Portability of genomic predictions trained on sparse factorial designs across two maize silage breeding cycles.

KEY MESSAGE: We validated the efficiency of genomic predictions calibrated on sparse factorial training sets to predict the next generation of hybrids and tested different strategies for updating predictions along generations. Genomic selection offers new prospects for revisiting hybrid breeding schemes by replacing extensive phenotyping of individuals with genomic predictions. Finding the ideal design for training genomic prediction models is still an open question. Previous studies have shown promising predictive abilities using sparse factorial instead of tester-based training sets to predict single-cross hybrids from the same generation. This study aims to further investigate the use of factorials and their optimization to predict line general combining abilities (GCAs) and hybrid values across breeding cycles. It relies on two breeding cycles of a maize reciprocal genomic selection scheme involving multiparental connected reciprocal populations from flint and dent complementary heterotic groups selected for silage performances. Selection based on genomic predictions trained on a factorial design resulted in a significant genetic gain for dry matter yield in the new generation. Results confirmed the efficiency of sparse factorial training sets to predict candidate line GCAs and hybrid values across breeding cycles. Compared to a previous study based on the first generation, the advantage of factorial over tester training sets appeared lower across generations. Updating factorial training sets by adding single-cross hybrids between selected lines from the previous generation or a random subset of hybrids from the new generation both improved predictive abilities. The CDmean criterion helped determine the set of single-crosses to phenotype to update the training set efficiently. Our results validated the efficiency of sparse factorial designs for calibrating hybrid genomic prediction experimentally and showed the benefit of updating it along generations.

Genetic regions determine tolerance to nitrogen deficiency in European elite bread wheats grown under contrasting nitrogen stress scenarios.

KEY MESSAGE: Clustering 24 environments in four contrasting nitrogen stress scenarios enabled the detection of genetic regions determining tolerance to nitrogen deficiency in European elite bread wheats. Increasing the nitrogen use efficiency of wheat varieties is an important goal for breeding. However, most genetic studies of wheat grown at different nitrogen levels in the field report significant interactions with the genotype. The chromosomal regions possibly involved in these interactions are largely unknown. The objective of this study was to quantify the response of elite bread wheat cultivars to different nitrogen field stress scenarios and identify genomic regions involved in this response. For this purpose, 212 elite bread wheat varieties were grown in a multi-environment trial at different nitrogen levels. Genomic regions associated with grain yield, protein concentration and grain protein deviation responses to nitrogen deficiency were identified. Environments were clustered according to adjusted means for grain yield, yield components and grain protein concentration. Four nitrogen availability scenarios were identified: optimal condition, moderate early deficiency, severe late deficiency, and severe continuous deficiency. A large range of tolerance to nitrogen deficiency was observed among varieties, which were ranked differently in different nitrogen deficiency scenarios. The well-known negative correlation between grain yield and grain protein concentration also existed between their respective tolerance indices. Interestingly, the tolerance indices for grain yield and grain protein deviation were either null or weakly positive meaning that breeding for the two traits should be less difficult than expected. Twenty-two QTL regions were identified for the tolerance indices. By selecting associated markers, these regions may be selected separately or combined to improve the tolerance to N deficiency within a breeding programme.

Identification of QTLs affecting post-anthesis heat stress responses in European bread wheat.

KEY MESSAGE: The response of a large panel of European elite wheat varieties to post-anthesis heat stress is influenced by 17 QTL linked to grain weight or the stay-green phenotype. Heat stress is a critical abiotic stress for winter bread wheat (Triticum aestivum L.) especially at the flowering and grain filling stages, limiting its growth and productivity in Europe and elsewhere. The breeding of new high-yield and stress-tolerant wheat varieties requires improved understanding of the physiological and genetic bases of heat tolerance. To identify genomic areas associated with plant and grain characteristics under heat stress, a panel of elite European wheat varieties (N = 199) was evaluated under controlled conditions in 2016 and 2017. A split-plot design was used to test the effects of high temperature for ten days after flowering. Flowering time, leaf chlorophyll content, the number of productive spikes, grain number, grain weight and grain size were measured, and the senescence process was modeled. Using genotyping data from a 280 K SNP chip, a genome-wide association study was carried out to test the main effect of each SNP and the effect of SNP × treatment interaction. Genotype × treatment interactions were mainly observed for grain traits measured on the main shoots and tillers. We identified 10 QTLs associated with the main effect of at least one trait and seven QTLs associated with the response to post-anthesis heat stress. Of these, two main QTLs associated with the heat tolerance of thousand-kernel weight were identified on chromosomes 4B and 6B. These QTLs will be useful for breeders to improve grain yield in environments where terminal heat stress is likely to occur.

Using environmental clustering to identify specific drought tolerance QTLs in bread wheat (T. aestivum L.).

KEY MESSAGE: Environmental clustering helps to identify QTLs associated with grain yield in different water stress scenarios. These QTLs could be useful for breeders to improve grain yields and increase genetic resilience in marginal environments. Drought is one of the main abiotic stresses limiting winter bread wheat growth and productivity around the world. The acquisition of new high-yielding and stress-tolerant varieties is therefore necessary and requires improved understanding of the physiological and genetic bases of drought resistance. A panel of 210 elite European varieties was evaluated in 35 field trials. Grain yield and its components were scored in each trial. A crop model was then run with detailed climatic data and soil water status to assess the dynamics of water stress in each environment. Varieties were registered from 1992 to 2011, allowing us to test timewise genetic progress. Finally, a genome-wide association study (GWAS) was carried out using genotyping data from a 280 K SNP chip. The crop model simulation allowed us to group the environments into four water stress scenarios: an optimal condition with no water stress, a post-anthesis water stress, a moderate-anthesis water stress and a high pre-anthesis water stress. Compared to the optimal water condition, grain yield losses in the stressed conditions were 3.3%, 12.4% and 31.2%, respectively. This environmental clustering improved understanding of the effect of drought on grain yields and explained 20% of the G × E interaction. The greatest genetic progress was obtained in the optimal condition, mostly represented in France. The GWAS identified several QTLs, some of which were specific of the different water stress patterns. Our results make breeding for improved drought resistance to specific environmental scenarios easier and will facilitate genetic progress in future environments, i.e., water stress environments.