生物
人口分层
遗传力
遗传关联
回归
全基因组关联研究
机器学习
推论
计算生物学
遗传建筑学
多效性
人口
遗传模型
遗传学
数量性状位点
人工智能
单核苷酸多态性
计算机科学
进化生物学
统计
基因
表型
基因型
社会学
人口学
数学
标识
DOI:10.1016/j.tig.2021.06.004
摘要
Accurate genetic prediction of complex traits can facilitate disease screening, improve early intervention, and aid in the development of personalized medicine. Genetic prediction of complex traits requires the development of statistical methods that can properly model polygenic architecture and construct a polygenic score (PGS). We present a comprehensive review of 46 methods for PGS construction. We connect the majority of these methods through a multiple linear regression framework which can be instrumental for understanding their prediction performance for traits with distinct genetic architectures. We discuss the practical considerations of PGS analysis as well as challenges and future directions of PGS method development. We hope our review serves as a useful reference both for statistical geneticists who develop PGS methods and for data analysts who perform PGS analysis.
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