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Jermy B, Läll K, Wolford BN, Wang Y, Zguro K, Cheng Y, Kanai M, Kanoni S, Yang Z, Hartonen T, Monti R, Wanner J, Youssef O, Lippert C, van Heel D, Okada Y, McCartney DL, Hayward C, Marioni RE, Furini S, Renieri A, Martin AR, Neale BM, Hveem K, Mägi R, Palotie A, Heyne H, Mars N, Ganna A, Ripatti S. A unified framework for estimating country-specific cumulative incidence for 18 diseases stratified by polygenic risk. Nat Commun 2024; 15:5007. [PMID: 38866767 PMCID: PMC11169548 DOI: 10.1038/s41467-024-48938-2] [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: 05/31/2023] [Accepted: 05/17/2024] [Indexed: 06/14/2024] Open
Abstract
Polygenic scores (PGSs) offer the ability to predict genetic risk for complex diseases across the life course; a key benefit over short-term prediction models. To produce risk estimates relevant to clinical and public health decision-making, it is important to account for varying effects due to age and sex. Here, we develop a novel framework to estimate country-, age-, and sex-specific estimates of cumulative incidence stratified by PGS for 18 high-burden diseases. We integrate PGS associations from seven studies in four countries (N = 1,197,129) with disease incidences from the Global Burden of Disease. PGS has a significant sex-specific effect for asthma, hip osteoarthritis, gout, coronary heart disease and type 2 diabetes (T2D), with all but T2D exhibiting a larger effect in men. PGS has a larger effect in younger individuals for 13 diseases, with effects decreasing linearly with age. We show for breast cancer that, relative to individuals in the bottom 20% of polygenic risk, the top 5% attain an absolute risk for screening eligibility 16.3 years earlier. Our framework increases the generalizability of results from biobank studies and the accuracy of absolute risk estimates by appropriately accounting for age- and sex-specific PGS effects. Our results highlight the potential of PGS as a screening tool which may assist in the early prevention of common diseases.
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Affiliation(s)
- Bradley Jermy
- Institute for Molecular Medicine Finland, FIMM, HiLIFE, University of Helsinki, Helsinki, Finland
| | - Kristi Läll
- Estonian Genome Centre, Institute of Genomics, University of Tartu, Tartu, Estonia
| | - Brooke N Wolford
- K. G. Jebsen Center for Genetic Epidemiology, Department of Public Health and Nursing, Faculty of Medicine and Health, Norwegian University of Science and Technology, Trondheim, Norway
| | - Ying Wang
- Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA, USA
- Stanley Center for Psychiatric Research and Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Kristina Zguro
- Med Biotech Hub and Competence Center, Department of Medical Biotechnologies, University of Siena, Siena, Italy
| | - Yipeng Cheng
- Centre for Genomic and Experimental Medicine, Institute of Genetics and Cancer, University of Edinburgh, Edinburgh, UK
| | - Masahiro Kanai
- Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA, USA
- Stanley Center for Psychiatric Research and Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Stavroula Kanoni
- William Harvey Research Institute, Barts and the London School of Medicine and Dentistry, Queen Mary University of London, London, UK
| | - Zhiyu Yang
- Institute for Molecular Medicine Finland, FIMM, HiLIFE, University of Helsinki, Helsinki, Finland
| | - Tuomo Hartonen
- Institute for Molecular Medicine Finland, FIMM, HiLIFE, University of Helsinki, Helsinki, Finland
| | - Remo Monti
- Hasso Plattner Institute, Digital Engineering Faculty, University of Potsdam, Potsdam, Germany
| | - Julian Wanner
- Institute for Molecular Medicine Finland, FIMM, HiLIFE, University of Helsinki, Helsinki, Finland
- Hasso Plattner Institute, Digital Engineering Faculty, University of Potsdam, Potsdam, Germany
| | - Omar Youssef
- Helsinki Biobank, Hospital District of Helsinki and Uusimaa (HUS), Helsinki, Finland
- Pathology Department, University of Helsinki, Helsinki, Finland
| | - Christoph Lippert
- Hasso Plattner Institute, Digital Engineering Faculty, University of Potsdam, Potsdam, Germany
- Hasso Plattner Institute for Digital Health, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - David van Heel
- Blizard Institute, Barts and the London School of Medicine and Dentistry, Queen Mary University of London, London, UK
| | - Yukinori Okada
- Department of Genome Informatics, Graduate School of Medicine, the University of Tokyo, Tokyo, Japan
- Department of Statistical Genetics, Osaka University Graduate School of Medicine, Suita, Japan
- Laboratory for Systems Genetics, RIKEN Center for Integrative Medical Sciences, Kanagawa, Japan
- Laboratory of Statistical Immunology, Immunology Frontier Research Center (WPI-IFReC), Osaka University, Suita, Japan
| | - Daniel L McCartney
- Centre for Genomic and Experimental Medicine, Institute of Genetics and Cancer, University of Edinburgh, Edinburgh, UK
| | - Caroline Hayward
- Medical Research Council Human Genetics Unit, Institute of Genetics and Cancer, University of Edinburgh, Edinburgh, UK
| | - Riccardo E Marioni
- Centre for Genomic and Experimental Medicine, Institute of Genetics and Cancer, University of Edinburgh, Edinburgh, UK
| | - Simone Furini
- Med Biotech Hub and Competence Center, Department of Medical Biotechnologies, University of Siena, Siena, Italy
- Department of Electrical, Electronic and Information Engineering "Guglielmo Marconi", University of Bologna, Bologna, Italy
| | - Alessandra Renieri
- Med Biotech Hub and Competence Center, Department of Medical Biotechnologies, University of Siena, Siena, Italy
- Medical Genetics, University of Siena, Siena, Italy
- Genetica Medica, Azienda Ospedaliera Universitaria Senese, Siena, Italy
| | - Alicia R Martin
- Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA, USA
- Stanley Center for Psychiatric Research and Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Benjamin M Neale
- Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA, USA
- Stanley Center for Psychiatric Research and Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Kristian Hveem
- K. G. Jebsen Center for Genetic Epidemiology, Department of Public Health and Nursing, Faculty of Medicine and Health, Norwegian University of Science and Technology, Trondheim, Norway
- Levanger Hospital, Nord-Trøndelag Hospital Trust, Levanger, Norway
| | - Reedik Mägi
- Estonian Genome Centre, Institute of Genomics, University of Tartu, Tartu, Estonia
| | - Aarno Palotie
- Institute for Molecular Medicine Finland, FIMM, HiLIFE, University of Helsinki, Helsinki, Finland
- Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA, USA
- Stanley Center for Psychiatric Research and Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Henrike Heyne
- Institute for Molecular Medicine Finland, FIMM, HiLIFE, University of Helsinki, Helsinki, Finland
- Hasso Plattner Institute, Digital Engineering Faculty, University of Potsdam, Potsdam, Germany
- Hasso Plattner Institute for Digital Health, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Nina Mars
- Institute for Molecular Medicine Finland, FIMM, HiLIFE, University of Helsinki, Helsinki, Finland
- Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA, USA
- Stanley Center for Psychiatric Research and Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Andrea Ganna
- Institute for Molecular Medicine Finland, FIMM, HiLIFE, University of Helsinki, Helsinki, Finland.
- Massachusetts General Hospital, Broad Institute of MIT and Harvard, Cambridge, MA, USA.
| | - Samuli Ripatti
- Institute for Molecular Medicine Finland, FIMM, HiLIFE, University of Helsinki, Helsinki, Finland.
- Massachusetts General Hospital, Broad Institute of MIT and Harvard, Cambridge, MA, USA.
- Department of Public Health, University of Helsinki, Helsinki, Finland.
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2
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Kim B, Kim DS, Shin JG, Leem S, Cho M, Kim H, Gu KN, Seo JY, You SW, Martin AR, Park SG, Kim Y, Jeong C, Kang NG, Won HH. Mapping and annotating genomic loci to prioritize genes and implicate distinct polygenic adaptations for skin color. Nat Commun 2024; 15:4874. [PMID: 38849341 PMCID: PMC11161515 DOI: 10.1038/s41467-024-49031-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/04/2023] [Accepted: 05/21/2024] [Indexed: 06/09/2024] Open
Abstract
Evidence for adaptation of human skin color to regional ultraviolet radiation suggests shared and distinct genetic variants across populations. However, skin color evolution and genetics in East Asians are understudied. We quantified skin color in 48,433 East Asians using image analysis and identified associated genetic variants and potential causal genes for skin color as well as their polygenic interplay with sun exposure. This genome-wide association study (GWAS) identified 12 known and 11 previously unreported loci and SNP-based heritability was 23-24%. Potential causal genes were determined through the identification of nonsynonymous variants, colocalization with gene expression in skin tissues, and expression levels in melanocytes. Genomic loci associated with pigmentation in East Asians substantially diverged from European populations, and we detected signatures of polygenic adaptation. This large GWAS for objectively quantified skin color in an East Asian population improves understanding of the genetic architecture and polygenic adaptation of skin color and prioritizes potential causal genes.
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Affiliation(s)
- Beomsu Kim
- Samsung Advanced Institute for Health Sciences and Technology (SAIHST), Sungkyunkwan University, Samsung Medical Center, Seoul, 06351, Republic of Korea
| | - Dan Say Kim
- Samsung Advanced Institute for Health Sciences and Technology (SAIHST), Sungkyunkwan University, Samsung Medical Center, Seoul, 06351, Republic of Korea
| | - Joong-Gon Shin
- Research and Innovation Center, CTO, LG Household & Healthcare (LG H&H), Seoul, 07795, Republic of Korea
| | - Sangseob Leem
- Research and Innovation Center, CTO, LG Household & Healthcare (LG H&H), Seoul, 07795, Republic of Korea
| | - Minyoung Cho
- Samsung Advanced Institute for Health Sciences and Technology (SAIHST), Sungkyunkwan University, Samsung Medical Center, Seoul, 06351, Republic of Korea
| | - Hanji Kim
- Research and Innovation Center, CTO, LG Household & Healthcare (LG H&H), Seoul, 07795, Republic of Korea
| | - Ki-Nam Gu
- Research and Innovation Center, CTO, LG Household & Healthcare (LG H&H), Seoul, 07795, Republic of Korea
| | - Jung Yeon Seo
- Research and Innovation Center, CTO, LG Household & Healthcare (LG H&H), Seoul, 07795, Republic of Korea
| | - Seung Won You
- Research and Innovation Center, CTO, LG Household & Healthcare (LG H&H), Seoul, 07795, Republic of Korea
| | - Alicia R Martin
- Analytic and Translational Genetics Unit, Department of Medicine, Massachusetts General Hospital and Harvard Medical School, Boston, MA, 02114, USA
- Stanley Center for Psychiatric Research, Broad Institute, Cambridge, MA, 02141, USA
| | - Sun Gyoo Park
- Research and Innovation Center, CTO, LG Household & Healthcare (LG H&H), Seoul, 07795, Republic of Korea
| | - Yunkwan Kim
- Research and Innovation Center, CTO, LG Household & Healthcare (LG H&H), Seoul, 07795, Republic of Korea
| | - Choongwon Jeong
- School of Biological Sciences, Seoul National University, Seoul, 08826, Republic of Korea
| | - Nae Gyu Kang
- Research and Innovation Center, CTO, LG Household & Healthcare (LG H&H), Seoul, 07795, Republic of Korea.
| | - Hong-Hee Won
- Samsung Advanced Institute for Health Sciences and Technology (SAIHST), Sungkyunkwan University, Samsung Medical Center, Seoul, 06351, Republic of Korea.
