Significant sparse polygenic risk scores across 813 traits in UK Biobank

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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

Tanigawa, Y, Qian, J, Venkataraman, G, Justesen, JM, Li, R, Tibshirani, R, Hastie, T & Rivas, MA 2022, 'Significant sparse polygenic risk scores across 813 traits in UK Biobank', PLOS Genetics, bind 18, nr. 3, e1010105. https://doi.org/10.1371/journal.pgen.1010105

APA

Tanigawa, Y., Qian, J., Venkataraman, G., Justesen, J. M., Li, R., Tibshirani, R., Hastie, T., & Rivas, M. A. (2022). Significant sparse polygenic risk scores across 813 traits in UK Biobank. PLOS Genetics, 18(3), [e1010105]. https://doi.org/10.1371/journal.pgen.1010105

Vancouver

Tanigawa Y, Qian J, Venkataraman G, Justesen JM, Li R, Tibshirani R o.a. Significant sparse polygenic risk scores across 813 traits in UK Biobank. PLOS Genetics. 2022;18(3). e1010105. https://doi.org/10.1371/journal.pgen.1010105

Author

Tanigawa, Yosuke ; Qian, Junyang ; Venkataraman, Guhan ; Justesen, Johanne Marie ; Li, Ruilin ; Tibshirani, Robert ; Hastie, Trevor ; Rivas, Manuel A. / Significant sparse polygenic risk scores across 813 traits in UK Biobank. I: PLOS Genetics. 2022 ; Bind 18, Nr. 3.

Bibtex

@article{1f1fb60c17b6498580f820afcaa1069f,
title = "Significant sparse polygenic risk scores across 813 traits in UK Biobank",
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).",
author = "Yosuke Tanigawa and Junyang Qian and Guhan Venkataraman and Justesen, {Johanne Marie} and Ruilin Li and Robert Tibshirani and Trevor Hastie and Rivas, {Manuel A}",
year = "2022",
doi = "10.1371/journal.pgen.1010105",
language = "English",
volume = "18",
journal = "P L o S Genetics",
issn = "1553-7390",
publisher = "Public Library of Science",
number = "3",

}

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