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Improved imputation accuracy of rare and low-frequency variants using population-specific high-coverage WGS-based imputation reference panel
Genetic imputation is a cost-efficient way to improve the power and resolution of genome-wide association (GWA) studies. Current publicly accessible imputation reference panels accurately predict genotypes for common variants with minor allele frequency (MAF)≥5% and low-frequency variants (0.5≤MAF&l...
Autores principales: | , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group
2017
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5520064/ https://www.ncbi.nlm.nih.gov/pubmed/28401899 http://dx.doi.org/10.1038/ejhg.2017.51 |
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author | Mitt, Mario Kals, Mart Pärn, Kalle Gabriel, Stacey B Lander, Eric S Palotie, Aarno Ripatti, Samuli Morris, Andrew P Metspalu, Andres Esko, Tõnu Mägi, Reedik Palta, Priit |
author_facet | Mitt, Mario Kals, Mart Pärn, Kalle Gabriel, Stacey B Lander, Eric S Palotie, Aarno Ripatti, Samuli Morris, Andrew P Metspalu, Andres Esko, Tõnu Mägi, Reedik Palta, Priit |
author_sort | Mitt, Mario |
collection | PubMed |
description | Genetic imputation is a cost-efficient way to improve the power and resolution of genome-wide association (GWA) studies. Current publicly accessible imputation reference panels accurately predict genotypes for common variants with minor allele frequency (MAF)≥5% and low-frequency variants (0.5≤MAF<5%) across diverse populations, but the imputation of rare variation (MAF<0.5%) is still rather limited. In the current study, we evaluate imputation accuracy achieved with reference panels from diverse populations with a population-specific high-coverage (30 ×) whole-genome sequencing (WGS) based reference panel, comprising of 2244 Estonian individuals (0.25% of adult Estonians). Although the Estonian-specific panel contains fewer haplotypes and variants, the imputation confidence and accuracy of imputed low-frequency and rare variants was significantly higher. The results indicate the utility of population-specific reference panels for human genetic studies. |
format | Online Article Text |
id | pubmed-5520064 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2017 |
publisher | Nature Publishing Group |
record_format | MEDLINE/PubMed |
spelling | pubmed-55200642017-08-23 Improved imputation accuracy of rare and low-frequency variants using population-specific high-coverage WGS-based imputation reference panel Mitt, Mario Kals, Mart Pärn, Kalle Gabriel, Stacey B Lander, Eric S Palotie, Aarno Ripatti, Samuli Morris, Andrew P Metspalu, Andres Esko, Tõnu Mägi, Reedik Palta, Priit Eur J Hum Genet Article Genetic imputation is a cost-efficient way to improve the power and resolution of genome-wide association (GWA) studies. Current publicly accessible imputation reference panels accurately predict genotypes for common variants with minor allele frequency (MAF)≥5% and low-frequency variants (0.5≤MAF<5%) across diverse populations, but the imputation of rare variation (MAF<0.5%) is still rather limited. In the current study, we evaluate imputation accuracy achieved with reference panels from diverse populations with a population-specific high-coverage (30 ×) whole-genome sequencing (WGS) based reference panel, comprising of 2244 Estonian individuals (0.25% of adult Estonians). Although the Estonian-specific panel contains fewer haplotypes and variants, the imputation confidence and accuracy of imputed low-frequency and rare variants was significantly higher. The results indicate the utility of population-specific reference panels for human genetic studies. Nature Publishing Group 2017-07 2017-04-12 /pmc/articles/PMC5520064/ /pubmed/28401899 http://dx.doi.org/10.1038/ejhg.2017.51 Text en Copyright © 2017 The Author(s) http://creativecommons.org/licenses/by-nc-sa/4.0/ This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in the credit line; if the material is not included under the Creative Commons license, users will need to obtain permission from the license holder to reproduce the material. To view a copy of this license, visit http://creativecommons.org/licenses/by-nc-sa/4.0/ |
spellingShingle | Article Mitt, Mario Kals, Mart Pärn, Kalle Gabriel, Stacey B Lander, Eric S Palotie, Aarno Ripatti, Samuli Morris, Andrew P Metspalu, Andres Esko, Tõnu Mägi, Reedik Palta, Priit Improved imputation accuracy of rare and low-frequency variants using population-specific high-coverage WGS-based imputation reference panel |
title | Improved imputation accuracy of rare and low-frequency variants using population-specific high-coverage WGS-based imputation reference panel |
title_full | Improved imputation accuracy of rare and low-frequency variants using population-specific high-coverage WGS-based imputation reference panel |
title_fullStr | Improved imputation accuracy of rare and low-frequency variants using population-specific high-coverage WGS-based imputation reference panel |
title_full_unstemmed | Improved imputation accuracy of rare and low-frequency variants using population-specific high-coverage WGS-based imputation reference panel |
title_short | Improved imputation accuracy of rare and low-frequency variants using population-specific high-coverage WGS-based imputation reference panel |
title_sort | improved imputation accuracy of rare and low-frequency variants using population-specific high-coverage wgs-based imputation reference panel |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5520064/ https://www.ncbi.nlm.nih.gov/pubmed/28401899 http://dx.doi.org/10.1038/ejhg.2017.51 |
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