Review of Crop Wild Relative Conservation and Use in West Asia and North Africa.

Ensuring global food security in the face of climate change is critical to human survival. With a predicted human population of 9.6 billion in 2050 and the demand for food supplies expected to increase by 60% globally, but with a parallel potential reduction in crop production for wheat by 6.0%, rice by 3.2%, maize by 7.4%, and soybean by 3.1% by the end of the century, maintaining future food security will be a challenge. One potential solution is new climate-smart varieties created using the breadth of diversity inherent in crop wild relatives (CWRs). Yet CWRs are threatened, with 16-35% regarded as threatened and a significantly higher percentage suffering genetic erosion. Additionally, they are under-conserved, 95% requiring additional ex situ collections and less than 1% being actively conserved in situ; they also often grow naturally in disturbed habitats limiting standard conservation measures. The urgent requirement for active CWR conservation is widely recognized in the global policy context (Convention on Biological Diversity post-2020 Global Biodiversity Framework, UN Sustainable Development Goals, the FAO Second Global Plan of Action for PGRFA, and the FAO Framework for Action on Biodiversity for Food and Agriculture) and breeders highlight that the lack of CWR diversity is unnecessarily limiting crop improvement. CWRs are not spread evenly across the globe; they are focused in hotspots and the hottest region for CWR diversity is in West Asia and North Africa (WANA). The region has about 40% of global priority taxa and the top 17 countries with maximum numbers of CWR taxa per unit area are all in WANA. Therefore, improved CWR active conservation in WANA is not only a regional but a critical global priority. To assist in the achievement of this goal, we will review the following topics for CWRs in the WANA region: (1) conservation status, (2) community-based conservation, (3) threat status, (4) diversity use, (5) CURE-CWR hub: (ICARDA Centre of Excellence), and (6) recommendations for research priorities. The implementation of the recommendations is likely to significantly improve CWRs in situ and ex situ conservation and will potentially at least double the availability of the full breadth of CWR diversity found in WANA to breeders, and so enhance regional and global food and nutritional security.

Genomic regions of durum wheat involved in water productivity.

Durum wheat is a staple food in the Mediterranean Basin, mostly cultivated under rainfed conditions. As such, the crop is often exposed to moisture stress. Therefore, the identification of genetic factors controlling the capacity of genotypes to convert moisture into grain yield (i.e., water productivity) is quintessential to stabilize production despite climatic variations. A global panel of 384 accessions was tested across 18 Mediterranean environments (in Morocco, Lebanon, and Jordan) representing a vast range of moisture levels. The accessions were assigned to water responsiveness classes, with genotypes 'Responsive to Low Moisture' reaching an average +1.5 kg ha-1 mm-1 yield advantage. Genome wide association studies revealed that six loci explained most of this variation. A second validation panel tested under moisture stress confirmed that carrying the positive allele at three loci on chromosomes 1B, 2A, and 7B generated an average water productivity gain of +2.2 kg ha-1 mm-1. These three loci were tagged by kompetitive allele specific PCR (KASP) markers, and these were used to screen a third independent validation panel composed of elites tested across moisture stressed sites. The three KASP combined predicted up to 10% of the variation for grain yield at 60% accuracy. These loci are now ready for molecular pyramiding and transfer across cultivars to improve the moisture conversion of durum wheat.

Identification of sources of resistance to scald (Rhynchosporium commune) and of related genomic regions using genome-wide association in a mapping panel of spring barley.

