Half-cost array-based genotyping of SNPs in bread wheat from pooled experiments and imputation

Abstract

Abstract The plant breeding industry has shown growing interest in using the genotype data of relevant markers for performing selection of new competitive varieties. The selection usually benefits from large amounts of marker data and it is therefore crucial to dispose of data collection methods that are both cost-effective and reliable.Computational methods such as genotype imputation have been proposed earlier in several plant science studies for addressing the cost challenge.Genotype imputation methods have though been used more frequently and investigated more extensively in human genetics research.The various algorithms that exist have shown lower accuracy at inferring the genotype of genetic variants occurring at low frequency, while these rare variants can have great significance and impact in the genetic studies that underlie selection.In contrast, pooling is a technique that can efficiently identify low-frequency items in a population and it has been successfully used for detecting the samples that carry rare variants in a population.In this study, we propose to combine pooling and imputation with microarray data for genotyping a population of recombinant inbred lines in a cost-effective and accurate manner, even for rare variants.We show that with an adequate imputation model, it is feasible to accurately predict the individual genotypes at half the cost of sample-wise genotyping and time-effectively. Moreover, we provide code resources for reproducing the results presented in this study in the form of a containerized workflow.