Breeding With Major and Minor Genes: Genomic Selection for Quantitative Disease Resistance

Low-input breeding potential in stone pine, a multipurpose forest tree with low genome diversity

Stone pine (Pinus pinea L.) is an emblematic tree species within the Mediterranean basin, with high ecological and economic relevance due to the production of edible nuts. Breeding programmes to improve pine nut production started decades ago in Southern Europe but have been hindered by the near absence of polymorphisms in the species genome and the lack of suitable genomic tools. In this study, we assessed new stone pine's genomic resources and their utilization in breeding and sustainable use, by using a commercial SNP-array (5,671 SNPs). Firstly, we confirmed the accurate clonal identification and identity check of 99 clones from the Spanish breeding programme. Secondly, we successfully estimated genomic relationships in clonal collections, an information needed for low-input breeding and genomic prediction. Thirdly, we applied this information to genomic prediction for the total number of cones unspoiled by pests and their weight measured in 3 Spanish clonal tests. Genomic prediction accuracy depends on the trait under consideration and possibly on the number of genotypes included in the test. Predictive ability (ry) was significant for the mean cone weight measured in the 3 clonal tests, while solely significant for the number of cones in one clonal test. The combination of a new SNP-array together with the phenotyping of relevant commercial traits into genomic prediction models, proved to be very promising to identify superior clones for cone weight. This approach opens new perspectives for early selection.

Mungbean G × E interaction unveiling resistance to Cercospora leaf spot through GGE biplot analysis

Cercospora leaf spot (CLS), caused by Cercospora canescens, is a major threat to mungbean production worldwide. The disease is complicated by its wide host range, diverse pathogenic strains, and the influence of environmental factors. Understanding the interplay between the host, pathogen, and environmental conditions is crucial for developing effective control measures. In a bid to identify and validate CLS resistant mungbean genotypes we conducted multi-environment trials; Genomic selection of mungbean for resistance against CLS necessitates pre-identification in diverse environments. Initially, 110 genotypes were screened under controlled conditions in which thirty-day-old mungbean plants were thoroughly sprayed with the spore suspension using a glass atomizer. After three weeks of revalidation under controlled conditions, Koch's postulates was adopted for disease identification before selecting 16 genotypes for field testing across four different environments over three successive years. The results obtained from the GGE biplot analysis emphasize the importance of taking both genetic and ambient factors in consideration when evaluating the potential of mungbean genotypes for resistance against CLS and therefore two genotypes "SK-89 (15)" and "WMB-9 (14)" was identified as desirable genotypes. The Additive Main Effects and Multiplicative Interaction (AMMI) model and the GGE biplot have emerged as potent tools for unraveling GEI complexities. It has expanded its application to include evaluation of resistant genotypes and to locate "ideal" evaluation sites and "mega environments" linked to resistance against infection. The study provides valuable insights for future breeding programs, allowing researchers to focus on incorporating these resistant traits into future varieties.

Can the molecular and transgenic breeding of crops be an alternative and sustainable technology to meet food demand?

The gradual increase in the worldwide population represents various challenges, and one of the most alarming being the food demand. Historically technological advances led to the development of crops that meets the requirements and demands. Currently, molecular breeding unlocks the genetic potential of crops for their improvement, positioning it as a key technology for the development of new crops. The implementation of OMICs sciences, such spatial and single cell transcriptomics is providing a large and precise information, which can be exploited for crop improvement related to increasing yield, improving the nutritional value; designing new strategies for diseases resistance and management and for conserving biodiversity. Furthermore, the use of new technologies such CRISPR/CAS9 brought us the ability to modify the selected regions of the genome to select the superior's genotypes at a short time and the use of artificial intelligence aid in the analysis of big data generated by OMICS sciences. On the other hand, the application of molecular improvement technologies open up discussion on global regulatory measures, the socio-economic and socio-ethics, as the frameworks on its global regulation and its impact on the society create the public perception on its acceptance. In this review, the use and impact of OMICs sciences and genetic engineering in crops development, the regulatory measures, the socio-economic impact and as well as the mediatic information on genetically modified crops worldwide is discussed along with comprehensive insights on the potential of molecular plant breeding as an alternative and sustainable technology to meet global food demand.

