Resolving the molecular processes that mediate genetic risk remains a challenge because most disease-associated variants are non-coding and functional characterization of these signals requires knowledge of the specific tissues and cell-types in which they operate. To address this challenge, we developed a framework for integrating tissue-specific gene expression and epigenomic maps to obtain “tissue-of-action” (TOA) scores for each association signal by systematically partitioning posterior probabilities from Bayesian fine-mapping. We applied this scheme to credible set variants for 380 association signals from a recent GWAS meta-analysis of type 2 diabetes (T2D) in Europeans. The resulting tissue profiles underscored a predominant role for pancreatic islets and, to a lesser extent, adipose and liver, particularly among signals with greater fine-mapping resolution. We incorporated resulting TOA scores into a rule-based classifier and validated the tissue assignments through comparison with data from cis-eQTL enrichment, functional fine-mapping, RNA co-expression, and patterns of physiological association. In addition to implicating signals with a single TOA, we found evidence for signals with shared effects in multiple tissues as well as distinct tissue profiles between independent signals within heterogeneous loci. Lastly, we demonstrated that TOA scores can be directly coupled with eQTL colocalization to further resolve effector transcripts at T2D signals. This framework guides mechanistic inference by directing functional validation studies to the most relevant tissues and can gain power as fine-mapping resolution and cell-specific annotations become richer. This method is generalizable to all complex traits with relevant annotation data and is made available as an R package.

解决介导遗传风险的分子过程仍然是一个挑战，因为大多数与疾病相关的变异是非编码的，并且这些信号的功能表征需要了解它们在其中运作的特定组织和细胞类型。为了应对这一挑战，我们开发了一个框架，用于整合组织特异性基因表达和表观基因组图谱，通过系统地划分贝叶斯精细映射的后验概率，获得每个关联信号的“作用组织”(TOA) 分数。我们将此方案应用于最近一项针对欧洲人 2 型糖尿病 (T2D) 的 GWAS 荟萃分析的 380 个关联信号的可信集变体。由此产生的组织概况强调了胰岛的主要作用，并且在较小程度上强调了脂肪和肝脏，特别是在具有更高精细映射分辨率的信号中。我们将生成的 TOA 分数纳入基于规则的分类器中，并通过与来自的数据进行比较来验证组织分配顺式-eQTL 富集、功能精细定位、RNA 共表达和生理关联模式。除了将信号与单个 TOA 联系起来之外，我们还发现了信号在多个组织中具有共同影响的证据，以及异质基因座内独立信号之间的不同组织概况。最后，我们证明了 TOA 分数可以直接与 eQTL 共定位相结合，以进一步解析 T2D 信号中的效应转录本。该框架通过将功能验证研究定向到最相关的组织来指导机制推断，并且随着精细映射分辨率和细胞特异性注释变得更加丰富，它可以获得强大的功能。此方法可推广到具有相关注释数据的所有复杂特征，并作为 R 包提供。