51
|
Edwards TL, Breeyear J, Piekos JA, Velez Edwards DR. Equity in Health: Consideration of Race and Ethnicity in Precision Medicine. Trends Genet 2020; 36:807-809. [PMID: 32709459 PMCID: PMC7373675 DOI: 10.1016/j.tig.2020.07.001] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/22/2020] [Revised: 06/30/2020] [Accepted: 07/01/2020] [Indexed: 12/13/2022]
Abstract
The causes for disparities in implementation of precision medicine are complex, due in part to differences in clinical care and a lack of engagement and recruitment of under-represented populations in studies. New tools and large genetic cohorts can change these circumstances and build access to personalized medicine for disadvantaged populations.
Collapse
Affiliation(s)
- Todd L Edwards
- Division of Epidemiology, Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, USA; Vanderbilt Genetics Institute, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Joseph Breeyear
- Division of Epidemiology, Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, USA; Vanderbilt Genetics Institute, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Jacqueline A Piekos
- Vanderbilt Genetics Institute, Vanderbilt University Medical Center, Nashville, TN, USA; Division of Quantitative Sciences, Department of Obstetrics and Gynecology, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Digna R Velez Edwards
- Vanderbilt Genetics Institute, Vanderbilt University Medical Center, Nashville, TN, USA; Division of Quantitative Sciences, Department of Obstetrics and Gynecology, Vanderbilt University Medical Center, Nashville, TN, USA; Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN, USA.
| |
Collapse
|
52
|
Babb de Villiers C, Kroese M, Moorthie S. Understanding polygenic models, their development and the potential application of polygenic scores in healthcare. J Med Genet 2020; 57:725-732. [PMID: 32376789 PMCID: PMC7591711 DOI: 10.1136/jmedgenet-2019-106763] [Citation(s) in RCA: 20] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/09/2019] [Revised: 03/09/2020] [Accepted: 03/28/2020] [Indexed: 02/06/2023]
Abstract
The use of genomic information to better understand and prevent common complex diseases has been an ongoing goal of genetic research. Over the past few years, research in this area has proliferated with several proposed methods of generating polygenic scores. This has been driven by the availability of larger data sets, primarily from genome-wide association studies and concomitant developments in statistical methodologies. Here we provide an overview of the methodological aspects of polygenic model construction. In addition, we consider the state of the field and implications for potential applications of polygenic scores for risk estimation within healthcare.
Collapse
Affiliation(s)
| | - Mark Kroese
- PHG Foundation, University of Cambridge, Cambridge, Cambridgeshire, UK
| | - Sowmiya Moorthie
- PHG Foundation, University of Cambridge, Cambridge, Cambridgeshire, UK
| |
Collapse
|
53
|
Erickson PA, Weller CA, Song DY, Bangerter AS, Schmidt P, Bergland AO. Unique genetic signatures of local adaptation over space and time for diapause, an ecologically relevant complex trait, in Drosophila melanogaster. PLoS Genet 2020; 16:e1009110. [PMID: 33216740 PMCID: PMC7717581 DOI: 10.1371/journal.pgen.1009110] [Citation(s) in RCA: 19] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/05/2019] [Revised: 12/04/2020] [Accepted: 09/10/2020] [Indexed: 02/07/2023] Open
Abstract
Organisms living in seasonally variable environments utilize cues such as light and temperature to induce plastic responses, enabling them to exploit favorable seasons and avoid unfavorable ones. Local adapation can result in variation in seasonal responses, but the genetic basis and evolutionary history of this variation remains elusive. Many insects, including Drosophila melanogaster, are able to undergo an arrest of reproductive development (diapause) in response to unfavorable conditions. In D. melanogaster, the ability to diapause is more common in high latitude populations, where flies endure harsher winters, and in the spring, reflecting differential survivorship of overwintering populations. Using a novel hybrid swarm-based genome wide association study, we examined the genetic basis and evolutionary history of ovarian diapause. We exposed outbred females to different temperatures and day lengths, characterized ovarian development for over 2800 flies, and reconstructed their complete, phased genomes. We found that diapause, scored at two different developmental cutoffs, has modest heritability, and we identified hundreds of SNPs associated with each of the two phenotypes. Alleles associated with one of the diapause phenotypes tend to be more common at higher latitudes, but these alleles do not show predictable seasonal variation. The collective signal of many small-effect, clinally varying SNPs can plausibly explain latitudinal variation in diapause seen in North America. Alleles associated with diapause are segregating in Zambia, suggesting that variation in diapause relies on ancestral polymorphisms, and both pro- and anti-diapause alleles have experienced selection in North America. Finally, we utilized outdoor mesocosms to track diapause under natural conditions. We found that hybrid swarms reared outdoors evolved increased propensity for diapause in late fall, whereas indoor control populations experienced no such change. Our results indicate that diapause is a complex, quantitative trait with different evolutionary patterns across time and space.
Collapse
Affiliation(s)
- Priscilla A. Erickson
- Department of Biology, University of Virginia, Charlottesville, Virginia, United States of America
| | - Cory A. Weller
- Department of Biology, University of Virginia, Charlottesville, Virginia, United States of America
| | - Daniel Y. Song
- Department of Biology, University of Virginia, Charlottesville, Virginia, United States of America
| | - Alyssa S. Bangerter
- Department of Biology, University of Virginia, Charlottesville, Virginia, United States of America
| | - Paul Schmidt
- Department of Biology, University of Pennsylvania, Philadelphia, Pennsylvania, United States of America
| | - Alan O. Bergland
- Department of Biology, University of Virginia, Charlottesville, Virginia, United States of America
| |
Collapse
|
54
|
Fuentes A. Biological anthropology's critical engagement with genomics, evolution, race/racism, and ourselves: Opportunities and challenges to making a difference in the academy and the world. AMERICAN JOURNAL OF PHYSICAL ANTHROPOLOGY 2020; 175:326-338. [PMID: 33098091 DOI: 10.1002/ajpa.24162] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/18/2020] [Revised: 07/25/2020] [Accepted: 10/02/2020] [Indexed: 12/31/2022]
Abstract
Biological anthropology can, and should, matter in the Anthropocene. Biological anthropologists are interested in human biology and the human experience in a broader ecological, evolutionary, and phylogenetic context. We are interested in the material of the body, the history of the body, and interactions of diverse bodies, communities, ecologies, and evolutionary processes. However, the cultural realities of bodies, histories, communities, livelihoods, perceptions, and experiences are as central to the endeavor and inquiry of biological anthropology as are their material aspects. Biological anthropology is a constant dialectic between the cultural and the biological. In this essay, I argue that Biological Anthropology has much to offer, a history to contend with, and a future that matters. To illustrate this, I highlight theoretical and methodological issues in genomics, evolutionary theory and connect them to the study of Race and Racism to emphasize specific arenas where Biological Anthropology has a great capacity, and a strong obligation, to play a central role. However, Biological Anthropology also has substantive internal issues that hinder our ability to do the best possible science. If we are to live up to our potential and make a difference in the 21st century we need to ameliorate our structural shortcomings and expand our voice, and impact, in academic and public discourse. The goal of this perspective is to offer suggestions for moving us toward this goal.