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3
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Lambert SA, Wingfield B, Gibson JT, Gil L, Ramachandran S, Yvon F, Saverimuttu S, Tinsley E, Lewis E, Ritchie SC, Wu J, Canovas R, McMahon A, Harris LW, Parkinson H, Inouye M. The Polygenic Score Catalog: new functionality and tools to enable FAIR research. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2024:2024.05.29.24307783. [PMID: 38853961 PMCID: PMC11160819 DOI: 10.1101/2024.05.29.24307783] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/11/2024]
Abstract
Polygenic scores (PGS) have transformed human genetic research and have multiple potential clinical applications, including risk stratification for disease prevention and prediction of treatment response. Here, we present a series of recent enhancements to the PGS Catalog (www.PGSCatalog.org), the largest findable, accessible, interoperable, and reusable (FAIR) repository of PGS. These include expansions in data content and ancestral diversity as well as the addition of new features. We further present the PGS Catalog Calculator (pgsc_calc, https://github.com/PGScatalog/pgsc_calc), an open-source, scalable and portable pipeline to reproducibly calculate PGS that securely democratizes equitable PGS applications by implementing genetic ancestry estimation and score normalization using reference data. With the PGS Catalog & calculator users can now quantify an individual's genetic predisposition for hundreds of common diseases and clinically relevant traits. Taken together, these updates and tools facilitate the next generation of PGS, thus lowering barriers to the clinical studies necessary to identify where PGS may be integrated into clinical practice.
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Affiliation(s)
- Samuel A. Lambert
- Cambridge Baker Systems Genomics Initiative, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
- British Heart Foundation Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
- Victor Phillip Dahdaleh Heart and Lung Research Institute, University of Cambridge, Cambridge, UK
- Health Data Research UK Cambridge, Wellcome Genome Campus and University of Cambridge, Cambridge, UK
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, Cambridge, UK
| | - Benjamin Wingfield
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, Cambridge, UK
| | - Joel T. Gibson
- Cambridge Baker Systems Genomics Initiative, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
- British Heart Foundation Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
- Victor Phillip Dahdaleh Heart and Lung Research Institute, University of Cambridge, Cambridge, UK
| | - Laurent Gil
- Health Data Research UK Cambridge, Wellcome Genome Campus and University of Cambridge, Cambridge, UK
- Wellcome Sanger Institute, Hinxton, UK
| | - Santhi Ramachandran
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, Cambridge, UK
| | - Florent Yvon
- Cambridge Baker Systems Genomics Initiative, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
- British Heart Foundation Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
- Victor Phillip Dahdaleh Heart and Lung Research Institute, University of Cambridge, Cambridge, UK
| | - Shirin Saverimuttu
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, Cambridge, UK
| | - Emily Tinsley
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, Cambridge, UK
| | - Elizabeth Lewis
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, Cambridge, UK
| | - Scott C. Ritchie
- Cambridge Baker Systems Genomics Initiative, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
- British Heart Foundation Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
- Victor Phillip Dahdaleh Heart and Lung Research Institute, University of Cambridge, Cambridge, UK
- British Heart Foundation Centre of Research Excellence, University of Cambridge, Cambridge, UK
| | - Jingqin Wu
- Cambridge Baker Systems Genomics Initiative, Baker Heart and Diabetes Institute, Melbourne, VIC, Australia
| | - Rodrigo Canovas
- Cambridge Baker Systems Genomics Initiative, Baker Heart and Diabetes Institute, Melbourne, VIC, Australia
| | - Aoife McMahon
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, Cambridge, UK
| | - Laura W. Harris
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, Cambridge, UK
| | - Helen Parkinson
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, Cambridge, UK
| | - Michael Inouye
- Cambridge Baker Systems Genomics Initiative, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
- British Heart Foundation Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
- Victor Phillip Dahdaleh Heart and Lung Research Institute, University of Cambridge, Cambridge, UK
- Health Data Research UK Cambridge, Wellcome Genome Campus and University of Cambridge, Cambridge, UK
- British Heart Foundation Centre of Research Excellence, University of Cambridge, Cambridge, UK
- Cambridge Baker Systems Genomics Initiative, Baker Heart and Diabetes Institute, Melbourne, VIC, Australia
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4
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Capalbo A, de Wert G, Mertes H, Klausner L, Coonen E, Spinella F, Van de Velde H, Viville S, Sermon K, Vermeulen N, Lencz T, Carmi S. Screening embryos for polygenic disease risk: a review of epidemiological, clinical, and ethical considerations. Hum Reprod Update 2024:dmae012. [PMID: 38805697 DOI: 10.1093/humupd/dmae012] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/10/2024] [Revised: 03/25/2024] [Indexed: 05/30/2024] Open
Abstract
BACKGROUND The genetic composition of embryos generated by in vitro fertilization (IVF) can be examined with preimplantation genetic testing (PGT). Until recently, PGT was limited to detecting single-gene, high-risk pathogenic variants, large structural variants, and aneuploidy. Recent advances have made genome-wide genotyping of IVF embryos feasible and affordable, raising the possibility of screening embryos for their risk of polygenic diseases such as breast cancer, hypertension, diabetes, or schizophrenia. Despite a heated debate around this new technology, called polygenic embryo screening (PES; also PGT-P), it is already available to IVF patients in some countries. Several articles have studied epidemiological, clinical, and ethical perspectives on PES; however, a comprehensive, principled review of this emerging field is missing. OBJECTIVE AND RATIONALE This review has four main goals. First, given the interdisciplinary nature of PES studies, we aim to provide a self-contained educational background about PES to reproductive specialists interested in the subject. Second, we provide a comprehensive and critical review of arguments for and against the introduction of PES, crystallizing and prioritizing the key issues. We also cover the attitudes of IVF patients, clinicians, and the public towards PES. Third, we distinguish between possible future groups of PES patients, highlighting the benefits and harms pertaining to each group. Finally, our review, which is supported by ESHRE, is intended to aid healthcare professionals and policymakers in decision-making regarding whether to introduce PES in the clinic, and if so, how, and to whom. SEARCH METHODS We searched for PubMed-indexed articles published between 1/1/2003 and 1/3/2024 using the terms 'polygenic embryo screening', 'polygenic preimplantation', and 'PGT-P'. We limited the review to primary research papers in English whose main focus was PES for medical conditions. We also included papers that did not appear in the search but were deemed relevant. OUTCOMES The main theoretical benefit of PES is a reduction in lifetime polygenic disease risk for children born after screening. The magnitude of the risk reduction has been predicted based on statistical modelling, simulations, and sibling pair analyses. Results based on all methods suggest that under the best-case scenario, large relative risk reductions are possible for one or more diseases. However, as these models abstract several practical limitations, the realized benefits may be smaller, particularly due to a limited number of embryos and unclear future accuracy of the risk estimates. PES may negatively impact patients and their future children, as well as society. The main personal harms are an unindicated IVF treatment, a possible reduction in IVF success rates, and patient confusion, incomplete counselling, and choice overload. The main possible societal harms include discarded embryos, an increasing demand for 'designer babies', overemphasis of the genetic determinants of disease, unequal access, and lower utility in people of non-European ancestries. Benefits and harms will vary across the main potential patient groups, comprising patients already requiring IVF, fertile people with a history of a severe polygenic disease, and fertile healthy people. In the United States, the attitudes of IVF patients and the public towards PES seem positive, while healthcare professionals are cautious, sceptical about clinical utility, and concerned about patient counselling. WIDER IMPLICATIONS The theoretical potential of PES to reduce risk across multiple polygenic diseases requires further research into its benefits and harms. Given the large number of practical limitations and possible harms, particularly unnecessary IVF treatments and discarded viable embryos, PES should be offered only within a research context before further clarity is achieved regarding its balance of benefits and harms. The gap in attitudes between healthcare professionals and the public needs to be narrowed by expanding public and patient education and providing resources for informative and unbiased genetic counselling.
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Affiliation(s)
- Antonio Capalbo
- Juno Genetics, Department of Reproductive Genetics, Rome, Italy
- Center for Advanced Studies and Technology (CAST), Department of Medical Genetics, "G. d'Annunzio" University of Chieti-Pescara, Chieti, Italy
| | - Guido de Wert
- Department of Health, Ethics & Society, CAPHRI-School for Public Health and Primary Care and GROW School for Oncology and Reproduction, Maastricht University, Maastricht, The Netherlands
| | - Heidi Mertes
- Department of Philosophy and Moral Sciences, Ghent University, Ghent, Belgium
- Department of Public Health and Primary Care, Ghent University, Ghent, Belgium
| | - Liraz Klausner
- Braun School of Public Health and Community Medicine, The Hebrew University of Jerusalem, Jerusalem, Israel
| | - Edith Coonen
- Departments of Clinical Genetics and Reproductive Medicine, Maastricht University Medical Centre, Maastricht, The Netherlands
- School for Oncology and Developmental Biology, GROW, Maastricht University, Maastricht, The Netherlands
| | - Francesca Spinella
- Eurofins GENOMA Group Srl, Molecular Genetics Laboratories, Department of Scientific Communication, Rome, Italy
| | - Hilde Van de Velde
- Research Group Genetics Reproduction and Development (GRAD), Vrije Universiteit Brussel, Brussel, Belgium
- Brussels IVF, UZ Brussel, Brussel, Belgium
| | - Stephane Viville
- Laboratoire de Génétique Médicale LGM, Institut de Génétique Médicale d'Alsace IGMA, INSERM UMR 1112, Université de Strasbourg, France
- Laboratoire de Diagnostic Génétique, Unité de Génétique de l'infertilité (UF3472), Hôpitaux Universitaires de Strasbourg, Strasbourg, France
| | - Karen Sermon
- Research Group Genetics Reproduction and Development (GRAD), Vrije Universiteit Brussel, Brussel, Belgium
| | | | - Todd Lencz
- Institute of Behavioral Science, Feinstein Institutes for Medical Research, Manhasset, NY, USA
- Departments of Psychiatry and Molecular Medicine, Zucker School of Medicine at Hofstra/Northwell, Hempstead, NY 11549, USA
| | - Shai Carmi
- Braun School of Public Health and Community Medicine, The Hebrew University of Jerusalem, Jerusalem, Israel
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5
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Jee YH, Wang Y, Jung KJ, Lee JY, Kimm H, Duan R, Price AL, Martin AR, Kraft P. Genome-wide association studies in a large Korean cohort identify novel quantitative trait loci for 36 traits and illuminates their genetic architectures. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2024:2024.05.17.24307550. [PMID: 38798434 PMCID: PMC11118625 DOI: 10.1101/2024.05.17.24307550] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/29/2024]
Abstract
Genome-wide association studies (GWAS) have been predominantly conducted in populations of European ancestry, limiting opportunities for biological discovery in diverse populations. We report GWAS findings from 153,950 individuals across 36 quantitative traits in the Korean Cancer Prevention Study-II (KCPS2) Biobank. We discovered 616 novel genetic loci in KCPS2, including an association between thyroid-stimulating hormone and CD36. Meta-analysis with the Korean Genome and Epidemiology Study, Biobank Japan, Taiwan Biobank, and UK Biobank identified 3,524 loci that were not significant in any contributing GWAS. We describe differences in genetic architectures across these East Asian and European samples. We also highlight East Asian specific associations, including a known pleiotropic missense variant in ALDH2, which fine-mapping identified as a likely causal variant for a diverse set of traits. Our findings provide insights into the genetic architecture of complex traits in East Asian populations and highlight how broadening the population diversity of GWAS samples can aid discovery.
