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KMID : 1132720230210020026
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Genomics & Informatics
2023 Volume.21 No. 2 p.26 ~ p.26
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A bioinformatic approach to identify pathogenic variants for Stevens-Johnson syndrome
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Muhammad Ma′ruf
Justitia Cahyani Fadli
Muhammad Reza Mahendra
Lalu Muhammad Irham
Nanik Sulistyani
Wirawan Adikusuma
Rockie Chong
Abdi Wira Septama
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Abstract
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Stevens-Johnson syndrome (SJS) produces a severe hypersensitivity reaction caused by Herpes simplex virus or mycoplasma infection, vaccination, systemic disease, or other agents. Several studies have investigated the genetic susceptibility involved in SJS. To provide further genetic insights into the pathogenesis of SJS, this study prioritized high-impact, SJS-associated pathogenic variants through integrating bioinformatic and population genetic data. First, we identified SJS-associated single nucleotide polymorphisms from the genome-wide association studies catalog, followed by genome annotation with HaploReg and variant validation with Ensembl. Subsequently, expression quantitative trait locus analysis (eQTL) from GTEx identified human genetic variants with differential gene expression across human tissues. Our results indicate that two variants, namely rs2074494 and rs5010528, which are encoded by the HLA-C (human leukocyte antigen C) gene, were found to be differentially expressed in skin. The allele frequencies for rs2074494 and rs5010528 also appear to significantly differ across continents. We highlight the utility of these population-specific HLA-C genetic variants for genetic association studies, and aid in early prognosis and disease treatment of SJS.
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KEYWORD
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bioinformatics, genetic variation, genomic, pathogenic variants, Stevens-Johnson syndrome
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