Developing functional insight into the causal molecular drivers of immunological disease is a critical challenge in genomic medicine. Here, we systematically apply Mendelian randomization (MR), genetic colocalization, immune-cell-type enrichment, and phenome-wide association methods to investigate the effects of genetically predicted gene expression on ten immune-associated diseases and four cancer outcomes. Using whole blood-derived estimates for regulatory variants from the eQTLGen consortium (n = 31,684), we constructed genetic risk scores for 10,104 genes. Applying the inverse-variance-weighted MR method transcriptome wide while accounting for linkage disequilibrium structure identified 664 unique genes with evidence of a genetically predicted effect on at least one disease outcome (p < 4.81 × 10⁻⁵). We next undertook genetic colocalization to investigate cell-type-specific effects at these loci by using gene expression data derived from 18 types of immune cells. This highlighted many cell-type-dependent effects, such as PRKCQ expression and asthma risk (posterior probability = 0.998), which was T cell specific. Phenome-wide analyses on 311 complex traits and endpoints allowed us to explore shared genetic architecture and prioritize key drivers of disease risk, such as CASP10, which provided evidence of an effect on seven cancer-related outcomes. Our atlas of results can be used to characterize known and novel loci in immune-associated disease and cancer susceptibility, both in terms of elucidating cell-type-dependent effects as well as dissecting shared disease pathways and pervasive pleiotropy. As an exemplar, we have highlighted several key findings in this study, although similar evaluations can be conducted via our interactive web platform.

对免疫疾病的分子驱动因素进行功能性洞察是基因组医学的一个关键挑战。在这里，我们系统地应用孟德尔随机化（MR）、遗传共定位、免疫细胞类型富集和全表型关联方法来研究遗传预测的基因表达对十种免疫相关疾病和四种癌症结果的影响。使用来自 eQTLGen 联盟 (n = 31,684) 的调节变异的全血估计值，我们构建了 10,104 个基因的遗传风险评分。在转录组范围内应用反方差加权 MR 方法，同时考虑连锁不平衡结构，确定了 664 个独特基因，并有证据表明对至少一种疾病结果具有遗传预测的影响 (p < 4.81 × 10 ^-5 )。接下来，我们使用来自 18 种免疫细胞的基因表达数据进行遗传共定位，以研究这些位点的细胞类型特异性效应。这凸显了许多细胞类型依赖性效应，例如PRKCQ表达和哮喘风险（后验概率 = 0.998），这是 T 细胞特异性的。对 311 个复杂性状和终点的表型范围分析使我们能够探索共享的遗传结构并优先考虑疾病风险的关键驱动因素，例如CASP10 ，它提供了对七种癌症相关结果影响的证据。我们的结果图谱可用于表征免疫相关疾病和癌症易感性中已知和新的位点，无论是在阐明细胞类型依赖性效应还是剖析共同的疾病途径和普遍的多效性方面。 作为一个例子，我们强调了这项研究中的几个关键发现，尽管可以通过我们的交互式网络平台进行类似的评估。