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DOI | 10.1038/s41467-017-01798-5 |
Efficient cross-trait penalized regression increases prediction accuracy in large cohorts using secondary phenotypes | |
Chung, Wonil1,2; Chen, Jun3,4; Turman, Constance1,2; Lindstrom, Sara5; Zhu, Zhaozhong1,2,6; Loh, Po-Ru1,2,7; Kraft, Peter1,2,8; Liang, Liming1,2,8 | |
2019-02-04 | |
发表期刊 | NATURE COMMUNICATIONS |
ISSN | 2041-1723 |
出版年 | 2019 |
卷号 | 10 |
文章类型 | Article |
语种 | 英语 |
国家 | USA |
英文摘要 | 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. |
领域 | 资源环境 |
收录类别 | SCI-E |
WOS记录号 | WOS:000457582900013 |
WOS关键词 | GENOME-WIDE ASSOCIATION ; VARIABLE SELECTION ; LINKAGE DISEQUILIBRIUM ; GENOTYPE IMPUTATION ; RISK PREDICTION ; COMPLEX TRAITS ; LASSO ; LOCI ; REGULARIZATION ; ARCHITECTURE |
WOS类目 | Multidisciplinary Sciences |
WOS研究方向 | Science & Technology - Other Topics |
URL | 查看原文 |
引用统计 | |
文献类型 | 期刊论文 |
条目标识符 | http://119.78.100.173/C666/handle/2XK7JSWQ/203542 |
专题 | 资源环境科学 |
作者单位 | 1.Harvard TH Chan Sch Publ Hlth, Program Genet Epidemiol & Stat Genet, Boston, MA 02115 USA; 2.Harvard TH Chan Sch Publ Hlth, Dept Epidemiol, Boston, MA 02115 USA; 3.Mayo Clin, Div Biomed Stat & Informat, Rochester, MN 55905 USA; 4.Mayo Clin, Ctr Individualized Med, Rochester, MN 55905 USA; 5.Univ Washington, Dept Epidemiol, Seattle, WA 98195 USA; 6.Harvard TH Chan Sch Publ Hlth, Dept Environm Hlth, Boston, MA 02115 USA; 7.Broad Inst Harvard & MIT, Program Med & Populat Genet, Cambridge, MA 02142 USA; 8.Harvard TH Chan Sch Publ Hlth, Dept Biostat, Boston, MA 02115 USA |
推荐引用方式 GB/T 7714 | Chung, Wonil,Chen, Jun,Turman, Constance,et al. Efficient cross-trait penalized regression increases prediction accuracy in large cohorts using secondary phenotypes[J]. NATURE COMMUNICATIONS,2019,10. |
APA | Chung, Wonil.,Chen, Jun.,Turman, Constance.,Lindstrom, Sara.,Zhu, Zhaozhong.,...&Liang, Liming.(2019).Efficient cross-trait penalized regression increases prediction accuracy in large cohorts using secondary phenotypes.NATURE COMMUNICATIONS,10. |
MLA | Chung, Wonil,et al."Efficient cross-trait penalized regression increases prediction accuracy in large cohorts using secondary phenotypes".NATURE COMMUNICATIONS 10(2019). |
条目包含的文件 | 条目无相关文件。 |
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