101
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Privé F, Aschard H, Carmi S, Folkersen L, Hoggart C, O'Reilly PF, Vilhjálmsson BJ. Portability of 245 polygenic scores when derived from the UK Biobank and applied to 9 ancestry groups from the same cohort. Am J Hum Genet 2022; 109:12-23. [PMID: 34995502 PMCID: PMC8764121 DOI: 10.1016/j.ajhg.2021.11.008] [Citation(s) in RCA: 123] [Impact Index Per Article: 61.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/26/2021] [Accepted: 11/04/2021] [Indexed: 12/25/2022] Open
Abstract
The low portability of polygenic scores (PGSs) across global populations is a major concern that must be addressed before PGSs can be used for everyone in the clinic. Indeed, prediction accuracy has been shown to decay as a function of the genetic distance between the training and test cohorts. However, such cohorts differ not only in their genetic distance but also in their geographical distance and their data collection and assaying, conflating multiple factors. In this study, we examine the extent to which PGSs are transferable between ancestries by deriving polygenic scores for 245 curated traits from the UK Biobank data and applying them in nine ancestry groups from the same cohort. By restricting both training and testing to the UK Biobank data, we reduce the risk of environmental and genotyping confounding from using different cohorts. We define the nine ancestry groups at a sub-continental level, based on a simple, robust, and effective method that we introduce here. We then apply two different predictive methods to derive polygenic scores for all 245 phenotypes and show a systematic and dramatic reduction in portability of PGSs trained using Northwestern European individuals and applied to nine ancestry groups. These analyses demonstrate that prediction already drops off within European ancestries and reduces globally in proportion to genetic distance. Altogether, our study provides unique and robust insights into the PGS portability problem.
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Affiliation(s)
- Florian Privé
- National Centre for Register-Based Research, Aarhus University, Aarhus 8210, Denmark.
| | - Hugues Aschard
- Department of Computational Biology, Institut Pasteur, Paris 75015, France; Program in Genetic Epidemiology and Statistical Genetics, Harvard T.H. Chan School of Public Health, Boston, MA 02115, USA
| | - Shai Carmi
- Braun School of Public Health and Community Medicine, The Hebrew University of Jerusalem, Jerusalem 9112102, Israel
| | | | - Clive Hoggart
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Paul F O'Reilly
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Bjarni J Vilhjálmsson
- National Centre for Register-Based Research, Aarhus University, Aarhus 8210, Denmark; Bioinformatics Research Centre, Aarhus University, Aarhus 8000, Denmark
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102
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Abstract
Cerebral small vessel disease (cSVD) is a leading cause of ischaemic and haemorrhagic stroke and a major contributor to dementia. Covert cSVD, which is detectable with brain MRI but does not manifest as clinical stroke, is highly prevalent in the general population, particularly with increasing age. Advances in technologies and collaborative work have led to substantial progress in the identification of common genetic variants that are associated with cSVD-related stroke (ischaemic and haemorrhagic) and MRI-defined covert cSVD. In this Review, we provide an overview of collaborative studies - mostly genome-wide association studies (GWAS) - that have identified >50 independent genetic loci associated with the risk of cSVD. We describe how these associations have provided novel insights into the biological mechanisms involved in cSVD, revealed patterns of shared genetic variation across cSVD traits, and shed new light on the continuum between rare, monogenic and common, multifactorial cSVD. We consider how GWAS summary statistics have been leveraged for Mendelian randomization studies to explore causal pathways in cSVD and provide genetic evidence for drug effects, and how the combination of findings from GWAS with gene expression resources and drug target databases has enabled identification of putative causal genes and provided proof-of-concept for drug repositioning potential. We also discuss opportunities for polygenic risk prediction, multi-ancestry approaches and integration with other omics data.
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103
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Pathak AK, Sukhavasi K, Marnetto D, Chaubey G, Pandey AK. Human population genomics approach in food metabolism. FUTURE FOODS 2022. [DOI: 10.1016/b978-0-323-91001-9.00033-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/19/2022] Open
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104
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Genetic risk scores and dementia risk across different ethnic groups in UK Biobank. PLoS One 2022; 17:e0277378. [PMID: 36477264 PMCID: PMC9728885 DOI: 10.1371/journal.pone.0277378] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/07/2022] [Accepted: 10/26/2022] [Indexed: 12/12/2022] Open
Abstract
BACKGROUND Genetic Risk Scores (GRS) for predicting dementia risk have mostly been used in people of European ancestry with limited testing in other ancestry groups. METHODS We conducted a logistic regression with all-cause dementia as the outcome and z-standardised GRS as the exposure across diverse ethnic groups. FINDINGS There was variation in frequency of APOE alleles across ethnic groups. Per standard deviation (SD) increase in z-GRS including APOE, the odds ratio (OR) for dementia was 1.73 (95%CI 1.69-1.77). Z-GRS excluding APOE also increased dementia risk (OR 1.21 per SD increase, 95% CI 1.18-1.24) and there was no evidence that ethnicity modified this association. Prediction of secondary outcomes was less robust in those not of European ancestry when APOE was excluded from the GRS. INTERPRETATION z-GRS derived from studies in people of European ancestry can be used to quantify genetic risk in people from more diverse ancestry groups. Urgent work is needed to include people from diverse ancestries in future genetic risk studies to make this field more inclusive.
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105
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Clark K, Leung YY, Lee WP, Voight B, Wang LS. Polygenic Risk Scores in Alzheimer's Disease Genetics: Methodology, Applications, Inclusion, and Diversity. J Alzheimers Dis 2022; 89:1-12. [PMID: 35848019 PMCID: PMC9484091 DOI: 10.3233/jad-220025] [Citation(s) in RCA: 17] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
The success of genome-wide association studies (GWAS) completed in the last 15 years has reinforced a key fact: polygenic architecture makes a substantial contribution to variation of susceptibility to complex disease, including Alzheimer's disease. One straight-forward way to capture this architecture and predict which individuals in a population are most at risk is to calculate a polygenic risk score (PRS). This score aggregates the risk conferred across multiple genetic variants, ultimately representing an individual's predicted genetic susceptibility for a disease. PRS have received increasing attention after having been successfully used in complex traits. This has brought with it renewed attention on new methods which improve the accuracy of risk prediction. While these applications are initially informative, their utility is far from equitable: the majority of PRS models use samples heavily if not entirely of individuals of European descent. This basic approach opens concerns of health equity if applied inaccurately to other population groups, or health disparity if we fail to use them at all. In this review we will examine the methods of calculating PRS and some of their previous uses in disease prediction. We also advocate for, with supporting scientific evidence, inclusion of data from diverse populations in these existing and future studies of population risk via PRS.
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Affiliation(s)
- Kaylyn Clark
- Penn Neurodegeneration Genomics Center, Department of Pathology and Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.,Department of Pathology and Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Yuk Yee Leung
- Penn Neurodegeneration Genomics Center, Department of Pathology and Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.,Department of Pathology and Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Wan-Ping Lee
- Penn Neurodegeneration Genomics Center, Department of Pathology and Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.,Department of Pathology and Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Benjamin Voight
- Department of Systems Pharmacology and Translational Therapeutics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.,Department of Genetics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.,Institute of Translational Medicine and Therapeutics, Perelman School or Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Li-San Wang
- Penn Neurodegeneration Genomics Center, Department of Pathology and Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.,Department of Pathology and Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
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106
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Sohail M, Izarraras-Gomez A, Ortega-Del Vecchyo D. Populations, Traits, and Their Spatial Structure in Humans. Genome Biol Evol 2021; 13:evab272. [PMID: 34894236 PMCID: PMC8715524 DOI: 10.1093/gbe/evab272] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 12/06/2021] [Indexed: 11/16/2022] Open
Abstract
The spatial distribution of genetic variants is jointly determined by geography, past demographic processes, natural selection, and its interplay with environmental variation. A fraction of these genetic variants are "causal alleles" that affect the manifestation of a complex trait. The effect exerted by these causal alleles on complex traits can be independent or dependent on the environment. Understanding the evolutionary processes that shape the spatial structure of causal alleles is key to comprehend the spatial distribution of complex traits. Natural selection, past population size changes, range expansions, consanguinity, assortative mating, archaic introgression, admixture, and the environment can alter the frequencies, effect sizes, and heterozygosities of causal alleles. This provides a genetic axis along which complex traits can vary. However, complex traits also vary along biogeographical and sociocultural axes which are often correlated with genetic axes in complex ways. The purpose of this review is to consider these genetic and environmental axes in concert and examine the ways they can help us decipher the variation in complex traits that is visible in humans today. This initiative necessarily implies a discussion of populations, traits, the ability to infer and interpret "genetic" components of complex traits, and how these have been impacted by adaptive events. In this review, we provide a history-aware discussion on these topics using both the recent and more distant past of our academic discipline and its relevant contexts.
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Affiliation(s)
- Mashaal Sohail
- Department of Human Genetics, University of Chicago, USA
- Centro de Ciencias Genómicas (CCG), Universidad Nacional Autónoma de México (UNAM), Cuernavaca, Morelos, México
| | - Alan Izarraras-Gomez
- Laboratorio Internacional de Investigación sobre el Genoma Humano (LIIGH), Universidad Nacional Autónoma de México (UNAM), Juriquilla, Querétaro, México
| | - Diego Ortega-Del Vecchyo
- Laboratorio Internacional de Investigación sobre el Genoma Humano (LIIGH), Universidad Nacional Autónoma de México (UNAM), Juriquilla, Querétaro, México
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107
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Wang Y, Zhu M, Ma H, Shen H. Polygenic risk scores: the future of cancer risk prediction, screening, and precision prevention. MEDICAL REVIEW (BERLIN, GERMANY) 2021; 1:129-149. [PMID: 37724297 PMCID: PMC10471106 DOI: 10.1515/mr-2021-0025] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/16/2021] [Accepted: 12/13/2021] [Indexed: 09/20/2023]
Abstract
Genome-wide association studies (GWASs) have shown that the genetic architecture of cancers are highly polygenic and enabled researchers to identify genetic risk loci for cancers. The genetic variants associated with a cancer can be combined into a polygenic risk score (PRS), which captures part of an individual's genetic susceptibility to cancer. Recently, PRSs have been widely used in cancer risk prediction and are shown to be capable of identifying groups of individuals who could benefit from the knowledge of their probabilistic susceptibility to cancer, which leads to an increased interest in understanding the potential utility of PRSs that might further refine the assessment and management of cancer risk. In this context, we provide an overview of the major discoveries from cancer GWASs. We then review the methodologies used for PRS construction, and describe steps for the development and evaluation of risk prediction models that include PRS and/or conventional risk factors. Potential utility of PRSs in cancer risk prediction, screening, and precision prevention are illustrated. Challenges and practical considerations relevant to the implementation of PRSs in health care settings are discussed.
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Affiliation(s)
- Yuzhuo Wang
- Department of Epidemiology, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing, Jiangsu, China
- Department of Medical Informatics, School of Biomedical Engineering and Informatics, Nanjing Medical University, Nanjing, Jiangsu, China
| | - Meng Zhu
- Department of Epidemiology, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing, Jiangsu, China
- Jiangsu Key Lab of Cancer Biomarkers, Prevention and Treatment, Collaborative Innovation Center for Cancer Personalized Medicine, Nanjing Medical University, Nanjing, Jiangsu, China
| | - Hongxia Ma
- Department of Epidemiology, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing, Jiangsu, China
- Jiangsu Key Lab of Cancer Biomarkers, Prevention and Treatment, Collaborative Innovation Center for Cancer Personalized Medicine, Nanjing Medical University, Nanjing, Jiangsu, China
- Research Units of Cohort Study on Cardiovascular Diseases and Cancers, Chinese Academy of Medical Sciences, Beijing, China
| | - Hongbing Shen
- Department of Epidemiology, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing, Jiangsu, China
- Jiangsu Key Lab of Cancer Biomarkers, Prevention and Treatment, Collaborative Innovation Center for Cancer Personalized Medicine, Nanjing Medical University, Nanjing, Jiangsu, China
- Research Units of Cohort Study on Cardiovascular Diseases and Cancers, Chinese Academy of Medical Sciences, Beijing, China
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108
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Marceau K, Brick LA, Pasman JA, Knopik VS, Reijneveld SA. Interactions between Genetic, Prenatal, Cortisol, and Parenting Influences on Adolescent Substance Use and Frequency: A TRAILS Study. Eur Addict Res 2021; 28:176-185. [PMID: 34847558 PMCID: PMC9117435 DOI: 10.1159/000519864] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/19/2021] [Accepted: 09/17/2021] [Indexed: 11/19/2022]
Abstract
INTRODUCTION Dynamic relations between genetic, hormone, and pre- and postnatal environments are theorized as critically important for adolescent substance use but are rarely tested in multifactorial models. This study assessed the impact of interactions of genetic risk and cortisol reactivity with prenatal and parenting influences on both any and frequency of adolescent substance use. METHODS Data are from the TRacking Adolescents' Individual Lives Survey (TRAILS), a prospective longitudinal, multi-rater study of 2,230 Dutch adolescents. Genetic risk was assessed via 3 substance-specific polygenic scores. Mothers retrospectively reported prenatal risk when adolescents were 11 years old. Adolescents rated their parents' warmth and hostility at age 11. Salivary cortisol reactivity was measured in response to a social stress task at age 16. Adolescents' self-reported cigarette, alcohol, and cannabis use frequency at age 16. RESULTS A multivariate hurdle regression model showed that polygenic risk for smoking, alcohol, and cannabis predicted any use of each substance, respectively, but predicted more frequent use only for smoking. Blunted cortisol reactivity predicted any use and more frequent use for all 3 outcomes. There were 2 interactions: blunted cortisol reactivity exacerbated the association of polygenic risk with any smoking and the association of prenatal risk with any alcohol use. CONCLUSION Polygenic risk seems of importance for early use but less so for frequency of use, whereas blunted cortisol reactivity was correlated with both. Blunted cortisol reactivity may also catalyze early risks for substance use, though to a limited degree. Gene-environment interactions play no role in the context of this multifactorial model.
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Affiliation(s)
- Kristine Marceau
- Department of Human Development and Family Studies, Purdue University; 1202 W. State Street, West Lafayette, IN 47907
| | - Leslie A. Brick
- Department of Psychiatry and Human Behavior, Alpert Medical School at Brown University; 700 Butler Drive, Providence, RI, 02906
| | - Joëlle A. Pasman
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Solnavägen 1, 171 77 Solna, Sweden
| | - Valerie S. Knopik
- Department of Human Development and Family Studies, Purdue University; 1202 W. State Street, West Lafayette, IN 47907
| | - Sijmen A. Reijneveld
- Department of Health Sciences, University Medical Center Groningen, University of Groningen; Hanzeplein 1, 9713 GZ Groningen, Netherlands
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109
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Abstract
Over the past decade, substantial progress has been made in the discovery of alleles contributing to the risk of coronary artery disease. In addition to providing causal insights into disease, these endeavours have yielded and enabled the refinement of polygenic risk scores. These scores can be used to predict incident coronary artery disease in multiple cohorts and indicate the clinical response to some preventive therapies in post hoc analyses of clinical trials. These observations and the widespread ability to calculate polygenic risk scores from direct-to-consumer and health-care-associated biobanks have raised many questions about responsible clinical adoption. In this Review, we describe technical and downstream considerations for the derivation and validation of polygenic risk scores and current evidence for their efficacy and safety. We discuss the implementation of these scores in clinical medicine for uses including risk prediction and screening algorithms for coronary artery disease, prioritization of patient subgroups that are likely to derive benefit from treatment, and efficient prospective clinical trial designs.
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110
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Ma Y, Zhou X. Genetic prediction of complex traits with polygenic scores: a statistical review. Trends Genet 2021; 37:995-1011. [PMID: 34243982 PMCID: PMC8511058 DOI: 10.1016/j.tig.2021.06.004] [Citation(s) in RCA: 47] [Impact Index Per Article: 15.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/04/2021] [Revised: 05/31/2021] [Accepted: 06/03/2021] [Indexed: 01/03/2023]
Abstract
Accurate genetic prediction of complex traits can facilitate disease screening, improve early intervention, and aid in the development of personalized medicine. Genetic prediction of complex traits requires the development of statistical methods that can properly model polygenic architecture and construct a polygenic score (PGS). We present a comprehensive review of 46 methods for PGS construction. We connect the majority of these methods through a multiple linear regression framework which can be instrumental for understanding their prediction performance for traits with distinct genetic architectures. We discuss the practical considerations of PGS analysis as well as challenges and future directions of PGS method development. We hope our review serves as a useful reference both for statistical geneticists who develop PGS methods and for data analysts who perform PGS analysis.
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Affiliation(s)
- Ying Ma
- Department of Biostatistics, 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.
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111
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Prakrithi P, Lakra P, Sundar D, Kapoor M, Mukerji M, Gupta I, The Indian Genome Variation Consortium. Genetic Risk Prediction of COVID-19 Susceptibility and Severity in the Indian Population. Front Genet 2021; 12:714185. [PMID: 34707636 PMCID: PMC8543005 DOI: 10.3389/fgene.2021.714185] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/24/2021] [Accepted: 09/08/2021] [Indexed: 01/09/2023] Open
Abstract
Host genetic variants can determine their susceptibility to COVID-19 infection and severity as noted in a recent Genome-wide Association Study (GWAS). Given the prominent genetic differences in Indian sub-populations as well as differential prevalence of COVID-19, here, we compute genetic risk scores in diverse Indian sub-populations that may predict differences in the severity of COVID-19 outcomes. We utilized the top 100 most significantly associated single-nucleotide polymorphisms (SNPs) from a GWAS by Pairo-Castineira et al. determining the genetic susceptibility to severe COVID-19 infection, to compute population-wise polygenic risk scores (PRS) for populations represented in the Indian Genome Variation Consortium (IGVC) database. Using a generalized linear model accounting for confounding variables, we found that median PRS was significantly associated (p < 2 x 10−16) with COVID-19 mortality in each district corresponding to the population studied and had the largest effect on mortality (regression coefficient = 10.25). As a control we repeated our analysis on randomly selected 100 non-associated SNPs several times and did not find significant association. Therefore, we conclude that genetic susceptibility may play a major role in determining the differences in COVID-19 outcomes and mortality across the Indian sub-continent. We suggest that combining PRS with other observed risk-factors in a Bayesian framework may provide a better prediction model for ascertaining high COVID-19 risk groups and to design more effective public health resource allocation and vaccine distribution schemes.
