A Statistical Framework for Joint eQTL Analysis in Multiple Tissues

Mapping expression Quantitative Trait Loci (eQTLs) represents a powerful and widely adopted approach to identifying putative regulatory variants and linking them to specific genes. Up to now eQTL studies have been conducted in a relatively narrow range of tissues or cell types. However, understanding the biology of organismal phenotypes will involve understanding regulation in multiple tissues, and ongoing studies are collecting eQTL data in dozens of cell types. Here we present a statistical framework for powerfully detecting eQTLs in multiple tissues or cell types (or, more generally, multiple subgroups). The framework explicitly models the potential for each eQTL to be active in some tissues and inactive in others. By modeling the sharing of active eQTLs among tissues, this framework increases power to detect eQTLs that are present in more than one tissue compared with “tissue-by-tissue” analyses that examine each tissue separately. Conversely, by modeling the inactivity of eQTLs in some tissues, the framework allows the proportion of eQTLs shared across different tissues to be formally estimated as parameters of a model, addressing the difficulties of accounting for incomplete power when comparing overlaps of eQTLs identified by tissue-by-tissue analyses. Applying our framework to re-analyze data from transformed B cells, T cells, and fibroblasts, we find that it substantially increases power compared with tissue-by-tissue analysis, identifying 63% more genes with eQTLs (at FDR = 0.05). Further, the results suggest that, in contrast to previous analyses of the same data, the majority of eQTLs detectable in these data are shared among all three tissues.

表达数量性状位点（expression Quantitative Trait Loci, eQTLs）的定位是一种功能强大且被广泛采用的研究策略，用于识别潜在调控变异并将其与特定基因建立关联。迄今为止，eQTL研究的开展范围相对局限于少数组织或细胞类型。然而，要理解机体表型的生物学机制，需要厘清多种组织中的基因调控过程，而当前正在进行的研究正从数十种细胞类型中收集eQTL数据。本研究提出了一种统计学分析框架，可在多种组织或细胞类型（或更广义地说，多个亚组）中高效精准检测eQTLs。该框架显式建模了每个eQTL在部分组织中具备活性、在其余组织中呈失活状态的潜在可能性。通过建模组织间活性eQTL的共享模式，相较于"tissue-by-tissue"分析（即单独检测每个组织的eQTL）的策略，该框架可提升对跨多种组织存在的eQTL的统计检测功效。反之，通过建模eQTL在部分组织中的失活状态，该框架可将不同组织间共享的eQTL比例作为模型参数进行正式估算，解决了在比较"tissue-by-tissue"分析所识别的eQTL重叠情况时，因统计功效不足而难以校正的难题。我们将该框架应用于重新分析转化型B细胞、T细胞和成纤维细胞的数据集，结果显示，相较于"tissue-by-tissue"分析策略，该框架显著提升了统计检测功效，在假发现率（false discovery rate, FDR）=0.05的条件下，可多识别出63%的携带eQTL的基因。此外，研究结果表明，与此前对该数据集的分析结论相反，本研究中可检测到的大多数eQTL在三种细胞类型间均存在共享现象。