201
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Wilde AAM, Semsarian C, Márquez MF, Sepehri Shamloo A, Ackerman MJ, Ashley EA, Sternick EB, Barajas-Martinez H, Behr ER, Bezzina CR, Breckpot J, Charron P, Chockalingam P, Crotti L, Gollob MH, Lubitz S, Makita N, Ohno S, Ortiz-Genga M, Sacilotto L, Schulze-Bahr E, Shimizu W, Sotoodehnia N, Tadros R, Ware JS, Winlaw DS, Kaufman ES, Aiba T, Bollmann A, Choi JI, Dalal A, Darrieux F, Giudicessi J, Guerchicoff M, Hong K, Krahn AD, MacIntyre C, Mackall JA, Mont L, Napolitano C, Ochoa JP, Peichl P, Pereira AC, Schwartz PJ, Skinner J, Stellbrink C, Tfelt-Hansen J, Deneke T. European Heart Rhythm Association (EHRA)/Heart Rhythm Society (HRS)/Asia Pacific Heart Rhythm Society (APHRS)/Latin American Heart Rhythm Society (LAHRS) Expert Consensus Statement on the State of Genetic Testing for Cardiac Diseases. Heart Rhythm 2022; 19:e1-e60. [PMID: 35390533 DOI: 10.1016/j.hrthm.2022.03.1225] [Citation(s) in RCA: 72] [Impact Index Per Article: 36.0] [Reference Citation Analysis] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/25/2022] [Accepted: 03/25/2022] [Indexed: 12/12/2022]
Affiliation(s)
- Arthur A M Wilde
- Heart Centre, Department of Cardiology, Amsterdam Universitair Medische Centra, Amsterdam, location AMC, The Netherlands.
| | - Christopher Semsarian
- Agnes Ginges Centre for Molecular Cardiology at Centenary Institute, University of Sydney, Sydney, Australia.
| | - Manlio F Márquez
- Instituto Nacional de Cardiología Ignacio Chávez, Ciudad de México, Mexico; and Member of the Latin American Heart Rhythm Society (LAHRS).
| | | | - Michael J Ackerman
- Departments of Cardiovascular Medicine, Pediatric and Adolescent Medicine, and Molecular Pharmacology & Experimental Therapeutics; Divisions of Heart Rhythm Services and Pediatric Cardiology; Windland Smith Rice Genetic Heart Rhythm Clinic and Windland Smith Rice Sudden Death Genomics Laboratory, Mayo Clinic, Rochester, MN, USA
| | - Euan A Ashley
- Department of Cardiovascular Medicine, Stanford University, Stanford, CA, USA
| | - Eduardo Back Sternick
- Arrhythmia and Electrophysiology Unit, Biocor Institute, Minas Gerais, Brazil; and Member of the Latin American Heart Rhythm Society (LAHRS)
| | | | - Elijah R Behr
- Cardiovascular Clinical Academic Group, Institute of Molecular and Clinical Sciences, St. George's, University of London; St. George's University Hospitals NHS Foundation Trust, London, UK; Mayo Clinic Healthcare, London
| | - Connie R Bezzina
- Amsterdam UMC Heart Center, Department of Experimental Cardiology, Amsterdam, The Netherlands
| | - Jeroen Breckpot
- Center for Human Genetics, University Hospitals Leuven, Leuven, Belgium
| | - Philippe Charron
- Sorbonne Université, APHP, Centre de Référence des Maladies Cardiaques Héréditaires, ICAN, Inserm UMR1166, Hôpital Pitié-Salpêtrière, Paris, France
| | | | - Lia Crotti
- Center for Cardiac Arrhythmias of Genetic Origin, Istituto Auxologico Italiano, IRCCS, Milan, Italy; Cardiomyopathy Unit and Cardiac Rehabilitation Unit, San Luca Hospital, Istituto Auxologico Italiano, IRCCS, Milan, Italy; Department of Medicine and Surgery, University of Milano-Bicocca, Milan, Italy
| | - Michael H Gollob
- Inherited Arrhythmia and Cardiomyopathy Program, Division of Cardiology, University of Toronto, Toronto, ON, Canada
| | - Steven Lubitz
- Cardiac Arrhythmia Service, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
| | - Naomasa Makita
- National Cerebral and Cardiovascular Center, Research Institute, Suita, Japan
| | - Seiko Ohno
- Department of Bioscience and Genetics, National Cerebral and Cardiovascular Center, Suita, Japan
| | - Martín Ortiz-Genga
- Clinical Department, Health in Code, A Coruña, Spain; and Member of the Latin American Heart Rhythm Society (LAHRS)
| | - Luciana Sacilotto
- Arrhythmia Unit, Instituto do Coracao, Hospital das Clinicas HCFMUSP, Faculdade de Medicina, Universidade de Sao Paulo, Sao Paulo, Brazil; and Member of the Latin American Heart Rhythm Society (LAHRS)
| | - Eric Schulze-Bahr
- Institute for Genetics of Heart Diseases, University Hospital Münster, Münster, Germany
| | - Wataru Shimizu
- Department of Cardiovascular Medicine, Graduate School of Medicine, Nippon Medical School, Bunkyo-ku, Tokyo, Japan
| | - Nona Sotoodehnia
- Cardiovascular Health Research Unit, Division of Cardiology, Department of Medicine, University of Washington, Seattle, WA, USA
| | - Rafik Tadros
- Cardiovascular Genetics Center, Department of Medicine, Montreal Heart Institute, Université de Montréal, Montreal, Canada
| | - James S Ware
- National Heart and Lung Institute and MRC London Institute of Medical Sciences, Imperial College London, London, UK; Royal Brompton & Harefield Hospitals, Guy's and St. Thomas' NHS Foundation Trust, London, UK
| | - David S Winlaw
- Cincinnati Children's Hospital Medical Centre, University of Cincinnati, Cincinnati, OH, USA
| | - Elizabeth S Kaufman
- Metrohealth Medical Center, Case Western Reserve University, Cleveland, OH, USA.
| | - Takeshi Aiba
- Department of Clinical Laboratory Medicine and Genetics, National Cerebral and Cardiovascular Center, Suita, Osaka, Japan
| | - Andreas Bollmann
- Department of Electrophysiology, Heart Center Leipzig at University of Leipzig, Leipzig, Germany; Leipzig Heart Institute, Leipzig Heart Digital, Leipzig, Germany
| | - Jong-Il Choi
- Division of Cardiology, Department of Internal Medicine, Korea University Anam Hospital, Korea University College of Medicine, Seoul, Republic of Korea
| | - Aarti Dalal
- Department of Pediatrics, Division of Cardiology, Vanderbilt University School of Medicine, Nashville, TN, USA
| | - Francisco Darrieux
- Arrhythmia Unit, Instituto do Coração, Hospital das Clínicas HCFMUSP, Faculdade de Medicina, Universidade de São Paulo, São Paulo, Brazil
| | - John Giudicessi
- Department of Cardiovascular Medicine (Divisions of Heart Rhythm Services and Circulatory Failure and the Windland Smith Rice Genetic Heart Rhythm Clinic), Mayo Clinic, Rochester, MN, USA
| | - Mariana Guerchicoff
- Division of Pediatric Arrhythmia and Electrophysiology, Italian Hospital of Buenos Aires, Buenos Aires, Argentina
| | - Kui Hong
- Department of Cardiovascular Medicine, The Second Affiliated Hospital of Nanchang University, Nanchang, China
| | - Andrew D Krahn
- Division of Cardiology, University of British Columbia, Vancouver, Canada
| | - Ciorsti MacIntyre
- Department of Cardiovascular Medicine, Division of Heart Rhythm Services, Windland Smith Rice Genetic Heart Rhythm Clinic, Mayo Clinic, Rochester, MN, USA
| | - Judith A Mackall
- Center for Cardiac Electrophysiology and Pacing, University Hospitals Cleveland Medical Center, Case Western Reserve University School of Medicine, Cleveland, OH, USA
| | - Lluís Mont
- Institut d'Investigacions Biomèdiques August Pi Sunyer (IDIBAPS), Barcelona, Spain; Centro de Investigacion Biomedica en Red en Enfermedades Cardiovasculares (CIBERCV), Madrid, Spain
| | - Carlo Napolitano
- Molecular Cardiology, Istituti Clinici Scientifici Maugeri, IRCCS, Pavia, Italy; Department of Molecular Medicine, University of Pavia, Pavia, Italy
| | - Juan Pablo Ochoa
- Centro Nacional de Investigaciones Cardiovasculares Carlos III (CNIC), Madrid, Spain; Heart Failure and Inherited Cardiac Diseases Unit, Department of Cardiology, Hospital Universitario Puerta de Hierro, Madrid, Spain; Centro de Investigacion Biomedica en Red en Enfermedades Cariovasculares (CIBERCV), Madrid, Spain
| | - Petr Peichl
- Department of Cardiology, Institute for Clinical and Experimental Medicine, Prague, Czech Republic
| | - Alexandre C Pereira
- Laboratory of Genetics and Molecular Cardiology, Heart Institute, University of São Paulo Medical School, São Paulo 05403-000, Brazil; Hipercol Brasil Program, São Paulo, Brazil
| | - Peter J Schwartz
- Center for Cardiac Arrhythmias of Genetic Origin, Istituto Auxologico Italiano, IRCCS, Milan, Italy
| | - Jon Skinner
- Sydney Childrens Hospital Network, University of Sydney, Sydney, Australia
| | - Christoph Stellbrink
- Department of Cardiology and Intensive Care Medicine, University Hospital Campus Klinikum Bielefeld, Bielefeld, Germany
| | - Jacob Tfelt-Hansen
- The Department of Cardiology, the Heart Centre, Copenhagen University Hospital, Rigshopitalet, Copenhagen, Denmark; Section of Genetics, Department of Forensic Medicine, Faculty of Medical Sciences, University of Copenhagen, Denmark
| | - Thomas Deneke
- Heart Center Bad Neustadt, Bad Neustadt a.d. Saale, Germany
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202
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Lu T, Forgetta V, Richards JB, Greenwood CMT. Polygenic risk score as a possible tool for identifying familial monogenic causes of complex diseases. Genet Med 2022; 24:1545-1555. [PMID: 35460399 DOI: 10.1016/j.gim.2022.03.022] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/08/2021] [Revised: 03/29/2022] [Accepted: 03/30/2022] [Indexed: 01/13/2023] Open
Abstract
PURPOSE The study aimed to evaluate whether polygenic risk scores could be helpful in addition to family history for triaging individuals to undergo deep-depth diagnostic sequencing for identifying monogenic causes of complex diseases. METHODS Among 44,550 exome-sequenced European ancestry UK Biobank participants, we identified individuals with a clinically reported or computationally predicted monogenic pathogenic variant for breast cancer, bowel cancer, heart disease, diabetes, or Alzheimer disease. We derived polygenic risk scores for these diseases. We tested whether a polygenic risk score could identify rare pathogenic variant heterozygotes among individuals with a parental disease history. RESULTS Monogenic causes of complex diseases were more prevalent among individuals with a parental disease history than in the rest of the population. Polygenic risk scores showed moderate discriminative power to identify familial monogenic causes. For instance, we showed that prescreening the patients with a polygenic risk score for type 2 diabetes can prioritize individuals to undergo diagnostic sequencing for monogenic diabetes variants and reduce needs for such sequencing by up to 37%. CONCLUSION Among individuals with a family history of complex diseases, those with a low polygenic risk score are more likely to have monogenic causes of the disease and could be prioritized to undergo genetic testing.
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Affiliation(s)
- Tianyuan Lu
- Centre for Clinical Epidemiology, Lady Davis Institute for Medical Research, Jewish General Hospital, Montreal, Quebec, Canada; Quantitative Life Sciences Program, McGill University, Montreal, Quebec, Canada
| | - Vincenzo Forgetta
- Centre for Clinical Epidemiology, Lady Davis Institute for Medical Research, Jewish General Hospital, Montreal, Quebec, Canada
| | - John Brent Richards
- Centre for Clinical Epidemiology, Lady Davis Institute for Medical Research, Jewish General Hospital, Montreal, Quebec, Canada; Department of Human Genetics, Faculty of Medicine and Health Sciences, McGill University, Montreal, Quebec, Canada; Department of Twin Research and Genetic Epidemiology, School of Life Course & Population Sciences, Faculty of Life Sciences & Medicine, King's College London, London, United Kingdom
| | - Celia M T Greenwood
- Centre for Clinical Epidemiology, Lady Davis Institute for Medical Research, Jewish General Hospital, Montreal, Quebec, Canada; Department of Human Genetics, Faculty of Medicine and Health Sciences, McGill University, Montreal, Quebec, Canada; Department of Epidemiology, Biostatistics and Occupational Health, Faculty of Medicine and Health Sciences, McGill University, Montreal, Quebec, Canada; Gerald Bronfman Department of Oncology, Gerald Bronfman Department of Oncology, McGill University, McGill University, Montreal, Quebec, Canada.
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203
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Cheng S, Cheng B, Liu L, Yang X, Meng P, Yao Y, Pan C, Zhang J, Li C, Zhang H, Chen Y, Zhang Z, Wen Y, Jia Y, Zhang F. Exome-wide screening identifies novel rare risk variants for major depression disorder. Mol Psychiatry 2022; 27:3069-3074. [PMID: 35365804 DOI: 10.1038/s41380-022-01536-4] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/28/2021] [Revised: 03/08/2022] [Accepted: 03/16/2022] [Indexed: 11/09/2022]
Abstract
Despite thousands of common genetic loci of major depression disorders (MDD) have been identified by GWAS to date, a large proportion of genetic variation predisposing to MDD remains unaccounted for. By utilizing the newly released UK Biobank 200,643 exome dataset, we conducted an exome-wide association study to identify rare risk variants contributing to MDD. After quality control, 120,033 participants with MDD polygenic risk scores (PRS) values were included. The individuals with lower 30% quantile of the PRS value were filtered for case and control selecting. Then the cases were set as the individuals with upper 10% quantile of the PHQ depression score and lower 10% quantile were set as controls. Finally, 1612 cases and 1612 controls were included in this study. The variants were annotated by ANNOVRA software. After exclusions, 34,761 qualifying variants, including 148 frameshift variant, 335 non-frameshift variant, 33,758 nonsynonymous, 91 start-loss, 393 stop-gain, 36 stop-loss variants were imported into the SKAT R-package to perform single variants, gene-based burden and robust burden tests with minor allele frequency (MAF) < 0.01. Single variant association testing identified one variant, rs4057749 (P = 5.39 × 10-9), within OR8B4 gene at an exome-wide significance level. The gene-based burden test of the exonic variants identified genome-wide significant associations in OR8B4 (PSKAT = 6.23 × 10-5, PSKAT Robust = 4.49 × 10-5), TRAPPC11 (PSKAT = 0.014, PSKAT Robust = 0.015), SBK3 (PSKAT = 0.020, PSKAT Robust = 0.025) and TNRC6B (PSKAT = 0.026, PSKAT Robust = 0.036). We identified multiple novel rare risk variants contributing to MDD in the individuals with lower PRS of MDD. The findings can help to broaden the genetic insights of the MDD pathogenesis.
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Affiliation(s)
- Shiqiang Cheng
- Key Laboratory of Trace Elements and Endemic Diseases of National Health and Family Planning Commission, Xi'an Jiaotong University, Xi'an, China.,Key Laboratory of Environment and Genes Related to Diseases of Ministry of Education of China, Xi'an Jiaotong University, Xi'an, China.,Key Laboratory for Disease Prevention and Control and Health Promotion of Shaanxi Province, Xi'an Jiaotong University, Xi'an, China
| | - Bolun Cheng
- Key Laboratory of Trace Elements and Endemic Diseases of National Health and Family Planning Commission, Xi'an Jiaotong University, Xi'an, China.,Key Laboratory of Environment and Genes Related to Diseases of Ministry of Education of China, Xi'an Jiaotong University, Xi'an, China.,Key Laboratory for Disease Prevention and Control and Health Promotion of Shaanxi Province, Xi'an Jiaotong University, Xi'an, China
| | - Li Liu
- Key Laboratory of Trace Elements and Endemic Diseases of National Health and Family Planning Commission, Xi'an Jiaotong University, Xi'an, China.,Key Laboratory of Environment and Genes Related to Diseases of Ministry of Education of China, Xi'an Jiaotong University, Xi'an, China.,Key Laboratory for Disease Prevention and Control and Health Promotion of Shaanxi Province, Xi'an Jiaotong University, Xi'an, China
| | - Xuena Yang
- Key Laboratory of Trace Elements and Endemic Diseases of National Health and Family Planning Commission, Xi'an Jiaotong University, Xi'an, China.,Key Laboratory of Environment and Genes Related to Diseases of Ministry of Education of China, Xi'an Jiaotong University, Xi'an, China.,Key Laboratory for Disease Prevention and Control and Health Promotion of Shaanxi Province, Xi'an Jiaotong University, Xi'an, China
| | - Peilin Meng
- Key Laboratory of Trace Elements and Endemic Diseases of National Health and Family Planning Commission, Xi'an Jiaotong University, Xi'an, China.,Key Laboratory of Environment and Genes Related to Diseases of Ministry of Education of China, Xi'an Jiaotong University, Xi'an, China.,Key Laboratory for Disease Prevention and Control and Health Promotion of Shaanxi Province, Xi'an Jiaotong University, Xi'an, China
| | - Yao Yao
- Key Laboratory of Trace Elements and Endemic Diseases of National Health and Family Planning Commission, Xi'an Jiaotong University, Xi'an, China.,Key Laboratory of Environment and Genes Related to Diseases of Ministry of Education of China, Xi'an Jiaotong University, Xi'an, China.,Key Laboratory for Disease Prevention and Control and Health Promotion of Shaanxi Province, Xi'an Jiaotong University, Xi'an, China
| | - Chuyu Pan
- Key Laboratory of Trace Elements and Endemic Diseases of National Health and Family Planning Commission, Xi'an Jiaotong University, Xi'an, China.,Key Laboratory of Environment and Genes Related to Diseases of Ministry of Education of China, Xi'an Jiaotong University, Xi'an, China.,Key Laboratory for Disease Prevention and Control and Health Promotion of Shaanxi Province, Xi'an Jiaotong University, Xi'an, China
| | - Jingxi Zhang
- Key Laboratory of Trace Elements and Endemic Diseases of National Health and Family Planning Commission, Xi'an Jiaotong University, Xi'an, China.,Key Laboratory of Environment and Genes Related to Diseases of Ministry of Education of China, Xi'an Jiaotong University, Xi'an, China.,Key Laboratory for Disease Prevention and Control and Health Promotion of Shaanxi Province, Xi'an Jiaotong University, Xi'an, China
| | - Chun'e Li
- Key Laboratory of Trace Elements and Endemic Diseases of National Health and Family Planning Commission, Xi'an Jiaotong University, Xi'an, China.,Key Laboratory of Environment and Genes Related to Diseases of Ministry of Education of China, Xi'an Jiaotong University, Xi'an, China.,Key Laboratory for Disease Prevention and Control and Health Promotion of Shaanxi Province, Xi'an Jiaotong University, Xi'an, China
| | - Huijie Zhang
- Key Laboratory of Trace Elements and Endemic Diseases of National Health and Family Planning Commission, Xi'an Jiaotong University, Xi'an, China.,Key Laboratory of Environment and Genes Related to Diseases of Ministry of Education of China, Xi'an Jiaotong University, Xi'an, China.,Key Laboratory for Disease Prevention and Control and Health Promotion of Shaanxi Province, Xi'an Jiaotong University, Xi'an, China
| | - Yujing Chen
- Key Laboratory of Trace Elements and Endemic Diseases of National Health and Family Planning Commission, Xi'an Jiaotong University, Xi'an, China.,Key Laboratory of Environment and Genes Related to Diseases of Ministry of Education of China, Xi'an Jiaotong University, Xi'an, China.,Key Laboratory for Disease Prevention and Control and Health Promotion of Shaanxi Province, Xi'an Jiaotong University, Xi'an, China
| | - Zhen Zhang
- Key Laboratory of Trace Elements and Endemic Diseases of National Health and Family Planning Commission, Xi'an Jiaotong University, Xi'an, China.,Key Laboratory of Environment and Genes Related to Diseases of Ministry of Education of China, Xi'an Jiaotong University, Xi'an, China.,Key Laboratory for Disease Prevention and Control and Health Promotion of Shaanxi Province, Xi'an Jiaotong University, Xi'an, China
| | - Yan Wen
- Key Laboratory of Trace Elements and Endemic Diseases of National Health and Family Planning Commission, Xi'an Jiaotong University, Xi'an, China.,Key Laboratory of Environment and Genes Related to Diseases of Ministry of Education of China, Xi'an Jiaotong University, Xi'an, China.,Key Laboratory for Disease Prevention and Control and Health Promotion of Shaanxi Province, Xi'an Jiaotong University, Xi'an, China
| | - Yumeng Jia
- Key Laboratory of Trace Elements and Endemic Diseases of National Health and Family Planning Commission, Xi'an Jiaotong University, Xi'an, China.,Key Laboratory of Environment and Genes Related to Diseases of Ministry of Education of China, Xi'an Jiaotong University, Xi'an, China.,Key Laboratory for Disease Prevention and Control and Health Promotion of Shaanxi Province, Xi'an Jiaotong University, Xi'an, China
| | - Feng Zhang
- Key Laboratory of Trace Elements and Endemic Diseases of National Health and Family Planning Commission, Xi'an Jiaotong University, Xi'an, China. .,Key Laboratory of Environment and Genes Related to Diseases of Ministry of Education of China, Xi'an Jiaotong University, Xi'an, China. .,Key Laboratory for Disease Prevention and Control and Health Promotion of Shaanxi Province, Xi'an Jiaotong University, Xi'an, China.
