1
|
Durvasula A, Price AL. Distinct explanations underlie gene-environment interactions in the UK Biobank. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2024:2023.09.22.23295969. [PMID: 37790574 PMCID: PMC10543037 DOI: 10.1101/2023.09.22.23295969] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/05/2023]
Abstract
The role of gene-environment (GxE) interaction in disease and complex trait architectures is widely hypothesized, but currently unknown. Here, we apply three statistical approaches to quantify and distinguish three different types of GxE interaction for a given trait and E variable. First, we detect locus-specific GxE interaction by testing for genetic correlation r g < 1 across E bins. Second, we detect genome-wide effects of the E variable on genetic variance by leveraging polygenic risk scores (PRS) to test for significant PRSxE in a regression of phenotypes on PRS, E, and PRSxE, together with differences in SNP-heritability across E bins. Third, we detect genome-wide proportional amplification of genetic and environmental effects as a function of the E variable by testing for significant PRSxE with no differences in SNP-heritability across E bins. Simulations show that these approaches achieve high sensitivity and specificity in distinguishing these three GxE scenarios. We applied our framework to 33 UK Biobank traits (25 quantitative traits and 8 diseases; average N = 325 K ) and 10 E variables spanning lifestyle, diet, and other environmental exposures. First, we identified 19 trait-E pairs with r g significantly < 1 (FDR<5%) (average r g = 0.95 ); for example, white blood cell count had r g = 0.95 (s.e. 0.01) between smokers and non-smokers. Second, we identified 28 trait-E pairs with significant PRSxE and significant SNP-heritability differences across E bins; for example, BMI had a significant PRSxE for physical activity (P=4.6e-5) with 5% larger SNP-heritability in the largest versus smallest quintiles of physical activity (P=7e-4). Third, we identified 15 trait-E pairs with significant PRSxE with no SNP-heritability differences across E bins; for example, waist-hip ratio adjusted for BMI had a significant PRSxE effect for time spent watching television (P=5e-3) with no SNP-heritability differences. Across the three scenarios, 8 of the trait-E pairs involved disease traits, whose interpretation is complicated by scale effects. Analyses using biological sex as the E variable produced additional significant findings in each of the three scenarios. Overall, we infer a significant contribution of GxE and GxSex effects to complex trait and disease variance.
Collapse
Affiliation(s)
- Arun Durvasula
- Center for Genetic Epidemiology, Department of Population and Public Health Sciences, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
- Department of Genetics, Harvard Medical School, Cambridge, MA, USA
- Department of Human Evolutionary Biology, Harvard University, Cambridge, MA, USA
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Alkes L Price
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| |
Collapse
|
2
|
LaBianca S, Brikell I, Helenius D, Loughnan R, Mefford J, Palmer CE, Walker R, Gådin JR, Krebs M, Appadurai V, Vaez M, Agerbo E, Pedersen MG, Børglum AD, Hougaard DM, Mors O, Nordentoft M, Mortensen PB, Kendler KS, Jernigan TL, Geschwind DH, Ingason A, Dahl AW, Zaitlen N, Dalsgaard S, Werge TM, Schork AJ. Polygenic profiles define aspects of clinical heterogeneity in attention deficit hyperactivity disorder. Nat Genet 2024; 56:234-244. [PMID: 38036780 DOI: 10.1038/s41588-023-01593-7] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/10/2021] [Accepted: 10/25/2023] [Indexed: 12/02/2023]
Abstract
Attention deficit hyperactivity disorder (ADHD) is a complex disorder that manifests variability in long-term outcomes and clinical presentations. The genetic contributions to such heterogeneity are not well understood. Here we show several genetic links to clinical heterogeneity in ADHD in a case-only study of 14,084 diagnosed individuals. First, we identify one genome-wide significant locus by comparing cases with ADHD and autism spectrum disorder (ASD) to cases with ADHD but not ASD. Second, we show that cases with ASD and ADHD, substance use disorder and ADHD, or first diagnosed with ADHD in adulthood have unique polygenic score (PGS) profiles that distinguish them from complementary case subgroups and controls. Finally, a PGS for an ASD diagnosis in ADHD cases predicted cognitive performance in an independent developmental cohort. Our approach uncovered evidence of genetic heterogeneity in ADHD, helping us to understand its etiology and providing a model for studies of other disorders.
Collapse
Affiliation(s)
- Sonja LaBianca
- Institute of Biological Psychiatry, Mental Health Center Sct. Hans, Mental Health Services Copenhagen, Roskilde, Denmark
- The Lundbeck Foundation Initiative for Integrative Psychiatric Research, Copenhagen, Denmark
| | - Isabell Brikell
- The Lundbeck Foundation Initiative for Integrative Psychiatric Research, Copenhagen, Denmark
- National Centre for Register-based Research, Department of Economics and Business Economics, Aarhus University, Aarhus, Denmark
| | - Dorte Helenius
- Institute of Biological Psychiatry, Mental Health Center Sct. Hans, Mental Health Services Copenhagen, Roskilde, Denmark
- The Lundbeck Foundation Initiative for Integrative Psychiatric Research, Copenhagen, Denmark
| | - Robert Loughnan
- Department of Cognitive Science, University of California, San Diego, La Jolla, CA, USA
- Center for Population Neuroscience and Genetics, Laureate Institute for Brain Research, Tulsa, OK, USA
| | - Joel Mefford
- Department of Neurology, University of California, Los Angeles, Los Angeles, CA, USA
| | - Clare E Palmer
- Center for Human Development, University of California, San Diego, La Jolla, CA, USA
| | - Rebecca Walker
- Department of Human Genetics, University of California, Los Angeles, Los Angeles, CA, USA
- Center for Autism Research and Treatment, Semel Institute, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA
| | - Jesper R Gådin
- Institute of Biological Psychiatry, Mental Health Center Sct. Hans, Mental Health Services Copenhagen, Roskilde, Denmark
- The Lundbeck Foundation Initiative for Integrative Psychiatric Research, Copenhagen, Denmark
| | - Morten Krebs
- Institute of Biological Psychiatry, Mental Health Center Sct. Hans, Mental Health Services Copenhagen, Roskilde, Denmark
- The Lundbeck Foundation Initiative for Integrative Psychiatric Research, Copenhagen, Denmark
| | - Vivek Appadurai
- Institute of Biological Psychiatry, Mental Health Center Sct. Hans, Mental Health Services Copenhagen, Roskilde, Denmark
- The Lundbeck Foundation Initiative for Integrative Psychiatric Research, Copenhagen, Denmark
| | - Morteza Vaez
- Institute of Biological Psychiatry, Mental Health Center Sct. Hans, Mental Health Services Copenhagen, Roskilde, Denmark
- The Lundbeck Foundation Initiative for Integrative Psychiatric Research, Copenhagen, Denmark
| | - Esben Agerbo
- The Lundbeck Foundation Initiative for Integrative Psychiatric Research, Copenhagen, Denmark
- National Centre for Register-based Research, Department of Economics and Business Economics, Aarhus University, Aarhus, Denmark
- Centre for Integrated Register-based Research, Aarhus University, Aarhus, Denmark
| | - Marianne Giørtz Pedersen
- The Lundbeck Foundation Initiative for Integrative Psychiatric Research, Copenhagen, Denmark
- National Centre for Register-based Research, Department of Economics and Business Economics, Aarhus University, Aarhus, Denmark
- Centre for Integrated Register-based Research, Aarhus University, Aarhus, Denmark
| | - Anders D Børglum
- The Lundbeck Foundation Initiative for Integrative Psychiatric Research, Copenhagen, Denmark
- Department of Biomedicine - Human Genetics, Aarhus University, Aarhus, Denmark
- Centre for Integrative Sequencing, Aarhus University, Aarhus, Denmark
| | - David M Hougaard
- The Lundbeck Foundation Initiative for Integrative Psychiatric Research, Copenhagen, Denmark
- Center for Neonatal Screening, Department for Congenital Disorders, Statens Serum Institut, Copenhagen, Denmark
| | - Ole Mors
- The Lundbeck Foundation Initiative for Integrative Psychiatric Research, Copenhagen, Denmark
- Psychosis Research Unit, Aarhus University Hospital - Psychiatry, Aarhus, Denmark
| | - Merete Nordentoft
- The Lundbeck Foundation Initiative for Integrative Psychiatric Research, Copenhagen, Denmark
- Copenhagen Mental Health Center, Mental Health Services Capital Region of Denmark Copenhagen, Copenhagen, Denmark
- Department of Clinical Medicine, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Preben Bo Mortensen
- The Lundbeck Foundation Initiative for Integrative Psychiatric Research, Copenhagen, Denmark
- National Centre for Register-based Research, Department of Economics and Business Economics, Aarhus University, Aarhus, Denmark
- Centre for Integrated Register-based Research, Aarhus University, Aarhus, Denmark
| | - Kenneth S Kendler
- Virginia Institute for Psychiatric and Behavioral Genetics, Virginia Commonwealth University, Richmond, VA, USA
- Department of Psychiatry, Virginia Commonwealth University, Richmond, VA, USA
| | - Terry L Jernigan
- Department of Cognitive Science, University of California, San Diego, La Jolla, CA, USA
- Center for Human Development, University of California, San Diego, La Jolla, CA, USA
- Department of Psychiatry, University of California, San Diego, La Jolla, CA, USA
- Department of Radiology, University of California, San Diego, La Jolla, CA, USA
| | - Daniel H Geschwind
- Department of Neurology, University of California, Los Angeles, Los Angeles, CA, USA
- Department of Human Genetics, University of California, Los Angeles, Los Angeles, CA, USA
- Center for Autism Research and Treatment, Semel Institute, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA
- Program in Neurobehavioral Genetics, Semel Institute, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA
| | - Andrés Ingason
- Institute of Biological Psychiatry, Mental Health Center Sct. Hans, Mental Health Services Copenhagen, Roskilde, Denmark
- The Lundbeck Foundation Initiative for Integrative Psychiatric Research, Copenhagen, Denmark
| | - Andrew W Dahl
- Section of Genetic Medicine, University of Chicago, Chicago, IL, USA
| | - Noah Zaitlen
- Department of Neurology, University of California, Los Angeles, Los Angeles, CA, USA
| | - Søren Dalsgaard
- The Lundbeck Foundation Initiative for Integrative Psychiatric Research, Copenhagen, Denmark
- National Centre for Register-based Research, Department of Economics and Business Economics, Aarhus University, Aarhus, Denmark
- Centre for Integrated Register-based Research, Aarhus University, Aarhus, Denmark
| | - Thomas M Werge
- Institute of Biological Psychiatry, Mental Health Center Sct. Hans, Mental Health Services Copenhagen, Roskilde, Denmark.
- The Lundbeck Foundation Initiative for Integrative Psychiatric Research, Copenhagen, Denmark.
- Department of Clinical Medicine, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark.
| | - Andrew J Schork
- Institute of Biological Psychiatry, Mental Health Center Sct. Hans, Mental Health Services Copenhagen, Roskilde, Denmark.
- The Lundbeck Foundation Initiative for Integrative Psychiatric Research, Copenhagen, Denmark.
- Neurogenomics Division, The Translational Genomics Research Institute, Phoenix, AZ, USA.
| |
Collapse
|
3
|
von Berg J, McArdle PF, Häppölä P, Haessler J, Kooperberg C, Lemmens R, Pezzini A, Thijs V, Pulit SL, Kittner SJ, Mitchell BD, de Ridder J, van der Laan SW. Evidence of survival bias in the association between APOE-ϵ4 and age of ischemic stroke onset. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2023:2023.12.01.23294385. [PMID: 38076909 PMCID: PMC10705635 DOI: 10.1101/2023.12.01.23294385] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 12/21/2023]
Abstract
Large genome-wide association studies (GWAS) employing case-control study designs have now identified tens of loci associated with ischemic stroke (IS). As a complement to these studies, we performed GWAS in a case-only design to identify loci influencing age at onset (AAO) of ischemic stroke. Analyses were conducted in a Discovery cohort of 10,857 ischemic stroke cases using a linear regression framework. We meta-analyzed all SNPs with p-value < 1×10-5 in a sex-combined or sex-stratified analysis using summary data from two additional replication cohorts. In the women-only meta-analysis, we detected significant evidence for association of AAO with rs429358, an exonic variant in APOE that encodes for the APOE-ϵ4 allele. Each copy of the rs429358:T>C allele was associated with a 1.29 years earlier stroke AOO (meta p-value = 2.48×10-11). This APOE variant has previously been associated with increased mortality and ischemic stroke AAO. We hypothesized that the association with AAO may reflect a survival bias attributable to an age-related decline in mortality among APOE-ϵ4 carriers and have no association to stroke AAO per se. Using a simulation study, we found that a variant associated with overall mortality might indeed be detected with an AAO analysis. A variant with a two-fold increase on mortality risk would lead to an observed effect of AAO that is comparable to what we found. In conclusion, we detected a robust association of the APOE locus with stroke AAO and provided simulations to suggest that this association may be unrelated to ischemic stroke per se but related to a general survival bias.
Collapse
Affiliation(s)
- Joanna von Berg
- Center for Molecular Medicine, Division Laboratories, Pharmacy, and Biomedical Genetics, University Medical Center Utrecht, Utrecht University, Utrecht, The Netherlands
- Oncode Institute, Utrecht, The Netherlands
| | - Patrick F. McArdle
- Division of Endocrinology, Diabetes and Nutrition, Department of Medicine, University of Maryland School of Medicine, Baltimore, MD, USA
| | - Paavo Häppölä
- Institute for Molecular Medicine Finland FIMM, HiLIFE, University of Helsinki, Helsinki, Finland
| | | | - Charles Kooperberg
- Division of Public Health Sciences, Fred Hutchinson Cancer Center, Seatle, WA, USA
| | - Robin Lemmens
- University Hospitals Leuven, Department of Neurology, Leuven, Belgium
- KU Leuven - University of Leuven, Department of Neurosciences, Experimental Neurology, Leuven, Belgium
| | - Alessandro Pezzini
- Department of Medicine and Surgery, University of Parma, Parma, Italy
- Stroke Care Program, Department of Emergency, Parma University Hospital, Parma, Italy
- Department of Clinical and Experimental Sciences, University of Brescia, Brescia, Italy
| | - Vincent Thijs
- Stroke Theme, The Florey, Heidelberg, Victoria, Australia
- Department of Medicine, University of Melbourne, Victoria, Australia
- Department of Neurology, Austin Health, Heidelberg, Victoria, Australia
| | | | - Sara L. Pulit
- Center for Molecular Medicine, Division Laboratories, Pharmacy, and Biomedical Genetics, University Medical Center Utrecht, Utrecht University, Utrecht, The Netherlands
| | - Steven J. Kittner
- Geriatric Research and Education Clinical Center, VA Maryland Health Care System, Baltimore, MD, USA
- Department of Neurology, University of Maryland School of Medicine, Baltimore, MD, USA
| | - Braxton D. Mitchell
- Division of Endocrinology, Diabetes and Nutrition, Department of Medicine, University of Maryland School of Medicine, Baltimore, MD, USA
- Geriatric Research and Education Clinical Center, VA Maryland Health Care System, Baltimore, MD, USA
| | - Jeroen de Ridder
- Center for Molecular Medicine, Division Laboratories, Pharmacy, and Biomedical Genetics, University Medical Center Utrecht, Utrecht University, Utrecht, The Netherlands
- Oncode Institute, Utrecht, The Netherlands
| | - Sander W. van der Laan
- Central Diagnostics Laboratory, Division Laboratories, Pharmacy, and Biomedical Genetics, University Medical Center Utrecht, Utrecht University, Utrecht, the Netherlands
- Center of Population Health and Genomics, University of Virginia, Charlottesville, VA, USA
| |
Collapse
|
4
|
Jiang X, Zhang MJ, Zhang Y, Durvasula A, Inouye M, Holmes C, Price AL, McVean G. Age-dependent topic modeling of comorbidities in UK Biobank identifies disease subtypes with differential genetic risk. Nat Genet 2023; 55:1854-1865. [PMID: 37814053 PMCID: PMC10632146 DOI: 10.1038/s41588-023-01522-8] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/28/2022] [Accepted: 08/31/2023] [Indexed: 10/11/2023]
Abstract
The analysis of longitudinal data from electronic health records (EHRs) has the potential to improve clinical diagnoses and enable personalized medicine, motivating efforts to identify disease subtypes from patient comorbidity information. Here we introduce an age-dependent topic modeling (ATM) method that provides a low-rank representation of longitudinal records of hundreds of distinct diseases in large EHR datasets. We applied ATM to 282,957 UK Biobank samples, identifying 52 diseases with heterogeneous comorbidity profiles; analyses of 211,908 All of Us samples produced concordant results. We defined subtypes of the 52 heterogeneous diseases based on their comorbidity profiles and compared genetic risk across disease subtypes using polygenic risk scores (PRSs), identifying 18 disease subtypes whose PRS differed significantly from other subtypes of the same disease. We further identified specific genetic variants with subtype-dependent effects on disease risk. In conclusion, ATM identifies disease subtypes with differential genome-wide and locus-specific genetic risk profiles.
Collapse
Affiliation(s)
- Xilin Jiang
- Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, University of Oxford, Oxford, UK.
- Department of Statistics, University of Oxford, Oxford, UK.
- Wellcome Centre for Human Genetics, University of Oxford, Oxford, UK.
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA.
- British Heart Foundation Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK.
- Victor Phillip Dahdaleh Heart and Lung Research Institute, University of Cambridge, Cambridge, UK.
| | - Martin Jinye Zhang
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Yidong Zhang
- Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, University of Oxford, Oxford, UK
- Chinese Academy of Medical Sciences Oxford Institute, Nuffield Department of Medicine, University of Oxford, Oxford, UK
- Department of Radiation Oncology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Arun Durvasula
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Department of Genetics, Harvard Medical School, Cambridge, MA, USA
- Department of Human Evolutionary Biology, Harvard University, Cambridge, MA, USA
| | - Michael Inouye
- British Heart Foundation Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
- Victor Phillip Dahdaleh Heart and Lung Research Institute, University of Cambridge, Cambridge, UK
- Cambridge Baker Systems Genomics Initiative, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
- Health Data Research UK Cambridge, Wellcome Genome Campus and University of Cambridge, Cambridge, UK
- British Heart Foundation Cambridge Centre of Research Excellence, Department of Clinical Medicine, University of Cambridge, Cambridge, UK
- Cambridge Baker Systems Genomics Initiative, Baker Heart and Diabetes Institute, Melbourne, Victoria, Australia
- The Alan Turing Institute, London, UK
| | - Chris Holmes
- Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, University of Oxford, Oxford, UK
- Department of Statistics, University of Oxford, Oxford, UK
- The Alan Turing Institute, London, UK
| | - Alkes L Price
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA.
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA.
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, USA.
| | - Gil McVean
- Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, University of Oxford, Oxford, UK.
| |
Collapse
|
5
|
Pedersen EM, Agerbo E, Plana-Ripoll O, Steinbach J, Krebs MD, Hougaard DM, Werge T, Nordentoft M, Børglum AD, Musliner KL, Ganna A, Schork AJ, Mortensen PB, McGrath JJ, Privé F, Vilhjálmsson BJ. ADuLT: An efficient and robust time-to-event GWAS. Nat Commun 2023; 14:5553. [PMID: 37689771 PMCID: PMC10492844 DOI: 10.1038/s41467-023-41210-z] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2022] [Accepted: 08/28/2023] [Indexed: 09/11/2023] Open
Abstract
Proportional hazards models have been proposed to analyse time-to-event phenotypes in genome-wide association studies (GWAS). However, little is known about the ability of proportional hazards models to identify genetic associations under different generative models and when ascertainment is present. Here we propose the age-dependent liability threshold (ADuLT) model as an alternative to a Cox regression based GWAS, here represented by SPACox. We compare ADuLT, SPACox, and standard case-control GWAS in simulations under two generative models and with varying degrees of ascertainment as well as in the iPSYCH cohort. We find Cox regression GWAS to be underpowered when cases are strongly ascertained (cases are oversampled by a factor 5), regardless of the generative model used. ADuLT is robust to ascertainment in all simulated scenarios. Then, we analyse four psychiatric disorders in iPSYCH, ADHD, Autism, Depression, and Schizophrenia, with a strong case-ascertainment. Across these psychiatric disorders, ADuLT identifies 20 independent genome-wide significant associations, case-control GWAS finds 17, and SPACox finds 8, which is consistent with simulation results. As more genetic data are being linked to electronic health records, robust GWAS methods that can make use of age-of-onset information will help increase power in analyses for common health outcomes.
