Redesigning the exploitation of wheat genetic resources

Tagging large CNV blocks in wheat boosts digitalization of germplasm resources by ultra-low-coverage sequencing

BACKGROUND

The massive structural variations and frequent introgression highly contribute to the genetic diversity of wheat, while the huge and complex genome of polyploid wheat hinders efficient genotyping of abundant varieties towards accurate identification, management, and exploitation of germplasm resources.

RESULTS

We develop a novel workflow that identifies 1240 high-quality large copy number variation blocks (CNVb) in wheat at the pan-genome level, demonstrating that CNVb can serve as an ideal DNA fingerprinting marker for discriminating massive varieties, with the accuracy validated by PCR assay. We then construct a digitalized genotyping CNVb map across 1599 global wheat accessions. Key CNVb markers are linked with trait-associated introgressions, such as the 1RS·1BL translocation and 2NvS translocation, and the beneficial alleles, such as the end-use quality allele Glu-D1d (Dx5 + Dy10) and the semi-dwarf r-e-z allele. Furthermore, we demonstrate that these tagged CNVb markers promote a stable and cost-effective strategy for evaluating wheat germplasm resources with ultra-low-coverage sequencing data, competing with SNP array for applications such as evaluating new varieties, efficient management of collections in gene banks, and describing wheat germplasm resources in a digitalized manner. We also develop a user-friendly interactive platform, WheatCNVb ( http://wheat.cau.edu.cn/WheatCNVb/ ), for exploring the CNVb profiles over ever-increasing wheat accessions, and also propose a QR-code-like representation of individual digital CNVb fingerprint. This platform also allows uploading new CNVb profiles for comparison with stored varieties.

CONCLUSIONS

The CNVb-based approach provides a low-cost and high-throughput genotyping strategy for enabling digitalized wheat germplasm management and modern breeding with precise and practical decision-making.

Advancing the Conservation and Utilization of Barley Genetic Resources: Insights into Germplasm Management and Breeding for Sustainable Agriculture

Barley is a very important crop particularly in marginal dry areas, where it often serves as the most viable option for farmers. Additionally, barley carries great significance in the Western world, serving not only as a fundamental crop for animal feed and malting but also as a nutritious food source. The broad adaptability of barley and its ability to withstand various biotic and abiotic stresses often make this species the sole cereal that can be cultivated in arid regions. The collection and utilization of barley genetic resources are crucial for identifying valuable traits to enhance productivity and mitigate the adverse effects of climate change. This review aims to provide an overview of the management and exploitation of barley genetic resources. Furthermore, the review explores the relationship between gene banks and participatory breeding, offering insights into the diversity and utilization of barley genetic resources through some examples such as the initiatives undertaken by ICARDA. Finally, this contribution highlights the importance of these resources for boosting barley productivity, addressing climate change impacts, and meeting the growing food demands in a rapidly changing agriculture. The understanding and utilizing the rich genetic diversity of barley can contribute to sustainable agriculture and ensure the success of this vital crop for future generations globally.

Maximization through optimization? On the relationship between hybrid performance and parental genetic distance

Heterosis is the improved performance of hybrids compared with their parental components and is widely exploited in agriculture. According to quantitative genetic theory, genetic distance between parents at heterotic quantitative trait loci is required for heterosis, but how heterosis varies with genetic distance has remained elusive, despite intensive research on the topic. Experimental studies have often found a positive association between heterosis and genetic distance that, however, varied in strength. Most importantly, it has remained unclear whether heterosis increases continuously with genetic distance or whether there is an optimum genetic distance after which heterosis declines again. Here, we revisit the relationship between heterosis and genetic distance and provide perspectives on how to maximize heterosis and hybrid performance in breeding, as well as the consequences for the design of heterotic groups and the utilization of more exotic material and genetic resources.

Physical mapping of the wheat genes in low-recombination regions: radiation hybrid mapping of the C-locus

KEY MESSAGE

This work reports the physical mapping of an important gene affecting spike compactness located in a low-recombination region of hexaploid wheat. This work paves the way for the eventual isolation and characterization of the factor involved but also opens up possibilities to use this approach to precisely map other wheat genes located on proximal parts of wheat chromosomes that show highly reduced recombination. Mapping wheat genes, in the centromeric and pericentromeric regions (~ 2/3rd of a given chromosome), poses a formidable challenge due to highly suppressed recombination. Using an example of compact spike locus (C-locus), this study provides an approach to precisely map wheat genes in the pericentromeric and centromeric regions that house ~ 30% of wheat genes. In club-wheat, spike compactness is controlled by the dominant C-locus, but previous efforts have failed to localize it, on a particular arm of chromosome 2D. We integrated radiation hybrid (RH) and high-resolution genetic mapping to locate C-locus on the short arm of chromosome 2D. Flanking markers of the C-locus span a physical distance of 11.0 Mb (231.0-242 Mb interval) and contain only 11 high-confidence annotated genes. This work demonstrates the value of this integrated strategy in mapping dominant genes in the low-recombination regions of the wheat genome. A comparison of the mapping resolutions of the RH and genetic maps using common anchored markers indicated that the RH map provides ~ 9 times better resolution that the genetic map even with much smaller population size. This study provides a broadly applicable approach to fine map wheat genes in regions of suppressed recombination.

More than 1000 genotypes are required to derive robust relationships between yield, yield stability and physiological parameters: a computational study on wheat crop

Using in silico experiment in crop model, we identified different physiological regulations of yield and yield stability, as well as quantify the genotype and environment numbers required for analysing yield stability convincingly. Identifying target traits for breeding stable and high-yielded cultivars simultaneously is difficult due to limited knowledge of physiological mechanisms behind yield stability. Besides, there is no consensus about the adequacy of a stability index (SI) and the minimal number of environments and genotypes required for evaluating yield stability. We studied this question using the crop model APSIM-Wheat to simulate 9100 virtual genotypes grown under 9000 environments. By analysing the simulated data, we showed that the shape of phenotype distributions affected the correlation between SI and mean yield and the genotypic superiority measure (P_i) was least affected among 11 SI. P_i was used as index to demonstrate that more than 150 environments were required to estimate yield stability of a genotype convincingly and more than 1000 genotypes were necessary to evaluate the contribution of a physiological parameter to yield stability. Network analyses suggested that a physiological parameter contributed preferentially to yield or P_i. For example, soil water absorption efficiency and potential grain filling rate explained better the variations in yield than in P_i; while light extinction coefficient and radiation use efficiency were more correlated with P_i than with yield. The high number of genotypes and environments required for studying P_i highlight the necessity and potential of in silico experiments to better understand the mechanisms behind yield stability.

Large-scale genotyping and phenotyping of a worldwide winter wheat genebank for its use in pre-breeding

Plant genetic resources (PGR) stored at genebanks are humanity's crop diversity savings for the future. Information on PGR contrasted with modern cultivars is key to select PGR parents for pre-breeding. Genotyping-by-sequencing was performed for 7,745 winter wheat PGR samples from the German Federal ex situ genebank at IPK Gatersleben and for 325 modern cultivars. Whole-genome shotgun sequencing was carried out for 446 diverse PGR samples and 322 modern cultivars and lines. In 19 field trials, 7,683 PGR and 232 elite cultivars were characterized for resistance to yellow rust - one of the major threats to wheat worldwide. Yield breeding values of 707 PGR were estimated using hybrid crosses with 36 cultivars - an approach that reduces the lack of agronomic adaptation of PGR and provides better estimates of their contribution to yield breeding. Cross-validations support the interoperability between genomic and phenotypic data. The here presented data are a stepping stone to unlock the functional variation of PGR for European pre-breeding and are the basis for future breeding and research activities.

