Identifying systematic heterogeneity patterns in genetic association meta-analysis studies

Progress in mapping loci associated with common complex diseases or quantitative inherited traits has been expedited by large-scale meta-analyses combining information across multiple studies, assembled through collaborative networks of researchers. Participating studies will usually have been independently designed and implemented in unique settings that are potential sources of phenotype, ancestry or other variability that could introduce between-study heterogeneity into a meta-analysis. Heterogeneity tests based on individual genetic variants (e.g. Q, I2) are not suited to identifying locus-specific from more systematic multi-locus or genome-wide patterns of heterogeneity. We have developed and evaluated an aggregate heterogeneity M statistic that combines between-study heterogeneity information across multiple genetic variants, to reveal systematic patterns of heterogeneity that elude conventional single variant analysis. Application to a GWAS meta-analysis of coronary disease with 48 contributing studies uncovered substantial systematic between-study heterogeneity, which could be partly explained by age-of-disease onset, family-history of disease and ancestry. Future meta-analyses of diseases and traits with multiple known genetic associations can use this approach to identify outlier studies and thereby optimize power to detect novel genetic associations.借助研究者协作网络整合多项研究数据构建的大规模荟萃分析，极大加快了常见复杂疾病或数量性状遗传位点的关联定位研究进程。参与荟萃分析的各项研究通常为独立设计并在独特场景下实施，这些场景可能成为表型、祖先背景或其他变量差异的来源，进而为荟萃分析引入研究间异质性。基于单个遗传变异的异质性检验（如Q统计量、I²统计量）无法有效区分位点特异性异质性与更具系统性的多位点或全基因组异质性模式。本研究开发并评估了一种聚合异质性M统计量，该统计量整合多个遗传变异的研究间异质性信息，可揭示常规单变异分析难以捕捉的系统性异质性模式。将该方法应用于一项纳入48项研究的冠心病全基因组关联研究（Genome-Wide Association Study, GWAS）荟萃分析后，发现存在显著的系统性研究间异质性，该异质性可部分由疾病发病年龄、疾病家族史及祖先背景解释。未来针对存在多个已知遗传关联的疾病与性状开展的荟萃分析，可采用该方法识别离群研究，从而提升检测新型遗传关联的统计效力。