Barley is an important crop worldwide known for its adaptation to harsh environments and used in multiple forms as feed, food and beverages. Its productivity is affected by major abiotic and biotic stresses. Scald caused by hemibiotrophic fungus Rhynchosporium commune is a major foliar disease in many parts of the world. Host plant resistance is targeted by breeders to efficiently control this disease. An association mapping panel of 316 spring barley genotypes (AM2017) was screened for seedling resistance in greenhouse against three R. commune isolates and for adult plant resistance in three field locations in Morocco. The phenotyping results showed different numbers of entries with resistant and moderately resistant reactions at both seedling and adult plant stages. The reactions differed between the isolates with the highest percentage of resistant genotypes observed for isolate SC-S611 (49.4%) and highest percentage of susceptible genotypes (73.8%) for isolate SC-1122. At adult plant stage, the highest percentage of scald resistant genotypes (64.5%) was observed at Rommani site compared to 56% at Guich site and only 28.8% at Marchouch site. Seven genotypes were resistant at the seedling and adult plant stages. Genome wide association study (GWAS) revealed 102 MTA (15 QTL) at the seedling stage, and 25 MTA (12 QTL) associated with scald resistance at the adult plant stage. In addition, the sequences of 92 out of 102 at SRT, and 24 out of 25 significant SNP markers at APR were located in genomic regions enriched with functional proteins involved in diverse cellular processes including disease resistance. These markers span over all chromosomes with the majority of SNPs located on 3H and 7H. This study has verified 18 QTL reported in previous studies. In addition, it was successful in identifying new sources of resistance and novel genomic regions which could help in enhancing scald resistance in barley breeding programs.

Genotypic and environmental effects on quality and nutritional attributes of Moroccan barley cultivars and elite breeding lines.

Although barley is mainly used for livestock feed and beverages, its use as human feed can enrich human diets with some health benefits. The development of new hulless varieties rich in ß-glucans and micronutrients can enhance the use of barley as food, but little is known about the effects of the environment on these nutritional traits. In this study, we evaluated five Moroccan varieties and two elite breeding lines of barley at four locations in Morocco during the 2016-2017 and 2017-2018 cropping seasons. The results showed highly significant differences between genotypes for ß-glucan, protein, iron, and selenium contents, as well as 1000 kernel weight, but not zinc content; significant to highly significant differences between environments for all traits except ß-glucan content; and significant to highly significant interactions for all traits. The highest level of ß-glucan content has reached 11.57% observed at the Sidi El Aydi site during the growing season 2017-2018 for the hulless variety Chifaa. This variety has shown the highest content of ß-glucan (6.2-11.57%) over all environments except at Tassaout during the 2016-2017 seasons. The breeding line M9V5 has achieved significantly higher protein content at all the locations during the two growing seasons, ranging from 12.38 to 20.14%. Most hulless lines had significantly higher ß-glucan and protein contents, but lower 1000 kernel weight. For micronutrients, the content ranges were 28.94 to 38.23 ppm for Fe, 28.78 to 36.49 ppm for Zn, and 0.14 to 0.18 ppm for Se, with the highest content for Fe and Zn shown by the breeding line M9V5 and Chifaa showing average contents of 33.39 ppm, 35.34 ppm, and 0.18 ppm for Fe, Zn, and Se, respectively. The GGE biplot confirmed the high and relatively stable content of ß-glucan and acceptable micronutrient contents of the Chifaa variety and identified Marchouch as the most discriminant site to breed for biofortified barley varieties.

Single- and multi-trait genomic prediction and genome-wide association analysis of grain yield and micronutrient-related traits in ICARDA wheat under drought environment.

