Comprehensive Computational Analysis of GWAS Loci Identifies CCR2 as a Candidate Gene for Celiac Disease Pathogenesis. Issue 8 (18th April 2017)

• Record Type: Journal Article
• Authors: Banaganapalli, Babajan; Rashidi, Omran; Saadah, Omar I.; Wang, Jun; Khan, Imran Ali; Al‐Aama, Jumana Y.; Shaik, Noor Ahmad; Elango, Ramu
• Is Part Of: Journal of cellular biochemistry. Volume 118:Issue 8(2017)
• Page Start: 2193
• Page End: 2207
• Publication Date: 2017-04-18
• Subjects: CELIAC DISEASE -- GWAS -- COMPUTATIONAL BIOLOGY -- CCR2; Cytochemistry -- Periodicals; 572
• DOI: 10.1002/jcb.25864
• Languages: English
• ISSNs: 0730-2312

Abstract

ABSTRACT: Celiac disease (CD) is a gluten intolerance disorder with known genetic contribution. The recent fine mapping and genome‐wide association studies (GWAS) have identified up to 57 non‐HLA CD susceptibility SNPs, majority of which are non‐coding variants lacking any functional annotation. Therefore, we adopted multidimensional computational approach for uncovering the plausible mechanisms through which these GWAS SNPs are connected to CD pathogenesis. At initial phase, we identified that 25 (43.85%) out of 57 CD‐SNPs lies in evolutionarily constrained genetic element regions. In follow‐up phases, through computational (CADD, GWAVA, and FATHMM algorithms) deleterious intensity measurements, we have discovered that 42 (3.94%) out of 1065 variants (57 CD‐lead and 1008‐linked SNPs; r 2 ≥ 0.8) are differentially deleterious in nature to CD. Further functional scrutinization of these CD variants by public domain eQTL mapping, gene expression, knockout mouse model, and pathway analyses revealed that deleterious SNPs of CCR2 gene influences its expression levels and may also elicit a cascade of T‐cell‐mediated immunological events leading to intestinal gluten intolerance in genetically susceptible individuals. This study demonstrates the utility of integrated in silico analysis of annotations, gene expression, and pathways in prioritizing the potential complex disease variants from large‐scale open source genomic data. J. Cell. Biochem. 118: 2193–2207, 2017. © 2017 Wiley Periodicals, Inc. Abstract : This study demonstrates the utility of integrated in silico analysis of annotations, gene expression, and pathways in prioritizing the potential complex disease variants from large‐scale open source genomic data.