全基因组关联分析研究进展  

Research progress of genome-wide association study

作　　者：段忠取[1] 朱军[1] 

机构地区：[1]浙江大学农业与生物技术学院生物信息学研究所,杭州310058

出　　处：《浙江大学学报（农业与生命科学版）》2015年第4期385-393,共9页Journal of Zhejiang University:Agriculture and Life Sciences

基　　金：国家自然科学基金面上项目"作物育种群体基因定位及分子选择育种新方法的研究"(31371250)

摘　　要：全基因组关联分析(genome-wide association study,GWAS)是近年来兴起的遗传分析方法,在人类和动植物复杂性状遗传研究中已取得初步成果。本文论述了GWAS研究的基本原理、主要分析方法及常用软件,在人类和动植物复杂性状研究中的应用;分析了GWAS研究中"丢失遗传率"的主要影响因素;介绍了上位性分析的新策略和基于GPU并行计算和混合线性模型的分析软件QTXNetwork;展望了GWAS研究的发展方向。Summary With the advent of molecular marker techniques in the past two decades, genome-wide association study （GWAS） was proved to be an effective tool to reveal genetic architecture of complex traits in human, animal and plants. GWAS typically focuses on associations between genetic markers and quantitative traits in natural populations and takes advantage of recombination events in the evolutionary history. In human, more than 6 000 variant loci were discovered to associate with ~ 500 quantitative traits and complex diseases. In animals, GWAS was conducted specially on economically important traits, genetic defect diseases and other complex diseases of the main livestock and poultries. In plants, GWAS has been applied to study flowering time, developmental traits and agronomical traits of Arabidopsis, rice, maize and cotton. Despite the initial success of GWAS that has been achieved, the uncovered associated loci usually have small effects on phenotype and only account for very limited phenotypic variation. The remaining unexplained genetic variance is the so-called ＂missing heritability＂. Three possible factors were responsible for the failure of detecting the cause loci. First, the efficiency of detecting the small-effect loci is very low and more small-effect loci are undiscovered. Most GWASs proceed on the base of the assumption that common phenotypic variation is caused by common genetic variation. The power to detect the cause loci is a function of allele frequency, thus it is difficult to identify the functional variants at low frequency though they have larger effects on the phenotype. Second, GWAS was unable to deal with the phenotypic variances caused by structural variation （i. e. copy number variation）.Third, current GWASs pay little attention to the interactions among the genetic variances and ones between genetic and environmental factors, which have been affirmed by the results of linkage analysis. New strategies for GWAS were discussed. The package GMDR-GPU was developed to analyze

关 键 词：全基因组关联分析 复杂性状 单核苷酸多态性 关联位点 

分 类 号：Q811.4[生物学—生物工程]