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Affiliation(s)
- Yon Ho Jee
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Ying Wang
- Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA 02114, USA
- Stanley Center for Psychiatric Research and Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
| | - Keum Ji Jung
- Institute for Health Promotion, Department of Epidemiology and Health Promotion, Graduate School of Public Health, Yonsei University, Seoul, Korea
| | - Ji-Young Lee
- Institute for Health Promotion, Department of Epidemiology and Health Promotion, Graduate School of Public Health, Yonsei University, Seoul, Korea
| | - Heejin Kimm
- Institute for Health Promotion, Department of Epidemiology and Health Promotion, Graduate School of Public Health, Yonsei University, Seoul, Korea
| | - Rui Duan
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, 02115, USA
| | - Alkes L. Price
- 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, 02115, USA
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Alicia R. Martin
- Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA 02114, USA
- Stanley Center for Psychiatric Research and Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
- Department of Medicine, Harvard Medical School, Boston, MA, USA
| | - Peter Kraft
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA
- Transdivisional Research Program, Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, MD, USA
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6
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Zhang S, Shu H, Zhou J, Rubin-Sigler J, Yang X, Liu Y, Cooper-Knock J, Monte E, Zhu C, Tu S, Li H, Tong M, Ecker JR, Ichida JK, Shen Y, Zeng J, Tsao PS, Snyder MP. Deconvolution of polygenic risk score in single cells unravels cellular and molecular heterogeneity of complex human diseases. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.05.14.594252. [PMID: 38798507 PMCID: PMC11118500 DOI: 10.1101/2024.05.14.594252] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/29/2024]
Abstract
Polygenic risk scores (PRSs) are commonly used for predicting an individual's genetic risk of complex diseases. Yet, their implication for disease pathogenesis remains largely limited. Here, we introduce scPRS, a geometric deep learning model that constructs single-cell-resolved PRS leveraging reference single-cell chromatin accessibility profiling data to enhance biological discovery as well as disease prediction. Real-world applications across multiple complex diseases, including type 2 diabetes (T2D), hypertrophic cardiomyopathy (HCM), and Alzheimer's disease (AD), showcase the superior prediction power of scPRS compared to traditional PRS methods. Importantly, scPRS not only predicts disease risk but also uncovers disease-relevant cells, such as hormone-high alpha and beta cells for T2D, cardiomyocytes and pericytes for HCM, and astrocytes, microglia and oligodendrocyte progenitor cells for AD. Facilitated by a layered multi-omic analysis, scPRS further identifies cell-type-specific genetic underpinnings, linking disease-associated genetic variants to gene regulation within corresponding cell types. We substantiate the disease relevance of scPRS-prioritized HCM genes and demonstrate that the suppression of these genes in HCM cardiomyocytes is rescued by Mavacamten treatment. Additionally, we establish a novel microglia-specific regulatory relationship between the AD risk variant rs7922621 and its target genes ANXA11 and TSPAN14. We further illustrate the detrimental effects of suppressing these two genes on microglia phagocytosis. Our work provides a multi-tasking, interpretable framework for precise disease prediction and systematic investigation of the genetic, cellular, and molecular basis of complex diseases, laying the methodological foundation for single-cell genetics.
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Affiliation(s)
- Sai Zhang
- Department of Epidemiology, University of Florida, Gainesville, FL, USA
- Departments of Biostatistics & Biomedical Engineering, Genetics Institute, McKnight Brain Institute, University of Florida, Gainesville, FL, USA
- Department of Genetics, Center for Genomics and Personalized Medicine, Stanford University School of Medicine, Stanford, CA, USA
- These authors contributed equally: Sai Zhang, Hantao Shu, and Jingtian Zhou
| | - Hantao Shu
- Institute for Interdisciplinary Information Sciences, Tsinghua University, Beijing, China
- These authors contributed equally: Sai Zhang, Hantao Shu, and Jingtian Zhou
| | - Jingtian Zhou
- Arc Institute, Palo Alto, CA, USA
- Genomic Analysis Laboratory, The Salk Institute for Biological Studies, La Jolla, CA, USA
- Bioinformatics and Systems Biology Program, University of California San Diego, La Jolla, CA, USA
- These authors contributed equally: Sai Zhang, Hantao Shu, and Jingtian Zhou
| | - Jasper Rubin-Sigler
- Department of Stem Cell Biology and Regenerative Medicine, Eli and Edythe Broad Center for Regenerative Medicine and Stem Cell Research, University of Southern California, Los Angeles, CA, USA
| | - Xiaoyu Yang
- Institute for Human Genetics, University of California San Francisco, San Francisco, CA, USA
| | - Yuxi Liu
- Institute for Human Genetics, University of California San Francisco, San Francisco, CA, USA
| | - Johnathan Cooper-Knock
- Sheffield Institute for Translational Neuroscience, University of Sheffield, Sheffield, UK
| | - Emma Monte
- Department of Genetics, Center for Genomics and Personalized Medicine, Stanford University School of Medicine, Stanford, CA, USA
| | - Chenchen Zhu
- Department of Genetics, Center for Genomics and Personalized Medicine, Stanford University School of Medicine, Stanford, CA, USA
| | - Sharon Tu
- Department of Stem Cell Biology and Regenerative Medicine, Eli and Edythe Broad Center for Regenerative Medicine and Stem Cell Research, University of Southern California, Los Angeles, CA, USA
| | - Han Li
- Institute for Interdisciplinary Information Sciences, Tsinghua University, Beijing, China
| | - Mingming Tong
- Department of Genetics, Center for Genomics and Personalized Medicine, Stanford University School of Medicine, Stanford, CA, USA
| | - Joseph R. Ecker
- Genomic Analysis Laboratory, The Salk Institute for Biological Studies, La Jolla, CA, USA
- Howard Hughes Medical Institute, The Salk Institute for Biological Studies, La Jolla, CA, USA
| | - Justin K. Ichida
- Department of Stem Cell Biology and Regenerative Medicine, Eli and Edythe Broad Center for Regenerative Medicine and Stem Cell Research, University of Southern California, Los Angeles, CA, USA
| | - Yin Shen
- Institute for Human Genetics, University of California San Francisco, San Francisco, CA, USA
- Department of Neurology, Weill Institute for Neurosciences, University of California San Francisco, San Francisco, CA, USA
| | - Jianyang Zeng
- School of Engineering, Research Center for Industries of the Future, Westlake University, Hangzhou, Zhejiang, China
| | - Philip S. Tsao
- VA Palo Alto Healthcare System, Palo Alto, CA, USA
- Department of Medicine, Stanford University School of Medicine, Stanford, CA, USA
| | - Michael P. Snyder
- Department of Genetics, Center for Genomics and Personalized Medicine, Stanford University School of Medicine, Stanford, CA, USA
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7
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Zheng Z, Liu S, Sidorenko J, Wang Y, Lin T, Yengo L, Turley P, Ani A, Wang R, Nolte IM, Snieder H, Yang J, Wray NR, Goddard ME, Visscher PM, Zeng J. Leveraging functional genomic annotations and genome coverage to improve polygenic prediction of complex traits within and between ancestries. Nat Genet 2024; 56:767-777. [PMID: 38689000 PMCID: PMC11096109 DOI: 10.1038/s41588-024-01704-y] [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: 10/01/2022] [Accepted: 03/05/2024] [Indexed: 05/02/2024]
Abstract
We develop a method, SBayesRC, that integrates genome-wide association study (GWAS) summary statistics with functional genomic annotations to improve polygenic prediction of complex traits. Our method is scalable to whole-genome variant analysis and refines signals from functional annotations by allowing them to affect both causal variant probability and causal effect distribution. We analyze 50 complex traits and diseases using ∼7 million common single-nucleotide polymorphisms (SNPs) and 96 annotations. SBayesRC improves prediction accuracy by 14% in European ancestry and up to 34% in cross-ancestry prediction compared to the baseline method SBayesR, which does not use annotations, and outperforms other methods, including LDpred2, LDpred-funct, MegaPRS, PolyPred-S and PRS-CSx. Investigation of factors affecting prediction accuracy identifies a significant interaction between SNP density and annotation information, suggesting whole-genome sequence variants with annotations may further improve prediction. Functional partitioning analysis highlights a major contribution of evolutionary constrained regions to prediction accuracy and the largest per-SNP contribution from nonsynonymous SNPs.
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Affiliation(s)
- Zhili Zheng
- Institute for Molecular Bioscience, The University of Queensland, Brisbane, Queensland, Australia.
- Program in Medical and Population Genetics, Broad Institute of Harvard and MIT, Cambridge, MA, USA.
- Stanley Center for Psychiatric Research, Broad Institute of Harvard and MIT, Cambridge, MA, USA.
| | - Shouye Liu
- Institute for Molecular Bioscience, The University of Queensland, Brisbane, Queensland, Australia
| | - Julia Sidorenko
- Institute for Molecular Bioscience, The University of Queensland, Brisbane, Queensland, Australia
| | - Ying Wang
- Institute for Molecular Bioscience, The University of Queensland, Brisbane, Queensland, Australia
| | - Tian Lin
- Institute for Molecular Bioscience, The University of Queensland, Brisbane, Queensland, Australia
| | - Loic Yengo
- Institute for Molecular Bioscience, The University of Queensland, Brisbane, Queensland, Australia
| | - Patrick Turley
- Center for Economic and Social Research, University of Southern California, Los Angeles, CA, USA
- Department of Economics, University of Southern California, Los Angeles, CA, USA
| | - Alireza Ani
- Department of Epidemiology, University of Groningen, University Medical Center Groningen, Groningen, the Netherlands
- Department of Bioinformatics, Isfahan University of Medical Sciences, Isfahan, Iran
| | - Rujia Wang
- Department of Epidemiology, University of Groningen, University Medical Center Groningen, Groningen, the Netherlands
| | - Ilja M Nolte
- Department of Epidemiology, University of Groningen, University Medical Center Groningen, Groningen, the Netherlands
| | - Harold Snieder
- Department of Epidemiology, University of Groningen, University Medical Center Groningen, Groningen, the Netherlands
| | - Jian Yang
- School of Life Sciences, Westlake University, Hangzhou, Zhejiang, China
- Westlake Laboratory of Life Sciences and Biomedicine, Hangzhou, Zhejiang, China
| | - Naomi R Wray
- Institute for Molecular Bioscience, The University of Queensland, Brisbane, Queensland, Australia
- Department of Psychiatry, University of Oxford, Oxford, UK
| | - Michael E Goddard
- Faculty of Veterinary and Agricultural Science, University of Melbourne, Parkville, Victoria, Australia
- Biosciences Research Division, Department of Economic Development, Jobs, Transport and Resources, Bundoora, Victoria, Australia
| | - Peter M Visscher
- Institute for Molecular Bioscience, The University of Queensland, Brisbane, Queensland, Australia
- Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | - Jian Zeng
- Institute for Molecular Bioscience, The University of Queensland, Brisbane, Queensland, Australia.