Advances in integrated genomic selection for rapid genetic gain in crop improvement: a review

Genomic selection and its importance in crop breeding. Integration of GS with new breeding tools and developing SOP for GS to achieve maximum genetic gain with low cost and time. The success of conventional breeding approaches is not sufficient to meet the demand of a growing population for nutritious food and other plant-based products. Whereas, marker assisted selection (MAS) is not efficient in capturing all the favorable alleles responsible for economic traits in the process of crop improvement. Genomic selection (GS) developed in livestock breeding and then adapted to plant breeding promised to overcome the drawbacks of MAS and significantly improve complicated traits controlled by gene/QTL with small effects. Large-scale deployment of GS in important crops, as well as simulation studies in a variety of contexts, addressed G × E interaction effects and non-additive effects, as well as lowering breeding costs and time. The current study provides a complete overview of genomic selection, its process, and importance in modern plant breeding, along with insights into its application. GS has been implemented in the improvement of complex traits including tolerance to biotic and abiotic stresses. Furthermore, this review hypothesises that using GS in conjunction with other crop improvement platforms accelerates the breeding process to increase genetic gain. The objective of this review is to highlight the development of an appropriate GS model, the global open source network for GS, and trans-disciplinary approaches for effective accelerated crop improvement. The current study focused on the application of data science, including machine learning and deep learning tools, to enhance the accuracy of prediction models. Present study emphasizes on developing plant breeding strategies centered on GS combined with routine conventional breeding principles by developing GS-SOP to achieve enhanced genetic gain.

Genetic Trends Estimation in IRRIs Rice Drought Breeding Program and Identification of High Yielding Drought-Tolerant Lines

Estimating genetic trends using historical data is an important parameter to check the success of the breeding programs. The estimated genetic trends can act as a guideline to target the appropriate breeding strategies and optimize the breeding program for improved genetic gains. In this study, 17 years of historical data from IRRI's rice drought breeding program was used to estimate the genetic trends and assess the breeding program's success. We also identified top-performing lines based on grain yield breeding values as an elite panel for implementing future population improvement-based breeding schemes. A two-stage approach of pedigree-based mixed model analysis was used to analyze the data and extract the breeding values and estimate the genetic trends for grain yield under non-stress, drought, and in combined data of non-stress and drought. Lower grain yield values were observed in all the drought trials. Heritability for grain yield estimates ranged between 0.20 and 0.94 under the drought trials and 0.43-0.83 under non-stress trials. Under non-stress conditions, the genetic gain of 0.21% (10.22 kg/ha/year) for genotypes and 0.17% (7.90 kg/ha/year) for checks was observed. The genetic trend under drought conditions exhibited a positive trend with the genetic gain of 0.13% (2.29 kg/ha/year) for genotypes and 0.55% (9.52 kg/ha/year) for checks. For combined analysis showed a genetic gain of 0.27% (8.32 kg/ha/year) for genotypes and 0.60% (13.69 kg/ha/year) for checks was observed. For elite panel selection, 200 promising lines were selected based on higher breeding values for grain yield and prediction accuracy of > 0.40. The breeding values of the 200 genotypes formulating the core panel ranged between 2366.17 and 4622.59 (kg/ha). A positive genetic rate was observed under all the three conditions; however, the rate of increase was lower than the required rate of 1.5% genetic gain. We propose a recurrent selection breeding strategy within the elite population with the integration of modern tools and technologies to boost the genetic gains in IRRI's drought breeding program. The elite breeding panel identified in this study forms an easily available and highly enriched genetic resource for future recurrent selection programs to boost the genetic gains.

Harnessing Genetic Diversity in the USDA Pea Germplasm Collection Through Genomic Prediction

Phenotypic evaluation and efficient utilization of germplasm collections can be time-intensive, laborious, and expensive. However, with the plummeting costs of next-generation sequencing and the addition of genomic selection to the plant breeder's toolbox, we now can more efficiently tap the genetic diversity within large germplasm collections. In this study, we applied and evaluated genomic prediction's potential to a set of 482 pea (Pisum sativum L.) accessions-genotyped with 30,600 single nucleotide polymorphic (SNP) markers and phenotyped for seed yield and yield-related components-for enhancing selection of accessions from the USDA Pea Germplasm Collection. Genomic prediction models and several factors affecting predictive ability were evaluated in a series of cross-validation schemes across complex traits. Different genomic prediction models gave similar results, with predictive ability across traits ranging from 0.23 to 0.60, with no model working best across all traits. Increasing the training population size improved the predictive ability of most traits, including seed yield. Predictive abilities increased and reached a plateau with increasing number of markers presumably due to extensive linkage disequilibrium in the pea genome. Accounting for population structure effects did not significantly boost predictive ability, but we observed a slight improvement in seed yield. By applying the best genomic prediction model (e.g., RR-BLUP), we then examined the distribution of genotyped but nonphenotyped accessions and the reliability of genomic estimated breeding values (GEBV). The distribution of GEBV suggested that none of the nonphenotyped accessions were expected to perform outside the range of the phenotyped accessions. Desirable breeding values with higher reliability can be used to identify and screen favorable germplasm accessions. Expanding the training set and incorporating additional orthogonal information (e.g., transcriptomics, metabolomics, physiological traits, etc.) into the genomic prediction framework can enhance prediction accuracy.

Reciprocal Recurrent Genomic Selection Is Impacted by Genotype-by-Environment Interactions

Reciprocal recurrent genomic selection is a breeding strategy aimed at improving the hybrid performance of two base populations. It promises to significantly advance hybrid breeding in wheat. Against this backdrop, the main objective of this study was to empirically investigate the potential and limitations of reciprocal recurrent genomic selection. Genome-wide predictive equations were developed using genomic and phenotypic data from a comprehensive population of 1,604 single crosses between 120 female and 15 male wheat lines. Twenty superior female lines were selected for initiation of the reciprocal recurrent genomic selection program. Focusing on the female pool, one cycle was performed with genomic selection steps at the F₂ (60 out of 629 plants) and the F₅ stage (49 out of 382 plants). Selection gain for grain yield was evaluated at six locations. Analyses of the phenotypic data showed pronounced genotype-by-environment interactions with two environments that formed an outgroup compared to the environments used for the genome-wide prediction equations. Removing these two environments for further analysis resulted in a selection gain of 1.0 dt ha^-1 compared to the hybrids of the original 20 parental lines. This underscores the potential of reciprocal recurrent genomic selection to promote hybrid wheat breeding, but also highlights the need to develop robust genome-wide predictive equations.

Challenges and prospects for a potential allohexaploid Brassica crop

The production of a new allohexaploid Brassica crop (2n = AABBCC) is increasingly attracting international interest: a new allohexaploid crop could benefit from several major advantages over the existing Brassica diploid and allotetraploid species, combining genetic diversity and traits from all six crop species with additional allelic heterosis from the extra genome. Although early attempts to produce allohexaploids showed mixed results, recent technological and conceptual advances have provided promising leads to follow. However, there are still major challenges which exist before this new crop type can be realized: (1) incorporation of sufficient genetic diversity to form a basis for breeding and improvement of this potential crop species; (2) restoration of regular meiosis, as most allohexaploids are genetically unstable after formation; and (3) improvement of agronomic traits to the level of "elite" breeding material in the diploid and allotetraploid crop species. In this review, we outline these major prospects and challenges and propose possible plans to produce a stable, diverse and agronomically viable allohexaploid Brassica crop.

