Genomic Selection at Preliminary Yield Trial Stage: Training Population Design to Predict Untested Lines

Abstract

Genomic selection (GS) is being applied routinely in wheat breeding programs. For the evaluation of preliminary lines, this tool is becoming important because preliminary lines are generally evaluated in few environments with no replications due to the minimal amount of seed available to the breeder. A total of 816 breeding lines belonging to advanced or preliminary yield trials were included in the study. We designed different training populations (TP) to predict lines in preliminary yield trials (PYT) consisting of: (i) advanced lines of the breeding program; (ii) 50% of the preliminary lines set belonging to many families; (iii) only full sibs, consisting of 50% of lines of each family. Results showed that the strategy of splitting the preliminary set in half, phenotyping only half of the lines to serve as the TP showed the most consistent results for the different traits. For a subset of the population of lines, we observed accuracies ranging from 0.49–0.65 for yield, 0.59–0.61 for test weight, 0.70–0.72 for heading date, and 0.49–0.50 for height. Accuracies decreased with the other training population designs, and were inconsistent across preliminary line sets and traits. From a breeder’s perspective, a prediction accuracy of 0.65 meant, at 0.2 selection intensity, 75% of the best yielding lines based on phenotypic information were correctly selected by the GS model. Our results demonstrate that, despite the small family size, an approach that includes lines from the same family in both the TP and VP, together with half sibs and more distant lines, and only phenotyping the lines included in the TP, could be a useful, efficient design for establishing a GS scheme to predict lines entering first year yield trials.

Training Population Design With the Use of Regional Fusarium Head Blight Nurseries to Predict Independent Breeding Lines for FHB Traits

“…In Figure 2, we analyzed the impact of these prediction accuracies when selecting lines based on DON BLUEs, and we observed an increase in proportion of lines correctly selected (up to 70%) when reducing the selection intensity, and with an advantage of 4-8% when selecting lines based on DSK index compare to DON index. These results confirm the usefulness of multiple trait indices as a source of information to distinguish the best genotypes for a trait, and also shows again that a prediction accuracy of 0.49 for example should be considered in terms of the percentage of lines "correctly" selected by the GS model, as discussed by Bassi et al (2015) and Verges and Van Sanford (2020). In our study again with a prediction accuracy of 0.49, a 50 to 70% of the lines are correctly selected at 30-40% SI.…”

Optical sorter–augmented genomic selection lowers deoxynivalenol accumulation in wheat

“…(2020), who reported, at a selection intensity of 20%, that 44% of lines selected based on DON GEBVs were in common with those selected based on actual DON values. However, both results are in sharp contrast to the findings of Verges and Van Sanford (2020), who observed up to 75% of lines selected for agronomic traits using GS in common with those selected using phenotypic selection.…”

Utilizing Genomic Selection for Wheat Population Development and Improvement

“…Genomic selection can also be implemented in the PD pipeline for across and within breeding cycles for selection and advancement (Figure 3) [9,15,24,25]. Early generation GS is superior to conventional PS in line breeding and can be strongly improved by including additional information from later generation PYTs and AYTs [9,25]. For instance, phenotypic data from the PYT or AYT can be used to predict advancements across breeding cycles as early as F2, DH, and inbred lines [26,27].…”

“…The implementation of GS for selection in PYTs has been extensively studied [15,21,24,25]. The PYT has been intuitively chosen because they are the first time that lines are yield tested and generally constitute the largest filtering stage before subjecting lines to resource-demanding replicated multi-location yield trials.…”

“…As mentioned previously, within cycle selection can be implemented in most stages in the breeding program. Verges and Van Sanford [25] demonstrated by utilizing within cycle selection, the number of lines in the PYT can be increased by phenotyping half of the lines and using the results to predict the performance of the other half. However, according to Michel et al [24], predicting across cycles rather than within cycles decreased protein yield and content bias.…”