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DOI | 10.1038/s41467-017-02773-w |
Non-parametric genetic prediction of complex traits with latent Dirichlet process regression models | |
Zeng, Ping1,2; Zhou, Xiang2,3 | |
2017-09-06 | |
发表期刊 | NATURE COMMUNICATIONS |
ISSN | 2041-1723 |
出版年 | 2017 |
卷号 | 8 |
文章类型 | Article |
语种 | 英语 |
国家 | Peoples R China; USA |
英文摘要 | Using genotype data to perform accurate genetic prediction of complex traits can facilitate genomic selection in animal and plant breeding programs, and can aid in the development of personalized medicine in humans. Because most complex traits have a polygenic architecture, accurate genetic prediction often requires modeling all genetic variants together via polygenic methods. Here, we develop such a polygenic method, which we refer to as the latent Dirichlet process regression model. Dirichlet process regression is non-parametric in nature, relies on the Dirichlet process to flexibly and adaptively model the effect size distribution, and thus enjoys robust prediction performance across a broad spectrum of genetic architectures. We compare Dirichlet process regression with several commonly used prediction methods with simulations. We further apply Dirichlet process regression to predict gene expressions, to conduct PrediXcan based gene set test, to perform genomic selection of four traits in two species, and to predict eight complex traits in a human cohort. |
领域 | 资源环境 |
收录类别 | SCI-E |
WOS记录号 | WOS:000409458000010 |
WOS关键词 | GENOME-WIDE ASSOCIATION ; BAYESIAN VARIABLE SELECTION ; RISK PREDICTION ; VARIATIONAL INFERENCE ; DAIRY-CATTLE ; HUMAN HEIGHT ; ACCURACY ; LOCI ; HERITABILITY ; ARCHITECTURE |
WOS类目 | Multidisciplinary Sciences |
WOS研究方向 | Science & Technology - Other Topics |
URL | 查看原文 |
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文献类型 | 期刊论文 |
条目标识符 | http://119.78.100.173/C666/handle/2XK7JSWQ/203646 |
专题 | 资源环境科学 |
作者单位 | 1.Xuzhou Med Univ, Dept Epidemiol & Biostat, Xuzhou 221004, Jiangsu, Peoples R China; 2.Univ Michigan, Dept Biostat, Ann Arbor, MI 48109 USA; 3.Univ Michigan, Ctr Stat Genet, Ann Arbor, MI 48109 USA |
推荐引用方式 GB/T 7714 | Zeng, Ping,Zhou, Xiang. Non-parametric genetic prediction of complex traits with latent Dirichlet process regression models[J]. NATURE COMMUNICATIONS,2017,8. |
APA | Zeng, Ping,&Zhou, Xiang.(2017).Non-parametric genetic prediction of complex traits with latent Dirichlet process regression models.NATURE COMMUNICATIONS,8. |
MLA | Zeng, Ping,et al."Non-parametric genetic prediction of complex traits with latent Dirichlet process regression models".NATURE COMMUNICATIONS 8(2017). |
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