Improving the baking quality of bread wheat by genomic selection in early generations

Abstract

Key message Genomic selection shows great promise for pre-selecting lines with superior bread baking quality in early generations, 3 years ahead of labour-intensive, time-consuming, and costly quality analysis. AbstractThe genetic improvement of baking quality is one of the grand challenges in wheat breeding as the assessment of the associated traits often involves time-consuming, labour-intensive, and costly testing forcing breeders to postpone sophisticated quality tests to the very last phases of variety development. The prospect of genomic selection for complex traits like grain yield has been shown in numerous studies, and might thus be also an interesting method to select for baking quality traits. Hence, we focused in this study on the accuracy of genomic selection for laborious and expensive to phenotype quality traits as well as its selection response in comparison with phenotypic selection. More than 400 genotyped wheat lines were, therefore, phenotyped for protein content, dough viscoelastic and mixing properties related to baking quality in multi-environment trials 2009–2016. The average prediction accuracy across three independent validation populations was r = 0.39 and could be increased to r = 0.47 by modelling major QTL as fixed effects as well as employing multi-trait prediction models, which resulted in an acceptable prediction accuracy for all dough rheological traits (r = 0.38–0.63). Genomic selection can furthermore be applied 2–3 years earlier than direct phenotypic selection, and the estimated selection response was nearly twice as high in comparison with indirect selection by protein content for baking quality related traits. This considerable advantage of genomic selection could accordingly support breeders in their selection decisions and aid in efficiently combining superior baking quality with grain yield in newly developed wheat varieties.Electronic supplementary materialThe online version of this article (doi:10.1007/s00122-017-2998-x) contains supplementary material, which is available to authorized users.

Gains through selection for grain yield in a winter wheat breeding program

“…Selection responses achieved by integrating GWAS-derived markers as fixed effects in the prediction model (GS2) was not significantly different than that of a standard GS approach (GS1), although 17% improvement in the mean R was observed. This demonstrated the potential to increase gains by incorporating fixed effect markers in the model, consistent with previous studies [43,44]. It should be noted that the markers used as fixed effects in the selection model were identified to be significant only in the training population (AMP) to disregard the effect of “inside trading,” which was previously observed to cause overestimated accuracies for FHB resistance in wheat [33].…”

Genomic prediction and quantitative trait locus discovery in a cassava training population constructed from multiple breeding stages

“…We also observed that using clones from the same breeding stage for TP gave higher prediction accuracy estimates than clones aggregated from multiple breeding stages. Our estimates agree with those found by Hofheinz, Borchardt, Weissleder, and Frisch (2012) for data from two consecutive breeding cycles of sugar beet (Beta vulgaris L.) and the study of Michel et al (2018) of GS using multiple breeding cycles in bread wheat (Triticum aestivum L.). Ceballos et al (2016) recommends the use of phenotypic information from clones at the advanced breeding stage during GS because of their "stable" genotypic performance.…”

“…Consistent with the findings of other researchers, we conclude that clones from the same breeding cycles are currently better option as candidates for GS TPs (Cericola et al, 2017;Michel et al, 2018;Song et al, 2017). In addition, it may not be useful to constitute TPs from programs with divergent populations or populations separated by barriers like the existing restriction on clone movement between the two Tanzanian programs.…”

Effectiveness of Genomic Selection by Response to Selection for Winter Wheat Variety Improvement

“…Suppose top performers ( 208 lines with desirable values, high or low) to be selected, the relative superiority of selection, phenotypes of the entire population; is the estimated average 212 phenotypes of the selected population. And byMichel et al (2018),…”

“…The 205 focus of such programs will be on a stable selection of superior lines over years. For that reason, 206we introduced a procedure that evaluates line selection by relative superiority of selection, as a 207 measurement of response to selection(Michel et al, 2018). Suppose top performers ( 208 lines with desirable values, high or low) to be selected, the relative superiority of selection, phenotypes of the entire population; is the estimated average 212 phenotypes of the selected population.…”