Pritzker School of Medicine, Growth & Development Training Program, University of Chicago, Chicago, IL, USA.
Department of Human Genetics, University of Chicago, Chicago, IL, USA.
Nat Genet. 2019 Jan;51(1):187-195. doi: 10.1038/s41588-018-0268-8. Epub 2018 Nov 26.
We introduce new statistical methods for analyzing genomic data sets that measure many effects in many conditions (for example, gene expression changes under many treatments). These new methods improve on existing methods by allowing for arbitrary correlations in effect sizes among conditions. This flexible approach increases power, improves effect estimates and allows for more quantitative assessments of effect-size heterogeneity compared to simple shared or condition-specific assessments. We illustrate these features through an analysis of locally acting variants associated with gene expression (cis expression quantitative trait loci (eQTLs)) in 44 human tissues. Our analysis identifies more eQTLs than existing approaches, consistent with improved power. We show that although genetic effects on expression are extensively shared among tissues, effect sizes can still vary greatly among tissues. Some shared eQTLs show stronger effects in subsets of biologically related tissues (for example, brain-related tissues), or in only one tissue (for example, testis). Our methods are widely applicable, computationally tractable for many conditions and available online.
我们介绍了新的统计方法，用于分析测量许多条件下许多效应的基因组数据集（例如，许多处理下的基因表达变化）。这些新方法通过允许条件之间的效应大小具有任意相关性，从而改进了现有方法。与简单的共享或特定于条件的评估相比，这种灵活的方法提高了功效，改善了效应估计，并允许对效应大小异质性进行更定量的评估。我们通过对 44 个人类组织中与基因表达（顺式表达数量性状基因座 (cis-eQTL)）相关的局部作用变体的分析说明了这些特征。我们的分析比现有方法识别出了更多的 eQTL，这与功效的提高是一致的。我们表明，尽管基因对表达的影响在组织中广泛共享，但效应大小在组织之间仍然可以有很大差异。一些共享的 eQTL 在生物学相关组织（例如，脑相关组织）的子集中或仅在一种组织（例如，睾丸）中表现出更强的效应。我们的方法具有广泛的适用性，对于许多条件具有可计算性，并且可以在线使用。
