Assessing the agronomic potential of sorghum B-lines using genomic prediction

In hybrid sorghum breeding, basic agronomic traits, such as days to flowering and plant height, of sorghum seed parents must be within a specific range for hybrid seed production. Because many sorghum programs select new B-lines outside of commercial seed production environments, the purpose of this study was to determine if genomic prediction is effective to evaluate new lines for their agronomic potential for seed production. Two B-line RIL populations were evaluated across several environments for days to mid-anthesis (DY), plant height (PH), panicle length (PL), and seed yield (SY). Across environments and populations, average prediction accuracies were between 0.47 and 0.61 for DY, 0.24 and 0.60 for PH, 0.16 and 0.37 for SY, and 0.37 and 0.57 for PL. The effect of training set size was assessed by subsampling various amounts of data into the training set, ranging from 5% to 65%. Prediction accuracies generally improved as the proportion of the total data in the training set increased for both inter- and intrapopulation predictions, but a relatively small portion (15%) of the total data still produced modest prediction accuracies. The effect of the marker density on genomic prediction accuracies was assessed by subsampling various numbers of single nucleotide polymorphisms. It was observed that as few as 500 markers were able to produce prediction accuracies similar to when all markers were used. The results of this study indicated that genomic prediction can be a useful and cost-effective tool that sorghum breeding programs should incorporate into their pipeline. Genomic prediction can be used to assess the agronomic value of seed parents prior to hybrid seed production.Genomic prediction can be used to predict agronomic value within and across environments.The amount of data in the training set can impact prediction accuracies.Increasing data in training set does not improve prediction accuracies uniformly across populations.Relatively low marker density can be used to predict sorghum B-line phenotypes.

Automated summary

This study investigates the application of genomic prediction in sorghum breeding. By evaluating two populations across different environments, it is found that genomic prediction can effectively assess the agronomic potential of sorghum parents. The size of the training set and marker density affect the accuracy of prediction.