Collapse
Affiliation(s)
- Emil M Pedersen
- National Centre for Register-Based Research, Aarhus University, Aarhus, Denmark.
- Lundbeck Foundation Initiative for Integrative Psychiatric Research, iPSYCH, Aarhus, Denmark.
| | - Esben Agerbo
- National Centre for Register-Based Research, Aarhus University, Aarhus, Denmark
- Lundbeck Foundation Initiative for Integrative Psychiatric Research, iPSYCH, Aarhus, Denmark
- Centre for Integrated Register-based Research at Aarhus University, Aarhus, Denmark
| | - Oleguer Plana-Ripoll
- National Centre for Register-Based Research, Aarhus University, Aarhus, Denmark
- Department of Clinical Epidemiology, Aarhus University and Aarhus University Hospital, Aarhus, Denmark
| | - Jette Steinbach
- National Centre for Register-Based Research, Aarhus University, Aarhus, Denmark
| | - Morten D Krebs
- Institute of Biological Psychiatry, Mental Health Center - Sct Hans, Copenhagen University Hospital - Mental Health Services CPH, Copenhagen, Denmark
| | - David M Hougaard
- Department for Congenital Disorders, Statens Serum Institut, Copenhagen, Denmark
| | - Thomas Werge
- Institute of Biological Psychiatry, Mental Health Center - Sct Hans, Copenhagen University Hospital - Mental Health Services CPH, Copenhagen, Denmark
- Department of Clinical Sciences, Copenhagen University, Copenhagen, Denmark
- Section for Geogenetics, GLOBE Institute, Faculty of Health and Medical Science, Copenhagen University, Copenhagen, Denmark
| | - Merete Nordentoft
- Lundbeck Foundation Initiative for Integrative Psychiatric Research, iPSYCH, Aarhus, Denmark
- CORE- Copenhagen Centre for Research in Mental Health, Mental Health Center-Copenhagen, Copenhagen University Hospital - Mental Health Services CPH, Copenhagen, Denmark
| | - Anders D Børglum
- Lundbeck Foundation Initiative for Integrative Psychiatric Research, iPSYCH, Aarhus, Denmark
- Department of Biomedicine and iSEQ Centre, Aarhus University, Aarhus, Denmark
- Center for Genomics and Personalized Medicine, CGPM, Aarhus University, Aarhus, Denmark
| | - Katherine L Musliner
- National Centre for Register-Based Research, Aarhus University, Aarhus, Denmark
- Department of Affective Disorders, Aarhus University Hospital-Psychiatry, Aarhus, Denmark
- Department of Clinical Medicine, Aarhus University, Aarhus, Denmark
| | - Andrea Ganna
- Institute for Molecular Medicine Finland, University of Helsinki, Helsinki, Finland
| | - Andrew J Schork
- Institute of Biological Psychiatry, Mental Health Center - Sct Hans, Copenhagen University Hospital - Mental Health Services CPH, Copenhagen, Denmark
- Section for Geogenetics, GLOBE Institute, Faculty of Health and Medical Science, Copenhagen University, Copenhagen, Denmark
- Neurogenomics Division, The Translational Genomics Research Institute (TGEN), Phoenix, AZ, USA
| | - Preben B Mortensen
- National Centre for Register-Based Research, Aarhus University, Aarhus, Denmark
- Lundbeck Foundation Initiative for Integrative Psychiatric Research, iPSYCH, Aarhus, Denmark
| | - John J McGrath
- National Centre for Register-Based Research, Aarhus University, Aarhus, Denmark
- Queensland Brain Institute, University of Queensland, St Lucia, QLD, Australia
- Queensland Centre for Mental Health Research, The Park Centre for Mental Health, Wacol, QLD, Australia
| | - Florian Privé
- National Centre for Register-Based Research, Aarhus University, Aarhus, Denmark
- Lundbeck Foundation Initiative for Integrative Psychiatric Research, iPSYCH, Aarhus, Denmark
| | - Bjarni J Vilhjálmsson
- National Centre for Register-Based Research, Aarhus University, Aarhus, Denmark.
- Lundbeck Foundation Initiative for Integrative Psychiatric Research, iPSYCH, Aarhus, Denmark.
- Bioinformatics Research Centre, Aarhus University, Aarhus, Denmark.
- Novo Nordisk Foundation Center for Genomic Mechanisms of Disease, the Broad Institute of MIT and Harvard, Massachusetts, USA.
| |
Collapse
|
6
|
Discerning asthma endotypes through comorbidity mapping. Nat Commun 2022; 13:6712. [PMID: 36344522 PMCID: PMC9640644 DOI: 10.1038/s41467-022-33628-8] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/01/2021] [Accepted: 09/27/2022] [Indexed: 11/09/2022] Open
Abstract
Asthma is a heterogeneous, complex syndrome, and identifying asthma endotypes has been challenging. We hypothesize that distinct endotypes of asthma arise in disparate genetic variation and life-time environmental exposure backgrounds, and that disease comorbidity patterns serve as a surrogate for such genetic and exposure variations. Here, we computationally discover 22 distinct comorbid disease patterns among individuals with asthma (asthma comorbidity subgroups) using diagnosis records for >151 M US residents, and re-identify 11 of the 22 subgroups in the much smaller UK Biobank. GWASs to discern asthma risk loci for individuals within each subgroup and in all subgroups combined reveal 109 independent risk loci, of which 52 are replicated in multi-ancestry meta-analysis across different ethnicity subsamples in UK Biobank, US BioVU, and BioBank Japan. Fourteen loci confer asthma risk in multiple subgroups and in all subgroups combined. Importantly, another six loci confer asthma risk in only one subgroup. The strength of association between asthma and each of 44 health-related phenotypes also varies dramatically across subgroups. This work reveals subpopulations of asthma patients distinguished by comorbidity patterns, asthma risk loci, gene expression, and health-related phenotypes, and so reveals different asthma endotypes.
Collapse
|
7
|
Hujoel ML, Loh PR, Neale BM, Price AL. Incorporating family history of disease improves polygenic risk scores in diverse populations. CELL GENOMICS 2022; 2:100152. [PMID: 35935918 PMCID: PMC9351615 DOI: 10.1016/j.xgen.2022.100152] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/07/2021] [Revised: 05/22/2022] [Accepted: 06/09/2022] [Indexed: 01/04/2023]
Abstract
Polygenic risk scores (PRSs) derived from genotype data and family history (FH) of disease provide valuable information for predicting disease risk, but PRSs perform poorly when applied to diverse populations. Here, we explore methods for combining both types of information (PRS-FH) in UK Biobank data. PRSs were trained using all British individuals (n = 409,000), and target samples consisted of unrelated non-British Europeans (n = 42,000), South Asians (n = 7,000), or Africans (n = 7,000). We evaluated PRS, FH, and PRS-FH using liability-scale R 2, primarily focusing on 3 well-powered diseases (type 2 diabetes, hypertension, and depression). PRS attained average prediction R 2s of 5.8%, 4.0%, and 0.53% in non-British Europeans, South Asians, and Africans, confirming poor cross-population transferability. In contrast, PRS-FH attained average prediction R 2s of 13%, 12%, and 10%, respectively, representing a large improvement in Europeans and an extremely large improvement in Africans. In conclusion, including family history improves the accuracy of polygenic risk scores, particularly in diverse populations.
Collapse
Affiliation(s)
- Margaux L.A. Hujoel
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA 02115, USA
- Division of Genetics, Department of Medicine, Brigham and Women’s Hospital and Harvard Medical School, Boston, MA 02115, USA
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
| | - Po-Ru Loh
- Division of Genetics, Department of Medicine, Brigham and Women’s Hospital and Harvard Medical School, Boston, MA 02115, USA
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
| | - Benjamin M. Neale
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
| | - Alkes L. Price
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA 02115, USA
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA 02115, USA
| |
Collapse
|
8
|
Sobrin L, Susarla G, Stanwyck L, Rouhana JM, Li A, Pollack S, Igo RP, Jensen RA, Li X, Ng MCY, Smith AV, Kuo JZ, Taylor KD, Freedman BI, Bowden DW, Penman A, Chen CJ, Craig JE, Adler SG, Chew EY, Cotch MF, Yaspan B, Mitchell P, Wang JJ, Klein BEK, Wong TY, Rotter JI, Burdon KP, Iyengar SK, Segrè AV. Gene Set Enrichment Analsyes Identify Pathways Involved in Genetic Risk for Diabetic Retinopathy. Am J Ophthalmol 2022; 233:111-123. [PMID: 34166655 PMCID: PMC8678352 DOI: 10.1016/j.ajo.2021.06.014] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/22/2020] [Revised: 04/19/2021] [Accepted: 06/12/2021] [Indexed: 01/03/2023]
Abstract
To identify functionally related genes associated with diabetic retinopathy (DR) risk using gene set enrichment analyses applied to genome-wide association study meta-analyses. METHODS We analyzed DR GWAS meta-analyses performed on 3246 Europeans and 2611 African Americans with type 2 diabetes. Gene sets relevant to 5 key DR pathophysiology processes were investigated: tissue injury, vascular events, metabolic events and glial dysregulation, neuronal dysfunction, and inflammation. Keywords relevant to these processes were queried in 4 pathway and ontology databases. Two GSEA methods, Meta-Analysis Gene set Enrichment of variaNT Associations (MAGENTA) and Multi-marker Analysis of GenoMic Annotation (MAGMA), were used. Gene sets were defined to be enriched for gene associations with DR if the P value corrected for multiple testing (Pcorr) was <.05. RESULTS Five gene sets were significantly enriched for numerous modest genetic associations with DR in one method (MAGENTA or MAGMA) and also at least nominally significant (uncorrected P < .05) in the other method. These pathways were regulation of the lipid catabolic process (2-fold enrichment, Pcorr = .014); nitric oxide biosynthesis (1.92-fold enrichment, Pcorr = .022); lipid digestion, mobilization, and transport (1.6-fold enrichment, P = .032); apoptosis (1.53-fold enrichment, P = .041); and retinal ganglion cell degeneration (2-fold enrichment, Pcorr = .049). The interferon gamma (IFNG) gene, previously implicated in DR by protein-protein interactions in our GWAS, was among the top ranked genes in the nitric oxide pathway (best variant P = .0001). CONCLUSIONS These GSEA indicate that variants in genes involved in oxidative stress, lipid transport and catabolism, and cell degeneration are enriched for genes associated with DR risk. NOTE: Publication of this article is sponsored by the American Ophthalmological Society.
Collapse
Affiliation(s)
- Lucia Sobrin
- From the Department of Ophthalmology, Harvard Medical School, Massachusetts Eye and Ear Infirmary.
| | - Gayatri Susarla
- From the Department of Ophthalmology, Harvard Medical School, Massachusetts Eye and Ear Infirmary
| | - Lynn Stanwyck
- From the Department of Ophthalmology, Harvard Medical School, Massachusetts Eye and Ear Infirmary
| | - John M Rouhana
- From the Department of Ophthalmology, Harvard Medical School, Massachusetts Eye and Ear Infirmary
| | - Ashley Li
- From the Department of Ophthalmology, Harvard Medical School, Massachusetts Eye and Ear Infirmary
| | - Samuela Pollack
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, Massachusetts
| | - Robert P Igo
- Department of Population and Quantitative Health Sciences, Case Western University, Cleveland, Ohio
| | - Richard A Jensen
- Cardiovascular Health Research Unit, Department of Medicine, Epidemiology and Health Services, University of Washington, Seattle, Washington
| | - Xiaohui Li
- Institute for Translational Genomics and Population Sciences, Department of Pediatrics, the Lundquist Institute for Biomedical Innovation at Harbor-UCLA Medical Center, Torrance, California
| | - Maggie C Y Ng
- Center for Genomics and Personalized Medicine Research, Wake Forest School of Medicine; Department of Biochemistry, Wake Forest School of Medicine, Winston-Salem, North Carolina, USA; Vanderbilt Genetics Institute and Division of Genetic Medicine, Vanderbilt University Medical Center, Nashville, Tennessee, USA
| | - Albert V Smith
- Department of Medicine, University of Iceland, Reykjavík, Iceland
| | - Jane Z Kuo
- Medical Affairs, Ophthalmology, Sun Pharmaceutical Industries, Inc, Princeton, New Jersey
| | - Kent D Taylor
- Institute for Translational Genomics and Population Sciences, Department of Pediatrics, the Lundquist Institute for Biomedical Innovation at Harbor-UCLA Medical Center, Torrance, California
| | - Barry I Freedman
- Center for Genomics and Personalized Medicine Research, Wake Forest School of Medicine; Department of Internal Medicine, Section on Nephrology, Wake Forest School of Medicine, Winston-Salem, North Carolina
| | - Donald W Bowden
- Center for Genomics and Personalized Medicine Research, Wake Forest School of Medicine; Department of Biochemistry, Wake Forest School of Medicine, Winston-Salem, North Carolina, USA
| | - Alan Penman
- Department of Preventive Medicine, John D. Bower School of Population Health (A.P.), Department of Ophthalmology
| | - Ching J Chen
- Department of Preventive Medicine, John D. Bower School of Population Health (A.P.), Department of Ophthalmology
| | - Jamie E Craig
- University of Mississippi Medical Center, Jackson, Mississippi, USA, FHMRI Eye & Vision, Flinders University, Bedford Park, SA, Australia
| | - Sharon G Adler
- Department of Nephrology and Hypertension, Los Angeles Biomedical Research Institute at Harbor-University of California, Torrance, California
| | - Emily Y Chew
- Division of Epidemiology and Clinical Applications, National Eye Institute, National Institutes of Health, Bethesda, Maryland
| | - Mary Frances Cotch
- Division of Epidemiology and Clinical Applications, National Eye Institute, National Institutes of Health, Bethesda, Maryland
| | - Brian Yaspan
- Genentech Inc, South San Francisco, California, USA
| | - Paul Mitchell
- Department of Ophthalmology, Centre for Vision Research, Westmead Institute for Medical Research, University of Sydney, Sydney, Australia
| | - Jie Jin Wang
- Department of Ophthalmology, Centre for Vision Research, Westmead Institute for Medical Research, University of Sydney, Sydney, Australia; Center of Clinician-Scientist Development, Duke-NUS Medical School, Singapore
| | - Barbara E K Klein
- Department of Ophthalmology and Visual Sciences, University of Wisconsin-Madison, Madison, Wisconsin, USA
| | - Tien Y Wong
- Center of Clinician-Scientist Development, Duke-NUS Medical School, Singapore; Singapore Eye Research Institute, Singapore National Eye Centre, Singapore
| | - Jerome I Rotter
- Institute for Translational Genomics and Population Sciences, Department of Pediatrics, the Lundquist Institute for Biomedical Innovation at Harbor-UCLA Medical Center, Torrance, California
| | - Kathyrn P Burdon
- Menzies Institute for Medical Research, University of Tasmania, Hobart, Australia
| | - Sudha K Iyengar
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, Massachusetts; Department of Population and Quantitative Health Sciences, Case Western University, Cleveland, Ohio
| | - Ayellet V Segrè
- From the Department of Ophthalmology, Harvard Medical School, Massachusetts Eye and Ear Infirmary; Broad Institute of Harvard and MIT, Cambridge, Massachusetts
| |
Collapse
|
9
|
Song Y, Yang L, Jiang L, Hao Z, Yang R, Xu P. Optimizing genomic control in mixed model associations with binary diseases. Brief Bioinform 2021; 23:6394993. [PMID: 34643219 DOI: 10.1093/bib/bbab426] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/06/2021] [Revised: 09/09/2021] [Accepted: 09/18/2021] [Indexed: 11/14/2022] Open
Abstract
Complex computation and approximate solution hinder the application of generalized linear mixed models (GLMM) into genome-wide association studies. We extended GRAMMAR to handle binary diseases by considering genomic breeding values (GBVs) estimated in advance as a known predictor in genomic logit regression, and then reduced polygenic effects by regulating downward genomic heritability to control false negative errors produced in the association tests. Using simulations and case analyses, we showed in optimizing GRAMMAR, polygenic effects and genomic controls could be evaluated using the fewer sampling markers, which extremely simplified GLMM-based association analysis in large-scale data. Further, joint association analysis for quantitative trait nucleotide (QTN) candidates chosen by multiple testing offered significant improved statistical power to detect QTNs over existing methods.
Collapse
Affiliation(s)
- Yuxin Song
- Wuxi Fisheries College, Nanjing Agricultural University, People's Republic of China
| | - Li'ang Yang
- College of Life Science, Northeast Agricultural University, People's Republic of China
| | - Li Jiang
- Research Centre for Aquatic biotechnology, Chinese Academy of Fishery Sciences, People's Republic of China
| | - Zhiyu Hao
- Institute of Animal Husbandry, Heilongjiang Academy of Agricultural Sciences, People's Republic of China
| | - Runqing Yang
- Research Centre for Aquatic biotechnology, Chinese Academy of Fishery Sciences, People's Republic of China
| | - Pao Xu
- Wuxi Fisheries College, Nanjing Agricultural University, People's Republic of China
| |
Collapse
|
10
|
Mamtani M, Jaisinghani MT, Jaiswal SG, Pipal KV, Patel AA, Kulkarni H. Genetic association of anthropometric traits with type 2 diabetes in ethnically endogamous Sindhi families. PLoS One 2021; 16:e0257390. [PMID: 34506595 PMCID: PMC8432747 DOI: 10.1371/journal.pone.0257390] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/17/2021] [Accepted: 08/31/2021] [Indexed: 12/20/2022] Open
Abstract
Background Ethnically endogamous populations can shed light on the genetics of type 2 diabetes. Such studies are lacking in India. We conducted this study to determine the genetic and environmental contributions of anthropometric traits to type 2 diabetes risk in the Sindhi families in central India. Methods We conducted a family study in Indian Sindhi families with at least one case of type 2 diabetes. Variance components methods were used to quantify the genetic association of 18 anthropometric traits with eight type 2 diabetes related traits. Univariate and bivariate polygenic models were used to determine the heritability, genetic and environmental correlation of anthropometric traits with type 2 diabetes related traits. Results We included 1,152 individuals from 112 phenotyped families. The ascertainment-bias corrected prevalence of type 2 diabetes was 35%. Waist circumference, hip circumference and the biceps, triceps, subscapular and medial calf skinfold thicknesses were polygenically and significantly associated with type 2 diabetes. The range of heritability of the anthropometric traits and type 2 diabetes related traits was 0.27–0.73 and 0.00–0.39, respectively. Heritability of type 2 diabetes as a discrete trait was 0.35. Heritability curves demonstrated a substantial local influence of type 2 diabetes related traits. Bivariate trait analyses showed that biceps and abdominal skinfold thickness and all waist-containing indexes were strongly genetically correlated with type 2 diabetes. Conclusions In this first study of Sindhi families, we found evidence for genetic and environmental concordance of anthropometric traits with type 2 diabetes. Future studies need to probe into the genetics of type 2 diabetes in this population.
Collapse
Affiliation(s)
- Manju Mamtani
- Lata Medical Research Foundation, Nagpur, India
- M&H Research, LLC, San Antonio, Texas, United States of America
- * E-mail:
| | | | | | | | | | - Hemant Kulkarni
- Lata Medical Research Foundation, Nagpur, India
- M&H Research, LLC, San Antonio, Texas, United States of America
| |
Collapse
|
11
|
Douglas GM, Bielawski JP, Langille MGI. Re-evaluating the relationship between missing heritability and the microbiome. MICROBIOME 2020; 8:87. [PMID: 32513310 PMCID: PMC7282175 DOI: 10.1186/s40168-020-00839-4] [Citation(s) in RCA: 17] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/30/2019] [Accepted: 04/15/2020] [Indexed: 06/07/2023]
Abstract
Human genome-wide association studies (GWASs) have recurrently estimated lower heritability estimates than familial studies. Many explanations have been suggested to explain these lower estimates, including that a substantial proportion of genetic variation and gene-by-environment interactions are unmeasured in typical GWASs. The human microbiome is potentially related to both of these explanations, but it has been more commonly considered as a source of unmeasured genetic variation. In particular, it has recently been argued that the genetic variation within the human microbiome should be included when estimating trait heritability. We outline issues with this argument, which in its strictest form depends on the holobiont model of human-microbiome interactions. Instead, we argue that the microbiome could be leveraged to help control for environmental variation across a population, although that remains to be determined. We discuss potential approaches that could be explored to determine whether integrating microbiome sequencing data into GWASs is useful. Video abstract.
