Imputation of low‐density marker chip data in plant breeding: Evaluation of methods based on sugar beet. Issue 4 (18th October 2022)

• Record Type: Journal Article
• Authors: Niehoff, Tobias; Pook, Torsten; Gholami, Mahmood; Beissinger, Timothy
• Is Part Of: œplant genome. Volume 15:Issue 4(2022)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2022-10-18
• Subjects: Plant genomes -- Periodicals; Plant genome mapping -- Periodicals; 572.862
• DOI: 10.1002/tpg2.20257
• Languages: English
• ISSNs: 1940-3372

Abstract

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 ( Beta vulgaris L. ssp. vulgaris ) 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 with 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 whereas Beagle was better for phasing. Combining both tools yielded the highest accuracies. Core Ideas: Beagle is sensitive to parameter tuning. Best imputation accuracies could be achieved by using a combination of Beagle and AlphaPlantImpute2. The population structure influenced imputation accuracy.