Bakir-Gungor Burcu, Ünlü Yazici Miray, Göy Gökhan, Temiz Mustafa
Department of Computer Engineering, Abdullah Gül University, Kayseri, Turkey.
Department of Bioengineering, Abdullah Gül University, Kayseri, Turkey.
Turk J Biol. 2022 Jul 18;46(4):318-341. doi: 10.55730/1300-0152.2620. eCollection 2022.
Type 2 diabetes mellitus (T2D) constitutes 90% of the diabetes cases, and it is a complex multifactorial disease. In the last decade, genome-wide association studies (GWASs) for T2D successfully pinpointed the genetic variants (typically single nucleotide polymorphisms, SNPs) that associate with disease risk. In order to diminish the burden of multiple testing in GWAS, researchers attempted to evaluate the collective effects of interesting variants. In this regard, pathway-based analyses of GWAS became popular to discover novel multigenic functional associations. Still, to reveal the unaccounted 85 to 90% of T2D variation, which lies hidden in GWAS datasets, new post-GWAS strategies need to be developed. In this respect, here we reanalyze three metaanalysis data of GWAS in T2D, using the methodology that we have developed to identify disease-associated pathways by combining nominally significant evidence of genetic association with the known biochemical pathways, protein-protein interaction (PPI) networks, and the functional information of selected SNPs. In this research effort, to enlighten the molecular mechanisms underlying T2D development and progress, we integrated different in silico approaches that proceed in top-down manner and bottom-up manner, and presented a comprehensive analysis at protein subnetwork, pathway, and pathway subnetwork levels. Using the mutual information based on the shared genes, the identified protein subnetworks and the affected pathways of each dataset were compared. While most of the identified pathways recapitulate the pathophysiology of T2D, our results show that incorporating SNP functional properties, PPI networks into GWAS can dissect leading molecular pathways, and it could offer improvement over traditional enrichment strategies.
2型糖尿病（T2D）占糖尿病病例的90%，是一种复杂的多因素疾病。在过去十年中，针对T2D的全基因组关联研究（GWAS）成功地确定了与疾病风险相关的基因变异（通常是单核苷酸多态性，SNP）。为了减轻GWAS中多重检验的负担，研究人员试图评估感兴趣变异的综合效应。在这方面，基于通路的GWAS分析开始流行，以发现新的多基因功能关联。然而，为了揭示隐藏在GWAS数据集中的85%至90%尚未得到解释的T2D变异，需要开发新的GWAS后分析策略。在这方面，我们使用已开发的方法，通过将遗传关联的名义显著证据与已知生化通路、蛋白质-蛋白质相互作用（PPI）网络以及选定SNP的功能信息相结合，重新分析了三个T2D的GWAS荟萃分析数据。在这项研究中，为了阐明T2D发生和发展的分子机制，我们整合了自上而下和自下而上的不同计算机模拟方法，并在蛋白质子网、通路和通路子网水平上进行了全面分析。使用基于共享基因的互信息，比较了每个数据集识别出的蛋白质子网和受影响的通路。虽然大多数识别出的通路概括了T2D的病理生理学，但我们的结果表明，将SNP功能特性、PPI网络纳入GWAS可以剖析主要分子通路，并且比传统的富集策略有所改进。
