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Evaluating the Accuracy of Imputation Methods in a Five-Way Admixed Population

Genotype imputation is a powerful tool for increasing statistical power in an association analysis. Meta-analysis of multiple study datasets also requires a substantial overlap of SNPs for a successful association analysis, which can be achieved by imputation. Quality of imputed datasets is largely...

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Autores principales: Schurz, Haiko, Müller, Stephanie J., van Helden, Paul David, Tromp, Gerard, Hoal, Eileen G., Kinnear, Craig J., Möller, Marlo
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6370942/
https://www.ncbi.nlm.nih.gov/pubmed/30804980
http://dx.doi.org/10.3389/fgene.2019.00034
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author Schurz, Haiko
Müller, Stephanie J.
van Helden, Paul David
Tromp, Gerard
Hoal, Eileen G.
Kinnear, Craig J.
Möller, Marlo
author_facet Schurz, Haiko
Müller, Stephanie J.
van Helden, Paul David
Tromp, Gerard
Hoal, Eileen G.
Kinnear, Craig J.
Möller, Marlo
author_sort Schurz, Haiko
collection PubMed
description Genotype imputation is a powerful tool for increasing statistical power in an association analysis. Meta-analysis of multiple study datasets also requires a substantial overlap of SNPs for a successful association analysis, which can be achieved by imputation. Quality of imputed datasets is largely dependent on the software used, as well as the reference populations chosen. The accuracy of imputation of available reference populations has not been tested for the five-way admixed South African Colored (SAC) population. In this study, imputation results obtained using three freely-accessible methods were evaluated for accuracy and quality. We show that the African Genome Resource is the best reference panel for imputation of missing genotypes in samples from the SAC population, implemented via the freely accessible Sanger Imputation Server.
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spelling pubmed-63709422019-02-25 Evaluating the Accuracy of Imputation Methods in a Five-Way Admixed Population Schurz, Haiko Müller, Stephanie J. van Helden, Paul David Tromp, Gerard Hoal, Eileen G. Kinnear, Craig J. Möller, Marlo Front Genet Genetics Genotype imputation is a powerful tool for increasing statistical power in an association analysis. Meta-analysis of multiple study datasets also requires a substantial overlap of SNPs for a successful association analysis, which can be achieved by imputation. Quality of imputed datasets is largely dependent on the software used, as well as the reference populations chosen. The accuracy of imputation of available reference populations has not been tested for the five-way admixed South African Colored (SAC) population. In this study, imputation results obtained using three freely-accessible methods were evaluated for accuracy and quality. We show that the African Genome Resource is the best reference panel for imputation of missing genotypes in samples from the SAC population, implemented via the freely accessible Sanger Imputation Server. Frontiers Media S.A. 2019-02-05 /pmc/articles/PMC6370942/ /pubmed/30804980 http://dx.doi.org/10.3389/fgene.2019.00034 Text en Copyright © 2019 Schurz, Müller, van Helden, Tromp, Hoal, Kinnear and Möller. http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Genetics
Schurz, Haiko
Müller, Stephanie J.
van Helden, Paul David
Tromp, Gerard
Hoal, Eileen G.
Kinnear, Craig J.
Möller, Marlo
Evaluating the Accuracy of Imputation Methods in a Five-Way Admixed Population
title Evaluating the Accuracy of Imputation Methods in a Five-Way Admixed Population
title_full Evaluating the Accuracy of Imputation Methods in a Five-Way Admixed Population
title_fullStr Evaluating the Accuracy of Imputation Methods in a Five-Way Admixed Population
title_full_unstemmed Evaluating the Accuracy of Imputation Methods in a Five-Way Admixed Population
title_short Evaluating the Accuracy of Imputation Methods in a Five-Way Admixed Population
title_sort evaluating the accuracy of imputation methods in a five-way admixed population
topic Genetics
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6370942/
https://www.ncbi.nlm.nih.gov/pubmed/30804980
http://dx.doi.org/10.3389/fgene.2019.00034
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