Collapse
Affiliation(s)
- Agustín Fuentes
- Department of Anthropology, 123 Aaron Burr Hall, Princeton University, Princeton, New Jersey, USA
| |
Collapse
|
55
|
Martin AR, Gignoux CR, Walters RK, Wojcik GL, Neale BM, Gravel S, Daly MJ, Bustamante CD, Kenny EE. Human Demographic History Impacts Genetic Risk Prediction across Diverse Populations. Am J Hum Genet 2020; 107:788-789. [PMID: 33007199 PMCID: PMC7536609 DOI: 10.1016/j.ajhg.2020.08.020] [Citation(s) in RCA: 17] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/02/2023] Open
|
56
|
Chande AT, Rishishwar L, Conley AB, Valderrama-Aguirre A, Medina-Rivas MA, Jordan IK. Ancestry effects on type 2 diabetes genetic risk inference in Hispanic/Latino populations. BMC MEDICAL GENETICS 2020; 21:132. [PMID: 32580712 PMCID: PMC7315475 DOI: 10.1186/s12881-020-01068-0] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/04/2020] [Accepted: 06/10/2020] [Indexed: 12/18/2022]
Abstract
Background Hispanic/Latino (HL) populations bear a disproportionately high burden of type 2 diabetes (T2D). The ability to predict T2D genetic risk using polygenic risk scores (PRS) offers great promise for improved screening and prevention. However, there are a number of complications related to the accurate inference of genetic risk across HL populations with distinct ancestry profiles. We investigated how ancestry affects the inference of T2D genetic risk using PRS in diverse HL populations from Colombia and the United States (US). In Colombia, we compared T2D genetic risk for the Mestizo population of Antioquia to the Afro-Colombian population of Chocó, and in the US, we compared European-American versus Mexican-American populations. Methods Whole genome sequences and genotypes from the 1000 Genomes Project and the ChocoGen Research Project were used for genetic ancestry inference and for T2D polygenic risk score (PRS) calculation. Continental ancestry fractions for HL genomes were inferred via comparison with African, European, and Native American reference genomes, and PRS were calculated using T2D risk variants taken from multiple genome-wide association studies (GWAS) conducted on cohorts with diverse ancestries. A correction for ancestry bias in T2D risk inference based on the frequencies of ancestral versus derived alleles was developed and applied to PRS calculations in the HL populations studied here. Results T2D genetic risk in Colombian and US HL populations is positively correlated with African and Native American ancestry and negatively correlated with European ancestry. The Afro-Colombian population of Chocó has higher predicted T2D risk than Antioquia, and the Mexican-American population has higher predicted risk than the European-American population. The inferred relative risk of T2D is robust to differences in the ancestry of the GWAS cohorts used for variant discovery. For trans-ethnic GWAS, population-specific variants and variants with same direction effects across populations yield consistent results. Nevertheless, the control for bias in T2D risk prediction confirms that explicit consideration of genetic ancestry can yield more reliable cross-population genetic risk inferences. Conclusions T2D associations that replicate across populations provide for more reliable risk inference, and modeling population-specific frequencies of ancestral and derived risk alleles can help control for biases in PRS estimation.
Collapse
Affiliation(s)
- Aroon T Chande
- School of Biological Sciences, Georgia Institute of Technology, 950 Atlantic Drive, Atlanta, GA, 30332, USA.,IHRC-Georgia Tech Applied Bioinformatics Laboratory, Atlanta, GA, USA.,PanAmerican Bioinformatics Institute, Cali, Valle del Cauca, Colombia
| | - Lavanya Rishishwar
- IHRC-Georgia Tech Applied Bioinformatics Laboratory, Atlanta, GA, USA.,PanAmerican Bioinformatics Institute, Cali, Valle del Cauca, Colombia
| | - Andrew B Conley
- IHRC-Georgia Tech Applied Bioinformatics Laboratory, Atlanta, GA, USA.,PanAmerican Bioinformatics Institute, Cali, Valle del Cauca, Colombia
| | - Augusto Valderrama-Aguirre
- School of Biological Sciences, Georgia Institute of Technology, 950 Atlantic Drive, Atlanta, GA, 30332, USA.,PanAmerican Bioinformatics Institute, Cali, Valle del Cauca, Colombia.,Biomedical Research Institute (COL0082529), Cali, Colombia.,Universidad Santiago de Cali, Cali, Colombia
| | - Miguel A Medina-Rivas
- PanAmerican Bioinformatics Institute, Cali, Valle del Cauca, Colombia.,Centro de Investigación en Biodiversidad y Hábitat, Universidad Tecnológica del Chocó, Quibdó, Chocó, Colombia
| | - I King Jordan
- School of Biological Sciences, Georgia Institute of Technology, 950 Atlantic Drive, Atlanta, GA, 30332, USA. .,IHRC-Georgia Tech Applied Bioinformatics Laboratory, Atlanta, GA, USA. .,PanAmerican Bioinformatics Institute, Cali, Valle del Cauca, Colombia.
