Correction: Miedaner et al. Effective Pollen-Fertility Restoration Is the Basis of Hybrid Rye Production and Ergot Mitigation. Plants 2022, 11, 1115.

In the original publication [...].

Effective Pollen-Fertility Restoration Is the Basis of Hybrid Rye Production and Ergot Mitigation.

Hybrid rye breeding leads to considerably higher grain yield and a higher revenue to the farmer. The basis of hybrid seed production is the CMS-inducing Pampa (P) cytoplasm derived from an Argentinean landrace and restorer-to-fertility (Rf) genes. European sources show an oligogenic inheritance, with major and minor Rf genes, and mostly result in low-to-moderate pollen-fertility levels. This results in higher susceptibility to ergot (Claviceps purpurea) because rye pollen and ergot spores are in strong competition for the unfertilized stigma. Rf genes from non-adapted Iranian primitive rye and old Argentinean cultivars proved to be most effective. The major Rf gene in these sources was localized on chromosome 4RL, which is also a hotspot of restoration in other Triticeae. Marker-based introgression into elite rye materials led to a yield penalty and taller progenies. The Rfp1 gene of IRAN IX was fine-mapped, and two linked genes of equal effects were detected. Commercial hybrids with this gene showed a similar low ergot infection when compared with population cultivars. The task of the future is to co-adapt these exotic Rfp genes to European elite gene pools by genomic-assisted breeding.

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.

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.

Analyzing Genetic Diversity for Virulence and Resistance Phenotypes in Populations of Stem Rust (Puccinia graminis f. sp. secalis) and Winter Rye (Secale cereale).

Stem rust (Puccinia graminis f. sp. secalis) leads to considerable yield losses in rye-growing areas with continental climate, from Eastern Germany to Siberia. For implementing resistance breeding, it is of utmost importance to (i) analyze the diversity of stem rust populations in terms of pathotypes (= virulence combinations) and (ii) identify resistance sources in winter rye populations. We analyzed 323 single-uredinial isolates mainly collected from German rye-growing areas across 3 years for their avirulence/virulence on 15 rye inbred differentials. Out of these, 226 pathotypes were detected and only 56 pathotypes occurred more than once. This high diversity was confirmed by a Simpson index of 1.0, a high Shannon index (5.27), and an evenness index of 0.97. In parallel, we investigated stem rust resistance among and within 121 heterogeneous rye populations originating mainly from Russia, Poland, Austria, and the United States across 3 to 15 environments (location-year combinations). While German rye populations had an average stem rust severity of 49.7%, 23 nonadapted populations were significantly (P < 0.01) more resistant with a stem rust severity ranging from 3 to 40%. Out of these, two modern Russian breeding populations and two old Austrian landraces were the best harboring 32 to 70% fully resistant plants across 8 to 10 environments. These populations with the lowest disease severity in adult-plant stage in the field also displayed resistance in leaf segment tests. In conclusion, stem rust populations are highly diverse and the majority of resistances in rye populations seems to be race specific.

Multiple-trait- and selection indices-genomic predictions for grain yield and protein content in rye for feeding purposes.

KEY MESSAGE: Exploiting the benefits from multiple-trait genomic selection for protein content prediction relying on additional grain yield information within training sets is a realistic genomic selection approach in rye breeding. ABSTRACT: Multiple-trait genomic selection (MTGS) was specially designed to benefit from the information of genetically correlated indicator traits in order to improve genomic prediction accuracies. Two segregating F3:4 rye testcross populations genotyped using diversity array technology markers and evaluated for grain yield (GY) and protein content (PC) were considered. The aims of our study were to explore the benefits of MTGS over single-trait genomic selection (STGS) for GY and PC prediction and to apply GS to predict different selection indices (SIs) for GY and PC improvement. Our results using a two-trait model (2TGS) empirically confirm that the ideal scenario to exploit the benefits of MTGS would be when the predictions of a relatively low heritable target trait with scarce phenotypic records are supported by an intensively phenotyped genetically correlated indicator trait which has higher heritability. This ideal scenario is expected for PC in practice. According to our GS implementation, MTGS can be performed in order to achieve more cycles of selection by unit of time. If the aim is to exclusively improve the prediction accuracy of a scarcely phenotyped trait, 2TGS will be a more accurate approach than a three-trait model which incorporates an additional correlated indicator trait. In general for balanced phenotypic information, we recommend to perform GS considering SIs as single traits, this method being a simple, direct and efficient way of prediction.