Collapse
Affiliation(s)
- Gavin M. Douglas
- Department of Microbiology and Immunology, Dalhousie University, Halifax, NS Canada
| | - Joseph P. Bielawski
- Department of Biology, Dalhousie University, Halifax, NS Canada
- Department of Mathematics and Statistics, Dalhousie University, Halifax, NS Canada
| | - Morgan G. I. Langille
- Department of Microbiology and Immunology, Dalhousie University, Halifax, NS Canada
- Department of Pharmacology, Dalhousie University, Halifax, NS Canada
| |
Collapse
|
12
|
Hujoel MLA, Gazal S, Loh PR, Patterson N, Price AL. Liability threshold modeling of case-control status and family history of disease increases association power. Nat Genet 2020; 52:541-547. [PMID: 32313248 PMCID: PMC7210076 DOI: 10.1038/s41588-020-0613-6] [Citation(s) in RCA: 40] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/25/2019] [Accepted: 03/12/2020] [Indexed: 12/22/2022]
Abstract
Family history of disease can provide valuable information in case-control association studies, but it is currently unclear how to best combine case-control status and family history of disease. We developed an association method based on posterior mean genetic liabilities under a liability threshold model, conditional on case-control status and family history (LT-FH). Analyzing 12 diseases from the UK Biobank (average N = 350,000) we compared LT-FH to genome-wide association without using family history (GWAS) and a previous proxy-based method incorporating family history (GWAX). LT-FH was 63% (standard error (s.e.) 6%) more powerful than GWAS and 36% (s.e. 4%) more powerful than the trait-specific maximum of GWAS and GWAX, based on the number of independent genome-wide-significant loci across all diseases (for example, 690 loci for LT-FH versus 423 for GWAS); relative improvements were similar when applying BOLT-LMM to GWAS, GWAX and LT-FH phenotypes. Thus, LT-FH greatly increases association power when family history of disease is available.
Collapse
Affiliation(s)
- Margaux L A Hujoel
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, USA.
| | - Steven Gazal
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Po-Ru Loh
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Brigham and Women's Hospital/Harvard Medical School, Boston, MA, USA
| | | | - Alkes L Price
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, USA.
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA.
- Broad Institute of MIT and Harvard, Cambridge, MA, USA.
| |
Collapse
|
13
|
MacQueen AH, White JW, Lee R, Osorno JM, Schmutz J, Miklas PN, Myers J, McClean PE, Juenger TE. Genetic Associations in Four Decades of Multienvironment Trials Reveal Agronomic Trait Evolution in Common Bean. Genetics 2020; 215:267-284. [PMID: 32205398 PMCID: PMC7198278 DOI: 10.1534/genetics.120.303038] [Citation(s) in RCA: 20] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/09/2019] [Accepted: 03/12/2020] [Indexed: 11/18/2022] Open
Abstract
Multienvironment trials (METs) are widely used to assess the performance of promising crop germplasm. Though seldom designed to elucidate genetic mechanisms, MET data sets are often much larger than could be duplicated for genetic research and, given proper interpretation, may offer valuable insights into the genetics of adaptation across time and space. The Cooperative Dry Bean Nursery (CDBN) is a MET for common bean (Phaseolus vulgaris) grown for > 70 years in the United States and Canada, consisting of 20-50 entries each year at 10-20 locations. The CDBN provides a rich source of phenotypic data across entries, years, and locations that is amenable to genetic analysis. To study stable genetic effects segregating in this MET, we conducted genome-wide association studies (GWAS) using best linear unbiased predictions derived across years and locations for 21 CDBN phenotypes and genotypic data (1.2 million SNPs) for 327 CDBN genotypes. The value of this approach was confirmed by the discovery of three candidate genes and genomic regions previously identified in balanced GWAS. Multivariate adaptive shrinkage (mash) analysis, which increased our power to detect significant correlated effects, found significant effects for all phenotypes. Mash found two large genomic regions with effects on multiple phenotypes, supporting a hypothesis of pleiotropic or linked effects that were likely selected on in pursuit of a crop ideotype. Overall, our results demonstrate that statistical genomics approaches can be used on MET phenotypic data to discover significant genetic effects and to define genomic regions associated with crop improvement.
Collapse
Affiliation(s)
- Alice H MacQueen
- Integrative Biology, The University of Texas at Austin, Texas 78712
| | - Jeffrey W White
- U.S. Arid Land Agricultural Research Center, U.S. Department of Agriculture-Agricultural Research Service, Maricopa, Arizona 85239
| | - Rian Lee
- Genomics and Bioinformatics Program, North Dakota State University, Fargo, North Dakota 58102
| | - Juan M Osorno
- Genomics and Bioinformatics Program, North Dakota State University, Fargo, North Dakota 58102
| | - Jeremy Schmutz
- Hudson-Alpha Institute for Biotechnology, Huntsville, Alabama 35806
| | - Phillip N Miklas
- Grain Legume Genetics and Physiology Research Unit, U.S. Department of Agriculture-Agricultural Research Service, Prosser, Washington 99350
| | - Jim Myers
- Department of Horticulture, Oregon State University, Corvallis, Oregon 97331
| | - Phillip E McClean
- Genomics and Bioinformatics Program, North Dakota State University, Fargo, North Dakota 58102
| | - Thomas E Juenger
- Integrative Biology, The University of Texas at Austin, Texas 78712
| |
Collapse
|
14
|
Mefford J, Park D, Zheng Z, Ko A, Ala-Korpela M, Laakso M, Pajukanta P, Yang J, Witte J, Zaitlen N. Efficient Estimation and Applications of Cross-Validated Genetic Predictions to Polygenic Risk Scores and Linear Mixed Models. J Comput Biol 2020; 27:599-612. [PMID: 32077750 PMCID: PMC7185352 DOI: 10.1089/cmb.2019.0325] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/19/2023] Open
Abstract
Large-scale cohorts with combined genetic and phenotypic data, coupled with methodological advances, have produced increasingly accurate genetic predictors of complex human phenotypes called polygenic risk scores (PRSs). In addition to the potential translational impacts of identifying at-risk individuals, PRS are being utilized for a growing list of scientific applications, including causal inference, identifying pleiotropy and genetic correlation, and powerful gene-based and mixed-model association tests. Existing PRS approaches rely on external large-scale genetic cohorts that have also measured the phenotype of interest. They further require matching on ancestry and genotyping platform or imputation quality. In this work, we present a novel reference-free method to produce a PRS that does not rely on an external cohort. We show that naive implementations of reference-free PRS either result in substantial overfitting or prohibitive increases in computational time. We show that our algorithm avoids both of these issues and can produce informative in-sample PRSs over a single cohort without overfitting. We then demonstrate several novel applications of reference-free PRSs, including detection of pleiotropy across 246 metabolic traits and efficient mixed-model association testing.
Collapse
Affiliation(s)
| | - Danny Park
- School of Medicine, UCSF, San Francisco, California
| | - Zhili Zheng
- Institute for Molecular Bioscience, University of Queensland, Brisbane, Queensland, Australia
| | - Arthur Ko
- Human Genetics, UCLA, Los Angeles, California
| | - Mika Ala-Korpela
- Baker IDI Heart and Diabetes Institute, Melbourne, Victoria, Australia
- University of Oulu Biocenter, Oulu, Finland
- NMR Metabolomics Laboratory, School of Pharmacy, University of Eastern Finland, Kuopio, Finland
- University of Bristol School of Medical Sciences, Population Health Science, Bristol, Bristol, United Kingdom
| | - Markku Laakso
- Department of Medicine, University of Eastern Finland School of Medicine, Kuopio, Finland
| | | | - Jian Yang
- Institute for Molecular Bioscience, University of Queensland, Brisbane, Queensland, Australia
| | - John Witte
- Departments of Epidemiology and Biostatistics, and Urology, UCSF, San Francisco, California
| | | |
Collapse
|
15
|
Trzaskowski M, Mehta D, Peyrot W, Hawkes D, Davies D, Howard D, Kemper KE, Sidorenko J, Maier R, Ripke S, Mattheisen M, Baune BT, Grabe HJ, Heath AC, Jones L, Jones I, Madden PAF, McIntosh AM, Breen G, Lewis CM, Børglum AD, Sullivan PF, Martin NG, Kendler KS, Levinson DF, Wray NR. Quantifying between-cohort and between-sex genetic heterogeneity in major depressive disorder. Am J Med Genet B Neuropsychiatr Genet 2019; 180:439-447. [PMID: 30708398 PMCID: PMC6675638 DOI: 10.1002/ajmg.b.32713] [Citation(s) in RCA: 24] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/05/2018] [Revised: 11/19/2018] [Accepted: 01/04/2019] [Indexed: 01/08/2023]
Abstract
Major depressive disorder (MDD) is clinically heterogeneous with prevalence rates twice as high in women as in men. There are many possible sources of heterogeneity in MDD most of which are not measured in a sufficiently comparable way across study samples. Here, we assess genetic heterogeneity based on two fundamental measures, between-cohort and between-sex heterogeneity. First, we used genome-wide association study (GWAS) summary statistics to investigate between-cohort genetic heterogeneity using the 29 research cohorts of the Psychiatric Genomics Consortium (PGC; N cases = 16,823, N controls = 25,632) and found that some of the cohort heterogeneity can be attributed to ascertainment differences (such as recruitment of cases from hospital vs. community sources). Second, we evaluated between-sex genetic heterogeneity using GWAS summary statistics from the PGC, Kaiser Permanente GERA, UK Biobank, and the Danish iPSYCH studies but did not find convincing evidence for genetic differences between the sexes. We conclude that there is no evidence that the heterogeneity between MDD data sets and between sexes reflects genetic heterogeneity. Larger sample sizes with detailed phenotypic records and genomic data remain the key to overcome heterogeneity inherent in assessment of MDD.
Collapse
Affiliation(s)
- Maciej Trzaskowski
- Institute for Molecular Bioscience, The University of Queensland, Brisbane, Queensland, Australia
| | - Divya Mehta
- Institute for Molecular Bioscience, The University of Queensland, Brisbane, Queensland, Australia
- School of Psychology and Counselling, Queensland University of Technology, Brisbane, Australia
| | - Wouter Peyrot
- Department of Psychiatry, Vrije Universiteit Medical Center and GGZ in Geest, Amsterdam, The Netherlands
| | - David Hawkes
- AGRF, The University of Queensland, Brisbane, Queensland, Australia
| | - Daniel Davies
- Department of Psychiatry, Behavioural and Clinical Neuroscience Institute and Developmental Psychiatry, Cambridge University, Cambridge, England, United Kingdom
| | - David Howard
- Division of Psychiatry, University of Edinburgh, Edinburgh, United Kingdom
| | - Kathryn E. Kemper
- Institute for Molecular Bioscience, The University of Queensland, Brisbane, Queensland, Australia
| | - Julia Sidorenko
- Institute for Molecular Bioscience, The University of Queensland, Brisbane, Queensland, Australia
| | - Robert Maier
- Institute for Molecular Bioscience, The University of Queensland, Brisbane, Queensland, Australia
- Analytic and Translational Genetics Unit, Massachusetts General Hospital and Harvard Medical School, Boston, Massachusetts
| | - Stephan Ripke
- Medical and Population Genetics, Broad Institute, Cambridge, Massachusetts
- Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, Massachusetts
- Department of Psychiatry and Psychotherapy, Universitätsmedizin Berlin Campus Charité Mitte, Berlin, Germany
| | - Manuel Mattheisen
- Department of Biomedicine, Aarhus University, Aarhus, Denmark
- iPSYCH, The Lundbeck Foundation Initiative for Integrative Psychiatric Research, Aarhus, Denmark
| | - Bernhard T Baune
- Department of Psychiatry, The University of Melbourne, Melbourne, Victoria, Australia
| | - Hans J. Grabe
- Department of Psychiatry and Psychotherapy, University Medicine Greifswald, Greifswald, Germany
| | - Andrew C Heath
- Department of Psychiatry, Washington University in Saint Louis School of Medicine, Saint Louis, Missouri
| | - Lisa Jones
- Institute of Health & Society, University of Worcester, Worcester, United Kingdom
| | - Ian Jones
- MRC Centre for Neuropsychiatric Genetics and Genomics, Cardiff University, Cardiff, United Kingdom
| | - Pamela AF Madden
- Department of Psychiatry, Washington University in Saint Louis School of Medicine, Saint Louis, Missouri
| | - Andrew M. McIntosh
- Division of Psychiatry, University of Edinburgh, Edinburgh, United Kingdom
- Centre for Cognitive Ageing and Cognitive Epidemiology, University of Edinburgh, Edinburgh, United Kingdom
| | - Gerome Breen
- MRC Social Genetic and Developmental Psychiatry Centre, King's College London, London, United Kingdom
- NIHR BRC for Mental Health, King's College London, London, United Kingdom
| | - Cathryn M. Lewis
- MRC Social Genetic and Developmental Psychiatry Centre, King's College London, London, United Kingdom
- Department of Medical and Molecular Genetics, King's College London, London, United Kingdom
| | - Anders D. Børglum
- iPSYCH, The Lundbeck Foundation Initiative for Integrative Psychiatric Research, Aarhus, Denmark
- Department of Biomedicine and iSEQ-Centre for Integrative Sequencing, Aarhus University, Aarhus, Denmark
| | - Patrick F. Sullivan
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
- Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina
- Department of Psychiatry, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina
| | - Nicholas G. Martin
- Genetics and Computational Biology, QIMR Berghofer Medical Research Institute, Brisbane, Queensland, Australia
| | - Kenneth S. Kendler
- Department of Psychiatry, Virginia Commonwealth University, Richmond, Virginia
| | - Douglas F. Levinson
- Psychiatry and Behavioral Sciences, Stanford University, Stanford, California
| | - Naomi R. Wray
- Institute for Molecular Bioscience, The University of Queensland, Brisbane, Queensland, Australia
- Queensland Brain Institute, The University of Queensland, Brisbane, Queensland, Australia
| | | |
Collapse
|
16
|
Oliynyk RT. Evaluating the Potential of Younger Cases and Older Controls Cohorts to Improve Discovery Power in Genome-Wide Association Studies of Late-Onset Diseases. J Pers Med 2019; 9:jpm9030038. [PMID: 31336617 PMCID: PMC6789773 DOI: 10.3390/jpm9030038] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/14/2019] [Revised: 07/15/2019] [Accepted: 07/16/2019] [Indexed: 11/25/2022] Open
Abstract
For more than a decade, genome-wide association studies have been making steady progress in discovering the causal gene variants that contribute to late-onset human diseases. Polygenic late-onset diseases in an aging population display a risk allele frequency decrease at older ages, caused by individuals with higher polygenic risk scores becoming ill proportionately earlier and bringing about a change in the distribution of risk alleles between new cases and the as-yet-unaffected population. This phenomenon is most prominent for diseases characterized by high cumulative incidence and high heritability, examples of which include Alzheimer’s disease, coronary artery disease, cerebral stroke, and type 2 diabetes, while for late-onset diseases with relatively lower prevalence and heritability, exemplified by cancers, the effect is significantly lower. In this research, computer simulations have demonstrated that genome-wide association studies of late-onset polygenic diseases showing high cumulative incidence together with high initial heritability will benefit from using the youngest possible age-matched cohorts. Moreover, rather than using age-matched cohorts, study cohorts combining the youngest possible cases with the oldest possible controls may significantly improve the discovery power of genome-wide association studies.
Collapse
Affiliation(s)
- Roman Teo Oliynyk
- Centre for Computational Evolution, University of Auckland, Auckland 1010, New Zealand.
- Department of Computer Science, University of Auckland, Auckland 1010, New Zealand.
| |
Collapse
|
17
|
Oliynyk RT. Age-related late-onset disease heritability patterns and implications for genome-wide association studies. PeerJ 2019; 7:e7168. [PMID: 31231601 PMCID: PMC6573810 DOI: 10.7717/peerj.7168] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/04/2018] [Accepted: 05/22/2019] [Indexed: 01/06/2023] Open
Abstract
Genome-wide association studies (GWASs) and other computational biology techniques are gradually discovering the causal gene variants that contribute to late-onset human diseases. After more than a decade of genome-wide association study efforts, these can account for only a fraction of the heritability implied by familial studies, the so-called “missing heritability” problem. Computer simulations of polygenic late-onset diseases (LODs) in an aging population have quantified the risk allele frequency decrease at older ages caused by individuals with higher polygenic risk scores (PRSs) becoming ill proportionately earlier. This effect is most prominent for diseases characterized by high cumulative incidence and high heritability, examples of which include Alzheimer’s disease, coronary artery disease, cerebral stroke, and type 2 diabetes. The incidence rate for LODs grows exponentially for decades after early onset ages, guaranteeing that the cohorts used for GWASs overrepresent older individuals with lower PRSs, whose disease cases are disproportionately due to environmental causes such as old age itself. This mechanism explains the decline in clinical predictive power with age and the lower discovery power of familial studies of heritability and GWASs. It also explains the relatively constant-with-age heritability found for LODs of lower prevalence, exemplified by cancers.
Collapse
Affiliation(s)
- Roman Teo Oliynyk
- Centre for Computational Evolution, University of Auckland, Auckland, New Zealand.,Department of Computer Science, University of Auckland, Auckland, New Zealand
| |
Collapse
|
18
|
Dahl A, Cai N, Ko A, Laakso M, Pajukanta P, Flint J, Zaitlen N. Reverse GWAS: Using genetics to identify and model phenotypic subtypes. PLoS Genet 2019; 15:e1008009. [PMID: 30951530 PMCID: PMC6469799 DOI: 10.1371/journal.pgen.1008009] [Citation(s) in RCA: 28] [Impact Index Per Article: 5.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/05/2018] [Revised: 04/17/2019] [Accepted: 02/07/2019] [Indexed: 12/16/2022] Open
Abstract
Recent and classical work has revealed biologically and medically significant subtypes in complex diseases and traits. However, relevant subtypes are often unknown, unmeasured, or actively debated, making automated statistical approaches to subtype definition valuable. We propose reverse GWAS (RGWAS) to identify and validate subtypes using genetics and multiple traits: while GWAS seeks the genetic basis of a given trait, RGWAS seeks to define trait subtypes with distinct genetic bases. Unlike existing approaches relying on off-the-shelf clustering methods, RGWAS uses a novel decomposition, MFMR, to model covariates, binary traits, and population structure. We use extensive simulations to show that modelling these features can be crucial for power and calibration. We validate RGWAS in practice by recovering a recently discovered stress subtype in major depression. We then show the utility of RGWAS by identifying three novel subtypes of metabolic traits. We biologically validate these metabolic subtypes with SNP-level tests and a novel polygenic test: the former recover known metabolic GxE SNPs; the latter suggests subtypes may explain substantial missing heritability. Crucially, statins, which are widely prescribed and theorized to increase diabetes risk, have opposing effects on blood glucose across metabolic subtypes, suggesting the subtypes have potential translational value.