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Affiliation(s)
- P Prakrithi
- Genomics and Molecular Medicine, CSIR Institute of Genomics and Integrative Biology, New Delhi, India.,Department of Biochemical Engineering and Biotechnology, Indian Institute of Technology Delhi, New Delhi, India
| | - Priya Lakra
- Department of Biochemical Engineering and Biotechnology, Indian Institute of Technology Delhi, New Delhi, India
| | - Durai Sundar
- Department of Biochemical Engineering and Biotechnology, Indian Institute of Technology Delhi, New Delhi, India
| | - Manav Kapoor
- Department of Neuroscience, Icahn School of Medicine at Mt. Sinai, New York, NY, United States
| | - Mitali Mukerji
- Genomics and Molecular Medicine, CSIR Institute of Genomics and Integrative Biology, New Delhi, India
| | - Ishaan Gupta
- Department of Biochemical Engineering and Biotechnology, Indian Institute of Technology Delhi, New Delhi, India
| | - The Indian Genome Variation Consortium
- Genomics and Molecular Medicine, CSIR Institute of Genomics and Integrative Biology, New Delhi, India.,Department of Biochemical Engineering and Biotechnology, Indian Institute of Technology Delhi, New Delhi, India.,Department of Neuroscience, Icahn School of Medicine at Mt. Sinai, New York, NY, United States
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112
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Márquez-Luna C, Gazal S, Loh PR, Kim SS, Furlotte N, Auton A, Price AL. Incorporating functional priors improves polygenic prediction accuracy in UK Biobank and 23andMe data sets. Nat Commun 2021; 12:6052. [PMID: 34663819 PMCID: PMC8523709 DOI: 10.1038/s41467-021-25171-9] [Citation(s) in RCA: 50] [Impact Index Per Article: 16.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/26/2019] [Accepted: 07/16/2021] [Indexed: 12/23/2022] Open
Abstract
Polygenic risk prediction is a widely investigated topic because of its promising clinical applications. Genetic variants in functional regions of the genome are enriched for complex trait heritability. Here, we introduce a method for polygenic prediction, LDpred-funct, that leverages trait-specific functional priors to increase prediction accuracy. We fit priors using the recently developed baseline-LD model, including coding, conserved, regulatory, and LD-related annotations. We analytically estimate posterior mean causal effect sizes and then use cross-validation to regularize these estimates, improving prediction accuracy for sparse architectures. We applied LDpred-funct to predict 21 highly heritable traits in the UK Biobank (avg N = 373 K as training data). LDpred-funct attained a +4.6% relative improvement in average prediction accuracy (avg prediction R2 = 0.144; highest R2 = 0.413 for height) compared to SBayesR (the best method that does not incorporate functional information). For height, meta-analyzing training data from UK Biobank and 23andMe cohorts (N = 1107 K) increased prediction R2 to 0.431. Our results show that incorporating functional priors improves polygenic prediction accuracy, consistent with the functional architecture of complex traits.
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Affiliation(s)
- Carla Márquez-Luna
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, USA.
- Program in Medical and Population Genetics, Broad Institute of Harvard and MIT, Cambridge, MA, USA.
- Charles R. Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
| | - Steven Gazal
- Program in Medical and Population Genetics, Broad Institute of Harvard and MIT, Cambridge, MA, USA
- Charles R. Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Po-Ru Loh
- Program in Medical and Population Genetics, Broad Institute of Harvard and MIT, Cambridge, MA, USA
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA
- Division of Genetics, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
| | - Samuel S Kim
- Program in Medical and Population Genetics, Broad Institute of Harvard and MIT, Cambridge, MA, USA
- Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge, MA, USA
| | | | | | - Alkes L Price
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, USA.
- Program in Medical and Population Genetics, Broad Institute of Harvard and MIT, Cambridge, MA, USA.
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA.
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113
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Ishigaki K. Beyond GWAS: from simple associations to functional insights. Semin Immunopathol 2021; 44:3-14. [PMID: 34605948 DOI: 10.1007/s00281-021-00894-5] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2021] [Accepted: 09/08/2021] [Indexed: 12/31/2022]
Abstract
Each human, when born, has slightly different DNA sequences, which make each of us unique. The variations in DNA sequences are called genetic variants. The primary aim of genome-wide association study (GWAS) is to detect associations between genetic variants and human phenotypes. Since GWAS focuses on germ-line variants, there is no reverse causation. Therefore, GWAS is one of the few tools that can assess the causality of human diseases. In the past 10 years, many large-scale GWAS have been conducted. Although the primary outputs of GWAS are just a series of statistics, its downstream analyses provided many insights beyond simple associations: the causal mechanisms for autoimmune diseases and shared etiology between diseases. Moreover, GWAS downstream analyses generated scores potentially helpful in predicting clinical outcomes of each patient. This review focuses on GWAS for autoimmune diseases and introduces significant achievements of its downstream analyses. We also provide future directions that potentially overcome current limitations. We restrict our discussion to common autoimmune diseases (e.g., rheumatoid arthritis) since rare Mendelian diseases possess distinct genetic etiologies and are not tested by GWAS.
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Affiliation(s)
- Kazuyoshi Ishigaki
- Laboratory for Human Immunogenetics, RIKEN Center for Integrative Medical Sciences, 1-7-22 Suehiro-cho, Tsurumi-ku, Yokohama City, Kanagawa, 230-0045, Japan.
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114
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Ghatan S, Costantini A, Li R, De Bruin C, Appelman-Dijkstra NM, Winter EM, Oei L, Medina-Gomez C. The Polygenic and Monogenic Basis of Paediatric Fractures. Curr Osteoporos Rep 2021; 19:481-493. [PMID: 33945105 PMCID: PMC8551106 DOI: 10.1007/s11914-021-00680-0] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 04/15/2021] [Indexed: 01/19/2023]
Abstract
PURPOSE OF REVIEW Fractures are frequently encountered in paediatric practice. Although recurrent fractures in children usually unveil a monogenic syndrome, paediatric fracture risk could be shaped by the individual genetic background influencing the acquisition of bone mineral density, and therefore, the skeletal fragility as shown in adults. Here, we examine paediatric fractures from the perspective of monogenic and complex trait genetics. RECENT FINDINGS Large-scale genome-wide studies in children have identified ~44 genetic loci associated with fracture or bone traits whereas ~35 monogenic diseases characterized by paediatric fractures have been described. Genetic variation can predispose to paediatric fractures through monogenic risk variants with a large effect and polygenic risk involving many variants of small effects. Studying genetic factors influencing peak bone attainment might help in identifying individuals at higher risk of developing early-onset osteoporosis and discovering drug targets to be used as bone restorative pharmacotherapies to prevent, or even reverse, bone loss later in life.
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Affiliation(s)
- S Ghatan
- Translational Skeletal Genomics Group, Department of Internal Medicine, Erasmus MC University Medical Centre, Doctor Molewaterplein 40, Ee-571, 3015, GD, Rotterdam, The Netherlands
- Department of Epidemiology, Erasmus MC University Medical Centre, Rotterdam, The Netherlands
| | - A Costantini
- Department of Molecular Medicine and Surgery and Center for Molecular Medicine, Karolinska Institutet, Stockholm, Sweden
| | - R Li
- Translational Skeletal Genomics Group, Department of Internal Medicine, Erasmus MC University Medical Centre, Doctor Molewaterplein 40, Ee-571, 3015, GD, Rotterdam, The Netherlands
- Department of Epidemiology, Erasmus MC University Medical Centre, Rotterdam, The Netherlands
| | - C De Bruin
- Department of Paediatrics, Leiden University Medical Centre, Leiden, The Netherlands
| | - N M Appelman-Dijkstra
- Department of Internal Medicine, Leiden University Medical Centre, Leiden, The Netherlands
| | - E M Winter
- Department of Internal Medicine, Leiden University Medical Centre, Leiden, The Netherlands
| | - L Oei
- Translational Skeletal Genomics Group, Department of Internal Medicine, Erasmus MC University Medical Centre, Doctor Molewaterplein 40, Ee-571, 3015, GD, Rotterdam, The Netherlands
- Department of Epidemiology, Erasmus MC University Medical Centre, Rotterdam, The Netherlands
- Department of Internal Medicine, Leiden University Medical Centre, Leiden, The Netherlands
| | - Carolina Medina-Gomez
- Translational Skeletal Genomics Group, Department of Internal Medicine, Erasmus MC University Medical Centre, Doctor Molewaterplein 40, Ee-571, 3015, GD, Rotterdam, The Netherlands.
- Department of Epidemiology, Erasmus MC University Medical Centre, Rotterdam, The Netherlands.
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115
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Neale ZE, Kuo SIC, Dick DM. A systematic review of gene-by-intervention studies of alcohol and other substance use. Dev Psychopathol 2021; 33:1410-1427. [PMID: 32602428 PMCID: PMC7772257 DOI: 10.1017/s0954579420000590] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/01/2023]
Abstract
Alcohol and other substance use problems are common, and the efficacy of current prevention and intervention programs is limited. Genetics may contribute to differential effectiveness of psychosocial prevention and intervention programs. This paper reviews gene-by-intervention (G×I) studies of alcohol and other substance use, and implications for integrating genetics into prevention science. Systematic review yielded 17 studies for inclusion. Most studies focused on youth substance prevention, alcohol was the most common outcome, and measures of genotype were heterogeneous. All studies reported at least one significant G×I interaction. We discuss these findings in the context of the history and current state of genetics, and provide recommendations for future G×I research. These include the integration of genome-wide polygenic scores into prevention studies, broad outcome measurement, recruitment of underrepresented populations, testing mediators of G×I effects, and addressing ethical implications. Integrating genetic research into prevention science, and training researchers to work fluidly across these fields, will enhance our ability to determine the best intervention for each individual across development. With growing public interest in obtaining personalized genetic information, we anticipate that the integration of genetics and prevention science will become increasingly important as we move into the era of precision medicine.
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Affiliation(s)
- Zoe E. Neale
- Department of Psychology, Virginia Commonwealth University
| | | | - Danielle M. Dick
- Department of Psychology, Virginia Commonwealth University
- Department of Human and Molecular Genetics, Virginia Commonwealth University
- College Behavioral and Emotional Health Institute, Virginia Commonwealth University
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116
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Fritsche LG, Ma Y, Zhang D, Salvatore M, Lee S, Zhou X, Mukherjee B. On cross-ancestry cancer polygenic risk scores. PLoS Genet 2021; 17:e1009670. [PMID: 34529658 PMCID: PMC8445431 DOI: 10.1371/journal.pgen.1009670] [Citation(s) in RCA: 30] [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: 01/26/2021] [Accepted: 06/17/2021] [Indexed: 11/29/2022] Open
Abstract
Polygenic risk scores (PRS) can provide useful information for personalized risk stratification and disease risk assessment, especially when combined with non-genetic risk factors. However, their construction depends on the availability of summary statistics from genome-wide association studies (GWAS) independent from the target sample. For best compatibility, it was reported that GWAS and the target sample should match in terms of ancestries. Yet, GWAS, especially in the field of cancer, often lack diversity and are predominated by European ancestry. This bias is a limiting factor in PRS research. By using electronic health records and genetic data from the UK Biobank, we contrast the utility of breast and prostate cancer PRS derived from external European-ancestry-based GWAS across African, East Asian, European, and South Asian ancestry groups. We highlight differences in the PRS distributions of these groups that are amplified when PRS methods condense hundreds of thousands of variants into a single score. While European-GWAS-derived PRS were not directly transferrable across ancestries on an absolute scale, we establish their predictive potential when considering them separately within each group. For example, the top 10% of the breast cancer PRS distributions within each ancestry group each revealed significant enrichments of breast cancer cases compared to the bottom 90% (odds ratio of 2.81 [95%CI: 2.69,2.93] in European, 2.88 [1.85, 4.48] in African, 2.60 [1.25, 5.40] in East Asian, and 2.33 [1.55, 3.51] in South Asian individuals). Our findings highlight a compromise solution for PRS research to compensate for the lack of diversity in well-powered European GWAS efforts while recruitment of diverse participants in the field catches up. The translation of results from genome-wide association studies (GWAS) into polygenic risk scores (PRS) to predict disease risk or outcomes is a major aspiration in the field of statistical genetics. While there has been significant progress in this area for many complex diseases, the lack of diversity in GWAS is transferred to PRS research. Discovery of genetic risk factors, especially for cancer traits, are almost exclusively based on individuals with European-ancestry, and it remains unclear if these results can be utilized for PRS applications across non-European ancestries. Here, we used external European-ancestry based GWAS results to construct breast and prostate cancer PRS and showcase their utility as predictors across African, East Asian, European, and South Asian ancestry groups using data from the UK Biobank. We observed ancestry-specific PRS distributions, that when scaled within each group, could identify individuals at higher risk of prostate and breast cancer in each group. Our study highlights an opportunity to use results from large European GWAS for the construction of PRS in diverse ancestry groups. To realize the full potential of PRS in early detection and prevention of cancer across ethnic groups, we need rapid expanded recruitment of diverse participants in the field of GWAS.
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Affiliation(s)
- Lars G. Fritsche
- Department of Biostatistics, University of Michigan School of Public Health, Ann Arbor, Michigan, United States of America
- Center for Statistical Genetics, University of Michigan School of Public Health, Ann Arbor, Michigan, United States of America
- Center for Precision Health Data Science, University of Michigan School of Public Health, Ann Arbor, Michigan, United States of America
- University of Michigan Rogel Cancer Center, University of Michigan, Ann Arbor, Michigan, United States of America
- * E-mail:
| | - Ying Ma
- Department of Biostatistics, University of Michigan School of Public Health, Ann Arbor, Michigan, United States of America
- Center for Statistical Genetics, University of Michigan School of Public Health, Ann Arbor, Michigan, United States of America
| | - Daiwei Zhang
- Department of Biostatistics, University of Michigan School of Public Health, Ann Arbor, Michigan, United States of America
- Center for Statistical Genetics, University of Michigan School of Public Health, Ann Arbor, Michigan, United States of America
| | - Maxwell Salvatore
- Department of Biostatistics, University of Michigan School of Public Health, Ann Arbor, Michigan, United States of America
- Center for Precision Health Data Science, University of Michigan School of Public Health, Ann Arbor, Michigan, United States of America
- Department of Epidemiology, University of Michigan School of Public Health, Ann Arbor, Michigan, United States of America
| | - Seunggeun Lee
- Department of Biostatistics, University of Michigan School of Public Health, Ann Arbor, Michigan, United States of America
- Center for Statistical Genetics, University of Michigan School of Public Health, Ann Arbor, Michigan, United States of America
- Graduate School of Data Science, Seoul National University, Seoul, South Korea
| | - Xiang Zhou
- Department of Biostatistics, University of Michigan School of Public Health, Ann Arbor, Michigan, United States of America
- Center for Statistical Genetics, University of Michigan School of Public Health, Ann Arbor, Michigan, United States of America
- Center for Precision Health Data Science, University of Michigan School of Public Health, Ann Arbor, Michigan, United States of America
| | - Bhramar Mukherjee
- Department of Biostatistics, University of Michigan School of Public Health, Ann Arbor, Michigan, United States of America
- Center for Statistical Genetics, University of Michigan School of Public Health, Ann Arbor, Michigan, United States of America
- Center for Precision Health Data Science, University of Michigan School of Public Health, Ann Arbor, Michigan, United States of America
- University of Michigan Rogel Cancer Center, University of Michigan, Ann Arbor, Michigan, United States of America
- Department of Epidemiology, University of Michigan School of Public Health, Ann Arbor, Michigan, United States of America
- Michigan Institute for Data Science, University of Michigan, Ann Arbor, Michigan, United States of America
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117
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Du Z, Gao G, Adedokun B, Ahearn T, Lunetta KL, Zirpoli G, Troester MA, Ruiz-Narváez EA, Haddad SA, PalChoudhury P, Figueroa J, John EM, Bernstein L, Zheng W, Hu JJ, Ziegler RG, Nyante S, Bandera EV, Ingles SA, Mancuso N, Press MF, Deming SL, Rodriguez-Gil JL, Yao S, Ogundiran TO, Ojengbe O, Bolla MK, Dennis J, Dunning AM, Easton DF, Michailidou K, Pharoah PDP, Sandler DP, Taylor JA, Wang Q, Weinberg CR, Kitahara CM, Blot W, Nathanson KL, Hennis A, Nemesure B, Ambs S, Sucheston-Campbell LE, Bensen JT, Chanock SJ, Olshan AF, Ambrosone CB, Olopade OI, Yarney J, Awuah B, Wiafe-Addai B, Conti DV, Palmer JR, Garcia-Closas M, Huo D, Haiman CA. Evaluating Polygenic Risk Scores for Breast Cancer in Women of African Ancestry. J Natl Cancer Inst 2021; 113:1168-1176. [PMID: 33769540 PMCID: PMC8418423 DOI: 10.1093/jnci/djab050] [Citation(s) in RCA: 37] [Impact Index Per Article: 12.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/07/2020] [Revised: 02/03/2021] [Accepted: 03/22/2021] [Indexed: 02/06/2023] Open
Abstract
BACKGROUND Polygenic risk scores (PRSs) have been demonstrated to identify women of European, Asian, and Latino ancestry at elevated risk of developing breast cancer (BC). We evaluated the performance of existing PRSs trained in European ancestry populations among women of African ancestry. METHODS We assembled genotype data for women of African ancestry, including 9241 case subjects and 10 193 control subjects. We evaluated associations of 179- and 313-variant PRSs with overall and subtype-specific BC risk. PRS discriminatory accuracy was assessed using area under the receiver operating characteristic curve. We also evaluated a recalibrated PRS, replacing the index variant with variants in each region that better captured risk in women of African ancestry and estimated lifetime absolute risk of BC in African Americans by PRS category. RESULTS For overall BC, the odds ratio per SD of the 313-variant PRS (PRS313) was 1.27 (95% confidence interval [CI] = 1.23 to 1.31), with an area under the receiver operating characteristic curve of 0.571 (95% CI = 0.562 to 0.579). Compared with women with average risk (40th-60th PRS percentile), women in the top decile of PRS313 had a 1.54-fold increased risk (95% CI = 1.38-fold to 1.72-fold). By age 85 years, the absolute risk of overall BC was 19.6% for African American women in the top 1% of PRS313 and 6.7% for those in the lowest 1%. The recalibrated PRS did not improve BC risk prediction. CONCLUSION The PRSs stratify BC risk in women of African ancestry, with attenuated performance compared with that reported in European, Asian, and Latina populations. Future work is needed to improve BC risk stratification for women of African ancestry.
