Ascertainment bias from imputation methods evaluation in wheat. (December 2016)

• Record Type: Journal Article
• Authors: Brandariz, Sofía; González Reymúndez, Agustín; Lado, Bettina; Malosetti, Marcos; Garcia, Antonio; Quincke, Martín; von Zitzewitz, Jarislav; Castro, Marina; Matus, Iván; del Pozo, Alejandro; Castro, Ariel; Gutiérrez, Lucía
• Is Part Of: BMC genomics. Volume 17:Number 1(2016)
• Page Start: 1
• Page End: 13
• Publication Date: 2016-12
• Subjects: GBS -- QTL -- GWAS -- Power -- False positive; Genomes -- Periodicals; Gene mapping -- Periodicals; Genomics -- Periodicals; Base Sequence -- Periodicals; Chromosome Mapping -- Periodicals; Genetic Techniques -- Periodicals; Sequence Analysis, DNA -- Periodicals; 572.8605
• DOI: 10.1186/s12864-016-3120-5
• Languages: English
• ISSNs: 1471-2164

Abstract

Abstract 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.