Collapse
Affiliation(s)
- Andy Dahl
- Department of Medicine, UCSF, San Francisco, California, United States of America
| | - Na Cai
- Wellcome Sanger Institute, Cambridge, United Kingdom
- European Bioinformatics Institute (EMBL-EBI), Cambridge, United Kingdom
| | - Arthur Ko
- Department of Human Genetics, David Geffen School of Medicine, UCLA, Los Angeles, California, United States of America
| | - Markku Laakso
- Institute of Clinical Medicine, Internal Medicine, University of Eastern Finland, Kuopio, Finland
- Kuopio University Hospital, Kuopio, Finland
| | - Päivi Pajukanta
- Department of Human Genetics, David Geffen School of Medicine, UCLA, Los Angeles, California, United States of America
| | - Jonathan Flint
- Center for Neurobehavioral Genetics, Semel Institute for Neuroscience and Human Behavior, UCLA, Los Angeles, California, United States of America
| | - Noah Zaitlen
- Department of Medicine, UCSF, San Francisco, California, United States of America
| |
Collapse
|
19
|
Pollack S, Igo RP, Jensen RA, Christiansen M, Li X, Cheng CY, Ng MCY, Smith AV, Rossin EJ, Segrè AV, Davoudi S, Tan GS, Chen YDI, Kuo JZ, Dimitrov LM, Stanwyck LK, Meng W, Hosseini SM, Imamura M, Nousome D, Kim J, Hai Y, Jia Y, Ahn J, Leong A, Shah K, Park KH, Guo X, Ipp E, Taylor KD, Adler SG, Sedor JR, Freedman BI, Lee IT, Sheu WHH, Kubo M, Takahashi A, Hadjadj S, Marre M, Tregouet DA, Mckean-Cowdin R, Varma R, McCarthy MI, Groop L, Ahlqvist E, Lyssenko V, Agardh E, Morris A, Doney ASF, Colhoun HM, Toppila I, Sandholm N, Groop PH, Maeda S, Hanis CL, Penman A, Chen CJ, Hancock H, Mitchell P, Craig JE, Chew EY, Paterson AD, Grassi MA, Palmer C, Bowden DW, Yaspan BL, Siscovick D, Cotch MF, Wang JJ, Burdon KP, Wong TY, Klein BEK, Klein R, Rotter JI, Iyengar SK, Price AL, Sobrin L. Multiethnic Genome-Wide Association Study of Diabetic Retinopathy Using Liability Threshold Modeling of Duration of Diabetes and Glycemic Control. Diabetes 2019; 68:441-456. [PMID: 30487263 PMCID: PMC6341299 DOI: 10.2337/db18-0567] [Citation(s) in RCA: 39] [Impact Index Per Article: 7.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/23/2018] [Accepted: 11/12/2018] [Indexed: 12/18/2022]
Abstract
To identify genetic variants associated with diabetic retinopathy (DR), we performed a large multiethnic genome-wide association study. Discovery included eight European cohorts (n = 3,246) and seven African American cohorts (n = 2,611). We meta-analyzed across cohorts using inverse-variance weighting, with and without liability threshold modeling of glycemic control and duration of diabetes. Variants with a P value <1 × 10-5 were investigated in replication cohorts that included 18,545 European, 16,453 Asian, and 2,710 Hispanic subjects. After correction for multiple testing, the C allele of rs142293996 in an intron of nuclear VCP-like (NVL) was associated with DR in European discovery cohorts (P = 2.1 × 10-9), but did not reach genome-wide significance after meta-analysis with replication cohorts. We applied the Disease Association Protein-Protein Link Evaluator (DAPPLE) to our discovery results to test for evidence of risk being spread across underlying molecular pathways. One protein-protein interaction network built from genes in regions associated with proliferative DR was found to have significant connectivity (P = 0.0009) and corroborated with gene set enrichment analyses. These findings suggest that genetic variation in NVL, as well as variation within a protein-protein interaction network that includes genes implicated in inflammation, may influence risk for DR.
Collapse
Affiliation(s)
- Samuela Pollack
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA
| | - Robert P Igo
- Department of Population and Quantitative Health Sciences, Case Western University, Cleveland, OH
| | - Richard A Jensen
- Cardiovascular Health Research Unit, Department of Medicine, Epidemiology and Health Services, University of Washington, Seattle, WA
| | - Mark Christiansen
- Cardiovascular Health Research Unit, Department of Medicine, Epidemiology and Health Services, University of Washington, Seattle, WA
| | - Xiaohui Li
- Institute for Translational Genomics and Population Sciences, LA BioMed and Department of Pediatrics, Harbor-UCLA Medical Center, Torrance, CA
| | - Ching-Yu Cheng
- Duke-NUS Medical School, Singapore
- Singapore Eye Research Institute, Singapore National Eye Centre, Singapore
| | - Maggie C Y Ng
- Center for Genomics and Personalized Medicine Research, Wake Forest School of Medicine, Winston-Salem, NC
- Department of Biochemistry, Wake Forest School of Medicine, Winston-Salem, NC
| | - Albert V Smith
- Department of Medicine, University of Iceland, Reykjavík, Iceland
| | - Elizabeth J Rossin
- Massachusetts Eye and Ear Department of Ophthalmology, Harvard Medical School, Boston, MA
| | - Ayellet V Segrè
- Massachusetts Eye and Ear Department of Ophthalmology, Harvard Medical School, Boston, MA
| | - Samaneh Davoudi
- Massachusetts Eye and Ear Department of Ophthalmology, Harvard Medical School, Boston, MA
| | - Gavin S Tan
- Duke-NUS Medical School, Singapore
- Singapore Eye Research Institute, Singapore National Eye Centre, Singapore
| | - Yii-Der Ida Chen
- Institute for Translational Genomics and Population Sciences, LA BioMed and Department of Pediatrics, Harbor-UCLA Medical Center, Torrance, CA
| | - Jane Z Kuo
- Institute for Translational Genomics and Population Sciences, LA BioMed and Department of Pediatrics, Harbor-UCLA Medical Center, Torrance, CA
- Medical Affairs, Ophthalmology, Sun Pharmaceutical Industries, Inc., Princeton, NJ
| | - Latchezar M Dimitrov
- Center for Genomics and Personalized Medicine Research, Wake Forest School of Medicine, Winston-Salem, NC
- Department of Biochemistry, Wake Forest School of Medicine, Winston-Salem, NC
| | - Lynn K Stanwyck
- Massachusetts Eye and Ear Department of Ophthalmology, Harvard Medical School, Boston, MA
| | - Weihua Meng
- Division of Population Health Sciences, Ninewells Hospital and Medical School, University of Dundee School of Medicine, Scotland, U.K
| | - S Mohsen Hosseini
- Program in Genetics and Genome Biology, The Hospital for Sick Children, Toronto, Ontario, Canada
| | - Minako Imamura
- Laboratory for Endocrinology, Metabolism and Kidney Diseases, RIKEN Center for Integrative Medical Sciences, Yokohama, Japan
- Department of Advanced Genomic and Laboratory Medicine, Graduate School of Medicine, University of the Ryukyus, Nishihara, Japan
- Division of Clinical Laboratory and Blood Transfusion, University of the Ryukyus Hospital, Nishihara, Japan
| | - Darryl Nousome
- Department of Preventive Medicine, Keck School of Medicine, University of Southern California, Los Angeles, CA
| | - Jihye Kim
- Human Genetics Center, School of Public Health, The University of Texas Health Science Center at Houston, Houston, TX
| | - Yang Hai
- Institute for Translational Genomics and Population Sciences, LA BioMed and Department of Pediatrics, Harbor-UCLA Medical Center, Torrance, CA
| | - Yucheng Jia
- Institute for Translational Genomics and Population Sciences, LA BioMed and Department of Pediatrics, Harbor-UCLA Medical Center, Torrance, CA
| | - Jeeyun Ahn
- Department of Ophthalmology, SMG-SNU Boramae Medical Center, Seoul National University College of Medicine, Seoul, Korea
| | - Aaron Leong
- Endocrine Unit and Diabetes Unit, Division of General Internal Medicine, Massachusetts General Hospital, Boston, MA
| | - Kaanan Shah
- Section of Genetic Medicine, University of Chicago, Chicago, IL
| | - Kyu Hyung Park
- Department of Ophthalmology, Seoul National University College of Medicine, Seoul National University Bundang Hospital, Seongnam, Korea
| | - Xiuqing Guo
- Institute for Translational Genomics and Population Sciences, LA BioMed and Department of Pediatrics, Harbor-UCLA Medical Center, Torrance, CA
| | - Eli Ipp
- Section of Diabetes and Metabolism, Harbor-UCLA Medical Center, University of California, Los Angeles, Los Angeles, CA
| | - Kent D Taylor
- Institute for Translational Genomics and Population Sciences, LA BioMed and Department of Pediatrics, Harbor-UCLA Medical Center, Torrance, CA
| | - Sharon G Adler
- Department of Nephrology and Hypertension, Los Angeles Biomedical Research Institute at Harbor-University of California, Torrance, CA
| | - John R Sedor
- Department of Medicine, Case Western Reserve University, Cleveland, OH
- Department of Physiology and Biophysics, Case Western Reserve University, Cleveland, OH
- Division of Nephrology, MetroHealth System, Cleveland, OH
| | - Barry I Freedman
- Section on Nephrology, Department of Internal Medicine, Wake Forest School of Medicine, Winston-Salem, NC
| | - I-Te Lee
- Division of Endocrinology and Metabolism, Department of Internal Medicine, Taichung Veterans General Hospital, Taichung, Taiwan
- School of Medicine, Chung Shan Medical University, Taichung, Taiwan
- School of Medicine, National Yang-Ming University, Taipei, Taiwan
| | - Wayne H-H Sheu
- Division of Endocrinology and Metabolism, Department of Internal Medicine, Taichung Veterans General Hospital, Taichung, Taiwan
- School of Medicine, Chung Shan Medical University, Taichung, Taiwan
- School of Medicine, National Yang-Ming University, Taipei, Taiwan
- School of Medicine, National Defense Medical Center, Taipei, Taiwan
| | - Michiaki Kubo
- RIKEN Center for Integrative Medical Sciences, Yokohama, Japan
| | - Atsushi Takahashi
- Laboratory for Statistical Analysis, RIKEN Center for Integrative Medical Sciences, Kanagawa, Japan
- Department of Genomic Medicine, Research Institute, National Cerebral and Cardiovascular Center, Osaka, Japan
| | - Samy Hadjadj
- CHU de Poitiers, Centre d'Investigation Clinique, Poitiers, France
- Université de Poitiers, UFR Médecine Pharmacie, Centre d'Investigation Clinique 1402, Poitiers, France
- INSERM, Centre d'Investigation Clinique 1402, Poitiers, France
- L'Institut du Thorax, INSERM, CNRS, CHU Nantes, Nantes, France
| | - Michel Marre
- Université Paris Diderot, Sorbonne Paris Cité, Paris, France
- Department of Diabetology, Endocrinology and Nutrition, Assistance Publique-Hôpitaux de Paris, Bichat Hospital, DHU FIRE, Paris, France
- INSERM U1138, Centre de Recherche des Cordeliers, Paris, France
| | - David-Alexandre Tregouet
- Team Genomics & Pathophysiology of Cardiovascular Diseases, UPMC, Sorbonne Universités, INSERM, UMR_S 1166, Paris, France
- Institute of Cardiometabolism and Nutrition, Paris, France
| | - Roberta Mckean-Cowdin
- Department of Preventive Medicine, Keck School of Medicine, University of Southern California, Los Angeles, CA
- Department of Ophthalmology, USC Roski Eye Institute, Keck School of Medicine of the University of Southern California, Los Angeles, CA
| | - Rohit Varma
- Department of Preventive Medicine, Keck School of Medicine, University of Southern California, Los Angeles, CA
- Department of Ophthalmology, USC Roski Eye Institute, Keck School of Medicine of the University of Southern California, Los Angeles, CA
| | - Mark I McCarthy
- Oxford Centre for Diabetes, Endocrinology and Metabolism, University of Oxford, Churchill Hospital, Oxford, U.K
- Wellcome Centre for Human Genetics, University of Oxford, Oxford, U.K
- NIHR Oxford Biomedical Research Centre, Churchill Hospital, Oxford, U.K
| | - Leif Groop
- Department of Clinical Sciences, Faculty of Medicine, Lund University, Malmö, Sweden
| | - Emma Ahlqvist
- Department of Clinical Sciences, Faculty of Medicine, Lund University, Malmö, Sweden
| | - Valeriya Lyssenko
- Department of Clinical Sciences, Faculty of Medicine, Lund University, Malmö, Sweden
- Department of Clinical Science, KG Jebsen Center for Diabetes Research, University of Bergen, Bergen, Norway
| | - Elisabet Agardh
- Department of Clinical Sciences, Faculty of Medicine, Lund University, Malmö, Sweden
| | - Andrew Morris
- Usher Institute of Population Health Sciences and Informatics, University of Edinburgh, Edinburgh, U.K
| | - Alex S F Doney
- Molecular and Clinical Medicine, Ninewells Hospital and Medical School, University of Dundee, Dundee, U.K
| | - Helen M Colhoun
- Institute of Genetics and Molecular Medicine, Western General Hospital, University of Edinburgh, Edinburgh, U.K
| | - Iiro Toppila
- Folkhälsan Institute of Genetics, Folkhälsan Research Center, Helsinki, Finland
- Abdominal Center, Nephrology, University of Helsinki and Helsinki University Hospital, Helsinki, Finland
- Research Programs Unit, Diabetes and Obesity, University of Helsinki, Helsinki, Finland
| | - Niina Sandholm
- Folkhälsan Institute of Genetics, Folkhälsan Research Center, Helsinki, Finland
- Abdominal Center, Nephrology, University of Helsinki and Helsinki University Hospital, Helsinki, Finland
- Research Programs Unit, Diabetes and Obesity, University of Helsinki, Helsinki, Finland
| | - Per-Henrik Groop
- Folkhälsan Institute of Genetics, Folkhälsan Research Center, Helsinki, Finland
- Abdominal Center, Nephrology, University of Helsinki and Helsinki University Hospital, Helsinki, Finland
- Research Programs Unit, Diabetes and Obesity, University of Helsinki, Helsinki, Finland
- Department of Diabetes, Central Clinical School, Monash University, Melbourne, Victoria, Australia
| | - Shiro Maeda
- Laboratory for Endocrinology, Metabolism and Kidney Diseases, RIKEN Center for Integrative Medical Sciences, Yokohama, Japan
- Department of Advanced Genomic and Laboratory Medicine, Graduate School of Medicine, University of the Ryukyus, Nishihara, Japan
- Division of Clinical Laboratory and Blood Transfusion, University of the Ryukyus Hospital, Nishihara, Japan
| | - Craig L Hanis
- Human Genetics Center, School of Public Health, The University of Texas Health Science Center at Houston, Houston, TX
| | - Alan Penman
- Department of Preventive Medicine, John D. Bower School of Population Health, University of Mississippi Medical Center, Jackson, MS
| | - Ching J Chen
- Department of Ophthalmology, University of Mississippi Medical Center, Jackson, MS
| | - Heather Hancock
- Department of Ophthalmology, University of Mississippi Medical Center, Jackson, MS
| | - Paul Mitchell
- Centre for Vision Research, Westmead Institute for Medical Research, The University of Sydney, Sydney, New South Wales, Australia
| | - Jamie E Craig
- Department of Ophthalmology, Flinders University, Bedford Park, South Australia, Australia
| | - Emily Y Chew
- Division of Epidemiology and Clinical Applications, National Eye Institute, National Institutes of Health, Bethesda, MD
| | - Andrew D Paterson
- Institute of Medical Sciences, University of Toronto, Toronto, Ontario, Canada
- Program in Genetics & Genome Biology, Hospital for Sick Children, Toronto, Ontario, Canada
- Epidemiology and Biostatistics, Dalla Lana School of Public Health, University of Toronto, Toronto, Ontario, Canada
| | - Michael A Grassi
- Grassi Retina, Naperville, IL
- Department of Ophthalmology and Visual Sciences, University of Illinois at Chicago, Chicago, IL
| | - Colin Palmer
- Pat MacPherson Centre for Pharmacogenetics and Pharmacogenomics, Ninewells Hospital and Medical School, University of Dundee, Dundee, U.K
| | - Donald W Bowden
- Center for Genomics and Personalized Medicine Research, Wake Forest School of Medicine, Winston-Salem, NC
- Department of Biochemistry, Wake Forest School of Medicine, Winston-Salem, NC
| | | | - David Siscovick
- Institute for Urban Health, New York Academy of Medicine, New York, NY
| | - Mary Frances Cotch
- Division of Epidemiology and Clinical Applications, National Eye Institute, National Institutes of Health, Bethesda, MD
| | - Jie Jin Wang
- Duke-NUS Medical School, Singapore
- Centre for Vision Research, Westmead Institute for Medical Research, The University of Sydney, Sydney, New South Wales, Australia
| | - Kathryn P Burdon
- Menzies Institute for Medical Research, University of Tasmania, Hobart, Tasmania, Australia
| | - Tien Y Wong
- Duke-NUS Medical School, Singapore
- Department of Ophthalmology, Yong Loo Lin School of Medicine, National University of Singapore, Singapore
| | - Barbara E K Klein
- Department of Ophthalmology and Visual Sciences, University of Wisconsin-Madison, Madison, WI
| | - Ronald Klein
- Department of Ophthalmology and Visual Sciences, University of Wisconsin-Madison, Madison, WI
| | - Jerome I Rotter
- Institute for Translational Genomics and Population Sciences, LA BioMed and Department of Pediatrics, Harbor-UCLA Medical Center, Torrance, CA
| | - Sudha K Iyengar
- Department of Population and Quantitative Health Sciences, Case Western University, Cleveland, OH
| | - Alkes L Price
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA
| | - Lucia Sobrin
- Massachusetts Eye and Ear Department of Ophthalmology, Harvard Medical School, Boston, MA
| |
Collapse
|
20
|
Banerjee S, Zeng L, Schunkert H, Söding J. Bayesian multiple logistic regression for case-control GWAS. PLoS Genet 2018; 14:e1007856. [PMID: 30596640 PMCID: PMC6329526 DOI: 10.1371/journal.pgen.1007856] [Citation(s) in RCA: 21] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/02/2018] [Revised: 01/11/2019] [Accepted: 11/28/2018] [Indexed: 12/21/2022] Open
Abstract
Genetic variants in genome-wide association studies (GWAS) are tested for disease association mostly using simple regression, one variant at a time. Standard approaches to improve power in detecting disease-associated SNPs use multiple regression with Bayesian variable selection in which a sparsity-enforcing prior on effect sizes is used to avoid overtraining and all effect sizes are integrated out for posterior inference. For binary traits, the logistic model has not yielded clear improvements over the linear model. For multi-SNP analysis, the logistic model required costly and technically challenging MCMC sampling to perform the integration. Here, we introduce the quasi-Laplace approximation to solve the integral and avoid MCMC sampling. We expect the logistic model to perform much better than multiple linear regression except when predicted disease risks are spread closely around 0.5, because only close to its inflection point can the logistic function be well approximated by a linear function. Indeed, in extensive benchmarks with simulated phenotypes and real genotypes, our Bayesian multiple LOgistic REgression method (B-LORE) showed considerable improvements (1) when regressing on many variants in multiple loci at heritabilities ≥ 0.4 and (2) for unbalanced case-control ratios. B-LORE also enables meta-analysis by approximating the likelihood functions of individual studies by multivariate normal distributions, using their means and covariance matrices as summary statistics. Our work should make sparse multiple logistic regression attractive also for other applications with binary target variables. B-LORE is freely available from: https://github.com/soedinglab/b-lore.
Collapse
Affiliation(s)
- Saikat Banerjee
- Max Planck Institute for Biophysical Chemistry, Göttingen, Germany
| | | | | | - Johannes Söding
- Max Planck Institute for Biophysical Chemistry, Göttingen, Germany
- * E-mail:
| |
Collapse
|
21
|
Abstract
A genome-wide association study (GWAS) seeks to identify genetic variants that contribute to the development and progression of a specific disease. Over the past 10 years, new approaches using mixed models have emerged to mitigate the deleterious effects of population structure and relatedness in association studies. However, developing GWAS techniques to accurately test for association while correcting for population structure is a computational and statistical challenge. Using laboratory mouse strains as an example, our review characterizes the problem of population structure in association studies and describes how it can cause false positive associations. We then motivate mixed models in the context of unmodeled factors.