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8
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Foreman AL, Warth B, Hessel EVS, Price EJ, Schymanski EL, Cantelli G, Parkinson H, Hecht H, Klánová J, Vlaanderen J, Hilscherova K, Vrijheid M, Vineis P, Araujo R, Barouki R, Vermeulen R, Lanone S, Brunak S, Sebert S, Karjalainen T. Adopting Mechanistic Molecular Biology Approaches in Exposome Research for Causal Understanding. ENVIRONMENTAL SCIENCE & TECHNOLOGY 2024; 58:7256-7269. [PMID: 38641325 PMCID: PMC11064223 DOI: 10.1021/acs.est.3c07961] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/25/2023] [Revised: 03/25/2024] [Accepted: 03/27/2024] [Indexed: 04/21/2024]
Abstract
Through investigating the combined impact of the environmental exposures experienced by an individual throughout their lifetime, exposome research provides opportunities to understand and mitigate negative health outcomes. While current exposome research is driven by epidemiological studies that identify associations between exposures and effects, new frameworks integrating more substantial population-level metadata, including electronic health and administrative records, will shed further light on characterizing environmental exposure risks. Molecular biology offers methods and concepts to study the biological and health impacts of exposomes in experimental and computational systems. Of particular importance is the growing use of omics readouts in epidemiological and clinical studies. This paper calls for the adoption of mechanistic molecular biology approaches in exposome research as an essential step in understanding the genotype and exposure interactions underlying human phenotypes. A series of recommendations are presented to make the necessary and appropriate steps to move from exposure association to causation, with a huge potential to inform precision medicine and population health. This includes establishing hypothesis-driven laboratory testing within the exposome field, supported by appropriate methods to read across from model systems research to human.
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Affiliation(s)
- Amy L. Foreman
- European
Molecular Biology Laboratory & European Bioinformatics Institute
(EMBL-EBI), Wellcome Trust Genome Campus, Hinxton CB10 1SD, U.K.
| | - Benedikt Warth
- Department
of Food Chemistry and Toxicology, University
of Vienna, 1090 Vienna, Austria
| | - Ellen V. S. Hessel
- National
Institute for Public Health and the Environment (RIVM), Antonie van Leeuwenhoeklaan 9, 3721 MA Bilthoven, The Netherlands
| | - Elliott J. Price
- RECETOX,
Faculty of Science, Masaryk University, Kotlarska 2, Brno 60200, Czech Republic
| | - Emma L. Schymanski
- Luxembourg
Centre for Systems Biomedicine, University
of Luxembourg, 6 avenue
du Swing, L-4367 Belvaux, Luxembourg
| | - Gaia Cantelli
- European
Molecular Biology Laboratory & European Bioinformatics Institute
(EMBL-EBI), Wellcome Trust Genome Campus, Hinxton CB10 1SD, U.K.
| | - Helen Parkinson
- European
Molecular Biology Laboratory & European Bioinformatics Institute
(EMBL-EBI), Wellcome Trust Genome Campus, Hinxton CB10 1SD, U.K.
| | - Helge Hecht
- RECETOX,
Faculty of Science, Masaryk University, Kotlarska 2, Brno 60200, Czech Republic
| | - Jana Klánová
- RECETOX,
Faculty of Science, Masaryk University, Kotlarska 2, Brno 60200, Czech Republic
| | - Jelle Vlaanderen
- Institute
for Risk Assessment Sciences, Division of Environmental Epidemiology, Utrecht University, Heidelberglaan 8 3584 CS Utrecht, The Netherlands
| | - Klara Hilscherova
- RECETOX,
Faculty of Science, Masaryk University, Kotlarska 2, Brno 60200, Czech Republic
| | - Martine Vrijheid
- Institute
for Global Health (ISGlobal), Barcelona
Biomedical Research Park (PRBB), Doctor Aiguader, 88, 08003 Barcelona, Spain
- Universitat
Pompeu Fabra, Carrer
de la Mercè, 12, Ciutat Vella, 08002 Barcelona, Spain
- Centro de Investigación Biomédica en Red
Epidemiología
y Salud Pública (CIBERESP), Av. Monforte de Lemos, 3-5. Pebellón 11, Planta 0, 28029 Madrid, Spain
| | - Paolo Vineis
- Department
of Epidemiology and Biostatistics, School of Public Health, Imperial College, London SW7 2AZ, U.K.
| | - Rita Araujo
- European Commission, DG Research and Innovation, Sq. Frère-Orban 8, 1000 Bruxelles, Belgium
| | | | - Roel Vermeulen
- Institute
for Risk Assessment Sciences, Division of Environmental Epidemiology, Utrecht University, Heidelberglaan 8 3584 CS Utrecht, The Netherlands
| | - Sophie Lanone
- Univ Paris Est Creteil, INSERM, IMRB, F-94010 Creteil, France
| | - Søren Brunak
- Novo
Nordisk Foundation Center for Protein Research, University of Copenhagen, Copenhagen, Blegdamsvej 3B, 2200 København, Denmark
| | - Sylvain Sebert
- Research
Unit of Population Health, University of
Oulu, P.O. Box 8000, FI-90014 Oulu, Finland
| | - Tuomo Karjalainen
- European Commission, DG Research and Innovation, Sq. Frère-Orban 8, 1000 Bruxelles, Belgium
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9
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Evans W, Meslin EM, Kai J, Qureshi N. Precision Medicine-Are We There Yet? A Narrative Review of Precision Medicine's Applicability in Primary Care. J Pers Med 2024; 14:418. [PMID: 38673045 PMCID: PMC11051552 DOI: 10.3390/jpm14040418] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/06/2024] [Revised: 03/27/2024] [Accepted: 04/06/2024] [Indexed: 04/28/2024] Open
Abstract
Precision medicine (PM), also termed stratified, individualised, targeted, or personalised medicine, embraces a rapidly expanding area of research, knowledge, and practice. It brings together two emerging health technologies to deliver better individualised care: the many "-omics" arising from increased capacity to understand the human genome and "big data" and data analytics, including artificial intelligence (AI). PM has the potential to transform an individual's health, moving from population-based disease prevention to more personalised management. There is however a tension between the two, with a real risk that this will exacerbate health inequalities and divert funds and attention from basic healthcare requirements leading to worse health outcomes for many. All areas of medicine should consider how this will affect their practice, with PM now strongly encouraged and supported by government initiatives and research funding. In this review, we discuss examples of PM in current practice and its emerging applications in primary care, such as clinical prediction tools that incorporate genomic markers and pharmacogenomic testing. We look towards potential future applications and consider some key questions for PM, including evidence of its real-world impact, its affordability, the risk of exacerbating health inequalities, and the computational and storage challenges of applying PM technologies at scale.
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Affiliation(s)
- William Evans
- Primary Care Stratified Medicine (PRISM), Division of Primary Care, University of Nottingham, Nottingham NG7 2RD, UK; (J.K.); (N.Q.)
| | - Eric M. Meslin
- PHG Foundation, Cambridge University, Cambridge CB1 8RN, UK;
- Dalla Lana School of Public Health, University of Toronto, Toronto, ON M5S 1A8, Canada
| | - Joe Kai
- Primary Care Stratified Medicine (PRISM), Division of Primary Care, University of Nottingham, Nottingham NG7 2RD, UK; (J.K.); (N.Q.)
| | - Nadeem Qureshi
- Primary Care Stratified Medicine (PRISM), Division of Primary Care, University of Nottingham, Nottingham NG7 2RD, UK; (J.K.); (N.Q.)
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10
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Xiang R, Kelemen M, Xu Y, Harris LW, Parkinson H, Inouye M, Lambert SA. Recent advances in polygenic scores: translation, equitability, methods and FAIR tools. Genome Med 2024; 16:33. [PMID: 38373998 PMCID: PMC10875792 DOI: 10.1186/s13073-024-01304-9] [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: 06/09/2023] [Accepted: 02/07/2024] [Indexed: 02/21/2024] Open
Abstract
Polygenic scores (PGS) can be used for risk stratification by quantifying individuals' genetic predisposition to disease, and many potentially clinically useful applications have been proposed. Here, we review the latest potential benefits of PGS in the clinic and challenges to implementation. PGS could augment risk stratification through combined use with traditional risk factors (demographics, disease-specific risk factors, family history, etc.), to support diagnostic pathways, to predict groups with therapeutic benefits, and to increase the efficiency of clinical trials. However, there exist challenges to maximizing the clinical utility of PGS, including FAIR (Findable, Accessible, Interoperable, and Reusable) use and standardized sharing of the genomic data needed to develop and recalculate PGS, the equitable performance of PGS across populations and ancestries, the generation of robust and reproducible PGS calculations, and the responsible communication and interpretation of results. We outline how these challenges may be overcome analytically and with more diverse data as well as highlight sustained community efforts to achieve equitable, impactful, and responsible use of PGS in healthcare.
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Affiliation(s)
- Ruidong Xiang
- Cambridge Baker Systems Genomics Initiative, Baker Heart and Diabetes Institute, Melbourne, VIC, Australia
- Cambridge Baker Systems Genomics Initiative, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
| | - Martin Kelemen
- Cambridge Baker Systems Genomics Initiative, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
- British Heart Foundation Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
- Victor Phillip Dahdaleh Heart and Lung Research Institute, University of Cambridge, Cambridge, UK
| | - Yu Xu
- Cambridge Baker Systems Genomics Initiative, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
- British Heart Foundation Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
- Victor Phillip Dahdaleh Heart and Lung Research Institute, University of Cambridge, Cambridge, UK
- Health Data Research UK Cambridge, Wellcome Genome Campus and University of Cambridge, Cambridge, UK
| | - Laura W Harris
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, Cambridge, UK
| | - Helen Parkinson
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, Cambridge, UK
| | - Michael Inouye
- Cambridge Baker Systems Genomics Initiative, Baker Heart and Diabetes Institute, Melbourne, VIC, Australia.
- Cambridge Baker Systems Genomics Initiative, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK.
- British Heart Foundation Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK.
- Victor Phillip Dahdaleh Heart and Lung Research Institute, University of Cambridge, Cambridge, UK.
- Health Data Research UK Cambridge, Wellcome Genome Campus and University of Cambridge, Cambridge, UK.
- British Heart Foundation Centre of Research Excellence, University of Cambridge, Cambridge, UK.
| | - Samuel A Lambert
- Cambridge Baker Systems Genomics Initiative, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
- British Heart Foundation Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
- Victor Phillip Dahdaleh Heart and Lung Research Institute, University of Cambridge, Cambridge, UK
- Health Data Research UK Cambridge, Wellcome Genome Campus and University of Cambridge, Cambridge, UK
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, Cambridge, UK
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11
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Aw AJ, McRae J, Rahmani E, Song YS. Highly parameterized polygenic scores tend to overfit to population stratification via random effects. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.01.27.577589. [PMID: 38352303 PMCID: PMC10862757 DOI: 10.1101/2024.01.27.577589] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 02/22/2024]
Abstract
Polygenic scores (PGSs), increasingly used in clinical settings, frequently include many genetic variants, with performance typically peaking at thousands of variants. Such highly parameterized PGSs often include variants that do not pass a genome-wide significance threshold. We propose a mathematical perspective that renders the effects of many of these non-significant variants random rather than causal, with the randomness capturing population structure. We devise methods to assess variant effect randomness and population stratification bias. Applying these methods to 141 traits from the UK Biobank, we find that, for many PGSs, the effects of non-significant variants are considerably random, with the extent of randomness associated with the degree of overfitting to population structure of the discovery cohort. Our findings explain why highly parameterized PGSs simultaneously have superior cohort-specific performance and limited generalizability, suggesting the critical need for variant randomness tests in PGS evaluation. Supporting code and a dashboard are available at https://github.com/songlab-cal/StratPGS.