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Affiliation(s)
- Zhaohui Du
- Center for Genetic Epidemiology, Department of Preventive Medicine, Keck School of Medicine, University of Southern California, Norris Comprehensive Cancer Center, Los Angeles, CA, USA
- Public Health Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, WA, USA
| | - Guimin Gao
- Department of Public Health Sciences, University of Chicago, Chicago, IL, USA
| | - Babatunde Adedokun
- Department of Public Health Sciences, University of Chicago, Chicago, IL, USA
| | - Thomas Ahearn
- Division of Cancer Epidemiology and Genetics, Department of Health and Human Services, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA
| | | | - Gary Zirpoli
- Slone Epidemiology Center, Boston University, Boston, MA, USA
| | - Melissa A Troester
- Department of Epidemiology, Lineberger Comprehensive Cancer Center, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | | | | | - Parichoy PalChoudhury
- Division of Cancer Epidemiology and Genetics, Department of Health and Human Services, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA
| | - Jonine Figueroa
- Division of Cancer Epidemiology and Genetics, Department of Health and Human Services, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA
- Usher Institute of Population Health Sciences and Informatics, The University of Edinburgh Medical School, Edinburgh, UK
- Cancer Research UK Edinburgh Centre, Edinburgh, UK
| | - Esther M John
- Department of Epidemiology and Population Health, Stanford University School of Medicine, Stanford, CA, USA
- Department of Medicine (Oncology), Stanford University School of Medicine, Stanford, CA, USA
| | - Leslie Bernstein
- Division of Biomarkers of Early Detection and Prevention Department of Population Sciences, Beckman Research Institute of the City of Hope, City of Hope Comprehensive Cancer Center, Los Angeles, CA, USA
| | - Wei Zheng
- Division of Epidemiology, Department of Medicine, Vanderbilt Epidemiology Center, and Vanderbilt-Ingram Cancer Center, Vanderbilt University School of Medicine, Nashville, TN, USA
| | - Jennifer J Hu
- Department of Public Health Sciences, Sylvester Comprehensive Cancer Center University of Miami Miller School of Medicine, Miami, FL, USA
| | - Regina G Ziegler
- Division of Cancer Epidemiology and Genetics, Department of Health and Human Services, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA
| | - Sarah Nyante
- Department of Epidemiology, Gillings School of Global Public Health and Lineberger Comprehensive Cancer Center, University of North Carolina, Chapel Hill, NC, USA
| | - Elisa V Bandera
- Department of Population Science, Rutgers Cancer Institute of New Jersey, New Brunswick, NJ, USA
| | - Sue A Ingles
- Center for Genetic Epidemiology, Department of Preventive Medicine, Keck School of Medicine, University of Southern California, Norris Comprehensive Cancer Center, Los Angeles, CA, USA
| | - Nicholas Mancuso
- Center for Genetic Epidemiology, Department of Preventive Medicine, Keck School of Medicine, University of Southern California, Norris Comprehensive Cancer Center, Los Angeles, CA, USA
| | - Michael F Press
- Department of Pathology, Keck School of Medicine and Norris Comprehensive Cancer Center, University of Southern California, Los Angeles, CA, USA
| | - Sandra L Deming
- Division of Epidemiology, Department of Medicine, Vanderbilt Epidemiology Center, and Vanderbilt-Ingram Cancer Center, Vanderbilt University School of Medicine, Nashville, TN, USA
| | - Jorge L Rodriguez-Gil
- Genomics, Development and Disease Section, Genetic Disease Research Branch, National Human Genome Research Institute, National Institutes of Health, Bethesda, MD, USA
- Medical Scientist Training Program, School of Medicine and Public Health, University of Wisconsin-Madison, Madison, WI, USA
| | - Song Yao
- Department of Cancer Prevention and Control, Roswell Park Cancer Institute, Buffalo, NY, USA
| | - Temidayo O Ogundiran
- Department of Surgery, College of Medicine, University of Ibadan, Ibadan, Nigeria
| | - Oladosu Ojengbe
- Center for Population and Reproductive Health, College of Medicine, University of Ibadan, University College Hospital, Ibadan, Nigeria
| | - Manjeet K Bolla
- Department of Public Health and Primary Care, Centre for Cancer Genetic Epidemiology, University of Cambridge, Cambridge, UK
| | - Joe Dennis
- Department of Public Health and Primary Care, Centre for Cancer Genetic Epidemiology, University of Cambridge, Cambridge, UK
| | - Alison M Dunning
- Department of Oncology, Centre for Cancer Genetic Epidemiology, University of Cambridge, Cambridge, UK
| | - Douglas F Easton
- Department of Oncology, Centre for Cancer Genetic Epidemiology, University of Cambridge, Cambridge, UK
| | - Kyriaki Michailidou
- Biostatistics Unit, The Cyprus Institute of Neurology and Genetics, Nicosia, Cyprus
| | - Paul D P Pharoah
- Department of Oncology, Centre for Cancer Genetic Epidemiology, University of Cambridge, Cambridge, UK
| | - Dale P Sandler
- Epidemiology Branch, National Institute of Environmental Health Sciences, National Institutes of Health, Research Triangle Park, NC, USA
| | - Jack A Taylor
- Epidemiology Branch, National Institute of Environmental Health Sciences, National Institutes of Health, Research Triangle Park, NC, USA
| | - Qin Wang
- Department of Public Health and Primary Care, Centre for Cancer Genetic Epidemiology, University of Cambridge, Cambridge, UK
| | - Clarice R Weinberg
- Biostatistics and Computational Biology Branch, National Institute of Environmental Health Sciences, National Institutes of Health, Research Triangle Park, NC, USA
| | - Cari M Kitahara
- Radiation Epidemiology Branch, Division of Cancer Epidemiology and Genetics, National Cancer Institute, Bethesda, MD, USA
| | - William Blot
- Division of Epidemiology, Department of Medicine, Vanderbilt Epidemiology Center, and Vanderbilt-Ingram Cancer Center, Vanderbilt University School of Medicine, Nashville, TN, USA
- International Epidemiology Institute, Rockville, MD, USA
| | - Katherine L Nathanson
- Department of Medicine, Abramson Cancer Center, The Perelman School of Medicine at the University of Pennsylvania, Philadelphia, PA, USA
| | - Anselm Hennis
- Chronic Disease Research Centre and Faculty of Medical Sciences, University of the West Indies, Bridgetown, Barbados
| | - Barbara Nemesure
- Department of Family, Population and Preventive Medicine, Stony Brook University, Stony Brook, NY, USA
| | - Stefan Ambs
- Laboratory of Human Carcinogenesis, National Cancer Institute, Bethesda, MD, USA
| | - Lara E Sucheston-Campbell
- College of Pharmacy, The Ohio State University, Columbus, OH, USA
- College of Veterinary Medicine, The Ohio State University, Columbus, OH, USA
| | - Jeannette T Bensen
- Department of Epidemiology, Lineberger Comprehensive Cancer Center, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Stephen J Chanock
- Division of Cancer Epidemiology and Genetics, Department of Health and Human Services, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA
| | - Andrew F Olshan
- Department of Epidemiology, Gillings School of Global Public Health, University of North Carolina, Chapel Hill, NC, USA
| | - Christine B Ambrosone
- Department of Cancer Prevention and Control, Roswell Park Cancer Institute, Buffalo, NY, USA
| | - Olufunmilayo I Olopade
- Department of Medicine, Center for Clinical Cancer Genetics and Global Health, University of Chicago, Chicago, IL, USA
| | | | | | | | | | | | - Julie R Palmer
- Slone Epidemiology Center, Boston University, Boston, MA, USA
| | - Montserrat Garcia-Closas
- Division of Cancer Epidemiology and Genetics, Department of Health and Human Services, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA
| | - Dezheng Huo
- Department of Public Health Sciences, University of Chicago, Chicago, IL, USA
| | - Christopher A Haiman
- Center for Genetic Epidemiology, Department of Preventive Medicine, Keck School of Medicine, University of Southern California, Norris Comprehensive Cancer Center, Los Angeles, CA, USA
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118
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Mathieson I. The omnigenic model and polygenic prediction of complex traits. Am J Hum Genet 2021; 108:1558-1563. [PMID: 34331855 PMCID: PMC8456163 DOI: 10.1016/j.ajhg.2021.07.003] [Citation(s) in RCA: 43] [Impact Index Per Article: 14.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2021] [Accepted: 07/08/2021] [Indexed: 12/16/2022] Open
Abstract
The omnigenic model was proposed as a framework to understand the highly polygenic architecture of complex traits revealed by genome-wide association studies (GWASs). I argue that this model also explains recent observations about cross-population genetic effects, specifically the low transferability of polygenic scores and the lack of clear evidence for polygenic selection. In particular, the omnigenic model explains why the effects of most GWAS variants vary between populations. This interpretation has several consequences for the evolutionary interpretation and practical use of GWAS summary statistics and polygenic scores. First, some polygenic scores may be applicable only in populations of the same ancestry and environment as the discovery population. Second, most GWAS associations will have differing effects between populations and are unlikely to be robust clinical targets. Finally, it may not always be possible to detect polygenic selection from population genetic data. These considerations make it difficult to interpret the clinical and evolutionary meanings of polygenic scores without an explicit model of genetic architecture.
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Affiliation(s)
- Iain Mathieson
- Department of Genetics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA.
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119
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Kuo SIC, Salvatore JE, Barr PB, Aliev F, Anokhin A, Bucholz KK, Chan G, Edenberg HJ, Hesselbrock V, Kamarajan C, Kramer JR, Lai D, Mallard TT, Nurnberger JI, Pandey G, Plawecki MH, Sanchez-Roige S, Waldman I, Palmer AA, Dick DM. Mapping Pathways by Which Genetic Risk Influences Adolescent Externalizing Behavior: The Interplay Between Externalizing Polygenic Risk Scores, Parental Knowledge, and Peer Substance Use. Behav Genet 2021; 51:543-558. [PMID: 34117972 PMCID: PMC8403154 DOI: 10.1007/s10519-021-10067-7] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2021] [Accepted: 05/26/2021] [Indexed: 12/25/2022]
Abstract
Genetic predispositions and environmental influences both play an important role in adolescent externalizing behavior; however, they are not always independent. To elucidate gene-environment interplay, we examined the interrelationships between externalizing polygenic risk scores, parental knowledge, and peer substance use in impacting adolescent externalizing behavior across two time-points in a high-risk longitudinal sample of 1,200 adolescents (764 European and 436 African ancestry; Mage = 12.99) from the Collaborative Study on the Genetics of Alcoholism. Results from multivariate path analysis indicated that externalizing polygenic scores were directly associated with adolescent externalizing behavior but also indirectly via peer substance use, in the European ancestry sample. No significant polygenic association nor indirect effects of genetic risk were observed in the African ancestry group, likely due to more limited power. Our findings underscore the importance of gene-environment interplay and suggest peer substance use may be a mechanism through which genetic risk influences adolescent externalizing behavior.
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Affiliation(s)
- Sally I-Chun Kuo
- Department of Psychology, Virginia Commonwealth University, Box 842018, 806 W Franklin St, Richmond, VA, 23284, USA.
| | - Jessica E Salvatore
- Department of Psychology, Virginia Commonwealth University, Box 842018, 806 W Franklin St, Richmond, VA, 23284, USA
| | - Peter B Barr
- Department of Psychology, Virginia Commonwealth University, Box 842018, 806 W Franklin St, Richmond, VA, 23284, USA
| | - Fazil Aliev
- Department of Psychology, Virginia Commonwealth University, Box 842018, 806 W Franklin St, Richmond, VA, 23284, USA
| | - Andrey Anokhin
- Department of Psychiatry, Washington University in St. Louis, St. Louis, MO, USA
| | - Kathleen K Bucholz
- Department of Psychiatry, Washington University in St. Louis, St. Louis, MO, USA
| | - Grace Chan
- Department of Psychiatry, University of Connecticut School of Medicine, Farmington, CT, USA
| | - Howard J Edenberg
- Department of Biochemistry and Molecular Biology, Indiana University, Indianapolis, IN, USA
- Department of Medical and Molecular Genetics, Indiana University, Indianapolis, IN, USA
| | - Victor Hesselbrock
- Department of Psychiatry, University of Connecticut School of Medicine, Farmington, CT, USA
| | - Chella Kamarajan
- Department of Psychiatry and Behavioral Sciences, State University of New York Downstate Medical Center, Brooklyn, NY, USA
| | - John R Kramer
- Department of Psychiatry, University of Iowa, Iowa City, IA, USA
| | - Dongbing Lai
- Department of Medical and Molecular Genetics, Indiana University, Indianapolis, IN, USA
| | - Travis T Mallard
- Department of Psychology, University of Texas at Austin, Austin, TX, USA
| | - John I Nurnberger
- Department of Medical and Molecular Genetics, Indiana University, Indianapolis, IN, USA
| | - Gayathri Pandey
- Department of Psychiatry and Behavioral Sciences, State University of New York Downstate Medical Center, Brooklyn, NY, USA
| | | | - Sandra Sanchez-Roige
- Department of Psychiatry, University of California San Diego, La Jolla, CA, USA
- Division of Genetic Medicine, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Irwin Waldman
- Department of Psychology, Emory University, Atlanta, GA, USA
| | - Abraham A Palmer
- Department of Psychiatry, University of California San Diego, La Jolla, CA, USA
- Institute of Genomic Medicine, University of California San Diego, La Jolla, CA, USA
| | - Danielle M Dick
- Department of Psychology, Virginia Commonwealth University, Box 842018, 806 W Franklin St, Richmond, VA, 23284, USA
- Department of Human and Molecular Genetics, Virginia Commonwealth University, Richmond, VA, USA
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120
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Elam KK, Mun CJ, Kutzner J, Ha T. Polygenic Risk for Aggression Predicts Adult Substance Use Disorder Diagnoses via Substance Use Offending in Emerging Adulthood and is Moderated by a Family-Centered Intervention. Behav Genet 2021; 51:607-618. [PMID: 34117582 PMCID: PMC8404142 DOI: 10.1007/s10519-021-10070-y] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/09/2021] [Accepted: 05/28/2021] [Indexed: 12/31/2022]
Abstract
A substance use offense reflects an encounter with law enforcement and the court system in response to breaking the law which may increase risk for substance use problems later in life. Individuals may also be at risk for substance use offending and substance use problems based on genetic predisposition. We examined a mediation model in which polygenic risk for aggression predicted adult substance use disorder diagnoses (SUD) via substance use offending in emerging adulthood. In addition, we explored for potential attenuation of genetic influences on these outcomes by a family-based intervention, the Family Check-Up (FCU). Secondary data analyses based upon the Project Alliance 1 sample was conducted among those with genetic data (n = 631; 322 from control and 309 from FCU intervention). The sample was ethnically diverse (30% African American, 44% European American, 6% Latinx, 4% Asian American, 3% Native American, and 13% Other). Greater polygenic risk for aggression was found to increase risk for substance use violations (age 19-23), which in turn was associated with greater likelihood of being diagnosed with SUD at age 27. A gene-by-intervention effect was found in which individuals in the control group had greater risk for SUD with increasing polygenic risk for aggression. Some convergence in results was found when replicating analyses in African American and European American subgroups. Results imply that genetic predisposition may increase risk for problematic substance use later in life via antisocial behavior, such as substance use offending, and that this can be attenuated by a family-centered intervention.
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Affiliation(s)
- Kit K Elam
- Department of Applied Health Science, Indiana University, 1025 E. 7th St., Suite 116, Bloomington, IN, 47405, USA.
| | - Chung Jung Mun
- Department of Psychiatry and Behavioral Sciences, Johns Hopkins School of Medicine, Baltimore, USA
- Edson College of Nursing and Health Innovation, Arizona State University, Tempe, USA
| | - Jodi Kutzner
- Department of Applied Health Science, Indiana University, 1025 E. 7th St., Suite 116, Bloomington, IN, 47405, USA
| | - Thao Ha
- Department of Psychology, Arizona State University, Tempe, USA
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121
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Irving-Pease EK, Muktupavela R, Dannemann M, Racimo F. Quantitative Human Paleogenetics: What can Ancient DNA Tell us About Complex Trait Evolution? Front Genet 2021; 12:703541. [PMID: 34422004 PMCID: PMC8371751 DOI: 10.3389/fgene.2021.703541] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/30/2021] [Accepted: 07/08/2021] [Indexed: 12/13/2022] Open
Abstract
Genetic association data from national biobanks and large-scale association studies have provided new prospects for understanding the genetic evolution of complex traits and diseases in humans. In turn, genomes from ancient human archaeological remains are now easier than ever to obtain, and provide a direct window into changes in frequencies of trait-associated alleles in the past. This has generated a new wave of studies aiming to analyse the genetic component of traits in historic and prehistoric times using ancient DNA, and to determine whether any such traits were subject to natural selection. In humans, however, issues about the portability and robustness of complex trait inference across different populations are particularly concerning when predictions are extended to individuals that died thousands of years ago, and for which little, if any, phenotypic validation is possible. In this review, we discuss the advantages of incorporating ancient genomes into studies of trait-associated variants, the need for models that can better accommodate ancient genomes into quantitative genetic frameworks, and the existing limits to inferences about complex trait evolution, particularly with respect to past populations.
