Please use this identifier to cite or link to this item: https://doi.org/10.1038/s41598-017-06905-6
Title: Inclusion of Population-specific Reference Panel from India to the 1000 Genomes Phase 3 Panel Improves Imputation Accuracy
Authors: Ahmad, M
Sinha, A
Ghosh, S
Kumar, V 
Davila, S 
Yajnik, C.S
Chandak, G.R
Keywords: controlled clinical trial
controlled study
female
gene frequency
genome
human
India
major clinical study
male
phase 3 clinical trial
stress
allele
Asia
Asian continental ancestry group
biology
Caucasian
genetic variation
genome-wide association study
haplotype
high throughput sequencing
human genome
human genome project
population genetics
reference value
statistics and numerical data
Alleles
Asia
Asian Continental Ancestry Group
Computational Biology
European Continental Ancestry Group
Gene Frequency
Genetic Variation
Genetics, Population
Genome, Human
Genome-Wide Association Study
Haplotypes
High-Throughput Nucleotide Sequencing
Human Genome Project
Humans
Reference Values
Issue Date: 2017
Citation: Ahmad, M, Sinha, A, Ghosh, S, Kumar, V, Davila, S, Yajnik, C.S, Chandak, G.R (2017). Inclusion of Population-specific Reference Panel from India to the 1000 Genomes Phase 3 Panel Improves Imputation Accuracy. Scientific Reports 7 (1) : 6733. ScholarBank@NUS Repository. https://doi.org/10.1038/s41598-017-06905-6
Rights: Attribution 4.0 International
Abstract: Imputation is a computational method based on the principle of haplotype sharing allowing enrichment of genome-wide association study datasets. It depends on the haplotype structure of the population and density of the genotype data. The 1000 Genomes Project led to the generation of imputation reference panels which have been used globally. However, recent studies have shown that population-specific panels provide better enrichment of genome-wide variants. We compared the imputation accuracy using 1000 Genomes phase 3 reference panel and a panel generated from genome-wide data on 407 individuals from Western India (WIP). The concordance of imputed variants was cross-checked with next-generation re-sequencing data on a subset of genomic regions. Further, using the genome-wide data from 1880 individuals, we demonstrate that WIP works better than the 1000 Genomes phase 3 panel and when merged with it, significantly improves the imputation accuracy throughout the minor allele frequency range. We also show that imputation using only South Asian component of the 1000 Genomes phase 3 panel works as good as the merged panel, making it computationally less intensive job. Thus, our study stresses that imputation accuracy using 1000 Genomes phase 3 panel can be further improved by including population-specific reference panels from South Asia. © 2017 The Author(s).
Source Title: Scientific Reports
URI: https://scholarbank.nus.edu.sg/handle/10635/178310
ISSN: 20452322
DOI: 10.1038/s41598-017-06905-6
Rights: Attribution 4.0 International
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