Winkler Thomas W, Justice Anne E, Cupples L Adrienne, Kronenberg Florian, Kutalik Zoltán, Heid Iris M
Department of Genetic Epidemiology, University of Regensburg, Regensburg, Germany.
Department of Epidemiology, University of North Carolina, Chapel Hill, NC, United States of America.
PLoS One. 2017 Jul 27;12(7):e0181038. doi: 10.1371/journal.pone.0181038. eCollection 2017.
Genome-wide association meta-analyses (GWAMAs) conducted separately by two strata have identified differences in genetic effects between strata, such as sex-differences for body fat distribution. However, there are several approaches to identify such differences and an uncertainty which approach to use. Assuming the availability of stratified GWAMA results, we compare various approaches to identify between-strata differences in genetic effects. We evaluate type I error and power via simulations and analytical comparisons for different scenarios of strata designs and for different types of between-strata differences. For strata of equal size, we find that the genome-wide test for difference without any filtering is the best approach to detect stratum-specific genetic effects with opposite directions, while filtering for overall association followed by the difference test is best to identify effects that are predominant in one stratum. When there is no a priori hypothesis on the type of difference, a combination of both approaches can be recommended. Some approaches violate type I error control when conducted in the same data set. For strata of unequal size, the best approach depends on whether the genetic effect is predominant in the larger or in the smaller stratum. Based on real data from GIANT (>175 000 individuals), we exemplify the impact of the approaches on the detection of sex-differences for body fat distribution (identifying up to 10 loci). Our recommendations provide tangible guidelines for future GWAMAs that aim at identifying between-strata differences. A better understanding of such effects will help pinpoint the underlying mechanisms.
由两个分层分别进行的全基因组关联荟萃分析（GWAMA）已经确定了各分层之间的基因效应差异，比如体脂分布的性别差异。然而，有几种方法可用于识别此类差异，且在使用哪种方法上存在不确定性。假设可获得分层的GWAMA结果，我们比较了各种用于识别基因效应分层间差异的方法。我们通过模拟以及针对不同分层设计方案和不同类型分层间差异的分析比较，评估了I型错误和检验效能。对于大小相等的分层，我们发现不进行任何筛选的全基因组差异检验是检测具有相反方向的分层特异性基因效应的最佳方法，而先进行总体关联筛选再进行差异检验则最适合识别在某一层中占主导的效应。当对差异类型没有先验假设时，可推荐两种方法结合使用。在同一数据集中进行某些方法时会违反I型错误控制。对于大小不等的分层，最佳方法取决于基因效应在较大分层还是较小分层中占主导。基于GIANT（超过17.5万人）的真实数据，我们举例说明了这些方法对体脂分布性别差异检测的影响（识别出多达10个基因座）。我们的建议为未来旨在识别分层间差异的GWAMA提供了切实可行的指导方针。对这类效应有更深入的了解将有助于查明潜在机制。
