Prospects and Challenges of Applied Genomic Selection—A New Paradigm in Breeding for Grain Yield in Bread Wheat

Abstract

Core Ideas Genomic prediction of grain yield across nurseries or years is challenging, because of genotype × environment interactions. Prediction accuracies can be improved by having at least one full‐sib in the training population. Genomic selection (GS) was less advantageous for within‐family selections in elite families with few full‐sibs and minimal Mendelian sampling variance. It is important to apply GS at the appropriate stage of the breeding cycle. Genomic selection (GS) has been promising for increasing genetic gains in several species. Therefore, we evaluated the potential integration of GS for grain yield (GY) in bread wheat (Triticum aestivum L.) in CIMMYT's elite yield trial nurseries. We observed that the genomic prediction accuracies within nurseries (0.44 and 0.35) were substantially higher than across‐nursery accuracies (0.15 and 0.05) for GY evaluated in the bed and flat planting systems, respectively. The accuracies from using only a subset of 251 genotyping‐by‐sequencing markers were comparable to the accuracies using all 2038 markers. We also used the item‐based collaborative filtering approach for incorporating other related traits in predicting GY and observed that it outperformed genomic predictions across nurseries, but was less predictive when trait correlations with GY were low. Furthermore, we compared GS and phenotypic selections (PS) and observed that at a selection intensity of 0.5, GS could select a maximum of 70.9 and 61.5% of the top lines and discard 71.5 and 60.5% of the poor lines selected or discarded by PS within and across nurseries, respectively. Comparisons of GS and pedigree‐based predictions revealed that the advantage of GS over the pedigree was moderate in populations without full‐sibs. However, GS was less advantageous for within‐family selections in elite families with few full‐sibs and minimal Mendelian sampling variance. Overall, our results demonstrate the importance of applying GS for GY at the appropriate stage of the breeding cycle, and we speculate that gains can be maximized if it is implemented in early‐generation within‐family selections.

Genomic Prediction and Indirect Selection for Grain Yield in US Pacific Northwest Winter Wheat Using Spectral Reflectance Indices from High-Throughput Phenotyping

“…Using the more related DH panels as test sets resulted in higher prediction accuracy compared with using the F5 in the presence of SRI as fixed effects (mean prediction accuracy of 0.23 vs. 0.15), indicating the relevance of relatedness between the training and validation panels. This is consistent with previous studies that show the importance of using genetically related populations for predictions [27,47,48]. Our results further indicate that when genetically unrelated populations are used for genomic predictions, the inclusion of secondary traits in the model could increase prediction accuracy for grain yield.…”

Multi-trait Genomic Prediction Model Increased the Predictive Ability for Agronomic and Malting Quality Traits in Barley (Hordeum vulgare L.)

“…Although poor predictive ability remains a major challenge in implementing genomic selection (Crossa et al, 2014), several studies have shown that genomic selection could be advantageous for complex traits with low heritability such as grain yield (Belamkar et al, 2018;Burgueño et al, 2012;. Genomic selection with low to moderate levels of predictive ability could be used in early-generation testing and selection (Belamkar et al, 2018;Juliana et al, 2018;Michel et al, 2018) and in off-season nurseries where field phenotypic information might be useless. Breeding program do not rely on predictive ability itself, but also on how genomic selection can be leveraged in selecting or discarding lines within a program, selecting parents, and phenotyping efforts (Belamkar et al, 2018;Juliana et al, 2018;Lado et al, 2018).…”

“…Genomic selection with low to moderate levels of predictive ability could be used in early-generation testing and selection (Belamkar et al, 2018;Juliana et al, 2018;Michel et al, 2018) and in off-season nurseries where field phenotypic information might be useless. Breeding program do not rely on predictive ability itself, but also on how genomic selection can be leveraged in selecting or discarding lines within a program, selecting parents, and phenotyping efforts (Belamkar et al, 2018;Juliana et al, 2018;Lado et al, 2018).…”

Simultaneous selection for grain yield and protein content in genomics-assisted wheat breeding

“…The implementation of genomic selection in many national and international plant breeding programmes in recent years (Guzmán et al 2016 ; Lado et al 2016 ; Michel et al 2016 ; Cericola et al 2017 ; Belamkar et al 2018 ; Juliana 2018 ) highlights the potential of this new breeding tool for variety development and accelerating the genetic improvement in crop plants. The merit of employing genomic predictions has been frequently tested by cross-validation, but also across families and years taking genomic relationship and genotype-by-environment interaction into account (Gezan et al 2017 ; Ben Hassen et al 2018 ; Kristensen et al 2018 ; Huang et al 2018; Pembleton et al 2018 ).…”