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ExPRSweb: An online repository with polygenic risk scores for common health-related exposures.
Ma, Ying; Patil, Snehal; Zhou, Xiang; Mukherjee, Bhramar; Fritsche, Lars G.
Affiliation
  • Ma Y; Department of Biostatistics, University of Michigan School of Public Health, Ann Arbor, MI 48109, USA; Center for Statistical Genetics, University of Michigan School of Public Health, Ann Arbor, MI 48109, USA.
  • Patil S; Department of Biostatistics, University of Michigan School of Public Health, Ann Arbor, MI 48109, USA; Center for Statistical Genetics, University of Michigan School of Public Health, Ann Arbor, MI 48109, USA.
  • Zhou X; Department of Biostatistics, University of Michigan School of Public Health, Ann Arbor, MI 48109, USA; Center for Statistical Genetics, University of Michigan School of Public Health, Ann Arbor, MI 48109, USA; Center for Precision Health Data Science, University of Michigan School of Public Health,
  • Mukherjee B; Department of Biostatistics, University of Michigan School of Public Health, Ann Arbor, MI 48109, USA; Center for Statistical Genetics, University of Michigan School of Public Health, Ann Arbor, MI 48109, USA; Center for Precision Health Data Science, University of Michigan School of Public Health,
  • Fritsche LG; Department of Biostatistics, University of Michigan School of Public Health, Ann Arbor, MI 48109, USA; Center for Statistical Genetics, University of Michigan School of Public Health, Ann Arbor, MI 48109, USA; Center for Precision Health Data Science, University of Michigan School of Public Health,
Am J Hum Genet ; 109(10): 1742-1760, 2022 10 06.
Article in En | MEDLINE | ID: mdl-36152628
ABSTRACT
Complex traits are influenced by genetic risk factors, lifestyle, and environmental variables, so-called exposures. Some exposures, e.g., smoking or lipid levels, have common genetic modifiers identified in genome-wide association studies. Because measurements are often unfeasible, exposure polygenic risk scores (ExPRSs) offer an alternative to study the influence of exposures on various phenotypes. Here, we collected publicly available summary statistics for 28 exposures and applied four common PRS methods to generate ExPRSs in two large biobanks the Michigan Genomics Initiative and the UK Biobank. We established ExPRSs for 27 exposures and demonstrated their applicability in phenome-wide association studies and as predictors for common chronic conditions. Especially the addition of multiple ExPRSs showed, for several chronic conditions, an improvement compared to prediction models that only included traditional, disease-focused PRSs. To facilitate follow-up studies, we share all ExPRS constructs and generated results via an online repository called ExPRSweb.
Subject(s)
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Full text: 1 Database: MEDLINE Main subject: Genetic Predisposition to Disease / Genome-Wide Association Study Type of study: Etiology_studies / Observational_studies / Prognostic_studies / Risk_factors_studies Limits: Humans Language: En Year: 2022 Type: Article

Full text: 1 Database: MEDLINE Main subject: Genetic Predisposition to Disease / Genome-Wide Association Study Type of study: Etiology_studies / Observational_studies / Prognostic_studies / Risk_factors_studies Limits: Humans Language: En Year: 2022 Type: Article