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204
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Bann D, Wright L, Hardy R, Williams DM, Davies NM. Polygenic and socioeconomic risk for high body mass index: 69 years of follow-up across life. PLoS Genet 2022; 18:e1010233. [PMID: 35834443 PMCID: PMC9282556 DOI: 10.1371/journal.pgen.1010233] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/19/2021] [Accepted: 05/03/2022] [Indexed: 11/29/2022] Open
Abstract
Genetic influences on body mass index (BMI) appear to markedly differ across life, yet existing research is equivocal and limited by a paucity of life course data. We thus used a birth cohort study to investigate differences in association and explained variance in polygenic risk for high BMI across infancy to old age (2-69 years). A secondary aim was to investigate how the association between BMI and a key purported environmental determinant (childhood socioeconomic position) differed across life, and whether this operated independently and/or multiplicatively of genetic influences. Data were from up to 2677 participants in the MRC National Survey of Health and Development, with measured BMI at 12 timepoints from 2-69 years. We used multiple polygenic indices from GWAS of adult and childhood BMI, and investigated their associations with BMI at each age. For polygenic liability to higher adult BMI, the trajectories of effect size (β) and explained variance (R2) diverged: explained variance peaked in early adulthood and plateaued thereafter, while absolute effect sizes increased throughout adulthood. For polygenic liability to higher childhood BMI, explained variance was largest in adolescence and early adulthood; effect sizes were marginally smaller in absolute terms from adolescence to adulthood. All polygenic indices were related to higher variation in BMI; quantile regression analyses showed that effect sizes were sizably larger at the upper end of the BMI distribution. Socioeconomic and polygenic risk for higher BMI across life appear to operate additively; we found little evidence of interaction. Our findings highlight the likely independent influences of polygenic and socioeconomic factors on BMI across life. Despite sizable associations, the BMI variance explained by each plateaued or declined across adulthood while BMI variance itself increased. This is suggestive of the increasing importance of chance ('non-shared') environmental influences on BMI across life.
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Affiliation(s)
- David Bann
- Centre for Longitudinal Studies, Social Research Institute, UCL, London, United Kingdom
- * E-mail: (DB); (LW)
| | - Liam Wright
- Centre for Longitudinal Studies, Social Research Institute, UCL, London, United Kingdom
- * E-mail: (DB); (LW)
| | - Rebecca Hardy
- School of Sport, Exercise and Health Sciences, Loughborough University, Loughborough, United Kingdom
- Social Research Institute, UCL, London, United Kingdom
| | - Dylan M. Williams
- MRC Unit for Lifelong Health and Ageing at UCL, London, United Kingdom
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
| | - Neil M. Davies
- MRC Integrative Epidemiology Unit, University of Bristol, Bristol, United Kingdom
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, United Kingdom
- K.G. Jebsen Center for Genetic Epidemiology, Department of Public Health and Nursing, NTNU, Norwegian University of Science and Technology, Trondheim, Norway
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205
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Khunsriraksakul C, Markus H, Olsen NJ, Carrel L, Jiang B, Liu DJ. Construction and Application of Polygenic Risk Scores in Autoimmune Diseases. Front Immunol 2022; 13:889296. [PMID: 35833142 PMCID: PMC9271862 DOI: 10.3389/fimmu.2022.889296] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/04/2022] [Accepted: 04/25/2022] [Indexed: 11/13/2022] Open
Abstract
Genome-wide association studies (GWAS) have identified hundreds of genetic variants associated with autoimmune diseases and provided unique mechanistic insights and informed novel treatments. These individual genetic variants on their own typically confer a small effect of disease risk with limited predictive power; however, when aggregated (e.g., via polygenic risk score method), they could provide meaningful risk predictions for a myriad of diseases. In this review, we describe the recent advances in GWAS for autoimmune diseases and the practical application of this knowledge to predict an individual’s susceptibility/severity for autoimmune diseases such as systemic lupus erythematosus (SLE) via the polygenic risk score method. We provide an overview of methods for deriving different polygenic risk scores and discuss the strategies to integrate additional information from correlated traits and diverse ancestries. We further advocate for the need to integrate clinical features (e.g., anti-nuclear antibody status) with genetic profiling to better identify patients at high risk of disease susceptibility/severity even before clinical signs or symptoms develop. We conclude by discussing future challenges and opportunities of applying polygenic risk score methods in clinical care.
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Affiliation(s)
- Chachrit Khunsriraksakul
- Graduate Program in Bioinformatics and Genomics, Pennsylvania State University College of Medicine, Hershey, PA, United States
- Institute for Personalized Medicine, Pennsylvania State University College of Medicine, Hershey, PA, United States
| | - Havell Markus
- Graduate Program in Bioinformatics and Genomics, Pennsylvania State University College of Medicine, Hershey, PA, United States
- Institute for Personalized Medicine, Pennsylvania State University College of Medicine, Hershey, PA, United States
| | - Nancy J. Olsen
- Department of Medicine, Division of Rheumatology, Pennsylvania State University College of Medicine, Hershey, PA, United States
| | - Laura Carrel
- Department of Biochemistry and Molecular Biology, Pennsylvania State University College of Medicine, Hershey, PA, United States
| | - Bibo Jiang
- Department of Public Health Sciences, Pennsylvania State University College of Medicine, Hershey, PA, United States
| | - Dajiang J. Liu
- Institute for Personalized Medicine, Pennsylvania State University College of Medicine, Hershey, PA, United States
- Department of Public Health Sciences, Pennsylvania State University College of Medicine, Hershey, PA, United States
- *Correspondence: Dajiang J. Liu,
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206
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Lu T, Forgetta V, Richards JB, Greenwood CMT. Capturing additional genetic risk from family history for improved polygenic risk prediction. Commun Biol 2022; 5:595. [PMID: 35710731 PMCID: PMC9203758 DOI: 10.1038/s42003-022-03532-4] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/17/2022] [Accepted: 05/24/2022] [Indexed: 12/01/2022] Open
Abstract
Family history of complex traits may reflect transmitted rare pathogenic variants, intra-familial shared exposures to environmental and lifestyle factors, as well as a common genetic predisposition. We developed a latent factor model to quantify trait heritability in excess of that captured by a common variant-based polygenic risk score, but inferable from family history. For 941 children in the Avon Longitudinal Study of Parents and Children cohort, a joint predictor combining a polygenic risk score for height and mid-parental height was able to explain ~55% of the total variance in sex-adjusted adult height z-scores, close to the estimated heritability. Marginal yet consistent risk prediction improvements were also achieved among ~400,000 European ancestry participants for 11 complex diseases in the UK Biobank. Our work showcases a paradigm for risk calculation, and supports incorporation of family history into polygenic risk score-based genetic risk prediction models.
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Affiliation(s)
- Tianyuan Lu
- Lady Davis Institute for Medical Research, Jewish General Hospital, Montreal, QC, Canada. .,Quantitative Life Sciences Program, McGill University, Montreal, QC, Canada.
| | - Vincenzo Forgetta
- Lady Davis Institute for Medical Research, Jewish General Hospital, Montreal, QC, Canada
| | - J Brent Richards
- Lady Davis Institute for Medical Research, Jewish General Hospital, Montreal, QC, Canada.,Department of Epidemiology, Biostatistics and Occupational Health, McGill University, Montreal, QC, Canada.,Department of Human Genetics, McGill University, Montreal, QC, Canada.,Department of Twin Research and Genetic Epidemiology, King's College London, London, UK
| | - Celia M T Greenwood
- Lady Davis Institute for Medical Research, Jewish General Hospital, Montreal, QC, Canada. .,Department of Epidemiology, Biostatistics and Occupational Health, McGill University, Montreal, QC, Canada. .,Department of Human Genetics, McGill University, Montreal, QC, Canada. .,Gerald Bronfman Department of Oncology, McGill University, Montreal, QC, Canada.
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207
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Hammer C, Mellman I. Coming of Age: Human Genomics and the Cancer-Immune Set Point. Cancer Immunol Res 2022; 10:674-679. [PMID: 35471657 PMCID: PMC9306278 DOI: 10.1158/2326-6066.cir-21-1017] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/29/2021] [Revised: 02/07/2022] [Accepted: 03/15/2022] [Indexed: 01/07/2023]
Abstract
Cancer is largely a disease of the tumor cell genome. As a result, the majority of genetics research in oncology has concentrated on the role of tumor somatic mutations, as well as inherited risk variants, in disease susceptibility and response to targeted treatments. The advent and success of cancer immunotherapies, however, have opened new perspectives for the investigation of the role of inherited genetic variation in codetermining outcome and safety. It is increasingly likely that the entirety of germline genetic variation involved in regulating immune responses accounts for a significant fraction of the observed variability in responses to cancer immunotherapies. Although germline genetic data from patients treated with cancer immunotherapies are still scarce, this line of research benefits from a vast body of knowledge derived from studies into autoimmune and infectious disease phenotypes, thus not requiring a start from a blank slate. Here, we discuss how a thorough investigation of genomic variation relevant for individuals' variability in (auto)immune responses can contribute to the discovery of novel treatment approaches and drug targets, and yield predictive biomarkers to stratify cancer patient populations in precision and personalized medicine settings.
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Affiliation(s)
- Christian Hammer
- Genentech, South San Francisco, California.,Corresponding Author: Christian Hammer, Genentech, Inc., South San Francisco, CA 94080. Phone: 650-452-9622; E-mail:
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Genetics in chronic kidney disease: conclusions from a Kidney Disease: Improving Global Outcomes (KDIGO) Controversies Conference. Kidney Int 2022; 101:1126-1141. [PMID: 35460632 PMCID: PMC9922534 DOI: 10.1016/j.kint.2022.03.019] [Citation(s) in RCA: 42] [Impact Index Per Article: 21.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/13/2022] [Revised: 03/16/2022] [Accepted: 03/29/2022] [Indexed: 01/19/2023]
Abstract
Numerous genes for monogenic kidney diseases with classical patterns of inheritance, as well as genes for complex kidney diseases that manifest in combination with environmental factors, have been discovered. Genetic findings are increasingly used to inform clinical management of nephropathies, and have led to improved diagnostics, disease surveillance, choice of therapy, and family counseling. All of these steps rely on accurate interpretation of genetic data, which can be outpaced by current rates of data collection. In March of 2021, Kidney Diseases: Improving Global Outcomes (KDIGO) held a Controversies Conference on "Genetics in Chronic Kidney Disease (CKD)" to review the current state of understanding of monogenic and complex (polygenic) kidney diseases, processes for applying genetic findings in clinical medicine, and use of genomics for defining and stratifying CKD. Given the important contribution of genetic variants to CKD, practitioners with CKD patients are advised to "think genetic," which specifically involves obtaining a family history, collecting detailed information on age of CKD onset, performing clinical examination for extrarenal symptoms, and considering genetic testing. To improve the use of genetics in nephrology, meeting participants advised developing an advanced training or subspecialty track for nephrologists, crafting guidelines for testing and treatment, and educating patients, students, and practitioners. Key areas of future research, including clinical interpretation of genome variation, electronic phenotyping, global representation, kidney-specific molecular data, polygenic scores, translational epidemiology, and open data resources, were also identified.
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209
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Affiliation(s)
- Iftikhar J Kullo
- Department of Cardiovascular Medicine, and the Gonda Vascular Center, Mayo Clinic, Rochester, MN, USA
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210
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Ashenhurst JR, Sazonova OV, Svrchek O, Detweiler S, Kita R, Babalola L, McIntyre M, Aslibekyan S, Fontanillas P, Shringarpure S, Pollard JD, Koelsch BL. A Polygenic Score for Type 2 Diabetes Improves Risk Stratification Beyond Current Clinical Screening Factors in an Ancestrally Diverse Sample. Front Genet 2022; 13:871260. [PMID: 35559025 PMCID: PMC9086969 DOI: 10.3389/fgene.2022.871260] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/08/2022] [Accepted: 03/31/2022] [Indexed: 11/13/2022] Open
Abstract
A substantial proportion of the adult United States population with type 2 diabetes (T2D) are undiagnosed, calling into question the comprehensiveness of current screening practices, which primarily rely on age, family history, and body mass index (BMI). We hypothesized that a polygenic score (PGS) may serve as a complementary tool to identify high-risk individuals. The T2D polygenic score maintained predictive utility after adjusting for family history and combining genetics with family history led to even more improved disease risk prediction. We observed that the PGS was meaningfully related to age of onset with implications for screening practices: there was a linear and statistically significant relationship between the PGS and T2D onset (-1.3 years per standard deviation of the PGS). Evaluation of U.S. Preventive Task Force and a simplified version of American Diabetes Association screening guidelines showed that addition of a screening criterion for those above the 90th percentile of the PGS provided a small increase the sensitivity of the screening algorithm. Among T2D-negative individuals, the T2D PGS was associated with prediabetes, where each standard deviation increase of the PGS was associated with a 23% increase in the odds of prediabetes diagnosis. Additionally, each standard deviation increase in the PGS corresponded to a 43% increase in the odds of incident T2D at one-year follow-up. Using complications and forms of clinical intervention (i.e., lifestyle modification, metformin treatment, or insulin treatment) as proxies for advanced illness we also found statistically significant associations between the T2D PGS and insulin treatment and diabetic neuropathy. Importantly, we were able to replicate many findings in a Hispanic/Latino cohort from our database, highlighting the value of the T2D PGS as a clinical tool for individuals with ancestry other than European. In this group, the T2D PGS provided additional disease risk information beyond that offered by traditional screening methodologies. The T2D PGS also had predictive value for the age of onset and for prediabetes among T2D-negative Hispanic/Latino participants. These findings strengthen the notion that a T2D PGS could play a role in the clinical setting across multiple ancestries, potentially improving T2D screening practices, risk stratification, and disease management.
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211
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Reay WR, Haslam R, Cairns MJ, Moschonis G, Clarke E, Attia J, Collins CE. Variation in cardiovascular disease risk factors among older adults in the Hunter Community Study cohort; a comparison of diet quality versus polygenic risk score. J Hum Nutr Diet 2022; 35:675-688. [PMID: 35560851 PMCID: PMC9542949 DOI: 10.1111/jhn.13031] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/01/2021] [Accepted: 01/16/2022] [Indexed: 11/29/2022]
Abstract
Background The interplay between cardiovascular disease (CVD) genetic risk indexed by a polygenic risk score (PRS) and diet quality still requires further investigation amongst older adults or those with established or treated CVD. The present study aimed to evaluate the relative contribution of diet quality, measured using the Australian Recommended Food Score (ARFS) and PRS, with respect to explaining variation in plasma lipids CVD outcomes in the Hunter Cohort. Methods The study comprised a secondary analysis of cross‐sectional data from the Hunter Cohort study. Single‐nucleotide polymorphisms from previously derived polygenic scores (PGSs) for three lipid classes were obtained: low‐density lipoprotein, high‐density lipoprotein and triglycerides, as well as PRS for coronary artery disease (CAD) from the PGS catalogue. Regression modelling and odds ratios were used to determine associations between PRS, ARFS and CVD risk. Results In total, 1703 participants were included: mean ± SD age 66 ± 7.4 years, 51% female, mean ± SD total ARFS 28.1 ± 8 (out of 74). Total diet quality and vegetable subscale were not significantly associated with measured lipids. By contrast, PGS for each lipid demonstrated a markedly strong, statistically significant correlation with its respective measured lipid. There was a significant association between CAD PRS and 5/6 CVD phenotypes (all except atrial fibrillation), with the largest effect size shown with coronary bypass. Adding dietary intake as a covariate did not change this relationship. Conclusions Lipid PGS explained more variance in measured lipids than diet quality. However, the poor diet quality observed in the current cohort may have limited the ability to observe any beneficial effects. Future research should investigate whether the diet quality of older adults can be improved and also the effect of these improvements on changes in polygenic risk. The Australian Recommended Food Score (ARFS) had little association with lipid and cardiovascular disease (CVD) endpoints. Lipid polygenic score (PGS) explained more variance in measured lipids than diet quality. The lipid PGS was associated with all three lipid parameters and some CVD endpoints, especially high cholesterol. Coronary artery disease polygenic risk score was associated with CVD endpoints angina, coronary bypass, heart attack, high cholesterol and hypertension and some lipid parameters (high‐density lipoprotein cholesterol).
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Affiliation(s)
- William R Reay
- School of Biomedical Sciences and Pharmacy, College of Health, Medicine and Wellbeing, The University of Newcastle, Callaghan, NSW, Australia.,Hunter Medical Research Institute, New Lambton, NSW, Australia
| | - Rebecca Haslam
- Hunter Medical Research Institute, New Lambton, NSW, Australia.,School of Health Sciences, College of Health, Medicine and Wellbeing, The University of Newcastle, Callaghan, NSW, Australia.,Priority Research Centre for Physical Activity and Nutrition, The University of Newcastle Callaghan, NSW, Australia
| | - Murray J Cairns
- School of Biomedical Sciences and Pharmacy, College of Health, Medicine and Wellbeing, The University of Newcastle, Callaghan, NSW, Australia.,Hunter Medical Research Institute, New Lambton, NSW, Australia
| | - George Moschonis
- Department of Dietetics, Nutrition and Sport, School of Allied Health, Human Services and Sport, La Trobe University, Melbourne, Australia
| | - Erin Clarke
- Hunter Medical Research Institute, New Lambton, NSW, Australia.,School of Health Sciences, College of Health, Medicine and Wellbeing, The University of Newcastle, Callaghan, NSW, Australia.,Priority Research Centre for Physical Activity and Nutrition, The University of Newcastle Callaghan, NSW, Australia
| | - John Attia
- Hunter Medical Research Institute, New Lambton, NSW, Australia.,School of Population Health and Medical Practice, College of Health, Medicine and Wellbeing, The University of Newcastle, Callaghan, NSW, Australia
| | - Clare Elizabeth Collins
- Hunter Medical Research Institute, New Lambton, NSW, Australia.,School of Health Sciences, College of Health, Medicine and Wellbeing, The University of Newcastle, Callaghan, NSW, Australia.,Priority Research Centre for Physical Activity and Nutrition, The University of Newcastle Callaghan, NSW, Australia
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212
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Schaid DJ, Sinnwell JP, Batzler A, McDonnell SK. Polygenic risk for prostate cancer: Decreasing relative risk with age but little impact on absolute risk. Am J Hum Genet 2022; 109:900-908. [PMID: 35353984 PMCID: PMC9118111 DOI: 10.1016/j.ajhg.2022.03.008] [Citation(s) in RCA: 13] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/25/2021] [Accepted: 03/09/2022] [Indexed: 12/14/2022] Open
Abstract
Polygenic risk scores (PRSs) for a variety of diseases have recently been shown to have relative risks that depend on age, and genetic relative risks decrease with increasing age. A refined understanding of the age dependency of PRSs for a disease is important for personalized risk predictions and risk stratification. To further evaluate how the PRS relative risk for prostate cancer depends on age, we refined analyses for a validated PRS for prostate cancer by using 64,274 prostate cancer cases and 46,432 controls of diverse ancestry (82.8% European, 9.8% African American, 3.8% Latino, 2.8% Asian, and 0.8% Ghanaian). Our strategy applied a novel weighted proportional hazards model to case-control data to fully utilize age to refine how the relative risk decreased with age. We found significantly greater relative risks for younger men (age 30-55 years) compared with older men (70-88 years) for both relative risk per standard deviation of the PRS and dichotomized according to the upper 90th percentile of the PRS distribution. For the largest European ancestral group that could provide reliable resolution, the log-relative risk decreased approximately linearly from age 50 to age 75. Despite strong evidence of age-dependent genetic relative risk, our results suggest that absolute risk predictions differed little from predictions that assumed a constant relative risk over ages, from short-term to long-term predictions, simplifying implementation of risk discussions into clinical practice.