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Affiliation(s)
- Evan K. Irving-Pease
- Lundbeck Foundation GeoGenetics Centre, GLOBE Institute, University of Copenhagen, Copenhagen, Denmark
| | - Rasa Muktupavela
- Lundbeck Foundation GeoGenetics Centre, GLOBE Institute, University of Copenhagen, Copenhagen, Denmark
| | - Michael Dannemann
- Center for Genomics, Evolution and Medicine, Institute of Genomics, University of Tartu, Tartu, Estonia
| | - Fernando Racimo
- Lundbeck Foundation GeoGenetics Centre, GLOBE Institute, University of Copenhagen, Copenhagen, Denmark
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122
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Lanca C, Kassam I, Patasova K, Foo LL, Li J, Ang M, Hoang QV, Teo YY, Hysi PG, Saw SM. New Polygenic Risk Score to Predict High Myopia in Singapore Chinese Children. Transl Vis Sci Technol 2021; 10:26. [PMID: 34319387 PMCID: PMC8322707 DOI: 10.1167/tvst.10.8.26] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022] Open
Abstract
Purpose The purpose of this study was to develop an Asian polygenic risk score (PRS) to predict high myopia (HM) in Chinese children in the Singapore Cohort of Risk factors for Myopia (SCORM) cohort. Methods We included children followed from 6 to 11 years old until teenage years (12–18 years old). Cycloplegic autorefraction, ultrasound biometry, Illumina HumanHap 550, or 550 Duo Beadarrays, demographics, and environmental factors data were obtained. The PRS was generated from the Consortium for Refractive Error and Myopia genomewide association study (n = 542,934) and the Strabismus, Amblyopia, and Refractive Error in Singapore children Study (n = 500). The Growing Up in Singapore Towards healthy Outcomes Cohort study (n = 339) was the replication cohort. The outcome was teenage HM (≤ −5.00 D) with predictive performance assessed using the area under the curve (AUC). Results Mean baseline age ± SD was 7.85 ± 0.84 (n = 1004) and 571 attended the teenage visit; 23.3% had HM. In multivariate analysis, the PRS was associated with a myopic spherical equivalent with an incremental R2 of 0.041 (95% confidence interval [CI] = 0.010, 0.073; P < 0.001). AUC for HM (0.77 [95% CI = 0.71–0.83]) performed better (P = 0.02) with the PRS compared with a model without (0.72 [95% CI = 0.65, 0.78]). Children at the top 25% PRS risk had a 2.34-fold-greater risk of HM (95% CI = 1.53, 3.55; P < 0.001). Conclusions The new Asian PRS improved the predictive performance to detect children at risk of HM. Translational Relevance Clinicians may use the PRS with other predictive factors to identify high risk children and guide interventions to reduce the risk of HM later in life.
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Affiliation(s)
- Carla Lanca
- Singapore Eye Research Institute, Singapore.,Comprehensive Health Research Center (CHRC), Escola Nacional de Saúde Pública, Universidade Nova de Lisboa, Lisboa, Portugal.,Escola Superior de Tecnologia da Saúde de Lisboa (ESTeSL), Instituto Politécnico de Lisboa, Lisboa, Portugal
| | - Irfahan Kassam
- Saw Swee Hock School of Public Health, National University of Singapore.,Life Sciences Institute, National University of Singapore
| | - Karina Patasova
- Section of Ophthalmology, School of Life Course Sciences, King's College London, United Kingdom Department of Twin Research and Genetic Epidemiology, School of Life Course Sciences, King's College London, UK
| | - Li-Lian Foo
- Singapore Eye Research Institute, Singapore.,Singapore National Eye Centre, Singapore.,Duke-NUS Medical School, Singapore
| | - Jonathan Li
- Department of Ophthalmology, University of California San Francisco, San Francisco, CA, USA
| | - Marcus Ang
- Singapore Eye Research Institute, Singapore.,Singapore National Eye Centre, Singapore.,Duke-NUS Medical School, Singapore
| | - Quan V Hoang
- Singapore Eye Research Institute, Singapore.,Singapore National Eye Centre, Singapore.,Duke-NUS Medical School, Singapore.,Department of Ophthalmology, Yong Loo Lin School of Medicine, National University of Singapore, Singapore
| | - Yik-Ying Teo
- Saw Swee Hock School of Public Health, National University of Singapore
| | - Pirro G Hysi
- Section of Ophthalmology, School of Life Course Sciences, King's College London, United Kingdom Department of Twin Research and Genetic Epidemiology, School of Life Course Sciences, King's College London, UK.,UCL Great Ormond Street Hospital Institute of Child Health, University College London, UK
| | - Seang-Mei Saw
- Singapore Eye Research Institute, Singapore.,Saw Swee Hock School of Public Health, National University of Singapore.,Duke-NUS Medical School, Singapore
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Liu C, Zeinomar N, Chung WK, Kiryluk K, Gharavi AG, Hripcsak G, Crew KD, Shang N, Khan A, Fasel D, Manolio TA, Jarvik GP, Rowley R, Justice AE, Rahm AK, Fullerton SM, Smoller JW, Larson EB, Crane PK, Dikilitas O, Wiesner GL, Bick AG, Terry MB, Weng C. Generalizability of Polygenic Risk Scores for Breast Cancer Among Women With European, African, and Latinx Ancestry. JAMA Netw Open 2021; 4:e2119084. [PMID: 34347061 PMCID: PMC8339934 DOI: 10.1001/jamanetworkopen.2021.19084] [Citation(s) in RCA: 28] [Impact Index Per Article: 9.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/23/2022] Open
Abstract
IMPORTANCE Multiple polygenic risk scores (PRSs) for breast cancer have been developed from large research consortia; however, their generalizability to diverse clinical settings is unknown. OBJECTIVE To examine the performance of previously developed breast cancer PRSs in a clinical setting for women of European, African, and Latinx ancestry. DESIGN, SETTING, AND PARTICIPANTS This cohort study using the Electronic Medical Records and Genomics (eMERGE) network data set included 39 591 women from 9 contributing medical centers in the US that had electronic medical records (EMR) linked to genotype data. Breast cancer cases and controls were identified through a validated EMR phenotyping algorithm. MAIN OUTCOMES AND MEASURES Multivariable logistic regression was used to assess the association between breast cancer risk and 7 previously developed PRSs, adjusting for age, study site, breast cancer family history, and first 3 ancestry informative principal components. RESULTS This study included 39 591 women: 33 594 with European, 3801 with African, and 2196 with Latinx ancestry. The mean (SD) age at breast cancer diagnosis was 60.7 (13.0), 58.8 (12.5), and 60.1 (13.0) years for women with European, African, and Latinx ancestry, respectively. PRSs derived from women with European ancestry were associated with breast cancer risk in women with European ancestry (highest odds ratio [OR] per 1-SD increase, 1.46; 95% CI, 1.41-1.51), women with Latinx ancestry (highest OR, 1.31; 95% CI, 1.09-1.58), and women with African ancestry (OR, 1.19; 95% CI, 1.05-1.35). For women with European ancestry, this association with breast cancer risk was largest in the extremes of the PRS distribution, with ORs ranging from 2.19 (95% CI, 1.84-2.53) to 2.48 (95% CI, 1.89-3.25) for the 3 different PRSs examined for those in the highest 1% of the PRS compared with those in the middle quantile. Among women with Latinx and African ancestries at the extremes of the PRS distribution, there were no statistically significant associations. CONCLUSIONS AND RELEVANCE This cohort study found that PRS models derived from women with European ancestry for breast cancer risk generalized well for women with European, Latinx, and African ancestries across different clinical settings, although the effect sizes for women with African ancestry were smaller, likely because of differences in risk allele frequencies and linkage disequilibrium patterns. These results highlight the need to improve representation of diverse population groups, particularly women with African ancestry, in genomic research cohorts.
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Affiliation(s)
- Cong Liu
- Department of Biomedical Informatics, Columbia University Irving Medical Center, New York, New York
| | - Nur Zeinomar
- Department of Epidemiology, Columbia University Irving Medical Center, New York, New York
- Division of Medical Oncology, Rutgers Cancer Institute of New Jersey, Robert Wood Johnson Medical School, New Brunswick, New Jersey
| | - Wendy K. Chung
- Department of Pediatrics, Columbia University Irving Medical Center, New York, New York
| | - Krzysztof Kiryluk
- Department of Medicine, Columbia University Irving Medical Center, New York, New York
| | - Ali G. Gharavi
- Department of Medicine, Columbia University Irving Medical Center, New York, New York
| | - George Hripcsak
- Department of Biomedical Informatics, Columbia University Irving Medical Center, New York, New York
| | - Katherine D. Crew
- Department of Medicine, Columbia University Irving Medical Center, New York, New York
| | - Ning Shang
- Department of Biomedical Informatics, Columbia University Irving Medical Center, New York, New York
| | - Atlas Khan
- Department of Medicine, Columbia University Irving Medical Center, New York, New York
| | - David Fasel
- Department of Biomedical Informatics, Columbia University Irving Medical Center, New York, New York
| | - Teri A. Manolio
- National Human Genome Research Institute, Bethesda, Maryland
| | - Gail P. Jarvik
- Department of Medicine, University of Washington, Seattle
| | - Robb Rowley
- National Human Genome Research Institute, Bethesda, Maryland
| | - Ann E. Justice
- Department of Population Health Sciences, Geisinger, Danville, Pennsylvania
| | - Alanna K. Rahm
- Genomic Medicine Institute, Geisinger, Danville, Pennsylvania
| | | | - Jordan W. Smoller
- Psychiatric and Neurodevelopmental Genetics Unit, Center for Genomic Medicine, Massachusetts General Hospital, Boston, Massachusetts
| | - Eric B. Larson
- Kaiser Permanente Washington Health Research Institute, Seattle, Washington
| | - Paul K. Crane
- Department of Medicine, University of Washington, Seattle
| | - Ozan Dikilitas
- Department of Cardiovascular Medicine, Mayo Clinic, Rochester, Minnesota
| | - Georgia L. Wiesner
- Department of Medicine, Division of Genetic Medicine, Vanderbilt University Medical Center, Nashville, Tennessee
| | - Alexander G. Bick
- Department of Medicine, Division of Genetic Medicine, Vanderbilt University Medical Center, Nashville, Tennessee
| | - Mary Beth Terry
- Department of Epidemiology, Columbia University Irving Medical Center, New York, New York
| | - Chunhua Weng
- Department of Biomedical Informatics, Columbia University Irving Medical Center, New York, New York
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Ota M, Fujio K. Multi-omics approach to precision medicine for immune-mediated diseases. Inflamm Regen 2021; 41:23. [PMID: 34332645 PMCID: PMC8325815 DOI: 10.1186/s41232-021-00173-8] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/11/2021] [Accepted: 06/26/2021] [Indexed: 12/23/2022] Open
Abstract
Recent innovation in high-throughput sequencing technologies has drastically empowered the scientific research. Consequently, now, it is possible to capture comprehensive profiles of samples at multiple levels including genome, epigenome, and transcriptome at a time. Applying these kinds of rich information to clinical settings is of great social significance. For some traits such as cardiovascular diseases, attempts to apply omics datasets in clinical practice for the prediction of the disease risk have already shown promising results, although still under way for immune-mediated diseases. Multiple studies have tried to predict treatment response in immune-mediated diseases using genomic, transcriptomic, or clinical information, showing various possible indicators. For better prediction of treatment response or disease outcome in immune-mediated diseases, combining multi-layer information together may increase the power. In addition, in order to efficiently pick up meaningful information from the massive data, high-quality annotation of genomic functions is also crucial. In this review, we discuss the achievement so far and the future direction of multi-omics approach to immune-mediated diseases.
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Affiliation(s)
- Mineto Ota
- Department of Allergy and Rheumatology, Graduate School of Medicine, The University of Tokyo, Tokyo, 113-0033, Japan. .,Department of Functional Genomics and Immunological Diseases, Graduate School of Medicine, The University of Tokyo, Tokyo, 113-0033, Japan.
| | - Keishi Fujio
- Department of Allergy and Rheumatology, Graduate School of Medicine, The University of Tokyo, Tokyo, 113-0033, Japan
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Hirtz R, Zheng Y, Rajcsanyi LS, Libuda L, Antel J, Peters T, Hebebrand J, Hinney A. [Genetic Analyses of Complex Phenotypes Through the Example of Anorexia Nervosa and Bodyweight Regulation]. ZEITSCHRIFT FUR KINDER-UND JUGENDPSYCHIATRIE UND PSYCHOTHERAPIE 2021; 50:175-185. [PMID: 34328348 DOI: 10.1024/1422-4917/a000829] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/19/2022]
Abstract
Genetic Analyses of Complex Phenotypes Through the Example of Anorexia Nervosa and Bodyweight Regulation Abstract. Genetics variants are important for the regulation of bodyweight and also contribute to the genetic architecture of eating disorders. For many decades, family studies, a subentity of so-called formal genetic studies, were employed to determine the genetic share of bodyweight and eating disorders and found heritability rates exceeding 50 % with both phenotypes. Because of this significant contribution of genetics, the search for those genes and their variants related to the variance in bodyweight and the etiology of eating disorders - or both - was commenced by the early 1990s. Initially, candidate genes studies were conducted targeting those genes most plausibly related to either phenotype, especially based on pathophysiological considerations. This approach, however, implicated only a few genes in the regulation of bodyweight and did not provide significant insights into the genetics of eating disorders. Driven by considerable methodological advances in genetic research, especially related to the introduction of so-called genome-wide association studies by the beginning of the 21st century, today more than 1,000 variants/loci have been detected that affect the regulation of bodyweight. Eight such loci have been identified regarding anorexia nervosa (AN). These results as well as those from cross-disorder analyses provide insights into the complex regulation of bodyweight and demonstrated unforeseen pathomechanisms for AN.
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Affiliation(s)
- Raphael Hirtz
- Klinik für Psychiatrie, Psychosomatik und Psychotherapie des Kindes- und Jugendalters, LVR-Klinikum Essen, Kliniken und Institut der Universität Duisburg-Essen, Universitätsklinikum Essen.,Abteilung für Pädiatrische Endokrinologie und Diabetologie, Kinderklinik II, Universitätsklinikum Essen
| | - Yiran Zheng
- Klinik für Psychiatrie, Psychosomatik und Psychotherapie des Kindes- und Jugendalters, LVR-Klinikum Essen, Kliniken und Institut der Universität Duisburg-Essen, Universitätsklinikum Essen
| | - Luisa S Rajcsanyi
- Klinik für Psychiatrie, Psychosomatik und Psychotherapie des Kindes- und Jugendalters, LVR-Klinikum Essen, Kliniken und Institut der Universität Duisburg-Essen, Universitätsklinikum Essen
| | - Lars Libuda
- Klinik für Psychiatrie, Psychosomatik und Psychotherapie des Kindes- und Jugendalters, LVR-Klinikum Essen, Kliniken und Institut der Universität Duisburg-Essen, Universitätsklinikum Essen.,Institut für Ernährung, Konsum und Gesundheit, Fakultät für Naturwissenschaften, Universität Paderborn
| | - Jochen Antel
- Klinik für Psychiatrie, Psychosomatik und Psychotherapie des Kindes- und Jugendalters, LVR-Klinikum Essen, Kliniken und Institut der Universität Duisburg-Essen, Universitätsklinikum Essen
| | - Triinu Peters
- Klinik für Psychiatrie, Psychosomatik und Psychotherapie des Kindes- und Jugendalters, LVR-Klinikum Essen, Kliniken und Institut der Universität Duisburg-Essen, Universitätsklinikum Essen
| | - Johannes Hebebrand
- Klinik für Psychiatrie, Psychosomatik und Psychotherapie des Kindes- und Jugendalters, LVR-Klinikum Essen, Kliniken und Institut der Universität Duisburg-Essen, Universitätsklinikum Essen
| | - Anke Hinney
- Klinik für Psychiatrie, Psychosomatik und Psychotherapie des Kindes- und Jugendalters, LVR-Klinikum Essen, Kliniken und Institut der Universität Duisburg-Essen, Universitätsklinikum Essen
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126
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Runcie DE, Qu J, Cheng H, Crawford L. MegaLMM: Mega-scale linear mixed models for genomic predictions with thousands of traits. Genome Biol 2021; 22:213. [PMID: 34301310 PMCID: PMC8299638 DOI: 10.1186/s13059-021-02416-w] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/02/2020] [Accepted: 06/23/2021] [Indexed: 12/21/2022] Open
Abstract
Large-scale phenotype data can enhance the power of genomic prediction in plant and animal breeding, as well as human genetics. However, the statistical foundation of multi-trait genomic prediction is based on the multivariate linear mixed effect model, a tool notorious for its fragility when applied to more than a handful of traits. We present MegaLMM, a statistical framework and associated software package for mixed model analyses of a virtually unlimited number of traits. Using three examples with real plant data, we show that MegaLMM can leverage thousands of traits at once to significantly improve genetic value prediction accuracy.
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Affiliation(s)
- Daniel E. Runcie
- Department of Plant Sciences, University of California Davis, Davis, CA USA
| | - Jiayi Qu
- Department of Plant Sciences, University of California Davis, Davis, CA USA
| | - Hao Cheng
- Department of Plant Sciences, University of California Davis, Davis, CA USA
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Muhammad A, Aka IT, Birdwell KA, Gordon AS, Roden DM, Wei WQ, Mosley JD, Van Driest SL. Genome-Wide Approach to Measure Variant-Based Heritability of Drug Outcome Phenotypes. Clin Pharmacol Ther 2021; 110:714-722. [PMID: 34151428 DOI: 10.1002/cpt.2323] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/30/2021] [Accepted: 05/16/2021] [Indexed: 12/18/2022]
Abstract
Pharmacogenomic studies have successfully identified variants-typically with large effect sizes in drug target and metabolism enzymes-that predict drug outcome phenotypes. However, these variants may account for a limited proportion of phenotype variability attributable to the genome. Using genome-wide common variation, we measured the narrow-sense heritability ( h SNP 2 ) of seven pharmacodynamic and five pharmacokinetic phenotypes across three cardiovascular drugs, two antibiotics, and three immunosuppressants. We used a Bayesian hierarchical mixed model, BayesR, to model the distribution of genome-wide variant effect sizes for each drug phenotype as a mixture of four normal distributions of fixed variance (0, 0.01%, 0.1%, and 1% of the total additive genetic variance). This model allowed us to parse h SNP 2 into bins representing contributions of no-effect, small-effect, moderate-effect, and large-effect variants, respectively. For the 12 phenotypes, a median of 969 (range 235-6,304) unique individuals of European ancestry and a median of 1,201,626 (range 777,427-1,514,275) variants were included in our analyses. The number of variants contributing to h SNP 2 ranged from 2,791 to 5,356 (median 3,347). Estimates for h SNP 2 ranged from 0.05 (angiotensin-converting enzyme inhibitor-induced cough) to 0.59 (gentamicin concentration). Small-effect and moderate-effect variants contributed a majority to h SNP 2 for every phenotype (range 61-95%). We conclude that drug outcome phenotypes are highly polygenic. Thus, larger genome-wide association studies of drug phenotypes are needed both to discover novel variants and to determine how genome-wide approaches may improve clinical prediction of drug outcomes.
