Brubaker Douglas, Liu Yu, Wang Junye, Tan Huiqing, Zhang Ge, Jacobsson Bo, Muglia Louis, Mesiano Sam, Chance Mark R
Center for Proteomics and Bioinformatics, and Department of Nutrition, School of Medicine.
Department of Reproductive Biology and Department of Obstetrics and Gynecology, University Hospitals Case Medical Center, Case Western Reserve University, Cleveland, OH, USA.
Hum Mol Genet. 2016 Dec 1;25(23):5254-5264. doi: 10.1093/hmg/ddw325.
Maternal genome influences associate with up to 40% of spontaneous preterm births (PTB). Multiple genome wide association studies (GWAS) have been completed to identify genetic variants associated with PTB. Disappointingly, no highly significant SNPs have replicated in independent cohorts so far. We developed an approach combining protein-protein interaction (PPI) network data with tissue specific gene expression data to "find" SNPs of modest significance to identify candidate genes of functional importance that would otherwise be overlooked. This approach is based on the assumption that "high-ranking" SNPs falling short of genome wide significance may nevertheless indicate genes that have substantial biological value in understanding PTB. We mapped highly-ranked candidate SNPs from a meta-analysis of PTB-GWAS to coding genes and developed a PPI network enriched with PTB-SNP carrying genes. This network was scored with gene expression data from term and preterm myometrium to identify subnetworks of PTB-SNP associated genes coordinately expressed with labour onset in myometrial tissue. Our analysis consistently identified significant sub-networks associated with the interacting transcription factors MEF2C and TWIST1, genes not previously associated with PTB, both of which regulate processes clearly relevant to birth timing. Other genes in the significant sub-networks were also associated with inflammatory pathways, as well as muscle function and ion channels. Gene expression level dysregulation was confirmed for eight of these networks by qRT-PCR in an independent set of term and pre-term subjects. Our method identifies novel genes dysregulated in PTB and provides a generalized framework to identify GWAS SNPs that would otherwise be overlooked.
母体基因组影响与高达40%的自发性早产（PTB）相关。已经完成了多项全基因组关联研究（GWAS）以确定与PTB相关的基因变异。令人失望的是，到目前为止，尚无高度显著的单核苷酸多态性（SNP）在独立队列中得到重复验证。我们开发了一种将蛋白质-蛋白质相互作用（PPI）网络数据与组织特异性基因表达数据相结合的方法，以“发现”具有适度显著性的SNP，从而识别具有功能重要性的候选基因，否则这些基因可能会被忽视。该方法基于这样一种假设，即未达到全基因组显著性的“高排名”SNP仍可能指示在理解PTB方面具有重要生物学价值的基因。我们将来自PTB-GWAS荟萃分析的高排名候选SNP映射到编码基因，并构建了一个富含携带PTB-SNP基因的PPI网络。利用足月和早产子宫肌层的基因表达数据对该网络进行评分，以识别与子宫肌层组织中与分娩开始协同表达的PTB-SNP相关基因的子网。我们的分析一致地识别出与相互作用的转录因子MEF2C和TWIST1相关的显著子网，这两个基因以前未与PTB相关联，它们都调节与分娩时间明显相关的过程。显著子网中的其他基因也与炎症途径以及肌肉功能和离子通道相关。通过qRT-PCR在一组独立的足月和早产受试者中对其中八个网络的基因表达水平失调进行了确认。我们的方法识别出在PTB中失调的新基因，并提供了一个通用框架来识别否则会被忽视的GWAS SNP。
