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Wheat (Triticum aestivum L.) cultivars must possess suitable enduse quality for release and consumer acceptability. However, breeding for quality traits is often considered a secondary target relative to yield largely because of amount of seed needed and expense. Without testing and selection, many undesirable materials are advanced, expending additional resources. Here, we develop and validate whole-genome prediction models for end-use quality phenotypes in the CIMMYT bread wheat breeding program. Model accuracy was tested using forward prediction on breeding lines (n = 5520) tested in unbalanced yield trials from 2009 to 2015 at Ciudad Obregon, Sonora, Mexico. Quality parameters included test weight, 1000-kernel weight, hardness, grain and flour protein, flour yield, sodium dodecyl sulfate sedimentation, Mixograph and Alveograph performance, and loaf volume. In general, prediction accuracy substantially increased over time as more data was available to train the model. Reflecting practical implementation of genomic selection (GS) in the breeding program, forward prediction accuracies (r) for quality parameters were assessed in 2015 and ranged from 0.32 (grain hardness) to 0.62 (mixing time). Increased selection intensity was possible with GS since more entries can be genotyped than phenotyped and expected genetic gain was 1.4 to 2.7 times higher across all traits than phenotypic selection. Given the limitations in measuring many lines for quality, we conclude that GS is a powerful tool to facilitate early generation selection for end-use quality in wheat, leaving larger populations for selection on yield during advanced testing and leading to better gain for both quality and yield in bread wheat breeding programs.

This study examines genomic prediction within 8416 Mexican landrace accessions and 2403 Iranian landrace accessions stored in gene banks. The Mexican and Iranian collections were evaluated in separate field trials, including an optimum environment for several traits, and in two separate environments (drought, D and heat, H) for the highly heritable traits, days to heading (DTH), and days to maturity (DTM). Analyses accounting and not accounting for population structure were performed. Genomic prediction models include genotype × environment interaction (G × E). Two alternative prediction strategies were studied: (1) random cross-validation of the data in 20% training (TRN) and 80% testing (TST) (TRN20-TST80) sets, and (2) two types of core sets, “diversity” and “prediction”, including 10% and 20%, respectively, of the total collections. Accounting for population structure decreased prediction accuracy by 15–20% as compared to prediction accuracy obtained when not accounting for population structure. Accounting for population structure gave prediction accuracies for traits evaluated in one environment for TRN20-TST80 that ranged from 0.407 to 0.677 for Mexican landraces, and from 0.166 to 0.662 for Iranian landraces. Prediction accuracy of the 20% diversity core set was similar to accuracies obtained for TRN20-TST80, ranging from 0.412 to 0.654 for Mexican landraces, and from 0.182 to 0.647 for Iranian landraces. The predictive core set gave similar prediction accuracy as the diversity core set for Mexican collections, but slightly lower for Iranian collections. Prediction accuracy when incorporating G × E for DTH and DTM for Mexican landraces for TRN20-TST80 was around 0.60, which is greater than without the G × E term. For Iranian landraces, accuracies were 0.55 for the G × E model with TRN20-TST80. Results show promising prediction accuracies for potential use in germplasm enhancement and rapid introgression of exotic germplasm into elite materials.

Knowledge of composition of high molecular weight glutenin subunits (HMW‐GS) and low molecular weight glutenin subunits (LMW‐GS) and their associations with pan bread and noodle quality will contribute to genetically improving processing quality of Chinese bread wheats. Two trials including a total of 158 winter and facultative cultivars and advanced lines were conducted to detect the allelic variation at Glu‐1 and Glu‐3 loci by SDS‐PAGE electrophoresis and to understand their effects on dough properties, pan bread, and dry white Chinese noodle (DWCN) quality. Results indicate that subunits/alleles 1 and null at Glu‐A1, 7+8 and 7+9 at Glu‐B1, 2+12 and 5+10 at Glu‐D1, alleles a and d at Glu‐A3, and alleles j and d at Glu‐B3 predominate in Chinese germplasm, and that 34.9% of the tested genotypes carry the 1B/1R translocation (allelic variation at Glu‐D3 was not determined because no significant effects were reported previously). Both variations at HMW‐GS and LMW‐GS/alleles and loci interactions contribute to dough properties and processing quality. For dough strength related traits such as farinograph stability and extensigraph maximum resistance and loaf volume, subunits/alleles 1, 7+8, 5+10, and Glu‐A3d are significantly better than those of their counterpart allelic variation, however, no significant difference was observed for the effects of d, b, and f at Glu‐B3 on these traits. For extensigraph extensibility, only subunits 1 and 7+8 are significantly better than their counterpart alleles, and alleles d and b at Glu‐B3 are slightly better than others. For DWCN quality, no significant difference is observed for HMW‐GS at Glu‐1, and Glu‐A3d and Glu‐B3d are slightly better than other alleles. Glu‐B3j, associated the 1B/1R translocation, has a strong negative effect on all quality traits except protein content. It is recommended that selection for subunits/alleles 1, 7+8, 5+10, and Glu‐A3d could contribute to improving gluten quality and pan bread quality. Reducing the frequency of the 1B/1R translocation will be crucial to wheat quality improvement in China.

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