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Affiliation(s)
- Daniel J Schaid
- Department of Quantitative Health Sciences, Mayo Clinic, Rochester, MN, USA.
| | - Jason P Sinnwell
- Department of Quantitative Health Sciences, Mayo Clinic, Rochester, MN, USA
| | - Anthony Batzler
- Department of Quantitative Health Sciences, Mayo Clinic, Rochester, MN, USA
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213
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Hodgson S, Huang QQ, Sallah N, Griffiths CJ, Newman WG, Trembath RC, Wright J, Lumbers RT, Kuchenbaecker K, van Heel DA, Mathur R, Martin HC, Finer S. Integrating polygenic risk scores in the prediction of type 2 diabetes risk and subtypes in British Pakistanis and Bangladeshis: A population-based cohort study. PLoS Med 2022; 19:e1003981. [PMID: 35587468 PMCID: PMC9119501 DOI: 10.1371/journal.pmed.1003981] [Citation(s) in RCA: 18] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/25/2021] [Accepted: 04/06/2022] [Indexed: 01/13/2023] Open
Abstract
BACKGROUND Type 2 diabetes (T2D) is highly prevalent in British South Asians, yet they are underrepresented in research. Genes & Health (G&H) is a large, population study of British Pakistanis and Bangladeshis (BPB) comprising genomic and routine health data. We assessed the extent to which genetic risk for T2D is shared between BPB and European populations (EUR). We then investigated whether the integration of a polygenic risk score (PRS) for T2D with an existing risk tool (QDiabetes) could improve prediction of incident disease and the characterisation of disease subtypes. METHODS AND FINDINGS In this observational cohort study, we assessed whether common genetic loci associated with T2D in EUR individuals were replicated in 22,490 BPB individuals in G&H. We replicated fewer loci in G&H (n = 76/338, 22%) than would be expected given power if all EUR-ascertained loci were transferable (n = 101, 30%; p = 0.001). Of the 27 transferable loci that were powered to interrogate this, only 9 showed evidence of shared causal variants. We constructed a T2D PRS and combined it with a clinical risk instrument (QDiabetes) in a novel, integrated risk tool (IRT) to assess risk of incident diabetes. To assess model performance, we compared categorical net reclassification index (NRI) versus QDiabetes alone. In 13,648 patients free from T2D followed up for 10 years, NRI was 3.2% for IRT versus QDiabetes (95% confidence interval (CI): 2.0% to 4.4%). IRT performed best in reclassification of individuals aged less than 40 years deemed low risk by QDiabetes alone (NRI 5.6%, 95% CI 3.6% to 7.6%), who tended to be free from comorbidities and slim. After adjustment for QDiabetes score, PRS was independently associated with progression to T2D after gestational diabetes (hazard ratio (HR) per SD of PRS 1.23, 95% CI 1.05 to 1.42, p = 0.028). Using cluster analysis of clinical features at diabetes diagnosis, we replicated previously reported disease subgroups, including Mild Age-Related, Mild Obesity-related, and Insulin-Resistant Diabetes, and showed that PRS distribution differs between subgroups (p = 0.002). Integrating PRS in this cluster analysis revealed a Probable Severe Insulin Deficient Diabetes (pSIDD) subgroup, despite the absence of clinical measures of insulin secretion or resistance. We also observed differences in rates of progression to micro- and macrovascular complications between subgroups after adjustment for confounders. Study limitations include the absence of an external replication cohort and the potential biases arising from missing or incorrect routine health data. CONCLUSIONS Our analysis of the transferability of T2D loci between EUR and BPB indicates the need for larger, multiancestry studies to better characterise the genetic contribution to disease and its varied aetiology. We show that a T2D PRS optimised for this high-risk BPB population has potential clinical application in BPB, improving the identification of T2D risk (especially in the young) on top of an established clinical risk algorithm and aiding identification of subgroups at diagnosis, which may help future efforts to stratify care and treatment of the disease.
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Affiliation(s)
- Sam Hodgson
- Primary Care Research Centre, University of Southampton, Southampton, United Kingdom
| | - Qin Qin Huang
- Department of Human Genetics, Wellcome Sanger Institute, Hinxton, United Kingdom
| | - Neneh Sallah
- Institute of Health Informatics, University College London, London, United Kingdom
- UCL Genetics Institute, University College London, London, United Kingdom
| | - Genes & Health Research Team
- Wolfson Institute of Population Health, Barts and the London School of Medicine and Dentistry, Queen Mary University of London, London, United Kingdom
- Blizard Institute, Barts and the London School of Medicine and Dentistry, Queen Mary University of London, London, United Kingdom
| | - Chris J. Griffiths
- Wolfson Institute of Population Health, Barts and the London School of Medicine and Dentistry, Queen Mary University of London, London, United Kingdom
| | - William G. Newman
- Manchester Centre for Genomic Medicine, Manchester University Hospitals NHS Foundation Trust, Manchester, United Kingdom
- Division of Evolution and Genomic Sciences, School of Biological Sciences, Faculty of Biology, Medicine and Health, University of Manchester, Manchester Academic Health Science Centre, Manchester, United Kingdom
| | - Richard C. Trembath
- School of Basic and Medical Biosciences, Faculty of Life Sciences and Medicine, King’s College London, London, United Kingdom
| | - John Wright
- Bradford Institute for Health Research, Bradford, United Kingdom
| | - R. Thomas Lumbers
- Institute of Health Informatics, University College London, London, United Kingdom
- British Heart Foundation Research Accelerator, University College London, London, United Kingdom
| | - Karoline Kuchenbaecker
- UCL Genetics Institute, University College London, London, United Kingdom
- Division of Psychiatry, University College London, London, United Kingdom
| | - David A. van Heel
- Blizard Institute, Barts and the London School of Medicine and Dentistry, Queen Mary University of London, London, United Kingdom
| | - Rohini Mathur
- London School of Hygiene & Tropical Medicine, London, United Kingdom
| | - Hilary C. Martin
- Department of Human Genetics, Wellcome Sanger Institute, Hinxton, United Kingdom
| | - Sarah Finer
- Wolfson Institute of Population Health, Barts and the London School of Medicine and Dentistry, Queen Mary University of London, London, United Kingdom
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214
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Hao L, Kraft P, Berriz GF, Hynes ED, Koch C, Korategere V Kumar P, Parpattedar SS, Steeves M, Yu W, Antwi AA, Brunette CA, Danowski M, Gala MK, Green RC, Jones NE, Lewis ACF, Lubitz SA, Natarajan P, Vassy JL, Lebo MS. Development of a clinical polygenic risk score assay and reporting workflow. Nat Med 2022; 28:1006-1013. [PMID: 35437332 PMCID: PMC9117136 DOI: 10.1038/s41591-022-01767-6] [Citation(s) in RCA: 66] [Impact Index Per Article: 33.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/22/2021] [Accepted: 03/02/2022] [Indexed: 12/31/2022]
Abstract
Implementation of polygenic risk scores (PRS) may improve disease prevention and management but poses several challenges: the construction of clinically valid assays, interpretation for individual patients, and the development of clinical workflows and resources to support their use in patient care. For the ongoing Veterans Affairs Genomic Medicine at Veterans Affairs (GenoVA) Study we developed a clinical genotype array-based assay for six published PRS. We used data from 36,423 Mass General Brigham Biobank participants and adjustment for population structure to replicate known PRS-disease associations and published PRS thresholds for a disease odds ratio (OR) of 2 (ranging from 1.75 (95% CI: 1.57-1.95) for type 2 diabetes to 2.38 (95% CI: 2.07-2.73) for breast cancer). After confirming the high performance and robustness of the pipeline for use as a clinical assay for individual patients, we analyzed the first 227 prospective samples from the GenoVA Study and found that the frequency of PRS corresponding to published OR > 2 ranged from 13/227 (5.7%) for colorectal cancer to 23/150 (15.3%) for prostate cancer. In addition to the PRS laboratory report, we developed physician- and patient-oriented informational materials to support decision-making about PRS results. Our work illustrates the generalizable development of a clinical PRS assay for multiple conditions and the technical, reporting and clinical workflow challenges for implementing PRS information in the clinic.
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Affiliation(s)
- Limin Hao
- Laboratory for Molecular Medicine, Mass General Brigham Personalized Medicine, Cambridge, MA, USA
| | - Peter Kraft
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Gabriel F Berriz
- Laboratory for Molecular Medicine, Mass General Brigham Personalized Medicine, Cambridge, MA, USA
| | - Elizabeth D Hynes
- Laboratory for Molecular Medicine, Mass General Brigham Personalized Medicine, Cambridge, MA, USA
| | - Christopher Koch
- Laboratory for Molecular Medicine, Mass General Brigham Personalized Medicine, Cambridge, MA, USA
| | | | - Shruti S Parpattedar
- Laboratory for Molecular Medicine, Mass General Brigham Personalized Medicine, Cambridge, MA, USA
| | - Marcie Steeves
- Laboratory for Molecular Medicine, Mass General Brigham Personalized Medicine, Cambridge, MA, USA
- Medical Genetics, Massachusetts General Hospital, Boston, MA, USA
| | - Wanfeng Yu
- Laboratory for Molecular Medicine, Mass General Brigham Personalized Medicine, Cambridge, MA, USA
| | - Ashley A Antwi
- Veterans Affairs Boston Healthcare System, Boston, MA, USA
| | | | | | - Manish K Gala
- Division of Gastroenterology, Massachusetts General Hospital, Boston, MA, USA
- Harvard Medical School, Boston, MA, USA
| | - Robert C Green
- Harvard Medical School, Boston, MA, USA
- Broad Institute of Harvard and the Massachusetts Institute of Technology, Cambridge, MA, USA
- Department of Medicine, Brigham and Women's Hospital, Boston, MA, USA
- Precision Population Health, Ariadne Labs, Boston, MA, USA
| | - Natalie E Jones
- Veterans Affairs Boston Healthcare System, Boston, MA, USA
- Broad Institute of Harvard and the Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Anna C F Lewis
- E J Safra Center for Ethics, Harvard University, Cambridge, MA, USA
| | - Steven A Lubitz
- Cardiovascular Disease Initiative, Broad Institute of Harvard and the Massachusetts Institute of Technology, Cambridge, MA, USA
- Cardiovascular Research Center, Massachusetts General Hospital, Boston, MA, USA
- Demoulas Center for Cardiac Arrhythmias, Massachusetts General Hospital, Boston, MA, USA
| | - Pradeep Natarajan
- Harvard Medical School, Boston, MA, USA
- Cardiovascular Disease Initiative, Broad Institute of Harvard and the Massachusetts Institute of Technology, Cambridge, MA, USA
- Cardiovascular Research Center, Massachusetts General Hospital, Boston, MA, USA
| | - Jason L Vassy
- Veterans Affairs Boston Healthcare System, Boston, MA, USA.
- Harvard Medical School, Boston, MA, USA.
- Department of Medicine, Brigham and Women's Hospital, Boston, MA, USA.
- Precision Population Health, Ariadne Labs, Boston, MA, USA.
| | - Matthew S Lebo
- Laboratory for Molecular Medicine, Mass General Brigham Personalized Medicine, Cambridge, MA, USA
- Harvard Medical School, Boston, MA, USA
- Department of Pathology, Brigham and Women's Hospital, Boston, MA, USA
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215
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Association between a polygenic and family risk score on the prevalence and incidence of myocardial infarction in the KORA-F3 study. Atherosclerosis 2022; 352:10-17. [DOI: 10.1016/j.atherosclerosis.2022.05.014] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/03/2022] [Revised: 05/03/2022] [Accepted: 05/18/2022] [Indexed: 11/23/2022]
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216
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Steinberg J, Iles MM, Lee JY, Wang X, Law MH, Smit AK, Nguyen‐Dumont T, Giles GG, Southey MC, Milne RL, Mann GJ, Bishop DT, MacInnis RJ, Cust AE. Independent evaluation of melanoma polygenic risk scores in UK and Australian prospective cohorts. Br J Dermatol 2022; 186:823-834. [PMID: 34921685 PMCID: PMC9545863 DOI: 10.1111/bjd.20956] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/03/2021] [Revised: 11/11/2021] [Accepted: 12/11/2021] [Indexed: 12/05/2022]
Abstract
BACKGROUND Previous studies suggest that polygenic risk scores (PRSs) may improve melanoma risk stratification. However, there has been limited independent validation of PRS-based risk prediction, particularly assessment of calibration (comparing predicted to observed risks). OBJECTIVES To evaluate PRS-based melanoma risk prediction in prospective UK and Australian cohorts with European ancestry. METHODS We analysed invasive melanoma incidence in the UK Biobank (UKB; n = 395 647, 1651 cases) and a case-cohort nested within the Melbourne Collaborative Cohort Study (MCCS, Australia; n = 4765, 303 cases). Three PRSs were evaluated: 68 single-nucleotide polymorphisms (SNPs) at 54 loci from a 2020 meta-analysis (PRS68), 50 SNPs significant in the 2020 meta-analysis excluding UKB (PRS50) and 45 SNPs at 21 loci known in 2018 (PRS45). Ten-year melanoma risks were calculated from population-level cancer registry data by age group and sex, with and without PRS adjustment. RESULTS Predicted absolute melanoma risks based on age and sex alone underestimated melanoma incidence in the UKB [ratio of expected/observed cases: E/O = 0·65, 95% confidence interval (CI) 0·62-0·68] and MCCS (E/O = 0·63, 95% CI 0·56-0·72). For UKB, calibration was improved by PRS adjustment, with PRS50-adjusted risks E/O = 0·91, 95% CI 0·87-0·95. The discriminative ability for PRS68- and PRS50-adjusted absolute risks was higher than for risks based on age and sex alone (Δ area under the curve 0·07-0·10, P < 0·0001), and higher than for PRS45-adjusted risks (Δ area under the curve 0·02-0·04, P < 0·001). CONCLUSIONS A PRS derived from a larger, more diverse meta-analysis improves risk prediction compared with an earlier PRS, and might help tailor melanoma prevention and early detection strategies to different risk levels. Recalibration of absolute risks may be necessary for application to specific populations.
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Affiliation(s)
- Julia Steinberg
- The Daffodil CentreThe University of Sydney, a joint venture with Cancer Council NSWSydneyNSWAustralia
| | - Mark M. Iles
- Leeds Institute for Data AnalyticsUniversity of LeedsLeedsUK
| | - Jin Yee Lee
- School of Public HealthThe University of SydneySydneyNSWAustralia
| | - Xiaochuan Wang
- Cancer Epidemiology DivisionCancer Council VictoriaMelbourneVICAustralia
| | - Matthew H. Law
- Statistical Genetics LaboratoryQIMR Berghofer Medical Research InstituteBrisbaneQLDAustralia
- School of Biomedical Sciences, Faculty of Health, and Institute of Health and Biomedical InnovationQueensland University of TechnologyKelvin GroveQLDAustralia
| | - Amelia K. Smit
- The Daffodil CentreThe University of Sydney, a joint venture with Cancer Council NSWSydneyNSWAustralia
| | - Tu Nguyen‐Dumont
- Precision Medicine, School of Clinical Sciences at Monash HealthMonash UniversityClaytonVICAustralia
- Department of Clinical PathologyThe University of MelbourneMelbourneVICAustralia
| | - Graham G. Giles
- Cancer Epidemiology DivisionCancer Council VictoriaMelbourneVICAustralia
- Precision Medicine, School of Clinical Sciences at Monash HealthMonash UniversityClaytonVICAustralia
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global HealthThe University of MelbourneMelbourneVICAustralia
| | - Melissa C. Southey
- Precision Medicine, School of Clinical Sciences at Monash HealthMonash UniversityClaytonVICAustralia
| | - Roger L. Milne
- Cancer Epidemiology DivisionCancer Council VictoriaMelbourneVICAustralia
- Precision Medicine, School of Clinical Sciences at Monash HealthMonash UniversityClaytonVICAustralia
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global HealthThe University of MelbourneMelbourneVICAustralia
| | - Graham J. Mann
- John Curtin School of Medical ResearchAustralian National UniversityCanberraACTAustralia
| | | | - Robert J. MacInnis
- Cancer Epidemiology DivisionCancer Council VictoriaMelbourneVICAustralia
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global HealthThe University of MelbourneMelbourneVICAustralia
| | - Anne E. Cust
- The Daffodil CentreThe University of Sydney, a joint venture with Cancer Council NSWSydneyNSWAustralia
- Melanoma Institute AustraliaThe University of SydneySydneyNSWAustralia
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217
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Prostate cancer polygenic risk score and prediction of lethal prostate cancer. NPJ Precis Oncol 2022; 6:25. [PMID: 35396534 PMCID: PMC8993880 DOI: 10.1038/s41698-022-00266-8] [Citation(s) in RCA: 18] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/26/2021] [Accepted: 02/11/2022] [Indexed: 11/23/2022] Open
Abstract
Polygenic risk scores (PRS) for prostate cancer incidence have been proposed to optimize prostate cancer screening. Prediction of lethal prostate cancer is key to any stratified screening program to avoid excessive overdiagnosis. Herein, PRS for incident prostate cancer was evaluated in two population-based cohorts of unscreened middle-aged men linked to cancer and death registries: the Västerbotten Intervention Project (VIP) and the Malmö Diet and Cancer study (MDC). SNP genotypes were measured by genome-wide SNP genotyping by array followed by imputation or genotyping of selected SNPs using mass spectrometry. The ability of PRS to predict lethal prostate cancer was compared to PSA and a commercialized pre-specified model based on four kallikrein markers. The PRS was associated with incident prostate cancer, replicating previously reported relative risks, and was also associated with prostate cancer death. However, unlike PSA, the PRS did not show stronger association with lethal disease: the hazard ratio for prostate cancer incidence vs. prostate cancer metastasis and death was 1.69 vs. 1.65 in VIP and 1.25 vs. 1.25 in MDC. PSA was a much stronger predictor of prostate cancer metastasis or death with an area-under-the-curve of 0.78 versus 0.63 for the PRS. Importantly, addition of PRS to PSA did not contribute additional risk stratification for lethal prostate cancer. We have shown that a PRS that predicts prostate cancer incidence does not have utility above and beyond that of PSA measured at baseline when applied to the clinically relevant endpoint of prostate cancer death. These findings have implications for public health policies for delivery of prostate cancer screening. Focusing polygenic risk scores on clinically significant endpoints such as prostate cancer metastasis or death would likely improve clinical utility.
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218
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Hämmerle M, Forer L, Schönherr S, Peters A, Grallert H, Kronenberg F, Gieger C, Lamina C. A Family and a Genome-Wide Polygenic Risk Score Are Independently Associated With Stroke in a Population-Based Study. Stroke 2022; 53:2331-2339. [PMID: 35387493 DOI: 10.1161/strokeaha.121.036551] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
BACKGROUND Positive family history and genetic risk scores have been shown to independently capture those individuals with high risk for stroke. The aim of our study was to evaluate the amount of shared information between family history and genetic risk and to investigate their combined effect on the association with prevalent and incident stroke cases. METHODS We obtained a family risk score (FamRS), weighted for disease onset and family size as well as genome-wide polygenic risk score (PGS) including over 3.2 million single-nucleotide polymorphisms in the population-based prospective KORA F3 (Cooperative Health Research in the Region of Augsburg) study (n=3071) from Southern Germany. FamRS and PGS were evaluated separately and combined. The measures were once treated as continuous variables but also divided in the highest 20%, 10%, 5%, and 1% percentiles. Odds ratios via logistic regression and hazard ratios via Cox regression were estimated. A stroke event was defined as a hospitalization for stroke that was self-reported in a standardized interview by certified and supervised personnel. RESULTS The FamRS outperformed other simplified family measures such as affected parents or number of affected family members. FamRS and PGS were not correlated, and no individuals were observed with both very high FamRS and very high PGS (top 1% percentile). In a combined model, both FamRS and PGS were independently from each other associated with risk of stroke, also independent of other traditional risk factors (p [FamRS]=0.02, p [PGS]=0.005). Individuals in the top 1% of either FamRS or PGS were found to have >5-fold risk for stroke (odds ratios, 5.82 [95% CI, 2.08-14]; P=0.0002). The results for incident stroke events showed the same trend but were not significant. CONCLUSIONS Our study shows that a family risk score and PGS capture different information concerning individual stroke risk. Combining the risk measures FamRS and PGS increases predictive power, as demonstrated in a population-based study.