Collapse
Affiliation(s)
- Jae Hoon Sul
- Department of Psychiatry and Biobehavioral Sciences, University of California Los Angeles, Los Angeles, California, United States of America
| | - Lana S. Martin
- Department of Computer Science, University of California, Los Angeles, California, United States of America
| | - Eleazar Eskin
- Department of Computer Science, University of California, Los Angeles, California, United States of America
- Department of Human Genetics, University of California Los Angeles, Los Angeles, California, United States of America
- * E-mail:
| |
Collapse
|
22
|
Zhang H, Chatterjee N, Rader D, Chen J. Adjustment of nonconfounding covariates in case-control genetic association studies. Ann Appl Stat 2018. [DOI: 10.1214/17-aoas1065] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/19/2022]
|
23
|
Traylor M, Malik R, Nalls MA, Cotlarciuc I, Radmanesh F, Thorleifsson G, Hanscombe KB, Langefeld C, Saleheen D, Rost NS, Yet I, Spector TD, Bell JT, Hannon E, Mill J, Chauhan G, Debette S, Bis JC, Longstreth WT, Ikram MA, Launer LJ, Seshadri S, Hamilton-Bruce MA, Jimenez-Conde J, Cole JW, Schmidt R, Słowik A, Lemmens R, Lindgren A, Melander O, Grewal RP, Sacco RL, Rundek T, Rexrode K, Arnett DK, Johnson JA, Benavente OR, Wasssertheil-Smoller S, Lee JM, Pulit SL, Wong Q, Rich SS, de Bakker PIW, McArdle PF, Woo D, Anderson CD, Xu H, Heitsch L, Fornage M, Jern C, Stefansson K, Thorsteinsdottir U, Gretarsdottir S, Lewis CM, Sharma P, Sudlow CLM, Rothwell PM, Boncoraglio GB, Thijs V, Levi C, Meschia JF, Rosand J, Kittner SJ, Mitchell BD, Dichgans M, Worrall BB, Markus HS. Genetic variation at 16q24.2 is associated with small vessel stroke. Ann Neurol 2017; 81:383-394. [PMID: 27997041 PMCID: PMC5366092 DOI: 10.1002/ana.24840] [Citation(s) in RCA: 63] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/10/2016] [Revised: 12/02/2016] [Accepted: 12/03/2016] [Indexed: 02/03/2023]
Abstract
Objective Genome‐wide association studies (GWAS) have been successful at identifying associations with stroke and stroke subtypes, but have not yet identified any associations solely with small vessel stroke (SVS). SVS comprises one quarter of all ischemic stroke and is a major manifestation of cerebral small vessel disease, the primary cause of vascular cognitive impairment. Studies across neurological traits have shown that younger‐onset cases have an increased genetic burden. We leveraged this increased genetic burden by performing an age‐at‐onset informed GWAS meta‐analysis, including a large younger‐onset SVS population, to identify novel associations with stroke. Methods We used a three‐stage age‐at‐onset informed GWAS to identify novel genetic variants associated with stroke. On identifying a novel locus associated with SVS, we assessed its influence on other small vessel disease phenotypes, as well as on messenger RNA (mRNA) expression of nearby genes, and on DNA methylation of nearby CpG sites in whole blood and in the fetal brain. Results We identified an association with SVS in 4,203 cases and 50,728 controls on chromosome 16q24.2 (odds ratio [OR; 95% confidence interval {CI}] = 1.16 [1.10–1.22]; p = 3.2 × 10−9). The lead single‐nucleotide polymorphism (rs12445022) was also associated with cerebral white matter hyperintensities (OR [95% CI] = 1.10 [1.05–1.16]; p = 5.3 × 10−5; N = 3,670), but not intracerebral hemorrhage (OR [95% CI] = 0.97 [0.84–1.12]; p = 0.71; 1,545 cases, 1,481 controls). rs12445022 is associated with mRNA expression of ZCCHC14 in arterial tissues (p = 9.4 × 10−7) and DNA methylation at probe cg16596957 in whole blood (p = 5.3 × 10−6). Interpretation 16q24.2 is associated with SVS. Associations of the locus with expression of ZCCHC14 and DNA methylation suggest the locus acts through changes to regulatory elements. Ann Neurol 2017;81:383–394
Collapse
Affiliation(s)
- Matthew Traylor
- Department of Medical and Molecular Genetics, King's College London, London, United Kingdom
| | - Rainer Malik
- Institute for Stroke and Dementia Research, Klinikum der Universität München, Ludwig-Maximilians University, Munich, Germany
| | - Mike A Nalls
- Laboratory of Neurogenetics, National Institute on Aging, Bethesda, MD
| | - Ioana Cotlarciuc
- Institute of Cardiovascular Research Royal Holloway University of London (ICR2UL), London, United Kingdom
| | - Farid Radmanesh
- Division of Neurocritical Care and Emergency Neurology, Massachusetts General Hospital, Boston, MA.,J. Philip Kistler Stroke Research Center, Department of Neurology, Massachusetts General Hospital, Boston, MA.,Center for Human Genetic Research, Massachusetts General Hospital, Boston, MA
| | | | - Ken B Hanscombe
- Department of Medical and Molecular Genetics, King's College London, London, United Kingdom
| | - Carl Langefeld
- Center for Public Health Genomics and Department of Biostatistical Sciences, Wake Forest School of Medicine, Winston-Salem, NC
| | - Danish Saleheen
- Department of Genetics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA
| | - Natalia S Rost
- J. Philip Kistler Stroke Research Center, Department of Neurology, Massachusetts General Hospital, Boston, MA
| | - Idil Yet
- Department of Twin Research & Genetic Epidemiology, King's College London, London, United Kingdom
| | - Tim D Spector
- Department of Twin Research & Genetic Epidemiology, King's College London, London, United Kingdom
| | - Jordana T Bell
- Department of Twin Research & Genetic Epidemiology, King's College London, London, United Kingdom
| | - Eilis Hannon
- University of Exeter Medical School, University of Exeter, Exeter, United Kingdom
| | - Jonathan Mill
- University of Exeter Medical School, University of Exeter, Exeter, United Kingdom.,Social, Genetic and Developmental Psychiatry Center, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom
| | - Ganesh Chauhan
- Inserm Research Center for Epidemiology and Biostatistics (U897)-Team Neuroepidemiology, Bordeaux, France.,University of Bordeaux, Bordeaux, France
| | - Stephanie Debette
- Inserm Research Center for Epidemiology and Biostatistics (U897)-Team Neuroepidemiology, Bordeaux, France.,University of Bordeaux, Bordeaux, France
| | - Joshua C Bis
- Cardiovascular Health Research Unit, Department of Medicine, University of Washington, Seattle, WA
| | - W T Longstreth
- Departments of Neurology and Epidemiology, University of Washington, Seattle, WA
| | - M Arfan Ikram
- Department of Neurology, Epidemiology and Radiology, Erasmus MC University Medical Center, Rotterdam, The Netherlands
| | - Lenore J Launer
- Laboratory of Epidemiology and Population Sciences, National Institute on Aging, Bethesda, MD
| | - Sudha Seshadri
- Boston University School of Medicine, Boston, MA.,Framingham Heart Study, Framingham, MA
| | | | | | - Jordi Jimenez-Conde
- Neurovascular Research Group (NEUVAS), Neurology Department, Institut Hospital del Mar d'Investigació Mèdica, Barcelona, Spain
| | - John W Cole
- Department of Neurology, University of Maryland School of Medicine and Baltimore VAMC, Baltimore, MD
| | - Reinhold Schmidt
- Department of Neurology, Clinical Division of Neurogeriatrics, Medical University Graz, Graz, Austria
| | - Agnieszka Słowik
- Department of Neurology, Jagiellonian University, Krakow, Poland
| | - Robin Lemmens
- KU Leuven-University of Leuven, Department of Neurosciences, Experimental Neurology and Leuven Research Institute for Neuroscience and Disease (LIND), Leuven, Belgium.,VIB, Vesalius Research Center, Laboratory of Neurobiology, Department of Neurology, Leuven, Belgium.,University Hospitals Leuven, Department of Neurology, Leuven, Belgium
| | - Arne Lindgren
- Department of Clinical Sciences Lund, Neurology, Lund University, Lund, Sweden.,Department of Neurology and Rehabilitation Medicine, Skåne University Hospital, Lund, Sweden
| | - Olle Melander
- Department of Clinical Sciences, Lund University, Malmö, Sweden
| | - Raji P Grewal
- Neuroscience Institute, Saint Francis Medical Center, School of Health and Medical Sciences, Seton Hall University, South Orange, NJ
| | - Ralph L Sacco
- Department of Neurology, Miller School of Medicine, University of Miami, Miami, FL
| | - Tatjana Rundek
- Department of Neurology, Miller School of Medicine, University of Miami, Miami, FL
| | - Kathryn Rexrode
- Harvard Medical School, Boston, MA, Center for Faculty Development and Diversity, Brigham and Women's Hospital, Boston, MA
| | - Donna K Arnett
- College of Public Health, University of Kentucky, Lexington, KY
| | - Julie A Johnson
- Department of Pharmacotherapy and Translational Research and Center for Pharmacogenomics, University of Florida, College of Pharmacy, Gainesville, FL.,Division of Cardiovascular Medicine, College of Medicine, University of Florida, Gainesville, FL
| | - Oscar R Benavente
- Department of Neurology, University of British Columbia, Vancouver, British Columbia, Canada
| | | | - Jin-Moo Lee
- Stroke Center, Department of Neurology, Washington University School of Medicine, Seattle, WA
| | - Sara L Pulit
- Department of Medical Genetics, University Medical Center Utrecht, Utrecht, The Netherlands
| | - Quenna Wong
- Department of Biostatistics, University of Washington, Seattle, WA
| | - Stephen S Rich
- Center for Public Health Genomics, University of Virginia School of Medicine, Charlottesville, VA
| | - Paul I W de Bakker
- Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht, The Netherlands.,Department of Medical Genetics, Center for Molecular Medicine, University Medical Center Utrecht, Utrecht, The Netherlands
| | - Patrick F McArdle
- Department of Medicine, University of Maryland School of Medicine, MD
| | - Daniel Woo
- University of Cincinnati College of Medicine, Cincinnati, OH
| | - Christopher D Anderson
- Division of Neurocritical Care and Emergency Neurology, Massachusetts General Hospital, Boston, MA.,J. Philip Kistler Stroke Research Center, Department of Neurology, Massachusetts General Hospital, Boston, MA.,Center for Human Genetic Research, Massachusetts General Hospital, Boston, MA.,Program in Medical and Population Genetics, Broad Institute, Boston, MA
| | - Huichun Xu
- Division of Endocrinology, Diabetes and Nutrition, University of Maryland School of Medicine, Baltimore, MD
| | - Laura Heitsch
- Division of Emergency Medicine, Washington University School of Medicine, St Louis, MO
| | - Myriam Fornage
- The University of Texas Health Science Center at Houston, Houston, TX
| | - Christina Jern
- Institute of Biomedicine, the Sahlgrenska Academy at University of Gothenburg, Gothenburg, Sweden
| | - Kari Stefansson
- deCODE genetics/AMGEN, Reykjavik, Iceland.,Faculty of Medicine, University of Iceland, Reykjavik, Iceland
| | - Unnur Thorsteinsdottir
- deCODE genetics/AMGEN, Reykjavik, Iceland.,Faculty of Medicine, University of Iceland, Reykjavik, Iceland
| | - Solveig Gretarsdottir
- Center for Public Health Genomics and Department of Biostatistical Sciences, Wake Forest School of Medicine, Winston-Salem, NC
| | - Cathryn M Lewis
- Department of Medical and Molecular Genetics, King's College London, London, United Kingdom.,Social, Genetic and Developmental Psychiatry Center, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom
| | - Pankaj Sharma
- Institute of Cardiovascular Research Royal Holloway University of London (ICR2UL), London, United Kingdom
| | - Cathie L M Sudlow
- Center for Clinical Brain Sciences & Institute of Genetics and Molecular Medicine, University of Edinburgh, Edinburgh, United Kingdom
| | - Peter M Rothwell
- Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, United Kingdom
| | - Giorgio B Boncoraglio
- Department of Cerebrovascular Diseases, Fondazione IRCCS Istituto Neurologico "Carlo Besta", Milano, Italy
| | - Vincent Thijs
- Framingham Heart Study, Framingham, MA.,Department of Neurology, Royal Adelaide Hospital, Adelaide, South Australia, Australia.,Department of Neurology, Austin Health and Florey Institute of Neuroscience and Mental Health, Heidelberg, Australia
| | - Chris Levi
- John Hunter Hospital, Hunter Medical Research Institute and University of Newcastle, Newcastle, NSW, Australia
| | - James F Meschia
- Department of Neurology, Mayo Clinic Jacksonville, Jacksonville, FL
| | - Jonathan Rosand
- Division of Neurocritical Care and Emergency Neurology, Massachusetts General Hospital, Boston, MA.,J. Philip Kistler Stroke Research Center, Department of Neurology, Massachusetts General Hospital, Boston, MA.,Center for Human Genetic Research, Massachusetts General Hospital, Boston, MA.,University of Cincinnati College of Medicine, Cincinnati, OH
| | - Steven J Kittner
- Department of Neurology, University of Maryland School of Medicine and Baltimore VAMC, Baltimore, MD
| | - Braxton D Mitchell
- Department of Medicine, University of Maryland School of Medicine, Baltimore, MD.,Geriatrics Research and Education Clinical Center, Baltimore Veterans Administration Medical Center, Baltimore, MD
| | - Martin Dichgans
- Institute for Stroke and Dementia Research, Klinikum der Universität München, Ludwig-Maximilians University, Munich, Germany.,Munich Cluster of Systems Neurology, SyNergy, Munich, Germany
| | - Bradford B Worrall
- Departments of Neurology and Public Health Sciences, University of Virginia School of Medicine, Charlottesville, VA
| | - Hugh S Markus
- Stroke Research Group, Division of Clinical Neurosciences, University of Cambridge, Cambridge, United Kingdom
| | | |
Collapse
|
24
|
Genome-wide Association for Major Depression Through Age at Onset Stratification: Major Depressive Disorder Working Group of the Psychiatric Genomics Consortium. Biol Psychiatry 2017; 81:325-335. [PMID: 27519822 PMCID: PMC5262436 DOI: 10.1016/j.biopsych.2016.05.010] [Citation(s) in RCA: 134] [Impact Index Per Article: 19.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/01/2015] [Revised: 03/26/2016] [Accepted: 05/05/2016] [Indexed: 01/06/2023]
Abstract
BACKGROUND Major depressive disorder (MDD) is a disabling mood disorder, and despite a known heritable component, a large meta-analysis of genome-wide association studies revealed no replicable genetic risk variants. Given prior evidence of heterogeneity by age at onset in MDD, we tested whether genome-wide significant risk variants for MDD could be identified in cases subdivided by age at onset. METHODS Discovery case-control genome-wide association studies were performed where cases were stratified using increasing/decreasing age-at-onset cutoffs; significant single nucleotide polymorphisms were tested in nine independent replication samples, giving a total sample of 22,158 cases and 133,749 control subjects for subsetting. Polygenic score analysis was used to examine whether differences in shared genetic risk exists between earlier and adult-onset MDD with commonly comorbid disorders of schizophrenia, bipolar disorder, Alzheimer's disease, and coronary artery disease. RESULTS We identified one replicated genome-wide significant locus associated with adult-onset (>27 years) MDD (rs7647854, odds ratio: 1.16, 95% confidence interval: 1.11-1.21, p = 5.2 × 10-11). Using polygenic score analyses, we show that earlier-onset MDD is genetically more similar to schizophrenia and bipolar disorder than adult-onset MDD. CONCLUSIONS We demonstrate that using additional phenotype data previously collected by genetic studies to tackle phenotypic heterogeneity in MDD can successfully lead to the discovery of genetic risk factor despite reduced sample size. Furthermore, our results suggest that the genetic susceptibility to MDD differs between adult- and earlier-onset MDD, with earlier-onset cases having a greater genetic overlap with schizophrenia and bipolar disorder.
Collapse
|
25
|
Mez J, Chung J, Jun G, Kriegel J, Bourlas AP, Sherva R, Logue MW, Barnes LL, Bennett DA, Buxbaum JD, Byrd GS, Crane PK, Ertekin-Taner N, Evans D, Fallin MD, Foroud T, Goate A, Graff-Radford NR, Hall KS, Kamboh MI, Kukull WA, Larson EB, Manly JJ, Haines JL, Mayeux R, Pericak-Vance MA, Schellenberg GD, Lunetta KL, Farrer LA. Two novel loci, COBL and SLC10A2, for Alzheimer's disease in African Americans. Alzheimers Dement 2017; 13:119-129. [PMID: 27770636 PMCID: PMC5318231 DOI: 10.1016/j.jalz.2016.09.002] [Citation(s) in RCA: 64] [Impact Index Per Article: 9.1] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2016] [Revised: 08/24/2016] [Accepted: 09/09/2016] [Indexed: 11/28/2022]
Abstract
INTRODUCTION African Americans' (AAs) late-onset Alzheimer's disease (LOAD) genetic risk profile is incompletely understood. Including clinical covariates in genetic analyses using informed conditioning might improve study power. METHODS We conducted a genome-wide association study (GWAS) in AAs employing informed conditioning in 1825 LOAD cases and 3784 cognitively normal controls. We derived a posterior liability conditioned on age, sex, diabetes status, current smoking status, educational attainment, and affection status, with parameters informed by external prevalence information. We assessed association between the posterior liability and a genome-wide set of single-nucleotide polymorphisms (SNPs), controlling for APOE and ABCA7, identified previously in a LOAD GWAS of AAs. RESULTS Two SNPs at novel loci, rs112404845 (P = 3.8 × 10-8), upstream of COBL, and rs16961023 (P = 4.6 × 10-8), downstream of SLC10A2, obtained genome-wide significant evidence of association with the posterior liability. DISCUSSION An informed conditioning approach can detect LOAD genetic associations in AAs not identified by traditional GWAS.
