Efficient cross-trait penalized regression increases prediction accuracy in large cohorts using secondary phenotypes
DC Field | Value | Language |
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dc.contributor.author | Chung, Wonil | - |
dc.contributor.author | Chen, Jun | - |
dc.contributor.author | Turman, Constance | - |
dc.contributor.author | Lindstrom, Sara | - |
dc.contributor.author | Zhu, Zhaozhong | - |
dc.contributor.author | Loh, Po-Ru | - |
dc.contributor.author | Kraft, Peter | - |
dc.contributor.author | Liang, Liming | - |
dc.date.available | 2020-09-14T08:12:29Z | - |
dc.date.created | 2020-06-25 | - |
dc.date.issued | 2019-02 | - |
dc.identifier.issn | 2041-1723 | - |
dc.identifier.uri | http://scholarworks.bwise.kr/ssu/handle/2018.sw.ssu/39084 | - |
dc.description.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 (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. | - |
dc.language | 영어 | - |
dc.language.iso | en | - |
dc.publisher | NATURE PUBLISHING GROUP | - |
dc.relation.isPartOf | NATURE COMMUNICATIONS | - |
dc.title | Efficient cross-trait penalized regression increases prediction accuracy in large cohorts using secondary phenotypes | - |
dc.type | Article | - |
dc.identifier.doi | 10.1038/s41467-019-08535-0 | - |
dc.type.rims | ART | - |
dc.identifier.bibliographicCitation | NATURE COMMUNICATIONS, v.10 | - |
dc.description.journalClass | 1 | - |
dc.identifier.wosid | 000457582900013 | - |
dc.identifier.scopusid | 2-s2.0-85061057577 | - |
dc.citation.title | NATURE COMMUNICATIONS | - |
dc.citation.volume | 10 | - |
dc.contributor.affiliatedAuthor | Chung, Wonil | - |
dc.type.docType | Article | - |
dc.description.isOpenAccess | N | - |
dc.subject.keywordPlus | GENOME-WIDE ASSOCIATION | - |
dc.subject.keywordPlus | VARIABLE SELECTION | - |
dc.subject.keywordPlus | LINKAGE DISEQUILIBRIUM | - |
dc.subject.keywordPlus | GENOTYPE IMPUTATION | - |
dc.subject.keywordPlus | RISK PREDICTION | - |
dc.subject.keywordPlus | COMPLEX TRAITS | - |
dc.subject.keywordPlus | LASSO | - |
dc.subject.keywordPlus | LOCI | - |
dc.subject.keywordPlus | REGULARIZATION | - |
dc.subject.keywordPlus | ARCHITECTURE | - |
dc.relation.journalResearchArea | Science & Technology - Other Topics | - |
dc.relation.journalWebOfScienceCategory | Multidisciplinary Sciences | - |
dc.description.journalRegisteredClass | scie | - |
dc.description.journalRegisteredClass | scopus | - |
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