基于IMPUTE2的全基因组关联性研究的基因型填补

多数全基因组关联性研究(GWAS)采用不同的分型芯片,导致遗传变异位点的数目及选择准则不同。基因型填补可以依据已有的基因分型数据,对未分型的位点进行填补。在应用IMPUTE2软件对基因型和表型数据库(db Ga P)中胃癌GWAS数据进行全基因组填补,以详细介绍全基因组填补的原理和过程。以第九号染色体为例,使用1000 Genome Project模板介绍全基因组填补的过程,包括填补前的质量控制、Pre-phasing、填补过程、填补的质量评估及填补后的关联性分析。第九号染色体在填补前有21 033个位点;而在填补后有1 630 406个SNP;其中INFO0.3的SNP位点有817 494个;而填补质量较高(INFO0.5)的位点数目有584 755个。IMPUTE2软件可以快速准确的对未分型的基因型进行填补,从而可以将多个GWAS数据整合到相同的位点数和密度上,再进行联合分析可以提高检验的把握度以便发现新的遗传易感性位点。

Imputation of Genome-wide association study using IMPUTE2

Multiple genome-wide association studies (GWASs) may apply different platforms which contain distinct sets of single-nucleotide polymorphism (SNPs) using diverse criterions. Imputation methods are developed to infer SNP genotypes by linkage disequilibrium (LD) with typed SNPs based on a reference panel. This study aims to introduce the process of imputation for GWAS data of gastric cancer deposited in the database of Genotypes and Phenotypes ( dbGaP ) used by IMPUTE2 software. Taking chromosome 9 as example, using the 1000 Genomes Project data (version 3, March 2012 release) as reference dataset, we described the process of whole genome-wide association imputation, including quality control before imputation, pre-phasing, imputation process, quality assessment and association analysis after imputation. There are 21,033 SNPs in chromosome nine before imputation, and after imputation the number of SNPs increased to 1,630,406, which include 817,494 SNPs with INFO > 0.3, while the number of higher quality SNPs (INFO > 0.5) is 584,755. IMPUTE2, a highly fast and accurate software, contributes to impute SNPs which have not genotyped in the original GWAS study. Combining results of multiple GWASs in a combined analysis is useful to have more power to identify new susceptibility loci.