Recent advances in polygenic scores: translation, equitability, methods and FAIR tools

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作者
Ruidong Xiang
Martin Kelemen
Yu Xu
Laura W. Harris
Helen Parkinson
Michael Inouye
Samuel A. Lambert
机构
[1] Baker Heart and Diabetes Institute,Cambridge Baker Systems Genomics Initiative
[2] University of Cambridge,Cambridge Baker Systems Genomics Initiative, Department of Public Health and Primary Care
[3] University of Cambridge,British Heart Foundation Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care
[4] Victor Phillip Dahdaleh Heart and Lung Research Institute,European Molecular Biology Laboratory
[5] University of Cambridge,British Heart Foundation Centre of Research Excellence
[6] Health Data Research UK Cambridge,undefined
[7] Wellcome Genome Campus and University of Cambridge,undefined
[8] European Bioinformatics Institute,undefined
[9] Wellcome Genome Campus,undefined
[10] University of Cambridge,undefined
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关键词
Polygenic score (PGS); Clinical utility; FAIR (Findable; Accessible; Interoperable; And Reusable); Genome-wide association studies (GWAS); Open-access; Transferability; Responsible use;
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摘要
Polygenic scores (PGS) can be used for risk stratification by quantifying individuals’ genetic predisposition to disease, and many potentially clinically useful applications have been proposed. Here, we review the latest potential benefits of PGS in the clinic and challenges to implementation. PGS could augment risk stratification through combined use with traditional risk factors (demographics, disease-specific risk factors, family history, etc.), to support diagnostic pathways, to predict groups with therapeutic benefits, and to increase the efficiency of clinical trials. However, there exist challenges to maximizing the clinical utility of PGS, including FAIR (Findable, Accessible, Interoperable, and Reusable) use and standardized sharing of the genomic data needed to develop and recalculate PGS, the equitable performance of PGS across populations and ancestries, the generation of robust and reproducible PGS calculations, and the responsible communication and interpretation of results. We outline how these challenges may be overcome analytically and with more diverse data as well as highlight sustained community efforts to achieve equitable, impactful, and responsible use of PGS in healthcare.
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