| |
Collapse
|
57
|
Joo YY, Actkins K, Pacheco JA, Basile AO, Carroll R, Crosslin DR, Day F, Denny JC, Velez Edwards DR, Hakonarson H, Harley JB, Hebbring SJ, Ho K, Jarvik GP, Jones M, Karaderi T, Mentch FD, Meun C, Namjou B, Pendergrass S, Ritchie MD, Stanaway IB, Urbanek M, Walunas TL, Smith M, Chisholm RL, Kho AN, Davis L, Hayes MG. A Polygenic and Phenotypic Risk Prediction for Polycystic Ovary Syndrome Evaluated by Phenome-Wide Association Studies. J Clin Endocrinol Metab 2020; 105:dgz326. [PMID: 31917831 PMCID: PMC7453038 DOI: 10.1210/clinem/dgz326] [Citation(s) in RCA: 31] [Impact Index Per Article: 7.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/14/2019] [Accepted: 01/07/2020] [Indexed: 11/19/2022]
Abstract
CONTEXT As many as 75% of patients with polycystic ovary syndrome (PCOS) are estimated to be unidentified in clinical practice. OBJECTIVE Utilizing polygenic risk prediction, we aim to identify the phenome-wide comorbidity patterns characteristic of PCOS to improve accurate diagnosis and preventive treatment. DESIGN, PATIENTS, AND METHODS Leveraging the electronic health records (EHRs) of 124 852 individuals, we developed a PCOS risk prediction algorithm by combining polygenic risk scores (PRS) with PCOS component phenotypes into a polygenic and phenotypic risk score (PPRS). We evaluated its predictive capability across different ancestries and perform a PRS-based phenome-wide association study (PheWAS) to assess the phenomic expression of the heightened risk of PCOS. RESULTS The integrated polygenic prediction improved the average performance (pseudo-R2) for PCOS detection by 0.228 (61.5-fold), 0.224 (58.8-fold), 0.211 (57.0-fold) over the null model across European, African, and multi-ancestry participants respectively. The subsequent PRS-powered PheWAS identified a high level of shared biology between PCOS and a range of metabolic and endocrine outcomes, especially with obesity and diabetes: "morbid obesity", "type 2 diabetes", "hypercholesterolemia", "disorders of lipid metabolism", "hypertension", and "sleep apnea" reaching phenome-wide significance. CONCLUSIONS Our study has expanded the methodological utility of PRS in patient stratification and risk prediction, especially in a multifactorial condition like PCOS, across different genetic origins. By utilizing the individual genome-phenome data available from the EHR, our approach also demonstrates that polygenic prediction by PRS can provide valuable opportunities to discover the pleiotropic phenomic network associated with PCOS pathogenesis.
Collapse
Affiliation(s)
- Yoonjung Yoonie Joo
- Division of Endocrinology, Metabolism, and Molecular Medicine, Department of Medicine, Northwestern University Feinberg School of Medicine, Chicago, Illinois
| | - Ky'Era Actkins
- Department of Microbiology, Immunology, and Physiology, Meharry Medical College, Nashville, Tennessee
| | - Jennifer A Pacheco
- Center for Genetic Medicine, Northwestern University Feinberg School of Medicine, Chicago, Illinois
| | - Anna O Basile
- Department of Biomedical Informatics, Columbia University New York, New York
| | - Robert Carroll
- Departments of Biomedical Informatics and Medicine, Vanderbilt University Medical Center, Nashville, Tennessee
| | - David R Crosslin
- Department of Biomedical Informatics and Medical Education, University of Washington School of Medicine, Seattle, Wahington
| | - Felix Day
- MRC Epidemiology Unit, Cambridge Biomedical Campus, University of Cambridge School of Clinical Medicine, Cambridge, United Kingdom
| | - Joshua C Denny
- Departments of Biomedical Informatics and Medicine, Vanderbilt University Medical Center, Nashville, Tennessee
| | - Digna R Velez Edwards
- Departments of Biomedical Informatics and Medicine, Vanderbilt University Medical Center, Nashville, Tennessee
- Division of Quantitative Sciences, Department of Obstetrics and Gynecology, Vanderbilt University Medical Center, Nashville, Tennessee
| | - Hakon Hakonarson
- Center for Applied Genomics, Children's Hospital of Philadelphia, Philadelphia, Pennsylvania
- Department of Pediatrics, The Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania
| | - John B Harley
- Center for Autoimmune Genomics and Etiology (CAGE), Cincinnati Children's Hospital Medical Center, Cincinnati, Ohio
- Department of Pediatrics, University of Cincinnati College of Medicine; US Department of Veterans Affairs, Cincinnati, Ohio
| | - Scott J Hebbring
- Center for Precision Medicine Research, Marshfield Clinic Research Institute, Marshfield, Wisconsin, USA
| | - Kevin Ho
- Biomedical and Translational Informatics, Geisinger, Danville, Pennsylvania
| | - Gail P Jarvik
- Division of Medical Genetics, Department of Medicine (Medical Genetics) and Genome Sciences, University of Washington Medical School, Seattle, Wahington
| | - Michelle Jones
- Center for Bioinformatics & Functional Genomics, Department of Biomedical Sciences, Cedars-Sinai Medical Center, Los Angeles, California
| | - Tugce Karaderi
- The Wellcome Trust Centre for Human Genetics, University of Oxford, Oxford OX3 7BN, United Kingdom
| | - Frank D Mentch
- Center for Applied Genomics, Children's Hospital of Philadelphia, Philadelphia, Pennsylvania
| | - Cindy Meun
- Department of Obstetrics and Gynecology, Erasmus MC, University Medical Center Rotterdam, Rotterdam, The Netherlands