Globally, over 2 billion people suffer from malnutrition due to inadequate intake of micronutrients. Genomic-assisted breeding is identified as a valuable method to facilitate developing new improved plant varieties targeting grain yield and micronutrient-related traits. In this study, a genome-wide association study (GWAS) and single- and multi-trait-based genomic prediction (GP) analysis was conducted using a set of 252 elite wheat genotypes from the International Center for Agricultural Research in Dry Areas (ICARDA). The objective was to identify linked SNP markers, putative candidate genes and to evaluate the genomic estimated breeding values (GEBVs) of grain yield and micronutrient-related traits.. For this purpose, a field trial was conducted at a drought-prone station, Merchouch, Morocco for 2 consecutive years (2018 and 2019) followed by GWAS and genomic prediction analysis with 10,173 quality SNP markers. The studied genotypes exhibited a significant genotypic variation in grain yield and micronutrient-related traits. The GWAS analysis identified highly significantly associated markers and linked putative genes on chromosomes 1B and 2B for zinc (Zn) and iron (Fe) contents, respectively. The genomic predictive ability of selenium (Se) and Fe traits with the multi-trait-based GP GBLUP model was 0.161 and 0.259 improving by 6.62 and 4.44%, respectively, compared to the corresponding single-trait-based models. The identified significantly linked SNP markers, associated putative genes, and developed GP models could potentially facilitate breeding programs targeting to improve the overall genetic gain of wheat breeding for grain yield and biofortification of micronutrients via marker-assisted (MAS) and genomic selection (GS) methods.

Genomic predictions to leverage phenotypic data across genebanks.

Genome-wide prediction is a powerful tool in breeding. Initial results suggest that genome-wide approaches are also promising for enhancing the use of the genebank material: predicting the performance of plant genetic resources can unlock their hidden potential and fill the information gap in genebanks across the world and, hence, underpin prebreeding programs. As a proof of concept, we evaluated the power of across-genebank prediction for extensive germplasm collections relying on historical data on flowering/heading date, plant height, and thousand kernel weight of 9,344 barley (Hordeum vulgare L.) plant genetic resources from the German Federal Ex situ Genebank for Agricultural and Horticultural Crops (IPK) and of 1,089 accessions from the International Center for Agriculture Research in the Dry Areas (ICARDA) genebank. Based on prediction abilities for each trait, three scenarios for predictive characterization were compared: 1) a benchmark scenario, where test and training sets only contain ICARDA accessions, 2) across-genebank predictions using IPK as training and ICARDA as test set, and 3) integrated genebank predictions that include IPK with 30% of ICARDA accessions as a training set to predict the rest of ICARDA accessions. Within the population of ICARDA accessions, prediction abilities were low to moderate, which was presumably caused by a limited number of accessions used to train the model. Interestingly, ICARDA prediction abilities were boosted up to ninefold by using training sets composed of IPK plus 30% of ICARDA accessions. Pervasive genotype × environment interactions (GEIs) can become a potential obstacle to train robust genome-wide prediction models across genebanks. This suggests that the potential adverse effect of GEI on prediction ability was counterbalanced by the augmented training set with certain connectivity to the test set. Therefore, across-genebank predictions hold the promise to improve the curation of the world's genebank collections and contribute significantly to the long-term development of traditional genebanks toward biodigital resource centers.

AlignStatPlot: An R package and online tool for robust sequence alignment statistics and innovative visualization of big data.

Multiple sequence alignment (MSA) is essential for understanding genetic variations controlling phenotypic traits in all living organisms. The post-analysis of MSA results is a difficult step for researchers who do not have programming skills. Especially those working with large scale data and looking for potential variations or variable sample groups. Generating bi-allelic data and the comparison of wild and alternative gene forms are important steps in population genetics. Customising MSA visualisation for a single page view is difficult, making viewing potential indels and variations challenging. There are currently no bioinformatics tools that permit post-MSA analysis, in which data on gene and single nucleotide scales could be combined with gene annotations and used for cluster analysis. We introduce "AlignStatPlot," a new R package and online tool that is well-documented and easy-to use for MSA and post-MSA analysis. This tool performs both traditional and cutting-edge analyses on sequencing data and generates new visualisation methods for MSA results. When compared to currently available tools, AlignStatPlot provides a robust ability to handle and visualise diversity data, while the online version will save time and encourage researchers to focus on explaining their findings. It is a simple tool that can be used in conjunction with population genetics software.

Identification, characterization, and validation of NBS-encoding genes in grass pea.