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Affiliation(s)
- Ayesha Muhammad
- Vanderbilt University School of Medicine, Nashville, Tennessee, USA
| | - Ida T Aka
- Department of Pediatrics, Vanderbilt University Medical Center, Nashville, Tennessee, USA
| | - Kelly A Birdwell
- Department of Medicine, Vanderbilt University Medical Center, Nashville, Tennessee, USA
| | - Adam S Gordon
- Department of Pharmacology, Northwestern University, Chicago, Illinois, USA.,Center for Genetic Medicine, Northwestern University, Chicago, Illinois, USA
| | - Dan M Roden
- Department of Medicine, Vanderbilt University Medical Center, Nashville, Tennessee, USA.,Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, Tennessee, USA.,Department of Pharmacology, Vanderbilt University Medical Center, Nashville, Tennessee, USA
| | - Wei-Qi Wei
- Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, Tennessee, USA
| | - Jonathan D Mosley
- Department of Medicine, Vanderbilt University Medical Center, Nashville, Tennessee, USA.,Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, Tennessee, USA
| | - Sara L Van Driest
- Department of Pediatrics, Vanderbilt University Medical Center, Nashville, Tennessee, USA.,Department of Medicine, Vanderbilt University Medical Center, Nashville, Tennessee, USA
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Ji Y, Long J, Kweon SS, Kang D, Kubo M, Park B, Shu XO, Zheng W, Tao R, Li B. Incorporating European GWAS findings improve polygenic risk prediction accuracy of breast cancer among East Asians. Genet Epidemiol 2021; 45:471-484. [PMID: 33739539 PMCID: PMC8372543 DOI: 10.1002/gepi.22382] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/22/2020] [Revised: 01/15/2021] [Accepted: 02/08/2021] [Indexed: 12/23/2022]
Abstract
Previous genome-wide association studies (GWASs) have been largely focused on European (EUR) populations. However, polygenic risk scores (PRSs) derived from EUR have been shown to perform worse in non-EURs compared with EURs. In this study, we aim to improve PRS prediction in East Asians (EASs). We introduce a rescaled meta-analysis framework to combine both EUR (N = 122,175) and EAS (N = 30,801) GWAS summary statistics. To improve PRS prediction in EASs, we use a scaling factor to up-weight the EAS data, such that the resulting effect size estimates are more relevant to EASs. We then derive PRSs for EAS from the rescaled meta-analysis results of EAS and EUR data. Evaluated in an independent EAS validation data set, this approach increases the prediction liability-adjusted Nagelkerke's pseudo R2 by 40%, 41%, and 5%, respectively, compared with PRSs derived from an EAS GWAS only, EUR GWAS only, and conventional fixed-effects meta-analysis of EAS and EUR data. The PRS derived from the rescaled meta-analysis approach achieved an area under the receiver operating characteristic curve (AUC) of 0.6059, higher than AUC = 0.5782, 0.5809, 0.6008 for EAS, EUR, and conventional meta-analysis of EAS and EUR. We further compare PRSs constructed by single-nucleotide polymorphisms that have different linkage disequilibrium (LD) scores and minor allele frequencies (MAFs) between EUR and EAS, and observe that lower LD scores or MAF in EAS correspond to poorer PRS performance (AUC = 0.5677, 0.5530, respectively) than higher LD scores or MAF (AUC = 0.589, 0.5993, respectively). We finally build a PRS stratified by LD score differences in EUR and EAS using rescaled meta-analysis, and obtain an AUC of 0.6096, with improvement over other strategies investigated.
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Affiliation(s)
- Ying Ji
- Vanderbilt Genetics Institute, Department of Molecular Physiology & Biophysics, Vanderbilt University, Nashville, TN, USA
| | - Jirong Long
- Division of Epidemiology, Department of Medicine, Vanderbilt Epidemiology Center, Vanderbilt-Ingram Cancer Center, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Sun-Seog Kweon
- Department of Preventive Medicine, Chonnam National University Medical School, Hwasun, Korea
- Jeonnam Regional Cancer Center, Chonnam National University Hwasun Hospital, Hwasun, Korea
| | - Daehee Kang
- Department of Preventive Medicine, Seoul National University College of Medicine, Seoul, Korea
- Cancer Research Institute, Seoul National University College of Medicine, Seoul, Korea
- Department of Biomedical Sciences, Seoul National University Graduate School, Seoul, Korea
- Institute of Environmental Medicine, Seoul National University Medical Research Center, Seoul, Korea
| | - Michiaki Kubo
- RIKEN Center for Integrative Medical Sciences, Yokohama, Japan
| | - Boyoung Park
- Department of Medicine, Hanyang University College of Medicine, Seoul, Korea
| | - Xiao-Ou Shu
- Division of Epidemiology, Department of Medicine, Vanderbilt Epidemiology Center, Vanderbilt-Ingram Cancer Center, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Wei Zheng
- Division of Epidemiology, Department of Medicine, Vanderbilt Epidemiology Center, Vanderbilt-Ingram Cancer Center, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Ran Tao
- Department of Biostatistics, Vanderbilt University Medical Center, Nashville, TN, USA
- Vanderbilt Genetics Institute, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Bingshan Li
- Vanderbilt Genetics Institute, Department of Molecular Physiology & Biophysics, Vanderbilt University, Nashville, TN, USA
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Franceschini N, Morris AP. Genetics of kidney traits in worldwide populations: the Continental Origins and Genetic Epidemiology Network (COGENT) Kidney Consortium. Kidney Int 2021; 98:35-41. [PMID: 32571486 DOI: 10.1016/j.kint.2020.02.036] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/13/2019] [Revised: 02/14/2020] [Accepted: 02/28/2020] [Indexed: 10/24/2022]
Affiliation(s)
- Nora Franceschini
- Department of Epidemiology, Gillings School of Global Public Health, University of North Carolina, Chapel Hill, North Carolina, USA.
| | - Andrew P Morris
- Division of Musculoskeletal and Dermatological Sciences, The University of Manchester, Manchester, UK
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130
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Konuma T, Okada Y. Statistical genetics and polygenic risk score for precision medicine. Inflamm Regen 2021; 41:18. [PMID: 34140035 PMCID: PMC8212479 DOI: 10.1186/s41232-021-00172-9] [Citation(s) in RCA: 19] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/28/2021] [Accepted: 06/09/2021] [Indexed: 12/27/2022] Open
Abstract
The prediction of disease risks is an essential part of personalized medicine, which includes early disease detection, prevention, and intervention. The polygenic risk score (PRS) has become the standard for quantifying genetic liability in predicting disease risks. PRS utilizes single-nucleotide polymorphisms (SNPs) with genetic risks elucidated by genome-wide association studies (GWASs) and is calculated as weighted sum scores of these SNPs with genetic risks using their effect sizes from GWASs as their weights. The utilities of PRS have been explored in many common diseases, such as cancer, coronary artery disease, obesity, and diabetes, and in various non-disease traits, such as clinical biomarkers. These applications demonstrated that PRS could identify a high-risk subgroup of these diseases as a predictive biomarker and provide information on modifiable risk factors driving health outcomes. On the other hand, there are several limitations to implementing PRSs in clinical practice, such as biased sensitivity for the ethnic background of PRS calculation and geographical differences even in the same population groups. Also, it remains unclear which method is the most suitable for the prediction with high accuracy among numerous PRS methods developed so far. Although further improvements of its comprehensiveness and generalizability will be needed for its clinical implementation in the future, PRS will be a powerful tool for therapeutic interventions and lifestyle recommendations in a wide range of diseases. Thus, it may ultimately improve the health of an entire population in the future.
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Affiliation(s)
- Takahiro Konuma
- Department of Statistical Genetics, Osaka University Graduate School of Medicine, 2-2 Yamadaoka, Suita, 565-0871, Japan.,Central Pharmaceutical Research Institute, Japan Tobacco Inc., Takatsuki, 569-1125, Japan
| | - Yukinori Okada
- Department of Statistical Genetics, Osaka University Graduate School of Medicine, 2-2 Yamadaoka, Suita, 565-0871, Japan. .,Laboratory of Statistical Immunology, Immunology Frontier Research Center (WPI-IFReC), Osaka University, Suita, 565-0871, Japan. .,Integrated Frontier Research for Medical Science Division, Institute for Open and Transdisciplinary Research Initiatives, Osaka University, Suita, 565-0871, Japan.
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131
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Abstract
As genome-wide association studies have continued to identify loci associated with complex traits, the implications of and necessity for proper use of these findings, including prediction of disease risk, have become apparent. Many complex diseases have numerous associated loci with detectable effects implicating risk for or protection from disease. A common contemporary approach to using this information for disease prediction is through the application of genetic risk scores. These scores estimate an individual's liability for a specific outcome by aggregating the effects of associated loci into a single measure as described in the previous version of this article. Although genetic risk scores have traditionally included variants that meet criteria for genome-wide significance, an extension known as the polygenic risk score has been developed to include the effects of more variants across the entire genome. Here, we describe common methods and software packages for calculating and interpreting polygenic risk scores. In this revised version of the article, we detail information that is needed to perform a polygenic risk score analysis, considerations for planning the analysis and interpreting results, as well as discussion of the limitations based on the choices made. We also provide simulated sample data and a walkthrough for four different polygenic risk score software. © 2021 Wiley Periodicals LLC.
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Affiliation(s)
- Michael D Osterman
- Cleveland Institute for Computational Biology, Department of Population and Quantitative Health Sciences, Case Western Reserve University, Cleveland, Ohio
| | - Tyler G Kinzy
- Cleveland Institute for Computational Biology, Department of Population and Quantitative Health Sciences, Case Western Reserve University, Cleveland, Ohio
| | - Jessica N Cooke Bailey
- Cleveland Institute for Computational Biology, Department of Population and Quantitative Health Sciences, Case Western Reserve University, Cleveland, Ohio
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Font-Porterias N, Giménez A, Carballo-Mesa A, Calafell F, Comas D. Admixture Has Shaped Romani Genetic Diversity in Clinically Relevant Variants. Front Genet 2021; 12:683880. [PMID: 34220960 PMCID: PMC8244592 DOI: 10.3389/fgene.2021.683880] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/22/2021] [Accepted: 05/13/2021] [Indexed: 01/04/2023] Open
Abstract
Genetic patterns of inter-population variation are a result of different demographic and adaptive histories, which gradually shape the frequency distribution of the variants. However, the study of clinically relevant mutations has a Eurocentric bias. The Romani, the largest transnational minority ethnic group in Europe, originated in South Asia and received extensive gene flow from West Eurasia. Most medical genetic studies have only explored founder mutations related to Mendelian disorders in this population. Here we analyze exome sequences and genome-wide array data of 89 healthy Spanish Roma individuals to study complex traits and disease. We apply a different framework and focus on variants with both increased and decreased allele frequencies, taking into account their local ancestry. We report several OMIM traits enriched for genes with deleterious variants showing increased frequencies in Roma or in non-Roma (e.g., obesity is enriched in Roma, with an associated variant linked to South Asian ancestry; while non-insulin dependent diabetes is enriched in non-Roma Europeans). In addition, previously reported pathogenic variants also show differences among populations, where some variants segregating at low frequency in non-Roma are virtually absent in the Roma. Lastly, we describe frequency changes in drug-response variation, where many of the variants increased in Roma are clinically associated with metabolic and cardiovascular-related drugs. These results suggest that clinically relevant variation in Roma cannot only be characterized in terms of founder mutations. Instead, we observe frequency differences compared to non-Roma: some variants are absent, while other have drifted to higher frequencies. As a result of the admixture events, these clinically damaging variants can be traced back to both European and South Asian-related ancestries. This can be attributed to a different prevalence of some genetic disorders or to the fact that genetic susceptibility variants are mostly studied in populations of European descent, and can differ in individuals with different ancestries.
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Affiliation(s)
- Neus Font-Porterias
- Departament de Ciències Experimentals i de la Salut, Institut de Biologia Evolutiva (UPF-CSIC), Universitat Pompeu Fabra, Barcelona, Spain
| | - Aaron Giménez
- Facultat de Sociologia, Universitat Autònoma de Barcelona, Barcelona, Spain
| | | | - Francesc Calafell
- Departament de Ciències Experimentals i de la Salut, Institut de Biologia Evolutiva (UPF-CSIC), Universitat Pompeu Fabra, Barcelona, Spain
| | - David Comas
- Departament de Ciències Experimentals i de la Salut, Institut de Biologia Evolutiva (UPF-CSIC), Universitat Pompeu Fabra, Barcelona, Spain
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133
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Lu T, Forgetta V, Wu H, Perry JRB, Ong KK, Greenwood CMT, Timpson NJ, Manousaki D, Richards JB. A Polygenic Risk Score to Predict Future Adult Short Stature Among Children. J Clin Endocrinol Metab 2021; 106:1918-1928. [PMID: 33788949 PMCID: PMC8266463 DOI: 10.1210/clinem/dgab215] [Citation(s) in RCA: 19] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/02/2021] [Indexed: 11/30/2022]
Abstract
CONTEXT Adult height is highly heritable, yet no genetic predictor has demonstrated clinical utility compared to mid-parental height. OBJECTIVE To develop a polygenic risk score for adult height and evaluate its clinical utility. DESIGN A polygenic risk score was constructed based on meta-analysis of genomewide association studies and evaluated on the Avon Longitudinal Study of Parents and Children (ALSPAC) cohort. SUBJECTS Participants included 442 599 genotyped White British individuals in the UK Biobank and 941 genotyped child-parent trios of European ancestry in the ALSPAC cohort. INTERVENTIONS None. MAIN OUTCOME MEASURES Standing height was measured using stadiometer; Standing height 2 SDs below the sex-specific population average was considered as short stature. RESULTS Combined with sex, a polygenic risk score captured 71.1% of the total variance in adult height in the UK Biobank. In the ALSPAC cohort, the polygenic risk score was able to identify children who developed adulthood short stature with an area under the receiver operating characteristic curve (AUROC) of 0.84, which is close to that of mid-parental height. Combining this polygenic risk score with mid-parental height or only one of the child's parent's height could improve the AUROC to at most 0.90. The polygenic risk score could also substitute mid-parental height in age-specific Khamis-Roche height predictors and achieve an equally strong discriminative power in identifying children with a short stature in adulthood. CONCLUSIONS A polygenic risk score could be considered as an alternative or adjunct to mid-parental height to improve screening for children at risk of developing short stature in adulthood in European ancestry populations.
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Affiliation(s)
- Tianyuan Lu
- Lady Davis Institute for Medical Research, Jewish General Hospital, Montréal, Canada
- Quantitative Life Sciences Program, McGill University, Montréal, Canada
| | - Vincenzo Forgetta
- Lady Davis Institute for Medical Research, Jewish General Hospital, Montréal, Canada
| | - Haoyu Wu
- Lady Davis Institute for Medical Research, Jewish General Hospital, Montréal, Canada
- Department of Epidemiology, Biostatistics and Occupational Health, McGill University, Montréal, Canada
| | - John R B Perry
- Medical Research Council Epidemiology Unit, Institute of Metabolic Science, University of Cambridge, Cambridge, UK
| | - Ken K Ong
- Medical Research Council Epidemiology Unit, Institute of Metabolic Science, University of Cambridge, Cambridge, UK
- Department of Pediatrics, University of Cambridge School of Clinical Medicine, Cambridge, UK
| | - Celia M T Greenwood
- Lady Davis Institute for Medical Research, Jewish General Hospital, Montréal, Canada
- Department of Epidemiology, Biostatistics and Occupational Health, McGill University, Montréal, Canada
- Department of Human Genetics, McGill University, Montréal, Canada
- Gerald Bronfman Department of Oncology, McGill University, Montréal, Canada
| | - Nicholas J Timpson
- Medical Research Council Integrative Epidemiology Unit, Department of Population Health Sciences, University of Bristol, Bristol, United Kingdom
| | - Despoina Manousaki
- Lady Davis Institute for Medical Research, Jewish General Hospital, Montréal, Canada
- Department of Pediatrics, Université de Montréal, Montréal, Canada
| | - J Brent Richards
- Lady Davis Institute for Medical Research, Jewish General Hospital, Montréal, Canada
- Department of Human Genetics, McGill University, Montréal, Canada
- Department of Twin Research and Genetic Epidemiology, King’s College London, London, UK
- Correspondence: J. Brent Richards, Jewish General Hospital, Room H-413, 3755 Chemin de la Côte-Sainte-Catherine, Montréal, Québec, H3T 1E2, Canada. E-mail:
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134
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Lu H, Wang T, Zhang J, Zhang S, Huang S, Zeng P. Evaluating marginal genetic correlation of associated loci for complex diseases and traits between European and East Asian populations. Hum Genet 2021; 140:1285-1297. [PMID: 34091770 DOI: 10.1007/s00439-021-02299-8] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/10/2020] [Accepted: 05/31/2021] [Indexed: 12/14/2022]
Abstract
Genome-wide association studies (GWASs) have successfully identified a large amount of single-nucleotide polymorphisms associated with many complex phenotypes in diverse populations. However, a comprehensive understanding of the genetic correlation of associated loci of phenotypes across populations remains lacking and the extent to which associations discovered in one population can be generalized to other populations or can be utilized for trans-ethnic genetic prediction is also unclear. By leveraging summary statistics, we proposed MAGIC to evaluate the trans-ethnic marginal genetic correlation (rm) of per-allele effect sizes for associated SNPs (P < 5E-8) under the framework of measurement error models. We confirmed the methodological advantage of MAGIC over general approaches through simulations and demonstrated its utility by analyzing 34 GWAS summary statistics of phenotypes from the East Asian (Nmax = 254,373) and European (Nmax = 1,220,901) populations. Among these phenotypes, rm was estimated to range from 0.584 (se = 0.140) for breast cancer to 0.949 (se = 0.035) for age of menarche, with an average of 0.835 (se = 0.045). We also uncovered that the trans-ethnic genetic prediction accuracy for phenotypes in the target population would substantially become low when using associated SNPs identified in non-target populations, indicating that associations discovered in the one population cannot be simply generalized to another population and that the accuracy of trans-ethnic phenotype prediction is generally dissatisfactory. Overall, our study provides in-depth insight into trans-ethnic genetic correlation and prediction for complex phenotypes across diverse populations.
