SG, Meurs KM. Genotype imputation in the domestic dog. Mamm Genome 2016; 27:485-94. [PMID: 27129452 DOI: 10.1007/s00335-016-9636-9] [Citation(s) in RCA: 33] [Impact Index Per Article: 4.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/20/2015] [Accepted: 04/11/2016] [Indexed: 01/08/2023]
Abstract
Application of imputation methods to accurately predict a dense array of SNP genotypes in the dog could provide an important supplement to current analyses of array-based genotyping data. Here, we developed a reference panel of 4,885,283 SNPs in 83 dogs across 15 breeds using whole genome sequencing. We used this panel to predict the genotypes of 268 dogs across three breeds with 84,193 SNP array-derived genotypes as inputs. We then (1) performed breed clustering of the actual and imputed data; (2) evaluated several reference panel breed combinations to determine an optimal reference panel composition; and (3) compared the accuracy of two commonly used software algorithms (Beagle and IMPUTE2). Breed clustering was well preserved in the imputation process across eigenvalues representing 75 % of the variation in the imputed data. Using Beagle with a target panel from a single breed, genotype concordance was highest using a multi-breed reference panel (92.4 %) compared to a breed-specific reference panel (87.0 %) or a reference panel containing no breeds overlapping with the target panel (74.9 %). This finding was confirmed using target panels derived from two other breeds. Additionally, using the multi-breed reference panel, genotype concordance was slightly higher with IMPUTE2 (94.1 %) compared to Beagle; Pearson correlation coefficients were slightly higher for both software packages (0.946 for Beagle, 0.961 for IMPUTE2). Our findings demonstrate that genotype imputation from SNP array-derived data to whole genome-level genotypes is both feasible and accurate in the dog with appropriate breed overlap between the target and reference panels.
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Affiliation(s)
- S G Friedenberg
- Department of Clinical Sciences and Comparative Medicine Institute, North Carolina State University College of Veterinary Medicine, 1060 William Moore Drive, Raleigh, NC, 27607, USA.
| | - K M Meurs
- Department of Clinical Sciences and Comparative Medicine Institute, North Carolina State University College of Veterinary Medicine, 1060 William Moore Drive, Raleigh, NC, 27607, USA
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83
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Genomewide association study of tenofovir pharmacokinetics and creatinine clearance in AIDS Clinical Trials Group protocol A5202. Pharmacogenet Genomics 2016; 25:450-61. [PMID: 26148204 DOI: 10.1097/fpc.0000000000000156] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/17/2022]
Abstract
BACKGROUND Tenofovir disoproxil fumarate (TDF) causes kidney toxicity in some patients. We carried out genomewide analyses to identify associations with plasma tenofovir clearance and change in creatinine clearance (CrCl) during the first 6 months after initiating therapy among patients randomized to TDF/emtricitabine-containing regimens in AIDS Clinical Trials Group protocol A5202. METHODS Pharmacokinetic analyses involved 501 patients randomized to the tenofovir arm. CrCl analyses involved 1096 patients, including 548 controls randomized to abacavir-containing regimens. All had been randomized to also receive atazanavir/ritonavir or efavirenz. Multivariable linear regression and generalized least squares models were used to test for associations between polymorphisms and tenofovir clearance and CrCl change, with Bonferroni correction. Planned subanalyses considered candidate genes and polymorphisms. RESULTS Median CrCl at baseline was 116 ml/min (interquartile range 99.8-135.5). The median change in CrCl after 6 months was -0.5 ml/min (-10.7 to +10.8) and 2.2 (interquartile range -9.9 to +13.2) in tenofovir and abacavir arms, respectively. In genomewide analyses SLC17A1 rs12662869 was found to be associated with an increase in tenofovir clearance (P=7.1×10). In candidate gene analysis for tenofovir clearance, most polymorphisms evaluated were in ABCC4. In the ABCC4 region, the lowest P-value was for CLDN10 rs12866697 (P=1.4×10). Among African Americans, SLC22A2 rs3127573 was associated with a greater 6-month CrCl increase in the tenofovir arm after correcting for multiple comparisons (P=3.3×10). CONCLUSION Among patients randomized to receive TDF/emtricitabine in A5202, there were no significant genomewide associations with change in CrCl. This study did not replicate polymorphisms previously implicated in tenofovir-associated renal injury.
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84
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Huang BE, Mulyasasmita W, Rajagopal G. The path from big data to precision medicine. EXPERT REVIEW OF PRECISION MEDICINE AND DRUG DEVELOPMENT 2016. [DOI: 10.1080/23808993.2016.1157686] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/22/2022]
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85
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Simonti CN, Vernot B, Bastarache L, Bottinger E, Carrell DS, Chisholm RL, Crosslin DR, Hebbring SJ, Jarvik GP, Kullo IJ, Li R, Pathak J, Ritchie MD, Roden DM, Verma SS, Tromp G, Prato JD, Bush WS, Akey JM, Denny JC, Capra JA. The phenotypic legacy of admixture between modern humans and Neandertals. Science 2016; 351:737-41. [PMID: 26912863 DOI: 10.1126/science.aad2149] [Citation(s) in RCA: 163] [Impact Index Per Article: 20.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Abstract
Many modern human genomes retain DNA inherited from interbreeding with archaic hominins, such as Neandertals, yet the influence of this admixture on human traits is largely unknown. We analyzed the contribution of common Neandertal variants to over 1000 electronic health record (EHR)-derived phenotypes in ~28,000 adults of European ancestry. We discovered and replicated associations of Neandertal alleles with neurological, psychiatric, immunological, and dermatological phenotypes. Neandertal alleles together explained a significant fraction of the variation in risk for depression and skin lesions resulting from sun exposure (actinic keratosis), and individual Neandertal alleles were significantly associated with specific human phenotypes, including hypercoagulation and tobacco use. Our results establish that archaic admixture influences disease risk in modern humans, provide hypotheses about the effects of hundreds of Neandertal haplotypes, and demonstrate the utility of EHR data in evolutionary analyses.