Grass pea is a promising crop with the potential to provide food and fodder, but its genomics has not been adequately explored. Identifying genes for desirable traits, such as drought tolerance and disease resistance, is critical for improving the plant. Grass pea currently lacks known R-genes, including the nucleotide-binding site-leucine-rich repeat (NBS-LRR) gene family, which plays a key role in protecting the plant from biotic and abiotic stresses. In our study, we used the recently published grass pea genome and available transcriptomic data to identify 274 NBS-LRR genes. The evolutionary relationships between the classified genes on the reported plants and LsNBS revealed that 124 genes have TNL domains, while 150 genes have CNL domains. All genes contained exons, ranging from 1 to 7. Ten conserved motifs with lengths ranging from 16 to 30 amino acids were identified. We found TIR-domain-containing genes in 132 LsNBSs, with 63 TIR-1 and 69 TIR-2, and RX-CCLike in 84 LsNBSs. We also identified several popular motifs, including P-loop, Uup, kinase-GTPase, ABC, ChvD, CDC6, Rnase_H, Smc, CDC48, and SpoVK. According to the gene enrichment analysis, the identified genes undergo several biological processes such as plant defense, innate immunity, hydrolase activity, and DNA binding. In the upstream regions, 103 transcription factors were identified that govern the transcription of nearby genes affecting the plant excretion of salicylic acid, methyl jasmonate, ethylene, and abscisic acid. According to RNA-Seq expression analysis, 85% of the encoded genes have high expression levels. Nine LsNBS genes were selected for qPCR under salt stress conditions. The majority of the genes showed upregulation at 50 and 200 µM NaCl. However, LsNBS-D18, LsNBS-D204, and LsNBS-D180 showed reduced or drastic downregulation compared to their respective expression levels, providing further insights into the potential functions of LsNBSs under salt stress conditions. They provide valuable insights into the potential functions of LsNBSs under salt stress conditions. Our findings also shed light on the evolution and classification of NBS-LRR genes in legumes, highlighting the potential of grass pea. Further research could focus on the functional analysis of these genes, and their potential use in breeding programs to improve the salinity, drought, and disease resistance of this important crop.

Genome-wide identification, characterization, and validation of the bHLH transcription factors in grass pea.

Background: The basic helix-loop-helix (bHLH) transcription factor is a vital component in plant biology, with a significant impact on various aspects of plant growth, cell development, and physiological processes. Grass pea is a vital agricultural crop that plays a crucial role in food security. However, the lack of genomic information presents a major challenge to its improvement and development. This highlights the urgency for deeper investigation into the function of bHLH genes in grass pea to improve our understanding of this important crop. Results: The identification of bHLH genes in grass pea was performed on a genome-wide scale using genomic and transcriptomic screening. A total of 122 genes were identified as having conserved bHLH domains and were functionally and fully annotated. The LsbHLH proteins could be classified into 18 subfamilies. There were variations in intron-exon distribution, with some genes lacking introns. The cis-element and gene enrichment analyses showed that the LsbHLHs were involved in various plant functions, including response to phytohormones, flower and fruit development, and anthocyanin synthesis. A total of 28 LsbHLHs were found to have cis-elements associated with light response and endosperm expression biosynthesis. Ten conserved motifs were identified across the LsbHLH proteins. The protein-protein interaction analysis showed that all LsbHLH proteins interacted with each other, and nine of them displayed high levels of interaction. RNA-seq analysis of four Sequence Read Archive (SRA) experiments showed high expression levels of LsbHLHs across a range of environmental conditions. Seven highly expressed genes were selected for qPCR validation, and their expression patterns in response to salt stress showed that LsbHLHD4, LsbHLHD5, LsbHLHR6, LsbHLHD8, LsbHLHR14, LsbHLHR68, and LsbHLHR86 were all expressed in response to salt stress. Conclusion: The study provides an overview of the bHLH family in the grass pea genome and sheds light on the molecular mechanisms underlying the growth and evolution of this crop. The report covers the diversity in gene structure, expression patterns, and potential roles in regulating plant growth and response to environmental stress factors in grass pea. The identified candidate LsbHLHs could be utilized as a tool to enhance the resilience and adaptation of grass pea to environmental stress.

