Bioinformatic Analysis of Coronary Disease Associated Snps and Genes To Identify Proteins Potentially Involved in the Pathogenesis of Atherosclerosis - Bioinformatic Analysis of Coronary Disease Associated Snps and Genes To Identify Proteins Potentially Involved in the Pathogenesis of Atherosclerosis - Open Access Pub

Factors that contribute to the onset of atherosclerosis may be elucidated by bioinformatic techniques applied to multiple sources of genomic and proteomic data. The results of genome wide association studies, such as the CardioGramPlusC4D study, expression data, such as that available from expression quantitative trait loci (eQTL) databases, along with protein interaction and pathway data available in Ingenuity Pathway Analysis (IPA), constitute a substantial set of data amenable to bioinformatics analysis. This study used bioinformatic analyses of recent genome wide association data to identify a seed set of genes likely associated with atherosclerosis. The set was expanded to include protein interaction candidates to create a network of proteins possibly influencing the onset and progression of atherosclerosis. Local average connectivity (LAC), eigenvector centrality, and betweenness metrics were calculated for the interaction network to identify top gene and protein candidates for a better understanding of the atherosclerotic disease process. The top ranking genes included some known to be involved with cardiovascular disease (APOA1, APOA5, APOB, APOC1, APOC2, APOE, CDKN1A, CXCL12, SCARB1, SMARCA4 and TERT), and others that are less obvious and require further investigation (TP53, MYC, PPARG, YWHAQ, RB1, AR, ESR1, EGFR, UBC and YWHAZ). Collectively these data help define a more focused set of genes that likely play a pivotal role in the pathogenesis of atherosclerosis and are therefore natural targets for novel therapeutic interventions. DOI 10.14302/issn.2326-0793.jpgr-17-1447 Atherosclerosis is a multifactorial disease with a strong genetic component. Genome wide association studies for coronary artery disease (CAD) related phenotypes have identified at least 56 susceptibility loci at genome wide significance 1, 2, and a study into the role of low-frequency (frequency 1% - 5%) and rare (frequency < 1%) DNA sequence variants in early onset myocardial infarction (MI) identified additional candidate genes 3. Investigation of proteins encoded by genes in close proximity to the susceptibility loci or implicated in the analysis of rare variants may lead to an enhanced understanding of the molecular mechanisms of atherosclerosis, and thereby facilitate the identification of novel candidates for targeted therapeutic interventions. As part of the Genomic and Proteomic Architecture of Atherosclerosis (GPAA) project, we plan to utilize sensitive and highly accurate targeted mass spectrometry to quantify and thereby validate proteins identified as putative