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Affiliation(s)
- Haojie Lu
- Department of Epidemiology and Biostatistics, School of Public Health, Xuzhou Medical University, Xuzhou, 221004, Jiangsu, China
| | - Ting Wang
- Department of Epidemiology and Biostatistics, School of Public Health, Xuzhou Medical University, Xuzhou, 221004, Jiangsu, China
| | - Jinhui Zhang
- Department of Epidemiology and Biostatistics, School of Public Health, Xuzhou Medical University, Xuzhou, 221004, Jiangsu, China
| | - Shuo Zhang
- Department of Epidemiology and Biostatistics, School of Public Health, Xuzhou Medical University, Xuzhou, 221004, Jiangsu, China
| | - Shuiping Huang
- Department of Epidemiology and Biostatistics, School of Public Health, Xuzhou Medical University, Xuzhou, 221004, Jiangsu, China.,Center for Medical Statistics and Data Analysis, School of Public Health, Xuzhou Medical University, Xuzhou, 221004, Jiangsu, China
| | - Ping Zeng
- Department of Epidemiology and Biostatistics, School of Public Health, Xuzhou Medical University, Xuzhou, 221004, Jiangsu, China. .,Center for Medical Statistics and Data Analysis, School of Public Health, Xuzhou Medical University, Xuzhou, 221004, Jiangsu, China.
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135
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Albiñana C, Grove J, McGrath JJ, Agerbo E, Wray NR, Bulik CM, Nordentoft M, Hougaard DM, Werge T, Børglum AD, Mortensen PB, Privé F, Vilhjálmsson BJ. Leveraging both individual-level genetic data and GWAS summary statistics increases polygenic prediction. Am J Hum Genet 2021; 108:1001-1011. [PMID: 33964208 PMCID: PMC8206385 DOI: 10.1016/j.ajhg.2021.04.014] [Citation(s) in RCA: 17] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/04/2021] [Accepted: 04/20/2021] [Indexed: 12/12/2022] Open
Abstract
The accuracy of polygenic risk scores (PRSs) to predict complex diseases increases with the training sample size. PRSs are generally derived based on summary statistics from large meta-analyses of multiple genome-wide association studies (GWASs). However, it is now common for researchers to have access to large individual-level data as well, such as the UK Biobank data. To the best of our knowledge, it has not yet been explored how best to combine both types of data (summary statistics and individual-level data) to optimize polygenic prediction. The most widely used approach to combine data is the meta-analysis of GWAS summary statistics (meta-GWAS), but we show that it does not always provide the most accurate PRS. Through simulations and using 12 real case-control and quantitative traits from both iPSYCH and UK Biobank along with external GWAS summary statistics, we compare meta-GWAS with two alternative data-combining approaches, stacked clumping and thresholding (SCT) and meta-PRS. We find that, when large individual-level data are available, the linear combination of PRSs (meta-PRS) is both a simple alternative to meta-GWAS and often more accurate.
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Affiliation(s)
- Clara Albiñana
- The Lundbeck Foundation Initiative for Integrative Psychiatric Research, iPSYCH, 8210 Aarhus V, Denmark; National Centre for Register-Based Research, Aarhus University, 8210 Aarhus V, Denmark.
| | - Jakob Grove
- The Lundbeck Foundation Initiative for Integrative Psychiatric Research, iPSYCH, 8210 Aarhus V, Denmark; Department of Biomedicine and Center for Integrative Sequencing, iSEQ, Aarhus University, 8000 Aarhus C, Denmark; Center for Genomics and Personalized Medicine, CGPM, Aarhus University, 8000 Aarhus C, Denmark; Bioinformatics Research Centre, Aarhus University, 8000 Aarhus C, Denmark
| | - John J McGrath
- National Centre for Register-Based Research, Aarhus University, 8210 Aarhus V, Denmark; Queensland Centre for Mental Health Research, The Park Centre for Mental Health, Brisbane, QLD 4076, Australia; Queensland Brain Institute, University of Queensland, Brisbane, QLD 4072, Australia
| | - Esben Agerbo
- The Lundbeck Foundation Initiative for Integrative Psychiatric Research, iPSYCH, 8210 Aarhus V, Denmark; National Centre for Register-Based Research, Aarhus University, 8210 Aarhus V, Denmark
| | - Naomi R Wray
- Institute for Molecular Bioscience, University of Queensland, Brisbane, QLD 4072, Australia; Queensland Brain Institute, University of Queensland, Brisbane, QLD 4072, Australia
| | - Cynthia M Bulik
- Department of Psychiatry, University of North Carolina at Chapel Hill, Chapel Hill, NC 27514, USA; Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, 171 77 Stockholm, Sweden; Department of Nutrition, University of North Carolina at Chapel Hill, Chapel Hill, NC 27514, USA
| | - Merete Nordentoft
- The Lundbeck Foundation Initiative for Integrative Psychiatric Research, iPSYCH, 8210 Aarhus V, Denmark; Copenhagen University Hospital, Mental Health Centre Copenhagen Mental Health Services in the Capital Region of Denmark, 2100 Copenhagen Ø, Denmark; Department of Clinical Medicine, University of Copenhagen, 2200 Copenhagen N, Denmark
| | - David M Hougaard
- The Lundbeck Foundation Initiative for Integrative Psychiatric Research, iPSYCH, 8210 Aarhus V, Denmark; Center for Neonatal Screening, Department for Congenital Disorders, Statens Serum Institut, 2300 Copenhagen S, Denmark
| | - Thomas Werge
- The Lundbeck Foundation Initiative for Integrative Psychiatric Research, iPSYCH, 8210 Aarhus V, Denmark; Institute of Biological Psychiatry, MHC Sct. Hans, Mental Health Services Copenhagen, 4000 Roskilde, Denmark; Department of Clinical Medicine, University of Copenhagen, 2200 Copenhagen N, Denmark; Lundbeck Foundation GeoGenetics Centre, GLOBE Institute, University of Copenhagen, 1350 Copenhagen K, Denmark
| | - Anders D Børglum
- The Lundbeck Foundation Initiative for Integrative Psychiatric Research, iPSYCH, 8210 Aarhus V, Denmark; Department of Biomedicine and Center for Integrative Sequencing, iSEQ, Aarhus University, 8000 Aarhus C, Denmark; Center for Genomics and Personalized Medicine, CGPM, Aarhus University, 8000 Aarhus C, Denmark
| | - Preben Bo Mortensen
- The Lundbeck Foundation Initiative for Integrative Psychiatric Research, iPSYCH, 8210 Aarhus V, Denmark; National Centre for Register-Based Research, Aarhus University, 8210 Aarhus V, Denmark
| | - Florian Privé
- The Lundbeck Foundation Initiative for Integrative Psychiatric Research, iPSYCH, 8210 Aarhus V, Denmark; National Centre for Register-Based Research, Aarhus University, 8210 Aarhus V, Denmark
| | - Bjarni J Vilhjálmsson
- The Lundbeck Foundation Initiative for Integrative Psychiatric Research, iPSYCH, 8210 Aarhus V, Denmark; National Centre for Register-Based Research, Aarhus University, 8210 Aarhus V, Denmark; Bioinformatics Research Centre, Aarhus University, 8000 Aarhus C, Denmark.
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136
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Abstract
Calcific aortic valve disease sits at the confluence of multiple world-wide epidemics of aging, obesity, diabetes, and renal dysfunction, and its prevalence is expected to nearly triple over the next 3 decades. This is of particularly dire clinical relevance, as calcific aortic valve disease can progress rapidly to aortic stenosis, heart failure, and eventually premature death. Unlike in atherosclerosis, and despite the heavy clinical toll, to date, no pharmacotherapy has proven effective to halt calcific aortic valve disease progression, with invasive and costly aortic valve replacement representing the only treatment option currently available. This substantial gap in care is largely because of our still-limited understanding of both normal aortic valve biology and the key regulatory mechanisms that drive disease initiation and progression. Drug discovery is further hampered by the inherent intricacy of the valvular microenvironment: a unique anatomic structure, a complex mixture of dynamic biomechanical forces, and diverse and multipotent cell populations collectively contributing to this currently intractable problem. One promising and rapidly evolving tactic is the application of multiomics approaches to fully define disease pathogenesis. Herein, we summarize the application of (epi)genomics, transcriptomics, proteomics, and metabolomics to the study of valvular heart disease. We also discuss recent forays toward the omics-based characterization of valvular (patho)biology at single-cell resolution; these efforts promise to shed new light on cellular heterogeneity in healthy and diseased valvular tissues and represent the potential to efficaciously target and treat key cell subpopulations. Last, we discuss systems biology- and network medicine-based strategies to extract meaning, mechanisms, and prioritized drug targets from multiomics datasets.
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Affiliation(s)
- Mark C. Blaser
- Center for Interdisciplinary Cardiovascular Sciences, Cardiovascular Division, Department of Medicine, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA
| | - Simon Kraler
- Center for Molecular Cardiology, University of Zurich, Schlieren, CH
| | - Thomas F. Lüscher
- Center for Molecular Cardiology, University of Zurich, Schlieren, CH
- Heart Division, Royal Brompton & Harefield Hospitals, London, UK
- National Heart and Lung Institute, Imperial College, London, UK
| | - Elena Aikawa
- Center for Interdisciplinary Cardiovascular Sciences, Cardiovascular Division, Department of Medicine, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA
- Center for Excellence in Vascular Biology, Cardiovascular Division, Department of Medicine, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA
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137
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Di Narzo AF, Hart A, Kosoy R, Peters L, Stojmirovic A, Cheng H, Zhang Z, Shan M, Cho J, Kasarskis A, Argmann C, Peter I, Schadt EE, Hao K. Polygenic risk score for alcohol drinking behavior improves prediction of inflammatory bowel disease risk. Hum Mol Genet 2021; 30:514-523. [PMID: 33601420 PMCID: PMC8599895 DOI: 10.1093/hmg/ddab045] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/21/2020] [Revised: 01/23/2021] [Accepted: 02/03/2021] [Indexed: 02/05/2023] Open
Abstract
Epidemiological studies have long recognized risky behaviors as potentially modifiable factors for the onset and flares of inflammatory bowel disease (IBD); yet, the underlying mechanisms are largely unknown. Recently, the genetic susceptibilities to cigarette smoking, alcohol and cannabis use [i.e. substance use (SU)] have been characterized by well-powered genome-wide association studies (GWASs). We aimed to assess the impact of genetic determinants of SU on IBD risk. Using Mount Sinai Crohn's and Colitis Registry (MSCCR) cohort of 1058 IBD cases and 188 healthy controls, we computed the polygenic risk score (PRS) for SU and correlated them with the observed IBD diagnoses, while adjusting for genetic ancestry, PRS for IBD and SU behavior at enrollment. The results were validated in a pediatric cohort with no SU exposure. PRS of alcohol consumption (DrnkWk), smoking cessation and age of smoking initiation, were associated with IBD risk in MSCCR even after adjustment for PRSIBD and actual smoking status. One interquartile range decrease in PRSDrnkWk was significantly associated to higher IBD risk (i.e. inverse association) (with odds ratio = 1.65 and 95% confidence interval: 1.32, 2.06). The association was replicated in a pediatric Crohn's disease cohort. Colocalization analysis identified a locus on chromosome 16 with polymorphisms in IL27, SULT1A2 and SH2B1, which reached genome-wide statistical significance in GWAS (P < 7.7e-9) for both alcohol consumption and IBD risk. This study demonstrated that the genetic predisposition to SU was associated with IBD risk, independent of PRSIBD and in the absence of SU behaviors. Our study may help further stratify individuals at risk of IBD.
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Affiliation(s)
- Antonio F Di Narzo
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Icahn Institute for Data Science and Genomic Technology, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Amy Hart
- Immunology Translational Sciences, Janssen R&D, LLC, Spring House, PA 19477, USA
| | - Roman Kosoy
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Icahn Institute for Data Science and Genomic Technology, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Lauren Peters
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Icahn Institute for Data Science and Genomic Technology, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Statistical Genomics, Sema4, Stamford, CT 06902, USA
| | | | - Haoxiang Cheng
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Icahn Institute for Data Science and Genomic Technology, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Zhongyang Zhang
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Icahn Institute for Data Science and Genomic Technology, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Mingxu Shan
- Statistical Genomics, Sema4, Stamford, CT 06902, USA
| | - Judy Cho
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Icahn Institute for Data Science and Genomic Technology, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Andrew Kasarskis
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Icahn Institute for Data Science and Genomic Technology, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Carmen Argmann
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Icahn Institute for Data Science and Genomic Technology, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Inga Peter
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Icahn Institute for Data Science and Genomic Technology, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Eric E Schadt
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Icahn Institute for Data Science and Genomic Technology, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Statistical Genomics, Sema4, Stamford, CT 06902, USA
| | - Ke Hao
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Icahn Institute for Data Science and Genomic Technology, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Statistical Genomics, Sema4, Stamford, CT 06902, USA
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138
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Polfus LM, Darst BF, Highland H, Sheng X, Ng MCY, Below JE, Petty L, Bien S, Sim X, Wang W, Fontanillas P, Patel Y, Preuss M, Schurmann C, Du Z, Lu Y, Rhie SK, Mercader JM, Tusie-Luna T, González-Villalpando C, Orozco L, Spracklen CN, Cade BE, Jensen RA, Sun M, Joo YY, An P, Yanek LR, Bielak LF, Tajuddin S, Nicolas A, Chen G, Raffield L, Guo X, Chen WM, Nadkarni GN, Graff M, Tao R, Pankow JS, Daviglus M, Qi Q, Boerwinkle EA, Liu S, Phillips LS, Peters U, Carlson C, Wikens LR, Marchand LL, North KE, Buyske S, Kooperberg C, Loos RJF, Stram DO, Haiman CA. Genetic discovery and risk characterization in type 2 diabetes across diverse populations. HGG ADVANCES 2021; 2. [PMID: 34604815 PMCID: PMC8486151 DOI: 10.1016/j.xhgg.2021.100029] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/23/2022] Open
Abstract
Genomic discovery and characterization of risk loci for type 2 diabetes (T2D) have been conducted primarily in individuals of European ancestry. We conducted a multiethnic genome-wide association study of T2D among 53,102 cases and 193,679 control subjects from African, Hispanic, Asian, Native Hawaiian, and European population groups in the Population Architecture Genomics and Epidemiology (PAGE) and Diabetes Genetics Replication and Meta-analysis (DIAGRAM) Consortia. In individuals of African ancestry, we discovered a risk variant in the TGFB1 gene (rs11466334, risk allele frequency (RAF) = 6.8%, odds ratio [OR] = 1.27, p = 2.06 × 10−8), which replicated in independent studies of African ancestry (p = 6.26 × 10−23). We identified a multiethnic risk variant in the BACE2 gene (rs13052926, RAF = 14.1%, OR = 1.08, p = 5.75 × 10−9), which also replicated in independent studies (p = 3.45 × 10−4). We also observed a significant difference in the performance of a multiethnic genetic risk score (GRS) across population groups (pheterogeneity = 3.85 × 10−20). Comparing individuals in the top GRS risk category (40%–60%), the OR was highest in Asians (OR = 3.08) and European (OR = 2.94) ancestry populations, followed by Hispanic (OR = 2.39), Native Hawaiian (OR = 2.02), and African ancestry (OR = 1.57) populations. These findings underscore the importance of genetic discovery and risk characterization in diverse populations and the urgent need to further increase representation of non-European ancestry individuals in genetics research to improve genetic-based risk prediction across populations.