| | - Bahram Namjou
- Center for Autoimmune Genomics and Etiology (CAGE), Cincinnati Children's Hospital Medical Center, Cincinnati, Ohio
| | - Sarah Pendergrass
- Biomedical and Translational Informatics, Geisinger, Danville, Pennsylvania
| | - Marylyn D Ritchie
- Department of Genetics, University of Pennsylvania, Philadelphia, Pennsylvania
| | - Ian B Stanaway
- Department of Biomedical Informatics and Medical Education, University of Washington School of Medicine, Seattle, Wahington
| | - Margrit Urbanek
- Division of Endocrinology, Metabolism, and Molecular Medicine, Department of Medicine, Northwestern University Feinberg School of Medicine, Chicago, Illinois
| | - Theresa L Walunas
- Division of General Internal Medicine and Geriatrics, Department of Medicine, Northwestern University Feinberg School of Medicine, Chicago, Illinois
| | - Maureen Smith
- Center for Genetic Medicine, Northwestern University Feinberg School of Medicine, Chicago, Illinois
| | - Rex L Chisholm
- Center for Genetic Medicine, Northwestern University Feinberg School of Medicine, Chicago, Illinois
| | - Abel N Kho
- Division of General Internal Medicine and Geriatrics, Department of Medicine, Northwestern University Feinberg School of Medicine, Chicago, Illinois
| | - Lea Davis
- Departments of Biomedical Informatics and Medicine, Vanderbilt University Medical Center, Nashville, Tennessee
| | - M Geoffrey Hayes
- Division of Endocrinology, Metabolism, and Molecular Medicine, Department of Medicine, Northwestern University Feinberg School of Medicine, Chicago, Illinois
- Center for Genetic Medicine, Northwestern University Feinberg School of Medicine, Chicago, Illinois
- Department of Anthropology, Northwestern University, Evanston, Illinois
| |
Collapse
|
58
|
Yang S, Zhou X. Accurate and Scalable Construction of Polygenic Scores in Large Biobank Data Sets. Am J Hum Genet 2020; 106:679-693. [PMID: 32330416 PMCID: PMC7212266 DOI: 10.1016/j.ajhg.2020.03.013] [Citation(s) in RCA: 56] [Impact Index Per Article: 14.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/12/2019] [Accepted: 03/30/2020] [Indexed: 01/24/2023] Open
Abstract
Accurate construction of polygenic scores (PGS) can enable early diagnosis of diseases and facilitate the development of personalized medicine. Accurate PGS construction requires prediction models that are both adaptive to different genetic architectures and scalable to biobank scale datasets with millions of individuals and tens of millions of genetic variants. Here, we develop such a method called Deterministic Bayesian Sparse Linear Mixed Model (DBSLMM). DBSLMM relies on a flexible modeling assumption on the effect size distribution to achieve robust and accurate prediction performance across a range of genetic architectures. DBSLMM also relies on a simple deterministic search algorithm to yield an approximate analytic estimation solution using summary statistics only. The deterministic search algorithm, when paired with further algebraic innovations, results in substantial computational savings. With simulations, we show that DBSLMM achieves scalable and accurate prediction performance across a range of realistic genetic architectures. We then apply DBSLMM to analyze 25 traits in UK Biobank. For these traits, compared to existing approaches, DBSLMM achieves an average of 2.03%-101.09% accuracy gain in internal cross-validations. In external validations on two separate datasets, including one from BioBank Japan, DBSLMM achieves an average of 14.74%-522.74% accuracy gain. In these real data applications, DBSLMM is 1.03-28.11 times faster and uses only 7.4%-24.8% of physical memory as compared to other multiple regression-based PGS methods. Overall, DBSLMM represents an accurate and scalable method for constructing PGS in biobank scale datasets.
Collapse
Affiliation(s)
- Sheng Yang
- Department of Biostatistics, Nanjing Medical University, Nanjing, Jiangsu 211166, China; Department of Biostatistics, University of Michigan, Ann Arbor, MI 48109, USA; Center for Statistical Genetics, University of Michigan, Ann Arbor, MI 48109, USA
| | - Xiang Zhou
- Department of Biostatistics, University of Michigan, Ann Arbor, MI 48109, USA; Center for Statistical Genetics, University of Michigan, Ann Arbor, MI 48109, USA.
| |
Collapse
|
59
|
Evans BJ, Javitt G, Hall R, Robertson M, Ossorio P, Wolf SM, Morgan T, Clayton EW. How Can Law and Policy Advance Quality in Genomic Analysis and Interpretation for Clinical Care? THE JOURNAL OF LAW, MEDICINE & ETHICS : A JOURNAL OF THE AMERICAN SOCIETY OF LAW, MEDICINE & ETHICS 2020; 48:44-68. [PMID: 32342785 PMCID: PMC7447152 DOI: 10.1177/1073110520916995] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/27/2023]
Abstract
Delivering high quality genomics-informed care to patients requires accurate test results whose clinical implications are understood. While other actors, including state agencies, professional organizations, and clinicians, are involved, this article focuses on the extent to which the federal agencies that play the most prominent roles - the Centers for Medicare and Medicaid Services enforcing CLIA and the FDA - effectively ensure that these elements are met and concludes by suggesting possible ways to improve their oversight of genomic testing.
