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A bioinformatic approach to identify pathogenic variants for Stevens-Johnson syndrome

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 th...

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Autores principales: Ma’ruf, Muhammad, Fadli, Justitia Cahyani, Mahendra, Muhammad Reza, Irham, Lalu Muhammad, Sulistyani, Nanik, Adikusuma, Wirawan, Chong, Rockie, Septama, Abdi Wira
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Korea Genome Organization 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10326529/
https://www.ncbi.nlm.nih.gov/pubmed/37704211
http://dx.doi.org/10.5808/gi.23010
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author Ma’ruf, Muhammad
Fadli, Justitia Cahyani
Mahendra, Muhammad Reza
Irham, Lalu Muhammad
Sulistyani, Nanik
Adikusuma, Wirawan
Chong, Rockie
Septama, Abdi Wira
author_facet Ma’ruf, Muhammad
Fadli, Justitia Cahyani
Mahendra, Muhammad Reza
Irham, Lalu Muhammad
Sulistyani, Nanik
Adikusuma, Wirawan
Chong, Rockie
Septama, Abdi Wira
author_sort Ma’ruf, Muhammad
collection PubMed
description 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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spelling pubmed-103265292023-07-08 A bioinformatic approach to identify pathogenic variants for Stevens-Johnson syndrome Ma’ruf, Muhammad Fadli, Justitia Cahyani Mahendra, Muhammad Reza Irham, Lalu Muhammad Sulistyani, Nanik Adikusuma, Wirawan Chong, Rockie Septama, Abdi Wira Genomics Inform Original Article 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. Korea Genome Organization 2023-06-30 /pmc/articles/PMC10326529/ /pubmed/37704211 http://dx.doi.org/10.5808/gi.23010 Text en (c) 2023, Korea Genome Organization https://creativecommons.org/licenses/by/4.0/(CC) This is an open-access article distributed under the terms of the Creative Commons Attribution license(https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Original Article
Ma’ruf, Muhammad
Fadli, Justitia Cahyani
Mahendra, Muhammad Reza
Irham, Lalu Muhammad
Sulistyani, Nanik
Adikusuma, Wirawan
Chong, Rockie
Septama, Abdi Wira
A bioinformatic approach to identify pathogenic variants for Stevens-Johnson syndrome
title A bioinformatic approach to identify pathogenic variants for Stevens-Johnson syndrome
title_full A bioinformatic approach to identify pathogenic variants for Stevens-Johnson syndrome
title_fullStr A bioinformatic approach to identify pathogenic variants for Stevens-Johnson syndrome
title_full_unstemmed A bioinformatic approach to identify pathogenic variants for Stevens-Johnson syndrome
title_short A bioinformatic approach to identify pathogenic variants for Stevens-Johnson syndrome
title_sort bioinformatic approach to identify pathogenic variants for stevens-johnson syndrome
topic Original Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10326529/
https://www.ncbi.nlm.nih.gov/pubmed/37704211
http://dx.doi.org/10.5808/gi.23010
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