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Affiliation(s)
- Corinne N Simonti
- Vanderbilt Genetics Institute, Vanderbilt University, Nashville, TN, USA
| | - Benjamin Vernot
- Department of Genome Sciences, University of Washington, Seattle, WA, USA
| | - Lisa Bastarache
- Department of Biomedical Informatics, Vanderbilt University, Nashville, TN, USA
| | | | - David S Carrell
- Department of Medicine (Medical Genetics), University of Washington Medical Center, Seattle, WA, USA
| | - Rex L Chisholm
- Center for Genetic Medicine, Feinberg School of Medicine, Northwestern University, Chicago, IL, USA
| | - David R Crosslin
- Department of Genome Sciences, University of Washington, Seattle, WA, USA. Department of Medicine (Medical Genetics), University of Washington Medical Center, Seattle, WA, USA
| | - Scott J Hebbring
- Center for Human Genetics, Marshfield Clinic, Marshfield, WI, USA
| | - Gail P Jarvik
- Department of Genome Sciences, University of Washington, Seattle, WA, USA. Department of Medicine (Medical Genetics), University of Washington Medical Center, Seattle, WA, USA
| | - Iftikhar J Kullo
- Division of Cardiovascular Diseases, Mayo Clinic, Rochester, MN, USA
| | - Rongling Li
- Division of Genomic Medicine, National Human Genome Research Institute, National Institutes of Health, Bethesda, MD, USA
| | - Jyotishman Pathak
- Division of Health Sciences Research, Mayo Clinic, Rochester, MN, USA
| | - Marylyn D Ritchie
- Department of Biochemistry and Molecular Biology, The Pennsylvania State University, University Park, PA, USA. Biomedical and Translational Informatics, Geisinger Health System, Danville, PA, USA
| | - Dan M Roden
- Vanderbilt Genetics Institute, Vanderbilt University, Nashville, TN, USA. Department of Biomedical Informatics, Vanderbilt University, Nashville, TN, USA. Department of Medicine, Vanderbilt University, Nashville, TN, USA. Department of Pharmacology, Vanderbilt University, Nashville, TN, USA
| | - Shefali S Verma
- Department of Biochemistry and Molecular Biology, The Pennsylvania State University, University Park, PA, USA
| | - Gerard Tromp
- Weis Center for Research, Geisinger Health System, Danville, PA, USA. Division of Molecular Biology and Human Genetics, Department of Biomedical Sciences, Faculty of Health Science, Stellenbosch University, Tygerberg, South Africa
| | - Jeffrey D Prato
- Department of Biomedical Informatics, Vanderbilt University, Nashville, TN, USA
| | - William S Bush
- Department of Epidemiology and Biostatistics, Case Western Reserve University, Cleveland, OH, USA
| | - Joshua M Akey
- Department of Genome Sciences, University of Washington, Seattle, WA, USA
| | - Joshua C Denny
- Vanderbilt Genetics Institute, Vanderbilt University, Nashville, TN, USA. Department of Biomedical Informatics, Vanderbilt University, Nashville, TN, USA. Department of Medicine, Vanderbilt University, Nashville, TN, USA
| | - John A Capra
- Vanderbilt Genetics Institute, Vanderbilt University, Nashville, TN, USA. Department of Biomedical Informatics, Vanderbilt University, Nashville, TN, USA. Department of Biological Sciences, Vanderbilt University, Nashville, TN, USA. Center for Quantitative Sciences, Vanderbilt University, Nashville, TN, USA
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High-density genotyping of immune-related loci identifies new SLE risk variants in individuals with Asian ancestry. Nat Genet 2016; 48:323-30. [PMID: 26808113 PMCID: PMC4767573 DOI: 10.1038/ng.3496] [Citation(s) in RCA: 183] [Impact Index Per Article: 22.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/20/2015] [Accepted: 12/23/2015] [Indexed: 01/04/2023]
Abstract
Systemic lupus erythematosus (SLE) has a strong but incompletely understood genetic architecture. We conducted an association study with replication in 4,478 SLE cases and 12,656 controls from six East Asian cohorts to identify new SLE susceptibility loci and better localize known loci. We identified ten new loci and confirmed 20 known loci with genome-wide significance. Among the new loci, the most significant locus was GTF2IRD1-GTF2I at 7q11.23 (rs73366469, Pmeta = 3.75 × 10(-117), odds ratio (OR) = 2.38), followed by DEF6, IL12B, TCF7, TERT, CD226, PCNXL3, RASGRP1, SYNGR1 and SIGLEC6. We identified the most likely functional variants at each locus by analyzing epigenetic marks and gene expression data. Ten candidate variants are known to alter gene expression in cis or in trans. Enrichment analysis highlights the importance of these loci in B cell and T cell biology. The new loci, together with previously known loci, increase the explained heritability of SLE to 24%. The new loci share functional and ontological characteristics with previously reported loci and are possible drug targets for SLE therapeutics.
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Leader JB, Pendergrass SA, Verma A, Carey DJ, Hartzel DN, Ritchie MD, Kirchner HL. Contrasting Association Results between Existing PheWAS Phenotype Definition Methods and Five Validated Electronic Phenotypes. AMIA ... ANNUAL SYMPOSIUM PROCEEDINGS. AMIA SYMPOSIUM 2015; 2015:824-32. [PMID: 26958218 PMCID: PMC4765620] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Subscribe] [Scholar Register] [Indexed: 06/05/2023]
Abstract
Phenome-Wide Association Studies (PheWAS) comprehensively investigate the association between genetic variation and a wide array of outcome traits. Electronic health record (EHR) based PheWAS uses various abstractions of International Classification of Diseases, Ninth Revision (ICD-9) codes to identify case/control status for diagnoses that are used as the phenotypic variables. However, there have not been comparisons within a PheWAS between results from high quality derived phenotypes and high-throughput but potentially inaccurate use of ICD-9 codes for case/control definition. For this study we first developed a group of high quality algorithms for five phenotypes. Next we evaluated the association of these "gold standard" phenotypes and 4,636,178 genetic variants with minor allele frequency > 0.01 and compared the results from high-throughput associations at the 3 digit, 5 digit, and PheWAS codes for defining case/control status. We found that certain diseases contained similar patient populations across phenotyping methods but had differences in PheWAS.
