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. Five populations were genotyped with a 10K SNP marker chip and one with DArTseqTM. In total, ten SR-QTLs were found that caused a reduction of 5-17 percentage points in stem coverage with urediniospores. Four QTLs thereof were mapped to positions of already known SR QTLs. An additional gene at the distal end of chromosome 2R, Pgs3.1, that caused a reduction of 40 percentage points SR infection, was validated. One SR-QTL on chromosome 3R, QTL-SR4, was found in three populations linked with the same marker. Further QTLs at similar positions, but from different populations, were also found on chromosomes 1R, 4R, and 6R. For SR, additionally seedling tests were used to separate between adult-plant and all-stage resistances and a statistical method accounting for the ordinal-scaled seedling test data was used to map seedling resistances. However, only Pgs3.1 could be detected based on seedling test data, even though genetic variance was observed in another population, too. For LR, in three of the populations, two new large-effect loci (Pr7 and Pr8) on chromosomes 1R and 2R were mapped that caused 34 and 21 percentage points reduction in leaf area covered with urediniospores and one new QTL on chromosome 1R causing 9 percentage points reduction.

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. In particular, machine learning techniques have gained substantial interest in recent years. Here we examined the predictive ability of machine learning-based models for two phenotypic traits in maize using data collected by the Maize Genomes to Fields (G2F) Initiative. The data we analyzed consisted of multi-environment trials (METs) dispersed across the United States and Canada from 2014 to 2017. An assortment of soil- and weather-related variables was derived and used in prediction models alongside genotypic data. Linear random effects models were compared to a linear regularized regression method (elastic net) and to two nonlinear gradient boosting methods based on decision tree algorithms (XGBoost, LightGBM). These models were evaluated under four prediction problems: (1) tested and new genotypes in a new year; (2) only unobserved genotypes in a new year; (3) tested and new genotypes in a new site; (4) only unobserved genotypes in a new site. Accuracy in forecasting grain yield performance of new genotypes in a new year was improved by up to 20% over the baseline model by including environmental predictors with gradient boosting methods. For plant height, an enhancement of predictive ability could neither be observed by using machine learning-based methods nor by using detailed environmental information. An investigation of key environmental factors using gradient boosting frameworks also revealed that temperature at flowering stage, frequency and amount of water received during the vegetative and grain filling stage, and soil organic matter content appeared as important predictors for grain yield in our panel of environments.

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

Rye stem rust caused by Puccinia graminis f. sp. secalis can be found in all European rye growing regions. When the summers are warm and dry, the disease can cause severe yield losses over large areas. To date only little research was done in Europe to trigger resistance breeding. To our knowledge, all varieties currently registered in Germany are susceptible. In this study, three biparental populations of inbred lines and one testcross population developed for mapping resistance were investigated. Over 2 years, 68-70 genotypes per population were tested, each in three locations. Combining the phenotypic data with genotyping results of a custom 10k Infinium iSelect single nucleotide polymorphism (SNP) array, we identified both quantitatively inherited adult plant resistance and monogenic all-stage resistance. A single resistance gene, tentatively named Pgs1, located at the distal end of chromosome 7R, could be identified in two independently developed populations. With high probability, it is closely linked to a nucleotide-binding leucine-rich repeat (NB-LRR) resistance gene homolog. A marker for a competitive allele-specific polymerase chain reaction (KASP) genotyping assay was designed that could explain 73 and 97% of the genetic variance in each of both populations, respectively. Additional investigation of naturally occurring rye leaf rust (caused by Puccinia recondita ROEBERGE) revealed a gene complex on chromosome 7R. The gene Pgs1 and further identified quantitative trait loci (QTL) have high potential to be used for breeding stem rust resistant rye.

Exploring new alleles for frost tolerance in winter rye.

KEY MESSAGE: Rye genetic resources provide a valuable source of new alleles for the improvement of frost tolerance in rye breeding programs. Frost tolerance is a must-have trait for winter cereal production in northern and continental cropping areas. Genetic resources should harbor promising alleles for the improvement of frost tolerance of winter rye elite lines. For frost tolerance breeding, the identification of quantitative trait loci (QTL) and the choice of optimum genome-based selection methods are essential. We identified genomic regions involved in frost tolerance of winter rye by QTL mapping in a biparental population derived from a highly frost tolerant selection from the Canadian cultivar Puma and the European elite line Lo157. Lines per se and their testcrosses were phenotyped in a controlled freeze test and in multi-location field trials in Russia and Canada. Three QTL on chromosomes 4R, 5R, and 7R were consistently detected across environments. The QTL on 5R is congruent with the genomic region harboring the Frost resistance locus 2 (Fr-2) in Triticeae. The Puma allele at the Fr-R2 locus was found to significantly increase frost tolerance. A comparison of predictive ability obtained from the QTL-based model with different whole-genome prediction models revealed that besides a few large, also small QTL effects contribute to the genomic variance of frost tolerance in rye. Genomic prediction models assigning a high weight to the Fr-R2 locus allow increasing the selection intensity for frost tolerance by genome-based pre-selection of promising candidates.

