Efficient cross-trait penalized regression increases prediction accuracy in large cohorts using secondary phenotypes
oleh: Wonil Chung, Jun Chen, Constance Turman, Sara Lindstrom, Zhaozhong Zhu, Po-Ru Loh, Peter Kraft, Liming Liang
| Format: | Article |
|---|---|
| Diterbitkan: | Nature Portfolio 2019-02-01 |
Deskripsi
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.