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Affiliation(s)
- Michelle Hämmerle
- Institute of Genetic Epidemiology, Department of Genetics, Medical University of Innsbruck, Innsbruck, Austria (M.H., L.F., S.H., F.K., C.L.)
| | - Lukas Forer
- Institute of Genetic Epidemiology, Department of Genetics, Medical University of Innsbruck, Innsbruck, Austria (M.H., L.F., S.H., F.K., C.L.)
| | - Sebastian Schönherr
- Institute of Genetic Epidemiology, Department of Genetics, Medical University of Innsbruck, Innsbruck, Austria (M.H., L.F., S.H., F.K., C.L.)
| | - Annette Peters
- Institute of Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany (A.P., C.G., H.G.).,German Center for Diabetes Research (DZD), Neuherberg, Germany (A.P., C.G., H.G.).,German Research Center for Cardiovascular Research (DZHK), Partner Site Munich Heart Alliance, Munich, Germany (A.P.)
| | - Harald Grallert
- Institute of Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany (A.P., C.G., H.G.).,German Center for Diabetes Research (DZD), Neuherberg, Germany (A.P., C.G., H.G.)
| | - Florian Kronenberg
- Institute of Genetic Epidemiology, Department of Genetics, Medical University of Innsbruck, Innsbruck, Austria (M.H., L.F., S.H., F.K., C.L.)
| | - Christian Gieger
- Institute of Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany (A.P., C.G., H.G.).,German Center for Diabetes Research (DZD), Neuherberg, Germany (A.P., C.G., H.G.)
| | - Claudia Lamina
- Institute of Genetic Epidemiology, Department of Genetics, Medical University of Innsbruck, Innsbruck, Austria (M.H., L.F., S.H., F.K., C.L.)
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Morton SU, Pereira AC, Quiat D, Richter F, Kitaygorodsky A, Hagen J, Bernstein D, Brueckner M, Goldmuntz E, Kim RW, Lifton RP, Porter GA, Tristani-Firouzi M, Chung WK, Roberts A, Gelb BD, Shen Y, Newburger JW, Seidman JG, Seidman CE. Genome-Wide De Novo Variants in Congenital Heart Disease Are Not Associated With Maternal Diabetes or Obesity. CIRCULATION. GENOMIC AND PRECISION MEDICINE 2022; 15:e003500. [PMID: 35130025 PMCID: PMC9295870 DOI: 10.1161/circgen.121.003500] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/21/2022]
Abstract
BACKGROUND Congenital heart disease (CHD) is the most common anomaly at birth, with a prevalence of ≈1%. While infants born to mothers with diabetes or obesity have a 2- to 3-fold increased incidence of CHD, the cause of the increase is unknown. Damaging de novo variants (DNV) in coding regions are more common among patients with CHD, but genome-wide rates of coding and noncoding DNVs associated with these prenatal exposures have not been studied in patients with CHD. METHODS DNV frequencies were determined for 1812 patients with CHD who had whole-genome sequencing and prenatal history data available from the Pediatric Cardiac Genomics Consortium's CHD GENES study (Genetic Network). The frequency of DNVs was compared between subgroups using t test or linear model. RESULTS Among 1812 patients with CHD, the number of DNVs per patient was higher with maternal diabetes (76.5 versus 72.1, t test P=3.03×10-11), but the difference was no longer significant after including parental ages in a linear model (paternal and maternal correction P=0.42). No interaction was observed between diabetes risk and parental age (paternal and maternal interaction P=0.80 and 0.68, respectively). No difference was seen in DNV count per patient based on maternal obesity (72.0 versus 72.2 for maternal body mass index <25 versus maternal body mass index >30, t test P=0.86). CONCLUSIONS After accounting for parental age, the offspring of diabetic or obese mothers have no increase in DNVs compared with other children with CHD. These results emphasize the role for other mechanisms in the cause of CHD associated with these prenatal exposures. REGISTRATION URL: https://clinicaltrials.gov; NCT01196182.
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Affiliation(s)
- Sarah U. Morton
- Division of Newborn Medicine, Department of Medicine, Boston Children’s Hospital, Boston, MA USA,Department of Pediatrics, Harvard Medical School, Boston, MA USA
| | | | - Daniel Quiat
- Department of Pediatrics, Harvard Medical School, Boston, MA USA,Department of Cardiology, Boston Children’s Hospital, Boston, MA USA
| | - Felix Richter
- Graduate School of Biomedical Sciences, Icahn School of Medicine at Mount Sinai, New York, NY USA
| | - Alexander Kitaygorodsky
- Departments of Systems Biology and Biomedical Informatics, Columbia University Medical Center, New York, NY USA
| | - Jacob Hagen
- Departments of Systems Biology and Biomedical Informatics, Columbia University Medical Center, New York, NY USA
| | - Daniel Bernstein
- Department of Pediatrics (Cardiology), Stanford University, Stanford, CA USA
| | - Martina Brueckner
- Departments of Genetics and Pediatrics; Yale University School of Medicine, New Haven, CT USA
| | - Elizabeth Goldmuntz
- Division of Cardiology, Children’s Hospital of Philadelphia, Department of Pediatrics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA USA
| | | | - Richard P. Lifton
- Laboratory of Human Genetics and Genomics, The Rockefeller University, New York, NY USA
| | - George A. Porter
- Department of Pediatrics, University of Rochester Medical Center, The School of Medicine and Dentistry, Rochester, NY USA
| | | | - Wendy K. Chung
- Departments of Pediatrics and Medicine, Columbia University Medical Center, New York, NY USA
| | - Amy Roberts
- Department of Pediatrics, Harvard Medical School, Boston, MA USA,Department of Cardiology, Boston Children’s Hospital, Boston, MA USA
| | - Bruce D. Gelb
- Mindich Child Health and Development Institute and Department of Pediatrics, Icahn School of Medicine at Mount Sinai, New York, NY USA
| | - Yufeng Shen
- Departments of Systems Biology and Biomedical Informatics, Columbia University Medical Center, New York, NY USA
| | - Jane W. Newburger
- Department of Pediatrics, Harvard Medical School, Boston, MA USA,Department of Cardiology, Boston Children’s Hospital, Boston, MA USA
| | - J. G. Seidman
- Department of Genetics, Harvard Medical School, Boston, MA USA
| | - Christine E. Seidman
- Department of Genetics, Harvard Medical School, Boston, MA USA,Cardiovascular Division, Brigham and Women’s Hospital, Boston, MA USA,Howard Hughes Medical Institute, Chevy Chase, MD USA
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Moorthie S, Hall A, Babb de Villiers C, Janus J, Brigden T, Blackburn L, Kroese M. How can we address the uncertainties regarding the potential clinical utility of polygenic score-based tests? Per Med 2022; 19:263-270. [PMID: 35289204 DOI: 10.2217/pme-2021-0148] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
Abstract
As common low penetrance variants associated with diseases are uncovered, attempts continue to be made to harness this knowledge for improving healthcare. Polygenic scores have been developed as the mechanism by which knowledge of common variants can be used to investigate genetic contributions to disease risk. They serve as a biomarker to provide an estimate of the genetic liability for a particular disease. Discussion continues as to whether polygenic scores are a useful biomarker and their readiness for incorporation into clinical and public health practice. In this paper, we investigate the key challenges that need to be addressed, in the description and assessment of the clinical utility of polygenic score-based tests for use in clinical and public health practice.
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Affiliation(s)
- Sowmiya Moorthie
- PHG Foundation, University of Cambridge, 2 Worts Causeway, Cambridge, CB1 8RN, UK.,Cambridge Public Health, University of Cambridge School of Clinical Medicine, Forvie Site, Cambridge Biomedical Campus, Cambridge, CB2 0SR, UK
| | - Alison Hall
- PHG Foundation, University of Cambridge, 2 Worts Causeway, Cambridge, CB1 8RN, UK
| | | | - Joanna Janus
- PHG Foundation, University of Cambridge, 2 Worts Causeway, Cambridge, CB1 8RN, UK
| | - Tanya Brigden
- PHG Foundation, University of Cambridge, 2 Worts Causeway, Cambridge, CB1 8RN, UK
| | - Laura Blackburn
- PHG Foundation, University of Cambridge, 2 Worts Causeway, Cambridge, CB1 8RN, UK
| | - Mark Kroese
- PHG Foundation, University of Cambridge, 2 Worts Causeway, Cambridge, CB1 8RN, UK
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Tanigawa Y, Qian J, Venkataraman G, Justesen JM, Li R, Tibshirani R, Hastie T, Rivas MA. Significant sparse polygenic risk scores across 813 traits in UK Biobank. PLoS Genet 2022; 18:e1010105. [PMID: 35324888 PMCID: PMC8946745 DOI: 10.1371/journal.pgen.1010105] [Citation(s) in RCA: 37] [Impact Index Per Article: 18.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/08/2021] [Accepted: 02/15/2022] [Indexed: 01/05/2023] Open
Abstract
We present a systematic assessment of polygenic risk score (PRS) prediction across more than 1,500 traits using genetic and phenotype data in the UK Biobank. We report 813 sparse PRS models with significant (p < 2.5 x 10-5) incremental predictive performance when compared against the covariate-only model that considers age, sex, types of genotyping arrays, and the principal component loadings of genotypes. We report a significant correlation between the number of genetic variants selected in the sparse PRS model and the incremental predictive performance (Spearman's ⍴ = 0.61, p = 2.2 x 10-59 for quantitative traits, ⍴ = 0.21, p = 9.6 x 10-4 for binary traits). The sparse PRS model trained on European individuals showed limited transferability when evaluated on non-European individuals in the UK Biobank. We provide the PRS model weights on the Global Biobank Engine (https://biobankengine.stanford.edu/prs).
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Affiliation(s)
- Yosuke Tanigawa
- Department of Biomedical Data Science, Stanford University, Stanford, California, United States of America
- Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, Massachusetts, United States of America
| | - Junyang Qian
- Department of Statistics, Stanford University, Stanford, California, United States of America
| | - Guhan Venkataraman
- Department of Biomedical Data Science, Stanford University, Stanford, California, United States of America
| | - Johanne Marie Justesen
- Department of Biomedical Data Science, Stanford University, Stanford, California, United States of America
| | - Ruilin Li
- Institute for Computational and Mathematical Engineering, Stanford University, Stanford, California, United States of America
| | - Robert Tibshirani
- Department of Biomedical Data Science, Stanford University, Stanford, California, United States of America
- Department of Statistics, Stanford University, Stanford, California, United States of America
| | - Trevor Hastie
- Department of Biomedical Data Science, Stanford University, Stanford, California, United States of America
- Department of Statistics, Stanford University, Stanford, California, United States of America
| | - Manuel A. Rivas
- Department of Biomedical Data Science, Stanford University, Stanford, California, United States of America
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Local genetic variation of inflammatory bowel disease in Basque population and its effect in risk prediction. Sci Rep 2022; 12:3386. [PMID: 35232999 PMCID: PMC8888637 DOI: 10.1038/s41598-022-07401-2] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/21/2021] [Accepted: 01/19/2022] [Indexed: 12/21/2022] Open
Abstract
Inflammatory bowel disease (IBD) is characterised by chronic inflammation of the gastrointestinal tract. Although its aetiology remains unknown, environmental and genetic factors are involved in its development. Regarding genetics, more than 200 loci have been associated with IBD but the transferability of those signals to the Basque population living in Northern Spain, a population with distinctive genetic background, remains unknown. We have analysed 5,411,568 SNPs in 498 IBD cases and 935 controls from the Basque population. We found 33 suggestive loci (p < 5 × 10−6) in IBD and its subtypes, namely Crohn’s Disease (CD) and Ulcerative Colitis (UC), detecting a genome-wide significant locus located in HLA region in patients with UC. Those loci contain previously associated genes with IBD (IL23R, JAK2 or HLA genes) and new genes that could be involved in its development (AGT, BZW2 or FSTL1). The overall genetic correlation between European populations and Basque population was high in IBD and CD, while in UC was lower. Finally, the use of genetic risk scores based on previous GWAS findings reached area under the curves > 0.68. In conclusion, we report on the genetic architecture of IBD in the Basque population, and explore the performance of European-descent genetic risk scores in this population.
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Pain O, Gillett AC, Austin JC, Folkersen L, Lewis CM. A tool for translating polygenic scores onto the absolute scale using summary statistics. Eur J Hum Genet 2022; 30:339-348. [PMID: 34983942 PMCID: PMC8904577 DOI: 10.1038/s41431-021-01028-z] [Citation(s) in RCA: 20] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/16/2021] [Revised: 09/16/2021] [Accepted: 12/09/2021] [Indexed: 12/21/2022] Open
Abstract
There is growing interest in the clinical application of polygenic scores as their predictive utility increases for a range of health-related phenotypes. However, providing polygenic score predictions on the absolute scale is an important step for their safe interpretation. We have developed a method to convert polygenic scores to the absolute scale for binary and normally distributed phenotypes. This method uses summary statistics, requiring only the area-under-the-ROC curve (AUC) or variance explained (R2) by the polygenic score, and the prevalence of binary phenotypes, or mean and standard deviation of normally distributed phenotypes. Polygenic scores are converted using normal distribution theory. We also evaluate methods for estimating polygenic score AUC/R2 from genome-wide association study (GWAS) summary statistics alone. We validate the absolute risk conversion and AUC/R2 estimation using data for eight binary and three continuous phenotypes in the UK Biobank sample. When the AUC/R2 of the polygenic score is known, the observed and estimated absolute values were highly concordant. Estimates of AUC/R2 from the lassosum pseudovalidation method were most similar to the observed AUC/R2 values, though estimated values deviated substantially from the observed for autoimmune disorders. This study enables accurate interpretation of polygenic scores using only summary statistics, providing a useful tool for educational and clinical purposes. Furthermore, we have created interactive webtools implementing the conversion to the absolute ( https://opain.github.io/GenoPred/PRS_to_Abs_tool.html ). Several further barriers must be addressed before clinical implementation of polygenic scores, such as ensuring target individuals are well represented by the GWAS sample.
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Affiliation(s)
- Oliver Pain
- Social, Genetic and Developmental Psychiatry Centre, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK.
- NIHR Maudsley Biomedical Research Centre, South London and Maudsley NHS Trust, London, SE5 8AF, UK.
| | - Alexandra C Gillett
- Social, Genetic and Developmental Psychiatry Centre, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
| | - Jehannine C Austin
- Department of Psychiatry and Medical Genetics, University of British Columbia, Vancouver, BC, Canada
| | | | - Cathryn M Lewis
- Social, Genetic and Developmental Psychiatry Centre, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
- NIHR Maudsley Biomedical Research Centre, South London and Maudsley NHS Trust, London, SE5 8AF, UK
- Department of Medical and Molecular Genetics, Faculty of Life Sciences and Medicine, King's College London, London, UK
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Integration of questionnaire-based risk factors improves polygenic risk scores for human coronary heart disease and type 2 diabetes. Commun Biol 2022; 5:158. [PMID: 35197564 PMCID: PMC8866413 DOI: 10.1038/s42003-021-02996-0] [Citation(s) in RCA: 17] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/21/2021] [Accepted: 12/16/2021] [Indexed: 12/14/2022] Open
Abstract
Large-scale biobank initiatives and commercial repositories store genomic data collected from millions of individuals, and tools to leverage the rapidly growing pool of health and genomic data in disease prevention are needed. Here, we describe the derivation and validation of genomics-enhanced risk tools for two common cardiometabolic diseases, coronary heart disease and type 2 diabetes. Data used for our analyses include the FinnGen study (N = 309,154) and the UK Biobank project (N = 343,672). The risk tools integrate contemporary genome-wide polygenic risk scores with simple questionnaire-based risk factors, including demographic, lifestyle, medication, and comorbidity data, enabling risk calculation across resources where genome data is available. Compared to routinely used clinical risk scores for coronary heart disease and type 2 diabetes prevention, the risk tools show at least equivalent risk discrimination, improved risk reclassification (overall net reclassification improvements ranging from 3.7 [95% CI 2.8–4.6] up to 6.2 [4.6–7.8]), and capacity to be improved even further with standard lipid and blood pressure measurements. Without the need for blood tests or evaluation by a health professional, the risk tools provide a powerful yet simple method for preliminary cardiometabolic risk assessment for individuals with genome data available. Max Tamlander et al. combine polygenic risk scores and clinical assessments to improve prediction of coronary artery disease and type 2 diabetes in European cohorts. Taken together, their results provide a useful method for preliminary cardiometabolic risk assessment in patients.
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Collister JA, Liu X, Clifton L. Calculating Polygenic Risk Scores (PRS) in UK Biobank: A Practical Guide for Epidemiologists. Front Genet 2022; 13:818574. [PMID: 35251129 PMCID: PMC8894758 DOI: 10.3389/fgene.2022.818574] [Citation(s) in RCA: 34] [Impact Index Per Article: 17.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/19/2021] [Accepted: 01/12/2022] [Indexed: 01/11/2023] Open
Abstract
A polygenic risk score estimates the genetic risk of an individual for some disease or trait, calculated by aggregating the effect of many common variants associated with the condition. With the increasing availability of genetic data in large cohort studies such as the UK Biobank, inclusion of this genetic risk as a covariate in statistical analyses is becoming more widespread. Previously this required specialist knowledge, but as tooling and data availability have improved it has become more feasible for statisticians and epidemiologists to calculate existing scores themselves for use in analyses. While tutorial resources exist for conducting genome-wide association studies and generating of new polygenic risk scores, fewer guides exist for the simple calculation and application of existing genetic scores. This guide outlines the key steps of this process: selection of suitable polygenic risk scores from the literature, extraction of relevant genetic variants and verification of their quality, calculation of the risk score and key considerations of its inclusion in statistical models, using the UK Biobank imputed data as a model data set. Many of the techniques in this guide will generalize to other datasets, however we also focus on some of the specific techniques required for using data in the formats UK Biobank have selected. This includes some of the challenges faced when working with large numbers of variants, where the computation time required by some tools is impractical. While we have focused on only a couple of tools, which may not be the best ones for every given aspect of the process, one barrier to working with genetic data is the sheer volume of tools available, and the difficulty for a novice to assess their viability. By discussing in depth a couple of tools that are adequate for the calculation even at large scale, we hope to make polygenic risk scores more accessible to a wider range of researchers.
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Affiliation(s)
- Jennifer A. Collister
- Nuffield Department of Population Health, University of Oxford, Oxford, United Kingdom
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Liu Y, Gusev A, Heng YJ, Alexandrov LB, Kraft P. Somatic mutational profiles and germline polygenic risk scores in human cancer. Genome Med 2022; 14:14. [PMID: 35144655 PMCID: PMC8832866 DOI: 10.1186/s13073-022-01016-y] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/01/2021] [Accepted: 01/24/2022] [Indexed: 02/03/2023] Open
Abstract
BACKGROUND The mutational profile of cancer reflects the activity of the mutagenic processes which have been operative throughout the lineage of the cancer cell. These processes leave characteristic profiles of somatic mutations called mutational signatures. Mutational signatures, including single-base substitution (SBS) signatures, may reflect the effects of exogenous or endogenous exposures. METHODS We used polygenic risk scores (PRS) to summarize common germline variation associated with cancer risk and other cancer-related traits and examined the association between somatic mutational profiles and germline PRS in 12 cancer types from The Cancer Genome Atlas. Somatic mutational profiles were constructed from whole-exome sequencing data of primary tumors. PRS were calculated for the 12 selected cancer types and 9 non-cancer traits, including cancer risk determinants, hormonal factors, and immune-mediated inflammatory diseases, using germline genetic data and published summary statistics from genome-wide association studies. RESULTS We found 17 statistically significant associations between somatic mutational profiles and germline PRS after Bonferroni correction (p < 3.15 × 10-5), including positive associations between germline inflammatory bowel disease PRS and number of somatic mutations attributed to signature SBS1 in prostate cancer and APOBEC-related signatures in breast cancer. Positive associations were also found between age at menarche PRS and mutation counts of SBS1 in overall and estrogen receptor-positive breast cancer. Consistent with prior studies that found an inverse association between the pubertal development PRS and risk of prostate cancer, likely reflecting hormone-related mechanisms, we found an inverse association between age at menarche PRS and mutation counts of SBS1 in prostate cancer. Inverse associations were also found between several cancer PRS and tumor mutation counts. CONCLUSIONS Our analysis suggests that there are robust associations between tumor somatic mutational profiles and germline PRS. These may reflect the mechanisms through hormone regulation and immune responses that contribute to cancer etiology and drive cancer progression.