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. Our principal aim was to evaluate different GP approaches to simultaneously model genotype-by-year (GY) effects and breeding values using multi-year data in terms of predictive ability. The models were evaluated under different scenarios reflecting common practice in plant breeding programs, such as different degrees of relatedness between training and validation sets, and using a selected fraction of genotypes in the training set. We used empirical grain yield data of a rye hybrid breeding program. A detailed description of the prediction approaches highlighting the use of kinship for modeling GY is presented. RESULTS: Using the kinship to model GY was advantageous in particular for datasets disconnected across years. On average, predictive abilities were 5% higher for models using kinship to model GY over models without kinship. We confirmed that using data from multiple selection stages provides valuable GY information and helps increasing predictive ability. This increase is on average 30% higher when the predicted genotypes are closely related with the genotypes in the training set. A selection of top-yielding genotypes together with the use of kinship to model GY improves the predictive ability in datasets composed of single years of several selection cycles. CONCLUSIONS: Our results clearly demonstrate that the use of multi-year data and appropriate modeling is beneficial for GP because it allows dissecting GY effects from genomic estimated breeding values. The model choice, as well as ensuring that the predicted candidates are sufficiently related to the genotypes in the training set, are crucial.

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. Through whole-genome shotgun sequencing of the 7.9-Gbp genome of the winter rye inbred line Lo7 we obtained a de novo assembly represented by 1.29 million scaffolds covering a total length of 2.8 Gbp. Our reference sequence represents nearly the entire low-copy portion of the rye genome. This genome assembly was used to predict 27 784 rye gene models based on homology to sequenced grass genomes. Through resequencing of 10 rye inbred lines and one accession of the wild relative S. vavilovii, we discovered more than 90 million single nucleotide variants and short insertions/deletions in the rye genome. From these variants, we developed the high-density Rye600k genotyping array with 600 843 markers, which enabled anchoring the sequence contigs along a high-density genetic map and establishing a synteny-based virtual gene order. Genotyping data were used to characterize the diversity of rye breeding pools and genetic resources, and to obtain a genome-wide map of selection signals differentiating the divergent gene pools. This rye whole-genome sequence closes a gap in Triticeae genome research, and will be highly valuable for comparative genomics, functional studies and genome-based breeding in rye.

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

KEY MESSAGE: 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. It is the latter from which a reduction in cycle length and consequently the strongest impact on selection gain is expected. We empirically investigated genome-based prediction of grain yield, plant height and thousand kernel weight within and across four selection cycles of a hybrid rye breeding program. Prediction performance was assessed using genomic and pedigree-based best linear unbiased prediction (GBLUP and PBLUP). A total of 1040 S2 lines were genotyped with 16 k SNPs and each year testcrosses of 260 S2 lines were phenotyped in seven or eight locations. The performance gap between GBLUP and PBLUP increased significantly for all traits when model calibration was performed on aggregated data from several cycles. Prediction accuracies obtained from cross-validation were in the order of 0.70 for all traits when data from all cycles (N CS = 832) were used for model training and exceeded within-cycle accuracies in all cases. As long as selection cycles are connected by a sufficient number of common ancestors and prediction accuracy has not reached a plateau when increasing sample size, aggregating data from several preceding cycles is recommended for predicting genetic values in subsequent cycles despite decreasing relatedness over time.

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. In this study, the potential to apply genomic selection to twelve malting quality traits in two commercial breeding programs of spring and winter barley (Hordeum vulgare L.) was assessed. Phenotypic means were calculated combining multilocational field trial data from 3 or 4 years, depending on the trait investigated. Three to five locations were available in each of these years. Heritabilities for malting traits ranged between 0.50 and 0.98. Predictive abilities (PA), as derived from cross validation, ranged between 0.14 to 0.58 for spring barley and 0.40-0.80 for winter barley. Small training sets were shown to be sufficient to obtain useful PAs, possibly due to the narrow genetic base in this breeding material. Deployment of genomic selection in malting barley breeding clearly has the potential to reduce cost intensive phenotyping for quality traits, increase selection intensity and to shorten breeding cycles.

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. A proper phenotypic analysis is a crucial prerequisite for accurate calibration of genomic prediction procedures. We compared stage-wise approaches to analyse a real dataset of a multi-environment trial (MET) in rye, which was connected between years only through one check, and used different spatial models to obtain better estimates, and thus, improved predictive abilities for genomic prediction. The aims of this study were to assess the advantage of using spatial models for the predictive abilities of genomic prediction, to identify suitable procedures to analyse a MET weakly connected across years using different stage-wise approaches, and to explore genomic prediction as a tool for selection of models for phenotypic data analysis. RESULTS: Using complex spatial models did not significantly improve the predictive ability of genomic prediction, but using row and column effects yielded the highest predictive abilities of all models. In the case of MET poorly connected between years, analysing each year separately and fitting year as a fixed effect in the genomic prediction stage yielded the most realistic predictive abilities. Predictive abilities can also be used to select models for phenotypic data analysis. The trend of the predictive abilities was not the same as the traditionally used Akaike information criterion, but favoured in the end the same models. CONCLUSIONS: Making predictions using weakly linked datasets is of utmost interest for plant breeders. We provide an example with suggestions on how to handle such cases. Rather than relying on checks we show how to use year means across all entries for integrating data across years. It is further shown that fitting of row and column effects captures most of the heterogeneity in the field trials analysed.