Assessing the Suitability of Elite Lines for Hybrid Seed Production and as Testers in Wide Crosses With Wheat Genetic Resources

The use of genetic resources in breeding is considered critical to ensure future selection gain, but the absence of important adaptation genes often masks the breeding value of genetic resources for grain yield. Testing genetic resources in a hybrid background has been proposed as a solution to obtain unbiased estimates of breeding values for grain yield. In our study, we evaluated the suitability of European wheat elite lines for implementing this hybrid strategy, focusing on maximizing seed yield in hybrid production and reducing masking effects due to susceptibility to lodging, yellow rust, and leaf rust of genetic resources. Over a 3-year period, 63 wheat elite female lines were crossed with eight male plant genetic resources in a multi-environment field experiment to evaluate seed yield on the female side. Then, the resulting hybrids and their parents were tested for plant height, lodging, and susceptibility to yellow rust and leaf rust in a further field experiment at multiple locations. We found that seed yield was strongly influenced by the elite wheat line choice in addition to environment and observed substantial differences among elite tester lines in their ability to reduce susceptibility to lodging, yellow rust, and leaf rust when the hybrid strategy was implemented. Consequently, breeders can significantly increase the amount of hybrid seed produced in wide crosses through appropriate tester choice and adapt genetic resources of wheat with the hybrid strategy to the modern cropping system.

Focused Identification of Germplasm Strategy (FIGS): polishing a rough diamond

Focused Identification of Germplasm Strategy (FIGS) has been advocated as an efficient approach to predict and harness variation in adaptive traits in genebanks or wild populations of plants. However, a weakness of the current FIGS approach is that it only utilizes a priori knowledge of one evolutionary factor: natural selection. Further optimization is needed to capture elusive traits, and this review shows that nonadaptive evolutionary processes (gene flow and genetic drift) should be incorporated to increase precision. Focusing on plant resistance to insect herbivores, we also note that historic selection pressures can be difficult to disentangle, and provide suggestions for successful mining based on eco-evolutionary theory. We conclude that with such refinement FIGS has high potential for enhancing breeding efforts and hence sustainable plant production.

Genebank Phenomics: A Strategic Approach to Enhance Value and Utilization of Crop Germplasm

Genetically diverse plant germplasm stored in ex-situ genebanks are excellent resources for breeding new high yielding and sustainable crop varieties to ensure future food security. Novel alleles have been discovered through routine genebank activities such as seed regeneration and characterization, with subsequent utilization providing significant genetic gains and improvements for the selection of favorable traits, including yield, biotic, and abiotic resistance. Although some genebanks have implemented cost-effective genotyping technologies through advances in DNA technology, the adoption of modern phenotyping is lagging. The introduction of advanced phenotyping technologies in recent decades has provided genebank scientists with time and cost-effective screening tools to obtain valuable phenotypic data for more traits on large germplasm collections during routine activities. The utilization of these phenotyping tools, coupled with high-throughput genotyping, will accelerate the use of genetic resources and fast-track the development of more resilient food crops for the future. In this review, we highlight current digital phenotyping methods that can capture traits during annual seed regeneration to enrich genebank phenotypic datasets. Next, we describe strategies for the collection and use of phenotypic data of specific traits for downstream research using high-throughput phenotyping technology. Finally, we examine the challenges and future perspectives of genebank phenomics.

Optimized breeding strategies to harness genetic resources with different performance levels

Background

The narrow genetic base of elite germplasm compromises long-term genetic gain and increases the vulnerability to biotic and abiotic stresses in unpredictable environmental conditions. Therefore, an efficient strategy is required to broaden the genetic base of commercial breeding programs while not compromising short-term variety release. Optimal cross selection aims at identifying the optimal set of crosses that balances the expected genetic value and diversity. We propose to consider genomic selection and optimal cross selection to recurrently improve genetic resources (i.e. pre-breeding), to bridge the improved genetic resources with elites (i.e. bridging), and to manage introductions into the elite breeding population. Optimal cross selection is particularly adapted to jointly identify bridging, introduction and elite crosses to ensure an overall consistency of the genetic base broadening strategy.

Results

We compared simulated breeding programs introducing donors with different performance levels, directly or indirectly after bridging. We also evaluated the effect of the training set composition on the success of introductions. We observed that with recurrent introductions of improved donors, it is possible to maintain the genetic diversity and increase mid- and long-term performances with only limited penalty at short-term. Considering a bridging step yielded significantly higher mid- and long-term genetic gain when introducing low performing donors. The results also suggested to consider marker effects estimated with a broad training population including donor by elite and elite by elite progeny to identify bridging, introduction and elite crosses.

Conclusion

Results of this study provide guidelines on how to harness polygenic variation present in genetic resources to broaden elite germplasm.

Suitability of Single-Nucleotide Polymorphism Arrays Versus Genotyping-By-Sequencing for Genebank Genomics in Wheat

Genebank genomics promises to unlock valuable diversity for plant breeding but first, one key question is which marker system is most suitable to fingerprint entire genebank collections. Using wheat as model species, we tested for the presence of an ascertainment bias and investigated its impact on estimates of genetic diversity and prediction ability obtained using three marker platforms: simple sequence repeat (SSR), genotyping-by-sequencing (GBS), and array-based SNP markers. We used a panel of 378 winter wheat genotypes including 190 elite lines and 188 plant genetic resources (PGR), which were phenotyped in multi-environmental trials for grain yield and plant height. We observed an ascertainment bias for the array-based SNP markers, which led to an underestimation of the molecular diversity within the population of PGR. In contrast, the marker system played only a minor role for the overall picture of the population structure and precision of genome-wide predictions. Interestingly, we found that rare markers contributed substantially to the prediction ability. This combined with the expectation that valuable novel diversity is most likely rare suggests that markers with minor allele frequency deserve careful consideration in the design of a pre-breeding program.

Comparative transcriptome analysis indicates that a core transcriptional network mediates isonuclear alloplasmic male sterility in wheat (Triticum aestivum L.)

BACKGROUND

Cytoplasmic male sterility (CMS) plays a crucial role in the utilization of heterosis and various types of CMS often have different abortion mechanisms. Therefore, it is important to understand the molecular mechanisms related to anther abortion in wheat, which remain unclear at present.

RESULTS

In this study, five isonuclear alloplasmic male sterile lines (IAMSLs) and their maintainer were investigated. Cytological analysis indicated that the abortion type was identical in IAMSLs, typical and stainable abortion, and the key abortive period was in the binucleate stage. Most of the 1,281 core shared differentially expressed genes identified by transcriptome sequencing compared with the maintainer in the vital abortive stage were involved in the metabolism of sugars, oxidative phosphorylation, phenylpropane biosynthesis, and phosphatidylinositol signaling, and they were downregulated in the IAMSLs. Key candidate genes encoding chalcone--flavonone isomerase, pectinesterase, and UDP-glucose pyrophosphorylase were screened and identified. Moreover, further verification elucidated that due to the impact of downregulated genes in these pathways, the male sterile anthers were deficient in sugar and energy, with excessive accumulations of ROS, blocked sporopollenin synthesis, and abnormal tapetum degradation.