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Affiliation(s)
- Alan J. Aw
- Department of Statistics, University of California, Berkeley
- Center for Computational Biology, University of California, Berkeley
- Artificial Intelligence Laboratory, Illumina Inc
| | - Jeremy McRae
- Artificial Intelligence Laboratory, Illumina Inc
| | - Elior Rahmani
- Department of Computational Medicine, University of California, Los Angeles
| | - Yun S. Song
- Department of Statistics, University of California, Berkeley
- Center for Computational Biology, University of California, Berkeley
- Computer Science Division, University of California, Berkeley
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12
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Pozdeyev N, Dighe M, Barrio M, Raeburn C, Smith H, Fisher M, Chavan S, Rafaels N, Shortt JA, Lin M, Leu MG, Clark T, Marshall C, Haugen BR, Subramanian D, Crooks K, Gignoux C, Cohen T. Thyroid Cancer Polygenic Risk Score Improves Classification of Thyroid Nodules as Benign or Malignant. J Clin Endocrinol Metab 2024; 109:402-412. [PMID: 37683082 DOI: 10.1210/clinem/dgad530] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/01/2023] [Revised: 08/29/2023] [Accepted: 09/05/2023] [Indexed: 09/10/2023]
Abstract
CONTEXT Thyroid nodule ultrasound-based risk stratification schemas rely on the presence of high-risk sonographic features. However, some malignant thyroid nodules have benign appearance on thyroid ultrasound. New methods for thyroid nodule risk assessment are needed. OBJECTIVE We investigated polygenic risk score (PRS) accounting for inherited thyroid cancer risk combined with ultrasound-based analysis for improved thyroid nodule risk assessment. METHODS The convolutional neural network classifier was trained on thyroid ultrasound still images and cine clips from 621 thyroid nodules. Phenome-wide association study (PheWAS) and PRS PheWAS were used to optimize PRS for distinguishing benign and malignant nodules. PRS was evaluated in 73 346 participants in the Colorado Center for Personalized Medicine Biobank. RESULTS When the deep learning model output was combined with thyroid cancer PRS and genetic ancestry estimates, the area under the receiver operating characteristic curve (AUROC) of the benign vs malignant thyroid nodule classifier increased from 0.83 to 0.89 (DeLong, P value = .007). The combined deep learning and genetic classifier achieved a clinically relevant sensitivity of 0.95, 95% CI [0.88-0.99], specificity of 0.63 [0.55-0.70], and positive and negative predictive values of 0.47 [0.41-0.58] and 0.97 [0.92-0.99], respectively. AUROC improvement was consistent in European ancestry-stratified analysis (0.83 and 0.87 for deep learning and deep learning combined with PRS classifiers, respectively). Elevated PRS was associated with a greater risk of thyroid cancer structural disease recurrence (ordinal logistic regression, P value = .002). CONCLUSION Augmenting ultrasound-based risk assessment with PRS improves diagnostic accuracy.
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Affiliation(s)
- Nikita Pozdeyev
- Department of Biomedical Informatics, University of Colorado Anschutz Medical Campus, Aurora, CO 80045, USA
- Division of Endocrinology Metabolism and Diabetes, Department of Medicine, University of Colorado Anschutz Medical Campus, Aurora, CO 80045, USA
- University of Colorado Cancer Center, University of Colorado Anschutz Medical Campus, Aurora, CO 80045, USA
| | - Manjiri Dighe
- Department of Radiology, University of Washington, Seattle, WA 98195, USA
| | - Martin Barrio
- Division of GI, Trauma, and Endocrine Surgery, Department of Surgery, University of Colorado Anschutz Medical Campus, Aurora, CO 80045, USA
| | - Christopher Raeburn
- Division of GI, Trauma, and Endocrine Surgery, Department of Surgery, University of Colorado Anschutz Medical Campus, Aurora, CO 80045, USA
| | - Harry Smith
- Colorado Center for Personalized Medicine, University of Colorado Anschutz Medical Campus, Aurora, CO 80045, USA
| | - Matthew Fisher
- Colorado Center for Personalized Medicine, University of Colorado Anschutz Medical Campus, Aurora, CO 80045, USA
| | - Sameer Chavan
- Colorado Center for Personalized Medicine, University of Colorado Anschutz Medical Campus, Aurora, CO 80045, USA
| | - Nicholas Rafaels
- Colorado Center for Personalized Medicine, University of Colorado Anschutz Medical Campus, Aurora, CO 80045, USA
| | - Jonathan A Shortt
- Department of Biomedical Informatics, University of Colorado Anschutz Medical Campus, Aurora, CO 80045, USA
- Colorado Center for Personalized Medicine, University of Colorado Anschutz Medical Campus, Aurora, CO 80045, USA
| | - Meng Lin
- Department of Biomedical Informatics, University of Colorado Anschutz Medical Campus, Aurora, CO 80045, USA
- Colorado Center for Personalized Medicine, University of Colorado Anschutz Medical Campus, Aurora, CO 80045, USA
| | - Michael G Leu
- Information Technology Services, UW Medicine, Seattle, WA 98195, USA
- Department of Biomedical Informatics and Medical Education, University of Washington, Seattle, WA 98195, USA
- Department of Pediatrics, University of Washington, Seattle, WA 98105, USA
- Division of Hospital Medicine, Seattle Children's Hospital, Seattle, WA 98105, USA
| | - Toshimasa Clark
- Department of Radiology, University of Colorado Anschutz Medical Campus, Aurora, CO 80045, USA
| | - Carrie Marshall
- Department of Pathology, University of Colorado Anschutz Medical Campus, Aurora, CO 80045, USA
| | - Bryan R Haugen
- Division of Endocrinology Metabolism and Diabetes, Department of Medicine, University of Colorado Anschutz Medical Campus, Aurora, CO 80045, USA
- University of Colorado Cancer Center, University of Colorado Anschutz Medical Campus, Aurora, CO 80045, USA
| | | | - Kristy Crooks
- Colorado Center for Personalized Medicine, University of Colorado Anschutz Medical Campus, Aurora, CO 80045, USA
- Department of Pathology, University of Colorado Anschutz Medical Campus, Aurora, CO 80045, USA
| | - Christopher Gignoux
- Department of Biomedical Informatics, University of Colorado Anschutz Medical Campus, Aurora, CO 80045, USA
- Colorado Center for Personalized Medicine, University of Colorado Anschutz Medical Campus, Aurora, CO 80045, USA
| | - Trevor Cohen
- Department of Biomedical Informatics and Medical Education, University of Washington, Seattle, WA 98195, USA
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13
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Kachuri L, Chatterjee N, Hirbo J, Schaid DJ, Martin I, Kullo IJ, Kenny EE, Pasaniuc B, Witte JS, Ge T. Principles and methods for transferring polygenic risk scores across global populations. Nat Rev Genet 2024; 25:8-25. [PMID: 37620596 PMCID: PMC10961971 DOI: 10.1038/s41576-023-00637-2] [Citation(s) in RCA: 32] [Impact Index Per Article: 32.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 07/11/2023] [Indexed: 08/26/2023]
Abstract
Polygenic risk scores (PRSs) summarize the genetic predisposition of a complex human trait or disease and may become a valuable tool for advancing precision medicine. However, PRSs that are developed in populations of predominantly European genetic ancestries can increase health disparities due to poor predictive performance in individuals of diverse and complex genetic ancestries. We describe genetic and modifiable risk factors that limit the transferability of PRSs across populations and review the strengths and weaknesses of existing PRS construction methods for diverse ancestries. Developing PRSs that benefit global populations in research and clinical settings provides an opportunity for innovation and is essential for health equity.
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Affiliation(s)
- Linda Kachuri
- Department of Epidemiology and Population Health, Stanford University School of Medicine, Stanford, CA, USA
- Stanford Cancer Institute, Stanford University School of Medicine, Stanford, CA, USA
| | - Nilanjan Chatterjee
- Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
| | - Jibril Hirbo
- Department of Medicine Division of Genetic Medicine, Vanderbilt University Medical Center, Nashville, TN, USA
- Vanderbilt Genetics Institute, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Daniel J Schaid
- Department of Quantitative Health Sciences, Mayo Clinic, Rochester, MN, USA
| | - Iman Martin
- Division of Genomic Medicine, National Human Genome Research Institute, Bethesda, MD, USA
| | - Iftikhar J Kullo
- Department of Cardiovascular Medicine, Mayo Clinic, Rochester, MN, USA
| | - Eimear E Kenny
- Institute for Genomic Health, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Department of Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Bogdan Pasaniuc
- Department of Computational Medicine, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA
- Department of Human Genetics, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA
- Department of Pathology and Laboratory Medicine, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA
| | - John S Witte
- Department of Epidemiology and Population Health, Stanford University School of Medicine, Stanford, CA, USA.
- Stanford Cancer Institute, Stanford University School of Medicine, Stanford, CA, USA.
- Department of Biomedical Data Science, Stanford University, Stanford, CA, USA.
- Department of Genetics, Stanford University, Stanford, CA, USA.
| | - Tian Ge
- Psychiatric and Neurodevelopmental Genetics Unit, Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA.
- Center for Precision Psychiatry, Department of Psychiatry, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA.
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, USA.
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14
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Chen CY, Chen TT, Feng YCA, Yu M, Lin SC, Longchamps RJ, Wang SH, Hsu YH, Yang HI, Kuo PH, Daly MJ, Chen WJ, Huang H, Ge T, Lin YF. Analysis across Taiwan Biobank, Biobank Japan, and UK Biobank identifies hundreds of novel loci for 36 quantitative traits. CELL GENOMICS 2023; 3:100436. [PMID: 38116116 PMCID: PMC10726425 DOI: 10.1016/j.xgen.2023.100436] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/05/2021] [Revised: 11/21/2021] [Accepted: 10/09/2023] [Indexed: 12/21/2023]
Abstract
Genome-wide association studies (GWASs) have identified tens of thousands of genetic loci associated with human complex traits. However, the majority of GWASs were conducted in individuals of European ancestries. Failure to capture global genetic diversity has limited genomic discovery and has impeded equitable delivery of genomic knowledge to diverse populations. Here we report findings from 102,900 individuals across 36 human quantitative traits in the Taiwan Biobank (TWB), a major biobank effort that broadens the population diversity of genetic studies in East Asia. We identified 968 novel genetic loci, pinpointed novel causal variants through statistical fine-mapping, compared the genetic architecture across TWB, Biobank Japan, and UK Biobank, and evaluated the utility of cross-phenotype, cross-population polygenic risk scores in disease risk prediction. These results demonstrated the potential to advance discovery through diversifying GWAS populations and provided insights into the common genetic basis of human complex traits in East Asia.
