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

Information of genetic architectures of complex traits can be leveraged for predicting phenotypes. Here, the authors develop CTPR (Cross-Trait Penalized Regression), a method for multi-trait polygenic risk prediction using individual-level genotypes and/or summary statistics from large cohorts.

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Auteurs principaux: Wonil Chung, Jun Chen, Constance Turman, Sara Lindstrom, Zhaozhong Zhu, Po-Ru Loh, Peter Kraft, Liming Liang
Format: article
Langue:EN
Publié: Nature Portfolio 2019
Sujets:
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Accès en ligne:https://doaj.org/article/bff51525773547649c9ec281d7ff076f
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