Wen Xiaoquan, Stephens Matthew
Department of Biostatistics, University of Michigan, 1415 Washington Heights, Ann Arbor, MI 48109, USA.
Department of Statistics and Department of Human Genetics, University of Chicago, 5734 S. University Avenue, Chicago, IL 60637, USA.
Ann Appl Stat. 2014;8(1):176-203. doi: 10.1214/13-AOAS695.
Genetic association analyses often involve data from multiple potentially-heterogeneous subgroups. The expected amount of heterogeneity can vary from modest (e.g. a typical meta-analysis), to large (e.g. a strong gene-environment interaction). However, existing statistical tools are limited in their ability to address such heterogeneity. Indeed, most genetic association meta-analyses use a "fixed effects" analysis, which assumes no heterogeneity. Here we develop and apply Bayesian association methods to address this problem. These methods are easy to apply (in the simplest case, requiring only a point estimate for the genetic effect, and its standard error, from each subgroup), and effectively include standard frequentist meta-analysis methods, including the usual "fixed effects" analysis, as special cases. We apply these tools to two large genetic association studies: one a meta-analysis of genome-wide association studies from the Global Lipids consortium, and the second a cross-population analysis for expression quantitative trait loci (eQTLs). In the Global Lipids data we find, perhaps surprisingly, that effects are generally quite homogeneous across studies. In the eQTL study we find that eQTLs are generally shared among different continental groups, and discuss consequences of this for study design.
基因关联分析通常涉及来自多个潜在异质性亚组的数据。预期的异质性程度可能从适度（例如典型的荟萃分析）到较大（例如强烈的基因-环境相互作用）不等。然而，现有的统计工具处理此类异质性的能力有限。实际上，大多数基因关联荟萃分析使用“固定效应”分析，该分析假定不存在异质性。在此，我们开发并应用贝叶斯关联方法来解决这一问题。这些方法易于应用（在最简单的情况下，每个亚组仅需遗传效应的点估计及其标准误差），并且有效地将标准的频率主义荟萃分析方法（包括通常的“固定效应”分析）作为特殊情况包含在内。我们将这些工具应用于两项大型基因关联研究：一项是对全球脂质联盟全基因组关联研究的荟萃分析，另一项是对表达数量性状位点（eQTL）的跨群体分析。在全球脂质数据中，我们发现，也许令人惊讶的是，各项研究之间的效应总体上相当一致。在eQTL研究中，我们发现eQTL通常在不同大陆群体之间共享，并讨论了这对研究设计的影响。
