In:
Nature Communications, Springer Science and Business Media LLC, Vol. 10, No. 1 ( 2019-02-04)
Kurzfassung:
We introduce cross-trait penalized regression (CTPR), a powerful and practical approach for multi-trait polygenic risk prediction in large cohorts. Specifically, we propose a novel cross-trait penalty function with the Lasso and the minimax concave penalty (MCP) to incorporate the shared genetic effects across multiple traits for large-sample GWAS data. Our approach extracts information from the secondary traits that is beneficial for predicting the primary trait based on individual-level genotypes and/or summary statistics. Our novel implementation of a parallel computing algorithm makes it feasible to apply our method to biobank-scale GWAS data. We illustrate our method using large-scale GWAS data (~1M SNPs) from the UK Biobank ( N = 456,837). We show that our multi-trait method outperforms the recently proposed multi-trait analysis of GWAS (MTAG) for predictive performance. The prediction accuracy for height by the aid of BMI improves from R 2 = 35.8% (MTAG) to 42.5% (MCP + CTPR) or 42.8% (Lasso + CTPR) with UK Biobank data.
Materialart:
Online-Ressource
ISSN:
2041-1723
DOI:
10.1038/s41467-019-08535-0
Sprache:
Englisch
Verlag:
Springer Science and Business Media LLC
Publikationsdatum:
2019
ZDB Id:
2553671-0