CONCLUSIONS

Through comparative transcriptome analysis, an intriguing core transcriptome-mediated male-sterility network was proposed and constructed for wheat and inferred that the downregulation of genes in important pathways may ultimately stunt the formation of the pollen outer wall in IAMSLs. These findings provide insights for predicting the functions of the candidate genes, and the comprehensive analysis of our results was helpful for studying the abortive interaction mechanism in CMS wheat.

Climatic changes and potatoes: How can we cope with the abiotic stresses?

Climate change triggers increases in temperature, drought, and/or salinity that threaten potato production, because they necessitate specific amounts and quality of water, meanwhile lower temperatures generally support stable crop yields. Various cultivation techniques have been developed to reduce the negative effects of drought, heat and/or salinity stresses on potato. Developing innovative varieties with relevant tolerance to abiotic stress is absolutely necessary to guarantee competitive production under sub-optimal environments. Commercial varieties are sensitive to abiotic stresses, and substantial changes to their higher tolerance levels are not easily achieved because their genetic base is narrow. Nonetheless, there are several other possibilities for genetic enhancement using landraces and wild relatives. The complexity of polysomic genetics and heterozygosity in potato hamper the phenotype evaluation over abiotic stresses and consequent conventional introgression of tolerance traits, which are more challenging than previous successes shown over diseases and insects resistances. Today, potatoes face more challenges with severe abiotic stresses. Potato wild relatives can be explored further using innovative genomic, transcriptomic, proteomic, and metabolomic approaches. At the field level, appropriate cultivation techniques must be applied along with precision farming technology and tolerant varieties developed from various breeding techniques, in order to realize high yield under multiple stresses.

Genebank genomics bridges the gap between the conservation of crop diversity and plant breeding

Genebanks have the long-term mission of preserving plant genetic resources as an agricultural legacy for future crop improvement. Operating procedures for seed storage and plant propagation have been in place for decades, but there is a lack of effective means for the discovery and transfer of beneficial alleles from landraces and wild relatives into modern varieties. Here, we review the prospects of using molecular passport data derived from genomic sequence information as a universal monitoring tool at the single-plant level within and between genebanks. Together with recent advances in breeding methodologies, the transformation of genebanks into bio-digital resource centers will facilitate the selection of useful genetic variation and its use in breeding programs, thus providing easy access to past crop diversity. We propose linking catalogs of natural genetic variation and enquiries into biological mechanisms of plant performance as a long-term joint research goal of genebanks, plant geneticists and breeders.

Accelerating crop genetic gains with genomic selection

Genomic prediction based on additive genetic effects can accelerate genetic gain. There are opportunities for further improvement by including non-additive effects that access untapped sources of genetic diversity. Several studies have reported a worrying gap between the projected global future demand for plant-based products and the current annual rates of production increase, indicating that enhancing the rate of genetic gain might be critical for future food security. Therefore, new breeding technologies and strategies are required to significantly boost genetic improvement of future crop cultivars. Genomic selection (GS) has delivered considerable genetic gain in animal breeding and is becoming an essential component of many modern plant breeding programmes as well. In this paper, we review the lessons learned from implementing GS in livestock and the impact of GS on crop breeding, and discuss important features for the success of GS under different breeding scenarios. We highlight major challenges associated with GS including rapid genotyping, phenotyping, genotype-by-environment interaction and non-additivity and give examples for opportunities to overcome these issues. Finally, the potential of combining GS with other modern technologies in order to maximise the rate of crop genetic improvement is discussed, including the potential of increasing prediction accuracy by integration of crop growth models in GS frameworks.

Phenotyping of isogenic chlorophyll-less bread and durum wheat mutant lines in relation to photoprotection and photosynthetic capacity

In our experiments, we examined high light responses and photosynthetic capacity of chlorophyll-less isogenic mutant lines of hexaploid bread wheat (Triticum aestivum L.) and tetraploid durum wheat (Triticum durum L.) in comparison to parental lines representing the wild type (WT), in two growth phases and two environments. In young plants, we observed a typical yellow-green phenotype with low chlorophyll content, significantly lower CO₂ assimilation rate, elevated chlorophyll a-to-b ratio and insufficient regulation of linear electron transport. In the mutants grown in a moderate light in the growth chamber, a typical "chlorina" phenotype almost disappeared or, at least, was significantly alleviated in later growth stages, including the values of CO₂ assimilation and the majority of the measured parameters related to photoprotective responses. On the other hand, in the case of the mutant lines grown in direct sunlight and fluctuating environment, the chlorophyll-less phenotype was evident also in latter growth phases. The chlorophyll-less phenotype was more severe in the durum wheat mutant lines compared to the bread wheat. For example, the durum wheat mutant lines grown outdoors expressed lower flexibility of photoprotective responses, including lower non-photochemical quenching and low rate of cyclic electron flow compared to WT or bread wheat mutants. Based on the analyses, we have identified a set of parameters providing information on the specific photosynthetic traits typical for the chlorophyll-less phenotype. Thus, the proposed way of phenotyping may serve for efficient selection of mutant genotypes for future research or screening activities. As a general result, we observed that the decrease of the chlorophyll content due to mutation was always associated with improper regulation of linear electron transport and a limited ability to prevent over-reduction of PSI acceptor side, regardless of the genotype, environment, and growth stage. This can partly explain why the low chlorophyll mutants were not successful in the evolution of higher plants, despite the photosynthetic capacity observed is high enough and they are fully competitive with wild-type plants in non-fluctuating controlled environment.

Discovering new alleles for yellow spot resistance in the Vavilov wheat collection

GWAS detected 11 yellow spot resistance QTL in the Vavilov wheat collection. Promising adult-plant resistance loci could provide a sustainable genetic solution to yellow spot in modern wheat varieties. Yellow spot, caused by the fungal pathogen Pyrenophora tritici-repentis (Ptr), is the most economically damaging foliar disease of wheat in Australia. Genetic resistance is considered to be the most sustainable means for disease management, yet the genomic regions underpinning resistance to Ptr, particularly adult-plant resistance (APR), remain vastly unknown. In this study, we report results of a genome-wide association study using 295 accessions from the Vavilov wheat collection which were extensively tested for response to Ptr infections in glasshouse and field trials at both seedling an adult growth stages. Combining phenotypic datasets from multiple experiments in Australia and Russia with 25,286 genome-wide, high-quality DArTseq markers, we detected a total of 11 QTL, of which 5 were associated with seedling resistance, 3 with all-stage resistance, and 3 with APR. Interestingly, the novel APR QTL were effective even in the presence of host sensitivity gene Tsn1. These genomic regions could offer broad-spectrum yellow spot protection, not just to ToxA but also other pathogenicity or virulence factors. Vavilov wheat accessions carrying APR QTL combinations displayed enhanced levels of resistance highlighting the potential for QTL stacking through breeding. We propose that the APR genetic factors discovered in our study could be used to improve resistance levels in modern wheat varieties and contribute to the sustainable control of yellow spot.

