Department of Medical and Molecular Genetics, King's College London, London, United Kingdom.
PLoS One. 2011;6(6):e20133. doi: 10.1371/journal.pone.0020133. Epub 2011 Jun 30.
Interpreting Genome-Wide Association Studies (GWAS) at a gene level is an important step towards understanding the molecular processes that lead to disease. In order to incorporate prior biological knowledge such as pathways and protein interactions in the analysis of GWAS data it is necessary to derive one measure of association for each gene. We compare three different methods to obtain gene-wide test statistics from Single Nucleotide Polymorphism (SNP) based association data: choosing the test statistic from the most significant SNP; the mean test statistics of all SNPs; and the mean of the top quartile of all test statistics. We demonstrate that the gene-wide test statistics can be controlled for the number of SNPs within each gene and show that all three methods perform considerably better than expected by chance at identifying genes with confirmed associations. By applying each method to GWAS data for Crohn's Disease and Type 1 Diabetes we identified new potential disease genes.
在基因水平上解释全基因组关联研究（GWAS）是理解导致疾病的分子过程的重要步骤。为了在 GWAS 数据分析中纳入先前的生物学知识（如途径和蛋白质相互作用），有必要为每个基因得出一个关联度量。我们比较了三种不同的方法，从基于单核苷酸多态性（SNP）的关联数据中获得基因范围的检验统计量：从最显著的 SNP 中选择检验统计量；所有 SNP 的平均检验统计量；以及所有检验统计量的四分位数的平均值。我们证明了基因范围的检验统计量可以控制每个基因中的 SNP 数量，并且表明所有三种方法在识别具有确认关联的基因方面的表现都明显优于随机预期。通过将每种方法应用于克罗恩病和 1 型糖尿病的 GWAS 数据，我们确定了新的潜在疾病基因。
