| Title: | Efficient cross-trait penalized regression increases prediction accuracy in large cohorts using secondary phenotypes |
| Journal: | Nature Communications |
| Published: | 4 Feb 2019 |
| Pubmed: | https://pubmed.ncbi.nlm.nih.gov/30718517/ |
| DOI: | https://doi.org/10.1038/s41467-019-08535-0 |
| URL: | https://www.nature.com/articles/s41467-019-08535-0.pdf |
Publication 7664
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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.8 Authors
- Wonil Chung
- Jun Chen
- Constance Turman
- Sara Lindstrom
- Zhaozhong Zhu
- Po-Ru Loh
- Peter Kraft
- Liming Liang
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