家畜全基因组关联分析研究进展

全基因组关联分析（genome-wide association study，GWAS）是一种研究经济性状候选基因的分析方法。近年来，随着家畜全基因组测序的完成，大量的单核苷酸多态性（single nucleotide polymorphisms，SNPs）被标识，GWAS也越来越多地应用于家畜重要性状的研究领域中，在动物遗传育种中，通过对家畜基因组进行GWAS分析研究，找到控制家畜主要经济性状的重要SNPs，从而挖掘重要经济性状的候选基因。作者详细综述了GWAS的分析方法及其在重要家畜育种中的研究进展。GWAS分析方法包括基因组控制法（genommic control）、分层分析法（stratification analysis）、主成分分析法（principal components analysis，PAC）和混合线性模型分析法（mixed-linear-model association，MLMA），通路分析方法包括非核算法（基因功能富集分析（gene set enrichment analysis，GSEA）和分层贝叶斯优取（hierarchical Bayes prioritization，HBP））和核算法。依据不同的目标性状选择合理的分析方法，提高GWAS分析结果的准确性，为进一步利用GWAS分析各种性状的遗传基础提供合理的借鉴。

Research Progress on Genome-wide Association in Livestock

Genome-wide association study (GWAS) is an analytical method, has been used to study candidate of economical traits in animals. In recent years, with the completion of the whole genome sequencing for livestock, a large number of single nucleotide polymorphisms (SNPs) have been identified. GWAS is also more and more applied in the field of important traits for livestock. Finding important SNPs that control the important economic traits of livestock by GWAS analysis to unearth candidate genes for important economic traits in animal genetics and breeding. This paper reviews the GWAS analysis methods and it's research progress in important livestock breeding. Statistical analysis methods include genommic control, stratification analysis, principal components analysis (PAC) and mixed-linear-model association (MLMA). The methods of pathway analysis include non-nuclear methods (gene enrichment analysis (GSEA) and hierarchical Bayes prioritization (HBP)) and algorithms. This review will provide reference for further research on genetic background of the important traits of livestock by GWAS.