Transcriptome-wide association studies and colocalization analysis are popular computational approaches for integrating genetic-association data from molecular and complex traits. They show the unique ability to go beyond variant-level genetic-association evidence and implicate critical functional units, e.g., genes, in disease etiology. However, in practice, when the two approaches are applied to the same molecular and complex-trait data, the inference results can be markedly different. This paper systematically investigates the inferential reproducibility between the two approaches through theoretical derivation, numerical experiments, and analyses of four complex trait GWAS and GTEx eQTL data. We identify two classes of inconsistent inference results. We find that the first class of inconsistent results (i.e., genes with strong colocalization but weak transcriptome-wide association study [TWAS] signals) might suggest an interesting biological phenomenon, i.e., horizontal pleiotropy; thus, the two approaches are truly complementary. The inconsistency in the second class (i.e., genes with weak colocalization but strong TWAS signals) can be understood and effectively reconciled. To this end, we propose a computational approach for locus-level colocalization analysis. We demonstrate that the joint TWAS and locus-level colocalization analysis improves specificity and sensitivity for implicating biologically relevant genes.

转录组范围内的关联研究和共定位分析是整合来自分子和复杂性状的遗传关联数据的流行计算方法。它们显示出超越变异水平遗传关联证据的独特能力，并将关键功能单元（例如基因）牵涉到疾病病因学中。然而，在实践中，当这两种方法应用于相同的分子和复杂性状数据时，推断结果可能会明显不同。本文通过理论推导、数值实验和四种复杂性状 GWAS 和 GTEx eQTL 数据的分析，系统地研究了两种方法之间的推理再现性。我们确定了两类不一致的推理结果。我们发现第一类不一致的结果（即 具有强共定位但弱转录组关联研究 [TWAS] 信号的基因）可能表明一种有趣的生物学现象，即水平多效性；因此，这两种方法是真正互补的。第二类中的不一致（即共定位较弱但 TWAS 信号较强的基因）可以理解并有效调和。为此，我们提出了一种用于基因座级共定位分析的计算方法。我们证明联合 TWAS 和基因座水平共定位分析提高了涉及生物学相关基因的特异性和敏感性。具有弱共定位但强 TWAS 信号的基因）可以被理解并有效地协调。为此，我们提出了一种用于基因座级共定位分析的计算方法。我们证明联合 TWAS 和基因座水平共定位分析提高了涉及生物学相关基因的特异性和敏感性。具有弱共定位但强 TWAS 信号的基因）可以被理解并有效地协调。为此，我们提出了一种用于基因座级共定位分析的计算方法。我们证明联合 TWAS 和基因座水平共定位分析提高了涉及生物学相关基因的特异性和敏感性。