Significant sparse polygenic risk scores across 813 traits in UK Biobank
Publikation: Bidrag til tidsskrift › Tidsskriftartikel › Forskning › fagfællebedømt
Standard
Significant sparse polygenic risk scores across 813 traits in UK Biobank. / Tanigawa, Yosuke; Qian, Junyang; Venkataraman, Guhan; Justesen, Johanne Marie; Li, Ruilin; Tibshirani, Robert; Hastie, Trevor; Rivas, Manuel A.
I: PLOS Genetics, Bind 18, Nr. 3, e1010105, 2022.Publikation: Bidrag til tidsskrift › Tidsskriftartikel › Forskning › fagfællebedømt
Harvard
APA
Vancouver
Author
Bibtex
}
RIS
TY - JOUR
T1 - Significant sparse polygenic risk scores across 813 traits in UK Biobank
AU - Tanigawa, Yosuke
AU - Qian, Junyang
AU - Venkataraman, Guhan
AU - Justesen, Johanne Marie
AU - Li, Ruilin
AU - Tibshirani, Robert
AU - Hastie, Trevor
AU - Rivas, Manuel A
PY - 2022
Y1 - 2022
N2 - 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).
AB - 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).
U2 - 10.1371/journal.pgen.1010105
DO - 10.1371/journal.pgen.1010105
M3 - Journal article
C2 - 35324888
VL - 18
JO - P L o S Genetics
JF - P L o S Genetics
SN - 1553-7390
IS - 3
M1 - e1010105
ER -
ID: 301160488