基因水平的关联分析方法

全基因组关联研究(Genome wide association study, GWAS)已经在国内外的医学遗传学研究中得到广泛应用, 但是GWAS数据中所蕴含的与多基因复杂性状疾病机制相关的丰富信息尚未得到深度挖掘。近年来, 研究者采用生物网络分析和生物通路分析等生物信息学和生物统计学手段分析GWAS数据, 并探索潜在的疾病机制。生物网络分析和生物通路分析主要是以基因为单位进行的, 因此必须在分析前将基因上全部或者部分单个单核苷酸多态性(Single nucleotide polymorphism, SNP)的遗传关联结果综合起来, 即基因水平的关联分析。基因水平的关联分析需要考虑单个SNP的遗传关联、基因上SNP数量和SNP之间的连锁不平衡结构等多种因素, 因此不仅在遗传学的概念上也在统计方法方面具有一定的复杂性和挑战性。文章对基因水平的关联分析的研究进展、原理和应用进行了综述。

Advances on gene-based association analysis

Genome-wide association studies (GWAS) have been widely used for hunting the susceptibility genes for common diseases in the past years; however, the abundant information for the disease mechanism based on the GWAS data has not been fully mined. Recently, some researchers focused on the biological network and pathway analysis for the GWAS data to explore the potential disease mechanism. Since genes are the basic units for the biological network and pathway, the genetic effects from all or part of single nucleotide polymorphisms (SNPs) in the genes should be integrated into genetic scores, which are so-called “gene-based association analysis”. Gene-based association analysis takes into account some important factors such as genetic effects of the SNPs, the number of the SNPs in the genes and the linkage disequilibrium structure of the SNPs. In this review, we will focus on the progress, principle and application of gene-based association analysis.