Feature engineering and parameter tuning: improving phenomic prediction ability in multi-environmental durum wheat breeding trials.

KEY MESSAGE: Optimized phenomic selection in durum wheat uses near-infrared spectra, feature engineering and parameter tuning. Our study reports improvements in predictive ability and emphasizes customized preprocessing for different traits and models. The success of plant breeding programs depends on efficient selection decisions. Phenomic selection has been proposed as a tool to predict phenotype performance based on near-infrared spectra (NIRS) to support selection decisions. In this study, we test the performance of phenomic selection in multi-environmental trials from our durum wheat breeding program for three breeding scenarios and use feature engineering as well as parameter tuning to improve the phenomic prediction ability. In addition, we investigate the influence of genotype and environment on the phenomic prediction ability for agronomic and quality traits. Preprocessing, based on a grid search over the Savitzky-Golay filter parameters based on 756,000 genotype best linear unbiased estimate (BLUE) computations, improved the phenomic prediction ability by up to 1500% (0.02-0.3). Furthermore, we show that preprocessing should be optimized depending on the dataset, trait, and model used for prediction. The phenomic prediction scenarios in our durum breeding program resulted in low-to-moderate prediction abilities with the highest and most stable prediction results when predicting new genotypes in the same environment as used for model training. This is consistent with the finding that NIRS capture both the genotype and genotype-by-environment ( G × E ) interaction variance.

Multiomics Based Association Mapping in Wheat Reveals Genetic Architecture of Quality and Allergenic Related Proteins.

Wheat is an important staple crop since its proteins contribute to human and animal nutrition and are important for its end-use quality. However, wheat proteins can also cause adverse human reactions for a large number of people. We performed a genome wide association study (GWAS) on 114 proteins quantified by LC-MS-based proteomics and expressed in an environmentally stable manner in 148 wheat cultivars with a heritability > 0.6. For 54 proteins, we detected quantitative trait loci (QTL) that exceeded the Bonferroni-corrected significance threshold and explained 17.3−84.5% of the genotypic variance. Proteins in the same family often clustered at a very close chromosomal position or the potential homeolog. Major QTLs were found for four well-known glutenin and gliadin subunits, and the QTL segregation pattern in the protein encoding the high molecular weight glutenin subunit Dx5 could be confirmed by SDS gel-electrophoresis. For nine potential allergenic proteins, large QTLs could be identified, and their measured allele frequencies open the possibility to select for low protein abundance by markers as long as their relevance for human health has been conclusively demonstrated. A potential allergen was introduced in the beginning of 1980s that may be linked to the cluster of resistance genes introgressed on chromosome 2AS from Triticum ventricosum. The reported sequence information for the 54 major QTLs can be used to design efficient markers for future wheat breeding.

Dissecting the genetics underlying the relationship between protein content and grain yield in a large hybrid wheat population.

KEY MESSAGE: Additive and dominance effect QTL for grain yield and protein content display antagonistic pleiotropic effects, making genomic selection based on the index grain protein deviation a promising method to alleviate the negative correlation between these traits in wheat breeding. Grain yield and quality-related traits such as protein content and sedimentation volume are key traits in wheat breeding. In this study, we used a large population of 1604 hybrids and their 135 parental components to investigate the genetics and metabolomics underlying the negative relationship of grain yield and quality, and evaluated approaches for their joint improvement. We identified a total of nine trait-associated metabolites and show that prediction using genomic data alone resulted in the highest prediction ability for all traits. We dissected the genetic architecture of grain yield and quality-determining traits and show results of the first mapping of the derived trait grain protein deviation. Further, we provide a genetic analysis of the antagonistic relation of grain yield and protein content and dissect the mode of gene action (pleiotropy vs linkage) of identified QTL. Lastly, we demonstrate that the composition of the training set for genomic prediction is crucial when considering different quality classes in wheat breeding.

Yield stability of hybrids versus lines in wheat, barley, and triticale.

