Genes act in concert with each other in specific contexts to perform their functions. Determining how these genes influence complex traits requires a mechanistic understanding of expression regulation across different conditions. It has been shown that this insight is critical for developing new therapies. Transcriptome-wide association studies have helped uncover the role of individual genes in disease-relevant mechanisms. However, modern models of the architecture of complex traits predict that gene-gene interactions play a crucial role in disease origin and progression. Here we introduce PhenoPLIER, a computational approach that maps gene-trait associations and pharmacological perturbation data into a common latent representation for a joint analysis. This representation is based on modules of genes with similar expression patterns across the same conditions. We observe that diseases are significantly associated with gene modules expressed in relevant cell types, and our approach is accurate in predicting known drug-disease pairs and inferring mechanisms of action. Furthermore, using a CRISPR screen to analyze lipid regulation, we find that functionally important players lack associations but are prioritized in trait-associated modules by PhenoPLIER. By incorporating groups of co-expressed genes, PhenoPLIER can contextualize genetic associations and reveal potential targets missed by single-gene strategies.

基因在特定环境下相互协调作用以发挥其功能。确定这些基因如何影响复杂性状需要对不同条件下的表达调控有机械的理解。事实证明，这种见解对于开发新疗法至关重要。全转录组关联研究有助于揭示单个基因在疾病相关机制中的作用。然而，复杂性状结构的现代模型预测基因间相互作用在疾病的起源和进展中发挥着至关重要的作用。在这里，我们介绍 PhenoPLIER，一种计算方法，可将基因性状关联和药理学扰动数据映射到联合分析的通用潜在表示中。这种表示基于在相同条件下具有相似表达模式的基因模块。我们观察到疾病与相关细胞类型中表达的基因模块显着相关，并且我们的方法在预测已知药物-疾病对和推断作用机制方面是准确的。此外，使用 CRISPR 筛选来分析脂质调节，我们发现功能上重要的参与者缺乏关联，但在 PhenoPLIER 的性状相关模块中被优先考虑。通过整合共表达基因组，PhenoPLIER 可以将遗传关联置于背景中，并揭示单基因策略错过的潜在目标。