Genebank genomics highlights the diversity of a global barley collection

Genebanks hold comprehensive collections of cultivars, landraces and crop wild relatives of all major food crops, but their detailed characterization has so far been limited to sparse core sets. The analysis of genome-wide genotyping-by-sequencing data for almost all barley accessions of the German ex situ genebank provides insights into the global population structure of domesticated barley and points out redundancies and coverage gaps in one of the world's major genebanks. Our large sample size and dense marker data afford great power for genome-wide association scans. We detect known and novel loci underlying morphological traits differentiating barley genepools, find evidence for convergent selection for barbless awns in barley and rice and show that a major-effect resistance locus conferring resistance to bymovirus infection has been favored by traditional farmers. This study outlines future directions for genomics-assisted genebank management and the utilization of germplasm collections for linking natural variation to human selection during crop evolution.

Sources for the breeding of soft spring wheat in the conditions of Novosibirsk region

The sources were identified among collection samples characterized by highly pronounced economic and valuable features, which allows new geographically remote source material to be taken to the regional breeding practices. This research aims to assess the agronomic traits (duration of the growing period, lodging resistance and plant height, 1000-grain weight, grain weight and yield) in soft spring wheat varieties of different ecological and geographical origin. Estimation was carried out by a 9-point system of expression of the trait during the study, which allows identifying samples with the greatest expression of the trait in the years of study with respect to the average experience. 5439 samples have been studied over 28 years, with 1106 of them, over two years or more. The study was carried out according to the methods of VIR on plots of 2 m2. It was shown that the samples mainly had no correlation between the yield and the duration of the growing period, while the average dependence (г = 0.6) was revealed between the yield and the height of the plants. Varieties forming the intermediate (4.5-5 points) and above average (6-7) yield in a short growing period (69-85 days) were identified (Lutescens 675, Irkut-skaya 49, Simbirca, Hybrid F3 S-141, Hybrid F4, Hybrid F3 S-289 and Hybrid F4 S-2300 and Pamyati Vavenko-va). A high average score (8.6-9) at 1000 grains weight was shown for 16 varieties with variation from 37 g (N43 and IAO-9) to 56 g (Hofed 1). A high average score (8-9) in the evaluation of grain weight was shown for Pamyati Leont'eva, Ekada 70, Simbirtsit, Don Jose, Yong-Liang 4 and Long-Mai 11, which formed ears with an average weight from 0.96 to 2.30 g. A consistently high score (9) reflecting the yield was in the varieties Condestavel, PF 843025, Prilenskaya 19, Pamyati Leont'eva, Omskaya Krasa.

Grain number and grain yield distribution along the spike remain stable despite breeding for high yield in winter wheat

Two winter wheat (Triticum aestivum L.) populations, i.e. 180 genetic resources and 210 elite varieties, were compared in a field trial to analyse how grain number and grain yield distribution along the spike changed during the breeding process and how this associates to yield-related traits. Elites showed in average 38% more yield compared to resources. This breeding improvement mainly derived from an increase in grains and yield per spike in addition to grains and yield per spikelet. These increments corresponded to 19, 23, 21 and 25%, respectively. Not much gain in thousand grain weight (4%) was observed in elites as compared to resources. The number of spikelets per spike was not, or even negatively, correlated with most traits, except of grains per spike, which suggests that this trait was not favoured during breeding. The grain number and grain yield distributions along the spike (GDAS and GYDAS) were measured and compared by using a novel mathematical tool. GDAS and GYDAS measure the deviation of a spike of interest from the architecture of a model spike with even grain and yield distribution along all spikelets, respectively. Both traits were positively correlated. Elites showed in average only a 1% improvement in GDAS and GYDAS values compared to resources. This comparison revealed that breeding increased grain number and yield uniformly along the spike without changing relative yield input of individual spikelets, thereby, maintaining the general spike architecture.

Genomic approaches for studying crop evolution

Understanding how crop plants evolved from their wild relatives and spread around the world can inform about the origins of agriculture. Here, we review how the rapid development of genomic resources and tools has made it possible to conduct genetic mapping and population genetic studies to unravel the molecular underpinnings of domestication and crop evolution in diverse crop species. We propose three future avenues for the study of crop evolution: establishment of high-quality reference genomes for crops and their wild relatives; genomic characterization of germplasm collections; and the adoption of novel methodologies such as archaeogenetics, epigenomics, and genome editing.

Advances in Wheat and Pathogen Genomics: Implications for Disease Control

The gene pool of wheat and its wild and domesticated relatives contains a plethora of resistance genes that can be exploited to make wheat more resilient to pathogens. Only a few of these genes have been isolated and studied at the molecular level. In recent years, we have seen a shift from classical breeding to genomics-assisted breeding, which makes use of the enormous advancements in DNA sequencing and high-throughput molecular marker technologies for wheat improvement. These genomic advancements have the potential to transform wheat breeding in the near future and to significantly increase the speed and precision at which new cultivars can be bred. This review highlights the genomic improvements that have been made in wheat and its pathogens over the past years and discusses their implications for disease-resistance breeding.

Genome-wide identification and analysis of the ALTERNATIVE OXIDASE gene family in diploid and hexaploid wheat

A comprehensive understanding of wheat responses to environmental stress will contribute to the long-term goal of feeding the planet. ALERNATIVE OXIDASE (AOX) genes encode proteins involved in a bypass of the electron transport chain and are also known to be involved in stress tolerance in multiple species. Here, we report the identification and characterization of the AOX gene family in diploid and hexaploid wheat. Four genes each were found in the diploid ancestors Triticum urartu, and Aegilops tauschii, and three in Aegilops speltoides. In hexaploid wheat (Triticum aestivum), 20 genes were identified, some with multiple splice variants, corresponding to a total of 24 proteins for those with observed transcription and translation. These proteins were classified as AOX1a, AOX1c, AOX1e or AOX1d via phylogenetic analysis. Proteins lacking most or all signature AOX motifs were assigned to putative regulatory roles. Analysis of protein-targeting sequences suggests mixed localization to the mitochondria and other organelles. In comparison to the most studied AOX from Trypanosoma brucei, there were amino acid substitutions at critical functional domains indicating possible role divergence in wheat or grasses in general. In hexaploid wheat, AOX genes were expressed at specific developmental stages as well as in response to both biotic and abiotic stresses such as fungal pathogens, heat and drought. These AOX expression patterns suggest a highly regulated and diverse transcription and expression system. The insights gained provide a framework for the continued and expanded study of AOX genes in wheat for stress tolerance through breeding new varieties, as well as resistance to AOX-targeted herbicides, all of which can ultimately be used synergistically to improve crop yield.

