Significant Sparse Polygenic Risk Scores across 813 traits in UK Biobank
Name
journal.pgen.1010105.pdf
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3.26 MB
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Author(s)
Tanigawa, Yosuke
Qian, Junyang
Venkataraman, Guhan
Justesen, Johanne Marie
Li, Ruilin
Tibshirani, Robert
Hastie, Trevor
Rivas, Manuel A.
Date Issued
September 6, 2021
Publisher
Cold Spring Harbor Laboratory
Citation
Tanigawa, Yosuke, Qian, Junyang, Venkataraman, Guhan, Justesen, Johanne Marie, Li, Ruilin et al. 2021. "Significant Sparse Polygenic Risk Scores across 813 traits in UK Biobank."
Version
Final published version
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).
MIT Department
Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory
Terms of Use
Creative Commons Attribution 4.0 International License
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DOI of Published Version
https://doi.org/10.1101/2021.09.02.21262942