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Affiliation(s)
| | | | - Anurag Verma
- Biomedical and Translational Informatics Program, Danville, PA, USA; The Center for Systems Genomics, The Pennsylvania State University, University Park, PA USA
| | - David J Carey
- Weis Center for Research, Geisinger Health System, Danville, PA, USA
| | | | - Marylyn D Ritchie
- Biomedical and Translational Informatics Program, Danville, PA, USA; The Center for Systems Genomics, The Pennsylvania State University, University Park, PA USA
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88
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Gallego CJ, Burt A, Sundaresan AS, Ye Z, Shaw C, Crosslin DR, Crane PK, Fullerton SM, Hansen K, Carrell D, Kuivaniemi H, Derr K, de Andrade M, McCarty CA, Kitchner TE, Ragon BK, Stallings SC, Papa G, Bochenek J, Smith ME, Aufox SA, Pacheco JA, Patel V, Friesema EM, Erwin AL, Gottesman O, Gerhard GS, Ritchie M, Motulsky AG, Kullo IJ, Larson EB, Tromp G, Brilliant MH, Bottinger E, Denny JC, Roden DM, Williams MS, Jarvik GP. Penetrance of Hemochromatosis in HFE Genotypes Resulting in p.Cys282Tyr and p.[Cys282Tyr];[His63Asp] in the eMERGE Network. Am J Hum Genet 2015; 97:512-20. [PMID: 26365338 PMCID: PMC4596892 DOI: 10.1016/j.ajhg.2015.08.008] [Citation(s) in RCA: 35] [Impact Index Per Article: 3.9] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/26/2015] [Accepted: 08/17/2015] [Indexed: 01/24/2023] Open
Abstract
Hereditary hemochromatosis (HH) is a common autosomal-recessive disorder associated with pathogenic HFE variants, most commonly those resulting in p.Cys282Tyr and p.His63Asp. Recommendations on returning incidental findings of HFE variants in individuals undergoing genome-scale sequencing should be informed by penetrance estimates of HH in unselected samples. We used the eMERGE Network, a multicenter cohort with genotype data linked to electronic medical records, to estimate the diagnostic rate and clinical penetrance of HH in 98 individuals homozygous for the variant coding for HFE p.Cys282Tyr and 397 compound heterozygotes with variants resulting in p.[His63Asp];[Cys282Tyr]. The diagnostic rate of HH in males was 24.4% for p.Cys282Tyr homozygotes and 3.5% for compound heterozygotes (p < 0.001); in females, it was 14.0% for p.Cys282Tyr homozygotes and 2.3% for compound heterozygotes (p < 0.001). Only males showed differences across genotypes in transferrin saturation levels (100% of homozygotes versus 37.5% of compound heterozygotes with transferrin saturation > 50%; p = 0.003), serum ferritin levels (77.8% versus 33.3% with serum ferritin > 300 ng/ml; p = 0.006), and diabetes (44.7% versus 28.0%; p = 0.03). No differences were found in the prevalence of heart disease, arthritis, or liver disease, except for the rate of liver biopsy (10.9% versus 1.8% [p = 0.013] in males; 9.1% versus 2% [p = 0.035] in females). Given the higher rate of HH diagnosis than in prior studies, the high penetrance of iron overload, and the frequency of at-risk genotypes, in addition to other suggested actionable adult-onset genetic conditions, opportunistic screening should be considered for p.[Cys282Tyr];[Cys282Tyr] individuals with existing genomic data.
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Affiliation(s)
- Carlos J Gallego
- Division of Medical Genetics, Department of Medicine, University of Washington, Seattle, WA 98195, USA; Pharmaceutical Outcomes Research and Policy Program, Department of Pharmacy, University of Washington, Seattle, WA 98195, USA.
| | - Amber Burt
- Division of Medical Genetics, Department of Medicine, University of Washington, Seattle, WA 98195, USA
| | - Agnes S Sundaresan
- Center for Health Research, Geisinger Health System, Danville, PA 17822, USA
| | - Zi Ye
- Division of Cardiovascular Diseases, Department of Internal Medicine, Mayo Clinic, Rochester, MN 55905, USA
| | - Christopher Shaw
- Division of Medical Genetics, Department of Medicine, University of Washington, Seattle, WA 98195, USA
| | - David R Crosslin
- Division of Medical Genetics, Department of Medicine, University of Washington, Seattle, WA 98195, USA
| | - Paul K Crane
- Division of General Internal Medicine, Department of Medicine, University of Washington, Seattle, WA 98104, USA
| | - S Malia Fullerton
- Department of Bioethics and Humanities, University of Washington, Seattle, WA 98195, USA
| | - Kris Hansen
- Group Health Research Institute, Group Health Cooperative, Seattle, WA 98101, USA
| | - David Carrell
- Group Health Research Institute, Group Health Cooperative, Seattle, WA 98101, USA
| | - Helena Kuivaniemi
- Siegfried and Janet Weis Center for Research, Geisinger Health System, Danville, PA 17822, USA
| | - Kimberly Derr
- Siegfried and Janet Weis Center for Research, Geisinger Health System, Danville, PA 17822, USA
| | - Mariza de Andrade
- Division of Biomedical Statistics and Informatics, Department of Health Sciences Research, Mayo Clinic, Rochester, MN 55905, USA
| | - Catherine A McCarty
- Research Division, Essentia Institute of Rural Health, Duluth, MN 55805, USA
| | - Terrie E Kitchner
- Center for Human Genetics, Marshfield Clinic Research Foundation, Marshfield, WI 54449, USA
| | - Brittany K Ragon
- Division of Cancer Medicine, MD Anderson Cancer Center, Houston, TX 77030, USA
| | - Sarah C Stallings
- Vanderbilt Institute for Clinical and Translational Research, Vanderbilt University School of Medicine, Nashville, TN 37203, USA
| | - Gabriella Papa
- Vanderbilt Institute for Clinical and Translational Research, Vanderbilt University School of Medicine, Nashville, TN 37203, USA
| | - Joseph Bochenek
- Department of Biomedical Informatics, Vanderbilt University School of Medicine, Nashville, TN 37232, USA
| | - Maureen E Smith
- Center for Genetic Medicine, Feinberg School of Medicine, Northwestern University, Chicago, IL 60611, USA
| | - Sharon A Aufox
- Center for Genetic Medicine, Feinberg School of Medicine, Northwestern University, Chicago, IL 60611, USA
| | - Jennifer A Pacheco
- Center for Genetic Medicine, Feinberg School of Medicine, Northwestern University, Chicago, IL 60611, USA
| | - Vaibhav Patel
- Center for Genetic Medicine, Feinberg School of Medicine, Northwestern University, Chicago, IL 60611, USA
| | - Elisha M Friesema
- Division of General Internal Medicine and Geriatrics, Feinberg School of Medicine, Northwestern University, Chicago, IL 60611, USA
| | - Angelika Ludtke Erwin
- Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Omri Gottesman
- Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Glenn S Gerhard
- Department of Medical Genetics and Molecular Biochemistry, Temple University School of Medicine, Philadelphia, PA 19140, USA
| | - Marylyn Ritchie
- Department of Biochemistry and Molecular Biology, Pennsylvania State University, University Park, PA 16802, USA
| | - Arno G Motulsky
- Division of Medical Genetics, Department of Medicine, University of Washington, Seattle, WA 98195, USA
| | - Iftikhar J Kullo
- Division of Cardiovascular Diseases, Department of Internal Medicine, Mayo Clinic, Rochester, MN 55905, USA
| | - Eric B Larson
- Group Health Research Institute, Group Health Cooperative, Seattle, WA 98101, USA
| | - Gerard Tromp
- Siegfried and Janet Weis Center for Research, Geisinger Health System, Danville, PA 17822, USA
| | - Murray H Brilliant
- Center for Human Genetics, Marshfield Clinic Research Foundation, Marshfield, WI 54449, USA
| | - Erwin Bottinger
- Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Joshua C Denny
- Department of Biomedical Informatics, Vanderbilt University School of Medicine, Nashville, TN 37232, USA; Department of Medicine, Vanderbilt University School of Medicine, Nashville, TN 37232, USA
| | - Dan M Roden
- Department of Medicine, Vanderbilt University School of Medicine, Nashville, TN 37232, USA; Department of Pharmacology, Vanderbilt University School of Medicine, Nashville, TN 37232, USA
| | - Marc S Williams
- Genomic Medicine Institute, Geisinger Health System, Danville, PA 17822, USA
| | - Gail P Jarvik
- Division of Medical Genetics, Department of Medicine, University of Washington, Seattle, WA 98195, USA.
