Dalapati Trisha, Wang Liuyang, Jones Angela G, Cardwell Jonathan, Konigsberg Iain R, Bossé Yohan, Sin Don D, Timens Wim, Hao Ke, Yang Ivana, Ko Dennis C
Medical Scientist Training Program, Duke University School of Medicine, Durham, NC, USA; Department of Molecular Genetics and Microbiology, Duke University School of Medicine, Durham, NC, USA.
Department of Molecular Genetics and Microbiology, Duke University School of Medicine, Durham, NC, USA.
HGG Adv. 2025 Apr 10;6(2):100410. doi: 10.1016/j.xhgg.2025.100410. Epub 2025 Jan 27.
Most genetic variants identified through genome-wide association studies (GWASs) are suspected to be regulatory in nature, but only a small fraction colocalize with expression quantitative trait loci (eQTLs, variants associated with expression of a gene). Therefore, it is hypothesized but largely untested that integration of disease GWAS with context-specific eQTLs will reveal the underlying genes driving disease associations. We used colocalization and transcriptomic analyses to identify shared genetic variants and likely causal genes associated with critically ill COVID-19 and idiopathic pulmonary fibrosis. We first identified five genome-wide significant variants associated with both diseases. Four of the variants did not demonstrate clear colocalization between GWAS and healthy lung eQTL signals. Instead, two of the four variants colocalized only in cell type- and disease-specific eQTL datasets. These analyses pointed to higher ATP11A expression from the C allele of rs12585036, in monocytes and in lung tissue from primarily smokers, which increased risk of idiopathic pulmonary fibrosis (IPF) and decreased risk of critically ill COVID-19. We also found lower DPP9 expression (and higher methylation at a specific CpG) from the G allele of rs12610495, acting in fibroblasts and in IPF lungs, and increased risk of IPF and critically ill COVID-19. We further found differential expression of the identified causal genes in diseased lungs when compared to non-diseased lungs, specifically in epithelial and immune cell types. These findings highlight the power of integrating GWASs, context-specific eQTLs, and transcriptomics of diseased tissue to harness human genetic variation to identify causal genes and where they function during multiple diseases.
通过全基因组关联研究（GWAS）鉴定出的大多数基因变异在本质上被怀疑具有调控作用，但只有一小部分与表达数量性状基因座（eQTL，即与基因表达相关的变异）共定位。因此，有人提出假说，但在很大程度上未经检验的是，将疾病GWAS与特定背景的eQTL整合将揭示驱动疾病关联的潜在基因。我们使用共定位和转录组分析来鉴定与重症COVID-19和特发性肺纤维化相关的共享基因变异和可能的因果基因。我们首先鉴定出与这两种疾病相关的五个全基因组显著变异。其中四个变异在GWAS和健康肺eQTL信号之间未显示出明显的共定位。相反，这四个变异中的两个仅在细胞类型和疾病特异性eQTL数据集中共定位。这些分析表明，在主要为吸烟者的单核细胞和肺组织中，rs12585036的C等位基因导致ATP11A表达升高，这增加了特发性肺纤维化（IPF）的风险并降低了重症COVID-19的风险。我们还发现，rs12610495的G等位基因在成纤维细胞和IPF肺中起作用，导致DPP9表达降低（以及特定CpG处的甲基化增加），并增加了IPF和重症COVID-19的风险。与非患病肺相比，我们进一步发现所鉴定的因果基因在患病肺中存在差异表达，特别是在上皮和免疫细胞类型中。这些发现突出了整合GWAS、特定背景的eQTL和患病组织转录组学以利用人类遗传变异来鉴定因果基因及其在多种疾病中的作用位点的能力。