Collapse
Affiliation(s)
- Jesse Mez
- Department of Neurology, Boston University School of Medicine, Boston, MA, USA; Alzheimer's Disease and CTE Center, Boston University School of Medicine, Boston, MA, USA.
| | - Jaeyoon Chung
- Department of Medicine (Biomedical Genetics), Boston University School of Medicine, Boston, MA, USA
| | - Gyungah Jun
- Department of Medicine (Biomedical Genetics), Boston University School of Medicine, Boston, MA, USA; Department of Biostatistics, Boston University School of Public Health, Boston, MA, USA; Department of Ophthalmology, Boston University School of Medicine, Boston, MA, USA
| | - Joshua Kriegel
- Department of Neurology, Boston University School of Medicine, Boston, MA, USA; Alzheimer's Disease and CTE Center, Boston University School of Medicine, Boston, MA, USA
| | - Alexandra P Bourlas
- Department of Neurology, Boston University School of Medicine, Boston, MA, USA; Alzheimer's Disease and CTE Center, Boston University School of Medicine, Boston, MA, USA
| | - Richard Sherva
- Department of Medicine (Biomedical Genetics), Boston University School of Medicine, Boston, MA, USA
| | - Mark W Logue
- Department of Medicine (Biomedical Genetics), Boston University School of Medicine, Boston, MA, USA; Department of Biostatistics, Boston University School of Public Health, Boston, MA, USA
| | - Lisa L Barnes
- Department of Neurological Sciences, Rush University Medical Center, Chicago, IL, USA
| | - David A Bennett
- Department of Neurological Sciences, Rush University Medical Center, Chicago, IL, USA; Rush Alzheimer's Disease Center, Rush University Medical Center, Chicago, IL, USA
| | - Joseph D Buxbaum
- Departments of Neuroscience, Mount Sinai School of Medicine, New York, NY, USA; Department of Genetics and Genomic Sciences, Mount Sinai School of Medicine, New York, NY, USA
| | - Goldie S Byrd
- Department of Biology, North Carolina A & T State University, Greensboro, NC, USA
| | - Paul K Crane
- Department of Medicine, University of Washington, Seattle, WA, USA
| | | | - Denis Evans
- Department of Neurological Sciences, Rush University Medical Center, Chicago, IL, USA
| | - M Daniele Fallin
- Department of Mental Health, Johns Hopkins School of Public Health, Baltimore, MD, USA; Department of Biostatistics, Johns Hopkins School of Public Health, Baltimore, MD, USA; Department of Epidemiology, Johns Hopkins School of Public Health, Baltimore, MD, USA
| | - Tatiana Foroud
- Department of Medical and Molecular Genetics, Indiana University School of Medicine, Indianapolis, IN, USA
| | - Alison Goate
- Departments of Neuroscience, Mount Sinai School of Medicine, New York, NY, USA; Department of Genetics and Genomic Sciences, Mount Sinai School of Medicine, New York, NY, USA
| | | | - Kathleen S Hall
- Department of Psychiatry, Indiana University School of Medicine, Indianapolis, IN, USA
| | - M Ilyas Kamboh
- Department of Human Genetics, University of Pittsburgh, Pittsburgh, PA, USA
| | - Walter A Kukull
- Department of Epidemiology, University of Washington, Seattle, WA, USA
| | - Eric B Larson
- Group Health, Group Health Research Institute, Seattle, WA, USA
| | - Jennifer J Manly
- Department of Neurology and the Taub Institute, Columbia University, New York, NY, USA
| | - Jonathan L Haines
- Department of Epidemiology and Biostatistics, Case Western Reserve University, Cleveland, OH, USA
| | - Richard Mayeux
- Department of Neurology and the Taub Institute, Columbia University, New York, NY, USA
| | | | - Gerard D Schellenberg
- Department of Pathology and Laboratory Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Kathryn L Lunetta
- Department of Biostatistics, Boston University School of Public Health, Boston, MA, USA
| | - Lindsay A Farrer
- Department of Neurology, Boston University School of Medicine, Boston, MA, USA; Alzheimer's Disease and CTE Center, Boston University School of Medicine, Boston, MA, USA; Department of Medicine (Biomedical Genetics), Boston University School of Medicine, Boston, MA, USA; Department of Biostatistics, Boston University School of Public Health, Boston, MA, USA; Department of Ophthalmology, Boston University School of Medicine, Boston, MA, USA; Department of Epidemiology, Boston University School of Public Health, Boston, MA, USA
| |
Collapse
|
26
|
Simultaneous Modeling of Disease Status and Clinical Phenotypes To Increase Power in Genome-Wide Association Studies. Genetics 2017; 205:1041-1047. [PMID: 28132020 DOI: 10.1534/genetics.116.198473] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/17/2016] [Accepted: 12/19/2016] [Indexed: 11/18/2022] Open
Abstract
Genome-wide association studies have identified thousands of variants implicated in dozens of complex diseases. Most studies collect individuals with and without disease and search for variants with different frequencies between the groups. For many of these studies, additional disease traits are also collected. Jointly modeling clinical phenotype and disease status is a promising way to increase power to detect true associations between genetics and disease. In particular, this approach increases the potential for discovering genetic variants that are associated with both a clinical phenotype and a disease. Standard multivariate techniques fail to effectively solve this problem, because their case-control status is discrete and not continuous. Standard approaches to estimate model parameters are biased due to the ascertainment in case-control studies. We present a novel method that resolves both of these issues for simultaneous association testing of genetic variants that have both case status and a clinical covariate. We demonstrate the utility of our method using both simulated data and the Northern Finland Birth Cohort data.
Collapse
|
27
|
Hayeck TJ, Loh PR, Pollack S, Gusev A, Patterson N, Zaitlen NA, Price AL. Mixed Model Association with Family-Biased Case-Control Ascertainment. Am J Hum Genet 2017; 100:31-39. [PMID: 28017371 DOI: 10.1016/j.ajhg.2016.11.015] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2016] [Accepted: 11/08/2016] [Indexed: 01/06/2023] Open
Abstract
Mixed models have become the tool of choice for genetic association studies; however, standard mixed model methods may be poorly calibrated or underpowered under family sampling bias and/or case-control ascertainment. Previously, we introduced a liability threshold-based mixed model association statistic (LTMLM) to address case-control ascertainment in unrelated samples. Here, we consider family-biased case-control ascertainment, where case and control subjects are ascertained non-randomly with respect to family relatedness. Previous work has shown that this type of ascertainment can severely bias heritability estimates; we show here that it also impacts mixed model association statistics. We introduce a family-based association statistic (LT-Fam) that is robust to this problem. Similar to LTMLM, LT-Fam is computed from posterior mean liabilities (PML) under a liability threshold model; however, LT-Fam uses published narrow-sense heritability estimates to avoid the problem of biased heritability estimation, enabling correct calibration. In simulations with family-biased case-control ascertainment, LT-Fam was correctly calibrated (average χ2 = 1.00-1.02 for null SNPs), whereas the Armitage trend test (ATT), standard mixed model association (MLM), and case-control retrospective association test (CARAT) were mis-calibrated (e.g., average χ2 = 0.50-1.22 for MLM, 0.89-2.65 for CARAT). LT-Fam also attained higher power than other methods in some settings. In 1,259 type 2 diabetes-affected case subjects and 5,765 control subjects from the CARe cohort, downsampled to induce family-biased ascertainment, LT-Fam was correctly calibrated whereas ATT, MLM, and CARAT were again mis-calibrated. Our results highlight the importance of modeling family sampling bias in case-control datasets with related samples.
Collapse
|
28
|
Genetic Mechanisms Leading to Sex Differences Across Common Diseases and Anthropometric Traits. Genetics 2016; 205:979-992. [PMID: 27974502 DOI: 10.1534/genetics.116.193623] [Citation(s) in RCA: 45] [Impact Index Per Article: 5.6] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/08/2016] [Accepted: 12/08/2016] [Indexed: 01/10/2023] Open
Abstract
Common diseases often show sex differences in prevalence, onset, symptomology, treatment, or prognosis. Although studies have been performed to evaluate sex differences at specific SNP associations, this work aims to comprehensively survey a number of complex heritable diseases and anthropometric traits. Potential genetically encoded sex differences we investigated include differential genetic liability thresholds or distributions, gene-sex interaction at autosomal loci, major contribution of the X-chromosome, or gene-environment interactions reflected in genes responsive to androgens or estrogens. Finally, we tested the overlap between sex-differential association with anthropometric traits and disease risk. We utilized complementary approaches of assessing GWAS association enrichment and SNP-based heritability estimation to explore explicit sex differences, as well as enrichment in sex-implicated functional categories. We do not find consistent increased genetic load in the lower-prevalence sex, or a disproportionate role for the X-chromosome in disease risk, despite sex-heterogeneity on the X for several traits. We find that all anthropometric traits show less than complete correlation between the genetic contribution to males and females, and find a convincing example of autosome-wide genome-sex interaction in multiple sclerosis (P = 1 × 10-9). We also find some evidence for hormone-responsive gene enrichment, and striking evidence of the contribution of sex-differential anthropometric associations to common disease risk, implying that general mechanisms of sexual dimorphism determining secondary sex characteristics have shared effects on disease risk.
Collapse
|
29
|
Yung G, Lin X. Validity of using ad hoc methods to analyze secondary traits in case-control association studies. Genet Epidemiol 2016; 40:732-743. [PMID: 27670932 DOI: 10.1002/gepi.21994] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/11/2015] [Revised: 06/23/2016] [Accepted: 06/26/2016] [Indexed: 11/10/2022]
Abstract
Case-control association studies often collect from their subjects information on secondary phenotypes. Reusing the data and studying the association between genes and secondary phenotypes provide an attractive and cost-effective approach that can lead to discovery of new genetic associations. A number of approaches have been proposed, including simple and computationally efficient ad hoc methods that ignore ascertainment or stratify on case-control status. Justification for these approaches relies on the assumption of no covariates and the correct specification of the primary disease model as a logistic model. Both might not be true in practice, for example, in the presence of population stratification or the primary disease model following a probit model. In this paper, we investigate the validity of ad hoc methods in the presence of covariates and possible disease model misspecification. We show that in taking an ad hoc approach, it may be desirable to include covariates that affect the primary disease in the secondary phenotype model, even though these covariates are not necessarily associated with the secondary phenotype. We also show that when the disease is rare, ad hoc methods can lead to severely biased estimation and inference if the true disease model follows a probit model instead of a logistic model. Our results are justified theoretically and via simulations. Applied to real data analysis of genetic associations with cigarette smoking, ad hoc methods collectively identified as highly significant (P<10-5) single nucleotide polymorphisms from over 10 genes, genes that were identified in previous studies of smoking cessation.
Collapse
Affiliation(s)
- Godwin Yung
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, Massachusetts, United States of America
| | - Xihong Lin
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, Massachusetts, United States of America
| |
Collapse
|
30
|
Local Joint Testing Improves Power and Identifies Hidden Heritability in Association Studies. Genetics 2016; 203:1105-16. [PMID: 27182951 DOI: 10.1534/genetics.116.188292] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/17/2016] [Accepted: 04/27/2016] [Indexed: 12/19/2022] Open
Abstract
There is mounting evidence that complex human phenotypes are highly polygenic, with many loci harboring multiple causal variants, yet most genetic association studies examine each SNP in isolation. While this has led to the discovery of thousands of disease associations, discovered variants account for only a small fraction of disease heritability. Alternative multi-SNP methods have been proposed, but issues such as multiple-testing correction, sensitivity to genotyping error, and optimization for the underlying genetic architectures remain. Here we describe a local joint-testing procedure, complete with multiple-testing correction, that leverages a genetic phenomenon we call linkage masking wherein linkage disequilibrium between SNPs hides their signal under standard association methods. We show that local joint testing on the original Wellcome Trust Case Control Consortium (WTCCC) data set leads to the discovery of 22 associated loci, 5 more than the marginal approach. These loci were later found in follow-up studies containing thousands of additional individuals. We find that these loci significantly increase the heritability explained by genome-wide significant associations in the WTCCC data set. Furthermore, we show that local joint testing in a cis-expression QTL (eQTL) study of the gEUVADIS data set increases the number of genes containing significant eQTL by 10.7% over marginal analyses. Our multiple-hypothesis correction and joint-testing framework are available in a python software package called Jester, available at github.com/brielin/Jester.
Collapse
|
31
|
Rakitsch B, Stegle O. Modelling local gene networks increases power to detect trans-acting genetic effects on gene expression. Genome Biol 2016; 17:33. [PMID: 26911988 PMCID: PMC4765046 DOI: 10.1186/s13059-016-0895-2] [Citation(s) in RCA: 22] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/19/2015] [Accepted: 02/09/2016] [Indexed: 01/05/2023] Open
Abstract
Expression quantitative trait loci (eQTL) mapping is a widely used tool to study the genetics of gene expression. Confounding factors and the burden of multiple testing limit the ability to map distal trans eQTLs, which is important to understand downstream genetic effects on genes and pathways. We propose a two-stage linear mixed model that first learns local directed gene-regulatory networks to then condition on the expression levels of selected genes. We show that this covariate selection approach controls for confounding factors and regulatory context, thereby increasing eQTL detection power and improving the consistency between studies. GNet-LMM is available at: https://github.com/PMBio/GNetLMM.
Collapse
Affiliation(s)
- Barbara Rakitsch
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge, UK.
| | - Oliver Stegle
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge, UK.
| |
Collapse
|
32
|
A Novel Test for Detecting SNP-SNP Interactions in Case-Only Trio Studies. Genetics 2016; 202:1289-97. [PMID: 26865367 DOI: 10.1534/genetics.115.179846] [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: 06/23/2015] [Accepted: 01/27/2016] [Indexed: 02/06/2023] Open
Abstract
Epistasis plays a significant role in the genetic architecture of many complex phenotypes in model organisms. To date, there have been very few interactions replicated in human studies due in part to the multiple-hypothesis burden implicit in genome-wide tests of epistasis. Therefore, it is of paramount importance to develop the most powerful tests possible for detecting interactions. In this work we develop a new SNP-SNP interaction test for use in case-only trio studies called the trio correlation (TC) test. The TC test computes the expected joint distribution of marker pairs in offspring conditional on parental genotypes. This distribution is then incorporated into a standard 1 d.f. correlation test of interaction. We show via extensive simulations under a variety of disease models that our test substantially outperforms existing tests of interaction in case-only trio studies. We also demonstrate a bias in a previous case-only trio interaction test and identify its origin. Finally, we show that a previously proposed permutation scheme in trio studies mitigates the known biases of case-only tests in the presence of population stratification. We conclude that the TC test shows improved power to identify interactions in existing, as well as emerging, trio association studies. The method is publicly available at www.github.com/BrunildaBalliu/TrioEpi.
Collapse
|
33
|
Mefford JA, Zaitlen NA, Witte JS. Comment: A Human Genetics Perspective. J Am Stat Assoc 2016. [DOI: 10.1080/01621459.2016.1149404] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/21/2022]
|
34
|
Waszczuk MA, Zavos HMS, Gregory AM, Eley TC. The stability and change of etiological influences on depression, anxiety symptoms and their co-occurrence across adolescence and young adulthood. Psychol Med 2016; 46:161-75. [PMID: 26310536 PMCID: PMC4673666 DOI: 10.1017/s0033291715001634] [Citation(s) in RCA: 34] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/01/2015] [Revised: 07/29/2015] [Accepted: 08/03/2015] [Indexed: 12/19/2022]
Abstract
BACKGROUND Depression and anxiety persist within and across diagnostic boundaries. The manner in which common v. disorder-specific genetic and environmental influences operate across development to maintain internalizing disorders and their co-morbidity is unclear. This paper investigates the stability and change of etiological influences on depression, panic, generalized, separation and social anxiety symptoms, and their co-occurrence, across adolescence and young adulthood. METHOD A total of 2619 twins/siblings prospectively reported symptoms of depression and anxiety at mean ages 15, 17 and 20 years. RESULTS Each symptom scale showed a similar pattern of moderate continuity across development, largely underpinned by genetic stability. New genetic influences contributing to change in the developmental course of the symptoms emerged at each time point. All symptom scales correlated moderately with one another over time. Genetic influences, both stable and time-specific, overlapped considerably between the scales. Non-shared environmental influences were largely time- and symptom-specific, but some contributed moderately to the stability of depression and anxiety symptom scales. These stable, longitudinal environmental influences were highly correlated between the symptoms. CONCLUSIONS The results highlight both stable and dynamic etiology of depression and anxiety symptom scales. They provide preliminary evidence that stable as well as newly emerging genes contribute to the co-morbidity between depression and anxiety across adolescence and young adulthood. Conversely, environmental influences are largely time-specific and contribute to change in symptoms over time. The results inform molecular genetics research and transdiagnostic treatment and prevention approaches.
Collapse
Affiliation(s)
- M. A. Waszczuk
- King's College London, MRC
Social, Genetic and Developmental Psychiatry Centre, Institute of
Psychiatry, Psychology and Neuroscience, London,
UK
| | - H. M. S. Zavos
- King's College London, MRC
Social, Genetic and Developmental Psychiatry Centre, Institute of
Psychiatry, Psychology and Neuroscience, London,
UK
| | - A. M. Gregory
- Department of Psychology,
Goldsmiths, University of London,
London, UK
| | - T. C. Eley
- King's College London, MRC
Social, Genetic and Developmental Psychiatry Centre, Institute of
Psychiatry, Psychology and Neuroscience, London,
UK
| |
Collapse
|
35
|
Variation in predictive ability of common genetic variants by established strata: the example of breast cancer and age. Epidemiology 2015; 26:51-8. [PMID: 25380502 DOI: 10.1097/ede.0000000000000195] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/31/2022]
Abstract
BACKGROUND Recent studies of breast cancer and common genetic markers have failed to identify pervasive gene-gene and gene-environment interactions. Theoretical considerations also suggest that the contribution of modest interactions to risk discrimination in the general population is likely small. However, the clinical utility of common breast cancer risk markers may nonetheless differ across strata defined by known risk factors, such as age. METHODS We examined the age-specific per-allele odds ratios of 15 common single nucleotide polymorphisms (SNPs) found to be associated with breast cancer in 1142 breast cancer cases and 1145 controls from the Nurses' Health Study. We calculated the age-specific discriminatory ability of risk models incorporating these SNPs. We then conducted simulation studies to explore how hypothetical underlying genetic models may fit the observed results. RESULTS Although all individual SNP-by-age interactions were modest, we found a negative interaction effect between age and a genetic risk score defined by the sum of risk alleles (P = 0.04). We also observed a decrease in discriminatory ability, as measured by the area under the curve (AUC), of the SNPs with age (P = 0.04). Simulation studies revealed models where the AUC can differ by strata defined by a risk factor without the presence of interactions; however, our study suggests that the observed differences in AUC are explained by the age-specific effect of the SNPs. CONCLUSION The identification of risk factors that alter the effect of multiple genetic variants can help to explain the genetic architecture of multifactorial diseases and identify subgroups of persons who may benefit from genetic screening.
Collapse
|
36
|
Shah S, Bonder MJ, Marioni RE, Zhu Z, McRae AF, Zhernakova A, Harris SE, Liewald D, Henders AK, Mendelson MM, Liu C, Joehanes R, Liang L, Levy D, Martin NG, Starr JM, Wijmenga C, Wray NR, Yang J, Montgomery GW, Franke L, Deary IJ, Visscher PM. Improving Phenotypic Prediction by Combining Genetic and Epigenetic Associations. Am J Hum Genet 2015; 97:75-85. [PMID: 26119815 PMCID: PMC4572498 DOI: 10.1016/j.ajhg.2015.05.014] [Citation(s) in RCA: 83] [Impact Index Per Article: 9.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2015] [Accepted: 05/21/2015] [Indexed: 01/23/2023] Open
Abstract
We tested whether DNA-methylation profiles account for inter-individual variation in body mass index (BMI) and height and whether they predict these phenotypes over and above genetic factors. Genetic predictors were derived from published summary results from the largest genome-wide association studies on BMI (n ∼ 350,000) and height (n ∼ 250,000) to date. We derived methylation predictors by estimating probe-trait effects in discovery samples and tested them in external samples. Methylation profiles associated with BMI in older individuals from the Lothian Birth Cohorts (LBCs, n = 1,366) explained 4.9% of the variation in BMI in Dutch adults from the LifeLines DEEP study (n = 750) but did not account for any BMI variation in adolescents from the Brisbane Systems Genetic Study (BSGS, n = 403). Methylation profiles based on the Dutch sample explained 4.9% and 3.6% of the variation in BMI in the LBCs and BSGS, respectively. Methylation profiles predicted BMI independently of genetic profiles in an additive manner: 7%, 8%, and 14% of variance of BMI in the LBCs were explained by the methylation predictor, the genetic predictor, and a model containing both, respectively. The corresponding percentages for LifeLines DEEP were 5%, 9%, and 13%, respectively, suggesting that the methylation profiles represent environmental effects. The differential effects of the BMI methylation profiles by age support previous observations of age modulation of genetic contributions. In contrast, methylation profiles accounted for almost no variation in height, consistent with a mainly genetic contribution to inter-individual variation. The BMI results suggest that combining genetic and epigenetic information might have greater utility for complex-trait prediction.