Using genome-wide associations and host-by-pathogen predictions to identify allelic interactions that control disease resistance

Characterizing the molecular mechanisms underlying disease symptom expression has been used to improve human health and disease resistance in crops and animal breeds. Quantitative trait loci and genome-wide association studies (GWAS) are widely used to identify genomic regions that are involved in disease progression. This study extends traditional GWAS significance tests of host and pathogen marker main effects by utilizing dual-genome reaction norm models to evaluate the importance of host-single nucleotide polymorphism (SNP) by pathogen-SNP interactions. Disease symptom severity data from Fusarium ear rot (FER) on maize (Zea mays L.) is used to demonstrate the use of both genomes in genomic selection models for breeding and the identification of loci that interact across organisms to impact FER disease development. Dual genome prediction models improved heritability estimates, error variances, and model accuracy while providing predictions for host-by-pathogen interactions that may be used to test the significance of SNP-SNP interactions. Independent GWAS for maize and Fusarium populations identified significantly associated loci and predictions that were used to evaluate the importance of interactions using two different association tests. Predictions from dual genome models were used to evaluate the significance of the SNP-SNP interactions that may be associated with population structure or polygenic effects. As well, association tests incorporating host and pathogen markers in models that also included genomic relationship matrices were used to account for population structure. Subsequent evaluation of protein-protein interactions from candidate genes near the interacting SNPs provides a further in silico evaluation method to expedite the identification of interacting genes.

CRISPR-mediated genome editing of wheat for enhancing disease resistance

Wheat is cultivated across diverse global environments, and its productivity is significantly impacted by various biotic stresses, most importantly but not limited to rust diseases, Fusarium head blight, wheat blast, and powdery mildew. The genetic diversity of modern cultivars has been eroded by domestication and selection, increasing their vulnerability to biotic stress due to uniformity. The rapid spread of new highly virulent and aggressive pathogen strains has exacerbated this situation. Three strategies can be used for enhancing disease resistance through genome editing: introducing resistance (R) gene-mediated resistance, engineering nucleotide-binding leucine-rich repeat receptors (NLRs), and manipulating susceptibility (S) genes to stop pathogens from exploiting these factors to support infection. Utilizing R gene-mediated resistance is the most common strategy for traditional breeding approaches, but the continuous evolution of pathogen effectors can eventually overcome this resistance. Moreover, modifying S genes can confer pleiotropic effects that hinder their use in agriculture. Enhancing disease resistance is paramount for sustainable wheat production and food security, and new tools and strategies are of great importance to the research community. The application of CRISPR-based genome editing provides promise to improve disease resistance, allowing access to a broader range of solutions beyond random mutagenesis or intraspecific variation, unlocking new ways to improve crops, and speeding up resistance breeding. Here, we first summarize the major disease resistance strategies in the context of important wheat diseases and their limitations. Next, we turn our attention to the powerful applications of genome editing technology in creating new wheat varieties against important wheat diseases.

Deciphering fungicide resistance in Phytophthora: mechanisms, prevalence, and sustainable management approaches

The genus Phytophthora contains more than 100 plant pathogenic species that parasitize a wide range of plants, including economically important fruits, vegetables, cereals, and forest trees, causing significant losses. Global agriculture is seriously threatened by fungicide resistance in Phytophthora species, which makes it imperative to fully comprehend the mechanisms, frequency, and non-chemical management techniques related to resistance mutations. The mechanisms behind fungicide resistance, such as target-site mutations, efflux pump overexpression, overexpression of target genes and metabolic detoxification routes for fungicides routinely used against Phytophthora species, are thoroughly examined in this review. Additionally, it assesses the frequency of resistance mutations in various Phytophthora species and geographical areas, emphasizing the rise of strains that are resistant to multiple drugs. The effectiveness of non-chemical management techniques, including biological control, host resistance, integrated pest management plans, and cultural practices, in reducing fungicide resistance is also thoroughly evaluated. The study provides important insights for future research and the development of sustainable disease management strategies to counter fungicide resistance in Phytophthora species by synthesizing current information and identifying knowledge gaps.