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Namjou B, Marsolo K, Lingren T, Ritchie MD, Verma SS, Cobb BL, Perry C, Kitchner TE, Brilliant MH, Peissig PL, Borthwick KM, Williams MS, Grafton J, Jarvik GP, Holm IA, Harley JB. A GWAS Study on Liver Function Test Using eMERGE Network Participants. PLoS One 2015; 10:e0138677. [PMID: 26413716 PMCID: PMC4586138 DOI: 10.1371/journal.pone.0138677] [Citation(s) in RCA: 16] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/01/2015] [Accepted: 09/02/2015] [Indexed: 11/18/2022] Open
Abstract
Introduction Liver enzyme levels and total serum bilirubin are under genetic control and in recent years genome-wide population-based association studies have identified different susceptibility loci for these traits. We conducted a genome-wide association study in European ancestry participants from the Electronic Medical Records and Genomics (eMERGE) Network dataset of patient medical records with available genotyping data in order to identify genetic contributors to variability in serum bilirubin levels and other liver function tests and to compare the effects between adult and pediatric populations. Methods The process of whole genome imputation of eMERGE samples with standard quality control measures have been described previously. After removing missing data and outliers based on principal components (PC) analyses, 3294 samples from European ancestry were used for the GWAS study. The association between each single nucleotide polymorphism (SNP) and total serum bilirubin and other liver function tests was tested using linear regression, adjusting for age, gender, site, platform and ancestry principal components (PC). Results Consistent with previous results, a strong association signal has been detected for UGT1A gene cluster (best SNP rs887829, beta = 0.15, p = 1.30x10-118) for total serum bilirubin level. Indeed, in this region more than 176 SNPs (or indels) had p<10−8 spanning 150Kb on the long arm of chromosome 2q37.1. In addition, we found a similar level of magnitude in a pediatric group (p = 8.26x10-47, beta = 0.17). Further imputation using sequencing data as a reference panel revealed association of other markers including known TA7 repeat indels (rs8175347) (p = 9.78x10-117) and rs111741722 (p = 5.41x10-119) which were in proxy (r2 = 0.99) with rs887829. Among rare variants, two Asian subjects homozygous for coding SNP rs4148323 (G71R) were identified. Additional known effects for total serum bilirubin were also confirmed including organic anion transporters SLCO1B1-SLCO1B3, TDRP and ZMYND8 at FDR<0.05 with no gene-gene interaction effects. Phenome-wide association studies (PheWAS) suggest a protective effect of TA7 repeat against cerebrovascular disease in an adult cohort (OR = 0.75, p = 0.0008). Among other liver function tests, we also confirmed the previous effect of the ABO blood group locus for variation in serum alkaline phosphatase (rs579459, p = 9.44x10-15). Conclusions Taken together, our data present interesting findings with strong confirmation of previous effects by simply using the eMERGE electronic health record phenotyping. In addition, our findings indicate that similar to the adult population, the UGT1A1 is the main locus responsible for normal variation of serum bilirubin in pediatric populations.
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Affiliation(s)
- Bahram Namjou
- Center for Autoimmune Genomics and Etiology, Cincinnati Children’s Hospital Medical Center (CCHMC), Cincinnati, OH, United States of America
- University of Cincinnati, College of Medicine, Cincinnati, OH, United States of America
- * E-mail:
| | - Keith Marsolo
- University of Cincinnati, College of Medicine, Cincinnati, OH, United States of America
- Division of Biomedical Informatics, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, United States of America
| | - Todd Lingren
- University of Cincinnati, College of Medicine, Cincinnati, OH, United States of America
- Division of Biomedical Informatics, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, United States of America
| | - Marylyn D. Ritchie
- Center for Systems Genomics, The Pennsylvania State University, University Park, PA, United States of America
| | - Shefali S. Verma
- Center for Systems Genomics, The Pennsylvania State University, University Park, PA, United States of America
| | - Beth L. Cobb
- Center for Autoimmune Genomics and Etiology, Cincinnati Children’s Hospital Medical Center (CCHMC), Cincinnati, OH, United States of America
| | - Cassandra Perry
- Division of Genetics and Genomics, Boston Children’s Hospital (BCH), Boston, MA, United States of America
| | - Terrie E. Kitchner
- Center for Human Genetics, Marshfield Clinic, Marshfield, Wisconsin, United States of America
| | - Murray H. Brilliant
- Center for Human Genetics, Marshfield Clinic, Marshfield, Wisconsin, United States of America
| | - Peggy L. Peissig
- Center for Human Genetics, Marshfield Clinic, Marshfield, Wisconsin, United States of America
| | - Kenneth M. Borthwick
- Genomic Medicine Institute, Geisinger Health System, Danville, PA, United States of America
| | - Marc S. Williams
- Genomic Medicine Institute, Geisinger Health System, Danville, PA, United States of America
| | - Jane Grafton
- Group Health Research Institute, Seattle, WA, United States of America
| | - Gail P. Jarvik
- Department of Medicine, University of Washington, Seattle, WA, United States of America
- Department of Genome Sciences, University of Washington, Seattle, WA, United States of America
| | - Ingrid A. Holm
- Division of Genetics and Genomics and The Manton Center for Orphan Disease Research, Boston Children’s Hospital, Boston, MA, United States of America
- Department of Pediatrics, Harvard Medical School, Boston, MA, United States of America
| | - John B. Harley
- Center for Autoimmune Genomics and Etiology, Cincinnati Children’s Hospital Medical Center (CCHMC), Cincinnati, OH, United States of America
- University of Cincinnati, College of Medicine, Cincinnati, OH, United States of America
- U.S. Department of Veterans Affairs Medical Center, Cincinnati, OH, United States of America
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90
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Simonett JM, Sohrab MA, Pacheco J, Armstrong LL, Rzhetskaya M, Smith M, Geoffrey Hayes M, Fawzi AA. A Validated Phenotyping Algorithm for Genetic Association Studies in Age-related Macular Degeneration. Sci Rep 2015; 5:12875. [PMID: 26255974 PMCID: PMC4530462 DOI: 10.1038/srep12875] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/10/2015] [Accepted: 07/13/2015] [Indexed: 11/09/2022] Open
Abstract
Age-related macular degeneration (AMD), a multifactorial, neurodegenerative disease, is a leading cause of vision loss. With the rapid advancement of DNA sequencing technologies, many AMD-associated genetic polymorphisms have been identified. Currently, the most time consuming steps of these studies are patient recruitment and phenotyping. In this study, we describe the development of an automated algorithm to identify neovascular (wet) AMD, non-neovascular (dry) AMD and control subjects using electronic medical record (EMR)-based criteria. Positive predictive value (91.7%) and negative predictive value (97.5%) were calculated using expert chart review as the gold standard to assess algorithm performance. We applied the algorithm to an EMR-linked DNA bio-repository to study previously identified AMD-associated single nucleotide polymorphisms (SNPs), using case/control status determined by the algorithm. Risk alleles of three SNPs, rs1061170 (CFH), rs1410996 (CFH), and rs10490924 (ARMS2) were found to be significantly associated with the AMD case/control status as defined by the algorithm. With the rapid growth of EMR-linked DNA biorepositories, patient selection algorithms can greatly increase the efficiency of genetic association study. We have found that stepwise validation of such an algorithm can result in reliable cohort selection and, when coupled within an EMR-linked DNA biorepository, replicates previously published AMD-associated SNPs.
