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


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Article Info

DATE

2019-02-04

AUTHORS

Wonil Chung, Jun Chen, Constance Turman, Sara Lindstrom, Zhaozhong Zhu, Po-Ru Loh, Peter Kraft, Liming Liang

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. More... »

PAGES

569

References to SciGraph publications

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    http://scigraph.springernature.com/pub.10.1038/s41467-019-08535-0

    DOI

    http://dx.doi.org/10.1038/s41467-019-08535-0

    DIMENSIONS

    https://app.dimensions.ai/details/publication/pub.1111910915

    PUBMED

    https://www.ncbi.nlm.nih.gov/pubmed/30718517


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