Novel Genomic Regions Linked to Ascochyta Blight Resistance in Two Differentially Resistant Cultivars of Chickpea.

Ascochyta blight (AB), caused by the fungal pathogen Ascochyta rabiei, is a devastating foliar disease of chickpea (Cicer arietinum L.). The genotyping-by-sequencing (GBS)-based approach was deployed for mapping QTLs associated with AB resistance in chickpea in two recombinant inbred line populations derived from two crosses (AB3279 derived from ILC 1929 × ILC 3279 and AB482 derived from ILC 1929 × ILC 482) and tested in six different environments. Twenty-one different genomic regions linked to AB resistance were identified in regions CalG02 and CalG04 in both populations AB3279 and AB482. These regions contain 1,118 SNPs significantly associated with AB resistance (p ≤ 0.001), which explained 11.2-39.3% of the phenotypic variation (PVE). Nine of the AB resistance-associated genomic regions were newly detected in this study, while twelve regions were known from previous AB studies. The proposed physical map narrows down AB resistance to consistent genomic regions identified across different environments. Gene ontology (GO) assigned these QTLs to 319 genes, many of which were associated with stress and disease resistance, and with most important genes belonging to resistance gene families such as leucine-rich repeat (LRR) and transcription factor families. Our results indicate that the flowering-associated gene GIGANTEA is a possible key factor in AB resistance in chickpea. The results have identified AB resistance-associated regions on the physical genetic map of chickpea and allowed for the identification of associated markers that will help in breeding of AB-resistant varieties.

State of ex situ conservation of landrace groups of 25 major crops.

Crop landraces have unique local agroecological and societal functions and offer important genetic resources for plant breeding. Recognition of the value of landrace diversity and concern about its erosion on farms have led to sustained efforts to establish ex situ collections worldwide. The degree to which these efforts have succeeded in conserving landraces has not been comprehensively assessed. Here we modelled the potential distributions of eco-geographically distinguishable groups of landraces of 25 cereal, pulse and starchy root/tuber/fruit crops within their geographic regions of diversity. We then analysed the extent to which these landrace groups are represented in genebank collections, using geographic and ecological coverage metrics as a proxy for genetic diversity. We find that ex situ conservation of landrace groups is currently moderately comprehensive on average, with substantial variation among crops; a mean of 63% ± 12.6% of distributions is currently represented in genebanks. Breadfruit, bananas and plantains, lentils, common beans, chickpeas, barley and bread wheat landrace groups are among the most fully represented, whereas the largest conservation gaps persist for pearl millet, yams, finger millet, groundnut, potatoes and peas. Geographic regions prioritized for further collection of landrace groups for ex situ conservation include South Asia, the Mediterranean and West Asia, Mesoamerica, sub-Saharan Africa, the Andean mountains of South America and Central to East Asia. With further progress to fill these gaps, a high degree of representation of landrace group diversity in genebanks is feasible globally, thus fulfilling international targets for their ex situ conservation.

Preparation and Curation of Multiyear, Multilocation, Multitrait Datasets.

Genome-wide association studies (GWAS) are a powerful approach to dissect genotype-phenotype associations and identify causative regions. However, this power is highly influenced by the accuracy of the phenotypic data. To obtain accurate phenotypic values, the phenotyping should be achieved through multienvironment trials (METs). In order to avoid any technical errors, the required time needs to be spent on exploring, understanding, curating and adjusting the phenotypic data in each trial before combining them using an appropriate linear mixed model (LMM). The LMM is chosen to minimize as much as possible any effect that can lead to misestimation of the phenotypic values. The purpose of this chapter is to explain a series of important steps to explore and analyze data from METs used to characterize an association panel. Two datasets are used to illustrate two different scenarios.