First insights into the genotype-phenotype map of phenotypic stability in rye.

Improving phenotypic stability of crops is pivotal for coping with the detrimental impacts of climate change. The goal of this study was to gain first insights into the genetic architecture of phenotypic stability in cereals. To this end, we determined grain yield, thousand kernel weight, test weight, falling number, and both protein and soluble pentosan content for two large bi-parental rye populations connected through one common parent and grown in multi-environmental field trials involving more than 15 000 yield plots. Based on these extensive phenotypic data, we calculated parameters for static and dynamic phenotypic stability of the different traits and applied linkage mapping using whole-genome molecular marker profiles. While we observed an absence of large-effect quantitative trait loci (QTLs) underlying yield stability, large and stable QTLs were found for phenotypic stability of test weight, soluble pentosan content, and falling number. Applying genome-wide selection, which in contrast to marker-assisted selection also takes into account loci with small-effect sizes, considerably increased the accuracy of prediction of phenotypic stability for all traits by exploiting both genetic relatedness and linkage between single-nucleotide polymorphisms and QTLs. We conclude that breeding for crop phenotypic stability can be improved in related populations using genomic selection approaches established upon extensive phenotypic data.

The accuracy of prediction of genomic selection in elite hybrid rye populations surpasses the accuracy of marker-assisted selection and is equally augmented by multiple field evaluation locations and test years.

BACKGROUND: Marker-assisted selection (MAS) and genomic selection (GS) based on genome-wide marker data provide powerful tools to predict the genotypic value of selection material in plant breeding. However, case-to-case optimization of these approaches is required to achieve maximum accuracy of prediction with reasonable input. RESULTS: Based on extended field evaluation data for grain yield, plant height, starch content and total pentosan content of elite hybrid rye derived from testcrosses involving two bi-parental populations that were genotyped with 1048 molecular markers, we compared the accuracy of prediction of MAS and GS in a cross-validation approach. MAS delivered generally lower and in addition potentially over-estimated accuracies of prediction than GS by ridge regression best linear unbiased prediction (RR-BLUP). The grade of relatedness of the plant material included in the estimation and test sets clearly affected the accuracy of prediction of GS. Within each of the two bi-parental populations, accuracies differed depending on the relatedness of the respective parental lines. Across populations, accuracy increased when both populations contributed to estimation and test set. In contrast, accuracy of prediction based on an estimation set from one population to a test set from the other population was low despite that the two bi-parental segregating populations under scrutiny shared one parental line. Limiting the number of locations or years in field testing reduced the accuracy of prediction of GS equally, supporting the view that to establish robust GS calibration models a sufficient number of test locations is of similar importance as extended testing for more than one year. CONCLUSIONS: In hybrid rye, genomic selection is superior to marker-assisted selection. However, it achieves high accuracies of prediction only for selection candidates closely related to the plant material evaluated in field trials, resulting in a rather pessimistic prognosis for distantly related material. Both, the numbers of evaluation locations and testing years in trials contribute equally to prediction accuracy.

Association between line per se and testcross performance for eight agronomic and quality traits in winter rye.

