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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. The 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.

SUBMITTER: Michel S

PROVIDER: S-EPMC5787228 | biostudies-literature | 2018 Feb

REPOSITORIES: biostudies-literature

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

Michel Sebastian S Kummer Christian C Gallee Martin M Hellinger Jakob J Ametz Christian C Akgöl Batuhan B Epure Doru D Güngör Huseyin H Löschenberger Franziska F Buerstmayr Hermann H

TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik 20171023 2

<h4>Key message</h4>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. The 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 phase ...[more]

PMID: 29063161

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Project description:KEY MESSAGE:Simultaneous genomic selection for grain yield, protein content and dough rheological traits enables the development of resource-use efficient varieties that combine superior yield potential with comparably high end-use quality. Selecting simultaneously for grain yield and baking quality is a major challenge in wheat breeding, and several concepts like grain protein deviations have been developed for shifting the undesirable negative correlation between both traits. The protein quality is, however, not considered in these concepts, although it is an important aspect and might facilitate the selection of genotypes that use available resources more efficiently with respect to the quantity and quality of the final end products. A population of 480 lines from an applied wheat breeding programme that was phenotyped for grain yield, protein content, protein yield and dough rheological traits was thus used to assess the potential of using integrated genomic selection indices to ease selection decisions with regard to the plethora of quality traits. Additionally, the feasibility of achieving a simultaneous genetic improvement in grain yield, protein content and protein quality was investigated to develop more resource-use efficient varieties. Dough rheological traits related to either gluten strength or viscosity were combined in two separate indices, both of which showed a substantially smaller negative trade-off with grain yield than the protein content. Genomic selection indices based on regression deviations for the two latter traits were subsequently extended by the gluten strength or viscosity indices. They revealed a large merit for identifying resource-use efficient genotypes that combine both superior yield potential with comparably high end-use quality. Hence, genomic selection opens up the opportunity for multi-trait selection in early generations, which will most likely increase the efficiency when developing new and improved varieties.

Project description:BackgroundHigh-molecular-weight glutenin subunits (HMW-GS) play important roles in the elasticity of dough made from wheat. The HMW-GS null line is useful for studying the contribution of HMW-GS to the end-use quality of wheat.MethodsIn a previous work, we cloned the Glu-1Ebx gene from Thinopyrum bessarabicum and introduced it into the wheat cultivar, Bobwhite. In addition to lines expressing the Glu-1Ebx gene, we also obtained a transgenic line (LH-11) with all the HMW-GS genes silenced. The HMW-GS deletion was stably inherited as a dominant and conformed to Mendel's laws. Expression levels of HMW-GS were determined by RT-PCR and epigenetic changes in methylation patterns and small RNAs were analyzed. Glutenins and gliadins were separated and quantitated by reversed-phase ultra-performance liquid chromatography. Measurement of glutenin macropolymer, and analysis of agronomic traits and end-use quality were also performed.ResultsDNA methylation and the presence of small double-stranded RNA may be the causes of post-transcriptional gene silencing in LH-11. The accumulation rate and final content of glutenin macropolymer (GMP) in LH-11 were significantly lower than in wild-type (WT) Bobwhite. The total protein content was not significantly affected as the total gliadin content increased in LH-11 compared to WT. Deletion of HMW-GS also changed the content of different gliadin fractions. The ratio of ω-gliadin increased, whereas α/β- and γ-gliadins declined in LH-11. The wet gluten content, sedimentation value, development time and stability time of LH-11 were remarkably lower than that of Bobwhite. Bread cannot be made using the flour of LH-11.ConclusionsPost-transcriptional gene silencing through epigenetic changes and RNA inhibition appear to be the causes for the gene expression deficiency in the transgenic line LH-11. The silencing of HMW-GW in LH-11 significantly reduced the dough properties, GMP content, wet gluten content, sedimentation value, development time and stability time of flour made from this wheat cultivar. The HMW-GS null line may provide a potential material for biscuit-making because of its low dough strength.

Project description:Key messageWe evaluated genomic selection across five breeding cycles of bread wheat breeding. Bias of within-cycle cross-validation and methods for improving the prediction accuracy were assessed. The prospect of genomic selection has been frequently shown by cross-validation studies using the same genetic material across multiple environments, but studies investigating genomic selection across multiple breeding cycles in applied bread wheat breeding are lacking. We estimated the prediction accuracy of grain yield, protein content and protein yield of 659 inbred lines across five independent breeding cycles and assessed the bias of within-cycle cross-validation. We investigated the influence of outliers on the prediction accuracy and predicted protein yield by its components traits. A high average heritability was estimated for protein content, followed by grain yield and protein yield. The bias of the prediction accuracy using populations from individual cycles using fivefold cross-validation was accordingly substantial for protein yield (17-712 %) and less pronounced for protein content (8-86 %). Cross-validation using the cycles as folds aimed to avoid this bias and reached a maximum prediction accuracy of [Formula: see text] = 0.51 for protein content, [Formula: see text] = 0.38 for grain yield and [Formula: see text] = 0.16 for protein yield. Dropping outlier cycles increased the prediction accuracy of grain yield to [Formula: see text] = 0.41 as estimated by cross-validation, while dropping outlier environments did not have a significant effect on the prediction accuracy. Independent validation suggests, on the other hand, that careful consideration is necessary before an outlier correction is undertaken, which removes lines from the training population. Predicting protein yield by multiplying genomic estimated breeding values of grain yield and protein content raised the prediction accuracy to [Formula: see text] = 0.19 for this derived trait.

Project description:Hybrid performance during wheat breeding can be improved by analyzing genetic distance (GD) among wheat genotypes and determining its correlation with heterosis. This study evaluated the GD between 16 wheat genotypes by using 60 simple sequence repeat (SSR) markers to classify them according to their relationships and select those with greater genetic diversity, evaluate the correlation of the SSR marker distance with heterotic performance and specific combining ability (SCA) for heat stress tolerance, and identify traits that most influence grain yield (GY). Eight parental genotypes with greater genetic diversity and their 28 F1 hybrids generated using diallel crossing were evaluated for 12 measured traits in two seasons. The GD varied from 0.235 to 0.911 across the 16 genotypes. Cluster analysis based on the GD estimated using SSRs classified the genotypes into three major groups and six sub-groups, almost consistent with the results of principal coordinate analysis. The combined data indicated that five hybrids showed 20% greater yield than mid-parent or better-parent. Two hybrids (P2 × P4) and (P2 × P5), which showed the highest performance of days to heading (DH), grain filling duration (GFD), and GY, and had large genetic diversity among themselves (0.883 and 0.911, respectively), were deemed as promising heat-tolerant hybrids. They showed the best mid-parent heterosis and better-parent heterosis (BPH) for DH (-11.57 and -7.65%; -13.39 and -8.36%, respectively), GFD (12.74 and 12.17%; 12.09 and 10.59%, respectively), and GY (36.04 and 20.04%; 44.06 and 37.73%, respectively). Correlation between GD and each of BPH and SCA effects based on SSR markers was significantly positive for GFD, hundred kernel weight, number of kernels per spike, harvest index, GY, and grain filling rate and was significantly negative for DH. These correlations indicate that the performance of wheat hybrids with high GY and earliness could be predicted by determining the GD of the parents by using SSR markers. Multivariate analysis (stepwise regression and path coefficient) suggested that GFD, hundred kernel weight, days to maturity, and number of kernels per spike had the highest influence on GY.

Dataset Information

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

Key message

Publications

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

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