Significant Sparse Polygenic Risk Scores Across 813 Traits in UK Biobank

Overview

Journal PLoS Genet

Specialty Genetics

Date 2022 Mar 24

PMID 35324888

Authors

Yosuke Tanigawa

Junyang Qian

Guhan Venkataraman

Johanne Marie Justesen

Ruilin Li

Robert Tibshirani

Trevor Hastie

Manuel A Rivas

Affiliations

Soon will be listed here.

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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