Correlation

Mathematics

Genetic variation

Biology

Gene

Biobank

Statistics

Genotype

Covariate

Spearman's rank correlation coefficient

Principal component analysis

Geometry

Genotyping

Genetic correlation

Genetics

Quantitative trait locus

PLoS Genetics

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 &lt; 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 ).

Significant sparse polygenic risk scores across 813 traits in UK Biobank