Single-Plant Selection at Ultra-Low Density Enhances Buffering Capacity of Barley Varieties and Landraces to Unpredictable Environments and Improves Their Agronomic Performance.

Rainfall and temperature are unpredictable factors in Mediterranean environments that result in irregular environmental conditions for crop growth, thus being a critical source of uncertainty for farmers. This study applied divergent single-plant selection for high and low yield within five barley varieties and two Tunisian landraces under semi-arid conditions at an ultra-low density of 1.2 plants/m2 for two consecutive years. Progeny evaluation under dense stands following farmers' practices was conducted in two semi-arid locations in Tunisia during one cropping season and in one location during a second season, totalling three environments. The results revealed significant genotypic effects for all recorded agronomic and physiological traits. No genotype × environment interaction was shown for biological yield, implying a biomass buffering capacity for selected lines under different environmental conditions. However, genotype × environment interaction was present in terms of grain yield since plasticity for biomass production under drought stress conditions was not translated directly to yield compensation for some of the lines. Nevertheless, several lines selected for high yield were identified to surpass their source material and best checks in each environment, while one line (IH4-4) outperformed consistently by 62.99% on average, in terms of grain yield, the best check across all environments. In addition, improved agronomic performance under drought conditions induced an indirect effect on some grain quality traits. Most of the lines selected for high yield maintained or even improved their grain protein content in comparison to their source material (average increase by 2.33%). On the other hand, most of the lines selected for low yield indicated a poor agronomic performance, further confirming the coherence between selection under ultra-low density and performance under dense stand.

Hybrid Seed Set in Relation with Male Floral Traits, Estimation of Heterosis and Combining Abilities for Yield and Its Components in Wheat (Triticum aestivum L.).

Breeding hybrids with maximum heterosis requires efficient cross-pollination and an improved male sterility system. Renewed efforts have been made to dissect the phenotypic variation and genetic basis of hybrid floral traits, although the potential of tailoring the appropriate flower design on seed setting is less known. To this end, elite wheat genotypes were crossed using a chemical hybridizing agent at different doses. A total of 23 hybrids were developed from a partial diallel design; and planted in an alpha lattice design with their parents at two locations in Morocco, for two years, to evaluate for yield components, heterosis and combining abilities. The 13.5 L ha-1 dose induced a maximum level of sterility (95%) and seed set showed large phenotypic variation and high heritability. In parallel, seed set showed tight correlation with pollen mass (0.97), visual anther extrusion (0.94) and pollen shedding (0.91) (p < 0.001), allowing direct selection of the associated traits. Using the combined data, mid-parent heterosis ranges were -7.64-14.55% for biomass (BM), -8.34-12.51% for thousand kernel weight (TKW) and -5.29-26.65% for grain yield (YLD); while best-parent heterosis showed ranges of -11.18-7.20%, -11.35-11.26% and -8.27-24.04% for BM, TKW and YLD, respectively. The magnitude of general combining ability (GCA) variance was greater than the specific combining ability (SCA) variance suggesting a greater additive gene action for BM, TKW and YLD. The favorable GCA estimates showed a simple method to predict additive effects contributing to high heterosis and thus could be an effective approach for the selection of promising parents in early generations.

Genetic diversity, distribution and domestication history of the neglected GGAtAt genepool of wheat.

