Complex traits and diseases can be influenced by both genetics and environment. However, given the large number of environmental stimuli and power challenges for gene-by-environment testing, it remains a critical challenge to identify and prioritize specific disease-relevant environmental exposures. We propose a framework for leveraging signals from transcriptional responses to environmental perturbations to identify disease-relevant perturbations that can modulate genetic risk for complex traits and inform the functions of genetic variants associated with complex traits. We perturbed human skeletal-muscle-, fat-, and liver-relevant cell lines with 21 perturbations affecting insulin resistance, glucose homeostasis, and metabolic regulation in humans and identified thousands of environmentally responsive genes. By combining these data with GWASs from 31 distinct polygenic traits, we show that the heritability of multiple traits is enriched in regions surrounding genes responsive to specific perturbations and, further, that environmentally responsive genes are enriched for associations with specific diseases and phenotypes from the GWAS Catalog. Overall, we demonstrate the advantages of large-scale characterization of transcriptional changes in diversely stimulated and pathologically relevant cells to identify disease-relevant perturbations.

复杂的性状和疾病会受到遗传和环境的影响。然而，考虑到逐个环境检测的大量环境刺激和功率挑战，识别和优先考虑与疾病相关的特定环境暴露仍然是一项重大挑战。我们提出了一个框架，利用转录反应对环境扰动的信号来识别与疾病相关的扰动，这些扰动可以调节复杂性状的遗传风险，并告知与复杂性状相关的遗传变异的功能。我们用 21 种影响人类胰岛素抵抗、葡萄糖稳态和代谢调节的扰动扰乱了人类骨骼肌、脂肪和肝脏相关细胞系，并确定了数千个环境响应基因。通过将这些数据与来自 31 个不同多基因性状的 GWAS 相结合，我们表明多个性状的遗传力在响应特定扰动的基因周围的区域中得到了丰富，此外，环境响应性基因因与来自 GWAS 的特定疾病和表型的关联而得到了丰富目录。总的来说，我们展示了大规模表征不同刺激和病理相关细胞中转录变化的优势，以识别与疾病相关的扰动。