Leveraging the Use of Historical Data Gathered During Seed Regeneration of an ex Situ Genebank Collection of Wheat

Genebanks are a rich source of genetic variation. Most of this variation is absent in breeding programs but may be useful for further crop plant improvement. However, the lack of phenotypic information forms a major obstacle for the educated choice of genebank accessions for research and breeding. A promising approach to fill this information gap is to exploit historical information gathered routinely during seed regeneration cycles. Still, this data is characterized by a high non-orthogonality hampering their analysis. By examining historical data records for flowering time, plant height, and thousand grain weight collected during 70 years of regeneration of 6,207 winter wheat (Triticum aestivum L.) accessions at the German Federal ex situ Genebank, we aimed to elaborate a strategy to analyze and validate non-orthogonal historical data in order to charge genebank information platforms with high quality ready-to-use phenotypic information. First, a three-step quality control assessment considering the plausibility of trait values and a standard as well as a weather parameter index based outlier detection was implemented, resulting in heritability estimates above 0.90 for all three traits. Then, the data was analyzed by estimating best linear unbiased estimations (BLUEs) applying a linear mixed-model approach. An in silico resampling study mimicking different missing data patterns revealed that accessions should be regenerated in a random fashion and not blocked by origin or acquisition date in order to minimize estimation biases in historical data sets. Validation data was obtained from multi-environmental orthogonal field trials considering a random subsample of 3,083 accessions. Correlations above 0.84 between BLUEs estimated for historical data and validation trials outperformed previous approaches and confirmed the robustness of our strategy as well as the high quality of the historical data. The results indicate that the IPK winter wheat collection reveals an extraordinary high phenotypic diversity compared to other collections. The quality checked ready-to-use phenotypic information resulting from this study is the first brick to extend traditional, conservation driven genebanks into bio-digital resource centers.

Can spelt wheat be used as heterotic group for hybrid wheat breeding?

Spelt wheat is a distinct genetic group to elite bread wheat, but heterosis for yield and protein quality is too low for spelt to be recommended as heterotic group for hybrid breeding in wheat. The feasibility to switch from line to hybrid breeding is currently a hot topic in the wheat community. One limitation seems to be the lack of divergent heterotic groups within wheat adapted to a certain region. Spelt wheat is a hexaploid wheat that can easily be crossed with bread wheat and that forms a divergent genetic group when compared to elite bread wheat. The aim of this study was to investigate the potential of Central European spelt as a heterotic group for Central European bread wheat. We performed two large experimental field studies comprising in total 43 spelt lines, 14 wheat lines, and 273 wheat-spelt hybrids, and determined yield, heading time, plant height, resistance against yellow rust, leaf rust, and powdery mildew, as well as protein content and sedimentation volume. Heterosis of yield was found to be lower than that of hybrids made between elite wheat lines. Moreover, heterosis of the quality trait sedimentation volume was negative. Consequently, spelt wheat does not appear suited to be used as heterotic group in hybrid wheat breeding. Nevertheless, high combining abilities of a few spelt lines with elite bread wheat lines make them interesting resources for pre-breeding in bread wheat. Thereby, the low correlation between line per se performance and combining ability of these spelt lines shows the potential to unravel the breeding value of genetic resources by crossing them to an elite tester.

Wheat genetic resources in the post-genomics era: promise and challenges

Background

Wheat genetic resources have been used for genetic improvement since 1876, when Stephen Wilson (Transactions and Proceedings of the Botanical Society of Edinburgh 12: 286) consciously made the first wide hybrid involving wheat and rye in Scotland. Wide crossing continued with sporadic attempts in the first half of 19th century and became a sophisticated scientific discipline during the last few decades with considerable impact in farmers' fields. However, a large diversity of untapped genetic resources could contribute in meeting future wheat production challenges.

Perspectives and Conclusion

Recently the complete reference genome of hexaploid (Chinese Spring) and tetraploid (Triticum turgidum ssp. dicoccoides) wheat became publicly available coupled with on-going international efforts on wheat pan-genome sequencing. We anticipate that an objective appraisal is required in the post-genomics era to prioritize genetic resources for use in the improvement of wheat production if the goal of doubling yield by 2050 is to be met. Advances in genomics have resulted in the development of high-throughput genotyping arrays, improved and efficient methods of gene discovery, genomics-assisted selection and gene editing using endonucleases. Likewise, ongoing advances in rapid generation turnover, improved phenotyping, envirotyping and analytical methods will significantly accelerate exploitation of exotic genes and increase the rate of genetic gain in breeding. We argue that the integration of these advances will significantly improve the precision and targeted identification of potentially useful variation in the wild relatives of wheat, providing new opportunities to contribute to yield and quality improvement, tolerance to abiotic stresses, resistance to emerging biotic stresses and resilience to weather extremes.

Genomic Prediction and Association Mapping of Curd-Related Traits in Gene Bank Accessions of Cauliflower

Genetic resources are an important source of genetic variation for plant breeding. Genome-wide association studies (GWAS) and genomic prediction greatly facilitate the analysis and utilization of useful genetic diversity for improving complex phenotypic traits in crop plants. We explored the potential of GWAS and genomic prediction for improving curd-related traits in cauliflower (Brassica oleracea var. botrytis) by combining 174 randomly selected cauliflower gene bank accessions from two different gene banks. The collection was genotyped with genotyping-by-sequencing (GBS) and phenotyped for six curd-related traits at two locations and three growing seasons. A GWAS analysis based on 120,693 single-nucleotide polymorphisms identified a total of 24 significant associations for curd-related traits. The potential for genomic prediction was assessed with a genomic best linear unbiased prediction model and BayesB. Prediction abilities ranged from 0.10 to 0.66 for different traits and did not differ between prediction methods. Imputation of missing genotypes only slightly improved prediction ability. Our results demonstrate that GWAS and genomic prediction in combination with GBS and phenotyping of highly heritable traits can be used to identify useful quantitative trait loci and genotypes among genetically diverse gene bank material for subsequent utilization as genetic resources in cauliflower breeding.

Sunflower Hybrid Breeding: From Markers to Genomic Selection

In sunflower, molecular markers for simple traits as, e.g., fertility restoration, high oleic acid content, herbicide tolerance or resistances to Plasmopara halstedii, Puccinia helianthi, or Orobanche cumana have been successfully used in marker-assisted breeding programs for years. However, agronomically important complex quantitative traits like yield, heterosis, drought tolerance, oil content or selection for disease resistance, e.g., against Sclerotinia sclerotiorum have been challenging and will require genome-wide approaches. Plant genetic resources for sunflower are being collected and conserved worldwide that represent valuable resources to study complex traits. Sunflower association panels provide the basis for genome-wide association studies, overcoming disadvantages of biparental populations. Advances in technologies and the availability of the sunflower genome sequence made novel approaches on the whole genome level possible. Genotype-by-sequencing, and whole genome sequencing based on next generation sequencing technologies facilitated the production of large amounts of SNP markers for high density maps as well as SNP arrays and allowed genome-wide association studies and genomic selection in sunflower. Genome wide or candidate gene based association studies have been performed for traits like branching, flowering time, resistance to Sclerotinia head and stalk rot. First steps in genomic selection with regard to hybrid performance and hybrid oil content have shown that genomic selection can successfully address complex quantitative traits in sunflower and will help to speed up sunflower breeding programs in the future. To make sunflower more competitive toward other oil crops higher levels of resistance against pathogens and better yield performance are required. In addition, optimizing plant architecture toward a more complex growth type for higher plant densities has the potential to considerably increase yields per hectare. Integrative approaches combining omic technologies (genomics, transcriptomics, proteomics, metabolomics and phenomics) using bioinformatic tools will facilitate the identification of target genes and markers for complex traits and will give a better insight into the mechanisms behind the traits.