KEY MESSAGE: We investigated associations between line per se and testcross performance in rye and suggested that selection for per se performance is valuable for several traits in multi-stage selection programs. Genotypic correlation between line per se and testcross performance is an important quantitative-genetic parameter for optimizing hybrid breeding programs. The main goal of this survey was to study the association of line per se and testcross performance at the phenotypic level. We used experimental data from the line per se and testcross performance of two segregating winter rye populations (A, B) with each of 220 progenies tested in six environments for eight agronomic and quality traits. Genotypic variances were considerably larger for per se than for testcross performance of all investigated traits resulting in higher heritabilities of the former in most instances. Genotypic correlations (r g) between testcross and line per se performance decreased with increasing complexity of the trait as shown by the respective heritabilities. They were highest (r g ≥ 0.7) for plant height and test weight in population B, and thousand-kernel weight, falling number and starch content in both populations. A selection of these traits for line per se performance in early generations will save field plots in further testing testcross performance and increase efficiency of hybrid breeding.

Hybrid rye performance under natural drought stress in Europe.

Several rye growing regions of Central Europe suffered from severe drought stress in the last decade. Rye is typically grown on sandy soils with low water-holding capacity in areas with low rainfall, thus drought-tolerant varieties are urgently needed. The main objective of our study was to evaluate the drought stress tolerance of rye hybrids using large-scaled field experiments. Two biparental populations (Pop-A, Pop-B) each consisting of 220 F(2:4) lines from the Petkus gene pool and their parents were evaluated for grain yield testcross performance under irrigated (I) and rainfed (R) regime in six environments. We observed for most environments severe drought stress leading to an average grain yield reduction of 23.8 % for rainfed compared to irrigated regime in drought stress environments. A decomposition of the variance revealed significant (P < 0.01) genotypic and genotype × environment interaction variances but only a minor effect of drought stress on the ranking of the genotypes with regard to grain yield. In conclusion, separate breeding programs for drought-tolerant genotypes are not superior to the currently practiced selection under rainfed conditions without irrigation in hybrid rye breeding in Central Europe.

Identification of quantitative trait loci in rye introgression lines carrying multiple donor chromosome segments.

Introgression libraries can be used to make favorable genetic variation of exotic donor genotypes available in the genetic background of elite breeding material. Our objective was to employ a combination of the Dunnett test and a linear model analysis to identify favorable donor alleles in introgression lines (ILs) that carry long or multiple donor chromosome segments (DCS). We reanalyzed a dataset of two rye introgression libraries that consisted of ILs carrying on average about four donor segments. After identifying ILs that had a significantly better per se or testcross performance than the recipient line with the Dunnett test, the linear model analysis was in most instances able to clearly identify the donor regions that were responsible for the superior performance. The precise localization of the favorable DCS allowed a detailed analysis of pleiotropic effects and the study of the consistency of effects for per se and testcross performance. We conclude that in many cases the linear model analysis allows the assignment of donor effects to individual DCS even for ILs with long or multiple donor segments. This may considerably increase the efficiency of producing sub-ILs, because only such segments need to be isolated that are known to have a significant effect on the phenotype.

Genetic architecture of complex agronomic traits examined in two testcross populations of rye (Secale cereale L.).

BACKGROUND: Rye is an important European crop used for food, feed, and bioenergy. Several quality and yield-related traits are of agronomic relevance for rye breeding programs. Profound knowledge of the genetic architecture of these traits is needed to successfully implement marker-assisted selection programs. Nevertheless, little is known on quantitative loci underlying important agronomic traits in rye. RESULTS: We used 440 F(3:4) inbred lines from two biparental populations (Pop-A, Pop-B) fingerprinted with about 800 to 900 SNP, SSR and/or DArT markers and outcrossed them to a tester for phenotyping. The resulting hybrids and their parents were evaluated for grain yield, single-ear weight, test weight, plant height, thousand-kernel weight, falling number, protein, starch, soluble and total pentosan contents in up to ten environments in Central Europe. The quality of the phenotypic data was high reflected by moderate to high heritability estimates. QTL analyses revealed a total of 31 QTL for Pop-A and 52 for Pop-B. QTL x environment interactions were significant (P < 0.01) in most cases but variance of QTL main effect was more prominent. CONCLUSIONS: QTL mapping was successfully applied based on two segregating rye populations. QTL underlying grain yield and several quality traits had small effects. In contrast, thousand-kernel weight, test weight, falling number and starch content were affected by several major QTL with a high frequency of occurrence in cross validation. These QTL explaining a large proportion of the genotypic variance can be exploited in marker-assisted selection programs and are candidates for further genetic dissection.

