Abstract
BackgroundWhole-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.ResultsIn 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.ConclusionsPoorer 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 materialThe online version of this article (doi:10.1186/s12864-016-3120-5) contains supplementary material, which is available to authorized users.
Highlights
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
We summarized the results with power (PO) and false positive rate (FPR)
Ascertainment bias in imputation performance comparison When we used a golden standard matrix of barley for simulating the Quantitative Trait Loci (QTL), we found that for major QTL effects, larger power was obtained without imputing the genotypic matrix
Summary
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. Next-generation sequencing (NGS) techniques allow discovering, sequencing, and genotyping thousands of Single Nucleotide Polymorphism (SNPs) covering the whole genome [1] These SNPs are being used in analyses like transcriptome assembly [2], Brandariz et al BMC Genomics (2016) 17:773 different strategies to impute missing data have been developed and used for genetic analyses [9]. IMPUTE considers the sequence of pairs of known haplotypes as hidden states, models the sequence of hidden states based on a recombination map estimated from the reference data, and it predicts unknown genotypes [14]. Beagle is a haplotype clustering based algorithm that uses the localized haplotype cluster model to group haplotypes at each marker and finds the most likely haplotype pairs based on the individual’s known genotypes [17]. For an overview of the imputation methods see [10]
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