Collapse
Affiliation(s)
- Barbara J Evans
- Barbara J. Evans, Ph.D., J.D., LL.M., is the Mary Ann and Lawrence E. Faust Professor of Law and Professor, Electrical and Computer Engineering at the University of Houston. Gail Javitt, J.D., is a Member of the Firm at Hyman, Phelps, and McNamara, P.C. Ralph Hall, J.D., is a Principal at Leavitt Partners and a Professor of Practice at the University of Minnesota Law School. Megan Robertson, J.D., is an Associate in the Health Care and Life Sciences practice, Epstein Becker & Green, P.C. Pilar Ossorio, Ph.D., J.D., is Professor of Law and Bioethics at the University of Wisconsin Law School and Ethics Scholar-in-Residence at the Morgridge Institute for Research. Susan M. Wolf, J.D., is McKnight Presidential Professor of Law, Medicine & Public Policy; Faegre Baker Daniels Professor of Law; Professor of Medicine; and Chair of the Consortium on Law and Values in Health, Environment & the Life Sciences at the University of Minnesota. Thomas Morgan, M.D., F.A.C.M.G., is Associate Professor of Pediatrics in Medical Genetics at the Vanderbilt University School of Medicine. Ellen W. Clayton, M.D., J.D., is Craig-Weaver Professor of Pediatrics and Professor of Law at Vanderbilt University Medical Center and Vanderbilt University
| | - Gail Javitt
- Barbara J. Evans, Ph.D., J.D., LL.M., is the Mary Ann and Lawrence E. Faust Professor of Law and Professor, Electrical and Computer Engineering at the University of Houston. Gail Javitt, J.D., is a Member of the Firm at Hyman, Phelps, and McNamara, P.C. Ralph Hall, J.D., is a Principal at Leavitt Partners and a Professor of Practice at the University of Minnesota Law School. Megan Robertson, J.D., is an Associate in the Health Care and Life Sciences practice, Epstein Becker & Green, P.C. Pilar Ossorio, Ph.D., J.D., is Professor of Law and Bioethics at the University of Wisconsin Law School and Ethics Scholar-in-Residence at the Morgridge Institute for Research. Susan M. Wolf, J.D., is McKnight Presidential Professor of Law, Medicine & Public Policy; Faegre Baker Daniels Professor of Law; Professor of Medicine; and Chair of the Consortium on Law and Values in Health, Environment & the Life Sciences at the University of Minnesota. Thomas Morgan, M.D., F.A.C.M.G., is Associate Professor of Pediatrics in Medical Genetics at the Vanderbilt University School of Medicine. Ellen W. Clayton, M.D., J.D., is Craig-Weaver Professor of Pediatrics and Professor of Law at Vanderbilt University Medical Center and Vanderbilt University
| | - Ralph Hall
- Barbara J. Evans, Ph.D., J.D., LL.M., is the Mary Ann and Lawrence E. Faust Professor of Law and Professor, Electrical and Computer Engineering at the University of Houston. Gail Javitt, J.D., is a Member of the Firm at Hyman, Phelps, and McNamara, P.C. Ralph Hall, J.D., is a Principal at Leavitt Partners and a Professor of Practice at the University of Minnesota Law School. Megan Robertson, J.D., is an Associate in the Health Care and Life Sciences practice, Epstein Becker & Green, P.C. Pilar Ossorio, Ph.D., J.D., is Professor of Law and Bioethics at the University of Wisconsin Law School and Ethics Scholar-in-Residence at the Morgridge Institute for Research. Susan M. Wolf, J.D., is McKnight Presidential Professor of Law, Medicine & Public Policy; Faegre Baker Daniels Professor of Law; Professor of Medicine; and Chair of the Consortium on Law and Values in Health, Environment & the Life Sciences at the University of Minnesota. Thomas Morgan, M.D., F.A.C.M.G., is Associate Professor of Pediatrics in Medical Genetics at the Vanderbilt University School of Medicine. Ellen W. Clayton, M.D., J.D., is Craig-Weaver Professor of Pediatrics and Professor of Law at Vanderbilt University Medical Center and Vanderbilt University
| | - Megan Robertson
- Barbara J. Evans, Ph.D., J.D., LL.M., is the Mary Ann and Lawrence E. Faust Professor of Law and Professor, Electrical and Computer Engineering at the University of Houston. Gail Javitt, J.D., is a Member of the Firm at Hyman, Phelps, and McNamara, P.C. Ralph Hall, J.D., is a Principal at Leavitt Partners and a Professor of Practice at the University of Minnesota Law School. Megan Robertson, J.D., is an Associate in the Health Care and Life Sciences practice, Epstein Becker & Green, P.C. Pilar Ossorio, Ph.D., J.D., is Professor of Law and Bioethics at the University of Wisconsin Law School and Ethics Scholar-in-Residence at the Morgridge Institute for Research. Susan M. Wolf, J.D., is McKnight Presidential Professor of Law, Medicine & Public Policy; Faegre Baker Daniels Professor of Law; Professor of Medicine; and Chair of the Consortium on Law and Values in Health, Environment & the Life Sciences at the University of Minnesota. Thomas Morgan, M.D., F.A.C.M.G., is Associate Professor of Pediatrics in Medical Genetics at the Vanderbilt University School of Medicine. Ellen W. Clayton, M.D., J.D., is Craig-Weaver Professor of Pediatrics and Professor of Law at Vanderbilt University Medical Center and Vanderbilt University
| | - Pilar Ossorio
- Barbara J. Evans, Ph.D., J.D., LL.M., is the Mary Ann and Lawrence E. Faust Professor of Law and Professor, Electrical and Computer Engineering at the University of Houston. Gail Javitt, J.D., is a Member of the Firm at Hyman, Phelps, and McNamara, P.C. Ralph Hall, J.D., is a Principal at Leavitt Partners and a Professor of Practice at the University of Minnesota Law School. Megan Robertson, J.D., is an Associate in the Health Care and Life Sciences practice, Epstein Becker & Green, P.C. Pilar Ossorio, Ph.D., J.D., is Professor of Law and Bioethics at the University of Wisconsin Law School and Ethics Scholar-in-Residence at the Morgridge Institute for Research. Susan M. Wolf, J.D., is McKnight Presidential Professor of Law, Medicine & Public Policy; Faegre Baker Daniels Professor of Law; Professor of Medicine; and Chair of the Consortium on Law and Values in Health, Environment & the Life Sciences at the University of Minnesota. Thomas Morgan, M.D., F.A.C.M.G., is Associate Professor of Pediatrics in Medical Genetics at the Vanderbilt University School of Medicine. Ellen W. Clayton, M.D., J.D., is Craig-Weaver Professor of Pediatrics and Professor of Law at Vanderbilt University Medical Center and Vanderbilt University
| | - Susan M Wolf