Collapse
Affiliation(s)
- Sonia Shah
- Queensland Brain Institute, University of Queensland, Brisbane 4072, Australia; University of Queensland Diamantina Institute, Translational Research Institute, University of Queensland, Brisbane, QLD 4072, Australia
| | - Marc J Bonder
- Department of Genetics, University Medical Center Groningen, University of Groningen, Groningen 9713 AV, the Netherlands
| | - Riccardo E Marioni
- Queensland Brain Institute, University of Queensland, Brisbane 4072, Australia; Centre for Cognitive Ageing and Cognitive Epidemiology, University of Edinburgh, Edinburgh EH8 9JZ, UK; Medical Genetics Section, Centre for Genomic and Experimental Medicine, Institute of Genetics and Molecular Medicine, University of Edinburgh, Edinburgh EH4 2XU, UK
| | - Zhihong Zhu
- Queensland Brain Institute, University of Queensland, Brisbane 4072, Australia
| | - Allan F McRae
- Queensland Brain Institute, University of Queensland, Brisbane 4072, Australia; University of Queensland Diamantina Institute, Translational Research Institute, University of Queensland, Brisbane, QLD 4072, Australia
| | - Alexandra Zhernakova
- Department of Genetics, University Medical Center Groningen, University of Groningen, Groningen 9713 AV, the Netherlands
| | - Sarah E Harris
- Centre for Cognitive Ageing and Cognitive Epidemiology, University of Edinburgh, Edinburgh EH8 9JZ, UK; Medical Genetics Section, Centre for Genomic and Experimental Medicine, Institute of Genetics and Molecular Medicine, University of Edinburgh, Edinburgh EH4 2XU, UK
| | - Dave Liewald
- Centre for Cognitive Ageing and Cognitive Epidemiology, University of Edinburgh, Edinburgh EH8 9JZ, UK
| | - Anjali K Henders
- Queensland Institute of Medical Research Berghofer Medical Research Institute, Brisbane, QLD 4029, Australia
| | - Michael M Mendelson
- Framingham Heart Study and Boston University School of Medicine, Boston, MA 01702, USA; Department of Cardiology, Boston Children's Hospital, Boston, MA 02115, USA; Population Studies Branch, National Heart, Lung, and Blood Institute, NIH, Bethesda, MD 20892-7936, USA
| | - Chunyu Liu
- Department of Biostatistics, Boston University, Boston, MA 02118, USA
| | - Roby Joehanes
- Hebrew Senior Life, Harvard Medical School, Boston, MA 02131, USA
| | - Liming Liang
- Departments of Epidemiology and Biostatistics, T.H. Chan School of Public Health, Harvard University, Boston, MA 02115, USA
| | - Daniel Levy
- Population Studies Branch, National Heart, Lung, and Blood Institute, NIH, Bethesda, MD 20892-7936, USA
| | - Nicholas G Martin
- Queensland Institute of Medical Research Berghofer Medical Research Institute, Brisbane, QLD 4029, Australia
| | - John M Starr
- Centre for Cognitive Ageing and Cognitive Epidemiology, University of Edinburgh, Edinburgh EH8 9JZ, UK; Department of Psychology, University of Edinburgh, Edinburgh EH8 9JZ, UK
| | - Cisca Wijmenga
- Department of Genetics, University Medical Center Groningen, University of Groningen, Groningen 9713 AV, the Netherlands
| | - Naomi R Wray
- Queensland Brain Institute, University of Queensland, Brisbane 4072, Australia
| | - Jian Yang
- Queensland Brain Institute, University of Queensland, Brisbane 4072, Australia
| | - Grant W Montgomery
- Queensland Institute of Medical Research Berghofer Medical Research Institute, Brisbane, QLD 4029, Australia
| | - Lude Franke
- Department of Genetics, University Medical Center Groningen, University of Groningen, Groningen 9713 AV, the Netherlands
| | - Ian J Deary
- Centre for Cognitive Ageing and Cognitive Epidemiology, University of Edinburgh, Edinburgh EH8 9JZ, UK; Department of Psychology, University of Edinburgh, Edinburgh EH8 9JZ, UK
| | - Peter M Visscher
- Queensland Brain Institute, University of Queensland, Brisbane 4072, Australia; University of Queensland Diamantina Institute, Translational Research Institute, University of Queensland, Brisbane, QLD 4072, Australia; Centre for Cognitive Ageing and Cognitive Epidemiology, University of Edinburgh, Edinburgh EH8 9JZ, UK.
| |
Collapse
|
37
|
Han SS, Rosenberg PS, Ghosh A, Landi MT, Caporaso NE, Chatterjee N. An exposure-weighted score test for genetic associations integrating environmental risk factors. Biometrics 2015; 71:596-605. [PMID: 26134142 DOI: 10.1111/biom.12328] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/01/2013] [Revised: 02/01/2015] [Accepted: 03/01/2015] [Indexed: 11/30/2022]
Abstract
Current methods for detecting genetic associations lack full consideration of the background effects of environmental exposures. Recently proposed methods to account for environmental exposures have focused on logistic regressions with gene-environment interactions. In this report, we developed a test for genetic association, encompassing a broad range of risk models, including linear, logistic and probit, for specifying joint effects of genetic and environmental exposures. We obtained the test statistics by maximizing over a class of score tests, each of which involves modified standard tests of genetic association through a weight function. This weight function reflects the potential heterogeneity of the genetic effects by levels of environmental exposures under a particular model. Simulation studies demonstrate the robust power of these methods for detecting genetic associations under a wide range of scenarios. Applications of these methods are further illustrated using data from genome-wide association studies of type 2 diabetes with body mass index and of lung cancer risk with smoking.
Collapse
Affiliation(s)
- Summer S Han
- Department of Radiology, Stanford University School of Medicine, Palo Alto, California 94305, U.S.A
| | - Philip S Rosenberg
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Department of Health and Human Services, 9609 Medical Center Drive Suite, Rockville, Bethesda, Maryland 20852, U.S.A
| | - Arpita Ghosh
- Public Health Foundation of India, Vasant Kunj, New Delhi 110070, India
| | - Maria Teresa Landi
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Department of Health and Human Services, 9609 Medical Center Drive Suite, Rockville, Bethesda, Maryland 20852, U.S.A
| | - Neil E Caporaso
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Department of Health and Human Services, 9609 Medical Center Drive Suite, Rockville, Bethesda, Maryland 20852, U.S.A
| | - Nilanjan Chatterjee
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Department of Health and Human Services, 9609 Medical Center Drive Suite, Rockville, Bethesda, Maryland 20852, U.S.A
| |
Collapse
|
38
|
Hayeck T, Zaitlen N, Loh PR, Vilhjalmsson B, Pollack S, Gusev A, Yang J, Chen GB, Goddard M, Visscher P, Patterson N, Price A. Mixed model with correction for case-control ascertainment increases association power. Am J Hum Genet 2015; 96:720-30. [PMID: 25892111 DOI: 10.1016/j.ajhg.2015.03.004] [Citation(s) in RCA: 37] [Impact Index Per Article: 4.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/21/2014] [Accepted: 03/05/2015] [Indexed: 01/06/2023] Open
Abstract
We introduce a liability-threshold mixed linear model (LTMLM) association statistic for case-control studies and show that it has a well-controlled false-positive rate and more power than existing mixed-model methods for diseases with low prevalence. Existing mixed-model methods suffer a loss in power under case-control ascertainment, but no solution has been proposed. Here, we solve this problem by using a χ(2) score statistic computed from posterior mean liabilities (PMLs) under the liability-threshold model. Each individual's PML is conditional not only on that individual's case-control status but also on every individual's case-control status and the genetic relationship matrix (GRM) obtained from the data. The PMLs are estimated with a multivariate Gibbs sampler; the liability-scale phenotypic covariance matrix is based on the GRM, and a heritability parameter is estimated via Haseman-Elston regression on case-control phenotypes and then transformed to the liability scale. In simulations of unrelated individuals, the LTMLM statistic was correctly calibrated and achieved higher power than existing mixed-model methods for diseases with low prevalence, and the magnitude of the improvement depended on sample size and severity of case-control ascertainment. In a Wellcome Trust Case Control Consortium 2 multiple sclerosis dataset with >10,000 samples, LTMLM was correctly calibrated and attained a 4.3% improvement (p = 0.005) in χ(2) statistics over existing mixed-model methods at 75 known associated SNPs, consistent with simulations. Larger increases in power are expected at larger sample sizes. In conclusion, case-control studies of diseases with low prevalence can achieve power higher than that in existing mixed-model methods.
Collapse
|
39
|
Accurate liability estimation improves power in ascertained case-control studies. Nat Methods 2015; 12:332-4. [PMID: 25664543 DOI: 10.1038/nmeth.3285] [Citation(s) in RCA: 34] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/05/2014] [Accepted: 12/18/2014] [Indexed: 12/31/2022]
Abstract
Linear mixed models (LMMs) have emerged as the method of choice for confounded genome-wide association studies. However, the performance of LMMs in nonrandomly ascertained case-control studies deteriorates with increasing sample size. We propose a framework called LEAP (liability estimator as a phenotype; https://github.com/omerwe/LEAP) that tests for association with estimated latent values corresponding to severity of phenotype, and we demonstrate that this can lead to a substantial power increase.
Collapse
|
40
|
Aschard H, Vilhjálmsson B, Joshi A, Price A, Kraft P. Adjusting for heritable covariates can bias effect estimates in genome-wide association studies. Am J Hum Genet 2015; 96:329-39. [PMID: 25640676 DOI: 10.1016/j.ajhg.2014.12.021] [Citation(s) in RCA: 175] [Impact Index Per Article: 19.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/28/2014] [Accepted: 12/17/2014] [Indexed: 11/25/2022] Open
Abstract
In recent years, a number of large-scale genome-wide association studies have been published for human traits adjusted for other correlated traits with a genetic basis. In most studies, the motivation for such an adjustment is to discover genetic variants associated with the primary outcome independently of the correlated trait. In this report, we contend that this objective is fulfilled when the tested variants have no effect on the covariate or when the correlation between the covariate and the outcome is fully explained by a direct effect of the covariate on the outcome. For all other scenarios, an unintended bias is introduced with respect to the primary outcome as a result of the adjustment, and this bias might lead to false positives. Here, we illustrate this point by providing examples from published genome-wide association studies, including large meta-analysis of waist-to-hip ratio and waist circumference adjusted for body mass index (BMI), where genetic effects might be biased as a result of adjustment for body mass index. Using both theory and simulations, we explore this phenomenon in detail and discuss the ramifications for future genome-wide association studies of correlated traits and diseases.
Collapse
|
41
|
Bowes J, Budu-Aggrey A, Huffmeier U, Uebe S, Steel K, Hebert HL, Wallace C, Massey J, Bruce IN, Bluett J, Feletar M, Morgan AW, Marzo-Ortega H, Donohoe G, Morris DW, Helliwell P, Ryan AW, Kane D, Warren RB, Korendowych E, Alenius GM, Giardina E, Packham J, McManus R, FitzGerald O, McHugh N, Brown MA, Ho P, Behrens F, Burkhardt H, Reis A, Barton A. Dense genotyping of immune-related susceptibility loci reveals new insights into the genetics of psoriatic arthritis. Nat Commun 2015; 6:6046. [PMID: 25651891 PMCID: PMC4327416 DOI: 10.1038/ncomms7046] [Citation(s) in RCA: 115] [Impact Index Per Article: 12.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/04/2014] [Accepted: 12/04/2014] [Indexed: 12/30/2022] Open
Abstract
Psoriatic arthritis (PsA) is a chronic inflammatory arthritis associated with psoriasis and, despite the larger estimated heritability for PsA, the majority of genetic susceptibility loci identified to date are shared with psoriasis. Here, we present results from a case-control association study on 1,962 PsA patients and 8,923 controls using the Immunochip genotyping array. We identify eight loci passing genome-wide significance, secondary independent effects at three loci and a distinct PsA-specific variant at the IL23R locus. We report two novel loci and evidence of a novel PsA-specific association at chromosome 5q31. Imputation of classical HLA alleles, amino acids and SNPs across the MHC region highlights three independent associations to class I genes. Finally, we find an enrichment of associated variants to markers of open chromatin in CD8(+) memory primary T cells. This study identifies key insights into the genetics of PsA that could begin to explain fundamental differences between psoriasis and PsA.
Collapse
MESH Headings
- Adult
- Aged
- Aged, 80 and over
- Alleles
- Arthritis, Psoriatic/genetics
- Arthritis, Psoriatic/immunology
- Arthritis, Psoriatic/metabolism
- Arthritis, Psoriatic/pathology
- CD4-Positive T-Lymphocytes/immunology
- CD4-Positive T-Lymphocytes/metabolism
- CD4-Positive T-Lymphocytes/pathology
- CD8-Positive T-Lymphocytes/immunology
- CD8-Positive T-Lymphocytes/metabolism
- CD8-Positive T-Lymphocytes/pathology
- Case-Control Studies
- Chromatin/chemistry
- Chromatin/immunology
- Chromosomes, Human, Pair 5
- Female
- Genetic Predisposition to Disease
- Genotype
- Genotyping Techniques
- Histocompatibility Antigens Class I/genetics
- Histocompatibility Antigens Class I/immunology
- Humans
- Immunologic Memory
- Male
- Microarray Analysis
- Middle Aged
- Polymorphism, Single Nucleotide
- Psoriasis/genetics
- Psoriasis/immunology
- Psoriasis/metabolism
- Psoriasis/pathology
- Quantitative Trait Loci/immunology
- Receptors, Interleukin/genetics
- Receptors, Interleukin/immunology
Collapse
Affiliation(s)
- John Bowes
- Arthritis Research UK Centre for Genetics and Genomics, The University of Manchester, Manchester M13 9PT, UK
| | - Ashley Budu-Aggrey
- Arthritis Research UK Centre for Genetics and Genomics, The University of Manchester, Manchester M13 9PT, UK
- NIHR Manchester Musculoskeletal Biomedical Research Unit, Central Manchester Foundation Trust and University of Manchester, Manchester Academy of Health Sciences, Manchester M13 9WU, UK
| | - Ulrike Huffmeier
- Institute of Human Genetics, University of Erlangen-Nuremberg, Erlangen 91054, Germany
| | - Steffen Uebe
- Institute of Human Genetics, University of Erlangen-Nuremberg, Erlangen 91054, Germany
| | - Kathryn Steel
- Arthritis Research UK Centre for Genetics and Genomics, The University of Manchester, Manchester M13 9PT, UK
| | - Harry L. Hebert
- Arthritis Research UK Centre for Genetics and Genomics, The University of Manchester, Manchester M13 9PT, UK
- The Dermatology Centre, Salford Royal NHS Foundation Trust, University of Manchester, Manchester Academic Health Science Centre, Manchester M6 8HD, UK
| | - Chris Wallace
- JDRF/Wellcome Trust Diabetes and Inflammation Laboratory, Department of Medical Genetics, NIHR Cambridge Biomedical Research Centre, Cambridge Institute for Medical Research, University of Cambridge, Wellcome Trust/MRC Building, Cambridge Biomedical Campus, Cambridge CB2 0XY, UK
- Centre for Biostatistics, Institute of Population Health, The University of Manchester, Jean McFarlane Building, Oxford Road, Manchester M13 9PL, UK
| | - Jonathan Massey
- Arthritis Research UK Centre for Genetics and Genomics, The University of Manchester, Manchester M13 9PT, UK
| | - Ian N. Bruce
- Arthritis Research UK Centre for Genetics and Genomics, The University of Manchester, Manchester M13 9PT, UK
- The Kellgren Centre for Rheumatology, Central Manchester Foundation Trust, NIHR Manchester Biomedical Research Centre, Manchester M13 9WL, UK
| | - James Bluett
- Arthritis Research UK Centre for Genetics and Genomics, The University of Manchester, Manchester M13 9PT, UK
- The Kellgren Centre for Rheumatology, Central Manchester Foundation Trust, NIHR Manchester Biomedical Research Centre, Manchester M13 9WL, UK
| | - Marie Feletar
- Monash University, Melbourne, Victoria 3800, Australia
| | - Ann W. Morgan
- NIHR-Leeds Musculoskeletal Biomedical Research Unit, Leeds Institute of Molecular Medicine, University of Leeds, Leeds LS7 4SA, UK
| | - Helena Marzo-Ortega
- NIHR-Leeds Musculoskeletal Biomedical Research Unit, Leeds Institute of Molecular Medicine, University of Leeds, Leeds LS7 4SA, UK
| | - Gary Donohoe
- CogGene Group, Discipline of Biochemistry and School of Psychology, National University of Ireland, Galway, Ireland
| | - Derek W. Morris
- CogGene Group, Discipline of Biochemistry and School of Psychology, National University of Ireland, Galway, Ireland
| | - Philip Helliwell
- NIHR-Leeds Musculoskeletal Biomedical Research Unit, Leeds Institute of Molecular Medicine, University of Leeds, Leeds LS7 4SA, UK
| | - Anthony W. Ryan
- Department of Clinical Medicine, Institute of Molecular Medicine, Trinity College Dublin, Dublin 8, Ireland
| | - David Kane
- Adelaide and Meath Hospital and Trinity College Dublin, Dublin 24, Ireland
| | - Richard B. Warren
- The Dermatology Centre, Salford Royal NHS Foundation Trust, University of Manchester, Manchester Academic Health Science Centre, Manchester M6 8HD, UK
| | - Eleanor Korendowych
- Royal National Hospital for Rheumatic Diseases and Department of Pharmacy and Pharmacology, University of Bath, Bath BA1 1RL, UK
| | - Gerd-Marie Alenius
- Department of Public Health and Clinical Medicine, Rheumatology, University Hospital, Umeå 901 87, Sweden
| | - Emiliano Giardina
- Department of Biopathology, Centre of Excellence for Genomic Risk Assessment in Multifactorial and Complex Diseases, School of Medicine, University of Rome ‘Tor Vergata’ and Fondazione PTV ‘Policlinico Tor Vergata’, Rome 18-00173, Italy
| | - Jonathan Packham
- Rheumatology Department, Haywood Hospital, Health Services Research Unit, Institute of Science and Technology in Medicine, Keele University, Keele ST5 5BG, UK
| | - Ross McManus
- Department of Clinical Medicine, Institute of Molecular Medicine, Trinity College Dublin, Dublin 8, Ireland
| | - Oliver FitzGerald
- Department of Rheumatology, St. Vincent’s University Hospital, UCD School of Medicine and Medical Sciences and Conway Institute of Biomolecular and Biomedical Research, University College Dublin, Dublin 4, Ireland
| | - Neil McHugh
- Royal National Hospital for Rheumatic Diseases and Department of Pharmacy and Pharmacology, University of Bath, Bath BA1 1RL, UK
| | - Matthew A. Brown
- The University of Queensland Diamantina Institute, Translational Research Institute, Princess Alexandra Hospital, Brisbane, Queensland QLD 4102, Australia
| | - Pauline Ho
- Arthritis Research UK Centre for Genetics and Genomics, The University of Manchester, Manchester M13 9PT, UK
- The Kellgren Centre for Rheumatology, Central Manchester Foundation Trust, NIHR Manchester Biomedical Research Centre, Manchester M13 9WL, UK
| | - Frank Behrens
- Division of Rheumatology and Fraunhofer IME-Project-Group Translational Medicine and Pharmacology, Goethe University, Frankfurt 60590, Germany
| | - Harald Burkhardt
- Division of Rheumatology and Fraunhofer IME-Project-Group Translational Medicine and Pharmacology, Goethe University, Frankfurt 60590, Germany
| | - Andre Reis
- Institute of Human Genetics, University of Erlangen-Nuremberg, Erlangen 91054, Germany
| | - Anne Barton
- Arthritis Research UK Centre for Genetics and Genomics, The University of Manchester, Manchester M13 9PT, UK
- NIHR Manchester Musculoskeletal Biomedical Research Unit, Central Manchester Foundation Trust and University of Manchester, Manchester Academy of Health Sciences, Manchester M13 9WU, UK
| |
Collapse
|
42
|
Balliu B, Tsonaka R, Boehringer S, Houwing-Duistermaat J. A retrospective likelihood approach for efficient integration of multiple omics factors in case-control association studies. Genet Epidemiol 2015; 39:156-65. [PMID: 25620726 DOI: 10.1002/gepi.21884] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/26/2014] [Revised: 10/08/2014] [Accepted: 12/02/2014] [Indexed: 11/09/2022]
Abstract
Integrative omics, the joint analysis of outcome and multiple types of omics data, such as genomics, epigenomics, and transcriptomics data, constitute a promising approach for powerful and biologically relevant association studies. These studies often employ a case-control design, and often include nonomics covariates, such as age and gender, that may modify the underlying omics risk factors. An open question is how to best integrate multiple omics and nonomics information to maximize statistical power in case-control studies that ascertain individuals based on the phenotype. Recent work on integrative omics have used prospective approaches, modeling case-control status conditional on omics, and nonomics risk factors. Compared to univariate approaches, jointly analyzing multiple risk factors with a prospective approach increases power in nonascertained cohorts. However, these prospective approaches often lose power in case-control studies. In this article, we propose a novel statistical method for integrating multiple omics and nonomics factors in case-control association studies. Our method is based on a retrospective likelihood function that models the joint distribution of omics and nonomics factors conditional on case-control status. The new method provides accurate control of Type I error rate and has increased efficiency over prospective approaches in both simulated and real data.
