| Title: | Significant sparse polygenic risk scores across 813 traits in UK Biobank |
| Journal: | PLOS Genetics |
| Published: | 24 Mar 2022 |
| Pubmed: | https://pubmed.ncbi.nlm.nih.gov/35324888/ |
| DOI: | https://doi.org/10.1371/journal.pgen.1010105 |
| URL: | https://journals.plos.org/plosgenetics/article/file?id=10.1371/journal.pgen.1010105&type=printable |
| Citations: | 55 (47 in last 2 years) as of 8 Aug 2024 |
Publication 12454
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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).</p>
8 Keywords
- Biological Specimen Banks
- Genetic Predisposition to Disease
- Genome-Wide Association Study
- Humans
- Multifactorial Inheritance
- Phenotype
- Risk Factors
- United Kingdom
8 Authors
- Yosuke Tanigawa
- Junyang Qian
- Guhan Venkataraman
- Johanne Marie Justesen
- Ruilin Li
- Robert Tibshirani
- Trevor Hastie
- Manuel A. Rivas
1 Application
| Application ID | Title |
|---|---|
| 24983 | Generating effective therapeutic hypotheses from genomic and hospital linkage data |
Enabling scientific discoveries that improve human health