scholarly journals Genetic diversity of ‘Very Important Pharmacogenes’ in two South-Asian populations

PeerJ ◽  
2021 ◽  
Vol 9 ◽  
pp. e12294
Author(s):  
Neeraj Bharti ◽  
Ruma Banerjee ◽  
Archana Achalere ◽  
Sunitha Manjari Kasibhatla ◽  
Rajendra Joshi
Keyword(s):  
Genetic Diversity ◽  
Allele Frequency ◽  
Population Stratification ◽  
Drug Response ◽  
Fixation Index ◽  
Frequency Variation ◽  
1000 Genomes Project ◽  
1000 Genomes ◽  
Link Type ◽  
Allele Frequency Variation

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.

scholarly journals Utilizing the Jaccard index to reveal population stratification in sequencing data: a simulation study and an application to the 1000 Genomes Project

2015 ◽  
Vol 32 (9) ◽  
pp. 1366-1372 ◽  
Author(s):  
Dmitry Prokopenko ◽  
Julian Hecker ◽  
Edwin K. Silverman ◽  
Marcello Pagano ◽  
Markus M. Nöthen ◽  
...  
Keyword(s):  
Simulation Study ◽  
Population Stratification ◽  
Jaccard Index ◽  
Sequencing Data ◽  
1000 Genomes Project ◽  
1000 Genomes

scholarly journals Legacy Data Confound Genomics Studies

2019 ◽  
Vol 37 (1) ◽  
pp. 2-10 ◽  
Author(s):  
Luke Anderson-Trocmé ◽  
Rick Farouni ◽  
Mathieu Bourgey ◽  
Yoichiro Kamatani ◽  
Koichiro Higasa ◽  
...  
Keyword(s):  
Population Stratification ◽  
Quality Data ◽  
Human Populations ◽  
Batch Effects ◽  
Sequencing Data ◽  
1000 Genomes Project ◽  
Mutational Spectra ◽  
1000 Genomes ◽  
Legacy Data ◽  
Early Phases

Abstract Recent reports have identified differences in the mutational spectra across human populations. Although some of these reports have been replicated in other cohorts, most have been reported only in the 1000 Genomes Project (1kGP) data. While investigating an intriguing putative population stratification within the Japanese population, we identified a previously unreported batch effect leading to spurious mutation calls in the 1kGP data and to the apparent population stratification. Because the 1kGP data are used extensively, we find that the batch effects also lead to incorrect imputation by leading imputation servers and a small number of suspicious GWAS associations. Lower quality data from the early phases of the 1kGP thus continue to contaminate modern studies in hidden ways. It may be time to retire or upgrade such legacy sequencing data.

scholarly journals Mapping bias overestimates reference allele frequencies at theHLAgenes in the 1000 Genomes Project phase I data

2014 ◽  
Author(s):  
Debora Yoshihara Caldeira Brandt ◽  
Vitor Rezende da Costa Aguiar ◽  
Bárbara Domingues Bitarello ◽  
Kelly Nunes ◽  
Jérôme Goudet ◽  
...  
Keyword(s):  
Phase I ◽  
Allele Frequency ◽  
Population Genomics ◽  
1000 Genomes Project ◽  
Reference Allele ◽  
1000 Genomes ◽  
Hla Genes ◽  
Mapping Bias ◽  
Genomic Regions ◽  
Ngs Data

Next Generation Sequencing (NGS) technologies have become the standard for data generation in studies of population genomics, as the 1000 Genomes Project (1000G). However, these techniques are known to be problematic when applied to highly polymorphic genomic regions, such as the Human Leukocyte Antigen (HLA) genes. Because accurate genotype calls and allele frequency estimations are crucial to population genomics analises, it is important to assess the reliability of NGS data. Here, we evaluate the reliability of genotype calls and allele frequency estimates of the SNPs reported by 1000G (phase I) at five HLA genes (HLA-A, -B, -C, -DRB1, -DQB1 ). We take advantage of the availability of HLA Sanger sequencing of 930 of the 1,092 1000G samples, and use this as a gold standard to benchmark the 1000G data. We document that 18.6% of SNP genotype calls in HLA genes are incorrect, and that allele frequencies are estimated with an error higher than ??0.1 at approximately 25% of the SNPs in HLA genes. We found a bias towards overestimation of reference allele frequency for the 1000G data, indicating mapping bias is an important cause of error in frequency estimation in this dataset. We provide a list of sites that have poor allele frequency estimates, and discuss the outcomes of including those sites in different kinds of analyses. Since the HLA region is the most polymorphic in the human genome, our results provide insights into the challenges of using of NGS data at other genomic regions of high diversity.

1000 Genomes Project Gives New Map of Genetic Diversity

Science ◽  
2010 ◽  
Vol 330 (6004) ◽  
pp. 574-575 ◽  
Author(s):  
Elizabeth Pennisi
Keyword(s):  
Genetic Diversity ◽  
1000 Genomes Project ◽  
1000 Genomes

scholarly journals Legacy Data Confounds Genomics Studies

2019 ◽  
Author(s):  
Luke Anderson-Trocmé ◽  
Rick Farouni ◽  
Mathieu Bourgey ◽  
Yoichiro Kamatani ◽  
Koichiro Higasa ◽  
...  
Keyword(s):  
Population Stratification ◽  
Quality Data ◽  
Human Populations ◽  
Batch Effects ◽  
Sequencing Data ◽  
1000 Genomes Project ◽  
Mutational Spectra ◽  
1000 Genomes ◽  
Legacy Data ◽  
Early Phases

AbstractRecent reports have identified differences in the mutational spectra across human populations. While some of these reports have been replicated in other cohorts, most have been reported only in the 1000 Genomes Project (1kGP) data. While investigating an intriguing putative population stratification within the Japanese population, we identified a previously unreported batch effect leading to spurious mutation calls in the 1kGP data and to the apparent population stratification. Because the 1kGP data is used extensively, we find that the batch effects also lead to incorrect imputation by leading imputation servers and a small number of suspicious GWAS associations. Lower-quality data from the early phases of the 1kGP thus continues to contaminate modern studies in hidden ways. It may be time to retire or upgrade such legacy sequencing data.

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