Bayesian optimization for genomic selection: a method for discovering the best genotype among a large number of candidates

A new pre-breeding strategy based on an optimization algorithm is proposed and evaluated via simulations. This strategy can find superior genotypes with less phenotyping effort. Genomic prediction is a promising approach to search for superior genotypes among a large number of accessions in germplasm collections preserved in gene banks. When some accessions are phenotyped and genotyped, a prediction model can be built, and the genotypic values of the remaining accessions can be predicted from their marker genotypes. In this study, we focused on the application of genomic prediction to pre-breeding, and propose a novel strategy that would reduce the cost of phenotyping needed to discover better accessions. We regarded the exploration of superior genotypes with genomic prediction as an optimization problem, and introduced Bayesian optimization to solve it. Bayesian optimization, that samples unobserved inputs according to the expected improvement (EI) as a selection criterion, seemed to be beneficial in pre-breeding. The EI depends on the predicted distribution of genotypic values, whereas usual selection depends only on the point estimate. We simulated a search for the best genotype among candidate genotypes and showed that the EI-based strategy required fewer genotypes to identify the best genotype than the usual and random selection strategy. Therefore, Bayesian optimization can be useful for applying genomic prediction to pre-breeding and would reduce the number of phenotyped accessions needed to find the best accession among a large number of candidates.

Assessing and Exploiting Functional Diversity in Germplasm Pools to Enhance Abiotic Stress Adaptation and Yield in Cereals and Food Legumes

There is a need to accelerate crop improvement by introducing alleles conferring host plant resistance, abiotic stress adaptation, and high yield potential. Elite cultivars, landraces and wild relatives harbor useful genetic variation that needs to be more easily utilized in plant breeding. We review genome-wide approaches for assessing and identifying alleles associated with desirable agronomic traits in diverse germplasm pools of cereals and legumes. Major quantitative trait loci and single nucleotide polymorphisms (SNPs) associated with desirable agronomic traits have been deployed to enhance crop productivity and resilience. These include alleles associated with variation conferring enhanced photoperiod and flowering traits. Genetic variants in the florigen pathway can provide both environmental flexibility and improved yields. SNPs associated with length of growing season and tolerance to abiotic stresses (precipitation, high temperature) are valuable resources for accelerating breeding for drought-prone environments. Both genomic selection and genome editing can also harness allelic diversity and increase productivity by improving multiple traits, including phenology, plant architecture, yield potential and adaptation to abiotic stresses. Discovering rare alleles and useful haplotypes also provides opportunities to enhance abiotic stress adaptation, while epigenetic variation has potential to enhance abiotic stress adaptation and productivity in crops. By reviewing current knowledge on specific traits and their genetic basis, we highlight recent developments in the understanding of crop functional diversity and identify potential candidate genes for future use. The storage and integration of genetic, genomic and phenotypic information will play an important role in ensuring broad and rapid application of novel genetic discoveries by the plant breeding community. Exploiting alleles for yield-related traits would allow improvement of selection efficiency and overall genetic gain of multigenic traits. An integrated approach involving multiple stakeholders specializing in management and utilization of genetic resources, crop breeding, molecular biology and genomics, agronomy, stress tolerance, and reproductive/seed biology will help to address the global challenge of ensuring food security in the face of growing resource demands and climate change induced stresses.

Emerging Avenues for Utilization of Exotic Germplasm

Breeders have been successful in increasing crop performance by exploiting genetic diversity over time. However, the reported annual yield increases are not sufficient in view of rapid human population growth and global environmental changes. Exotic germplasm possesses high levels of genetic diversity for valuable traits. However, only a small fraction of naturally occurring genetic diversity is utilized. Moreover, the yield gap between elite and exotic germplasm widens, which increases the effort needed to use exotic germplasm and to identify beneficial alleles and for their introgression. The advent of high-throughput genotyping and phenotyping technologies together with emerging biotechnologies provide new opportunities to explore exotic genetic variation. This review will summarize potential challenges for utilization of exotic germplasm and provide solutions.

Tapping the genetic diversity of landraces in allogamous crops with doubled haploid lines: a case study from European flint maize

Using landraces for broadening the genetic base of elite maize germplasm is hampered by heterogeneity and high genetic load. Production of DH line libraries can help to overcome these problems. Landraces of maize (Zea mays L.) represent a huge reservoir of genetic diversity largely untapped by breeders. Genetic heterogeneity and a high genetic load hamper their use in hybrid breeding. Production of doubled haploid line libraries (DHL) by the in vivo haploid induction method promises to overcome these problems. To test this hypothesis, we compared the line per se performance of 389 doubled haploid (DH) lines across six DHL produced from European flint landraces with that of four flint founder lines (FFL) and 53 elite flint lines (EFL) for 16 agronomic traits evaluated in four locations. The genotypic variance ([Formula: see text]) within DHL was generally much larger than that among DHL and exceeded also [Formula: see text] of the EFL. For most traits, the means and [Formula: see text] differed considerably among the DHL, resulting in different expected selection gains. Mean grain yield of the EFL was 25 and 62% higher than for the FFL and DHL, respectively, indicating considerable breeding progress in the EFL and a remnant genetic load in the DHL. Usefulness of the best 20% lines was for individual DHL comparable to the EFL and grain yield (GY) in the top lines from both groups was similar. Our results corroborate the tremendous potential of landraces for broadening the narrow genetic base of elite germplasm. To make best use of these "gold reserves", we propose a multi-stage selection approach with optimal allocation of resources to (1) choose the most promising landraces for DHL production and (2) identify the top DH lines for further breeding.

The Predicted Cross Value for Genetic Introgression of Multiple Alleles

We consider the plant genetic improvement challenge of introgressing multiple alleles from a homozygous donor to a recipient. First, we frame the project as an algorithmic process that can be mathematically formulated. We then introduce a novel metric for selecting breeding parents that we refer to as the predicted cross value (PCV). Unlike estimated breeding values, which represent predictions of general combining ability, the PCV predicts specific combining ability. The PCV takes estimates of recombination frequencies as an input vector and calculates the probability that a pair of parents will produce a gamete with desirable alleles at all specified loci. We compared the PCV approach with existing estimated-breeding-value approaches in two simulation experiments, in which 7 and 20 desirable alleles were to be introgressed from a donor line into a recipient line. Results suggest that the PCV is more efficient and effective for multi-allelic trait introgression. We also discuss how operations research can be used for other crop genetic improvement projects and suggest several future research directions.

Back to the Future - Tapping into Ancient Grains for Food Diversity

Einkorn, emmer, and spelt are old wheat species that have fed the world for centuries before they have nearly completely been replaced by modern bread wheat. Nowadays, the diversity of these old species lies frozen in gene banks and rare attempts aim to exploit them as a source for genetic diversity in modern wheat breeding. Here, we want to raise a debate on a more holistic exploitation of ancient species via their direct introduction to the consumer market as high quality products. Although exemplified only for ancient wheat species, this innovative self-financing strategy can be directly extended to other species. A central requirement for this concept is intensive communication, coordination, and interdisciplinary research along the entire production chain from farm to fork.