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Affiliation(s)
- Joseph M Simonett
- Department of Ophthalmology, Northwestern University Feinberg School of Medicine, Chicago, IL 60611
| | - Mahsa A Sohrab
- Department of Ophthalmology, Northwestern University Feinberg School of Medicine, Chicago, IL 60611
| | - Jennifer Pacheco
- Center for Genetic Medicine, Northwestern University Feinberg School of Medicine, Chicago, IL 60611
| | - Loren L Armstrong
- Division of Endocrinology, Metabolism, and Molecular Medicine, Department of Medicine, Northwestern University Feinberg School of Medicine, Chicago, IL 60611
| | - Margarita Rzhetskaya
- Division of Endocrinology, Metabolism, and Molecular Medicine, Department of Medicine, Northwestern University Feinberg School of Medicine, Chicago, IL 60611
| | - Maureen Smith
- Center for Genetic Medicine, Northwestern University Feinberg School of Medicine, Chicago, IL 60611
| | - M Geoffrey Hayes
- 1] Center for Genetic Medicine, Northwestern University Feinberg School of Medicine, Chicago, IL 60611 [2] Division of Endocrinology, Metabolism, and Molecular Medicine, Department of Medicine, Northwestern University Feinberg School of Medicine, Chicago, IL 60611 [3] Department of Anthropology, Northwestern University, Evanston, IL [4] Northwestern Comprehensive Center on Obesity, Northwestern University Feinberg School of Medicine, Chicago, IL 60611
| | - Amani A Fawzi
- Department of Ophthalmology, Northwestern University Feinberg School of Medicine, Chicago, IL 60611
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van Leeuwen EM, Kanterakis A, Deelen P, Kattenberg MV, Slagboom PE, de Bakker PIW, Wijmenga C, Swertz MA, Boomsma DI, van Duijn CM, Karssen LC, Hottenga JJ. Population-specific genotype imputations using minimac or IMPUTE2. Nat Protoc 2015; 10:1285-96. [PMID: 26226460 DOI: 10.1038/nprot.2015.077] [Citation(s) in RCA: 70] [Impact Index Per Article: 7.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/22/2023]
Abstract
In order to meaningfully analyze common and rare genetic variants, results from genome-wide association studies (GWASs) of multiple cohorts need to be combined in a meta-analysis in order to obtain enough power. This requires all cohorts to have the same single-nucleotide polymorphisms (SNPs) in their GWASs. To this end, genotypes that have not been measured in a given cohort can be imputed on the basis of a set of reference haplotypes. This protocol provides guidelines for performing imputations with two widely used tools: minimac and IMPUTE2. These guidelines were developed and used by the Genome of the Netherlands (GoNL) consortium, which has created a population-specific reference panel for genetic imputations and used this reference to impute various Dutch biobanks. We also describe several factors that might influence the final imputation quality. This protocol, which has been used by the largest Dutch biobanks, should take approximately several days, depending on the sample size of the biobank and the computer resources available.
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Affiliation(s)
| | - Alexandros Kanterakis
- 1] University of Groningen, University Medical Center Groningen, Genomics Coordination Center, Groningen, the Netherlands. [2] University of Groningen, University Medical Center Groningen, Department of Genetics, Groningen, the Netherlands
| | - Patrick Deelen
- 1] University of Groningen, University Medical Center Groningen, Genomics Coordination Center, Groningen, the Netherlands. [2] University of Groningen, University Medical Center Groningen, Department of Genetics, Groningen, the Netherlands
| | | | | | - P Eline Slagboom
- 1] Department of Molecular Epidemiology, Leiden University Medical Center, Leiden, the Netherlands. [2] Netherlands Consortium for Healthy Ageing, Leiden University Medical Center, Leiden, the Netherlands
| | - Paul I W de Bakker
- 1] Department of Medical Genetics, University Medical Center Utrecht, Utrecht, the Netherlands. [2] Department of Epidemiology, University Medical Center Utrecht, Utrecht, the Netherlands
| | - Cisca Wijmenga
- 1] University of Groningen, University Medical Center Groningen, Genomics Coordination Center, Groningen, the Netherlands. [2] University of Groningen, University Medical Center Groningen, Department of Genetics, Groningen, the Netherlands
| | - Morris A Swertz
- 1] University of Groningen, University Medical Center Groningen, Genomics Coordination Center, Groningen, the Netherlands. [2] University of Groningen, University Medical Center Groningen, Department of Genetics, Groningen, the Netherlands
| | - Dorret I Boomsma
- Department of Biological Psychology, VU University, Amsterdam, the Netherlands
| | | | - Lennart C Karssen
- 1] Department of Epidemiology, Erasmus Medical Center, Rotterdam, the Netherlands. [2] PolyOmica, Groningen, the Netherlands
| | - Jouke Jan Hottenga
- Department of Biological Psychology, VU University, Amsterdam, the Netherlands
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A genome-wide association study identifies variants in KCNIP4 associated with ACE inhibitor-induced cough. THE PHARMACOGENOMICS JOURNAL 2015; 16:231-7. [PMID: 26169577 PMCID: PMC4713364 DOI: 10.1038/tpj.2015.51] [Citation(s) in RCA: 41] [Impact Index Per Article: 4.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/30/2014] [Revised: 04/13/2015] [Accepted: 06/03/2015] [Indexed: 12/30/2022]
Abstract
The most common side effect of angiotensin-converting enzyme inhibitor (ACEi) drugs is cough. We conducted a genome-wide association study (GWAS) of ACEi-induced cough among 7080 subjects of diverse ancestries in the Electronic Medical Records and Genomics (eMERGE) network. Cases were subjects diagnosed with ACEi-induced cough. Controls were subjects with at least 6 months of ACEi use and no cough. A GWAS (1595 cases and 5485 controls) identified associations on chromosome 4 in an intron of KCNIP4. The strongest association was at rs145489027 (minor allele frequency=0.33, odds ratio (OR)=1.3 (95% confidence interval (CI): 1.2–1.4), P=1.0 × 10−8). Replication for six single-nucleotide polymorphisms (SNPs) in KCNIP4 was tested in a second eMERGE population (n=926) and in the Genetics of Diabetes Audit and Research in Tayside, Scotland (GoDARTS) cohort (n=4309). Replication was observed at rs7675300 (OR=1.32 (1.01–1.70), P=0.04) in eMERGE and at rs16870989 and rs1495509 (OR=1.15 (1.01–1.30), P=0.03 for both) in GoDARTS. The combined association at rs1495509 was significant (OR=1.23 (1.15–1.32), P=1.9 × 10−9). These results indicate that SNPs in KCNIP4 may modulate ACEi-induced cough risk.