KEY MESSAGE: We present a comprehensive survey of cytogenetic and genomic diversity of the GGAtAt genepool of wheat, thereby unlocking these plant genetic resources for wheat improvement. Wheat yields are stagnating around the world and new sources of genes for resistance or tolerances to abiotic traits are required. In this context, the tetraploid wheat wild relatives are among the key candidates for wheat improvement. Despite its potential huge value for wheat breeding, the tetraploid GGAtAt genepool is largely neglected. Understanding the population structure, native distribution range, intraspecific variation of the entire tetraploid GGAtAt genepool and its domestication history would further its use for wheat improvement. The paper provides the first comprehensive survey of genomic and cytogenetic diversity sampling the full breadth and depth of the tetraploid GGAtAt genepool. According to the results obtained, the extant GGAtAt genepool consists of three distinct lineages. We provide detailed insights into the cytogenetic composition of GGAtAt wheats, revealed group- and population-specific markers and show that chromosomal rearrangements play an important role in intraspecific diversity of T. araraticum. The origin and domestication history of the GGAtAt lineages is discussed in the context of state-of-the-art archaeobotanical finds. We shed new light on the complex evolutionary history of the GGAtAt wheat genepool and provide the basis for an increased use of the GGAtAt wheat genepool for wheat improvement. The findings have implications for our understanding of the origins of agriculture in southwest Asia.

Population Dynamics and Yield Loss Assessment for Pea Aphid, Acyrthosiphon pisum (Harris) (Homoptera: Aphididae), on Lentil in Morocco.

Pea aphid (Acyrthosiphon pisum Harris) is the major insect pest of lentil in Morocco. We investigated pea aphid mean numbers and yield losses on three lentil varieties at one location during three successive cropping seasons during 2015-2018. The effects of several weather factors on pea aphid population dynamics were investigated. Population density increased in early spring followed by several peaks during March-April and then steeply declined during the late spring. Aphid populations peaked at different times during the three years of the study. In 2016, higher populations occurred during the second and third weeks of April for Abda and Zaria varieties with averages of 27 and 28 aphids/20 twigs, respectively. In 2017, higher populations occurred on the 12th and 13th standard meteorological weeks (SMWs) for Zaria with averages of 24.7 and 27.03 aphids/20 twigs, respectively. In 2018, the population peaked for all varieties at three different times, 11th, 13th, and 17th SMW, with the highest for Zaria being 26.00, 47.41, and 32.33 aphids/20 twigs. Pea aphid population dynamics changed with weather conditions. The number of aphids significantly and positively correlated with maximum temperature, but significantly negatively correlated with relative humidity and wind speed. The minimum temperature and rainfall had non-significant correlations. Pea aphid infestation resulted in losses of total seed weight for all lentil varieties, with the highest avoidable losses for Bakria being 12.51% followed by Zaria with 7.72% and Abda with 4.56%. These losses may justify the development of integrated management options for control of this pest.

Assessment and modeling using machine learning of resistance to scald (Rhynchosporium commune) in two specific barley genetic resources subsets.

Barley production worldwide is limited by several abiotic and biotic stresses and breeding of highly productive and adapted varieties is key to overcome these challenges. Leaf scald, caused by Rhynchosporium commune is a major disease of barley that requires the identification of novel sources of resistance. In this study two subsets of genebank accessions were used: one extracted from the Reference set developed within the Generation Challenge Program (GCP) with 191 accessions, and the other with 101 accessions selected using the filtering approach of the Focused Identification of Germplasm Strategy (FIGS). These subsets were evaluated for resistance to scald at the seedling stage under controlled conditions using two Moroccan isolates, and at the adult plant stage in Ethiopia and Morocco. The results showed that both GCP and FIGS subsets were able to identify sources of resistance to leaf scald at both plant growth stages. In addition, the test of independence and goodness of fit showed that FIGS filtering approach was able to capture higher percentages of resistant accessions compared to GCP subset at the seedling stage against two Moroccan scald isolates, and at the adult plant stage against four field populations of Morocco and Ethiopia, with the exception of Holetta nursery 2017. Furthermore, four machine learning models were tuned on training sets to predict scald reactions on the test sets based on diverse metrics (accuracy, specificity, and Kappa). All models efficiently identified resistant accessions with specificities higher than 0.88 but showed different performances between isolates at the seedling and to field populations at the adult plant stage. The findings of our study will help in fine-tuning FIGS approach using machine learning for the selection of best-bet subsets for resistance to scald disease from the large number of genebank accessions.

