中国畜牧杂志2017，Vol.53Issue(11)：21-24,4.DOI:10.19556/j.0258-7033.2017-11-021
主成分分析在动物科学的应用研究进展
Research Progress on Principal Component Analysis in Animal Science
摘要
Abstract
Principal component analysis (PCA) takes the idea of dimensionality reduction and also maintains the characteristics of the largest contribution data to the difference.In livestock production,PCA is used to study variables of traits and expected to simplify the number of variables as well as obtain sufficient information to reduce the complexity of research.In genome-wide association analysis (GWAS),PCA can be used to correct population stratification and reduce the false positive results of population stratification for association results.The PCA diagram can be shown whether the study population is stratified.In this paper,the principle of PCA,analysis software and its application in livestock production and GWAS are reviewed.关键词
主成分分析/群体分层/降维/假阳性/GWASKey words
Principal component analysis/Population stratification/Dimensionality reduction/False positive/GWAS分类
农业科技引用本文复制引用
宋志芳,解佑志,芦春莲,李赛,曹洪战..主成分分析在动物科学的应用研究进展[J].中国畜牧杂志,2017,53(11):21-24,4.基金项目
河北省科技计划项目(15226301D) （15226301D）