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Affiliation(s)
- Linda M Polfus
- Department of Preventive Medicine, Center for Genetic Epidemiology, University of Southern California, Los Angeles, CA, USA.,These authors contributed equally
| | - Burcu F Darst
- Department of Preventive Medicine, Center for Genetic Epidemiology, University of Southern California, Los Angeles, CA, USA.,These authors contributed equally
| | - Heather Highland
- Department of Epidemiology, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Xin Sheng
- Department of Preventive Medicine, Center for Genetic Epidemiology, University of Southern California, Los Angeles, CA, USA
| | - Maggie C Y Ng
- Division of Genetic Medicine, Vanderbilt Genetics Institute, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Jennifer E Below
- The Vanderbilt Genetics Institute, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Lauren Petty
- The Vanderbilt Genetics Institute, Vanderbilt University Medical Center, Nashville, TN, USA
| | | | - Xueling Sim
- Saw Swee Hock School of Public Health, National University of Singapore and National University Health System, Singapore, Singapore
| | | | | | - Yesha Patel
- Department of Preventive Medicine, Center for Genetic Epidemiology, University of Southern California, Los Angeles, CA, USA
| | | | | | | | - Michael Preuss
- Icahn School of Medicine at Mount Sinai, The Charles Bronfman Institute for Personalized Medicine, New York, NY, USA
| | - Claudia Schurmann
- Icahn School of Medicine at Mount Sinai, The Charles Bronfman Institute for Personalized Medicine, New York, NY, USA
| | - Zhaohui Du
- Department of Preventive Medicine, Center for Genetic Epidemiology, University of Southern California, Los Angeles, CA, USA
| | - Yingchang Lu
- Division of Genetic Medicine, Vanderbilt Genetics Institute, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Suhn K Rhie
- Department of Biochemistry and Molecular Medicine, University of Southern California, Los Angeles, CA, USA
| | | | - Teresa Tusie-Luna
- Unidad de Biología Molecular y Medicina Genómica, Instituto Nacional de Ciencias Médicas y Nutrición Salvador Zubirán, Universidad Nacional Autónoma de Mexico, Mexico City, Mexico
| | - Clicerio González-Villalpando
- Centro de Estudios en Diabetes, Unidad de Investigacion en Diabetes y Riesgo Cardiovascular, Centro de Investigacion en Salud Poblacional, Instituto Nacional de Salud Pública, Mexico City, Mexico
| | - Lorena Orozco
- Instituto Nacional de Medicina Genómica, Mexico City, Mexico
| | - Cassandra N Spracklen
- Department of Biostatistics and Epidemiology, University of Massachusetts, Amherst, MA, USA
| | - Brian E Cade
- Division of Sleep and Circadian Disorders, Brigham and Women's Hospital, Boston, MA, USA
| | - Richard A Jensen
- Cardiovascular Health Research Unit, Department of Medicine, University of Washington, Seattle, WA, USA
| | - Meng Sun
- Wellcome Centre for Human Genetics, Nuffield Department of Medicine, University of Oxford, Oxford, UK, USA
| | - Yoonjung Yoonie Joo
- Division of Endocrinology, Metabolism, and Molecular Medicine, Department of Medicine, Northwestern University Feinberg School of Medicine, Chicago, IL, USA
| | - Ping An
- Division of Statistical Genomics, School of Medicine, Washington University, St. Louis, MO, USA
| | - Lisa R Yanek
- Department of Medicine, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Lawrence F Bielak
- Department of Epidemiology, School of Public Health, University of Michigan, Ann Arbor, MI, USA
| | - Salman Tajuddin
- Laboratory of Epidemiology and Population Sciences, National Institute on Aging, National Institutes of Health, Baltimore, MD, USA
| | - Aude Nicolas
- Laboratory of Neurogenetics, National Institute on Aging, National Institutes of Health, Bethesda, MD, USA
| | - Guanjie Chen
- Center for Research on Genomics and Global Health, National Human Genome Institute, National Institutes of Health, Bethesda, MD, USA
| | - Laura Raffield
- Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Xiuqing Guo
- Institute for Translational Genomics and Population Sciences, Los Angeles Biomedical Research Institute at Harbor-UCLA Medical Center, Torrance, CA, USA
| | - Wei-Min Chen
- Department of Public Health Sciences and Center for Public Health Genomics, University of Virginia School of Medicine, Charlottesville, VA, USA
| | - Girish N Nadkarni
- Icahn School of Medicine at Mount Sinai, The Charles Bronfman Institute for Personalized Medicine, New York, NY, USA
| | - Mariaelisa Graff
- Department of Epidemiology, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Ran Tao
- Department of Biostatistics, Vanderbilt Genetics Institute, Vanderbilt University, Nashville, TN, USA
| | - James S Pankow
- Division of Epidemiology and Community Health, School of Public Health, University of Minnesota, Minneapolis, MN, USA
| | - Martha Daviglus
- Institute for Minority Health Research, University of Illinois Chicago, Chicago, IL, USA
| | - Qibin Qi
- Center for Population Cohorts, Department of Epidemiology & Population Health, Albert Einstein College of Medicine, Bronx, NY, USA
| | - Eric A Boerwinkle
- Human Genetics Center, University of Texas Health Science Center, Houston, TX, USA
| | - Simin Liu
- School of Public Health, Brown University, Providence, RI, USA
| | - Lawrence S Phillips
- Atlanta VA Medical Center, Decatur, GA, USA.,Division of Endocrinology and Metabolism, Department of Medicine, Emory University School of Medicine, Atlanta, GA, USA
| | - Ulrike Peters
- Division of Public Health Sciences, University of Washington, Department of Epidemiology, Fred Hutchinson Cancer Research Center, Seattle, WA, USA
| | - Chris Carlson
- Division of Public Health Sciences, Fred Hutchinson Cancer Research Center, Seattle, WA, USA
| | - Lynne R Wikens
- University of Hawaii Cancer Center, University of Hawaii at Manoa, Honolulu, HI, USA
| | - Loic Le Marchand
- University of Hawaii Cancer Center, University of Hawaii at Manoa, Honolulu, HI, USA
| | - Kari E North
- Department of Epidemiology, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Steven Buyske
- Department of Statistics, Rutgers University, Piscataway, NJ, USA
| | - Charles Kooperberg
- Division of Public Health Sciences, Fred Hutchinson Cancer Research Center, Seattle, WA, USA
| | - Ruth J F Loos
- Icahn School of Medicine at Mount Sinai, The Mindich Child Health and Development Institute, The Charles Bronfman Institute for Personalized Medicine, New York, NY, USA
| | - Daniel O Stram
- Department of Preventive Medicine, Center for Genetic Epidemiology, University of Southern California, Los Angeles, CA, USA
| | - Christopher A Haiman
- Department of Preventive Medicine, Center for Genetic Epidemiology, University of Southern California, Los Angeles, CA, USA
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Abstract
The known genetic architecture of blood pressure now comprises >30 genes, with rare variants resulting in monogenic forms of hypertension or hypotension and >1,477 common single-nucleotide polymorphisms (SNPs) being associated with the blood pressure phenotype. Monogenic blood pressure syndromes predominantly involve the renin-angiotensin-aldosterone system and the adrenal glucocorticoid pathway, with a smaller fraction caused by neuroendocrine tumours of the sympathetic and parasympathetic nervous systems. The SNPs identified in genome-wide association studies (GWAS) as being associated with the blood pressure phenotype explain only approximately 27% of the 30-50% estimated heritability of blood pressure, and the effect of each SNP on the blood pressure phenotype is small. A paucity of SNPs from GWAS are mapped to known genes causing monogenic blood pressure syndromes. For example, a GWAS signal mapped to the gene encoding uromodulin has been shown to affect blood pressure by influencing sodium homeostasis, and the effects of another GWAS signal were mediated by endothelin. However, the majority of blood pressure-associated SNPs show pleiotropic associations. Unravelling these associations can potentially help us to understand the underlying biological pathways. In this Review, we appraise the current knowledge of blood pressure genomics, explore the causal pathways for hypertension identified in Mendelian randomization studies and highlight the opportunities for drug repurposing and pharmacogenomics for the treatment of hypertension.
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Affiliation(s)
- Sandosh Padmanabhan
- BHF Glasgow Cardiovascular Research Centre, Institute of Cardiovascular and Medical Sciences, University of Glasgow, Glasgow, UK
| | - Anna F Dominiczak
- BHF Glasgow Cardiovascular Research Centre, Institute of Cardiovascular and Medical Sciences, University of Glasgow, Glasgow, UK.
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140
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Gettler K, Levantovsky R, Moscati A, Giri M, Wu Y, Hsu NY, Chuang LS, Sazonovs A, Venkateswaran S, Korie U, Chasteau C, Duerr RH, Silverberg MS, Snapper SB, Daly MJ, McGovern DP, Brant SR, Rioux JD, Kugathasan S, Anderson CA, Itan Y, Cho JH. Common and Rare Variant Prediction and Penetrance of IBD in a Large, Multi-ethnic, Health System-based Biobank Cohort. Gastroenterology 2021; 160:1546-1557. [PMID: 33359885 PMCID: PMC8237248 DOI: 10.1053/j.gastro.2020.12.034] [Citation(s) in RCA: 42] [Impact Index Per Article: 14.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/19/2020] [Revised: 11/24/2020] [Accepted: 12/10/2020] [Indexed: 12/16/2022]
Abstract
BACKGROUND AND AIMS Polygenic risk scores (PRS) may soon be used to predict inflammatory bowel disease (IBD) risk in prevention efforts. We leveraged exome-sequence and single nucleotide polymorphism (SNP) array data from 29,358 individuals in the multiethnic, randomly ascertained health system-based BioMe biobank to define effects of common and rare IBD variants on disease prediction and pathophysiology. METHODS PRS were calculated from European, African American, and Ashkenazi Jewish (AJ) reference case-control studies, and a meta-GWAS run using all three association datasets. PRS were then combined using regression to assess which combination of scores best predicted IBD status in European, AJ, Hispanic, and African American cohorts in BioMe. Additionally, rare variants were assessed in genes associated with very early-onset IBD (VEO-IBD), by estimating genetic penetrance in each BioMe population. RESULTS Combining risk scores based on association data from distinct ancestral populations improved IBD prediction for every population in BioMe and significantly improved prediction among European ancestry UK Biobank individuals. Lower predictive power for non-Europeans was observed, reflecting in part substantially lower African IBD case-control reference sizes. We replicated associations for two VEO-IBD genes, ADAM17 and LRBA, with high dominant model penetrance in BioMe. Autosomal recessive LRBA risk alleles are associated with severe, early-onset autoimmunity; we show that heterozygous carriage of an African-predominant LRBA protein-altering allele is associated with significantly decreased LRBA and CTLA-4 expression with T-cell activation. CONCLUSIONS Greater genetic diversity in African populations improves prediction across populations, and generalizes some VEO-IBD genes. Increasing African American IBD case-collections should be prioritized to reduce health disparities and enhance pathophysiological insight.
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Affiliation(s)
- Kyle Gettler
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, New York
| | - Rachel Levantovsky
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, New York
| | - Arden Moscati
- The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, New York
| | - Mamta Giri
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, New York
| | - Yiming Wu
- The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, New York
| | - Nai-Yun Hsu
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, New York
| | - Ling-Shiang Chuang
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, New York
| | - Aleksejs Sazonovs
- Human Genetics, Wellcome Sanger Institute, Hinxton, Cambridgeshire, United Kingdom
| | - Suresh Venkateswaran
- Division of Pediatric Gastroenterology, Hepatology & Nutrition, Emory University School of Medicine, Atlanta, Georgia
| | - Ujunwa Korie
- The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, New York
| | - Colleen Chasteau
- The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, New York
| | - Richard H Duerr
- Department of Medicine, University of Pittsburgh, Pittsburgh, Pennsylvania
| | - Mark S Silverberg
- Division of Gastroenterology, Mount Sinai Hospital Inflammatory Bowel Disease Centre, Toronto, Ontario, Canada
| | - Scott B Snapper
- Division of Gastroenterology, Hepatology & Nutrition, Boston Children's Hospital, Boston, Massachusetts
| | - Mark J Daly
- Medical and Population Genetics, Broad Institute, Cambridge, Massachusetts; Institute for Molecular Medicine Finland, University of Helsinki, Helsinki, Finland
| | - Dermot P McGovern
- Medicine and Biomedical Sciences, Cedars-Sinai, Los Angeles, California
| | - Steven R Brant
- Division of Gastroenterology and Hepatology, Department of Medicine, Rutgers Robert Wood Johnson Medical School, and Department of Genetics and The Human Genetics Institute of New Jersey, Rutgers University, New Brunswick, New Jersey; Harvey M. and Lyn P. Meyerhoff Inflammatory Bowel Disease Center, Division of Gastroenterology, Johns Hopkins University School of Medicine, Baltimore, Maryland
| | - John D Rioux
- Montreal Heart Institute, University of Montreal, Montreal, Canada
| | - Subra Kugathasan
- Division of Pediatric Gastroenterology, Hepatology & Nutrition, Emory University School of Medicine, Atlanta, Georgia; Department of Human Genetics, Emory University School of Medicine, Atlanta, Georgia
| | - Carl A Anderson
- Human Genetics, Wellcome Sanger Institute, Hinxton, Cambridgeshire, United Kingdom
| | - Yuval Itan
- The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, New York
| | - Judy H Cho
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, New York; The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, New York; Department of Medicine, Icahn School of Medicine at Mount Sinai, New York, New York.
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141
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Zhao B, Zou F. On polygenic risk scores for complex traits prediction. Biometrics 2021; 78:499-511. [PMID: 33786811 DOI: 10.1111/biom.13466] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/04/2021] [Revised: 03/10/2021] [Accepted: 03/15/2021] [Indexed: 12/01/2022]
Abstract
Polygenic risk scores (PRS) have gained substantial attention for complex traits prediction in genome-wide association studies (GWAS). Motivated by the polygenic model of complex traits, we study the statistical properties of PRS under the high-dimensional but sparsity free setting where the triplet ( n , p , m ) → ( ∞ , ∞ , ∞ ) with n , p , m being the sample size, the number of assayed single-nucleotide polymorphisms (SNPs), and the number of assayed causal SNPs, respectively. First, we derive asymptotic results on the out-of-sample (prediction) R-squared for PRS. These results help understand the widespread observed gap between the in-sample heritability (or partial R-squared due to the genetic features) estimate and the out-of-sample R-squared for most complex traits. Next, we investigate how features should be selected (e.g., by a p-value threshold) for constructing optimal PRS. We reveal that the optimal threshold depends largely on the genetic architecture underlying the complex trait and the sample size of the training GWAS, or the m / n ratio. For highly polygenic traits with a large m / n ratio, it is difficult to separate causal and null SNPs and stringent feature selection in principle often leads to poor PRS prediction. We numerically illustrate the theoretical results with intensive simulation studies and real data analysis on 33 complex traits with a wide range of genetic architectures in the UK Biobank database.
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Affiliation(s)
- Bingxin Zhao
- Department of Biostatistics, University of North Carolina, Chapel Hill, North Carolina, USA
| | - Fei Zou
- Department of Biostatistics, University of North Carolina, Chapel Hill, North Carolina, USA
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142
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Somineni HK, Nagpal S, Venkateswaran S, Cutler DJ, Okou DT, Haritunians T, Simpson CL, Begum F, Datta LW, Quiros AJ, Seminerio J, Mengesha E, Alexander JS, Baldassano RN, Dudley-Brown S, Cross RK, Dassopoulos T, Denson LA, Dhere TA, Iskandar H, Dryden GW, Hou JK, Hussain SZ, Hyams JS, Isaacs KL, Kader H, Kappelman MD, Katz J, Kellermayer R, Kuemmerle JF, Lazarev M, Li E, Mannon P, Moulton DE, Newberry RD, Patel AS, Pekow J, Saeed SA, Valentine JF, Wang MH, McCauley JL, Abreu MT, Jester T, Molle-Rios Z, Palle S, Scherl EJ, Kwon J, Rioux JD, Duerr RH, Silverberg MS, Zwick ME, Stevens C, Daly MJ, Cho JH, Gibson G, McGovern DP, Brant SR, Kugathasan S. Whole-genome sequencing of African Americans implicates differential genetic architecture in inflammatory bowel disease. Am J Hum Genet 2021; 108:431-445. [PMID: 33600772 PMCID: PMC8008495 DOI: 10.1016/j.ajhg.2021.02.001] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/21/2020] [Accepted: 02/01/2021] [Indexed: 12/20/2022] Open
Abstract
Whether or not populations diverge with respect to the genetic contribution to risk of specific complex diseases is relevant to understanding the evolution of susceptibility and origins of health disparities. Here, we describe a large-scale whole-genome sequencing study of inflammatory bowel disease encompassing 1,774 affected individuals and 1,644 healthy control Americans with African ancestry (African Americans). Although no new loci for inflammatory bowel disease are discovered at genome-wide significance levels, we identify numerous instances of differential effect sizes in combination with divergent allele frequencies. For example, the major effect at PTGER4 fine maps to a single credible interval of 22 SNPs corresponding to one of four independent associations at the locus in European ancestry individuals but with an elevated odds ratio for Crohn disease in African Americans. A rare variant aggregate analysis implicates Ca2+-binding neuro-immunomodulator CALB2 in ulcerative colitis. Highly significant overall overlap of common variant risk for inflammatory bowel disease susceptibility between individuals with African and European ancestries was observed, with 41 of 241 previously known lead variants replicated and overall correlations in effect sizes of 0.68 for combined inflammatory bowel disease. Nevertheless, subtle differences influence the performance of polygenic risk scores, and we show that ancestry-appropriate weights significantly improve polygenic prediction in the highest percentiles of risk. The median amount of variance explained per locus remains the same in African and European cohorts, providing evidence for compensation of effect sizes as allele frequencies diverge, as expected under a highly polygenic model of disease.
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143
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Shi H, Gazal S, Kanai M, Koch EM, Schoech AP, Siewert KM, Kim SS, Luo Y, Amariuta T, Huang H, Okada Y, Raychaudhuri S, Sunyaev SR, Price AL. Population-specific causal disease effect sizes in functionally important regions impacted by selection. Nat Commun 2021; 12:1098. [PMID: 33597505 PMCID: PMC7889654 DOI: 10.1038/s41467-021-21286-1] [Citation(s) in RCA: 52] [Impact Index Per Article: 17.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/15/2019] [Accepted: 01/15/2021] [Indexed: 01/31/2023] Open
Abstract
Many diseases exhibit population-specific causal effect sizes with trans-ethnic genetic correlations significantly less than 1, limiting trans-ethnic polygenic risk prediction. We develop a new method, S-LDXR, for stratifying squared trans-ethnic genetic correlation across genomic annotations, and apply S-LDXR to genome-wide summary statistics for 31 diseases and complex traits in East Asians (average N = 90K) and Europeans (average N = 267K) with an average trans-ethnic genetic correlation of 0.85. We determine that squared trans-ethnic genetic correlation is 0.82× (s.e. 0.01) depleted in the top quintile of background selection statistic, implying more population-specific causal effect sizes. Accordingly, causal effect sizes are more population-specific in functionally important regions, including conserved and regulatory regions. In regions surrounding specifically expressed genes, causal effect sizes are most population-specific for skin and immune genes, and least population-specific for brain genes. Our results could potentially be explained by stronger gene-environment interaction at loci impacted by selection, particularly positive selection.
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Affiliation(s)
- Huwenbo Shi
- 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.
| | - Steven Gazal
- 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
| | - Masahiro Kanai
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA, USA
- Stanley Center for Psychiatric Research, Broad Institute of Harvard and MIT, Cambridge, MA, USA
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
- Department of Statistical Genetics, Osaka University Graduate School of Medicine, Suita, Japan
| | - Evan M Koch
- Division of Genetics, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
- Department of Medicine, Harvard Medical School, Boston, MA, USA
| | - Armin P Schoech
- 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
| | - Katherine M Siewert
- 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
| | - Samuel S Kim
- 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 Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Yang Luo
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
- Division of Genetics, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
- Division of Rheumatology, Immunology, and Allergy, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
- Center for Data Sciences, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Tiffany Amariuta
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
- Division of Rheumatology, Immunology, and Allergy, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
- Center for Data Sciences, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
- Graduate School of Arts and Sciences, Harvard University, Cambridge, MA, USA
| | - Hailiang Huang
- Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA, USA
- Stanley Center for Psychiatric Research, Broad Institute of Harvard and MIT, Cambridge, MA, USA
- Department of Medicine, Harvard Medical School, Boston, MA, USA
| | - Yukinori Okada
- Department of Statistical Genetics, Osaka University Graduate School of Medicine, Suita, Japan
- Laboratory of Statistical Immunology, Immunology Frontier Research Center (WPI-IFReC), Osaka University, Suita, Japan
| | - Soumya Raychaudhuri
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
- Division of Genetics, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
- Division of Rheumatology, Immunology, and Allergy, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
- Center for Data Sciences, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
- Arthritis Research UK Centre for Genetics and Genomics, Centre for Musculoskeletal Research, Manchester Academic Health Science Centre, The University of Manchester, Manchester, UK
| | - Shamil R Sunyaev
- Division of Genetics, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
- Department of Medicine, Harvard Medical School, Boston, MA, USA
| | - 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.
