Scripts and data for the paper: gsQTL: Associating genetic risk variants with gene sets by exploiting their shared variability

Scripts and data used for the paper gsQTL: Associating genetic risk variants with gene sets by exploiting their shared variability.<br>To investigate the functional significance of genetic risk loci identified through genome-wide association studies (GWASs), genetic loci are linked to genes based on their capacity to account for variation in gene expression, resulting in expression quantitative trait loci (eQTL). Following this, gene set analyses are commonly used to gain insights into functionality. However, the efficacy of this approach is hampered by small effect sizes and the burden of multiple testing. We propose an alternative approach: instead of examining the cumulative associations of individual genes within a gene set, we consider the collective variation of the entire gene set. We introduce the concept of gene set QTL (gsQTL), and show it to be more adept at identifying links between genetic risk variants and specific gene sets. Notably, gsQTL experiences less susceptibility to inflation or deflation of significant enrichments compared with conventional methods. Furthermore, we demonstrate the broader applicability of shared variability within gene sets. This is evident in scenarios such as the coordinated regulation of genes by a transcription factor or coordinated differential expression.

用于论文《gsQTL：利用共享变异特性将遗传风险变异与基因集关联》的脚本与数据集。为探究通过全基因组关联研究（Genome-Wide Association Studies, GWAS）鉴定得到的遗传风险位点的功能意义，现有研究通常基于遗传位点解释基因表达变异的能力，将其与基因进行关联，由此得到表达数量性状位点（expression Quantitative Trait Locus, eQTL）。在此基础上，基因集分析常被用于解析相关生物学功能，但该方法的效能受限于较小的效应量与繁重的多重检验负担。我们提出一种替代分析方案：不再检验基因集中单个基因的累积关联，转而考量整个基因集的集体变异特性。我们引入基因集数量性状位点（gene set Quantitative Trait Locus, gsQTL）这一概念，并证明其在识别遗传风险变异与特定基因集之间的关联方面更具优势。值得注意的是，相较于传统方法，gsQTL受显著富集结果的通胀或通缩影响更小。此外，我们还验证了基因集内共享变异特性的更广泛应用场景，例如转录因子（transcription factor）对基因的协同调控以及协同差异表达（coordinated differential expression）等情形。