While genome-wide association studies are increasingly successful in discovering genomic loci associated with complex human traits and disorders, the biological interpretation of these findings remains challenging. Here we developed the GSA-MiXeR analytical tool for gene set analysis (GSA), which fits a model for the heritability of individual genes, accounting for linkage disequilibrium across variants and allowing the quantification of partitioned heritability and fold enrichment for small gene sets. We validated the method using extensive simulations and sensitivity analyses. When applied to a diverse selection of complex traits and disorders, including schizophrenia, GSA-MiXeR prioritizes gene sets with greater biological specificity compared to standard GSA approaches, implicating voltage-gated calcium channel function and dopaminergic signaling for schizophrenia. Such biologically relevant gene sets, often with fewer than ten genes, are more likely to provide insights into the pathobiology of complex diseases and highlight potential drug targets.

尽管全基因组关联研究在发现与复杂人类特征和疾病相关的基因组位点方面越来越成功，但这些发现的生物学解释仍然具有挑战性。在这里，我们开发了用于基因集分析（GSA）的 GSA-MiXeR 分析工具，该工具适合单个基因的遗传力模型，解释变体之间的连锁不平衡，并允许量化小基因集的分区遗传力和倍数富集。我们使用广泛的模拟和敏感性分析验证了该方法。当应用于包括精神分裂症在内的多种复杂特征和疾病时，GSA-MiXeR 优先考虑与标准 GSA 方法相比具有更大生物学特异性的基因集，这暗示了精神分裂症的电压门控钙通道功能和多巴胺能信号传导。这种生物学相关的基因集通常少于十个基因，更有可能提供对复杂疾病病理学的见解并突出潜在的药物靶点。