Division of Biostatistics, University of Minnesota, Minneapolis, Minnesota 55455.
Division of Biostatistics, University of Minnesota, Minneapolis, Minnesota 55455
Genetics. 2018 Jul;209(3):699-709. doi: 10.1534/genetics.118.300805. Epub 2018 May 4.
It remains challenging to boost statistical power of genome-wide association studies (GWASs) to identify more risk variants or loci that can account for "missing heritability." Furthermore, since most identified variants are not in gene-coding regions, a biological interpretation of their function is largely lacking. On the other hand, recent biotechnological advances have made it feasible to experimentally measure the three-dimensional organization of the genome, including enhancer-promoter interactions in high resolutions. Due to the well-known critical roles of enhancer-promoter interactions in regulating gene expression programs, such data have been applied to link GWAS risk variants to their putative target genes, gaining insights into underlying biological mechanisms. However, their direct use in GWAS association testing is yet to be exploited. Here we propose integrating enhancer-promoter interactions into GWAS association analysis to both boost statistical power and enhance interpretability. We demonstrate that through an application to two large-scale schizophrenia (SCZ) GWAS summary data sets, the proposed method could identify some novel SCZ-associated genes and pathways (containing no significant SNPs). For example, after the Bonferroni correction, for the larger SCZ data set with 36,989 cases and 113,075 controls, our method applied to the gene body and enhancer regions identified 27 novel genes and 11 novel KEGG pathways to be significant, all missed by the transcriptome-wide association study (TWAS) approach. We conclude that our proposed method is potentially useful and is complementary to TWAS and other standard gene- and pathway-based methods.
提高全基因组关联研究（GWAS）识别更多风险变异或基因座的统计能力以解释“遗传缺失”仍然具有挑战性。此外，由于大多数已鉴定的变异不在基因编码区域，因此它们的功能的生物学解释在很大程度上是缺乏的。另一方面，最近的生物技术进步使得以高分辨率实验测量基因组的三维结构，包括增强子-启动子相互作用成为可能。由于增强子-启动子相互作用在调节基因表达程序方面的作用是众所周知的，因此这些数据已被用于将 GWAS 风险变异与其假定的靶基因联系起来，从而深入了解潜在的生物学机制。然而，它们在 GWAS 关联测试中的直接应用尚未得到开发。在这里，我们提出将增强子-启动子相互作用整合到 GWAS 关联分析中，以提高统计能力和增强可解释性。我们证明，通过对两个大规模精神分裂症（SCZ）GWAS 汇总数据集的应用，该方法可以识别一些新的与 SCZ 相关的基因和途径（不包含显著的 SNP）。例如，在经过 Bonferroni 校正后，对于较大的 SCZ 数据集，其中包含 36989 例病例和 113075 例对照，我们应用于基因体和增强子区域的方法确定了 27 个新的与 SCZ 相关的基因和 11 个新的 KEGG 途径是显著的，而转录组全基因组关联研究（TWAS）方法都遗漏了这些基因和途径。我们的结论是，我们提出的方法具有潜在的实用性，并且与 TWAS 和其他标准的基因和途径方法互补。
