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Ascertainment bias from imputation methods evaluation in wheat

BACKGROUND: Whole-genome genotyping techniques like Genotyping-by-sequencing (GBS) are being used for genetic studies such as Genome-Wide Association (GWAS) and Genomewide Selection (GS), where different strategies for imputation have been developed. Nevertheless, imputation error may lead to poor p...

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Autores principales: Brandariz, Sofía P., González Reymúndez, Agustín, Lado, Bettina, Malosetti, Marcos, Garcia, Antonio Augusto Franco, Quincke, Martín, von Zitzewitz, Jarislav, Castro, Marina, Matus, Iván, del Pozo, Alejandro, Castro, Ariel J., Gutiérrez, Lucía
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2016
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5050639/
https://www.ncbi.nlm.nih.gov/pubmed/27716058
http://dx.doi.org/10.1186/s12864-016-3120-5
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author Brandariz, Sofía P.
González Reymúndez, Agustín
Lado, Bettina
Malosetti, Marcos
Garcia, Antonio Augusto Franco
Quincke, Martín
von Zitzewitz, Jarislav
Castro, Marina
Matus, Iván
del Pozo, Alejandro
Castro, Ariel J.
Gutiérrez, Lucía
author_facet Brandariz, Sofía P.
González Reymúndez, Agustín
Lado, Bettina
Malosetti, Marcos
Garcia, Antonio Augusto Franco
Quincke, Martín
von Zitzewitz, Jarislav
Castro, Marina
Matus, Iván
del Pozo, Alejandro
Castro, Ariel J.
Gutiérrez, Lucía
author_sort Brandariz, Sofía P.
collection PubMed
description BACKGROUND: Whole-genome genotyping techniques like Genotyping-by-sequencing (GBS) are being used for genetic studies such as Genome-Wide Association (GWAS) and Genomewide Selection (GS), where different strategies for imputation have been developed. Nevertheless, imputation error may lead to poor performance (i.e. smaller power or higher false positive rate) when complete data is not required as it is for GWAS, and each marker is taken at a time. The aim of this study was to compare the performance of GWAS analysis for Quantitative Trait Loci (QTL) of major and minor effect using different imputation methods when no reference panel is available in a wheat GBS panel. RESULTS: In this study, we compared the power and false positive rate of dissecting quantitative traits for imputed and not-imputed marker score matrices in: (1) a complete molecular marker barley panel array, and (2) a GBS wheat panel with missing data. We found that there is an ascertainment bias in imputation method comparisons. Simulating over a complete matrix and creating missing data at random proved that imputation methods have a poorer performance. Furthermore, we found that when QTL were simulated with imputed data, the imputation methods performed better than the not-imputed ones. On the other hand, when QTL were simulated with not-imputed data, the not-imputed method and one of the imputation methods performed better for dissecting quantitative traits. Moreover, larger differences between imputation methods were detected for QTL of major effect than QTL of minor effect. We also compared the different marker score matrices for GWAS analysis in a real wheat phenotype dataset, and we found minimal differences indicating that imputation did not improve the GWAS performance when a reference panel was not available. CONCLUSIONS: Poorer performance was found in GWAS analysis when an imputed marker score matrix was used, no reference panel is available, in a wheat GBS panel. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s12864-016-3120-5) contains supplementary material, which is available to authorized users.
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spelling pubmed-50506392016-10-05 Ascertainment bias from imputation methods evaluation in wheat Brandariz, Sofía P. González Reymúndez, Agustín Lado, Bettina Malosetti, Marcos Garcia, Antonio Augusto Franco Quincke, Martín von Zitzewitz, Jarislav Castro, Marina Matus, Iván del Pozo, Alejandro Castro, Ariel J. Gutiérrez, Lucía BMC Genomics Research Article BACKGROUND: Whole-genome genotyping techniques like Genotyping-by-sequencing (GBS) are being used for genetic studies such as Genome-Wide Association (GWAS) and Genomewide Selection (GS), where different strategies for imputation have been developed. Nevertheless, imputation error may lead to poor performance (i.e. smaller power or higher false positive rate) when complete data is not required as it is for GWAS, and each marker is taken at a time. The aim of this study was to compare the performance of GWAS analysis for Quantitative Trait Loci (QTL) of major and minor effect using different imputation methods when no reference panel is available in a wheat GBS panel. RESULTS: In this study, we compared the power and false positive rate of dissecting quantitative traits for imputed and not-imputed marker score matrices in: (1) a complete molecular marker barley panel array, and (2) a GBS wheat panel with missing data. We found that there is an ascertainment bias in imputation method comparisons. Simulating over a complete matrix and creating missing data at random proved that imputation methods have a poorer performance. Furthermore, we found that when QTL were simulated with imputed data, the imputation methods performed better than the not-imputed ones. On the other hand, when QTL were simulated with not-imputed data, the not-imputed method and one of the imputation methods performed better for dissecting quantitative traits. Moreover, larger differences between imputation methods were detected for QTL of major effect than QTL of minor effect. We also compared the different marker score matrices for GWAS analysis in a real wheat phenotype dataset, and we found minimal differences indicating that imputation did not improve the GWAS performance when a reference panel was not available. CONCLUSIONS: Poorer performance was found in GWAS analysis when an imputed marker score matrix was used, no reference panel is available, in a wheat GBS panel. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s12864-016-3120-5) contains supplementary material, which is available to authorized users. BioMed Central 2016-10-04 /pmc/articles/PMC5050639/ /pubmed/27716058 http://dx.doi.org/10.1186/s12864-016-3120-5 Text en © The Author(s). 2016 Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
spellingShingle Research Article
Brandariz, Sofía P.
González Reymúndez, Agustín
Lado, Bettina
Malosetti, Marcos
Garcia, Antonio Augusto Franco
Quincke, Martín
von Zitzewitz, Jarislav
Castro, Marina
Matus, Iván
del Pozo, Alejandro
Castro, Ariel J.
Gutiérrez, Lucía
Ascertainment bias from imputation methods evaluation in wheat
title Ascertainment bias from imputation methods evaluation in wheat
title_full Ascertainment bias from imputation methods evaluation in wheat
title_fullStr Ascertainment bias from imputation methods evaluation in wheat
title_full_unstemmed Ascertainment bias from imputation methods evaluation in wheat
title_short Ascertainment bias from imputation methods evaluation in wheat
title_sort ascertainment bias from imputation methods evaluation in wheat
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5050639/
https://www.ncbi.nlm.nih.gov/pubmed/27716058
http://dx.doi.org/10.1186/s12864-016-3120-5
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