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Genetic diversity of ‘Very Important Pharmacogenes’ in two South-Asian populations
OBJECTIVES: Reliable identification of population-specific variants is important for building the single nucleotide polymorphism (SNP) profile. In this study, genomic variation using allele frequency differences of pharmacologically important genes for Gujarati Indians in Houston (GIH) and Indian Te...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
PeerJ Inc.
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8590392/ https://www.ncbi.nlm.nih.gov/pubmed/34824904 http://dx.doi.org/10.7717/peerj.12294 |
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author | Bharti, Neeraj Banerjee, Ruma Achalere, Archana Kasibhatla, Sunitha Manjari Joshi, Rajendra |
author_facet | Bharti, Neeraj Banerjee, Ruma Achalere, Archana Kasibhatla, Sunitha Manjari Joshi, Rajendra |
author_sort | Bharti, Neeraj |
collection | PubMed |
description | OBJECTIVES: Reliable identification of population-specific variants is important for building the single nucleotide polymorphism (SNP) profile. In this study, genomic variation using allele frequency differences of pharmacologically important genes for Gujarati Indians in Houston (GIH) and Indian Telugu in the U.K. (ITU) from the 1000 Genomes Project vis-à-vis global population data was studied to understand its role in drug response. METHODS: Joint genotyping approach was used to derive variants of GIH and ITU independently. SNPs of both these populations with significant allele frequency variation (minor allele frequency ≥ 0.05) with super-populations from the 1000 Genomes Project and gnomAD based on Chi-square distribution with p-value of ≤ 0.05 and Bonferroni’s multiple adjustment tests were identified. Population stratification and fixation index analysis was carried out to understand genetic differentiation. Functional annotation of variants was carried out using SnpEff, VEP and CADD score. RESULTS: Population stratification of VIP genes revealed four clusters viz., single cluster of GIH and ITU, one cluster each of East Asian, European, African populations and Admixed American was found to be admixed. A total of 13 SNPs belonging to ten pharmacogenes were identified to have significant allele frequency variation in both GIH and ITU populations as compared to one or more super-populations. These SNPs belong to VKORC1 (rs17708472, rs2359612, rs8050894) involved in Vitamin K cycle, cytochrome P450 isoforms CYP2C9 (rs1057910), CYP2B6 (rs3211371), CYP2A2 (rs4646425) and CYP2A4 (rs4646440); ATP-binding cassette (ABC) transporter ABCB1 (rs12720067), DPYD1 (rs12119882, rs56160474) involved in pyrimidine metabolism, methyltransferase COMT (rs9332377) and transcriptional factor NR1I2 (rs6785049). SNPs rs1544410 (VDR), rs2725264 (ABCG2), rs5215 and rs5219 (KCNJ11) share high fixation index (≥ 0.5) with either EAS/AFR populations. Missense variants rs1057910 (CYP2C9), rs1801028 (DRD2) and rs1138272 (GSTP1), rs116855232 (NUDT15); intronic variants rs1131341 (NQO1) and rs115349832 (DPYD) are identified to be ‘deleterious’. CONCLUSIONS: Analysis of SNPs pertaining to pharmacogenes in GIH and ITU populations using population structure, fixation index and allele frequency variation provides a premise for understanding the role of genetic diversity in drug response in Asian Indians. |
format | Online Article Text |
id | pubmed-8590392 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | PeerJ Inc. |
record_format | MEDLINE/PubMed |
spelling | pubmed-85903922021-11-24 Genetic diversity of ‘Very Important Pharmacogenes’ in two South-Asian populations Bharti, Neeraj Banerjee, Ruma Achalere, Archana Kasibhatla, Sunitha Manjari Joshi, Rajendra PeerJ Bioinformatics OBJECTIVES: Reliable identification of population-specific variants is important for building the single nucleotide polymorphism (SNP) profile. In this study, genomic variation using allele frequency differences of pharmacologically important genes for Gujarati Indians in Houston (GIH) and Indian Telugu in the U.K. (ITU) from the 1000 Genomes Project vis-à-vis global population data was studied to understand its role in drug response. METHODS: Joint genotyping approach was used to derive variants of GIH and ITU independently. SNPs of both these populations with significant allele frequency variation (minor allele frequency ≥ 0.05) with super-populations from the 1000 Genomes Project and gnomAD based on Chi-square distribution with p-value of ≤ 0.05 and Bonferroni’s multiple adjustment tests were identified. Population stratification and fixation index analysis was carried out to understand genetic differentiation. Functional annotation of variants was carried out using SnpEff, VEP and CADD score. RESULTS: Population stratification of VIP genes revealed four clusters viz., single cluster of GIH and ITU, one cluster each of East Asian, European, African populations and Admixed American was found to be admixed. A total of 13 SNPs belonging to ten pharmacogenes were identified to have significant allele frequency variation in both GIH and ITU populations as compared to one or more super-populations. These SNPs belong to VKORC1 (rs17708472, rs2359612, rs8050894) involved in Vitamin K cycle, cytochrome P450 isoforms CYP2C9 (rs1057910), CYP2B6 (rs3211371), CYP2A2 (rs4646425) and CYP2A4 (rs4646440); ATP-binding cassette (ABC) transporter ABCB1 (rs12720067), DPYD1 (rs12119882, rs56160474) involved in pyrimidine metabolism, methyltransferase COMT (rs9332377) and transcriptional factor NR1I2 (rs6785049). SNPs rs1544410 (VDR), rs2725264 (ABCG2), rs5215 and rs5219 (KCNJ11) share high fixation index (≥ 0.5) with either EAS/AFR populations. Missense variants rs1057910 (CYP2C9), rs1801028 (DRD2) and rs1138272 (GSTP1), rs116855232 (NUDT15); intronic variants rs1131341 (NQO1) and rs115349832 (DPYD) are identified to be ‘deleterious’. CONCLUSIONS: Analysis of SNPs pertaining to pharmacogenes in GIH and ITU populations using population structure, fixation index and allele frequency variation provides a premise for understanding the role of genetic diversity in drug response in Asian Indians. PeerJ Inc. 2021-11-10 /pmc/articles/PMC8590392/ /pubmed/34824904 http://dx.doi.org/10.7717/peerj.12294 Text en ©2021 Bharti et al. https://creativecommons.org/licenses/by/4.0/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, reproduction and adaptation in any medium and for any purpose provided that it is properly attributed. For attribution, the original author(s), title, publication source (PeerJ) and either DOI or URL of the article must be cited. |
spellingShingle | Bioinformatics Bharti, Neeraj Banerjee, Ruma Achalere, Archana Kasibhatla, Sunitha Manjari Joshi, Rajendra Genetic diversity of ‘Very Important Pharmacogenes’ in two South-Asian populations |
title | Genetic diversity of ‘Very Important Pharmacogenes’ in two South-Asian populations |
title_full | Genetic diversity of ‘Very Important Pharmacogenes’ in two South-Asian populations |
title_fullStr | Genetic diversity of ‘Very Important Pharmacogenes’ in two South-Asian populations |
title_full_unstemmed | Genetic diversity of ‘Very Important Pharmacogenes’ in two South-Asian populations |
title_short | Genetic diversity of ‘Very Important Pharmacogenes’ in two South-Asian populations |
title_sort | genetic diversity of ‘very important pharmacogenes’ in two south-asian populations |
topic | Bioinformatics |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8590392/ https://www.ncbi.nlm.nih.gov/pubmed/34824904 http://dx.doi.org/10.7717/peerj.12294 |
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