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93
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Ormond KE, Rashkin M, Faucett WA. Standardizing Variant Interpretation in Genomic Sequencing: Implications for Genetic Counseling Practice. CURRENT GENETIC MEDICINE REPORTS 2015. [DOI: 10.1007/s40142-015-0073-y] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
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94
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Hall MA, Verma SS, Wallace J, Lucas A, Berg RL, Connolly J, Crawford DC, Crosslin DR, de Andrade M, Doheny KF, Haines JL, Harley JB, Jarvik GP, Kitchner T, Kuivaniemi H, Larson EB, Carrell DS, Tromp G, Vrabec TR, Pendergrass SA, McCarty CA, Ritchie MD. Biology-Driven Gene-Gene Interaction Analysis of Age-Related Cataract in the eMERGE Network. Genet Epidemiol 2015; 39:376-84. [PMID: 25982363 PMCID: PMC4550090 DOI: 10.1002/gepi.21902] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/05/2014] [Revised: 02/27/2015] [Accepted: 03/13/2015] [Indexed: 01/19/2023]
Abstract
Bioinformatics approaches to examine gene-gene models provide a means to discover interactions between multiple genes that underlie complex disease. Extensive computational demands and adjusting for multiple testing make uncovering genetic interactions a challenge. Here, we address these issues using our knowledge-driven filtering method, Biofilter, to identify putative single nucleotide polymorphism (SNP) interaction models for cataract susceptibility, thereby reducing the number of models for analysis. Models were evaluated in 3,377 European Americans (1,185 controls, 2,192 cases) from the Marshfield Clinic, a study site of the Electronic Medical Records and Genomics (eMERGE) Network, using logistic regression. All statistically significant models from the Marshfield Clinic were then evaluated in an independent dataset of 4,311 individuals (742 controls, 3,569 cases), using independent samples from additional study sites in the eMERGE Network: Mayo Clinic, Group Health/University of Washington, Vanderbilt University Medical Center, and Geisinger Health System. Eighty-three SNP-SNP models replicated in the independent dataset at likelihood ratio test P < 0.05. Among the most significant replicating models was rs12597188 (intron of CDH1)-rs11564445 (intron of CTNNB1). These genes are known to be involved in processes that include: cell-to-cell adhesion signaling, cell-cell junction organization, and cell-cell communication. Further Biofilter analysis of all replicating models revealed a number of common functions among the genes harboring the 83 replicating SNP-SNP models, which included signal transduction and PI3K-Akt signaling pathway. These findings demonstrate the utility of Biofilter as a biology-driven method, applicable for any genome-wide association study dataset.
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Affiliation(s)
- Molly A Hall
- Department of Biochemistry and Molecular Biology, Center for Systems Genomics, Eberly College of Science, The Pennsylvania State University, University Park, Pennsylvania, United States of America
| | - Shefali S Verma
- Department of Biochemistry and Molecular Biology, Center for Systems Genomics, Eberly College of Science, The Pennsylvania State University, University Park, Pennsylvania, United States of America
| | - John Wallace
- Department of Biochemistry and Molecular Biology, Center for Systems Genomics, Eberly College of Science, The Pennsylvania State University, University Park, Pennsylvania, United States of America
| | - Anastasia Lucas
- Department of Biochemistry and Molecular Biology, Center for Systems Genomics, Eberly College of Science, The Pennsylvania State University, University Park, Pennsylvania, United States of America
| | - Richard L Berg
- Marshfield Clinic, Marshfield, Wisconsin, United States of America
| | - John Connolly
- Center for Applied Genomics, Children's Hospital of Philadelphia, Philadelphia, Pennsylvania, United States of America
| | - Dana C Crawford
- Department of Epidemiology and Biostatistics, Institute for Computational Biology, Case Western Reserve University, Cleveland, Ohio, United States of America
| | - David R Crosslin
- Department of Genome Sciences, University of Washington, Seattle, Washington, United States of America
| | | | - Kimberly F Doheny
- Center for Inherited Disease Research, IGM, Johns Hopkins University SOM, Baltimore, Maryland, United States of America
| | - Jonathan L Haines
- Department of Epidemiology and Biostatistics, Institute for Computational Biology, Case Western Reserve University, Cleveland, Ohio, United States of America
| | - John B Harley
- Department of Pediatrics, Cincinnati Children's Hospital, University of Cincinnati, Cincinnati, Ohio, United States of America
| | - Gail P Jarvik
- Department of Genome Sciences, University of Washington, Seattle, Washington, United States of America.,Division of Medical Genetics, Department of Medicine, University of Washington, Seattle, Washington, United States of America
| | - Terrie Kitchner
- Marshfield Clinic, Marshfield, Wisconsin, United States of America
| | - Helena Kuivaniemi
- Geisinger Health System, Danville, Pennsylvania, United States of America
| | - Eric B Larson
- Group Health Research Institute, Seattle, Washington, United States of America
| | - David S Carrell
- Group Health Research Institute, Seattle, Washington, United States of America
| | - Gerard Tromp
- Geisinger Health System, Danville, Pennsylvania, United States of America
| | - Tamara R Vrabec
- Geisinger Health System, Danville, Pennsylvania, United States of America
| | | | | | - Marylyn D Ritchie
- Department of Biochemistry and Molecular Biology, Center for Systems Genomics, Eberly College of Science, The Pennsylvania State University, University Park, Pennsylvania, United States of America.,Geisinger Health System, Danville, Pennsylvania, United States of America
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Ritchie MD, de Andrade M, Kuivaniemi H. The foundation of precision medicine: integration of electronic health records with genomics through basic, clinical, and translational research. Front Genet 2015; 6:104. [PMID: 25852745 PMCID: PMC4362332 DOI: 10.3389/fgene.2015.00104] [Citation(s) in RCA: 19] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/17/2015] [Accepted: 02/27/2015] [Indexed: 12/30/2022] Open
Affiliation(s)
- Marylyn D Ritchie
- Biochemistry and Molecular Biology, Center for Systems Genomics, The Pennsylvania State University University Park, PA, USA ; Institute of Biomedical and Translational Informatics, Geisinger Health System Danville, PA, USA
