Prospects and limits of marker imputation in quantitative genetic studies in European elite wheat (Triticum aestivum L.).

Sang He,Yusheng Zhao,Erhard Ebmeyer,Reiner Bothe,Jochen C Reif,Yong Jiang,M Florian Mette,Timothy F Sharbel

doi:10.1186/s12864-015-1366-y

Sang He, Yusheng Zhao + Show 6 more

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https://doi.org/10.1186/s12864-015-1366-y

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Abstract

BackgroundThe main goal of our study was to investigate the implementation, prospects, and limits of marker imputation for quantitative genetic studies contrasting map-independent and map-dependent algorithms. We used a diversity panel consisting of 372 European elite wheat (Triticum aestivum L.) varieties, which had been genotyped with SNP arrays, and performed intensive simulation studies.ResultsOur results clearly showed that imputation accuracy was substantially higher for map-dependent compared to map-independent methods. The accuracy of marker imputation depended strongly on the linkage disequilibrium between the markers in the reference panel and the markers to be imputed. For the decay of linkage disequilibrium present in European wheat, we concluded that around 45,000 markers are needed for low cost, low-density marker profiling. This will facilitate high imputation accuracy, also for rare alleles. Genomic selection and diversity studies profited only marginally from imputing missing values. In contrast, the power of association mapping increased substantially when missing values were imputed.ConclusionsImputing missing values is especially of interest for an economic implementation of association mapping in breeding populations.Electronic supplementary materialThe online version of this article (doi:10.1186/s12864-015-1366-y) contains supplementary material, which is available to authorized users.

Highlights

The main goal of our study was to investigate the implementation, prospects, and limits of marker imputation for quantitative genetic studies contrasting map-independent and map-dependent algorithms
The findings revealed that estimating heterozygosity, inbreeding coefficients, and genetic differentiation were substantially biased when missing values were imputed
After performing quality checks to exclude those markers that were monomorphic and for which genetic map information was unavailable [15], 9,926 Single nucleotide polymorphism (SNP) remained for the 90 k SNP array and 1,573 SNPs remained for the 9 k SNP array

Summary

Introduction

The main goal of our study was to investigate the implementation, prospects, and limits of marker imputation for quantitative genetic studies contrasting map-independent and map-dependent algorithms. Imputing missing values is crucial for molecular marker data sets generated by methods with inherent high levels of missing data, for example genotyping-by-sequencing (GBS) [1]. This holds for approaches aiming to reduce genotyping expenses by combining high-density marker profiling of a population subsample with medium-density marker profiling for the majority of population members. Imputation algorithms can be classified into mapdependent and map-independent algorithms. Several factors can influence the accuracy of imputing missing values for particular markers [5]. The accuracy of imputation benefits if genotypic information is available for markers tightly linked to those being imputed [5,8].

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