Approaches to detect genetic effects that differ between two strata in genome-wide meta-analyses: Recommendations based on a systematic evaluation

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.由两个分层分别开展的全基因组关联荟萃分析（Genome-wide association meta-analyses, GWAMAs）已证实不同分层间的遗传效应存在差异，例如体脂分布的性别差异。然而，目前针对此类差异的识别存在多种分析策略，且缺乏公认的最优方法选择依据。本研究假设已获取分层全基因组关联荟萃分析结果，对多种识别不同分层间遗传效应差异的分析策略展开对比。我们通过模拟实验与解析对比，针对不同分层设计场景以及不同类型的分层间差异场景，评估了各类方法的一类错误控制情况与检验效能。针对样本量相等的分层，研究发现：未经过滤的全基因组差异检验，是检测方向相反的分层特异性遗传效应的最优策略；而先进行总体关联过滤、再开展差异检验的方法，则最适合识别仅在某一分层中占主导的遗传效应。若未预先设定差异类型的研究假设，则可推荐联合使用上述两种策略。部分方法若在同一数据集内开展分析，则会破坏一类错误的控制水平。针对样本量不等的分层，最优策略取决于遗传效应在较大分层还是较小分层中占主导地位。基于GIANT联盟（涵盖超过175000名受试者）的真实数据，我们举例说明了不同分析策略对体脂分布性别差异检测结果的影响（最多可识别10个基因座）。本研究提出的建议可为未来旨在识别分层间遗传效应差异的全基因组关联荟萃分析提供切实可行的指导规范。对这类遗传效应的更深入理解，将有助于精准定位其背后的分子机制。