School of Public Health and Tropical Medicine, Tulane University, New Orleans, Louisiana, United States of America.
PLoS One. 2012;7(4):e34486. doi: 10.1371/journal.pone.0034486. Epub 2012 Apr 5.
Genotype imputation is often used in the meta-analysis of genome-wide association studies (GWAS), for combining data from different studies and/or genotyping platforms, in order to improve the ability for detecting disease variants with small to moderate effects. However, how genotype imputation affects the performance of the meta-analysis of GWAS is largely unknown. In this study, we investigated the effects of genotype imputation on the performance of meta-analysis through simulations based on empirical data from the Framingham Heart Study. We found that when fix-effects models were used, considerable between-study heterogeneity was detected when causal variants were typed in only some but not all individual studies, resulting in up to ∼25% reduction of detection power. For certain situations, the power of the meta-analysis can be even less than that of individual studies. Additional analyses showed that the detection power was slightly improved when between-study heterogeneity was partially controlled through the random-effects model, relative to that of the fixed-effects model. Our study may aid in the planning, data analysis, and interpretation of GWAS meta-analysis results when genotype imputation is necessary.
基因型推断常用于全基因组关联研究（GWAS）的荟萃分析，以合并来自不同研究和/或基因分型平台的数据，从而提高检测具有小至中等效应的疾病变异的能力。然而，基因型推断如何影响 GWAS 荟萃分析的性能在很大程度上是未知的。在这项研究中，我们通过基于弗雷明汉心脏研究的经验数据进行模拟，研究了基因型推断对荟萃分析性能的影响。我们发现，当使用固定效应模型时，当因果变异仅在部分而非所有个体研究中进行分型时，会检测到相当大的研究间异质性，导致检测能力最多降低约 25%。在某些情况下，荟萃分析的功效甚至可能低于个别研究。进一步的分析表明，与固定效应模型相比，通过随机效应模型部分控制研究间异质性，检测功效略有提高。当基因型推断是必要时，我们的研究可能有助于 GWAS 荟萃分析结果的规划、数据分析和解释。