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144
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Zhang Y, Lee MTM. Serum Urate Polygenic Risk Score Can Improve Gout Risk Prediction: A Large-Scale Cohort Study. Front Genet 2021; 11:604219. [PMID: 33613619 PMCID: PMC7889590 DOI: 10.3389/fgene.2020.604219] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/09/2020] [Accepted: 12/30/2020] [Indexed: 11/21/2022] Open
Abstract
Gout is a painful inflammatory arthritis affecting more than 8 million Americans. Identifying high-risk patients in early life could potentially encourage people to adopt lifestyle changes to prevent gout. Polygenic risk score (PRS) provides an overall estimate of an individual's genetic liability to develop a disease and can be used for early identification of high-risk individuals. In this study, we validated a previously reported PRS in an independent cohort. The urate-PRS was constructed from 110 significant urate-associated variants identified in Europeans. Phenome-wide and PRS-wide association study showed the urate-PRS is highly specifically associated with gout (phecode: 274.10; beta = 1.495 [1.372, 1.619], p = 4.37e-124). Urate-PRS alone did not performed in the gout prediction (area under the receiver operating characteristic curve, AUROC = 0.640); however, the addition of PRS upon demographics significantly improved the model performance, yielding an AUROC of 0.804 from 0.777 (DeLong test p = 3.66e−9). Trans-ethnic PRS and European-specific PRS showed similar prediction performance. We observed increasing gout prevalence and odds ratio (OR) across the PRS quintiles. Our study showed 8.2% of the cohort had more than 2.5 odds for gout than remainders, indicating that urate-PRS may be a better marker than age and sex to stratify patient risk. With the rapid growth of large biorepositories, such as All of Us, urate-PRS can be applied quickly and widely in population to estimate individual's risk, providing a powerful tool for gout preventive purpose in population health.
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Affiliation(s)
- Yanfei Zhang
- Genomic Medicine Institute, Geisinger, Danville, PA, United States
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145
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Thomas NS, Salvatore JE, Gillespie NA, Aliev F, Ksinan AJ, Dick DM. Cannabis use in college: Genetic predispositions, peers, and activity participation. Drug Alcohol Depend 2021; 219:108489. [PMID: 33373877 PMCID: PMC8369492 DOI: 10.1016/j.drugalcdep.2020.108489] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/09/2020] [Revised: 11/10/2020] [Accepted: 11/22/2020] [Indexed: 12/01/2022]
Abstract
BACKGROUND Among adult college students in the US, cannabis use is common and associated with considerable negative consequences to health, cognition, and academic functioning, underscoring the importance of identifying risk and protective factors. Cannabis use is influenced by genetic factors, but genetic risk is not determinative. Accordingly, it is critical to identify environments that reduce risk among those who are at elevated genetic risk. This study examined the impact of polygenic scores for cannabis initiation, various forms of social activity participation, and peer deviance on recent cannabis use. Our aim was to test whether these environments moderate genetic risk for cannabis use. METHODS Data came from a longitudinal sample of undergraduate college students of European American (EA; NEA = 750) and African American (AA; NAA = 405) ancestry. Generalized estimating equations with a logit link function were used to examine main effects and two-way interactions. RESULTS Engagement with church activities was associated with lower probability of cannabis use. Peer deviance was associated with higher probability of cannabis use. Engagement with community activities moderated the influence of the polygenic risk score in the EA sample, such that PRS was associated with recent cannabis use among those who never engaged in community activities. This effect did not replicate in AAs, which may have been due to the portability of PRS based on EA discovery samples. CONCLUSIONS Results suggest that community activities may limit the influence of genetic risk, as associations between PRS and cannabis use were only observed among individuals who never engaged in community activities.
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Affiliation(s)
- Nathaniel S Thomas
- Department of Psychology, Virginia Commonwealth University, Box 842018, Richmond, VA, 23284-2018, United States; College Behavioral and Emotional Health Institute, Virginia Commonwealth University, Box 843092, Richmond, VA, 23284-3092, United States.
| | - Jessica E Salvatore
- Department of Psychology, Virginia Commonwealth University, Box 842018, Richmond, VA, 23284-2018, United States; Virginia Institute for Psychiatric and Behavioral Genetics, Box 980126, Richmond, VA, 23298-0126, United States
| | - Nathan A Gillespie
- Department of Psychiatry, Virginia Commonwealth University, Box 980308, Richmond, VA, 23219-1359, United States; Virginia Institute for Psychiatric and Behavioral Genetics, Box 980126, Richmond, VA, 23298-0126, United States; Genetic Epidemiology, QIMR Berghofer Medical Research Institute, Locked Bag 2000, Royal Brisbane Hospital, QLD, 4029, Brisbane, Australia
| | - Fazil Aliev
- Department of Psychology, Virginia Commonwealth University, Box 842018, Richmond, VA, 23284-2018, United States; Karabuk University, Faculty of Business, Turkey
| | - Albert J Ksinan
- Department of Health Behavior and Policy, Virginia Commonwealth University, 830 E Main St., Richmond, VA, 23219, United States
| | - Danielle M Dick
- Department of Psychology, Virginia Commonwealth University, Box 842018, Richmond, VA, 23284-2018, United States; College Behavioral and Emotional Health Institute, Virginia Commonwealth University, Box 843092, Richmond, VA, 23284-3092, United States; Department of Human & Molecular Genetics, Virginia Commonwealth University, Box 980033, Richmond, VA, 23298-0033, United States.
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146
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Benton ML, Abraham A, LaBella AL, Abbot P, Rokas A, Capra JA. The influence of evolutionary history on human health and disease. Nat Rev Genet 2021; 22:269-283. [PMID: 33408383 PMCID: PMC7787134 DOI: 10.1038/s41576-020-00305-9] [Citation(s) in RCA: 97] [Impact Index Per Article: 32.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 10/26/2020] [Indexed: 01/29/2023]
Abstract
Nearly all genetic variants that influence disease risk have human-specific origins; however, the systems they influence have ancient roots that often trace back to evolutionary events long before the origin of humans. Here, we review how advances in our understanding of the genetic architectures of diseases, recent human evolution and deep evolutionary history can help explain how and why humans in modern environments become ill. Human populations exhibit differences in the prevalence of many common and rare genetic diseases. These differences are largely the result of the diverse environmental, cultural, demographic and genetic histories of modern human populations. Synthesizing our growing knowledge of evolutionary history with genetic medicine, while accounting for environmental and social factors, will help to achieve the promise of personalized genomics and realize the potential hidden in an individual's DNA sequence to guide clinical decisions. In short, precision medicine is fundamentally evolutionary medicine, and integration of evolutionary perspectives into the clinic will support the realization of its full potential.
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Affiliation(s)
- Mary Lauren Benton
- grid.152326.10000 0001 2264 7217Department of Biomedical Informatics, Vanderbilt University School of Medicine, Nashville, TN USA ,grid.252890.40000 0001 2111 2894Department of Computer Science, Baylor University, Waco, TX USA
| | - Abin Abraham
- grid.152326.10000 0001 2264 7217Vanderbilt Genetics Institute, Vanderbilt University, Nashville, TN USA ,grid.152326.10000 0001 2264 7217Vanderbilt University Medical Center, Vanderbilt University, Nashville, TN USA
| | - Abigail L. LaBella
- grid.152326.10000 0001 2264 7217Department of Biological Sciences, Vanderbilt University, Nashville, TN USA
| | - Patrick Abbot
- grid.152326.10000 0001 2264 7217Department of Biological Sciences, Vanderbilt University, Nashville, TN USA
| | - Antonis Rokas
- grid.152326.10000 0001 2264 7217Department of Biomedical Informatics, Vanderbilt University School of Medicine, Nashville, TN USA ,grid.152326.10000 0001 2264 7217Vanderbilt Genetics Institute, Vanderbilt University, Nashville, TN USA ,grid.152326.10000 0001 2264 7217Department of Biological Sciences, Vanderbilt University, Nashville, TN USA
| | - John A. Capra
- grid.152326.10000 0001 2264 7217Department of Biomedical Informatics, Vanderbilt University School of Medicine, Nashville, TN USA ,grid.152326.10000 0001 2264 7217Department of Biological Sciences, Vanderbilt University, Nashville, TN USA ,grid.266102.10000 0001 2297 6811Bakar Computational Health Sciences Institute and Department of Epidemiology and Biostatistics, University of California, San Francisco, CA USA
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147
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Genotype-environment correlation by intervention effects underlying middle childhood peer rejection and associations with adolescent marijuana use. Dev Psychopathol 2020; 34:171-182. [PMID: 33349288 DOI: 10.1017/s0954579420001066] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/22/2022]
Abstract
Aggressive behavior in middle childhood can contribute to peer rejection, subsequently increasing risk for substance use in adolescence. However, the quality of peer relationships a child experiences can be associated with his or her genetic predisposition, a genotype-environment correlation (rGE). In addition, recent evidence indicates that psychosocial preventive interventions can buffer genetic predispositions for negative behavior. The current study examined associations between polygenic risk for aggression, aggressive behavior, and peer rejection from 8.5 to 10.5 years, and the subsequent influence of peer rejection on marijuana use in adolescence (n = 515; 256 control, 259 intervention). Associations were examined separately in control and intervention groups for children of families who participated in a randomized controlled trial of the family-based preventive intervention, the Family Check-Up . Using time-varying effect modeling (TVEM), polygenic risk for aggression was associated with peer rejection from approximately age 8.50 to 9.50 in the control group but no associations were present in the intervention group. Subsequent analyses showed peer rejection mediated the association between polygenic risk for aggression and adolescent marijuana use in the control group. The role of rGEs in middle childhood peer processes and implications for preventive intervention programs for adolescent substance use are discussed.
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148
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Cavazos TB, Witte JS. Inclusion of variants discovered from diverse populations improves polygenic risk score transferability. HGG ADVANCES 2020; 2. [PMID: 33564748 PMCID: PMC7869832 DOI: 10.1016/j.xhgg.2020.100017] [Citation(s) in RCA: 51] [Impact Index Per Article: 12.8] [Reference Citation Analysis] [Abstract] [Grants] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/27/2022] Open
Abstract
The majority of polygenic risk scores (PRSs) have been developed and optimized in individuals of European ancestry and may have limited generalizability across other ancestral populations. Understanding aspects of PRSs that contribute to this issue and determining solutions is complicated by disease-specific genetic architecture and limited knowledge of sharing of causal variants and effect sizes across populations. Motivated by these challenges, we undertook a simulation study to assess the relationship between ancestry and the potential bias in PRSs developed in European ancestry populations. Our simulations show that the magnitude of this bias increases with increasing divergence from European ancestry, and this is attributed to population differences in linkage disequilibrium and allele frequencies of European-discovered variants, likely as a result of genetic drift. Importantly, we find that including into the PRS variants discovered in African ancestry individuals has the potential to achieve unbiased estimates of genetic risk across global populations and admixed individuals. We confirm our simulation findings in an analysis of hemoglobin A1c (HbA1c), asthma, and prostate cancer in the UK Biobank. Given the demonstrated improvement in PRS prediction accuracy, recruiting larger diverse cohorts will be crucial—and potentially even necessary—for enabling accurate and equitable genetic risk prediction across populations.
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Affiliation(s)
- Taylor B. Cavazos
- Biological and Medical Informatics, University of California, San Francisco, San Francisco, CA 94158, USA
| | - John S. Witte
- Biological and Medical Informatics, University of California, San Francisco, San Francisco, CA 94158, USA
- Department of Epidemiology and Biostatistics, University of California, San Francisco, San Francisco, CA 94158, USA
- Institute for Human Genetics, University of California, San Francisco, San Francisco, CA 94143, USA
- Corresponding author
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149
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Amariuta T, Ishigaki K, Sugishita H, Ohta T, Koido M, Dey KK, Matsuda K, Murakami Y, Price AL, Kawakami E, Terao C, Raychaudhuri S. Improving the trans-ancestry portability of polygenic risk scores by prioritizing variants in predicted cell-type-specific regulatory elements. Nat Genet 2020; 52:1346-1354. [PMID: 33257898 PMCID: PMC8049522 DOI: 10.1038/s41588-020-00740-8] [Citation(s) in RCA: 94] [Impact Index Per Article: 23.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/21/2020] [Accepted: 10/19/2020] [Indexed: 12/15/2022]
Abstract
Poor trans-ancestry portability of polygenic risk scores is a consequence of Eurocentric genetic studies and limited knowledge of shared causal variants. Leveraging regulatory annotations may improve portability by prioritizing functional over tagging variants. We constructed a resource of 707 cell-type-specific IMPACT regulatory annotations by aggregating 5,345 epigenetic datasets to predict binding patterns of 142 transcription factors across 245 cell types. We then partitioned the common SNP heritability of 111 genome-wide association study summary statistics of European (average n ≈ 189,000) and East Asian (average n ≈ 157,000) origin. IMPACT annotations captured consistent SNP heritability between populations, suggesting prioritization of shared functional variants. Variant prioritization using IMPACT resulted in increased trans-ancestry portability of polygenic risk scores from Europeans to East Asians across all 21 phenotypes analyzed (49.9% mean relative increase in R2). Our study identifies a crucial role for functional annotations such as IMPACT to improve the trans-ancestry portability of genetic data.
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Affiliation(s)
- Tiffany Amariuta
- Center for Data Sciences, Harvard Medical School, Boston, MA, USA
- Divisions of Genetics and Rheumatology, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
- Graduate School of Arts and Sciences, Harvard University, Cambridge, MA, USA
| | - Kazuyoshi Ishigaki
- Center for Data Sciences, Harvard Medical School, Boston, MA, USA
- Divisions of Genetics and Rheumatology, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Laboratory for Statistical and Translational Genetics, RIKEN Center for Integrative Medical Sciences, Kanagawa, Japan
| | - Hiroki Sugishita
- Laboratory for Developmental Genetics, RIKEN Center for Integrative Medical Sciences (IMS), Kanagawa, Japan
| | - Tazro Ohta
- Medical Sciences Innovation Hub Program, RIKEN, Kanagawa, Japan
- Database Center for Life Science, Joint Support-Center for Data Science Research, Research Organization of Information and Systems, Shizuoka, Japan
| | - Masaru Koido
- Laboratory for Statistical and Translational Genetics, RIKEN Center for Integrative Medical Sciences, Kanagawa, Japan
- Division of Molecular Pathology, Institute of Medical Science, The University of Tokyo, Tokyo, Japan
| | - Kushal K Dey
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Koichi Matsuda
- Laboratory of Genome Technology, Human Genome Center, Institute of Medical Science, The University of Tokyo, Tokyo, Japan
- Laboratory of Clinical Genome Sequencing, Department of Computational Biology and Medical Sciences, Graduate School of Frontier Sciences, The University of Tokyo, Tokyo, Japan
| | - Yoshinori Murakami
- Division of Molecular Pathology, Institute of Medical Science, The University of Tokyo, Tokyo, Japan
| | - 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
| | - Eiryo Kawakami
- Medical Sciences Innovation Hub Program, RIKEN, Kanagawa, Japan
- Artificial Intelligence Medicine, Graduate School of Medicine, Chiba University, Chiba, Japan
| | - Chikashi Terao
- Laboratory for Statistical and Translational Genetics, RIKEN Center for Integrative Medical Sciences, Kanagawa, Japan
- Clinical Research Center, Shizuoka General Hospital, Shizuoka, Japan
- Department of Applied Genetics, The School of Pharmaceutical Sciences, University of Shizuoka, Shizuoka, Japan
| | - Soumya Raychaudhuri
- Center for Data Sciences, Harvard Medical School, Boston, MA, USA.
- Divisions of Genetics and Rheumatology, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA.
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA.
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA.
- Centre for Genetics and Genomics Versus Arthritis, Centre for Musculoskeletal Research, Manchester Academic Health Science Centre, The University of Manchester, Manchester, UK.
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150
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Weissbrod O, Hormozdiari F, Benner C, Cui R, Ulirsch J, Gazal S, Schoech AP, van de Geijn B, Reshef Y, Márquez-Luna C, O'Connor L, Pirinen M, Finucane HK, Price AL. Functionally informed fine-mapping and polygenic localization of complex trait heritability. Nat Genet 2020; 52:1355-1363. [PMID: 33199916 PMCID: PMC7710571 DOI: 10.1038/s41588-020-00735-5] [Citation(s) in RCA: 162] [Impact Index Per Article: 40.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/28/2019] [Accepted: 10/02/2020] [Indexed: 01/16/2023]
Abstract
Fine-mapping aims to identify causal variants impacting complex traits. We propose PolyFun, a computationally scalable framework to improve fine-mapping accuracy by leveraging functional annotations across the entire genome-not just genome-wide-significant loci-to specify prior probabilities for fine-mapping methods such as SuSiE or FINEMAP. In simulations, PolyFun + SuSiE and PolyFun + FINEMAP were well calibrated and identified >20% more variants with a posterior causal probability >0.95 than identified in their nonfunctionally informed counterparts. In analyses of 49 UK Biobank traits (average n = 318,000), PolyFun + SuSiE identified 3,025 fine-mapped variant-trait pairs with posterior causal probability >0.95, a >32% improvement versus SuSiE. We used posterior mean per-SNP heritabilities from PolyFun + SuSiE to perform polygenic localization, constructing minimal sets of common SNPs causally explaining 50% of common SNP heritability; these sets ranged in size from 28 (hair color) to 3,400 (height) to 2 million (number of children). In conclusion, PolyFun prioritizes variants for functional follow-up and provides insights into complex trait architectures.
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Affiliation(s)
- Omer Weissbrod
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA.
| | - Farhad Hormozdiari
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Christian Benner
- Institute for Molecular Medicine Finland (FIMM), University of Helsinki, Helsinki, Finland
| | - Ran Cui
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Jacob Ulirsch
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Program in Biological and Biomedical Sciences, Harvard Medical School, Cambridge, MA, USA
| | - Steven Gazal
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Armin P Schoech
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Bryce van de Geijn
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Yakir Reshef
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Carla Márquez-Luna
- The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Luke O'Connor
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Matti Pirinen
- Institute for Molecular Medicine Finland (FIMM), University of Helsinki, Helsinki, Finland
- Department of Public Health, University of Helsinki, Helsinki, Finland
- Department of Mathematics and Statistics, University of Helsinki, Helsinki, Finland
| | - Hilary K Finucane
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Department of Medicine, Massachusetts General Hospital, Boston, MA, USA
| | - 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.
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