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Affiliation(s)
- Yuxi Liu
- grid.38142.3c000000041936754XDepartment of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA 02115 USA ,grid.38142.3c000000041936754XProgram in Genetic Epidemiology and Statistical Genetics, Harvard T.H. Chan School of Public Health, 655 Huntington Avenue, Boston, MA 02115 USA
| | - Alexander Gusev
- grid.65499.370000 0001 2106 9910Department of Medical Oncology, Dana-Farber Cancer Institute and Harvard Medical School, Boston, MA 02215 USA
| | - Yujing J. Heng
- grid.38142.3c000000041936754XDepartment of Pathology, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA 02215 USA
| | - Ludmil B. Alexandrov
- grid.266100.30000 0001 2107 4242Department of Cellular and Molecular Medicine, University of California San Diego, La Jolla, CA 92093 USA
| | - Peter Kraft
- grid.38142.3c000000041936754XDepartment of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA 02115 USA ,grid.38142.3c000000041936754XProgram in Genetic Epidemiology and Statistical Genetics, Harvard T.H. Chan School of Public Health, 655 Huntington Avenue, Boston, MA 02115 USA ,grid.38142.3c000000041936754XDepartment of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA 02115 USA
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Xu Y, Vuckovic D, Ritchie SC, Akbari P, Jiang T, Grealey J, Butterworth AS, Ouwehand WH, Roberts DJ, Di Angelantonio E, Danesh J, Soranzo N, Inouye M. Machine learning optimized polygenic scores for blood cell traits identify sex-specific trajectories and genetic correlations with disease. CELL GENOMICS 2022; 2:None. [PMID: 35072137 PMCID: PMC8758502 DOI: 10.1016/j.xgen.2021.100086] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/20/2020] [Revised: 08/24/2021] [Accepted: 12/13/2021] [Indexed: 12/13/2022]
Abstract
Genetic association studies for blood cell traits, which are key indicators of health and immune function, have identified several hundred associations and defined a complex polygenic architecture. Polygenic scores (PGSs) for blood cell traits have potential clinical utility in disease risk prediction and prevention, but designing PGS remains challenging and the optimal methods are unclear. To address this, we evaluated the relative performance of 6 methods to develop PGS for 26 blood cell traits, including a standard method of pruning and thresholding (P + T) and 5 learning methods: LDpred2, elastic net (EN), Bayesian ridge (BR), multilayer perceptron (MLP) and convolutional neural network (CNN). We evaluated these optimized PGSs on blood cell trait data from UK Biobank and INTERVAL. We find that PGSs designed using common machine learning methods EN and BR show improved prediction of blood cell traits and consistently outperform other methods. Our analyses suggest EN/BR as the top choices for PGS construction, showing improved performance for 25 blood cell traits in the external validation, with correlations with the directly measured traits increasing by 10%-23%. Ten PGSs showed significant statistical interaction with sex, and sex-specific PGS stratification showed that all of them had substantial variation in the trajectories of blood cell traits with age. Genetic correlations between the PGSs for blood cell traits and common human diseases identified well-known as well as new associations. We develop machine learning-optimized PGS for blood cell traits, demonstrate their relationships with sex, age, and disease, and make these publicly available as a resource.
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Affiliation(s)
- Yu Xu
- Cambridge Baker Systems Genomics Initiative, Department of Public Health and Primary Care, University of Cambridge, Cambridge CB1 8RN, UK
- Cambridge Baker Systems Genomics Initiative, Baker Heart and Diabetes Institute, Melbourne, VIC 3004, Australia
- British Heart Foundation Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge CB1 8RN, UK
| | - Dragana Vuckovic
- Department of Human Genetics, Wellcome Sanger Institute, Hinxton CB10 1SA, UK
- National Institute for Health Research Blood and Transplant Research Unit in Donor Health and Genomics, University of Cambridge, Cambridge CB1 8RN, UK
| | - Scott C. Ritchie
- Cambridge Baker Systems Genomics Initiative, Department of Public Health and Primary Care, University of Cambridge, Cambridge CB1 8RN, UK
- Cambridge Baker Systems Genomics Initiative, Baker Heart and Diabetes Institute, Melbourne, VIC 3004, Australia
- British Heart Foundation Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge CB1 8RN, UK
- British Heart Foundation Centre of Research Excellence, University of Cambridge, Cambridge CB1 8RN, UK
| | - Parsa Akbari
- British Heart Foundation Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge CB1 8RN, UK
- National Institute for Health Research Blood and Transplant Research Unit in Donor Health and Genomics, University of Cambridge, Cambridge CB1 8RN, UK
| | - Tao Jiang
- British Heart Foundation Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge CB1 8RN, UK
| | - Jason Grealey
- Cambridge Baker Systems Genomics Initiative, Baker Heart and Diabetes Institute, Melbourne, VIC 3004, Australia
- Department of Mathematics and Statistics, La Trobe University, Bundoora, VIC 3086, Australia
| | - Adam S. Butterworth
- British Heart Foundation Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge CB1 8RN, UK
- National Institute for Health Research Blood and Transplant Research Unit in Donor Health and Genomics, University of Cambridge, Cambridge CB1 8RN, UK
- British Heart Foundation Centre of Research Excellence, University of Cambridge, Cambridge CB1 8RN, UK
- Health Data Research UK Cambridge, Wellcome Genome Campus and University of Cambridge, Cambridge CB10 1SA, UK
| | - Willem H. Ouwehand
- Department of Human Genetics, Wellcome Sanger Institute, Hinxton CB10 1SA, UK
- British Heart Foundation Centre of Research Excellence, University of Cambridge, Cambridge CB1 8RN, UK
- National Health Service (NHS) Blood and Transplant, Cambridge Biomedical Campus, Cambridge CB2 0PT, UK
- Department of Haematology, University of Cambridge, Cambridge CB2 0PT, UK
| | - David J. Roberts
- National Institute for Health Research Blood and Transplant Research Unit in Donor Health and Genomics, University of Cambridge, Cambridge CB1 8RN, UK
- National Health Service (NHS) Blood and Transplant, Cambridge Biomedical Campus, Cambridge CB2 0PT, UK
- National Institute for Health Research Oxford Biomedical Research Centre, University of Oxford and John Radcliffe Hospital, Oxford OX3 9DU, UK
| | - Emanuele Di Angelantonio
- British Heart Foundation Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge CB1 8RN, UK
- National Institute for Health Research Blood and Transplant Research Unit in Donor Health and Genomics, University of Cambridge, Cambridge CB1 8RN, UK
- British Heart Foundation Centre of Research Excellence, University of Cambridge, Cambridge CB1 8RN, UK
- Health Data Science Research Centre, Human Technopole, Milan 20157, Italy
- Health Data Research UK Cambridge, Wellcome Genome Campus and University of Cambridge, Cambridge CB10 1SA, UK
| | - John Danesh
- British Heart Foundation Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge CB1 8RN, UK
- Department of Human Genetics, Wellcome Sanger Institute, Hinxton CB10 1SA, UK
- National Institute for Health Research Blood and Transplant Research Unit in Donor Health and Genomics, University of Cambridge, Cambridge CB1 8RN, UK
- British Heart Foundation Centre of Research Excellence, University of Cambridge, Cambridge CB1 8RN, UK
- Health Data Research UK Cambridge, Wellcome Genome Campus and University of Cambridge, Cambridge CB10 1SA, UK
| | - Nicole Soranzo
- Department of Human Genetics, Wellcome Sanger Institute, Hinxton CB10 1SA, UK
- National Institute for Health Research Blood and Transplant Research Unit in Donor Health and Genomics, University of Cambridge, Cambridge CB1 8RN, UK
- British Heart Foundation Centre of Research Excellence, University of Cambridge, Cambridge CB1 8RN, UK
| | - Michael Inouye
- Cambridge Baker Systems Genomics Initiative, Department of Public Health and Primary Care, University of Cambridge, Cambridge CB1 8RN, UK
- Cambridge Baker Systems Genomics Initiative, Baker Heart and Diabetes Institute, Melbourne, VIC 3004, Australia
- British Heart Foundation Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge CB1 8RN, UK
- British Heart Foundation Centre of Research Excellence, University of Cambridge, Cambridge CB1 8RN, UK
- Health Data Research UK Cambridge, Wellcome Genome Campus and University of Cambridge, Cambridge CB10 1SA, UK
- The Alan Turing Institute, London NW1 2DB, UK
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229
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Plagnol V. Polygenic score development in the era of large-scale biobanks. CELL GENOMICS 2022; 2:100088. [PMID: 36777037 PMCID: PMC9903709 DOI: 10.1016/j.xgen.2021.100088] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
Abstract
In this issue of Cell Genomics, Xu et al. report a comprehensive analysis of the genetics of 26 blood cell traits, leveraging data from two large biobanks to construct and make available machine-learning optimized polygenic scores (PGSs). In addition to delivering insights into the biology and clinical associations of these traits, the authors evaluate and provide recommendations on methods for PGS construction.
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Affiliation(s)
- Vincent Plagnol
- Genomics plc, King Charles House, Park End Street, Oxford OX1 1JD, UK
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230
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Sotnikova EA, Kiseleva AV, Meshkov AN, Ershova AI, Ivanova AA, Kolchina MA, Kutsenko VA, Skripnikova IA, Drapkina OM. Biobank data for studying the genetic architecture of osteoporosis and developing genetic risk scores. КАРДИОВАСКУЛЯРНАЯ ТЕРАПИЯ И ПРОФИЛАКТИКА 2022. [DOI: 10.15829/1728-8800-2021-3045] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/01/2022] Open
Abstract
Osteoporosis is a chronic systemic disease of the skeleton, characterized by a decrease in bone mass and an impairment of bone microarchitecture, which can lead to a decrease in bone strength and an increase in the risk of minor trauma fractures. Osteoporosis is diagnosed on the basis of bone mineral density (BMD). BMD is characterized by high heritability that ranges according to various sources from 50 to 85%. As in the case of other complex traits, the most common approach to searching for genetic variants that affect BMD is a genome-wide association study. The lower effect size or frequency of a variant is, the larger the sample size is required to achieve statistically significant data on associations. Therefore, the studies involving hundreds of thousands of participants based on biobank data can identify the largest number of variants associated with BMD. In addition, biobank data are used in the development of genetic risk scores for osteoporosis that can be used both in combination with existing prognosis algorithms and independently of them. The aim of this review was to present the most significant studies of osteoporosis genetics, including those based on biobank data and genome-wide association studies, as well as studies on the genetic risk scores and the contribution of rare variants.
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Affiliation(s)
- E. A. Sotnikova
- National Research Center for Therapy and Preventive Medicine
| | - A. V. Kiseleva
- National Research Center for Therapy and Preventive Medicine
| | - A. N. Meshkov
- National Medical Research Center for Therapy and Preventive Medicine; Russian National Research Medical University
| | - A. I. Ershova
- National Research Center for Therapy and Preventive Medicine
| | - A. A. Ivanova
- National Research Center for Therapy and Preventive Medicine
| | - M. A. Kolchina
- National Research Center for Therapy and Preventive Medicine
| | - V. A. Kutsenko
- National Medical Research Center for Therapy and Preventive Medicine; Lomonosov Moscow State University
| | - I. A. Skripnikova
- National Medical Research Center for Therapy and Preventive Medicine
| | - O. M. Drapkina
- National Medical Research Center for Therapy and Preventive Medicine
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231
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Cantelli G, Bateman A, Brooksbank C, Petrov AI, Malik-Sheriff R, Ide-Smith M, Hermjakob H, Flicek P, Apweiler R, Birney E, McEntyre J. The European Bioinformatics Institute (EMBL-EBI) in 2021. Nucleic Acids Res 2022; 50:D11-D19. [PMID: 34850134 PMCID: PMC8690175 DOI: 10.1093/nar/gkab1127] [Citation(s) in RCA: 29] [Impact Index Per Article: 14.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2021] [Revised: 10/14/2021] [Accepted: 11/23/2021] [Indexed: 11/28/2022] Open
Abstract
The European Bioinformatics Institute (EMBL-EBI) maintains a comprehensive range of freely available and up-to-date molecular data resources, which includes over 40 resources covering every major data type in the life sciences. This year's service update for EMBL-EBI includes new resources, PGS Catalog and AlphaFold DB, and updates on existing resources, including the COVID-19 Data Platform, trRosetta and RoseTTAfold models introduced in Pfam and InterPro, and the launch of Genome Integrations with Function and Sequence by UniProt and Ensembl. Furthermore, we highlight projects through which EMBL-EBI has contributed to the development of community-driven data standards and guidelines, including the Recommended Metadata for Biological Images (REMBI), and the BioModels Reproducibility Scorecard. Training is one of EMBL-EBI's core missions and a key component of the provision of bioinformatics services to users: this year's update includes many of the improvements that have been developed to EMBL-EBI's online training offering.
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Affiliation(s)
- Gaia Cantelli
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Hinxton, Cambridge CB10 1SD, UK
| | - Alex Bateman
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Hinxton, Cambridge CB10 1SD, UK
| | - Cath Brooksbank
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Hinxton, Cambridge CB10 1SD, UK
| | - Anton I Petrov
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Hinxton, Cambridge CB10 1SD, UK
| | - Rahuman S Malik-Sheriff
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Hinxton, Cambridge CB10 1SD, UK
| | - Michele Ide-Smith
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Hinxton, Cambridge CB10 1SD, UK
| | - Henning Hermjakob
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Hinxton, Cambridge CB10 1SD, UK
| | - Paul Flicek
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Hinxton, Cambridge CB10 1SD, UK
| | - Rolf Apweiler
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Hinxton, Cambridge CB10 1SD, UK
| | - Ewan Birney
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Hinxton, Cambridge CB10 1SD, UK
| | - Johanna McEntyre
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Hinxton, Cambridge CB10 1SD, UK
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232
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Privé F, Aschard H, Carmi S, Folkersen L, Hoggart C, O'Reilly PF, Vilhjálmsson BJ. Portability of 245 polygenic scores when derived from the UK Biobank and applied to 9 ancestry groups from the same cohort. Am J Hum Genet 2022; 109:12-23. [PMID: 34995502 PMCID: PMC8764121 DOI: 10.1016/j.ajhg.2021.11.008] [Citation(s) in RCA: 112] [Impact Index Per Article: 56.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/26/2021] [Accepted: 11/04/2021] [Indexed: 12/25/2022] Open
Abstract
The low portability of polygenic scores (PGSs) across global populations is a major concern that must be addressed before PGSs can be used for everyone in the clinic. Indeed, prediction accuracy has been shown to decay as a function of the genetic distance between the training and test cohorts. However, such cohorts differ not only in their genetic distance but also in their geographical distance and their data collection and assaying, conflating multiple factors. In this study, we examine the extent to which PGSs are transferable between ancestries by deriving polygenic scores for 245 curated traits from the UK Biobank data and applying them in nine ancestry groups from the same cohort. By restricting both training and testing to the UK Biobank data, we reduce the risk of environmental and genotyping confounding from using different cohorts. We define the nine ancestry groups at a sub-continental level, based on a simple, robust, and effective method that we introduce here. We then apply two different predictive methods to derive polygenic scores for all 245 phenotypes and show a systematic and dramatic reduction in portability of PGSs trained using Northwestern European individuals and applied to nine ancestry groups. These analyses demonstrate that prediction already drops off within European ancestries and reduces globally in proportion to genetic distance. Altogether, our study provides unique and robust insights into the PGS portability problem.
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Affiliation(s)
- Florian Privé
- National Centre for Register-Based Research, Aarhus University, Aarhus 8210, Denmark.
| | - Hugues Aschard
- Department of Computational Biology, Institut Pasteur, Paris 75015, France; Program in Genetic Epidemiology and Statistical Genetics, Harvard T.H. Chan School of Public Health, Boston, MA 02115, USA
| | - Shai Carmi
- Braun School of Public Health and Community Medicine, The Hebrew University of Jerusalem, Jerusalem 9112102, Israel
| | | | - Clive Hoggart
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Paul F O'Reilly
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Bjarni J Vilhjálmsson
- National Centre for Register-Based Research, Aarhus University, Aarhus 8210, Denmark; Bioinformatics Research Centre, Aarhus University, Aarhus 8000, Denmark
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233
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Zadissa A, Apweiler R. Data Mining, Quality and Management in the Life Sciences. Methods Mol Biol 2022; 2449:3-25. [PMID: 35507257 DOI: 10.1007/978-1-0716-2095-3_1] [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] [Indexed: 06/14/2023]
Abstract
With the evermore emphasis put on open science and its invaluable benefits to the scientific community, it is no longer the case where a research project simply ends with a scientific publication. The benefits of data sharing and reproducibility of results have taken the centerpiece within the life science research supported by FAIR principles that firmly underline the importance of open data. The current data-intensive multidisciplinary research has also highlighted the significance of how data is mined and managed. Here we describe some of the features adopted by EMBL-EBI data resources to support data mining, data quality, and data management. We also highlight how EMBL-EBI has responded to the current pandemic through its data resources.
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Affiliation(s)
- Amonida Zadissa
- EMBL-EBI, Wellcome Genome Campus, Hinxton, Cambridgeshire, UK.
| | - Rolf Apweiler
- EMBL-EBI, Wellcome Genome Campus, Hinxton, Cambridgeshire, UK
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234
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Raben TG, Lello L, Widen E, Hsu SDH. From Genotype to Phenotype: Polygenic Prediction of Complex Human Traits. Methods Mol Biol 2022; 2467:421-446. [PMID: 35451785 DOI: 10.1007/978-1-0716-2205-6_15] [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] [Indexed: 06/14/2023]
Abstract
Decoding the genome confers the capability to predict characteristics of the organism (phenotype) from DNA (genotype). We describe the present status and future prospects of genomic prediction of complex traits in humans. Some highly heritable complex phenotypes such as height and other quantitative traits can already be predicted with reasonable accuracy from DNA alone. For many diseases, including important common conditions such as coronary artery disease, breast cancer, type I and II diabetes, individuals with outlier polygenic scores (e.g., top few percent) have been shown to have 5 or even 10 times higher risk than average. Several psychiatric conditions such as schizophrenia and autism also fall into this category. We discuss related topics such as the genetic architecture of complex traits, sibling validation of polygenic scores, and applications to adult health, in vitro fertilization (embryo selection), and genetic engineering.
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Affiliation(s)
| | - Louis Lello
- Michigan State University, East Lansing, MI, USA
- Genomic Prediction, North Brunswick, NJ, USA
| | - Erik Widen
- Michigan State University, East Lansing, MI, USA
| | - Stephen D H Hsu
- Michigan State University, East Lansing, MI, USA.
- Genomic Prediction, North Brunswick, NJ, USA.
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235
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Saurabh R, Fouodo CJK, König IR, Busch H, Wohlers I. A survey of genome-wide association studies, polygenic scores and UK Biobank highlights resources for autoimmune disease genetics. Front Immunol 2022; 13:972107. [PMID: 35990650 PMCID: PMC9388859 DOI: 10.3389/fimmu.2022.972107] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/17/2022] [Accepted: 07/12/2022] [Indexed: 12/04/2022] Open
Abstract
Autoimmune diseases share a general mechanism of auto-antigens harming tissues. Still. they are phenotypically diverse, with genetic as well as environmental factors contributing to their etiology at varying degrees. Associated genomic loci and variants have been identified in numerous genome-wide association studies (GWAS), whose results are increasingly used for polygenic scores (PGS) that are used to predict disease risk. At the same time, a technological shift from genotyping arrays to next generation sequencing (NGS) is ongoing. NGS allows the identification of virtually all - including rare - genetic variants, which in combination with methodological developments promises to improve the prediction of disease risk and elucidate molecular mechanisms underlying disease. Here we review current, publicly available autoimmune disease GWAS and PGS data based on information from the GWAS and PGS catalog, respectively. We summarize autoimmune diseases investigated, respective studies conducted and their results. Further, we review genetic data and autoimmune disease patients in the UK Biobank (UKB), the largest resource for genetic and phenotypic data available for academic research. We find that only comparably prevalent autoimmune diseases are covered by the UKB and at the same time assessed by both GWAS and PGS catalogs. These are systemic (systemic lupus erythematosus) as well as organ-specific, affecting the gastrointestinal tract (inflammatory bowel disease as well as specifically Crohn's disease and ulcerative colitis), joints (juvenile ideopathic arthritis, psoriatic arthritis, rheumatoid arthritis, ankylosing spondylitis), glands (Sjögren syndrome), the nervous system (multiple sclerosis), and the skin (vitiligo).