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Affiliation(s)
- Chia-Yen Chen
- Biogen, Cambridge, MA 02142, USA
- Psychiatric and Neurodevelopmental Genetics Unit, Massachusetts General Hospital, Boston, MA 02114, USA
- Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA 02114, USA
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
| | - Tzu-Ting Chen
- Center for Neuropsychiatric Research, National Health Research Institutes, Miaoli 35053, Taiwan
| | - Yen-Chen Anne Feng
- Psychiatric and Neurodevelopmental Genetics Unit, Massachusetts General Hospital, Boston, MA 02114, USA
- Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA 02114, USA
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
- Department of Public Health & Institute of Epidemiology and Preventive Medicine, College of Public Health, National Taiwan University, Taipei 100025, Taiwan
- Institute of Health Data Analytics and Statistics, College of Public Health, National Taiwan University, Taipei 100025, Taiwan
| | - Mingrui Yu
- Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA 02114, USA
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
| | - Shu-Chin Lin
- Center for Neuropsychiatric Research, National Health Research Institutes, Miaoli 35053, Taiwan
| | - Ryan J. Longchamps
- Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA 02114, USA
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
| | - Shi-Heng Wang
- National Center for Geriatrics and Welfare Research, National Health Research Institutes, Miaoli 35053, Taiwan
- Department of Public Health, College of Public Health, China Medical University, Taichung 40678, Taiwan
| | - Yi-Hsiang Hsu
- Marcus Institute for Aging Research and Harvard Medical School, Boston, MA 02131, USA
- Beth Israel Deaconess Medical Center, Boston, MA 02215, USA
- Harvard School of Public Health, Boston, MA 02115, USA
- Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
| | - Hwai-I. Yang
- Genomics Research Center, Academia Sinica, Taipei 115201, Taiwan
- Institute of Clinical Medicine, National Yang-Ming University, Taipei 112304, Taiwan
- Doctoral Program of Clinical and Experimental Medicine, National Sun Yat-Sen University, Kaohsiung 80424, Taiwan
- Biomedical Translation Research Center, Academia Sinica, Taipei 115021, Taiwan
| | - Po-Hsiu Kuo
- Department of Public Health & Institute of Epidemiology and Preventive Medicine, College of Public Health, National Taiwan University, Taipei 100025, Taiwan
- Department of Psychiatry, College of Medicine and National Taiwan University Hospital, Taipei 106319, Taiwan
| | - Mark J. Daly
- Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA 02114, USA
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
- Institute for Molecular Medicine Finland FIMM, University of Helsinki, 00014 Helsinki, Finland
| | - Wei J. Chen
- Center for Neuropsychiatric Research, National Health Research Institutes, Miaoli 35053, Taiwan
- Department of Public Health & Institute of Epidemiology and Preventive Medicine, College of Public Health, National Taiwan University, Taipei 100025, Taiwan
- Department of Psychiatry, College of Medicine and National Taiwan University Hospital, Taipei 106319, Taiwan
| | - Hailiang Huang
- Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA 02114, USA
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
- Department of Medicine, Harvard Medical School, Boston, MA 02114, USA
| | - Tian Ge
- Psychiatric and Neurodevelopmental Genetics Unit, Massachusetts General Hospital, Boston, MA 02114, USA
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
- Department of Psychiatry, Massachusetts General Hospital, Harvard Medical School, Boston, MA 02114, USA
| | - Yen-Feng Lin
- Center for Neuropsychiatric Research, National Health Research Institutes, Miaoli 35053, Taiwan
- Department of Public Health & Medical Humanities, School of Medicine, National Yang Ming Chiao Tung University, Taipei 112304, Taiwan
- Institute of Behavioral Medicine, College of Medicine, National Cheng Kung University, Tainan 70101, Taiwan
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15
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Jeng XJ, Hu Y, Venkat V, Lu TP, Tzeng JY. Transfer learning with false negative control improves polygenic risk prediction. PLoS Genet 2023; 19:e1010597. [PMID: 38011285 PMCID: PMC10723713 DOI: 10.1371/journal.pgen.1010597] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/01/2023] [Revised: 12/15/2023] [Accepted: 11/09/2023] [Indexed: 11/29/2023] Open
Abstract
Polygenic risk score (PRS) is a quantity that aggregates the effects of variants across the genome and estimates an individual's genetic predisposition for a given trait. PRS analysis typically contains two input data sets: base data for effect size estimation and target data for individual-level prediction. Given the availability of large-scale base data, it becomes more common that the ancestral background of base and target data do not perfectly match. In this paper, we treat the GWAS summary information obtained in the base data as knowledge learned from a pre-trained model, and adopt a transfer learning framework to effectively leverage the knowledge learned from the base data that may or may not have similar ancestral background as the target samples to build prediction models for target individuals. Our proposed transfer learning framework consists of two main steps: (1) conducting false negative control (FNC) marginal screening to extract useful knowledge from the base data; and (2) performing joint model training to integrate the knowledge extracted from base data with the target training data for accurate trans-data prediction. This new approach can significantly enhance the computational and statistical efficiency of joint-model training, alleviate over-fitting, and facilitate more accurate trans-data prediction when heterogeneity level between target and base data sets is small or high.
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Affiliation(s)
- Xinge Jessie Jeng
- Department of Statistics, North Carolina State University, Raleigh, North Carolina, United States of America
| | - Yifei Hu
- Department of Statistics, North Carolina State University, Raleigh, North Carolina, United States of America
| | - Vaishnavi Venkat
- Bioinformatics Research Center, North Carolina State University, Raleigh, North Carolina, United States of America
| | - Tzu-Pin Lu
- Institute of Health Data Analytics and Statistics, National Taiwan University, Taipei, Taiwan
- Department of Public Health, National Taiwan University, Taipei, Taiwan
| | - Jung-Ying Tzeng
- Department of Statistics, North Carolina State University, Raleigh, North Carolina, United States of America
- Bioinformatics Research Center, North Carolina State University, Raleigh, North Carolina, United States of America
- Institute of Health Data Analytics and Statistics, National Taiwan University, Taipei, Taiwan
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16
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Alpeeva EV, Sharov KS. Global Biobank Meta-analysis Initiative: How can global health benefit by its use? J Glob Health 2023; 13:03054. [PMID: 37885313 PMCID: PMC10603307 DOI: 10.7189/jogh.13.03054] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/28/2023] Open
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17
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Dapas M, Lee YL, Wentworth-Sheilds W, Im HK, Ober C, Schoettler N. Revealing polygenic pleiotropy using genetic risk scores for asthma. HGG ADVANCES 2023; 4:100233. [PMID: 37663543 PMCID: PMC10474095 DOI: 10.1016/j.xhgg.2023.100233] [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: 06/02/2023] [Accepted: 08/11/2023] [Indexed: 09/05/2023] Open
Abstract
In this study we examined how genetic risk for asthma associates with different features of the disease and with other medical conditions and traits. Using summary statistics from two multi-ancestry genome-wide association studies of asthma, we modeled polygenic risk scores (PRSs) and validated their predictive performance in the UK Biobank. We then performed phenome-wide association studies of the asthma PRSs with 371 heritable traits in the UK Biobank. We identified 228 total significant associations across a variety of organ systems, including associations that varied by PRS model, sex, age of asthma onset, ancestry, and human leukocyte antigen region alleles. Our results highlight pervasive pleiotropy between asthma and numerous other traits and conditions and elucidate pathways that contribute to asthma and its comorbidities.
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Affiliation(s)
- Matthew Dapas
- Department of Human Genetics, University of Chicago, Chicago, IL, USA
| | - Yu Lin Lee
- Department of Human Genetics, University of Chicago, Chicago, IL, USA
- Biological Sciences Collegiate Division, University of Chicago, Chicago, IL, USA
| | | | - Hae Kyung Im
- Section of Genetic Medicine, Department of Medicine, University of Chicago, Chicago, IL, USA
| | - Carole Ober
- Department of Human Genetics, University of Chicago, Chicago, IL, USA
| | - Nathan Schoettler
- Section of Pulmonary and Critical Care Medicine, Department of Medicine, University of Chicago, Chicago, IL, USA
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18
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Wang Y, Kanai M, Tan T, Kamariza M, Tsuo K, Yuan K, Zhou W, Okada Y, Huang H, Turley P, Atkinson EG, Martin AR. Polygenic prediction across populations is influenced by ancestry, genetic architecture, and methodology. CELL GENOMICS 2023; 3:100408. [PMID: 37868036 PMCID: PMC10589629 DOI: 10.1016/j.xgen.2023.100408] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/31/2023] [Revised: 06/21/2023] [Accepted: 08/22/2023] [Indexed: 10/24/2023]
Abstract
Polygenic risk scores (PRSs) developed from multi-ancestry genome-wide association studies (GWASs), PRSmulti, hold promise for improving PRS accuracy and generalizability across populations. To establish best practices for leveraging the increasing diversity of genomic studies, we investigated how various factors affect the performance of PRSmulti compared with PRSs constructed from single-ancestry GWASs (PRSsingle). Through extensive simulations and empirical analyses, we showed that PRSmulti overall outperformed PRSsingle in understudied populations, except when the understudied population represented a small proportion of the multi-ancestry GWAS. Furthermore, integrating PRSs based on local ancestry-informed GWASs and large-scale, European-based PRSs improved predictive performance in understudied African populations, especially for less polygenic traits with large-effect ancestry-enriched variants. Our work highlights the importance of diversifying genomic studies to achieve equitable PRS performance across ancestral populations and provides guidance for developing PRSs from multiple studies.
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Affiliation(s)
- Ying Wang
- Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA 02114, USA
- Stanley Center for Psychiatric Research and Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
| | - Masahiro Kanai
- Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA 02114, USA
- Stanley Center for Psychiatric Research and Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
- Department of Statistical Genetics, Osaka University Graduate School of Medicine, Suita, Japan
| | - Taotao Tan
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX, USA
| | | | - Kristin Tsuo
- Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA 02114, USA
- Stanley Center for Psychiatric Research and Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
| | - Kai Yuan
- Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA 02114, USA
- Stanley Center for Psychiatric Research and Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
| | - Wei Zhou
- Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA 02114, USA
- Stanley Center for Psychiatric Research and Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
| | - Yukinori Okada
- Department of Statistical Genetics, Osaka University Graduate School of Medicine, Suita, Japan
- Laboratory for Systems Genetics, RIKEN Center for Integrative Medical Sciences, Yokohama, Japan
- Laboratory of Statistical Immunology, Immunology Frontier Research Center (WPI-IFReC), Center for Infectious Disease Education and Research (CiDER), and Integrated Frontier Research for Medical Science Division, Institute for Open and Transdisciplinary Research Initiatives, Osaka University, Suita 565-0871, Japan
- Department of Genome Informatics, Graduate School of Medicine, the University of Tokyo, Tokyo 113-0033, Japan
| | - the BioBank Japan Project
- Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA 02114, USA
- Stanley Center for Psychiatric Research and Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
- Department of Statistical Genetics, Osaka University Graduate School of Medicine, Suita, Japan
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX, USA
- Society of Fellows, Harvard University, Cambridge, MA 02138, USA
- Laboratory for Systems Genetics, RIKEN Center for Integrative Medical Sciences, Yokohama, Japan
- Laboratory of Statistical Immunology, Immunology Frontier Research Center (WPI-IFReC), Center for Infectious Disease Education and Research (CiDER), and Integrated Frontier Research for Medical Science Division, Institute for Open and Transdisciplinary Research Initiatives, Osaka University, Suita 565-0871, Japan
- Department of Genome Informatics, Graduate School of Medicine, the University of Tokyo, Tokyo 113-0033, Japan
- Department of Economics, and Center for Economic and Social Research, University of Southern California, Los Angeles, CA, USA
| | - Hailiang Huang
- Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA 02114, USA
- Stanley Center for Psychiatric Research and Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
| | - Patrick Turley
- Department of Economics, and Center for Economic and Social Research, University of Southern California, Los Angeles, CA, USA
| | - Elizabeth G. Atkinson
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX, USA
| | - Alicia R. Martin
- Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA 02114, USA
- Stanley Center for Psychiatric Research and Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
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19
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Moll M, Peljto AL, Kim JS, Xu H, Debban CL, Chen X, Menon A, Putman RK, Ghosh AJ, Saferali A, Nishino M, Hatabu H, Hobbs BD, Hecker J, McDermott G, Sparks JA, Wain LV, Allen RJ, Tobin MD, Raby BA, Chun S, Silverman EK, Zamora AC, Ortega VE, Garcia CK, Barr RG, Bleecker ER, Meyers DA, Kaner RJ, Rich SS, Manichaikul A, Rotter JI, Dupuis J, O’Connor GT, Fingerlin TE, Hunninghake GM, Schwartz DA, Cho MH. A Polygenic Risk Score for Idiopathic Pulmonary Fibrosis and Interstitial Lung Abnormalities. Am J Respir Crit Care Med 2023; 208:791-801. [PMID: 37523715 PMCID: PMC10563194 DOI: 10.1164/rccm.202212-2257oc] [Citation(s) in RCA: 10] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/14/2022] [Accepted: 07/31/2023] [Indexed: 08/02/2023] Open
Abstract
Rationale: In addition to rare genetic variants and the MUC5B locus, common genetic variants contribute to idiopathic pulmonary fibrosis (IPF) risk. The predictive power of common variants outside the MUC5B locus for IPF and interstitial lung abnormalities (ILAs) is unknown. Objectives: We tested the predictive value of IPF polygenic risk scores (PRSs) with and without the MUC5B region on IPF, ILA, and ILA progression. Methods: We developed PRSs that included (PRS-M5B) and excluded (PRS-NO-M5B) the MUC5B region (500-kb window around rs35705950-T) using an IPF genome-wide association study. We assessed PRS associations with area under the receiver operating characteristic curve (AUC) metrics for IPF, ILA, and ILA progression. Measurements and Main Results: We included 14,650 participants (1,970 IPF; 1,068 ILA) from six multi-ancestry population-based and case-control cohorts. In cases excluded from genome-wide association study, the PRS-M5B (odds ratio [OR] per SD of the score, 3.1; P = 7.1 × 10-95) and PRS-NO-M5B (OR per SD, 2.8; P = 2.5 × 10-87) were associated with IPF. Participants in the top PRS-NO-M5B quintile had ∼sevenfold odds for IPF compared with those in the first quintile. A clinical model predicted IPF (AUC, 0.61); rs35705950-T and PRS-NO-M5B demonstrated higher AUCs (0.73 and 0.7, respectively), and adding both genetic predictors to a clinical model yielded the highest performance (AUC, 0.81). The PRS-NO-M5B was associated with ILA (OR, 1.25) and ILA progression (OR, 1.16) in European ancestry participants. Conclusions: A common genetic variant risk score complements the MUC5B variant to identify individuals at high risk of interstitial lung abnormalities and pulmonary fibrosis.