Collapse
Affiliation(s)
- Brunilda Balliu
- Department of Medical Statistics and Bioinformatics, Leiden University Medical Center, The Netherlands
| | | | | | | |
Collapse
|
43
|
Qin J, Zhang H, Li P, Albanes D, Yu K. Using covariate-specific disease prevalence information to increase the power of case-control studies. Biometrika 2014. [DOI: 10.1093/biomet/asu048] [Citation(s) in RCA: 28] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/01/2023] Open
|
44
|
Golan D, Rosset S. Effective genetic-risk prediction using mixed models. Am J Hum Genet 2014; 95:383-93. [PMID: 25279982 PMCID: PMC4185122 DOI: 10.1016/j.ajhg.2014.09.007] [Citation(s) in RCA: 41] [Impact Index Per Article: 4.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/13/2014] [Accepted: 09/12/2014] [Indexed: 12/24/2022] Open
Abstract
For predicting genetic risk, we propose a statistical approach that is specifically adapted to dealing with the challenges imposed by disease phenotypes and case-control sampling. Our approach (termed Genetic Risk Scores Inference [GeRSI]), combines the power of fixed-effects models (which estimate and aggregate the effects of single SNPs) and random-effects models (which rely primarily on whole-genome similarities between individuals) within the framework of the widely used liability-threshold model. We demonstrate in extensive simulation that GeRSI produces predictions that are consistently superior to current state-of-the-art approaches. When applying GeRSI to seven phenotypes from the Wellcome Trust Case Control Consortium (WTCCC) study, we confirm that the use of random effects is most beneficial for diseases that are known to be highly polygenic: hypertension (HT) and bipolar disorder (BD). For HT, there are no significant associations in the WTCCC data. The fixed-effects model yields an area under the ROC curve (AUC) of 54%, whereas GeRSI improves it to 59%. For BD, using GeRSI improves the AUC from 55% to 62%. For individuals ranked at the top 10% of BD risk predictions, using GeRSI substantially increases the BD relative risk from 1.4 to 2.5.
Collapse
Affiliation(s)
- David Golan
- Department of Statistics, Tel Aviv University, Tel Aviv 69978, Israel.
| | - Saharon Rosset
- Department of Statistics, Tel Aviv University, Tel Aviv 69978, Israel.
| |
Collapse
|
45
|
Homogeneous case subgroups increase power in genetic association studies. Eur J Hum Genet 2014; 23:863-9. [PMID: 25271086 DOI: 10.1038/ejhg.2014.194] [Citation(s) in RCA: 21] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/25/2014] [Revised: 07/16/2014] [Accepted: 08/20/2014] [Indexed: 12/12/2022] Open
Abstract
Genome-wide association studies of clinically defined cases against controls have transformed our understanding of the genetic causes of many diseases. However, there are limitations to the simple clinical definitions used in these studies, and GWAS analyses are beginning to explore more refined phenotypes in subgroups of the existing data sets. These analyses are often performed ad hoc without considering the power requirements to justify such analyses. Here we derive expressions for the relative power of such subgroup analyses and determine the genotypic relative risks (GRRs) required to achieve equivalent power to a full analysis for relevant scenarios. We show that only modest increases in GRRs may be required to offset the reduction in power from analysing fewer cases, implying that analyses of more genetically homogenous case subgroups may have the potential to identify further associations. We find that, for lower genotypic relative risks in the full sample, subgroup analyses of more homogeneous cases have relatively more power than for higher index genotypic relative risks and that this effect is stronger for rare as opposed to common variants. As GWA studies are likely to have now identified the majority of SNPs with stronger effects, these results strongly advocate a renewed effort to identify phenotypically homogeneous disease groups, in which power to detect genetic variants with small effects will be greater. These results suggest that analysis of case subsets could be a powerful strategy to uncover some of the hidden heritability for common complex disorders, particularly in identifying rarer variants of modest effect.
Collapse
|
46
|
|
47
|
Abstract
Our understanding of the genetic basis of disease has evolved from descriptions of overall heritability or familiality to the identification of large numbers of risk loci. One can quantify the impact of such loci on disease using a plethora of measures, which can guide future research decisions. However, different measures can attribute varying degrees of importance to a variant. In this Analysis, we consider and contrast the most commonly used measures - specifically, the heritability of disease liability, approximate heritability, sibling recurrence risk, overall genetic variance using a logarithmic relative risk scale, the area under the receiver-operating curve for risk prediction and the population attributable fraction - and give guidelines for their use that should be explicitly considered when assessing the contribution of genetic variants to disease.
Collapse
|
48
|
Traylor M, Mäkelä KM, Kilarski LL, Holliday EG, Devan WJ, Nalls MA, Wiggins KL, Zhao W, Cheng YC, Achterberg S, Malik R, Sudlow C, Bevan S, Raitoharju E, Oksala N, Thijs V, Lemmens R, Lindgren A, Slowik A, Maguire JM, Walters M, Algra A, Sharma P, Attia JR, Boncoraglio GB, Rothwell PM, de Bakker PIW, Bis JC, Saleheen D, Kittner SJ, Mitchell BD, Rosand J, Meschia JF, Levi C, Dichgans M, Lehtimäki T, Lewis CM, Markus HS. A novel MMP12 locus is associated with large artery atherosclerotic stroke using a genome-wide age-at-onset informed approach. PLoS Genet 2014; 10:e1004469. [PMID: 25078452 PMCID: PMC4117446 DOI: 10.1371/journal.pgen.1004469] [Citation(s) in RCA: 61] [Impact Index Per Article: 6.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/21/2013] [Accepted: 05/14/2014] [Indexed: 11/25/2022] Open
Abstract
Genome-wide association studies (GWAS) have begun to identify the common genetic component to ischaemic stroke (IS). However, IS has considerable phenotypic heterogeneity. Where clinical covariates explain a large fraction of disease risk, covariate informed designs can increase power to detect associations. As prevalence rates in IS are markedly affected by age, and younger onset cases may have higher genetic predisposition, we investigated whether an age-at-onset informed approach could detect novel associations with IS and its subtypes; cardioembolic (CE), large artery atherosclerosis (LAA) and small vessel disease (SVD) in 6,778 cases of European ancestry and 12,095 ancestry-matched controls. Regression analysis to identify SNP associations was performed on posterior liabilities after conditioning on age-at-onset and affection status. We sought further evidence of an association with LAA in 1,881 cases and 50,817 controls, and examined mRNA expression levels of the nearby genes in atherosclerotic carotid artery plaques. Secondly, we performed permutation analyses to evaluate the extent to which age-at-onset informed analysis improves significance for novel loci. We identified a novel association with an MMP12 locus in LAA (rs660599; p = 2.5×10⁻⁷), with independent replication in a second population (p = 0.0048, OR(95% CI) = 1.18(1.05-1.32); meta-analysis p = 2.6×10⁻⁸). The nearby gene, MMP12, was significantly overexpressed in carotid plaques compared to atherosclerosis-free control arteries (p = 1.2×10⁻¹⁵; fold change = 335.6). Permutation analyses demonstrated improved significance for associations when accounting for age-at-onset in all four stroke phenotypes (p<0.001). Our results show that a covariate-informed design, by adjusting for age-at-onset of stroke, can detect variants not identified by conventional GWAS.
Collapse
Affiliation(s)
- Matthew Traylor
- Stroke and Dementia Research Centre, St George's University of London, London, United Kingdom
| | - Kari-Matti Mäkelä
- Department of Clinical Chemistry, Fimlab Laboratories, Tampere, Finland
- School of Medicine, University of Tampere, Tampere, Finland
| | - Laura L. Kilarski
- Stroke and Dementia Research Centre, St George's University of London, London, United Kingdom
| | - Elizabeth G. Holliday
- Center for Clinical Epidemiology and Biostatistics, School of Medicine and Public Health, University of Newcastle, Callaghan, New South Wales, Australia
- Hunter Medical Research Institute, New Lambton Heights, New South Wales, Australia
| | - William J. Devan
- Center for Human Genetic Research, Massachusetts General Hospital, Boston, Massachusetts, United States of America
- Program in Medical and Population Genetics, Broad Institute of Harvard and MIT, Cambridge, Massachusetts, United States of America
| | - Mike A. Nalls
- Laboratory of Neurogenetics, National Institute on Aging, Bethesda, Maryland, United States of America
| | - Kerri L. Wiggins
- Cardiovascular Health Research Unit, Department of Medicine, University of Washington, Seattle, Washington, United States of America
| | - Wei Zhao
- Perelman School of Medicine, Division of Translational Medicine and Human Genetics, University of Pennsylvania, Philadelphia, Pennsylvania, United States of America
| | - Yu-Ching Cheng
- Department of Medicine, University of Maryland School of Medicine, Baltimore, Maryland, United States of America
- Research and Development Program, Veterans Affairs Maryland Health Care System, Baltimore, Maryland, United States of America
| | - Sefanja Achterberg
- Department of Neurology and Neurosurgery, Brain Center Rudolf Magnus, University Medical Center Utrecht, Utrecht, The Netherlands
| | - Rainer Malik
- Institute for Stroke and Dementia Research, Klinikum der Universität München, Ludwig-Maximilians-Universität, Munich, Germany
| | - Cathie Sudlow
- Division of Clinical Neurosciences and Insititute of Genetics and Molecular Medicine, University of Edinburgh, Edinburgh, United Kingdom
| | - Steve Bevan
- Clinical Neurosciences, University of Cambridge, Cambridge, United Kingdom
| | - Emma Raitoharju
- Department of Clinical Chemistry, Fimlab Laboratories, Tampere, Finland
- School of Medicine, University of Tampere, Tampere, Finland
| | | | - Niku Oksala
- Department of Clinical Chemistry, Fimlab Laboratories, Tampere, Finland
- School of Medicine, University of Tampere, Tampere, Finland
- Department of Surgery, Tampere University Hospital, Tampere, Finland
| | - Vincent Thijs
- KU Leuven - University of Leuven, Department of Neurosciences, Experimental Neurology - Laboratory of Neurobiology, Leuven, Belgium
- VIB - Vesalius Research Center, Leuven, Belgium
- University Hospitals Leuven, Department of Neurology, Leuven, Belgium
| | - Robin Lemmens
- KU Leuven - University of Leuven, Department of Neurosciences, Experimental Neurology - Laboratory of Neurobiology, Leuven, Belgium
- VIB - Vesalius Research Center, Leuven, Belgium
- University Hospitals Leuven, Department of Neurology, Leuven, Belgium
| | - Arne Lindgren
- Department of Clinical Sciences Lund, Neurology, Lund University, Lund, Sweden
- Department of Neurology and Rehabilitation Medicine, Skåne University Hospital, Lund, Sweden
| | - Agnieszka Slowik
- Department of Neurology, Jagiellonian University, Krakow, Poland
| | - Jane M. Maguire
- Center for Clinical Epidemiology and Biostatistics, School of Medicine and Public Health, University of Newcastle, Callaghan, New South Wales, Australia
- Hunter Medical Research Institute, New Lambton Heights, New South Wales, Australia
- School of Nursing and Midwifery, University of Newcastle, Callaghan, New South Wales, Australia
- Centre for Translational Neuroscience and Mental Health, University of Newcastle, Callaghan, New South Wales, Australia
| | - Matthew Walters
- Institute of Cardiovascular and Medical Sciences, University of Glasgow, Glasgow, United Kingdom
| | - Ale Algra
- Department of Neurology and Neurosurgery, Brain Center Rudolf Magnus, University Medical Center Utrecht, Utrecht, The Netherlands
- Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht, The Netherlands
| | - Pankaj Sharma
- Imperial College Cerebrovascular Research Unit (ICCRU), Imperial College London, London, United Kingdom
| | - John R. Attia
- Center for Clinical Epidemiology and Biostatistics, School of Medicine and Public Health, University of Newcastle, Callaghan, New South Wales, Australia
- Hunter Medical Research Institute, New Lambton Heights, New South Wales, Australia
- Centre for Translational Neuroscience and Mental Health, University of Newcastle, Callaghan, New South Wales, Australia
| | - Giorgio B. Boncoraglio
- Department of Cereberovascular Disease, Fondazione Istituto di Ricovero e Cura a Carattere Scientifico (IRCCS) Istituto Neurologico Carlo Besta, Milan, Italy
| | - Peter M. Rothwell
- Stroke Prevention Research Unit, Nuffield Department of Clinical Neuroscience, University of Oxford, Oxford, United Kingdom
| | - Paul I. W. de Bakker
- Program in Medical and Population Genetics, Broad Institute of Harvard and MIT, Cambridge, Massachusetts, United States of America
- Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht, The Netherlands
- Department of Medical Genetics, University Medical Centre, Utrecht, The Netherlands
- Division of Genetics, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts, United States of America
| | - Joshua C. Bis
- Cardiovascular Health Research Unit, Department of Medicine, University of Washington, Seattle, Washington, United States of America
| | - Danish Saleheen
- Department of Biostatistics and Epidemiology, University of Pennsylvania, Philadelphia, Pennsylvania, United States of America
- Center for Non-Communicable Diseases, Karachi, Pakistan
| | - Steven J. Kittner
- Research and Development Program, Veterans Affairs Maryland Health Care System, Baltimore, Maryland, United States of America
| | - Braxton D. Mitchell
- Department of Medicine, University of Maryland School of Medicine, Baltimore, Maryland, United States of America
| | - Jonathan Rosand
- Center for Human Genetic Research, Massachusetts General Hospital, Boston, Massachusetts, United States of America
- Program in Medical and Population Genetics, Broad Institute of Harvard and MIT, Cambridge, Massachusetts, United States of America
| | - James F. Meschia
- Department of Neurology, Mayo Clinic, Jacksonville, Florida, United States of America
| | - Christopher Levi
- Hunter Medical Research Institute, New Lambton Heights, New South Wales, Australia
- Centre for Translational Neuroscience and Mental Health, University of Newcastle, Callaghan, New South Wales, Australia
| | - Martin Dichgans
- Institute for Stroke and Dementia Research, Klinikum der Universität München, Ludwig-Maximilians-Universität, Munich, Germany
- Munich Cluster for Systems Neurology (SyNergy), Ludwig-Maximilians-Universität, Munich, Germany
| | - Terho Lehtimäki
- Department of Clinical Chemistry, Fimlab Laboratories, Tampere, Finland
- School of Medicine, University of Tampere, Tampere, Finland
| | - Cathryn M. Lewis
- Department of Medical & Molecular Genetics, King's College London, London, United Kingdom
- Social, Genetic and Developmental Psychiatry Centre, Institute of Psychiatry, King's College London, London, United Kingdom
| | - Hugh S. Markus
- Clinical Neurosciences, University of Cambridge, Cambridge, United Kingdom
| |
Collapse
|
49
|
Brown R, Pasaniuc B. Enhanced methods for local ancestry assignment in sequenced admixed individuals. PLoS Comput Biol 2014; 10:e1003555. [PMID: 24743331 PMCID: PMC3990492 DOI: 10.1371/journal.pcbi.1003555] [Citation(s) in RCA: 23] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/23/2013] [Accepted: 02/10/2014] [Indexed: 01/22/2023] Open
Abstract
Inferring the ancestry at each locus in the genome of recently admixed individuals (e.g., Latino Americans) plays a major role in medical and population genetic inferences, ranging from finding disease-risk loci, to inferring recombination rates, to mapping missing contigs in the human genome. Although many methods for local ancestry inference have been proposed, most are designed for use with genotyping arrays and fail to make use of the full spectrum of data available from sequencing. In addition, current haplotype-based approaches are very computationally demanding, requiring large computational time for moderately large sample sizes. Here we present new methods for local ancestry inference that leverage continent-specific variants (CSVs) to attain increased performance over existing approaches in sequenced admixed genomes. A key feature of our approach is that it incorporates the admixed genomes themselves jointly with public datasets, such as 1000 Genomes, to improve the accuracy of CSV calling. We use simulations to show that our approach attains accuracy similar to widely used computationally intensive haplotype-based approaches with large decreases in runtime. Most importantly, we show that our method recovers comparable local ancestries, as the 1000 Genomes consensus local ancestry calls in the real admixed individuals from the 1000 Genomes Project. We extend our approach to account for low-coverage sequencing and show that accurate local ancestry inference can be attained at low sequencing coverage. Finally, we generalize CSVs to sub-continental population-specific variants (sCSVs) and show that in some cases it is possible to determine the sub-continental ancestry for short chromosomal segments on the basis of sCSVs. Advances in sequencing technologies are dramatically changing the volume and type of data collected in genetic studies. Although most genetic studies so far have focused on individuals of European ancestry, recent studies are increasingly being performed in individuals of admixed ancestry (i.e., with recent ancestors from multiple continents, e.g., Latino Americans). A key component in such studies is the accurate inference of continental ancestry at each segment in the genome of these individuals. In this work we present accurate and robust methods that use continent-specific variants (i.e., genetic variants observed only in individuals of a given continent), now readily accessible through sequencing technology, to perform extremely fast and accurate inference of the ancestral origin of each genomic segment in recently admixed individuals.
Collapse
Affiliation(s)
- Robert Brown
- Bioinformatics Interdepartmental Program, University of California Los Angeles, Los Angeles, California, United States of America
- Department of Pathology and Laboratory Medicine, Geffen School of Medicine, University of California Los Angeles, Los Angeles, California, United States of America
- * E-mail: (RB); (BP)
| | - Bogdan Pasaniuc
- Bioinformatics Interdepartmental Program, University of California Los Angeles, Los Angeles, California, United States of America
- Department of Pathology and Laboratory Medicine, Geffen School of Medicine, University of California Los Angeles, Los Angeles, California, United States of America
- Jonsson Comprehensive Cancer Center, University of California Los Angeles, Los Angeles, California, United States of America
- * E-mail: (RB); (BP)
| |
Collapse
|
50
|
Williams AL, Jacobs SBR, Moreno-Macías H, Huerta-Chagoya A, Churchhouse C, Márquez-Luna C, García-Ortíz H, Gómez-Vázquez MJ, Burtt NP, Aguilar-Salinas CA, González-Villalpando C, Florez JC, Orozco L, Haiman CA, Tusié-Luna T, Altshuler D. Sequence variants in SLC16A11 are a common risk factor for type 2 diabetes in Mexico. Nature 2014; 506:97-101. [PMID: 24390345 PMCID: PMC4127086 DOI: 10.1038/nature12828] [Citation(s) in RCA: 322] [Impact Index Per Article: 32.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2012] [Accepted: 11/04/2013] [Indexed: 12/19/2022]
Abstract
Performing genetic studies in multiple human populations can identify disease risk alleles that are common in one population but rare in others, with the potential to illuminate pathophysiology, health disparities, and the population genetic origins of disease alleles. Here we analysed 9.2 million single nucleotide polymorphisms (SNPs) in each of 8,214 Mexicans and other Latin Americans: 3,848 with type 2 diabetes and 4,366 non-diabetic controls. In addition to replicating previous findings, we identified a novel locus associated with type 2 diabetes at genome-wide significance spanning the solute carriers SLC16A11 and SLC16A13 (P = 3.9 × 10(-13); odds ratio (OR) = 1.29). The association was stronger in younger, leaner people with type 2 diabetes, and replicated in independent samples (P = 1.1 × 10(-4); OR = 1.20). The risk haplotype carries four amino acid substitutions, all in SLC16A11; it is present at ~50% frequency in Native American samples and ~10% in east Asian, but is rare in European and African samples. Analysis of an archaic genome sequence indicated that the risk haplotype introgressed into modern humans via admixture with Neanderthals. The SLC16A11 messenger RNA is expressed in liver, and V5-tagged SLC16A11 protein localizes to the endoplasmic reticulum. Expression of SLC16A11 in heterologous cells alters lipid metabolism, most notably causing an increase in intracellular triacylglycerol levels. Despite type 2 diabetes having been well studied by genome-wide association studies in other populations, analysis in Mexican and Latin American individuals identified SLC16A11 as a novel candidate gene for type 2 diabetes with a possible role in triacylglycerol metabolism.
Collapse
|