Genome-Wide Association and Prediction of Male and Female Floral Hybrid Potential Traits in Elite Spring Bread Wheat Genotypes.

Hybrid wheat breeding is one of the most promising technologies for further sustainable yield increases. However, the cleistogamous nature of wheat displays a major bottleneck for a successful hybrid breeding program. Thus, an optimized breeding strategy by developing appropriate parental lines with favorable floral trait combinations is the best way to enhance the outcrossing ability. This study, therefore, aimed to dissect the genetic basis of various floral traits using genome-wide association study (GWAS) and to assess the potential of genome-wide prediction (GP) for anther extrusion (AE), visual anther extrusion (VAE), pollen mass (PM), pollen shedding (PSH), pollen viability (PV), anther length (AL), openness of the flower (OPF), duration of floret opening (DFO) and stigma length. To this end, we employed 196 ICARDA spring bread wheat lines evaluated for three years and genotyped with 10,477 polymorphic SNP. In total, 70 significant markers were identified associated to the various assessed traits at FDR ≤ 0.05 contributing a minor to large proportion of the phenotypic variance (8-26.9%), affecting the traits either positively or negatively. GWAS revealed multi-marker-based associations among AE, VAE, PM, OPF and DFO, most likely linked markers, suggesting a potential genomic region controlling the genetic association of these complex traits. Of these markers, Kukri_rep_c103359_233 and wsnp_Ex_rep_c107911_91350930 deserve particular attention. The consistently significant markers with large effect could be useful for marker-assisted selection. Genomic selection revealed medium to high prediction accuracy ranging between 52% and 92% for the assessed traits with the least and maximum value observed for stigma length and visual anther extrusion, respectively. This indicates the feasibility to implement genomic selection to predict the performance of hybrid floral traits with high reliability.

Comparison of Genomic Prediction Methods for Yellow, Stem, and Leaf Rust Resistance in Wheat Landraces from Afghanistan.

Wheat rust diseases, including yellow rust (Yr; also known as stripe rust) caused by Puccinia striiformis Westend. f. sp. tritici, leaf rust (Lr) caused by Puccinia triticina Eriks. and stem rust (Sr) caused by Puccinia graminis Pres f. sp. tritici are major threats to wheat production all around the globe. Durable resistance to wheat rust diseases can be achieved through genomic-assisted prediction of resistant accessions to increase genetic gain per unit time. Genomic prediction (GP) is a promising technology that uses genomic markers to estimate genomic-assisted breeding values (GBEVs) for selecting resistant plant genotypes and accumulating favorable alleles for adult plant resistance (APR) to wheat rust diseases. To evaluate GP we compared the predictive ability of nine different parametric, semi-parametric and Bayesian models including Genomic Unbiased Linear Prediction (GBLUP), Ridge Regression (RR), Least Absolute Shrinkage and Selection Operator (LASSO), Elastic Net (EN), Bayesian Ridge Regression (BRR), Bayesian A (BA), Bayesian B (BB), Bayesian C (BC) and Reproducing Kernel Hilbert Spacing model (RKHS) to estimate GEBV's for APR to yellow, leaf and stem rust of wheat in a panel of 363 bread wheat landraces of Afghanistan origin. Based on five-fold cross validation the mean predictive abilities were 0.33, 0.30, 0.38, and 0.33 for Yr (2016), Yr (2017), Lr, and Sr, respectively. No single model outperformed the rest of the models for all traits. LASSO and EN showed the lowest predictive ability in four of the five traits. GBLUP and RR gave similar predictive abilities, whereas Bayesian models were not significantly different from each other as well. We also investigated the effect of the number of genotypes and the markers used in the analysis on the predictive ability of the GP model. The predictive ability was highest with 1000 markers and there was a linear trend in the predictive ability and the size of the training population. The results of the study are encouraging, confirming the feasibility of GP to be effectively applied in breeding programs for resistance to all three wheat rust diseases.