Beecham Gary W, Martin Eden R, Gilbert John R, Haines Jonathan L, Pericak-Vance Margaret A
John P Hussman Institute for Human Genomics, University of Miami, Miller School of Medicine, Miami, Florida 33136, USA.
Ann Hum Genet. 2010 May;74(3):189-94. doi: 10.1111/j.1469-1809.2010.00573.x.
With the advent of publicly available genome-wide genotyping data, the use of genotype imputation methods is becoming increasingly common. These methods are of particular use in joint analyses, where data from different genotyping platforms are imputed to a reference set and combined in a single analysis. We show here that such an analysis can miss strong genetic association signals, such as that of the apolipoprotein-e gene in late-onset Alzheimer disease. This can occur in regions of weak to moderate LD; unobserved SNPs are not imputed with confidence so there is no consensus SNP set on which to perform association tests. Both IMPUTE and Mach software are tested, with similar results. Additionally, we show that a meta-analysis that properly accounts for the genotype uncertainty can recover association signals that were lost under a joint analysis. This shows that joint analyses of imputed genotypes, particularly failure to replicate strong signals, should be considered critically and examined on a case-by-case basis.
随着公开可用的全基因组基因分型数据的出现,基因型填充方法的使用越来越普遍。这些方法在联合分析中特别有用,在联合分析中,来自不同基因分型平台的数据被填充到一个参考集中,并在一次分析中进行合并。我们在此表明,这样的分析可能会遗漏强遗传关联信号,比如晚发性阿尔茨海默病中的载脂蛋白E基因的关联信号。这可能发生在连锁不平衡(LD)程度较弱到中等的区域;未观察到的单核苷酸多态性(SNP)无法可靠地填充,因此没有可用于进行关联测试的一致SNP集。对IMPUTE和Mach软件都进行了测试,结果相似。此外,我们表明,一个适当考虑基因型不确定性的荟萃分析能够找回在联合分析中丢失的关联信号。这表明,对填充基因型的联合分析,尤其是未能重复强信号的情况,应予以审慎考虑并逐案审查。