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Ascertainment bias from imputation methods evaluation in wheat

Overview of attention for article published in BMC Genomics, October 2016
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Title
Ascertainment bias from imputation methods evaluation in wheat
Published in
BMC Genomics, October 2016
DOI 10.1186/s12864-016-3120-5
Pubmed ID
Authors

Sofía P. Brandariz, Agustín González Reymúndez, Bettina Lado, Marcos Malosetti, Antonio Augusto Franco Garcia, Martín Quincke, Jarislav von Zitzewitz, Marina Castro, Iván Matus, Alejandro del Pozo, Ariel J. Castro, Lucía Gutiérrez

Abstract

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. 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. 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.

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 36 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Italy 1 3%
France 1 3%
Brazil 1 3%
Unknown 33 92%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 10 28%
Researcher 8 22%
Student > Master 4 11%
Other 3 8%
Student > Postgraduate 2 6%
Other 6 17%
Unknown 3 8%
Readers by discipline Count As %
Agricultural and Biological Sciences 26 72%
Computer Science 3 8%
Mathematics 1 3%
Engineering 1 3%
Unknown 5 14%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 1. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 02 September 2017.
All research outputs
#17,818,042
of 22,890,496 outputs
Outputs from BMC Genomics
#7,583
of 10,670 outputs
Outputs of similar age
#228,467
of 319,862 outputs
Outputs of similar age from BMC Genomics
#174
of 269 outputs
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