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Affiliation(s)
- Matthew Moll
- Division of Pulmonary and Critical Care Medicine, and
- Channing Division of Network Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts
| | - Anna L. Peljto
- Department of Medicine and
- Department of Immunology, Division of Pulmonary Medicine, University of Colorado, Aurora, Colorado
| | - John S. Kim
- Division of Pulmonary and Critical Care Medicine, Department of Medicine, University of Virginia, Charlottesville, Virginia
| | - Hanfei Xu
- Department of Biostatistics, Boston University School of Public Health, Boston, Massachusetts
| | - Catherine L. Debban
- Center for Public Health Genomics, University of Virginia School of Medicine, Charlottesville, Virginia
| | - Xianfeng Chen
- Division of Pulmonary and Critical Care Medicine, Department of Medicine, Mayo Clinic, Phoenix, Arizona
| | - Aravind Menon
- Division of Pulmonary and Critical Care Medicine, and
| | | | - Auyon J. Ghosh
- Department of Medicine, Division of Pulmonary and Critical Care Medicine, State University of New York Upstate Medical Center, Syracuse, New York
| | - Aabida Saferali
- Channing Division of Network Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts
| | - Mizuki Nishino
- Center for Pulmonary Functional Imaging, Department of Radiology
| | - Hiroto Hatabu
- Center for Pulmonary Functional Imaging, Department of Radiology
| | - Brian D. Hobbs
- Division of Pulmonary and Critical Care Medicine, and
- Channing Division of Network Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts
| | - Julian Hecker
- Channing Division of Network Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts
| | - Gregory McDermott
- Division of Rheumatology, Department of Medicine, Brigham and Women’s Hospital, Boston, Massachusetts
| | - Jeffrey A. Sparks
- Division of Rheumatology, Department of Medicine, Brigham and Women’s Hospital, Boston, Massachusetts
| | - Louise V. Wain
- Department of Health Sciences, University of Leicester, Leicester, United Kingdom
- National Institute for Health Research, Leicester Respiratory Biomedical Research Centre, Glenfield Hospital, Leicester, United Kingdom
| | - Richard J. Allen
- Department of Health Sciences, University of Leicester, Leicester, United Kingdom
- National Institute for Health Research, Leicester Respiratory Biomedical Research Centre, Glenfield Hospital, Leicester, United Kingdom
| | - Martin D. Tobin
- Department of Health Sciences, University of Leicester, Leicester, United Kingdom
- National Institute for Health Research, Leicester Respiratory Biomedical Research Centre, Glenfield Hospital, Leicester, United Kingdom
| | - Benjamin A. Raby
- Channing Division of Network Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts
- Department of Pediatrics
- Division of Pulmonary Medicine, Boston Children’s Hospital, Harvard Medical School, Boston, Massachusetts
| | - Sung Chun
- Division of Pulmonary Medicine, Boston Children’s Hospital, Harvard Medical School, Boston, Massachusetts
| | - Edwin K. Silverman
- Division of Pulmonary and Critical Care Medicine, and
- Channing Division of Network Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts
| | - Ana C. Zamora
- Division of Pulmonary and Critical Care Medicine, Department of Medicine, Mayo Clinic, Phoenix, Arizona
| | - Victor E. Ortega
- Division of Pulmonary and Critical Care Medicine, Department of Medicine, Mayo Clinic, Phoenix, Arizona
| | - Christine K. Garcia
- Division of Pulmonary and Critical Care Medicine, Department of Medicine, Columbia University Irving Medical Center, New York, New York
| | - R. Graham Barr
- Department of Medicine and
- Division of General Medicine, Department of Epidemiology, Columbia University Medical Center, New York, New York
| | - Eugene R. Bleecker
- Division of Genetics, Genomics, and Precision Medicine, Department of Medicine, University of Arizona, Tucson, Arizona
| | - Deborah A. Meyers
- Division of Genetics, Genomics, and Precision Medicine, Department of Medicine, University of Arizona, Tucson, Arizona
| | - Robert J. Kaner
- Division of Pulmonary Medicine, Weill Cornell School of Medicine, New York, New York
| | - Stephen S. Rich
- Center for Public Health Genomics, University of Virginia School of Medicine, Charlottesville, Virginia
| | - Ani Manichaikul
- Center for Public Health Genomics, University of Virginia School of Medicine, Charlottesville, Virginia
| | - Jerome I. Rotter
- The Institute for Translational Genomics and Population Sciences, Department of Pediatrics, The Lundquist Institute for Biomedical Innovation at Harbor-University of California, Los Angeles Medical Center, Torrance, California
| | - Josée Dupuis
- Department of Biostatistics, Boston University School of Public Health, Boston, Massachusetts
- Department of Epidemiology, Biostatistics and Occupational Health, School of Population and Global Health, McGill University Faculty of Medicine and Health Sciences, Montreal, Quebec, Canada
| | - George T. O’Connor
- Department of Medicine, Pulmonary Center, Boston University School of Medicine, Boston, Massachusetts; and
| | - Tasha E. Fingerlin
- The National Jewish Health Cohen Family Asthma Institute, Division of Allergy and Immunology, National Jewish Health, Denver, Colorado
| | | | - David A. Schwartz
- Department of Medicine and
- Department of Immunology, Division of Pulmonary Medicine, University of Colorado, Aurora, Colorado
| | - Michael H. Cho
- Division of Pulmonary and Critical Care Medicine, and
- Channing Division of Network Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts
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20
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Lo Faro V, Johansson T, Höglund J, Hadizadeh F, Johansson Å. Polygenic risk scores and risk stratification in deep vein thrombosis. Thromb Res 2023; 228:151-162. [PMID: 37331118 DOI: 10.1016/j.thromres.2023.06.011] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/03/2023] [Revised: 05/18/2023] [Accepted: 06/09/2023] [Indexed: 06/20/2023]
Abstract
INTRODUCTION Deep vein thrombosis (DVT) is a complex disease, where 60 % of risk is due to genetic factors, such as the Factor V Leiden (FVL) variant. DVT is either asymptomatic or manifests with unspecific symptoms and, if left untreated, DVT leads to severe complications. The impact is dramatic and currently, there is still a research gap in DVT prevention. We characterized the genetic contribution and stratified individuals based on genetic makeup to evaluate if it favorably impacts risk prediction. METHODS In the UK Biobank (UKB), we performed gene-based association tests using exome sequencing data, as well as a genome-wide association study. We also constructed polygenic risk scores (PRS) in a subset of the cohort (Number of cases = 8231; Number of controls = 276,360) and calculated the impact on the prediction capacity of the PRS in a non-overlapping part of the cohort (Number of cases = 4342; Number of controls = 142,822). We generated additional PRSs that excluded the known causative variants. RESULTS We discovered and replicated a novel common variant (rs11604583) near the region where are located the TRIM51 and LRRC55 genes and identified a novel rare variant (rs187725533) located near the CREB3L1 gene, associated with 2.5-fold higher risk of DVT. In one of the PRS models constructed, the top decile of risk is associated with 3.4-fold increased risk, an effect that is 2.3-fold when excluding FVL carriers. In the top PRS decile, the cumulative risk of DVT at the age of 80 years is 10 % for FVL carriers, contraposed to 5 % for non-carriers. The population attributable fractions of having a high polygenic risk on the rate of DVT was estimated to be around 20 % in our cohort. CONCLUSION Individuals with a high polygenic risk of DVT, and not only carriers of well-studied variants such as FVL, may benefit from prevention strategies.
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Affiliation(s)
- Valeria Lo Faro
- Department of Immunology, Genetics and Pathology, Genomics and Neurobiology, Science for Life Laboratory, Uppsala University, Uppsala, Sweden.
| | - Therese Johansson
- Department of Immunology, Genetics and Pathology, Genomics and Neurobiology, Science for Life Laboratory, Uppsala University, Uppsala, Sweden; Centre for Women's Mental Health during the Reproductive Lifespan - Womher, Uppsala University, Uppsala, Sweden
| | - Julia Höglund
- Department of Immunology, Genetics and Pathology, Genomics and Neurobiology, Science for Life Laboratory, Uppsala University, Uppsala, Sweden
| | - Fatemeh Hadizadeh
- Department of Immunology, Genetics and Pathology, Genomics and Neurobiology, Science for Life Laboratory, Uppsala University, Uppsala, Sweden
| | - Åsa Johansson
- Department of Immunology, Genetics and Pathology, Genomics and Neurobiology, Science for Life Laboratory, Uppsala University, Uppsala, Sweden
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21
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Cañadas-Garre M, Kunzmann AT, Anderson K, Brennan EP, Doyle R, Patterson CC, Godson C, Maxwell AP, McKnight AJ. Albuminuria-Related Genetic Biomarkers: Replication and Predictive Evaluation in Individuals with and without Diabetes from the UK Biobank. Int J Mol Sci 2023; 24:11209. [PMID: 37446387 PMCID: PMC10342310 DOI: 10.3390/ijms241311209] [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: 05/31/2023] [Revised: 06/26/2023] [Accepted: 07/04/2023] [Indexed: 07/15/2023] Open
Abstract
Increased albuminuria indicates underlying glomerular pathology and is associated with worse renal disease outcomes, especially in diabetic kidney disease. Many single nucleotide polymorphisms (SNPs), associated with albuminuria, could be potentially useful to construct polygenic risk scores (PRSs) for kidney disease. We investigated the diagnostic accuracy of SNPs, previously associated with albuminuria-related traits, on albuminuria and renal injury in the UK Biobank population, with a particular interest in diabetes. Multivariable logistic regression was used to evaluate the influence of 91 SNPs on urine albumin-to-creatinine ratio (UACR)-related traits and kidney damage (any pathology indicating renal injury), stratifying by diabetes. Weighted PRSs for microalbuminuria and UACR from previous studies were used to calculate the area under the receiver operating characteristic curve (AUROC). CUBN-rs1801239 and DDR1-rs116772905 were associated with all the UACR-derived phenotypes, in both the overall and non-diabetic cohorts, but not with kidney damage. Several SNPs demonstrated different effects in individuals with diabetes compared to those without. SNPs did not improve the AUROC over currently used clinical variables. Many SNPs are associated with UACR or renal injury, suggesting a role in kidney dysfunction, dependent on the presence of diabetes in some cases. However, individual SNPs or PRSs did not improve the diagnostic accuracy for albuminuria or renal injury compared to standard clinical variables.
