Research Progress on Principal Component Analysis in Animal Science
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.
Principal component analysisPopulation stratificationDimensionality reductionFalse positiveGWAS
国家科技期刊平台
NSTL
万方数据
维普