- Barbara J. Evans, Ph.D., J.D., LL.M., is the Mary Ann and Lawrence E. Faust Professor of Law and Professor, Electrical and Computer Engineering at the University of Houston. Gail Javitt, J.D., is a Member of the Firm at Hyman, Phelps, and McNamara, P.C. Ralph Hall, J.D., is a Principal at Leavitt Partners and a Professor of Practice at the University of Minnesota Law School. Megan Robertson, J.D., is an Associate in the Health Care and Life Sciences practice, Epstein Becker & Green, P.C. Pilar Ossorio, Ph.D., J.D., is Professor of Law and Bioethics at the University of Wisconsin Law School and Ethics Scholar-in-Residence at the Morgridge Institute for Research. Susan M. Wolf, J.D., is McKnight Presidential Professor of Law, Medicine & Public Policy; Faegre Baker Daniels Professor of Law; Professor of Medicine; and Chair of the Consortium on Law and Values in Health, Environment & the Life Sciences at the University of Minnesota. Thomas Morgan, M.D., F.A.C.M.G., is Associate Professor of Pediatrics in Medical Genetics at the Vanderbilt University School of Medicine. Ellen W. Clayton, M.D., J.D., is Craig-Weaver Professor of Pediatrics and Professor of Law at Vanderbilt University Medical Center and Vanderbilt University
| | - Thomas Morgan
- Barbara J. Evans, Ph.D., J.D., LL.M., is the Mary Ann and Lawrence E. Faust Professor of Law and Professor, Electrical and Computer Engineering at the University of Houston. Gail Javitt, J.D., is a Member of the Firm at Hyman, Phelps, and McNamara, P.C. Ralph Hall, J.D., is a Principal at Leavitt Partners and a Professor of Practice at the University of Minnesota Law School. Megan Robertson, J.D., is an Associate in the Health Care and Life Sciences practice, Epstein Becker & Green, P.C. Pilar Ossorio, Ph.D., J.D., is Professor of Law and Bioethics at the University of Wisconsin Law School and Ethics Scholar-in-Residence at the Morgridge Institute for Research. Susan M. Wolf, J.D., is McKnight Presidential Professor of Law, Medicine & Public Policy; Faegre Baker Daniels Professor of Law; Professor of Medicine; and Chair of the Consortium on Law and Values in Health, Environment & the Life Sciences at the University of Minnesota. Thomas Morgan, M.D., F.A.C.M.G., is Associate Professor of Pediatrics in Medical Genetics at the Vanderbilt University School of Medicine. Ellen W. Clayton, M.D., J.D., is Craig-Weaver Professor of Pediatrics and Professor of Law at Vanderbilt University Medical Center and Vanderbilt University
| | - Ellen Wright Clayton
- Barbara J. Evans, Ph.D., J.D., LL.M., is the Mary Ann and Lawrence E. Faust Professor of Law and Professor, Electrical and Computer Engineering at the University of Houston. Gail Javitt, J.D., is a Member of the Firm at Hyman, Phelps, and McNamara, P.C. Ralph Hall, J.D., is a Principal at Leavitt Partners and a Professor of Practice at the University of Minnesota Law School. Megan Robertson, J.D., is an Associate in the Health Care and Life Sciences practice, Epstein Becker & Green, P.C. Pilar Ossorio, Ph.D., J.D., is Professor of Law and Bioethics at the University of Wisconsin Law School and Ethics Scholar-in-Residence at the Morgridge Institute for Research. Susan M. Wolf, J.D., is McKnight Presidential Professor of Law, Medicine & Public Policy; Faegre Baker Daniels Professor of Law; Professor of Medicine; and Chair of the Consortium on Law and Values in Health, Environment & the Life Sciences at the University of Minnesota. Thomas Morgan, M.D., F.A.C.M.G., is Associate Professor of Pediatrics in Medical Genetics at the Vanderbilt University School of Medicine. Ellen W. Clayton, M.D., J.D., is Craig-Weaver Professor of Pediatrics and Professor of Law at Vanderbilt University Medical Center and Vanderbilt University
| |
Collapse
|
60
|
Mostafavi H, Harpak A, Agarwal I, Conley D, Pritchard JK, Przeworski M. Variable prediction accuracy of polygenic scores within an ancestry group. eLife 2020; 9:48376. [PMID: 31999256 DOI: 10.1101/629949] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/10/2019] [Accepted: 01/28/2020] [Indexed: 05/25/2023] Open
Abstract
Fields as diverse as human genetics and sociology are increasingly using polygenic scores based on genome-wide association studies (GWAS) for phenotypic prediction. However, recent work has shown that polygenic scores have limited portability across groups of different genetic ancestries, restricting the contexts in which they can be used reliably and potentially creating serious inequities in future clinical applications. Using the UK Biobank data, we demonstrate that even within a single ancestry group (i.e., when there are negligible differences in linkage disequilibrium or in causal alleles frequencies), the prediction accuracy of polygenic scores can depend on characteristics such as the socio-economic status, age or sex of the individuals in which the GWAS and the prediction were conducted, as well as on the GWAS design. Our findings highlight both the complexities of interpreting polygenic scores and underappreciated obstacles to their broad use.
Collapse
Affiliation(s)
| | - Arbel Harpak
- Department of Biological Sciences, Columbia University, New York, United States
| | - Ipsita Agarwal
- Department of Biological Sciences, Columbia University, New York, United States
| | - Dalton Conley
- Department of Sociology, Princeton University, Princeton, United States
- Office of Population Research, Princeton University, Princeton, United States
| | - Jonathan K Pritchard
- Department of Genetics, Stanford University, Stanford, United States
- Department of Biology, Stanford University, Stanford, United States
- Howard Hughes Medical Institute, Stanford University, Stanford, United States
| | - Molly Przeworski
- Department of Biological Sciences, Columbia University, New York, United States
- Department of Systems Biology, Columbia University, New York, United States
| |
Collapse
|
61
|
Mostafavi H, Harpak A, Agarwal I, Conley D, Pritchard JK, Przeworski M. Variable prediction accuracy of polygenic scores within an ancestry group. eLife 2020; 9:e48376. [PMID: 31999256 PMCID: PMC7067566 DOI: 10.7554/elife.48376] [Citation(s) in RCA: 215] [Impact Index Per Article: 53.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/10/2019] [Accepted: 01/28/2020] [Indexed: 12/13/2022] Open
Abstract
Fields as diverse as human genetics and sociology are increasingly using polygenic scores based on genome-wide association studies (GWAS) for phenotypic prediction. However, recent work has shown that polygenic scores have limited portability across groups of different genetic ancestries, restricting the contexts in which they can be used reliably and potentially creating serious inequities in future clinical applications. Using the UK Biobank data, we demonstrate that even within a single ancestry group (i.e., when there are negligible differences in linkage disequilibrium or in causal alleles frequencies), the prediction accuracy of polygenic scores can depend on characteristics such as the socio-economic status, age or sex of the individuals in which the GWAS and the prediction were conducted, as well as on the GWAS design. Our findings highlight both the complexities of interpreting polygenic scores and underappreciated obstacles to their broad use.
