Studying Stem Rust and Leaf Rust Resistances of Self-Fertile Rye Breeding Populations.

Stem rust (SR) and leaf rust (LR) are currently the two most important rust diseases of cultivated rye in Central Europe and resistant cultivars promise to prevent yield losses caused by those pathogens. To secure long-lasting resistance, ideally pyramided monogenic resistances and race-nonspecific resistances are applied. To find respective genes, we screened six breeding populations and one testcross population for resistance to artificially inoculated SR and naturally occurring LR in multi-environmental field trials.

Prediction of Maize Phenotypic Traits With Genomic and Environmental Predictors Using Gradient Boosting Frameworks.

The development of crop varieties with stable performance in future environmental conditions represents a critical challenge in the context of climate change. Environmental data collected at the field level, such as soil and climatic information, can be relevant to improve predictive ability in genomic prediction models by describing more precisely genotype-by-environment interactions, which represent a key component of the phenotypic response for complex crop agronomic traits. Modern predictive modeling approaches can efficiently handle various data types and are able to capture complex nonlinear relationships in large datasets.

Mapping Stem Rust ( f. sp. ) Resistance in Self-Fertile Winter Rye Populations.

Rye stem rust caused by f. sp. can be found in all European rye growing regions.

Genomic prediction in early selection stages using multi-year data in a hybrid rye breeding program.

Background: The use of multiple genetic backgrounds across years is appealing for genomic prediction (GP) because past years' data provide valuable information on marker effects. Nonetheless, single-year GP models are less complex and computationally less demanding than multi-year GP models. In devising a suitable analysis strategy for multi-year data, we may exploit the fact that even if there is no replication of genotypes across years, there is plenty of replication at the level of marker loci.

Towards a whole-genome sequence for rye (Secale cereale L.).

We report on a whole-genome draft sequence of rye (Secale cereale L.). Rye is a diploid Triticeae species closely related to wheat and barley, and an important crop for food and feed in Central and Eastern Europe.

Model training across multiple breeding cycles significantly improves genomic prediction accuracy in rye (Secale cereale L.).

Genomic prediction accuracy can be significantly increased by model calibration across multiple breeding cycles as long as selection cycles are connected by common ancestors. In hybrid rye breeding, application of genome-based prediction is expected to increase selection gain because of long selection cycles in population improvement and development of hybrid components. Essentially two prediction scenarios arise: (1) prediction of the genetic value of lines from the same breeding cycle in which model training is performed and (2) prediction of lines from subsequent cycles.

Prediction of malting quality traits in barley based on genome-wide marker data to assess the potential of genomic selection.

Key Message: Genomic prediction of malting quality traits in barley shows the potential of applying genomic selection to improve selection for malting quality and speed up the breeding process.

Abstract: Genomic selection has been applied to various plant species, mostly for yield or yield-related traits such as grain dry matter yield or thousand kernel weight, and improvement of resistances against diseases. Quality traits have not been the main scope of analysis for genomic selection, but have rather been addressed by marker-assisted selection.

The importance of phenotypic data analysis for genomic prediction - a case study comparing different spatial models in rye.

Background: Genomic prediction is becoming a daily tool for plant breeders. It makes use of genotypic information to make predictions used for selection decisions. The accuracy of the predictions depends on the number of genotypes used in the calibration; hence, there is a need of combining data across years.