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Affiliation(s)
- Marisa Cañadas-Garre
- Molecular Epidemiology and Public Health Research Group, Centre for Public Health, Queen’s University Belfast, Institute for Clinical Sciences A, Royal Victoria Hospital, Belfast BT12 6BA, UK
- Genomic Oncology Area, GENYO, Centre for Genomics and Oncological Research, Pfizer-University of Granada-Andalusian Regional Government, PTS Granada, Avenida de la Ilustración 114, 18016 Granada, Spain
- Hematology Department, Hospital Universitario Virgen de las Nieves, Avenida de las Fuerzas Armadas 2, 18014 Granada, Spain
- Instituto de Investigación Biosanitaria de Granada (ibs.GRANADA), Avenida de Madrid, 15, 18012 Granada, Spain
| | - Andrew T. Kunzmann
- Cancer Epidemiology Research Group, Centre for Public Health, Queen’s University Belfast, Institute for Clinical Sciences A, Royal Victoria Hospital, Belfast BT12 6BA, UK
| | - Kerry Anderson
- Molecular Epidemiology and Public Health Research Group, Centre for Public Health, Queen’s University Belfast, Institute for Clinical Sciences A, Royal Victoria Hospital, Belfast BT12 6BA, UK
| | - Eoin P. Brennan
- UCD Diabetes Complications Research Centre, Conway Institute of Biomolecular and Biomedical Research, University College Dublin, D04 V1W8 Dublin, Ireland
| | - Ross Doyle
- UCD Diabetes Complications Research Centre, Conway Institute of Biomolecular and Biomedical Research, University College Dublin, D04 V1W8 Dublin, Ireland
- School of Medicine, University College Dublin, Health Sciences Centre, Belfield, D04 V1W8 Dublin, Ireland
- Mater Misericordiae University Hospital, Eccles St., D07 R2WY Dublin, Ireland
| | - Christopher C. Patterson
- Molecular Epidemiology and Public Health Research Group, Centre for Public Health, Queen’s University Belfast, Institute for Clinical Sciences A, Royal Victoria Hospital, Belfast BT12 6BA, UK
| | - Catherine Godson
- UCD Diabetes Complications Research Centre, Conway Institute of Biomolecular and Biomedical Research, University College Dublin, D04 V1W8 Dublin, Ireland
- School of Medicine, University College Dublin, Health Sciences Centre, Belfield, D04 V1W8 Dublin, Ireland
| | - Alexander P. Maxwell
- Molecular Epidemiology and Public Health Research Group, Centre for Public Health, Queen’s University Belfast, Institute for Clinical Sciences A, Royal Victoria Hospital, Belfast BT12 6BA, UK
- Regional Nephrology Unit, Level 11, Belfast City Hospital, Lisburn Road, Belfast BT9 7AB, UK
| | - Amy Jayne McKnight
- Molecular Epidemiology and Public Health Research Group, Centre for Public Health, Queen’s University Belfast, Institute for Clinical Sciences A, Royal Victoria Hospital, Belfast BT12 6BA, UK
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22
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Berga-Švītiņa E, Maksimenko J, Miklaševičs E, Fischer K, Vilne B, Mägi R. Polygenic Risk Score Predicts Modified Risk in BRCA1 Pathogenic Variant c.4035del and c.5266dup Carriers in Breast Cancer Patients. Cancers (Basel) 2023; 15:cancers15112957. [PMID: 37296919 DOI: 10.3390/cancers15112957] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/28/2023] [Revised: 05/22/2023] [Accepted: 05/23/2023] [Indexed: 06/12/2023] Open
Abstract
The aim of this study was to assess the power of the polygenic risk score (PRS) in estimating the overall genetic risk of women carrying germline BRCA1 pathogenic variants (PVs) c.4035del or c.5266dup to develop breast (BC) or ovarian cancer (OC) due to additional genetic variations. In this study, PRSs previously developed from two joint models using summary statistics of age-at-onset (BayesW model) and case-control data (BayesRR-RC model) from a genome-wide association analysis (GWAS) were applied to 406 germline BRCA1 PV (c.4035del or c.5266dup) carriers affected by BC or OC, compared with unaffected individuals. A binomial logistic regression model was used to assess the association of PRS with BC or OC development risk. We observed that the best-fitting BayesW PRS model effectively predicted the individual's BC risk (OR = 1.37; 95% CI = 1.03-1.81, p = 0.02905 with AUC = 0.759). However, none of the applied PRS models was a good predictor of OC risk. The best-fitted PRS model (BayesW) contributed to assessing the risk of developing BC for germline BRCA1 PV (c.4035del or c.5266dup) carriers and may facilitate more precise and timely patient stratification and decision-making to improve the current BC treatment or even prevention strategies.
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Affiliation(s)
- Egija Berga-Švītiņa
- Bioinformatics Lab, Rīga Stradiņš University, Dzirciema Street 16, LV-1007 Riga, Latvia
- Institute of Oncology, Rīga Stradiņš University, Pilsoņu Street 13, Block 13, LV-1002 Riga, Latvia
| | - Jeļena Maksimenko
- Institute of Oncology, Rīga Stradiņš University, Pilsoņu Street 13, Block 13, LV-1002 Riga, Latvia
- Pauls Stradiņš Clinical University Hospital, Pilsoņu Street 13, LV-1002 Riga, Latvia
| | - Edvīns Miklaševičs
- Institute of Oncology, Rīga Stradiņš University, Pilsoņu Street 13, Block 13, LV-1002 Riga, Latvia
- Department of Biology and Microbiology, Rīga Stradiņš University, LV-1007 Riga, Latvia
| | - Krista Fischer
- Institute of Genomics, University of Tartu, Riia 23b, 51010 Tartu, Estonia
- Institute of Mathematics and Statistics, University of Tartu, Narva mnt 18, 51009 Tartu, Estonia
| | - Baiba Vilne
- Bioinformatics Lab, Rīga Stradiņš University, Dzirciema Street 16, LV-1007 Riga, Latvia
| | - Reedik Mägi
- Institute of Genomics, University of Tartu, Riia 23b, 51010 Tartu, Estonia
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23
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Majara L, Kalungi A, Koen N, Tsuo K, Wang Y, Gupta R, Nkambule LL, Zar H, Stein DJ, Kinyanda E, Atkinson EG, Martin AR. Low and differential polygenic score generalizability among African populations due largely to genetic diversity. HGG ADVANCES 2023; 4:100184. [PMID: 36873096 PMCID: PMC9982687 DOI: 10.1016/j.xhgg.2023.100184] [Citation(s) in RCA: 12] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/10/2022] [Accepted: 02/04/2023] [Indexed: 02/15/2023] Open
Abstract
African populations are vastly underrepresented in genetic studies but have the most genetic variation and face wide-ranging environmental exposures globally. Because systematic evaluations of genetic prediction had not yet been conducted in ancestries that span African diversity, we calculated polygenic risk scores (PRSs) in simulations across Africa and in empirical data from South Africa, Uganda, and the United Kingdom to better understand the generalizability of genetic studies. PRS accuracy improves with ancestry-matched discovery cohorts more than from ancestry-mismatched studies. Within ancestrally and ethnically diverse South African individuals, we find that PRS accuracy is low for all traits but varies across groups. Differences in African ancestries contribute more to variability in PRS accuracy than other large cohort differences considered between individuals in the United Kingdom versus Uganda. We computed PRS in African ancestry populations using existing European-only versus ancestrally diverse genetic studies; the increased diversity produced the largest accuracy gains for hemoglobin concentration and white blood cell count, reflecting large-effect ancestry-enriched variants in genes known to influence sickle cell anemia and the allergic response, respectively. Differences in PRS accuracy across African ancestries originating from diverse regions are as large as across out-of-Africa continental ancestries, requiring commensurate nuance.
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Affiliation(s)
- Lerato Majara
- Global Initiative for Neuropsychiatric Genetics Education in Research (GINGER), Harvard T.H. Chan School of Public Health, Department of Epidemiology, Boston, MA, USA
- MRC Human Genetics Research Unit, Division of Human Genetics, Institute of Infectious Disease and Molecular Medicine, Faculty of Health Sciences, University of Cape Town, Observatory 7925, South Africa
- Department of Psychiatry and Neuroscience Institute, University of Cape Town, Cape Town, South Africa
| | - Allan Kalungi
- Global Initiative for Neuropsychiatric Genetics Education in Research (GINGER), Harvard T.H. Chan School of Public Health, Department of Epidemiology, Boston, MA, USA
- Department of Psychiatry, College of Health Sciences, Makerere University, Kampala, Uganda
- Department of Psychiatry, Faculty of Medicine and Health Sciences, Stellenbosch University, Cape Town, South Africa
- Mental Health Project, Medical Research Council/Uganda Virus Research Institute (MRC/UVRI) & London School of Hygiene and Tropical Medicine (LSHTM), Uganda Research Unit, Entebbe, Uganda
| | - Nastassja Koen
- Global Initiative for Neuropsychiatric Genetics Education in Research (GINGER), Harvard T.H. Chan School of Public Health, Department of Epidemiology, Boston, MA, USA
- Department of Psychiatry and Neuroscience Institute, University of Cape Town, Cape Town, South Africa
- South African Medical Research Council (SAMRC) Unit on Risk and Resilience in Mental Disorders, Cape Town, South Africa
| | - Kristin Tsuo
- Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA 02114, USA
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
- Program in Biological and Biomedical Sciences, Harvard Medical School, Boston, MA 02115, USA
| | - Ying Wang
- Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA 02114, USA
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
| | - Rahul Gupta
- Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA 02114, USA
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
- Program in Biological and Biomedical Sciences, Harvard Medical School, Boston, MA 02115, USA
| | - Lethukuthula L. Nkambule
- Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA 02114, USA
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
| | - Heather Zar
- Department of Paediatrics and Child Health, Red Cross Children’s Hospital and Medical Research Council Unit on Child and Adolescent Health, University of Cape Town, Cape Town, South Africa
| | - Dan J. Stein
- Department of Psychiatry and Neuroscience Institute, University of Cape Town, Cape Town, South Africa
- South African Medical Research Council (SAMRC) Unit on Risk and Resilience in Mental Disorders, Cape Town, South Africa
| | - Eugene Kinyanda
- Mental Health Project, Medical Research Council/Uganda Virus Research Institute (MRC/UVRI) & London School of Hygiene and Tropical Medicine (LSHTM), Uganda Research Unit, Entebbe, Uganda
| | - Elizabeth G. Atkinson
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX 77030, USA
| | - Alicia R. Martin
- Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA 02114, USA
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
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