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 (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.

Improvement Technologies for Data Imputation in Bioinformatics

“…Additionally, we compared mean imputation accuracy and mean execution time with similar ones from [15]. The authors of this work conducted a set of experiments related to Beagle parameter optimization.…”

“…. Imputation results comparison: (a)-imputation time for Algorithm [15] and Algorithm A (using r5a.2xlarge); (b)-imputation accuracy for Algorithm [15] and Algorithm B (using r5a.2xlarge).…”

“…Imputation accuracy comparison Algorithm [15] vs. Algorithm B (r5a.2xlarge) Algorithm [15] Algorithm B Figure 16. Imputation results comparison: (a)-imputation time for Algorithm [15] and Algorithm A (using r5a.2xlarge); (b)-imputation accuracy for Algorithm [15] and Algorithm B (using r5a.2xlarge).…”

“…The paper [15] attracts particular attention, where the problem of enhancing the quality of imputed genotype data by utilizing a combination of established software solutions, Beagle and AlphaPlantImpute2, and optimizing their input parameters is explored. Imputation accuracy was measured as the Pearson correlation coefficient between the simulated and imputed data.…”

“…As a last step, we compared the obtained mean imputation accuracy and mean execution time with those from [15]. One of the most impactful changes related to the parameter "iterations" (set to 5000) significantly increases total execution time in any case (although mean accuracy is also increased).…”