A comprehensive meta-analysis and prioritization study to identify vitiligo associated coding and non-coding SNV candidates using web-based bioinformatics tools.

Vitiligo is a prevalent depigmentation disorder affecting around 1% of the general population. So far, various Genome Wide Association Studies (GWAS) and Candidate Gene Association Studies (CGAS) have identified several single nucleotide variants (SNVs) as a risk factor for vitiligo. Nonetheless, little has been discerned regarding their direct functional significance to the disease pathogenesis. In this study, we did extensive data mining and downstream analysis using several experimentally validated datasets like GTEx Portal and web tools like rSNPBase, RegulomeDB, HaploReg and STRING to prioritize 13 SNVs from a set of 291SNVs that have been previously reported to be associated with vitiligo. We also prioritized their underlying/target genes and tried annotating their functional contribution to vitiligo pathogenesis. Our analysis revealed genes like FGFR10P, SUOX, CDK5RAP1 and RERE that have never been implicated in vitiligo previously to have strong potentials to contribute to the disease pathogenesis. The study is the first of its kind to prioritize and functionally annotate vitiligo-associated GWAS and CGAS SNVs and their underlying/target genes, based on functional data available in the public domain database.

Prioritization of human well-being spectrum related GWAS-SNVs using ENCODE-based web-tools predict interplay between PSMC3, ITIH4, and SERPINC1 genes in modulating well-being.

Several traits related to positive and negative affect show a high genetic as well as phenotypic correlation with well-being in humans, and are therefore collectively termed as "Well-being spectrum". Genome-Wide Association studies (GWA studies) on "well-being measurement" have led to identification of several genomic variants (Single Nucleotide Variants - SNVs), but very little has been explained with respect to their functionality and mode of alteration of well-being. Utilizing a pool of 1258 GWA studies based SNVs on "well-being measurement", we prioritized the SNVs and tried to annotate well-being related functionality through several bioinformatic tools to predict whether a protein sequence variation affects protein function, as well as experimentally validated datasets available in ENCODE based web-tools namely rSNPBase, RegulomeDB, Haploreg, along with GTEx Portal and STRING based protein interaction networks. Prioritization yielded three key SNVs; rs3781627-A, rs13072536-T and 5877-C potentially regulating three genes, PSMC3, ITIH4 and SERPINC1, respectively. Interestingly, the genes showed well clustered protein-protein interaction (maximum combined confidence score >0.4) with other well-being candidate genes, namely TNF and CRP genes suggesting their important role in modulation of well-being. PSMC3 and ITIH4 genes are also involved in driving acute phase responses signifying a probable cross-talk between well-being and psychoneuroimmunological system. To best of our knowledge this study is the first of its kind where the well-being associated GWA studies-SNVs were prioritized and functionally annotated, majorly based on functional data available in public domain, which revealed PSMC3, ITIH4 and SERPINC1 genes as probable candidates in regulation of well-being spectrum.