Collapse
Affiliation(s)
| | - Arbel Harpak
- Department of Biological Sciences, Columbia UniversityNew YorkUnited States
| | - Ipsita Agarwal
- Department of Biological Sciences, Columbia UniversityNew YorkUnited States
| | - Dalton Conley
- Department of Sociology, Princeton UniversityPrincetonUnited States
- Office of Population Research, Princeton UniversityPrincetonUnited States
| | - Jonathan K Pritchard
- Department of Genetics, Stanford UniversityStanfordUnited States
- Department of Biology, Stanford UniversityStanfordUnited States
- Howard Hughes Medical Institute, Stanford UniversityStanfordUnited States
| | - Molly Przeworski
- Department of Biological Sciences, Columbia UniversityNew YorkUnited States
- Department of Systems Biology, Columbia UniversityNew YorkUnited States
| |
Collapse
|
62
|
Uricchio LH. Evolutionary perspectives on polygenic selection, missing heritability, and GWAS. Hum Genet 2020; 139:5-21. [PMID: 31201529 PMCID: PMC8059781 DOI: 10.1007/s00439-019-02040-6] [Citation(s) in RCA: 23] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/29/2018] [Accepted: 06/06/2019] [Indexed: 12/26/2022]
Abstract
Genome-wide association studies (GWAS) have successfully identified many trait-associated variants, but there is still much we do not know about the genetic basis of complex traits. Here, we review recent theoretical and empirical literature regarding selection on complex traits to argue that "missing heritability" is as much an evolutionary problem as it is a statistical problem. We discuss empirical findings that suggest a role for selection in shaping the effect sizes and allele frequencies of causal variation underlying complex traits, and the limitations of these studies. We then use simulations of selection, realistic genome structure, and complex human demography to illustrate the results of recent theoretical work on polygenic selection, and show that statistical inference of causal loci is sharply affected by evolutionary processes. In particular, when selection acts on causal alleles, it hampers the ability to detect causal loci and constrains the transferability of GWAS results across populations. Last, we discuss the implications of these findings for future association studies, and suggest that future statistical methods to infer causal loci for genetic traits will benefit from explicit modeling of the joint distribution of effect sizes and allele frequencies under plausible evolutionary models.
Collapse
Affiliation(s)
- Lawrence H Uricchio
- Department of Biology, Stanford University, Stanford, CA, USA.
- Department of Integrative Biology, University of California, Berkeley, Berkeley, CA, USA.
| |
Collapse
|
63
|
|
64
|
Oetjens MT, Kelly MA, Sturm AC, Martin CL, Ledbetter DH. Quantifying the polygenic contribution to variable expressivity in eleven rare genetic disorders. Nat Commun 2019; 10:4897. [PMID: 31653860 PMCID: PMC6814771 DOI: 10.1038/s41467-019-12869-0] [Citation(s) in RCA: 60] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/14/2019] [Accepted: 10/03/2019] [Indexed: 12/02/2022] Open
Abstract
Rare genetic disorders (RGDs) often exhibit significant clinical variability among affected individuals, a disease characteristic termed variable expressivity. Recently, the aggregate effect of common variation, quantified as polygenic scores (PGSs), has emerged as an effective tool for predictions of disease risk and trait variation in the general population. Here, we measure the effect of PGSs on 11 RGDs including four sex-chromosome aneuploidies (47,XXX; 47,XXY; 47,XYY; 45,X) that affect height; two copy-number variant (CNV) disorders (16p11.2 deletions and duplications) and a Mendelian disease (melanocortin 4 receptor deficiency (MC4R)) that affect BMI; and two Mendelian diseases affecting cholesterol: familial hypercholesterolemia (FH; LDLR and APOB) and familial hypobetalipoproteinemia (FHBL; PCSK9 and APOB). Our results demonstrate that common, polygenic factors of relevant complex traits frequently contribute to variable expressivity of RGDs and that PGSs may be a useful metric for predicting clinical severity in affected individuals and for risk stratification.
Collapse
MESH Headings
- Apolipoproteins B/genetics
- Autistic Disorder/genetics
- Body Height/genetics
- Body Mass Index
- Cholesterol, LDL/blood
- Cholesterol, LDL/genetics
- Chromosome Deletion
- Chromosome Disorders/genetics
- Chromosome Duplication/genetics
- Chromosomes, Human, Pair 16/genetics
- Chromosomes, Human, X/genetics
- Female
- Humans
- Hyperlipoproteinemia Type II/genetics
- Hypobetalipoproteinemias/genetics
- Intellectual Disability/genetics
- Klinefelter Syndrome/genetics
- Male
- Middle Aged
- Multifactorial Inheritance
- Obesity/genetics
- Proprotein Convertase 9/genetics
- Rare Diseases/genetics
- Receptor, Melanocortin, Type 4/deficiency
- Receptor, Melanocortin, Type 4/genetics
- Receptors, LDL/genetics
- Sex Chromosome Aberrations
- Sex Chromosome Disorders of Sex Development/genetics
- Trisomy/genetics
- Turner Syndrome/genetics
- XYY Karyotype/genetics
Collapse
Affiliation(s)
| | - M A Kelly
- Geisinger Health System, Danville, PA, USA
| | - A C Sturm
- Geisinger Health System, Danville, PA, USA
| | - C L Martin
- Geisinger Health System, Danville, PA, USA
| | | |
Collapse
|
65
|
Abstract
Using data from the Philadelphia Neurodevelopmental Cohort, we examined whether European ancestry predicted cognitive ability over and above both parental socioeconomic status (SES) and measures of eye, hair, and skin color. First, using multi-group confirmatory factor analysis, we verified that strict factorial invariance held between self-identified African and European-Americans. The differences between these groups, which were equivalent to 14.72 IQ points, were primarily (75.59%) due to difference in general cognitive ability (g), consistent with Spearman’s hypothesis. We found a relationship between European admixture and g. This relationship existed in samples of (a) self-identified monoracial African-Americans (B = 0.78, n = 2,179), (b) monoracial African and biracial African-European-Americans, with controls added for self-identified biracial status (B = 0.85, n = 2407), and (c) combined European, African-European, and African-American participants, with controls for self-identified race/ethnicity (B = 0.75, N = 7,273). Controlling for parental SES modestly attenuated these relationships whereas controlling for measures of skin, hair, and eye color did not. Next, we validated four sets of polygenic scores for educational attainment (eduPGS). MTAG, the multi-trait analysis of genome-wide association study (GWAS) eduPGS (based on 8442 overlapping variants) predicted g in both the monoracial African-American (r = 0.111, n = 2179, p < 0.001), and the European-American (r = 0.227, n = 4914, p < 0.001) subsamples. We also found large race differences for the means of eduPGS (d = 1.89). Using the ancestry-adjusted association between MTAG eduPGS and g from the monoracial African-American sample as an estimate of the transracially unbiased validity of eduPGS (B = 0.124), the results suggest that as much as 20%–25% of the race difference in g can be naïvely explained by known cognitive ability-related variants. Moreover, path analysis showed that the eduPGS substantially mediated associations between cognitive ability and European ancestry in the African-American sample. Subtest differences, together with the effects of both ancestry and eduPGS, had near-identity with subtest g-loadings. This finding confirmed a Jensen effect acting on ancestry-related differences. Finally, we confirmed measurement invariance along the full range of European ancestry in the combined sample using local structural equation modeling. Results converge on genetics as a potential partial explanation for group mean differences in intelligence.
