Year: 2019
Type: preprint
Abstract: ABSTRACT Large-scale cohorts with combined genetic and phenotypic data, coupled with methodological advances, have produced increasingly accurate genetic predictors of complex human phenotypes called polygenic risk scores (PRS). In addition to the potential translational impacts of identifying at-risk individuals, PRS are being utilized for a growing list of scientific applications including causal inference, identifying pleiotropy and genetic correlation, and powerful gene-based and mixed model association tests. Existing PRS approaches rely on external large-scale genetic cohorts that have also measured the phenotype of interest. They further require matching on ancestry and genotyping platform or imputation quality. In this work we present a novel reference-free method to produce PRS that does not rely on an external cohort. We show that naive implementations of reference-free PRS either result in substantial over-fitting or prohibitive increases in computational time. We show that our algorithm avoids both of these issues, and can produce informative in-sample PRS over any existing cohort without over-fitting. We then demonstrate several novel applications of reference-free PRS including detection of pleiotropy across 246 metabolic traits and efficient mixed-model association testing.
DOI: 10.1101/517821
Institutions: University of California, San Francisco, Broad Institute, The University of Queensland, Institute for Molecular Bioscience, University of California, Los Angeles, Baker Heart and Diabetes Institute, University of Eastern Finland, The Alfred Hospital, University of Bristol, Monash University, Medical Research Council, University of Oulu, Chinese Academy of Medical Sciences & Peking Union Medical College
Language: English
Subfield: Genetics
Domain: Life Sciences
Open Access status: green
Awards: Richer Models of Asthma Risk: Bridging the Environment-Genetics Divide, Leveraging functional data to predict disease risk in multi-ethnic populations, Functional genetic variants for type 2 diabetes, Genomics of dyslipidemia in Mexicans, Center for Multi- and Trans-ethnic Mapping of Mendelian and Complex Diseases, G9000061537
OpenAlex ID: W2950873898