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Affiliation(s)
- Rochi Saurabh
- Medical Systems Biology, Lübeck Institute for Experimental Dermatology (LIED) and Institute for Cardiogenetics, University of Lübeck, Lübeck, Germany
| | - Césaire J K Fouodo
- Institute of Medical Biometry and Statistics (IMBS), University of Lübeck, Lübeck, Germany
| | - Inke R König
- Institute of Medical Biometry and Statistics (IMBS), University of Lübeck, Lübeck, Germany
| | - Hauke Busch
- Medical Systems Biology, Lübeck Institute for Experimental Dermatology (LIED) and Institute for Cardiogenetics, University of Lübeck, Lübeck, Germany
| | - Inken Wohlers
- Medical Systems Biology, Lübeck Institute for Experimental Dermatology (LIED) and Institute for Cardiogenetics, University of Lübeck, Lübeck, Germany
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236
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Johnson D, Wilke MA, Lyle SM, Kowalec K, Jorgensen A, Wright GE, Drögemöller BI. A systematic review and analysis of the use of polygenic scores in pharmacogenomics. Clin Pharmacol Ther 2021; 111:919-930. [PMID: 34953075 DOI: 10.1002/cpt.2520] [Citation(s) in RCA: 20] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/07/2021] [Accepted: 12/18/2021] [Indexed: 11/09/2022]
Abstract
Polygenic scores (PGS) have emerged as promising tools for complex trait risk prediction. The application of these scores to pharmacogenomics provides new opportunities to improve the prediction of treatment outcomes. To gain insight into this area of research, we conducted a systematic review and accompanying analysis. This review uncovered 51 papers examining the use of PGS for drug-related outcomes, with the majority of these papers focusing on the treatment of psychiatric disorders (n=30). Due to difficulties in collecting large cohorts of uniformly treated patients, the majority of pharmacogenomic PGS were derived from large-scale genome-wide association studies of disease phenotypes that were related to the pharmacogenomic phenotypes under investigation (e.g. schizophrenia-derived PGS for antipsychotic response prediction). Examination of the research participants included in these studies revealed that the majority of cohort participants were of European descent (78.4%). These biases were also reflected in research affiliations, which were heavily weighted towards institutions located in Europe and North America, with no first or last authors originating from institutions in Africa or South Asia. There was also substantial variability in the methods used to develop PGS, with between 3 and 6.6 million variants included in the PGS. Finally, we observed significant inconsistencies in the reporting of PGS analyses and results, particularly in terms of risk model development and application, coupled with a lack of data transparency and availability, with only three pharmacogenomics PGS deposited on the PGS Catalog. These findings highlight current gaps and key areas for future pharmacogenomic PGS research.
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Affiliation(s)
- Danielle Johnson
- Department of Health Data Science, University of Liverpool, Liverpool, UK
| | - MacKenzie Ap Wilke
- Department of Biochemistry and Medical Genetics, Rady Faculty of Health Sciences, University of Manitoba, Winnipeg, MB, Canada
| | - Sarah M Lyle
- Department of Biochemistry and Medical Genetics, Rady Faculty of Health Sciences, University of Manitoba, Winnipeg, MB, Canada
| | - Kaarina Kowalec
- College of Pharmacy, Rady Faculty of Health Sciences, University of Manitoba, Winnipeg, MB, Canada.,Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
| | - Andrea Jorgensen
- Department of Health Data Science, University of Liverpool, Liverpool, UK
| | - Galen Eb Wright
- Department of Biochemistry and Medical Genetics, Rady Faculty of Health Sciences, University of Manitoba, Winnipeg, MB, Canada.,Department of Pharmacology and Therapeutics, Rady Faculty of Health Sciences, University of Manitoba, Winnipeg, Manitoba, Canada.,Neuroscience Research Program, Kleysen Institute for Advanced Medicine, Health Sciences Centre and Max Rady College of Medicine, University of Manitoba, Winnipeg, MB, Canada
| | - Britt I Drögemöller
- Department of Biochemistry and Medical Genetics, Rady Faculty of Health Sciences, University of Manitoba, Winnipeg, MB, Canada.,CancerCare Manitoba Research Institute, Winnipeg, MB, Canada.,Children's Hospital Research Institute of Manitoba, Winnipeg, MB, Canada
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237
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Castela Forte J, Folkertsma P, Gannamani R, Kumaraswamy S, Mount S, de Koning TJ, van Dam S, Wolffenbuttel BHR. Development and Validation of Decision Rules Models to Stratify Coronary Artery Disease, Diabetes, and Hypertension Risk in Preventive Care: Cohort Study of Returning UK Biobank Participants. J Pers Med 2021; 11:1322. [PMID: 34945794 PMCID: PMC8707007 DOI: 10.3390/jpm11121322] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/26/2021] [Revised: 10/23/2021] [Accepted: 11/20/2021] [Indexed: 12/25/2022] Open
Abstract
Many predictive models exist that predict risk of common cardiometabolic conditions. However, a vast majority of these models do not include genetic risk scores and do not distinguish between clinical risk requiring medical or pharmacological interventions and pre-clinical risk, where lifestyle interventions could be first-choice therapy. In this study, we developed, validated, and compared the performance of three decision rule algorithms including biomarkers, physical measurements, and genetic risk scores for incident coronary artery disease (CAD), diabetes (T2D), and hypertension against commonly used clinical risk scores in 60,782 UK Biobank participants. The rules models were tested for an association with incident CAD, T2D, and hypertension, and hazard ratios (with 95% confidence interval) were calculated from survival models. Model performance was assessed using the area under the receiver operating characteristic curve (AUROC), and Net Reclassification Index (NRI). The higher risk group in the decision rules model had a 40-, 40.9-, and 21.6-fold increased risk of CAD, T2D, and hypertension, respectively (p < 0.001 for all). Risk increased significantly between the three strata for all three conditions (p < 0.05). Based on genetic risk alone, we identified not only a high-risk group, but also a group at elevated risk for all health conditions. These decision rule models comprising blood biomarkers, physical measurements, and polygenic risk scores moderately improve commonly used clinical risk scores at identifying individuals likely to benefit from lifestyle intervention for three of the most common lifestyle-related chronic health conditions. Their utility as part of digital data or digital therapeutics platforms to support the implementation of lifestyle interventions in preventive and primary care should be further validated.
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Affiliation(s)
- José Castela Forte
- Department of Clinical Pharmacy and Pharmacology, University Medical Center Groningen, University of Groningen, Hanzeplein 1, 9713 GZ Groningen, The Netherlands
- Ancora Health B.V., Herestraat 106, 9711 LM Groningen, The Netherlands; (P.F.); (R.G.); (S.K.); (S.M.); (S.v.D.)
| | - Pytrik Folkertsma
- Ancora Health B.V., Herestraat 106, 9711 LM Groningen, The Netherlands; (P.F.); (R.G.); (S.K.); (S.M.); (S.v.D.)
- Department of Endocrinology, University Medical Center Groningen, University of Groningen, Hanzeplein 1, 9713 GZ Groningen, The Netherlands;
| | - Rahul Gannamani
- Ancora Health B.V., Herestraat 106, 9711 LM Groningen, The Netherlands; (P.F.); (R.G.); (S.K.); (S.M.); (S.v.D.)
- Department of Neurology, University Medical Center Groningen, University of Groningen, Hanzeplein 1, 9713 GZ Groningen, The Netherlands;
| | - Sridhar Kumaraswamy
- Ancora Health B.V., Herestraat 106, 9711 LM Groningen, The Netherlands; (P.F.); (R.G.); (S.K.); (S.M.); (S.v.D.)
| | - Sarah Mount
- Ancora Health B.V., Herestraat 106, 9711 LM Groningen, The Netherlands; (P.F.); (R.G.); (S.K.); (S.M.); (S.v.D.)
| | - Tom J. de Koning
- Department of Neurology, University Medical Center Groningen, University of Groningen, Hanzeplein 1, 9713 GZ Groningen, The Netherlands;
- Pediatrics, Department of Clinical Sciences, Lund University, Sölvegatan 19-BMC F12, 221 84 Lund, Sweden
| | - Sipko van Dam
- Ancora Health B.V., Herestraat 106, 9711 LM Groningen, The Netherlands; (P.F.); (R.G.); (S.K.); (S.M.); (S.v.D.)
- Department of Endocrinology, University Medical Center Groningen, University of Groningen, Hanzeplein 1, 9713 GZ Groningen, The Netherlands;
| | - Bruce H. R. Wolffenbuttel
- Department of Endocrinology, University Medical Center Groningen, University of Groningen, Hanzeplein 1, 9713 GZ Groningen, The Netherlands;
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238
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Becker J, Burik CAP, Goldman G, Wang N, Jayashankar H, Bennett M, Belsky DW, Karlsson Linnér R, Ahlskog R, Kleinman A, Hinds DA, Caspi A, Corcoran DL, Moffitt TE, Poulton R, Sugden K, Williams BS, Harris KM, Steptoe A, Ajnakina O, Milani L, Esko T, Iacono WG, McGue M, Magnusson PKE, Mallard TT, Harden KP, Tucker-Drob EM, Herd P, Freese J, Young A, Beauchamp JP, Koellinger PD, Oskarsson S, Johannesson M, Visscher PM, Meyer MN, Laibson D, Cesarini D, Benjamin DJ, Turley P, Okbay A. Resource profile and user guide of the Polygenic Index Repository. Nat Hum Behav 2021; 5:1744-1758. [PMID: 34140656 PMCID: PMC8678380 DOI: 10.1038/s41562-021-01119-3] [Citation(s) in RCA: 44] [Impact Index Per Article: 14.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/18/2020] [Accepted: 04/16/2021] [Indexed: 02/05/2023]
Abstract
Polygenic indexes (PGIs) are DNA-based predictors. Their value for research in many scientific disciplines is growing rapidly. As a resource for researchers, we used a consistent methodology to construct PGIs for 47 phenotypes in 11 datasets. To maximize the PGIs' prediction accuracies, we constructed them using genome-wide association studies-some not previously published-from multiple data sources, including 23andMe and UK Biobank. We present a theoretical framework to help interpret analyses involving PGIs. A key insight is that a PGI can be understood as an unbiased but noisy measure of a latent variable we call the 'additive SNP factor'. Regressions in which the true regressor is this factor but the PGI is used as its proxy therefore suffer from errors-in-variables bias. We derive an estimator that corrects for the bias, illustrate the correction, and make a Python tool for implementing it publicly available.
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Affiliation(s)
- Joel Becker
- Department of Economics, New York University, New York, NY, USA
| | - Casper A P Burik
- Department of Economics, School of Business and Economics, Vrije Universiteit Amsterdam, Amsterdam, the Netherlands
| | - Grant Goldman
- National Bureau of Economic Research, Cambridge, MA, USA
| | - Nancy Wang
- National Bureau of Economic Research, Cambridge, MA, USA
| | | | | | - Daniel W Belsky
- Department of Epidemiology, Columbia University Mailman School of Public Health, New York, NY, USA
- Robert N. Butler Columbia Aging Center, Columbia University, New York, NY, USA
| | - Richard Karlsson Linnér
- Department of Economics, School of Business and Economics, Vrije Universiteit Amsterdam, Amsterdam, the Netherlands
| | - Rafael Ahlskog
- Department of Government, Uppsala University, Uppsala, Sweden
| | | | | | - Avshalom Caspi
- Department of Psychology and Neuroscience, Duke University, Durham, NC, USA
- Social, Genetic, and Developmental Psychiatry Centre, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, UK
- Center for Genomic and Computational Biology, Duke University, Durham, NC, USA
- Department of Psychiatry and Behavioral Sciences, Duke University, Durham, NC, USA
| | - David L Corcoran
- Center for Genomic and Computational Biology, Duke University, Durham, NC, USA
| | - Terrie E Moffitt
- Department of Psychology and Neuroscience, Duke University, Durham, NC, USA
- Social, Genetic, and Developmental Psychiatry Centre, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, UK
- Center for Genomic and Computational Biology, Duke University, Durham, NC, USA
- Department of Psychiatry and Behavioral Sciences, Duke University, Durham, NC, USA
| | - Richie Poulton
- Dunedin Multidisciplinary Health and Development Research Unit, University of Otago, Dunedin, New Zealand
| | - Karen Sugden
- Department of Psychology and Neuroscience, Duke University, Durham, NC, USA
| | | | - Kathleen Mullan Harris
- Department of Sociology, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
- Carolina Population Center, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Andrew Steptoe
- Department of Behavioural Science and Health, University College London, London, UK
| | - Olesya Ajnakina
- Department of Behavioural Science and Health, University College London, London, UK
- Department of Biostatistics and Health Informatics, Institute of Psychiatry, Psychology, and Neuroscience, King's College London, London, UK
| | - Lili Milani
- Institute of Genomics, University of Tartu, Tartu, Estonia
| | - Tõnu Esko
- Institute of Genomics, University of Tartu, Tartu, Estonia
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - William G Iacono
- Department of Psychology, University of Minnesota, Minneapolis, MN, USA
| | - Matt McGue
- Department of Psychology, University of Minnesota, Minneapolis, MN, USA
| | - Patrik K E Magnusson
- Swedish Twin Registry, Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
| | - Travis T Mallard
- Department of Psychology, The University of Texas at Austin, Austin, TX, USA
| | - K Paige Harden
- Department of Psychology, The University of Texas at Austin, Austin, TX, USA
- Population Research Center, The University of Texas at Austin, Austin, TX, USA
| | - Elliot M Tucker-Drob
- Department of Psychology, The University of Texas at Austin, Austin, TX, USA
- Population Research Center, The University of Texas at Austin, Austin, TX, USA
| | - Pamela Herd
- McCourt School of Public Policy, Georgetown University, Washington, DC, USA
| | - Jeremy Freese
- Department of Sociology, Stanford University, Stanford, CA, USA
| | - Alexander Young
- UCLA Anderson School of Management, Los Angeles, CA, USA
- Human Genetics Department, UCLA David Geffen School of Medicine, Los Angeles, CA, USA
| | - Jonathan P Beauchamp
- Interdisciplinary Center for Economic Science and Department of Economics, George Mason University, Fairfax, VA, USA
| | - Philipp D Koellinger
- Department of Economics, School of Business and Economics, Vrije Universiteit Amsterdam, Amsterdam, the Netherlands
- Robert M. La Follette School of Public Affairs, University of Wisconsin-Madison, Madison, WI, USA
| | - Sven Oskarsson
- Department of Government, Uppsala University, Uppsala, Sweden
| | - Magnus Johannesson
- Department of Economics, Stockholm School of Economics, Stockholm, Sweden
| | - Peter M Visscher
- Institute for Molecular Bioscience, The University of Queensland, Brisbane, Queensland, Australia
| | - Michelle N Meyer
- Center for Translational Bioethics and Health Care Policy, Geisinger Health System, Danville, PA, USA
| | - David Laibson
- National Bureau of Economic Research, Cambridge, MA, USA
- Department of Economics, Harvard University, Cambridge, MA, USA
| | - David Cesarini
- Department of Economics, New York University, New York, NY, USA.
- National Bureau of Economic Research, Cambridge, MA, USA.
| | - Daniel J Benjamin
- National Bureau of Economic Research, Cambridge, MA, USA.
- UCLA Anderson School of Management, Los Angeles, CA, USA.
- Human Genetics Department, UCLA David Geffen School of Medicine, Los Angeles, CA, USA.
| | - 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.
| | - Aysu Okbay
- Department of Economics, School of Business and Economics, Vrije Universiteit Amsterdam, Amsterdam, the Netherlands.
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239
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Abstract
Over the past decade, substantial progress has been made in the discovery of alleles contributing to the risk of coronary artery disease. In addition to providing causal insights into disease, these endeavours have yielded and enabled the refinement of polygenic risk scores. These scores can be used to predict incident coronary artery disease in multiple cohorts and indicate the clinical response to some preventive therapies in post hoc analyses of clinical trials. These observations and the widespread ability to calculate polygenic risk scores from direct-to-consumer and health-care-associated biobanks have raised many questions about responsible clinical adoption. In this Review, we describe technical and downstream considerations for the derivation and validation of polygenic risk scores and current evidence for their efficacy and safety. We discuss the implementation of these scores in clinical medicine for uses including risk prediction and screening algorithms for coronary artery disease, prioritization of patient subgroups that are likely to derive benefit from treatment, and efficient prospective clinical trial designs.
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240
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Bakshi A, Riaz M, Orchard SG, Carr PR, Joshi AD, Cao Y, Rebello R, Nguyen-Dumont T, Southey MC, Millar JL, Gately L, Gibbs P, Ford LG, Parnes HL, Chan AT, McNeil JJ, Lacaze P. A Polygenic Risk Score Predicts Incident Prostate Cancer Risk in Older Men but Does Not Select for Clinically Significant Disease. Cancers (Basel) 2021; 13:5815. [PMID: 34830967 PMCID: PMC8616400 DOI: 10.3390/cancers13225815] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/29/2021] [Revised: 11/17/2021] [Accepted: 11/18/2021] [Indexed: 12/24/2022] Open
Abstract
Despite the high prevalence of prostate cancer in older men, the predictive value of a polygenic risk score (PRS) remains uncertain in men aged ≥70 years. We used a 6.6 million-variant PRS to predict the risk of incident prostate cancer in a prospective study of 5701 men of European descent aged ≥70 years (mean age 75 years) enrolled in the ASPirin in Reducing Events in the Elderly (ASPREE) clinical trial. The study endpoint was prostate cancer, including metastatic or non-metastatic disease, confirmed by an expert panel. After excluding participants with a history of prostate cancer at enrolment, we used a multivariable Cox proportional hazards model to assess the association between the PRS and incident prostate cancer risk, adjusting for covariates. Additionally, we examined the distribution of Gleason grade groups by PRS group to determine if a higher PRS was associated with higher grade disease. We tested for interaction between the PRS and aspirin treatment. Logistic regression was used to independently assess the association of the PRS with prevalent (pre-trial) prostate cancer, reported in medical histories. During a median follow-up time of 4.6 years, 218 of the 5701 participants (3.8%) were diagnosed with prostate cancer. The PRS predicted incident risk with a hazard ratio (HR) of 1.52 per standard deviation (SD) (95% confidence interval (CI) 1.33-1.74, p < 0.001). Men in the top quintile of the PRS distribution had an almost three times higher risk of prostate cancer than men in the lowest quintile (HR = 2.99 (95% CI 1.90-4.27), p < 0.001). However, a higher PRS was not associated with a higher Gleason grade groups. We found no interaction between aspirin treatment and the PRS for prostate cancer risk. The PRS was also associated with prevalent prostate cancer (odds ratio = 1.80 per SD (95% CI 1.65-1.96), p < 0.001).While a PRS for prostate cancer is strongly associated with incident risk in men aged ≥70 years, the clinical utility of the PRS as a biomarker is currently limited by its inability to select for clinically significant disease.
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Affiliation(s)
- Andrew Bakshi
- Department of Epidemiology and Preventive Medicine, School of Public Health and Preventive Medicine, Monash University, Melbourne, VIC 3004, Australia; (M.R.); (S.G.O.); (P.R.C.); (J.L.M.); (J.J.M.); (P.L.)
| | - Moeen Riaz
- Department of Epidemiology and Preventive Medicine, School of Public Health and Preventive Medicine, Monash University, Melbourne, VIC 3004, Australia; (M.R.); (S.G.O.); (P.R.C.); (J.L.M.); (J.J.M.); (P.L.)
| | - Suzanne G. Orchard
- Department of Epidemiology and Preventive Medicine, School of Public Health and Preventive Medicine, Monash University, Melbourne, VIC 3004, Australia; (M.R.); (S.G.O.); (P.R.C.); (J.L.M.); (J.J.M.); (P.L.)
| | - Prudence R. Carr
- Department of Epidemiology and Preventive Medicine, School of Public Health and Preventive Medicine, Monash University, Melbourne, VIC 3004, Australia; (M.R.); (S.G.O.); (P.R.C.); (J.L.M.); (J.J.M.); (P.L.)
| | - Amit D. Joshi
- Clinical and Translational Epidemiology Unit, MGH Cancer Center, Massachusetts General Hospital and Harvard Medical School, Boston, MA 02108, USA; (A.D.J.); (A.T.C.)
| | - Yin Cao
- Alvin J. Siteman Cancer Center, Division of Public Health Sciences, Department of Surgery, Washington University School of Medicine, St. Louis, MO 63110, USA;
| | - Richard Rebello
- Centre for Cancer Research, Department of Clinical Pathology, University of Melbourne, Melbourne, VIC 3010, Australia;
- Sir Peter MacCallum Department of Oncology, University of Melbourne, Melbourne, VIC 3010, Australia
| | - Tú Nguyen-Dumont
- Precision Medicine, School of Clinical Sciences at Monash Health, Monash University, Melbourne, VIC 3168, Australia; (T.N.-D.); (M.C.S.)
- Department of Clinical Pathology, University of Melbourne, Melbourne, VIC 3010, Australia
| | - Melissa C. Southey
- Precision Medicine, School of Clinical Sciences at Monash Health, Monash University, Melbourne, VIC 3168, Australia; (T.N.-D.); (M.C.S.)
