Efficient cross-trait penalized regression increases prediction accuracy in large cohorts using secondary phenotypes

Wonil Chung, Jun Chen, Constance Turman, Sara Lindstrom, Zhaozhong Zhu, Po-Ru Loh, Peter Kraft and Liming Liang ()
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Wonil Chung: Harvard T.H. Chan School of Public Health
Jun Chen: Mayo Clinic
Constance Turman: Harvard T.H. Chan School of Public Health
Sara Lindstrom: University of Washington
Zhaozhong Zhu: Harvard T.H. Chan School of Public Health
Po-Ru Loh: Harvard T.H. Chan School of Public Health
Peter Kraft: Harvard T.H. Chan School of Public Health
Liming Liang: Harvard T.H. Chan School of Public Health

Nature Communications, 2019, vol. 10, issue 1, 1-11

Abstract: Abstract 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 R2 = 35.8% (MTAG) to 42.5% (MCP + CTPR) or 42.8% (Lasso + CTPR) with UK Biobank data.

Date: 2019
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DOI: 10.1038/s41467-019-08535-0

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