Collapse
|
66
|
Wünnemann F, Sin Lo K, Langford-Avelar A, Busseuil D, Dubé MP, Tardif JC, Lettre G. Validation of Genome-Wide Polygenic Risk Scores for Coronary Artery Disease in French Canadians. CIRCULATION. GENOMIC AND PRECISION MEDICINE 2019; 12:e002481. [PMID: 31184202 PMCID: PMC6587223 DOI: 10.1161/circgen.119.002481] [Citation(s) in RCA: 42] [Impact Index Per Article: 8.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/06/2019] [Accepted: 05/17/2019] [Indexed: 11/16/2022]
Abstract
BACKGROUND Coronary artery disease (CAD) represents one of the leading causes of morbidity and mortality worldwide. Given the healthcare risks and societal impacts associated with CAD, their clinical management would benefit from improved prevention and prediction tools. Polygenic risk scores (PRS) based on an individual's genome sequence are emerging as potentially powerful biomarkers to predict the risk to develop CAD. Two recently derived genome-wide PRS have shown high specificity and sensitivity to identify CAD cases in European-ancestry participants from the UK Biobank. However, validation of the PRS predictive power and transferability in other populations is now required to support their clinical utility. METHODS We calculated both PRS (GPSCAD and metaGRSCAD) in French-Canadian individuals from 3 cohorts totaling 3639 prevalent CAD cases and 7382 controls and tested their power to predict prevalent, incident, and recurrent CAD. We also estimated the impact of the founder French-Canadian familial hypercholesterolemia deletion ( LDLR delta >15 kb deletion) on CAD risk in one of these cohorts and used this estimate to calibrate the impact of the PRS. RESULTS Our results confirm the ability of both PRS to predict prevalent CAD comparable to the original reports (area under the curve=0.72-0.89). Furthermore, the PRS identified about 6% to 7% of individuals at CAD risk similar to carriers of the LDLR delta >15 kb mutation, consistent with previous estimates. However, the PRS did not perform as well in predicting an incident or recurrent CAD (area under the curve=0.56-0.60), maybe because of confounding because 76% of the participants were on statin treatment. This result suggests that additional work is warranted to better understand how ascertainment biases and study design impact PRS for CAD. CONCLUSIONS Collectively, our results confirm that novel, genome-wide PRS is able to predict CAD in French Canadians; with further improvements, this is likely to pave the way towards more targeted strategies to predict and prevent CAD-related adverse events.
Collapse
Affiliation(s)
- Florian Wünnemann
- Montreal Heart Institute (F.W., K.S.L., A.L.-A., D.B., M.-P.D., J.-C.T., G.L.), Université de Montréal, Québec, Canada
| | - Ken Sin Lo
- Montreal Heart Institute (F.W., K.S.L., A.L.-A., D.B., M.-P.D., J.-C.T., G.L.), Université de Montréal, Québec, Canada
| | - Alexandra Langford-Avelar
- Montreal Heart Institute (F.W., K.S.L., A.L.-A., D.B., M.-P.D., J.-C.T., G.L.), Université de Montréal, Québec, Canada
| | - David Busseuil
- Montreal Heart Institute (F.W., K.S.L., A.L.-A., D.B., M.-P.D., J.-C.T., G.L.), Université de Montréal, Québec, Canada
| | - Marie-Pierre Dubé
- Montreal Heart Institute (F.W., K.S.L., A.L.-A., D.B., M.-P.D., J.-C.T., G.L.), Université de Montréal, Québec, Canada
- Faculté de Médecine (M.-P.D., J-C.T., G.L.), Université de Montréal, Québec, Canada
| | - Jean-Claude Tardif
- Montreal Heart Institute (F.W., K.S.L., A.L.-A., D.B., M.-P.D., J.-C.T., G.L.), Université de Montréal, Québec, Canada
- Faculté de Médecine (M.-P.D., J-C.T., G.L.), Université de Montréal, Québec, Canada
| | - Guillaume Lettre
- Montreal Heart Institute (F.W., K.S.L., A.L.-A., D.B., M.-P.D., J.-C.T., G.L.), Université de Montréal, Québec, Canada
- Faculté de Médecine (M.-P.D., J-C.T., G.L.), Université de Montréal, Québec, Canada
| |
Collapse
|
67
|
Abstract
Great care is needed when interpreting claims about the genetic basis of human variation based on data from genome-wide association studies.
Collapse
Affiliation(s)
| | - Joachim Hermisson
- Department of Mathematics, University of Vienna, Vienna, Austria.,Max F. Perutz Laboratories, University of Vienna, Vienna, Austria
| | - Magnus Nordborg
- Gregor Mendel Institute, Austrian Academy of Sciences, Vienna BioCenter, Vienna, Austria
| |
Collapse
|