- Department of Clinical Pathology, University of Melbourne, Melbourne, VIC 3010, Australia
- Cancer Epidemiology Division, Cancer Council Victoria, Melbourne, VIC 3004, Australia
| | - Jeremy L. Millar
- Department of Epidemiology and Preventive Medicine, School of Public Health and Preventive Medicine, Monash University, Melbourne, VIC 3004, Australia; (M.R.); (S.G.O.); (P.R.C.); (J.L.M.); (J.J.M.); (P.L.)
- Alfred Health Radiation Oncology, Alfred Hospital, Melbourne, VIC 3004, Australia
- Central Clinical School, Monash University, Melbourne, VIC 3168, Australia
| | - Lucy Gately
- Personalised Oncology Division, Walter and Eliza Hall Institute Medical Research, Faculty of Medicine, University of Melbourne, Melbourne, VIC 3052, Australia; (L.G.); (P.G.)
| | - Peter Gibbs
- Personalised Oncology Division, Walter and Eliza Hall Institute Medical Research, Faculty of Medicine, University of Melbourne, Melbourne, VIC 3052, Australia; (L.G.); (P.G.)
| | - Leslie G. Ford
- Division of Cancer Prevention, National Cancer Institute, Rockville, MD 20892, USA; (L.G.F.); (H.L.P.)
| | - Howard L. Parnes
- Division of Cancer Prevention, National Cancer Institute, Rockville, MD 20892, USA; (L.G.F.); (H.L.P.)
| | - Andrew T. Chan
- Clinical and Translational Epidemiology Unit, MGH Cancer Center, Massachusetts General Hospital and Harvard Medical School, Boston, MA 02108, USA; (A.D.J.); (A.T.C.)
| | - John J. McNeil
- Department of Epidemiology and Preventive Medicine, School of Public Health and Preventive Medicine, Monash University, Melbourne, VIC 3004, Australia; (M.R.); (S.G.O.); (P.R.C.); (J.L.M.); (J.J.M.); (P.L.)
| | - Paul Lacaze
- Department of Epidemiology and Preventive Medicine, School of Public Health and Preventive Medicine, Monash University, Melbourne, VIC 3004, Australia; (M.R.); (S.G.O.); (P.R.C.); (J.L.M.); (J.J.M.); (P.L.)
- Clinical and Translational Epidemiology Unit, MGH Cancer Center, Massachusetts General Hospital and Harvard Medical School, Boston, MA 02108, USA; (A.D.J.); (A.T.C.)
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241
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Responsible use of polygenic risk scores in the clinic: potential benefits, risks and gaps. Nat Med 2021; 27:1876-1884. [PMID: 34782789 DOI: 10.1038/s41591-021-01549-6] [Citation(s) in RCA: 186] [Impact Index Per Article: 62.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/22/2021] [Accepted: 09/22/2021] [Indexed: 01/24/2023]
Abstract
Polygenic risk scores (PRSs) aggregate the many small effects of alleles across the human genome to estimate the risk of a disease or disease-related trait for an individual. The potential benefits of PRSs include cost-effective enhancement of primary disease prevention, more refined diagnoses and improved precision when prescribing medicines. However, these must be weighed against the potential risks, such as uncertainties and biases in PRS performance, as well as potential misunderstanding and misuse of these within medical practice and in wider society. By addressing key issues including gaps in best practices, risk communication and regulatory frameworks, PRSs can be used responsibly to improve human health. Here, the International Common Disease Alliance's PRS Task Force, a multidisciplinary group comprising expertise in genetics, law, ethics, behavioral science and more, highlights recent research to provide a comprehensive summary of the state of polygenic score research, as well as the needs and challenges as PRSs move closer to widespread use in the clinic.
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242
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Heterogeneity in how women value risk-stratified breast screening. Genet Med 2021; 24:146-156. [PMID: 34906505 DOI: 10.1016/j.gim.2021.09.002] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/09/2020] [Revised: 07/04/2021] [Accepted: 09/10/2021] [Indexed: 12/28/2022] Open
Abstract
PURPOSE Risk-stratified screening has potential to improve the cost effectiveness of national breast cancer screening programs. This study aimed to inform a socially acceptable and equitable implementation framework by determining what influences a woman's decision to accept a personalized breast cancer risk assessment and what the relative impact of these key determinants is. METHODS Multicriteria decision analysis was used to elicit the relative weights for 8 criteria that women reported influenced their decision. Preference heterogeneity was explored through cluster analysis. RESULTS The 2 criteria valued most by the 347 participants related to program access, "Mode of invitation" and "Testing process". Both criteria significantly influenced participation (P < .001). A total of 73% preferred communication by letter/online. Almost all women preferred a multidisease risk assessment with potential for a familial high-risk result. Four preference-based subgroups were identified. Membership to the largest subgroup was predicted by lower educational attainment, and women in this subgroup were concerned with program access. Higher relative perceived breast cancer risk predicted membership to the smallest subgroup that was focused on test parameters, namely "Scope of test" and "Test specificity". CONCLUSION Overall, Australian women would accept a personalized multidisease risk assessment, but when aligning with their preferences, it will necessitate a focus on program access and the development of online communication frameworks.
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243
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Rehm HL, Page AJ, Smith L, Adams JB, Alterovitz G, Babb LJ, Barkley MP, Baudis M, Beauvais MJ, Beck T, Beckmann JS, Beltran S, Bernick D, Bernier A, Bonfield JK, Boughtwood TF, Bourque G, Bowers SR, Brookes AJ, Brudno M, Brush MH, Bujold D, Burdett T, Buske OJ, Cabili MN, Cameron DL, Carroll RJ, Casas-Silva E, Chakravarty D, Chaudhari BP, Chen SH, Cherry JM, Chung J, Cline M, Clissold HL, Cook-Deegan RM, Courtot M, Cunningham F, Cupak M, Davies RM, Denisko D, Doerr MJ, Dolman LI, Dove ES, Dursi LJ, Dyke SO, Eddy JA, Eilbeck K, Ellrott KP, Fairley S, Fakhro KA, Firth HV, Fitzsimons MS, Fiume M, Flicek P, Fore IM, Freeberg MA, Freimuth RR, Fromont LA, Fuerth J, Gaff CL, Gan W, Ghanaim EM, Glazer D, Green RC, Griffith M, Griffith OL, Grossman RL, Groza T, Guidry Auvil JM, Guigó R, Gupta D, Haendel MA, Hamosh A, Hansen DP, Hart RK, Hartley DM, Haussler D, Hendricks-Sturrup RM, Ho CW, Hobb AE, Hoffman MM, Hofmann OM, Holub P, Hsu JS, Hubaux JP, Hunt SE, Husami A, Jacobsen JO, Jamuar SS, Janes EL, Jeanson F, Jené A, Johns AL, Joly Y, Jones SJ, Kanitz A, Kato K, Keane TM, Kekesi-Lafrance K, Kelleher J, Kerry G, Khor SS, Knoppers BM, Konopko MA, Kosaki K, Kuba M, Lawson J, Leinonen R, Li S, Lin MF, Linden M, Liu X, Liyanage IU, Lopez J, Lucassen AM, Lukowski M, Mann AL, Marshall J, Mattioni M, Metke-Jimenez A, Middleton A, Milne RJ, Molnár-Gábor F, Mulder N, Munoz-Torres MC, Nag R, Nakagawa H, Nasir J, Navarro A, Nelson TH, Niewielska A, Nisselle A, Niu J, Nyrönen TH, O’Connor BD, Oesterle S, Ogishima S, Ota Wang V, Paglione LA, Palumbo E, Parkinson HE, Philippakis AA, Pizarro AD, Prlic A, Rambla J, Rendon A, Rider RA, Robinson PN, Rodarmer KW, Rodriguez LL, Rubin AF, Rueda M, Rushton GA, Ryan RS, Saunders GI, Schuilenburg H, Schwede T, Scollen S, Senf A, Sheffield NC, Skantharajah N, Smith AV, Sofia HJ, Spalding D, Spurdle AB, Stark Z, Stein LD, Suematsu M, Tan P, Tedds JA, Thomson AA, Thorogood A, Tickle TL, Tokunaga K, Törnroos J, Torrents D, Upchurch S, Valencia A, Guimera RV, Vamathevan J, Varma S, Vears DF, Viner C, Voisin C, Wagner AH, Wallace SE, Walsh BP, Williams MS, Winkler EC, Wold BJ, Wood GM, Woolley JP, Yamasaki C, Yates AD, Yung CK, Zass LJ, Zaytseva K, Zhang J, Goodhand P, North K, Birney E. GA4GH: International policies and standards for data sharing across genomic research and healthcare. CELL GENOMICS 2021; 1:100029. [PMID: 35072136 PMCID: PMC8774288 DOI: 10.1016/j.xgen.2021.100029] [Citation(s) in RCA: 75] [Impact Index Per Article: 25.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 01/08/2023]
Abstract
The Global Alliance for Genomics and Health (GA4GH) aims to accelerate biomedical advances by enabling the responsible sharing of clinical and genomic data through both harmonized data aggregation and federated approaches. The decreasing cost of genomic sequencing (along with other genome-wide molecular assays) and increasing evidence of its clinical utility will soon drive the generation of sequence data from tens of millions of humans, with increasing levels of diversity. In this perspective, we present the GA4GH strategies for addressing the major challenges of this data revolution. We describe the GA4GH organization, which is fueled by the development efforts of eight Work Streams and informed by the needs of 24 Driver Projects and other key stakeholders. We present the GA4GH suite of secure, interoperable technical standards and policy frameworks and review the current status of standards, their relevance to key domains of research and clinical care, and future plans of GA4GH. Broad international participation in building, adopting, and deploying GA4GH standards and frameworks will catalyze an unprecedented effort in data sharing that will be critical to advancing genomic medicine and ensuring that all populations can access its benefits.
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Affiliation(s)
- Heidi L. Rehm
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Massachusetts General Hospital, Boston, MA, USA
- Harvard Medical School, Boston, MA, USA
| | - Angela J.H. Page
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Global Alliance for Genomics and Health, Toronto, ON, Canada
| | - Lindsay Smith
- Global Alliance for Genomics and Health, Toronto, ON, Canada
- Ontario Institute for Cancer Research, Toronto, ON, Canada
| | - Jeremy B. Adams
- Global Alliance for Genomics and Health, Toronto, ON, Canada
- Ontario Institute for Cancer Research, Toronto, ON, Canada
| | - Gil Alterovitz
- Brigham and Women’s Hospital, Boston, MA, USA
- Harvard Medical School, Boston, MA, USA
| | | | | | - Michael Baudis
- University of Zurich, Zurich, Switzerland
- SIB Swiss Institute of Bioinformatics, Lausanne, Switzerland
| | - Michael J.S. Beauvais
- Global Alliance for Genomics and Health, Toronto, ON, Canada
- McGill University, Montreal, QC, Canada
| | - Tim Beck
- University of Leicester, Leicester, UK
| | | | - Sergi Beltran
- CNAG-CRG, Centre for Genomic Regulation (CRG), The Barcelona Institute of Science and Technology, Barcelona, Spain
- Universitat Pompeu Fabra (UPF), Barcelona, Spain
- Universitat de Barcelona, Barcelona, Spain
| | - David Bernick
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | | | | | - Tiffany F. Boughtwood
- Australian Genomics, Parkville, VIC, Australia
- Murdoch Children’s Research Institute, Parkville, VIC, Australia
| | - Guillaume Bourque
- McGill University, Montreal, QC, Canada
- Canadian Center for Computational Genomics, Montreal, QC, Canada
| | | | | | - Michael Brudno
- Canadian Center for Computational Genomics, Montreal, QC, Canada
- University of Toronto, Toronto, ON, Canada
- University Health Network, Toronto, ON, Canada
- Vector Institute, Toronto, ON, Canada
- Canadian Distributed Infrastructure for Genomics (CanDIG), Toronto, ON, Canada
| | | | - David Bujold
- McGill University, Montreal, QC, Canada
- Canadian Center for Computational Genomics, Montreal, QC, Canada
- Canadian Distributed Infrastructure for Genomics (CanDIG), Toronto, ON, Canada
| | - Tony Burdett
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Hinxton, UK
| | | | | | - Daniel L. Cameron
- Walter and Eliza Hall Institute of Medical Research, Parkville, VIC, Australia
- University of Melbourne, Melbourne, VIC, Australia
| | | | | | | | - Bimal P. Chaudhari
- Nationwide Children’s Hospital, Columbus, OH, USA
- The Ohio State University, Columbus, OH, USA
| | - Shu Hui Chen
- National Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, MD, USA
| | | | - Justina Chung
- Global Alliance for Genomics and Health, Toronto, ON, Canada
- Ontario Institute for Cancer Research, Toronto, ON, Canada
| | - Melissa Cline
- UC Santa Cruz Genomics Institute, Santa Cruz, CA, USA
| | | | | | - Mélanie Courtot
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Hinxton, UK
| | - Fiona Cunningham
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Hinxton, UK
| | | | | | | | | | | | | | - L. Jonathan Dursi
- University Health Network, Toronto, ON, Canada
- Canadian Distributed Infrastructure for Genomics (CanDIG), Toronto, ON, Canada
| | | | | | | | | | - Susan Fairley
- Global Alliance for Genomics and Health, Toronto, ON, Canada
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Hinxton, UK
| | - Khalid A. Fakhro
- Sidra Medicine, Doha, Qatar
- Weill Cornell Medicine - Qatar, Doha, Qatar
| | - Helen V. Firth
- Wellcome Sanger Institute, Hinxton, UK
- Addenbrooke’s Hospital, Cambridge, UK
| | | | | | - Paul Flicek
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Hinxton, UK
| | - Ian M. Fore
- National Cancer Institute, National Institutes of Health, Bethesda, MD, USA
| | - Mallory A. Freeberg
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Hinxton, UK
| | | | - Lauren A. Fromont
- Centre for Genomic Regulation (CRG), The Barcelona Institute of Science and Technology, Barcelona, Spain
| | | | - Clara L. Gaff
- Australian Genomics, Parkville, VIC, Australia
- Murdoch Children’s Research Institute, Parkville, VIC, Australia
- Walter and Eliza Hall Institute of Medical Research, Parkville, VIC, Australia
- University of Melbourne, Melbourne, VIC, Australia
| | - Weiniu Gan
- National Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, MD, USA
| | - Elena M. Ghanaim
- National Human Genome Research Institute, National Institutes of Health, Bethesda, MD, USA
| | - David Glazer
- Verily Life Sciences, South San Francisco, CA, USA
| | - Robert C. Green
- Brigham and Women’s Hospital, Boston, MA, USA
- Harvard Medical School, Boston, MA, USA
| | - Malachi Griffith
- Washington University School of Medicine in St. Louis, St. Louis, MO, USA
| | - Obi L. Griffith
- Washington University School of Medicine in St. Louis, St. Louis, MO, USA
| | | | | | | | - Roderic Guigó
- Universitat Pompeu Fabra (UPF), Barcelona, Spain
- Centre for Genomic Regulation (CRG), The Barcelona Institute of Science and Technology, Barcelona, Spain
| | - Dipayan Gupta
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Hinxton, UK
| | | | - Ada Hamosh
- Johns Hopkins University, Baltimore, MD, USA
| | - David P. Hansen
- Australian Genomics, Parkville, VIC, Australia
- The Australian e-Health Research Centre, CSIRO, Herston, QLD, Australia
| | - Reece K. Hart
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Invitae, San Francisco, CA, USA
- MyOme, Inc, San Bruno, CA, USA
| | | | - David Haussler
- UC Santa Cruz Genomics Institute, Santa Cruz, CA, USA
- Howard Hughes Medical Institute, University of California, Santa Cruz, CA, USA
| | | | | | | | - Michael M. Hoffman
- University of Toronto, Toronto, ON, Canada
- University Health Network, Toronto, ON, Canada
- Vector Institute, Toronto, ON, Canada
| | - Oliver M. Hofmann
- University of Toronto, Toronto, ON, Canada
- University of Melbourne, Melbourne, VIC, Australia
| | - Petr Holub
- BBMRI-ERIC, Graz, Austria
- Masaryk University, Brno, Czech Republic
| | | | | | - Sarah E. Hunt
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Hinxton, UK
| | - Ammar Husami
- Cincinnati Children’s Hospital Medical Center, Cincinnati, OH, USA
| | | | - Saumya S. Jamuar
- SingHealth Duke-NUS Genomic Medicine Centre, Singapore, Republic of Singapore
- SingHealth Duke-NUS Institute of Precision Medicine, Singapore, Republic of Singapore
| | - Elizabeth L. Janes
- Global Alliance for Genomics and Health, Toronto, ON, Canada
- University of Waterloo, Waterloo, ON, Canada
| | | | - Aina Jené
- Centre for Genomic Regulation (CRG), The Barcelona Institute of Science and Technology, Barcelona, Spain
| | - Amber L. Johns
- Garvan Institute of Medical Research, Darlinghurst, NSW, Australia
| | - Yann Joly
- McGill University, Montreal, QC, Canada
| | - Steven J.M. Jones
- Canada’s Michael Smith Genome Sciences Centre, BC Cancer, Vancouver, BC, Canada
| | - Alexander Kanitz
- SIB Swiss Institute of Bioinformatics, Lausanne, Switzerland
- University of Basel, Basel, Switzerland
| | | | - Thomas M. Keane
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Hinxton, UK
- University of Nottingham, Nottingham, UK
| | - Kristina Kekesi-Lafrance
- Global Alliance for Genomics and Health, Toronto, ON, Canada
- McGill University, Montreal, QC, Canada
| | | | - Giselle Kerry
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Hinxton, UK
| | - Seik-Soon Khor
- National Center for Global Health and Medicine Hospital, Tokyo, Japan
- University of Tokyo, Tokyo, Japan
| | | | | | | | | | | | - Rasko Leinonen
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Hinxton, UK
| | - Stephanie Li
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Global Alliance for Genomics and Health, Toronto, ON, Canada
| | | | - Mikael Linden
- CSC–IT Center for Science, Espoo, Finland
- ELIXIR Finland, Espoo, Finland
| | | | - Isuru Udara Liyanage
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Hinxton, UK
| | | | | | | | - Alice L. Mann
- Global Alliance for Genomics and Health, Toronto, ON, Canada
- Wellcome Sanger Institute, Hinxton, UK
| | | | | | | | - Anna Middleton
- Wellcome Connecting Science, Hinxton, UK
- University of Cambridge, Cambridge, UK
| | - Richard J. Milne
- Wellcome Connecting Science, Hinxton, UK
- University of Cambridge, Cambridge, UK
| | | | - Nicola Mulder
- H3ABioNet, Computational Biology Division, IDM, Faculty of Health Sciences, Cape Town, South Africa
| | | | - Rishi Nag
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Hinxton, UK
| | - Hidewaki Nakagawa
- Japan Agency for Medical Research & Development (AMED), Tokyo, Japan
- RIKEN Center for Integrative Medical Sciences, Yokohama, Japan
| | | | - Arcadi Navarro
- Centre for Genomic Regulation (CRG), The Barcelona Institute of Science and Technology, Barcelona, Spain
- Institute of Evolutionary Biology (UPF-CSIC), Universitat Pompeu Fabra (UPF), Barcelona, Spain
- Institució Catalana de Recerca i Estudis Avançats, Barcelona, Spain
- Barcelonaβeta Brain Research Center (BBRC), Pasqual Maragall Foundation, Barcelona, Spain
| | | | - Ania Niewielska
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Hinxton, UK
| | - Amy Nisselle
- Murdoch Children’s Research Institute, Parkville, VIC, Australia
- University of Melbourne, Melbourne, VIC, Australia
- Human Genetics Society of Australasia Education, Ethics & Social Issues Committee, Alexandria, NSW, Australia
| | - Jeffrey Niu
- University Health Network, Toronto, ON, Canada
| | - Tommi H. Nyrönen
- CSC–IT Center for Science, Espoo, Finland
- ELIXIR Finland, Espoo, Finland
| | | | - Sabine Oesterle
- SIB Swiss Institute of Bioinformatics, Lausanne, Switzerland
| | | | - Vivian Ota Wang
- National Cancer Institute, National Institutes of Health, Bethesda, MD, USA
| | | | - Emilio Palumbo
- Universitat Pompeu Fabra (UPF), Barcelona, Spain
- Centre for Genomic Regulation (CRG), The Barcelona Institute of Science and Technology, Barcelona, Spain
| | - Helen E. Parkinson
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Hinxton, UK
| | | | | | | | - Jordi Rambla
- Universitat Pompeu Fabra (UPF), Barcelona, Spain
- Centre for Genomic Regulation (CRG), The Barcelona Institute of Science and Technology, Barcelona, Spain
| | | | - Renee A. Rider
- National Human Genome Research Institute, National Institutes of Health, Bethesda, MD, USA
| | - Peter N. Robinson
- The Jackson Laboratory, Farmington, CT, USA
- University of Connecticut, Farmington, CT, USA
| | - Kurt W. Rodarmer
- National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Bethesda, MD, USA
| | | | - Alan F. Rubin
- Walter and Eliza Hall Institute of Medical Research, Parkville, VIC, Australia
- University of Melbourne, Melbourne, VIC, Australia
| | - Manuel Rueda
- Centre for Genomic Regulation (CRG), The Barcelona Institute of Science and Technology, Barcelona, Spain
| | | | | | | | - Helen Schuilenburg
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Hinxton, UK
| | - Torsten Schwede
- SIB Swiss Institute of Bioinformatics, Lausanne, Switzerland
- University of Basel, Basel, Switzerland
| | | | | | | | - Neerjah Skantharajah
- Global Alliance for Genomics and Health, Toronto, ON, Canada
- Ontario Institute for Cancer Research, Toronto, ON, Canada
| | | | - Heidi J. Sofia
- National Human Genome Research Institute, National Institutes of Health, Bethesda, MD, USA
| | - Dylan Spalding
- CSC–IT Center for Science, Espoo, Finland
- ELIXIR Finland, Espoo, Finland
| | | | - Zornitza Stark
- Australian Genomics, Parkville, VIC, Australia
- Murdoch Children’s Research Institute, Parkville, VIC, Australia
- University of Melbourne, Melbourne, VIC, Australia
| | - Lincoln D. Stein
- Ontario Institute for Cancer Research, Toronto, ON, Canada
- University of Toronto, Toronto, ON, Canada
| | | | - Patrick Tan
- SingHealth Duke-NUS Genomic Medicine Centre, Singapore, Republic of Singapore
- Precision Health Research Singapore, Singapore, Republic of Singapore
- Genome Institute of Singapore, Singapore, Republic of Singapore
| | | | - Alastair A. Thomson
- National Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, MD, USA
| | - Adrian Thorogood
- McGill University, Montreal, QC, Canada
- University of Luxembourg, Esch-sur-Alzette, Luxembourg
| | | | - Katsushi Tokunaga
