4.8 Article

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

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NATURE COMMUNICATIONS
卷 10, 期 -, 页码 -

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NATURE PUBLISHING GROUP
DOI: 10.1038/s41467-019-08535-0

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资金

  1. National Institutes of Health [P01CA87969, P01CA055075, P01DK070756, U01HG004728, UM1CA186107, UM1CA176726, R01CA49449, R01CA50385, R01CA67262, R01CA131332, R01HL034594, R01HL088521, R01HL35464, R01HL116854, R01EY015473, R01EY022305, P30EY014104, R03DC013373, R03CA165131]
  2. [R01 GM105857-01A1]

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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 (similar to 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.

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