KEY MESSAGE: We present experimental data for wheat, barley, and triticale suggesting that hybrids manifest on average higher yield stability than inbred lines. Yield stability is assumed to be higher for hybrids than for inbred lines, but experimental data proving this hypothesis is scarce for autogamous cereals. We used multi-location grain yield trials and compared the yield stability of hybrids versus lines for wheat (Triticum aestivum L.), barley (Hordeum vulgare L.), and triticale (×Triticosecale Wittmack). Our study comprised three phenotypic data sets of 1,749 wheat, 96 barley, and 130 triticale genotypes, which were evaluated for grain yield in up to five contrasting locations. Yield stability of the group of hybrids was compared with that of the group of inbred lines estimating the stability variance. For all three crops we observed a significantly (P < 0.05) higher yield stability of hybrids compared to lines. The enhanced yield stability of hybrids as compared to lines represents a major step forward, facilitating coping with the increasing abiotic stress expected from the predicted climate change.

Hybrid wheat: quantitative genetic parameters and consequences for the design of breeding programs.

KEY MESSAGE: Commercial heterosis for grain yield is present in hybrid wheat but long-term competiveness of hybrid versus line breeding depends on the development of heterotic groups to improve hybrid prediction. Detailed knowledge of the amount of heterosis and quantitative genetic parameters are of paramount importance to assess the potential of hybrid breeding. Our objectives were to (1) examine the extent of midparent, better-parent and commercial heterosis in a vast population of 1,604 wheat (Triticum aestivum L.) hybrids and their parental elite inbred lines and (2) discuss the consequences of relevant quantitative parameters for the design of hybrid wheat breeding programs. Fifteen male lines were crossed in a factorial mating design with 120 female lines, resulting in 1,604 of the 1,800 potential single-cross hybrid combinations. The hybrids, their parents, and ten commercial wheat varieties were evaluated in multi-location field experiments for grain yield, plant height, heading time and susceptibility to frost, lodging, septoria tritici blotch, yellow rust, leaf rust, and powdery mildew at up to five locations. We observed that hybrids were superior to the mean of their parents for grain yield (10.7 %) and susceptibility to frost (-7.2 %), leaf rust (-8.4 %) and septoria tritici blotch (-9.3 %). Moreover, 69 hybrids significantly (P < 0.05) outyielded the best commercial inbred line variety underlining the potential of hybrid wheat breeding. The estimated quantitative genetic parameters suggest that the establishment of reciprocal recurrent selection programs is pivotal for a successful long-term hybrid wheat breeding.

Hybrid breeding in autogamous cereals.

Hybrid breeding in autogamous cereals has a long history of attempts with moderate success. There is a vast amount of literature investigating the potential problems and solutions, but until now, market share of hybrids is still a niche compared to line varieties. Our aim was to summarize the status quo of hybrid breeding efforts for the autogamous cereals wheat, rice, barley, and triticale. Furthermore, the research needs for a successful hybrid breeding in autogamous cereals are intensively discussed. To our opinion, the basic requirements for a successful hybrid breeding in autogamous cereals are fulfilled. Nevertheless, optimization of the existing hybridization systems is urgently required and should be coupled with the development of clear male and female pool concepts. We present a quantitative genetic framework as a first step to compare selection gain of hybrid versus line breeding. The lack of precise empirical estimates of relevant quantitative genetic parameters, however, is currently the major bottleneck for a robust evaluation of the potential of hybrid breeding in autogamous cereals.

Association mapping in an elite maize breeding population.

Association mapping (AM) is a powerful approach to dissect the genetic architecture of quantitative traits. The main goal of our study was to empirically compare several statistical methods of AM using data of an elite maize breeding program with respect to QTL detection power and possibility to correct for population stratification. These models were based on the inclusion of cofactors (Model A), cofactors and population effect (Model B), and SNP effects nested within populations (Model C). A total of 930 testcross progenies of an elite maize breeding population were field-evaluated for grain yield and grain moisture in multi-location trials and fingerprinted with 425 SNP markers. For grain yield, population stratification was effectively controlled by Model A. For grain moisture with a high ratio of variance among versus within populations, Model B should be applied in order to avoid potential false positives. Model C revealed large differences among allele substitution effects for trait-associated SNPs across multiple plant breeding populations. This heterogeneous SNP allele substitution effects have a severe impact for genomic selection studies, where SNP effects are often assumed to be independent of the genetic background.