Association analysis of frost tolerance in rye using candidate genes and phenotypic data from controlled, semi-controlled, and field phenotyping platforms.

BACKGROUND: Frost is an important abiotic stress that limits cereal production in the temperate zone. As the most frost tolerant small grain cereal, rye (Secale cereale L.) is an ideal cereal model for investigating the genetic basis of frost tolerance (FT), a complex trait with polygenic inheritance. Using 201 genotypes from five Eastern and Middle European winter rye populations, this study reports a multi-platform candidate gene-based association analysis in rye using 161 single nucleotide polymorphisms (SNPs) and nine insertion-deletion (Indel) polymorphisms previously identified from twelve candidate genes with a putative role in the frost responsive network. RESULTS: Phenotypic data analyses of FT in three different phenotyping platforms, controlled, semi-controlled and field, revealed significant genetic variations in the plant material under study. Statistically significant (P < 0.05) associations between FT and SNPs/haplotypes of candidate genes were identified. Two SNPs in ScCbf15 and one in ScCbf12, all leading to amino acid exchanges, were significantly associated with FT over all three phenotyping platforms. Distribution of SNP effect sizes expressed as percentage of the genetic variance explained by individual SNPs was highly skewed towards zero with a few SNPs obtaining large effects. Two-way epistasis was found between 14 pairs of candidate genes. Relatively low to medium empirical correlations of SNP-FT associations were observed across the three platforms underlining the need for multi-level experimentation for dissecting complex associations between genotypes and FT in rye. CONCLUSIONS: Candidate gene based-association studies are a powerful tool for investigating the genetic basis of FT in rye. Results of this study support the findings of bi-parental linkage mapping and expression studies that the Cbf gene family plays an essential role in FT.

High levels of nucleotide diversity and fast decline of linkage disequilibrium in rye (Secale cereale L.) genes involved in frost response.

BACKGROUND: Rye (Secale cereale L.) is the most frost tolerant cereal species. As an outcrossing species, rye exhibits high levels of intraspecific diversity, which makes it well-suited for allele mining in genes involved in the frost responsive network. For investigating genetic diversity and the extent of linkage disequilibrium (LD) we analyzed eleven candidate genes and 37 microsatellite markers in 201 lines from five Eastern and Middle European rye populations. RESULTS: A total of 147 single nucleotide polymorphisms (SNPs) and nine insertion-deletion polymorphisms were found within 7,639 bp of DNA sequence from eleven candidate genes, resulting in an average SNP frequency of 1 SNP/52 bp. Nucleotide and haplotype diversity of candidate genes were high with average values π = 5.6 × 10(-3) and Hd = 0.59, respectively. According to an analysis of molecular variance (AMOVA), most of the genetic variation was found between individuals within populations. Haplotype frequencies varied markedly between the candidate genes. ScCbf14, ScVrn1, and ScDhn1 were dominated by a single haplotype, while the other 8 genes (ScCbf2, ScCbf6, ScCbf9b, ScCbf11, ScCbf12, ScCbf15, ScIce2, and ScDhn3) had a more balanced haplotype frequency distribution. Intra-genic LD decayed rapidly, within approximately 520 bp on average. Genome-wide LD based on microsatellites was low. CONCLUSIONS: The Middle European population did not differ substantially from the four Eastern European populations in terms of haplotype frequencies or in the level of nucleotide diversity. The low LD in rye compared to self-pollinating species promises a high resolution in genome-wide association mapping. SNPs discovered in the promoters or coding regions, which attribute to non-synonymous substitutions, are suitable candidates for association mapping.