- University of Tokyo, Tokyo, Japan
- National Center for Global Health and Medicine, Tokyo, Japan
| | - Juha Törnroos
- CSC–IT Center for Science, Espoo, Finland
- ELIXIR Finland, Espoo, Finland
| | - David Torrents
- Institució Catalana de Recerca i Estudis Avançats, Barcelona, Spain
- Barcelona Supercomputing Center, Barcelona, Spain
| | - Sean Upchurch
- California Institute of Technology, Pasadena, CA, USA
| | - Alfonso Valencia
- Institució Catalana de Recerca i Estudis Avançats, Barcelona, Spain
- Barcelona Supercomputing Center, Barcelona, Spain
| | | | - Jessica Vamathevan
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Hinxton, UK
| | - Susheel Varma
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Hinxton, UK
- Health Data Research UK, London, UK
| | - Danya F. Vears
- Murdoch Children’s Research Institute, Parkville, VIC, Australia
- University of Melbourne, Melbourne, VIC, Australia
- Human Genetics Society of Australasia Education, Ethics & Social Issues Committee, Alexandria, NSW, Australia
- Melbourne Law School, University of Melbourne, Parkville, VIC, Australia
| | - Coby Viner
- University of Toronto, Toronto, ON, Canada
- University Health Network, Toronto, ON, Canada
| | | | - Alex H. Wagner
- Nationwide Children’s Hospital, Columbus, OH, USA
- The Ohio State University, Columbus, OH, USA
| | | | | | | | - Eva C. Winkler
- Section of Translational Medical Ethics, University Hospital Heidelberg, Heidelberg, Germany
| | | | | | | | | | - Andrew D. Yates
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Hinxton, UK
| | - Christina K. Yung
- Ontario Institute for Cancer Research, Toronto, ON, Canada
- Indoc Research, Toronto, ON, Canada
| | - Lyndon J. Zass
- H3ABioNet, Computational Biology Division, IDM, Faculty of Health Sciences, Cape Town, South Africa
| | - Ksenia Zaytseva
- McGill University, Montreal, QC, Canada
- Canadian Centre for Computational Genomics, Montreal, QC, Canada
| | - Junjun Zhang
- Ontario Institute for Cancer Research, Toronto, ON, Canada
| | - Peter Goodhand
- Global Alliance for Genomics and Health, Toronto, ON, Canada
- Ontario Institute for Cancer Research, Toronto, ON, Canada
| | - Kathryn North
- Murdoch Children’s Research Institute, Parkville, VIC, Australia
- University of Toronto, Toronto, ON, Canada
- University of Melbourne, Melbourne, VIC, Australia
| | - Ewan Birney
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Hinxton, UK
- European Molecular Biology Laboratory, Heidelberg, Germany
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MacArthur JA, Buniello A, Harris LW, Hayhurst J, McMahon A, Sollis E, Cerezo M, Hall P, Lewis E, Whetzel PL, Bahcall OG, Barroso I, Carroll RJ, Inouye M, Manolio TA, Rich SS, Hindorff LA, Wiley K, Parkinson H. Workshop proceedings: GWAS summary statistics standards and sharing. CELL GENOMICS 2021; 1:100004. [PMID: 36082306 PMCID: PMC9451133 DOI: 10.1016/j.xgen.2021.100004] [Citation(s) in RCA: 17] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 12/21/2022]
Abstract
Genome-wide association studies (GWASs) have enabled robust mapping of complex traits in humans. The open sharing of GWAS summary statistics (SumStats) is essential in facilitating the larger meta-analyses needed for increased power in resolving the genetic basis of disease. However, most GWAS SumStats are not readily accessible because of limited sharing and a lack of defined standards. With the aim of increasing the availability, quality, and utility of GWAS SumStats, the National Human Genome Research Institute-European Bioinformatics Institute (NHGRI-EBI) GWAS Catalog organized a community workshop to address the standards, infrastructure, and incentives required to promote and enable sharing. We evaluated the barriers to SumStats sharing, both technological and sociological, and developed an action plan to address those challenges and ensure that SumStats and study metadata are findable, accessible, interoperable, and reusable (FAIR). We encourage early deposition of datasets in the GWAS Catalog as the recognized central repository. We recommend standard requirements for reporting elements and formats for SumStats and accompanying metadata as guidelines for community standards and a basis for submission to the GWAS Catalog. Finally, we provide recommendations to enable, promote, and incentivize broader data sharing, standards and FAIRness in order to advance genomic medicine.
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Affiliation(s)
- Jacqueline A.L. MacArthur
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, UK
- BHF Data Science Centre, Health Data Research UK, London, UK
| | - Annalisa Buniello
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, UK
| | - Laura W. Harris
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, UK
| | - James Hayhurst
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, UK
| | - Aoife McMahon
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, UK
| | - Elliot Sollis
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, UK
| | - Maria Cerezo
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, UK
| | - Peggy Hall
- Division of Genomic Medicine, National Human Genome Research Institute, National Institutes of Health, Bethesda, MD 20892, USA
| | - Elizabeth Lewis
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, UK
| | - Patricia L. Whetzel
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, UK
| | - Orli G. Bahcall
- Cell Genomics, Cell Press, 50 Hampshire St., 5th Floor, Cambridge, MA 02139, USA
| | - Inês Barroso
- Exeter Centre of Excellence in Diabetes (EXCEED), University of Exeter Medical School, Exeter, UK
| | - Robert J. Carroll
- Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Michael Inouye
- Cambridge Baker Systems Genomics Initiative, Department of Public Health and Primary Care, University of Cambridge, Cambridge CB1 8RN, UK
- Cambridge Baker Systems Genomics Initiative, Baker Heart and Diabetes Institute, 75 Commercial Rd., Melbourne 3004, VIC, Australia
- The Alan Turing Institute, London, UK
| | - Teri A. Manolio
- Division of Genomic Medicine, National Human Genome Research Institute, National Institutes of Health, Bethesda, MD 20892, USA
| | - Stephen S. Rich
- Center for Public Health Genomics, University of Virginia, Charlottesville, VA 22908, USA
| | - Lucia A. Hindorff
- Division of Genomic Medicine, National Human Genome Research Institute, National Institutes of Health, Bethesda, MD 20892, USA
| | - Ken Wiley
- Division of Genomic Medicine, National Human Genome Research Institute, National Institutes of Health, Bethesda, MD 20892, USA
| | - Helen Parkinson
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, UK
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Lencz T, Backenroth D, Granot-Hershkovitz E, Green A, Gettler K, Cho JH, Weissbrod O, Zuk O, Carmi S. Utility of polygenic embryo screening for disease depends on the selection strategy. eLife 2021; 10:e64716. [PMID: 34635206 PMCID: PMC8510582 DOI: 10.7554/elife.64716] [Citation(s) in RCA: 35] [Impact Index Per Article: 11.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/09/2020] [Accepted: 08/09/2021] [Indexed: 12/13/2022] Open
Abstract
Polygenic risk scores (PRSs) have been offered since 2019 to screen in vitro fertilization embryos for genetic liability to adult diseases, despite a lack of comprehensive modeling of expected outcomes. Here we predict, based on the liability threshold model, the expected reduction in complex disease risk following polygenic embryo screening for a single disease. A strong determinant of the potential utility of such screening is the selection strategy, a factor that has not been previously studied. When only embryos with a very high PRS are excluded, the achieved risk reduction is minimal. In contrast, selecting the embryo with the lowest PRS can lead to substantial relative risk reductions, given a sufficient number of viable embryos. We systematically examine the impact of several factors on the utility of screening, including: variance explained by the PRS, number of embryos, disease prevalence, parental PRSs, and parental disease status. We consider both relative and absolute risk reductions, as well as population-averaged and per-couple risk reductions, and also examine the risk of pleiotropic effects. Finally, we confirm our theoretical predictions by simulating 'virtual' couples and offspring based on real genomes from schizophrenia and Crohn's disease case-control studies. We discuss the assumptions and limitations of our model, as well as the potential emerging ethical concerns.
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Affiliation(s)
- Todd Lencz
- Departments of Psychiatry and Molecular Medicine, Zucker School of Medicine at Hofstra/NorthwellHempsteadUnited States
- Department of Psychiatry, Division of Research, The Zucker Hillside Hospital Division of Northwell HealthGlen OaksUnited States
- Institute for Behavioral Science, The Feinstein Institutes for Medical ResearchManhassetUnited States
| | - Daniel Backenroth
- Braun School of Public Health and Community Medicine, The Hebrew University of JerusalemJerusalemIsrael
| | - Einat Granot-Hershkovitz
- Braun School of Public Health and Community Medicine, The Hebrew University of JerusalemJerusalemIsrael
| | - Adam Green
- Braun School of Public Health and Community Medicine, The Hebrew University of JerusalemJerusalemIsrael
| | - Kyle Gettler
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount SinaiNew YorkUnited States
| | - Judy H Cho
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount SinaiNew YorkUnited States
- The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount SinaiNew YorkUnited States
- Department of Medicine, Icahn School of Medicine at Mount SinaiNew YorkUnited States
| | - Omer Weissbrod
- Department of Epidemiology, Harvard T.H. Chan School of Public HealthBostonUnited States
| | - Or Zuk
- Department of Statistics and Data Science, The Hebrew University of JerusalemJerusalemIsrael
| | - Shai Carmi
- Braun School of Public Health and Community Medicine, The Hebrew University of JerusalemJerusalemIsrael
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Clarke SL, Assimes TL, Tcheandjieu C. The Propagation of Racial Disparities in Cardiovascular Genomics Research. CIRCULATION. GENOMIC AND PRECISION MEDICINE 2021; 14:e003178. [PMID: 34461749 PMCID: PMC8530858 DOI: 10.1161/circgen.121.003178] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/30/2022]
Abstract
Genomics research has improved our understanding of the genetic basis for human traits and diseases. This progress is now being translated into clinical care as we move toward a future of precision medicine. Many hope that expanded use of genomic testing will improve disease screening, diagnosis, risk stratification, and treatment. In many respects, cardiovascular medicine is leading this charge. However, most cardiovascular genomics research has been conducted in populations of primarily European ancestry. This bias has critical downstream effects. Here, we review the current disparities in cardiovascular genomics research, and we outline how these disparities propagate forward through all phases of the translational pipeline. If not adequately addressed, biases in genomics research will further compound the existing health disparities that face underrepresented and marginalized populations.
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Affiliation(s)
- Shoa L. Clarke
- VA Palo Alto Health Care system, Palo Alto
- Department of Medicine, Division of Cardiovascular Medicine, Stanford University School of Medicine, Stanford, CA
| | - Themistocles L. Assimes
- VA Palo Alto Health Care system, Palo Alto
- Department of Medicine, Division of Cardiovascular Medicine, Stanford University School of Medicine, Stanford, CA
| | - Catherine Tcheandjieu
- VA Palo Alto Health Care system, Palo Alto
- Department of Medicine, Division of Cardiovascular Medicine, Stanford University School of Medicine, Stanford, CA
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248
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Brockman DG, Petronio L, Dron JS, Kwon BC, Vosburg T, Nip L, Tang A, O'Reilly M, Lennon N, Wong B, Ng K, Huang KH, Fahed AC, Khera AV. Design and user experience testing of a polygenic score report: a qualitative study of prospective users. BMC Med Genomics 2021; 14:238. [PMID: 34598685 PMCID: PMC8485114 DOI: 10.1186/s12920-021-01056-0] [Citation(s) in RCA: 28] [Impact Index Per Article: 9.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/25/2021] [Accepted: 08/12/2021] [Indexed: 12/28/2022] Open
Abstract
Background Polygenic scores—which quantify inherited risk by integrating information from many common sites of DNA variation—may enable a tailored approach to clinical medicine. However, alongside considerable enthusiasm, we and others have highlighted a lack of standardized approaches for score disclosure. Here, we review the landscape of polygenic score reporting and describe a generalizable approach for development of a polygenic score disclosure tool for coronary artery disease. Methods We assembled a working group of clinicians, geneticists, data visualization specialists, and software developers. The group reviewed existing polygenic score reports and then designed a two-page mock report for coronary artery disease. We then conducted a qualitative user-experience study with this report using an interview guide focused on comprehension, experience, and attitudes. Interviews were transcribed and analyzed for themes identification to inform report revision. Results Review of nine existing polygenic score reports from commercial and academic groups demonstrated significant heterogeneity, reinforcing the need for additional efforts to study and standardize score disclosure. Using a newly developed mock score report, we conducted interviews with ten adult individuals (50% females, 70% without prior genetic testing experience, age range 20–70 years) recruited via an online platform. We identified three themes from interviews: (1) visual elements, such as color and simple graphics, enable participants to interpret, relate to, and contextualize their polygenic score, (2) word-based descriptions of risk and polygenic scores presented as percentiles were the best recognized and understood, (3) participants had varying levels of interest in understanding complex genomic information and therefore would benefit from additional resources that can adapt to their individual needs in real time. In response to user feedback, colors used for communicating risk were modified to minimize unintended color associations and odds ratios were removed. All 10 participants expressed interest in receiving a polygenic score report based on their personal genomic information. Conclusions Our findings describe a generalizable approach to develop a polygenic score report understandable by potential patients. Although additional studies are needed across a wider spectrum of patient populations, these results are likely to inform ongoing efforts related to polygenic score disclosure within clinical practice. Supplementary Information The online version contains supplementary material available at 10.1186/s12920-021-01056-0.
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Affiliation(s)
- Deanna G Brockman
- Center for Genomic Medicine, Department of Medicine, Massachusetts General Hospital, 185 Cambridge Street, Simches Research Building
- CPZN 6.256, Boston, MA, 02114, USA.,Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Lia Petronio
- Pattern Visualization Team, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Jacqueline S Dron
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Bum Chul Kwon
- Center for Computational Health, IBM Research, Cambridge, MA, USA
| | - Trish Vosburg
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA.,Genomics Platform, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Lisa Nip
- Pattern Visualization Team, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Andrew Tang
- Pattern Visualization Team, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Mary O'Reilly
- Pattern Visualization Team, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Niall Lennon
- Genomics Platform, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Bang Wong
- Pattern Visualization Team, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Kenney Ng
- Center for Computational Health, IBM Research, Cambridge, MA, USA
| | - Katherine H Huang
- Pattern Visualization Team, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Akl C Fahed
- Center for Genomic Medicine, Department of Medicine, Massachusetts General Hospital, 185 Cambridge Street, Simches Research Building
- CPZN 6.256, Boston, MA, 02114, USA.,Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA.,Department of Medicine, Harvard Medical School, Boston, MA, USA
| | - Amit V Khera
- Center for Genomic Medicine, Department of Medicine, Massachusetts General Hospital, 185 Cambridge Street, Simches Research Building
- CPZN 6.256, Boston, MA, 02114, USA. .,Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA. .,Department of Medicine, Harvard Medical School, Boston, MA, USA.
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249
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Liou L, Kaptoge S, Dennis J, Shah M, Tyrer J, Inouye M, Easton DF, Pharoah PDP. Genomic risk prediction of coronary artery disease in women with breast cancer: a prospective cohort study. Breast Cancer Res 2021; 23:94. [PMID: 34593009 PMCID: PMC8482562 DOI: 10.1186/s13058-021-01465-0] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/12/2021] [Accepted: 08/24/2021] [Indexed: 02/02/2023] Open
Abstract
BACKGROUND Advancements in cancer therapeutics have resulted in increases in cancer-related survival; however, there is a growing clinical dilemma. The current balancing of survival benefits and future cardiotoxic harms of oncotherapies has resulted in an increased burden of cardiovascular disease in breast cancer survivors. Risk stratification may help address this clinical dilemma. This study is the first to assess the association between a coronary artery disease-specific polygenic risk score and incident coronary artery events in female breast cancer survivors. METHODS We utilized the Studies in Epidemiology and Research in Cancer Heredity prospective cohort involving 12,413 women with breast cancer with genotype information and without a baseline history of cardiovascular disease. Cause-specific hazard ratios for association of the polygenic risk score and incident coronary artery disease (CAD) were obtained using left-truncated Cox regression adjusting for age, genotype array, conventional risk factors such as smoking and body mass index, as well as other sociodemographic, lifestyle, and medical variables. RESULTS Over a median follow-up of 10.3 years (IQR: 16.8) years, 750 incident fatal or non-fatal coronary artery events were recorded. A 1 standard deviation higher polygenic risk score was associated with an adjusted hazard ratio of 1.33 (95% CI 1.20, 1.47) for incident CAD. CONCLUSIONS This study provides evidence that a coronary artery disease-specific polygenic risk score can risk-stratify breast cancer survivors independently of other established cardiovascular risk factors.
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Affiliation(s)
- Lathan Liou
- British Heart Foundation Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK.
| | - Stephen Kaptoge
- British Heart Foundation Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
| | - Joe Dennis
- Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
| | - Mitul Shah
- Department of Oncology, University of Cambridge, Cambridge, UK
| | - Jonathan Tyrer
- Department of Oncology, University of Cambridge, Cambridge, UK
| | - Michael Inouye
- British Heart Foundation Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
- Cambridge Baker Systems Genomics Initiative, Baker Heart and Diabetes Institute, Melbourne, VIC, Australia
| | - Douglas F Easton
- Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
- Department of Oncology, University of Cambridge, Cambridge, UK
| | - Paul D P Pharoah
- Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
- Department of Oncology, University of Cambridge, Cambridge, UK
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250
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Visseren FLJ, Mach F, Smulders YM, Carballo D, Koskinas KC, Bäck M, Benetos A, Biffi A, Boavida JM, Capodanno D, Cosyns B, Crawford C, Davos CH, Desormais I, Di Angelantonio E, Franco OH, Halvorsen S, Hobbs FDR, Hollander M, Jankowska EA, Michal M, Sacco S, Sattar N, Tokgozoglu L, Tonstad S, Tsioufis KP, van Dis I, van Gelder IC, Wanner C, Williams B. 2021 ESC Guidelines on cardiovascular disease prevention in clinical practice. Eur J Prev Cardiol 2021; 29:5-115. [PMID: 34558602 DOI: 10.1093/eurjpc/zwab154] [Citation(s) in RCA: 211] [Impact Index Per Article: 70.3] [Reference Citation Analysis] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/24/2022]
Affiliation(s)
| | | | | | | | | | | | | | - Alessandro Biffi
- European Federation of Sports Medicine Association (EFSMA).,International Federation of Sport Medicine (FIMS)
| | | | | | | | | | | | | | | | | | | | - F D Richard Hobbs
- World Organization of National Colleges, Academies and Academic Associations of General Practitioners/Family Physicians (WONCA) - Europe
| | | | | | | | | | | | | | | | | | | | | | - Christoph Wanner
- European Renal Association - European Dialysis and Transplant Association (ERA-EDTA)
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