An integrated analysis of public genomic data unveils a possible functional mechanism of psoriasis risk via a long-range ERRFI1 enhancer

Naoto Kubota, Mikita Suyama

Research output: Contribution to journalArticle

Abstract

Background: Psoriasis is a chronic inflammatory skin disease, for which genome-wide association studies (GWAS) have identified many genetic variants as risk markers. However, the details of underlying molecular mechanisms, especially which variants are functional, are poorly understood. Methods: We utilized a computational approach to survey psoriasis-associated functional variants that might affect protein functions or gene expression levels. We developed a pipeline by integrating publicly available datasets provided by GWAS Catalog, FANTOM5, GTEx, SNP2TFBS, and DeepBlue. To identify functional variants on exons or splice sites, we used a web-based annotation tool in the Ensembl database. To search for noncoding functional variants within promoters or enhancers, we used eQTL data calculated by GTEx. The data of variants lying on transcription factor binding sites provided by SNP2TFBS were used to predict detailed functions of the variants. Results: We discovered 22 functional variant candidates, of which 8 were in noncoding regions. We focused on the enhancer variant rs72635708 (T > C) in the 1p36.23 region; this variant is within the enhancer region of the ERRFI1 gene, which regulates lipid metabolism in the liver and skin morphogenesis via EGF signaling. Further analysis showed that the ERRFI1 promoter spatially contacts with the enhancer, despite the 170 kb distance between them. We found that this variant lies on the AP-1 complex binding motif and may modulate binding levels. Conclusions: The minor allele rs72635708 (rs72635708-C) might affect the ERRFI1 promoter activity, which results in unstable expression of ERRFI1, enhancing the risk of psoriasis via disruption of lipid metabolism and skin cell proliferation. Our study represents a successful example of predicting molecular pathogenesis by integration and reanalysis of public data.

Original languageEnglish
Article number8
JournalBMC Medical Genomics
Volume13
Issue number1
DOIs
Publication statusPublished - Jan 22 2020

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All Science Journal Classification (ASJC) codes

  • Genetics
  • Genetics(clinical)

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