| | - Mariza de Andrade
- Division of Biomedical Statistics and Informatics, Department of Health Science Research, Mayo Clinic Rochester, MN, USA
| | - Helena Kuivaniemi
- The Sigfried and Janet Weis Center for Research, Geisinger Health System Danville, PA, USA ; Department of Surgery, Temple University School of Medicine Philadelphia, PA, USA
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Moore CB, Verma A, Pendergrass S, Verma SS, Johnson DH, Daar ES, Gulick RM, Haubrich R, Robbins GK, Ritchie MD, Haas DW. Phenome-wide Association Study Relating Pretreatment Laboratory Parameters With Human Genetic Variants in AIDS Clinical Trials Group Protocols. Open Forum Infect Dis 2015; 2:ofu113. [PMID: 25884002 PMCID: PMC4396430 DOI: 10.1093/ofid/ofu113] [Citation(s) in RCA: 29] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/19/2014] [Accepted: 12/02/2014] [Indexed: 01/11/2023] Open
Abstract
Background. Phenome-Wide Association Studies (PheWAS) identify genetic associations across multiple phenotypes. Clinical trials offer opportunities for PheWAS to identify pharmacogenomic associations. We describe the first PheWAS to use genome-wide genotypic data and to utilize human immunodeficiency virus (HIV) clinical trials data. As proof-of-concept, we focused on baseline laboratory phenotypes from antiretroviral therapy-naive individuals. Methods. Data from 4 AIDS Clinical Trials Group (ACTG) studies were split into 2 datasets: Dataset I (1181 individuals from protocol A5202) and Dataset II (1366 from protocols A5095, ACTG 384, and A5142). Final analyses involved 2547 individuals and 5 954 294 imputed polymorphisms. We calculated comprehensive associations between these polymorphisms and 27 baseline laboratory phenotypes. Results. A total of 10 584 (0.17%) polymorphisms had associations with P < .01 in both datasets and with the same direction of association. Twenty polymorphisms replicated associations with identical or related phenotypes reported in the Catalog of Published Genome-Wide Association Studies, including several not previously reported in HIV-positive cohorts. We also identified several possibly novel associations. Conclusions. These analyses define PheWAS properties and principles with baseline laboratory data from HIV clinical trials. This approach may be useful for evaluating on-treatment HIV clinical trials data for associations with various clinical phenotypes.
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Affiliation(s)
- Carrie B. Moore
- Vanderbilt University School of Medicine, Nashville, Tennessee
- The Center for Systems Genomics, The Pennsylvania State University, University Park
| | - Anurag Verma
- The Center for Systems Genomics, The Pennsylvania State University, University Park
| | - Sarah Pendergrass
- The Center for Systems Genomics, The Pennsylvania State University, University Park
| | - Shefali S. Verma
- The Center for Systems Genomics, The Pennsylvania State University, University Park
| | | | - Eric S. Daar
- Los Angeles Biomed Research Institute at Harbor-UCLA Medical Center, Torrance, California
| | | | | | | | - Marylyn D. Ritchie
- The Center for Systems Genomics, The Pennsylvania State University, University Park
| | - David W. Haas
- Vanderbilt University School of Medicine, Nashville, Tennessee
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Namjou B, Marsolo K, Caroll RJ, Denny JC, Ritchie MD, Verma SS, Lingren T, Porollo A, Cobb BL, Perry C, Kottyan LC, Rothenberg ME, Thompson SD, Holm IA, Kohane IS, Harley JB. Phenome-wide association study (PheWAS) in EMR-linked pediatric cohorts, genetically links PLCL1 to speech language development and IL5-IL13 to Eosinophilic Esophagitis. Front Genet 2014; 5:401. [PMID: 25477900 PMCID: PMC4235428 DOI: 10.3389/fgene.2014.00401] [Citation(s) in RCA: 62] [Impact Index Per Article: 6.2] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/29/2014] [Accepted: 10/31/2014] [Indexed: 02/06/2023] Open
Abstract
Objective: We report the first pediatric specific Phenome-Wide Association Study (PheWAS) using electronic medical records (EMRs). Given the early success of PheWAS in adult populations, we investigated the feasibility of this approach in pediatric cohorts in which associations between a previously known genetic variant and a wide range of clinical or physiological traits were evaluated. Although computationally intensive, this approach has potential to reveal disease mechanistic relationships between a variant and a network of phenotypes. Method: Data on 5049 samples of European ancestry were obtained from the EMRs of two large academic centers in five different genotyped cohorts. Recently, these samples have undergone whole genome imputation. After standard quality controls, removing missing data and outliers based on principal components analyses (PCA), 4268 samples were used for the PheWAS study. We scanned for associations between 2476 single-nucleotide polymorphisms (SNP) with available genotyping data from previously published GWAS studies and 539 EMR-derived phenotypes. The false discovery rate was calculated and, for any new PheWAS findings, a permutation approach (with up to 1,000,000 trials) was implemented. Results: This PheWAS found a variety of common variants (MAF > 10%) with prior GWAS associations in our pediatric cohorts including Juvenile Rheumatoid Arthritis (JRA), Asthma, Autism and Pervasive Developmental Disorder (PDD) and Type 1 Diabetes with a false discovery rate < 0.05 and power of study above 80%. In addition, several new PheWAS findings were identified including a cluster of association near the NDFIP1 gene for mental retardation (best SNP rs10057309, p = 4.33 × 10−7, OR = 1.70, 95%CI = 1.38 − 2.09); association near PLCL1 gene for developmental delays and speech disorder [best SNP rs1595825, p = 1.13 × 10−8, OR = 0.65(0.57 − 0.76)]; a cluster of associations in the IL5-IL13 region with Eosinophilic Esophagitis (EoE) [best at rs12653750, p = 3.03 × 10−9, OR = 1.73 95%CI = (1.44 − 2.07)], previously implicated in asthma, allergy, and eosinophilia; and association of variants in GCKR and JAZF1 with allergic rhinitis in our pediatric cohorts [best SNP rs780093, p = 2.18 × 10−5, OR = 1.39, 95%CI = (1.19 − 1.61)], previously demonstrated in metabolic disease and diabetes in adults. Conclusion: The PheWAS approach with re-mapping ICD-9 structured codes for our European-origin pediatric cohorts, as with the previous adult studies, finds many previously reported associations as well as presents the discovery of associations with potentially important clinical implications.
