Imputation of Low-density Marker Chip Data in Plant Breeding: Evaluation of Methods Based on Sugar Beet

Abstract

Low-density genotyping followed by imputation reduces genotyping costs while still providing high-density marker information. An increased marker density has the potential to improve the outcome of all applications that are based on genomic data. This study investigates techniques for 1k to 20k genomic marker imputation for plant breeding programs with sugar beet as an example crop, where these are realistic marker numbers for modern breeding applications. The generally accepted "gold standard" for imputation, Beagle 5.1, was compared to the recently developed software AlphaPlantImpute2 which is designed specifically for plant breeding. For Beagle 5.1 and AlphaPlantImpute2, the imputation strategy as well as the imputation parameters were optimized in this study. We found that the imputation accuracy of Beagle could be tremendously improved (0.22 to 0.67) by tuning parameters, mainly by lowering the values for the parameter for the effective population size and increasing the number of iterations performed. Separating the phasing and imputation steps also improved accuracies when optimized parameters were used (0.67 to 0.82). We also found that the imputation accuracy of Beagle decreased when more low-density lines were included for imputation. AlphaPlantImpute2 produced very high accuracies without optimization (0.89) and was generally less responsive to optimization. Overall, AlphaPlantImpute2 performed relatively better for imputation while Beagle was better for phasing. Combining both tools yielded the highest accuracies.

A multiple phenotype imputation method for genetic diversity and core collection in Taiwanese vegetable soybean

“…The MI method can be applied to many kinds of data, including phenotypes and genotypes (Soley-Bori, 2013). Several success examples in plant and human studies, for instance Plant-Impute DB (Gao et al, 2021), imputed low-density marker chip data in plant breeding (Niehoff et al, 2022) and GWAS genotypes in rice (Wang et al, 2018). We noticed that the MI method outperforms the single imputation methods (e.g.…”