Prospects of Genomic Prediction in the USDA Soybean Germplasm Collection: Historical Data Creates Robust Models for Enhancing Selection of Accessions

The identification and mobilization of useful genetic variation from germplasm banks for use in breeding programs is critical for future genetic gain and protection against crop pests. Plummeting costs of next-generation sequencing and genotyping is revolutionizing the way in which researchers and breeders interface with plant germplasm collections. An example of this is the high density genotyping of the entire USDA Soybean Germplasm Collection. We assessed the usefulness of 50K single nucleotide polymorphism data collected on 18,480 domesticated soybean (Glycine max) accessions and vast historical phenotypic data for developing genomic prediction models for protein, oil, and yield. Resulting genomic prediction models explained an appreciable amount of the variation in accession performance in independent validation trials, with correlations between predicted and observed reaching up to 0.92 for oil and protein and 0.79 for yield. The optimization of training set design was explored using a series of cross-validation schemes. It was found that the target population and environment need to be well represented in the training set. Second, genomic prediction training sets appear to be robust to the presence of data from diverse geographical locations and genetic clusters. This finding, however, depends on the influence of shattering and lodging, and may be specific to soybean with its presence of maturity groups. The distribution of 7608 nonphenotyped accessions was examined through the application of genomic prediction models. The distribution of predictions of phenotyped accessions was representative of the distribution of predictions for nonphenotyped accessions, with no nonphenotyped accessions being predicted to fall far outside the range of predictions of phenotyped accessions.

Plant Genetic Resources: Needs, Rights, and Opportunities

Technological advances allow us to tap into genetic resources to address food and nutritional security in the face of population growth, urbanization, climate change, and environmental degradation. It is vital, particularly for developing countries, to ensure that the policy framework regulating access and use of genetic resources keeps pace with technological developments.

Initiating maize pre-breeding programs using genomic selection to harness polygenic variation from landrace populations

Background

The limited genetic diversity of elite maize germplasms raises concerns about the potential to breed for new challenges. Initiatives have been formed over the years to identify and utilize useful diversity from landraces to overcome this issue. The aim of this study was to evaluate the proposed designs to initiate a pre-breeding program within the Seeds of Discovery (SeeD) initiative with emphasis on harnessing polygenic variation from landraces using genomic selection. We evaluated these designs with stochastic simulation to provide decision support about the effect of several design factors on the quality of resulting (pre-bridging) germplasm. The evaluated design factors were: i) the approach to initiate a pre-breeding program from the selected landraces, doubled haploids of the selected landraces, or testcrosses of the elite hybrid and selected landraces, ii) the genetic parameters of landraces and phenotypes, and iii) logistical factors related to the size and management of a pre-breeding program.

Results

The results suggest a pre-breeding program should be initiated directly from landraces. Initiating from testcrosses leads to a rapid reconstruction of the elite donor genome during further improvement of the pre-bridging germplasm. The analysis of accuracy of genomic predictions across the various design factors indicate the power of genomic selection for pre-breeding programs with large genetic diversity and constrained resources for data recording. The joint effect of design factors was summarized with decision trees with easy to follow guidelines to optimize pre-breeding efforts of SeeD and similar initiatives.

Conclusions

Results of this study provide guidelines for SeeD and similar initiatives on how to initiate pre-breeding programs that aim to harness polygenic variation from landraces.

Electronic supplementary material

The online version of this article (doi:10.1186/s12864-015-2345-z) contains supplementary material, which is available to authorized users.

Breeding schemes for the implementation of genomic selection in wheat (Triticum spp.)

In the last decade the breeding technology referred to as 'genomic selection' (GS) has been implemented in a variety of species, with particular success in animal breeding. Recent research shows the potential of GS to reshape wheat breeding. Many authors have concluded that the estimated genetic gain per year applying GS is several times that of conventional breeding. GS is, however, a new technology for wheat breeding and many programs worldwide are still struggling to identify the best strategy for its implementation. This article provides practical guidelines on the key considerations when implementing GS. A review of the existing GS literature for a range of species is provided and used to prime breeder-oriented considerations on the practical applications of GS. Furthermore, this article discusses potential breeding schemes for GS, genotyping considerations, and methods for effective training population design. The components of selection intensity, progress toward inbreeding in half- or full-sibs recurrent schemes, and the generation of selection are also presented.

Subgenomic Diversity Patterns Caused by Directional Selection in Bread Wheat Gene Pools

Genetic diversity represents the fundamental key to breeding success, providing the basis for breeders to select varieties with constantly improving yield performance. On the other hand, strong selection during domestication and breeding have eliminated considerable genetic diversity in the breeding pools of major crops, causing erosion of genetic potential for adaptation to emerging challenges like climate change. High-throughput genomic technologies can address this dilemma by providing detailed knowledge to characterize and replenish genetic diversity in breeding programs. In hexaploid bread wheat (Triticum aestivum L.), the staple food for 35% of the world's population, bottlenecks during allopolyploidisation followed by strong artificial selection have considerably narrowed diversity to the extent that yields in many regions appear to be unexpectedly stagnating. In this study, we used a 90,000 single nucleotide polymorphism (SNP) wheat genotyping array to assay high-frequency, polymorphic SNP markers in 460 accessions representing different phenological diversity groups from Asian, Australian, European, and North American bread wheat breeding materials. Detailed analysis of subgroup diversity at the chromosome and subgenome scale revealed highly distinct patterns of conserved linkage disequilibrium between different gene pools. The data enable identification of genome regions in most need of rejuvenation with novel diversity and provide a high-resolution molecular basis for genomic-assisted introgression of new variation into chromosome segments surrounding directionally selected metaloci conferring important adaptation and quality traits.

Genetic architecture is more complex for resistance to Septoria tritici blotch than to Fusarium head blight in Central European winter wheat

BACKGROUND

Fusarium head blight (FHB) and Septoria tritici blotch (STB) severely impair wheat production. With the aim to further elucidate the genetic architecture underlying FHB and STB resistance, we phenotyped 1604 European wheat hybrids and their 135 parental lines for FHB and STB disease severities and determined genotypes at 17,372 single-nucleotide polymorphic loci.

RESULTS

Cross-validated association mapping revealed the absence of large effect QTL for both traits. Genomic selection showed a three times higher prediction accuracy for FHB than STB disease severity for test sets largely unrelated to the training sets.

CONCLUSIONS

Our findings suggest that the genetic architecture is less complex and, hence, can be more properly tackled to perform accurate prediction for FHB than STB disease severity. Consequently, FHB disease severity is an interesting model trait to fine-tune genomic selection models exploiting beyond relatedness also knowledge of the genetic architecture.

Variance components, heritability and correlation analysis of anther and ovary size during the floral development of bread wheat

Anther and ovary development play an important role in grain setting, a crucial factor determining wheat (Triticum aestivum L.) yield. One aim of this study was to determine the heritability of anther and ovary size at different positions within a spikelet at seven floral developmental stages and conduct a variance components analysis. Relationships between anther and ovary size and other traits were also assessed. The thirty central European winter wheat genotypes used in this study were based on reduced height (Rht) and photoperiod sensitivity (Ppd) genes with variable genetic backgrounds. Identical experimental designs were conducted in a greenhouse and field simultaneously. Heritability of anther and ovary size indicated strong genetic control. Variance components analysis revealed that anther and ovary sizes of floret 3 (i.e. F3, the third floret from the spikelet base) and floret 4 (F4) were more sensitive to the environment compared with those in floret 1 (F1). Good correlations were found between spike dry weight and anther and ovary size in both greenhouse and field, suggesting that anther and ovary size are good predictors of each other, as well as spike dry weight in both conditions. Relationships between spike dry weight and anther and ovary size at F3/4 positions were stronger than at F1, suggesting that F3/4 anther and ovary size are better predictors of spike dry weight. Generally, ovary size showed a closer relationship with spike dry weight than anther size, suggesting that ovary size is a more reliable predictor of spike dry weight.