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Affiliation(s)
- Bahram Namjou
- Center for Autoimmune Genomics and Etiology, Cincinnati Children's Hospital Medical Center Cincinnati, OH, USA ; College of Medicine, University of Cincinnati Cincinnati, OH, USA
| | - Keith Marsolo
- College of Medicine, University of Cincinnati Cincinnati, OH, USA ; Division of Biomedical Informatics, Cincinnati Children's Hospital Medical Center Cincinnati, OH, USA
| | - Robert J Caroll
- Department of Biomedical Informatics, Vanderbilt University School of Medicine Nashville, TN, USA
| | - Joshua C Denny
- Department of Biomedical Informatics, Vanderbilt University School of Medicine Nashville, TN, USA ; Department of Medicine, Vanderbilt University School of Medicine Nashville, TN, USA
| | - Marylyn D Ritchie
- Center for Systems Genomics, The Pennsylvania State University Philadelphia, PA, USA
| | - Shefali S Verma
- Center for Systems Genomics, The Pennsylvania State University Philadelphia, PA, USA
| | - Todd Lingren
- College of Medicine, University of Cincinnati Cincinnati, OH, USA ; Division of Biomedical Informatics, Cincinnati Children's Hospital Medical Center Cincinnati, OH, USA
| | - Aleksey Porollo
- Center for Autoimmune Genomics and Etiology, Cincinnati Children's Hospital Medical Center Cincinnati, OH, USA ; College of Medicine, University of Cincinnati Cincinnati, OH, USA ; Division of Biomedical Informatics, Cincinnati Children's Hospital Medical Center Cincinnati, OH, USA
| | - Beth L Cobb
- Center for Autoimmune Genomics and Etiology, Cincinnati Children's Hospital Medical Center Cincinnati, OH, USA
| | - Cassandra Perry
- Division of Genetics and Genomics, Boston Children's Hospital Boston, MA, USA
| | - Leah C Kottyan
- Center for Autoimmune Genomics and Etiology, Cincinnati Children's Hospital Medical Center Cincinnati, OH, USA ; College of Medicine, University of Cincinnati Cincinnati, OH, USA ; Division of Allergy and Immunology, Department of Pediatrics, Cincinnati Children's Hospital Medical Center Cincinnati, OH, USA
| | - Marc E Rothenberg
- Division of Allergy and Immunology, Department of Pediatrics, Cincinnati Children's Hospital Medical Center Cincinnati, OH, USA
| | - Susan D Thompson
- Center for Autoimmune Genomics and Etiology, Cincinnati Children's Hospital Medical Center Cincinnati, OH, USA ; College of Medicine, University of Cincinnati Cincinnati, OH, USA
| | - Ingrid A Holm
- Division of Genetics and Genomics, Department of Pediatrics, The Manton Center for Orphan Disease Research, Harvard Medical School, Boston Children's Hospital Boston, MA, USA
| | - Isaac S Kohane
- Children's Hospital Informatics Program, Center for Biomedical Informatics, Harvard Medical School Boston, MA, USA
| | - John B Harley
- Center for Autoimmune Genomics and Etiology, Cincinnati Children's Hospital Medical Center Cincinnati, OH, USA ; College of Medicine, University of Cincinnati Cincinnati, OH, USA ; U.S. Department of Veterans Affairs Medical Center Cincinnati, OH, USA
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Crawford DC, Crosslin DR, Tromp G, Kullo IJ, Kuivaniemi H, Hayes MG, Denny JC, Bush WS, Haines JL, Roden DM, McCarty CA, Jarvik GP, Ritchie MD. eMERGEing progress in genomics-the first seven years. Front Genet 2014; 5:184. [PMID: 24987407 PMCID: PMC4060012 DOI: 10.3389/fgene.2014.00184] [Citation(s) in RCA: 67] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2014] [Accepted: 05/30/2014] [Indexed: 12/15/2022] Open
Abstract
The electronic MEdical Records & GEnomics (eMERGE) network was established in 2007 by the National Human Genome Research Institute (NHGRI) of the National Institutes of Health (NIH) in part to explore the utility of electronic medical records (EMRs) in genome science. The initial focus was on discovery primarily using the genome-wide association paradigm, but more recently, the network has begun evaluating mechanisms to implement new genomic information coupled to clinical decision support into EMRs. Herein, we describe this evolution including the development of the individual and merged eMERGE genomic datasets, the contribution the network has made toward genomic discovery and human health, and the steps taken toward the next generation genotype-phenotype association studies and clinical implementation.
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Affiliation(s)
- Dana C Crawford
- Center for Human Genetics Research, Vanderbilt University Nashville, TN, USA ; Department of Molecular Physiology and Biophysics, Vanderbilt University Nashville, TN, USA
| | - David R Crosslin
- Medical Genetics, Department of Medicine, School of Medicine, University of Washington Seattle, WA, USA ; Department of Genome Sciences, University of Washington Seattle, WA, USA
| | - Gerard Tromp
- The Sigfried and Janet Weis Center for Research, Geisinger Health System Danville, PA, USA
| | - Iftikhar J Kullo
- Division of Cardiovascular Diseases and the Gonda Vascular Center, Mayo Clinic Rochester, MN, USA
| | - Helena Kuivaniemi
- The Sigfried and Janet Weis Center for Research, Geisinger Health System Danville, PA, USA
| | - M Geoffrey Hayes
- Division of Endocrinology, Metabolism, and Molecular Medicine, Department of Medicine, Feinberg School of Medicine, Northwestern University Chicago, IL, USA
| | - Joshua C Denny
- Department of Biomedical Informatics, Vanderbilt University Nashville, TN, USA ; Department of Medicine, Vanderbilt University Nashville, TN, USA
| | - William S Bush
- Center for Human Genetics Research, Vanderbilt University Nashville, TN, USA ; Department of Biomedical Informatics, Vanderbilt University Nashville, TN, USA
| | - Jonathan L Haines
- Department of Epidemiology and Biostatistics, Case Western Reserve University Cleveland, OH, USA ; Institute for Computational Biology, Case Western Reserve University Cleveland, OH, USA
| | - Dan M Roden
- Department of Medicine, Vanderbilt University Nashville, TN, USA ; Department of Pharmacology, Vanderbilt University Nashville, TN, USA
| | | | - Gail P Jarvik
- Medical Genetics, Department of Medicine, School of Medicine, University of Washington Seattle, WA, USA ; Department of Genome Sciences, University of Washington Seattle, WA, USA
| | - Marylyn D Ritchie
- Department of Biochemistry and Molecular Biology, Pennsylvania State University University Park, PA, USA ; Center for Systems Genomics, Pennsylvania State University University Park, PA, USA
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