Identification of novel sources of partial and incomplete hypersensitive resistance to rust and associated genomic regions in common bean

Common bean (Phaseolus vulgaris) is one of the legume crops most consumed worldwide and bean rust is one of the most severe foliar biotrophic fungal diseases impacting its production. In this work, we searched for new sources of rust resistance (Uromyces appendiculatus) in a representative collection of the Portuguese germplasm, known to have accessions with an admixed genetic background between Mesoamerican and Andean gene pools. We identified six accessions with incomplete hypersensitive resistance and 20 partially resistant accessions of Andean, Mesoamerican, and admixed origin. We detected 11 disease severity-associated single-nucleotide polymorphisms (SNPs) using a genome-wide association approach. Six of the associations were related to partial (incomplete non-hypersensitive) resistance and five to incomplete hypersensitive resistance, and the proportion of variance explained by each association varied from 4.7 to 25.2%. Bean rust severity values ranged from 0.2 to 49.1% and all the infection types were identified, reflecting the diversity of resistance mechanisms deployed by the Portuguese germplasm.The associations with U. appendiculatus partial resistance were located in chromosome Pv08, and with incomplete hypersensitive resistance in chromosomes Pv06, Pv07, and Pv08, suggesting an oligogenic inheritance of both types of resistance. A resolution to the gene level was achieved for eight of the associations. The candidate genes proposed included several resistance-associated enzymes, namely β-amylase 7, acyl-CoA thioesterase, protein kinase, and aspartyl protease. Both SNPs and candidate genes here identified constitute promising genomics targets to develop functional molecular tools to support bean rust resistance precision breeding.

Genome-wide transcriptomic and functional analyses provide new insights into the response of spring barley to drought stress

Drought is a major abiotic stress that impairs the physiology and development of plants, ultimately leading to crop yield losses. Drought tolerance is a complex quantitative trait influenced by multiple genes and metabolic pathways. However, molecular intricacies and subsequent morphological and physiological changes in response to drought stress remain elusive. Herein, we combined morpho-physiological and comparative RNA-sequencing analyses to identify core drought-induced marker genes and regulatory networks in the barley cultivar 'Giza134'. Based on field trials, drought-induced declines occurred in crop growth rate, relative water content, leaf area duration, flag leaf area, concentration of chlorophyll (Chl) a, b and a + b, net photosynthesis, and yield components. In contrast, the Chl a/b ratio, stoma resistance, and proline concentration increased significantly. RNA-sequence analysis identified a total of 2462 differentially expressed genes (DEGs), of which 1555 were up-regulated and 907 were down-regulated in response to water-deficit stress (WD). Comparative transcriptomics analysis highlighted three unique metabolic pathways (carbohydrate metabolism, iron ion binding, and oxidoreductase activity) as containing genes differentially expressed that could mitigate water stress. Our results identified several drought-induced marker genes belonging to diverse physiochemical functions like chlorophyll concentration, photosynthesis, light harvesting, gibberellin biosynthetic, iron homeostasis as well as Cis-regulatory elements. These candidate genes can be utilized to identify gene-associated markers to develop drought-resilient barley cultivars over a short period of time. Our results provide new insights into the understanding of water stress response mechanisms in barley.

Oligogenic Control of Quantitative Resistance Against Powdery Mildew Revealed in Portuguese Common Bean Germplasm

Common bean (Phaseolus vulgaris L.) is one of the most important food legumes worldwide, and its production is severely affected by fungal diseases such as powdery mildew. Portugal has a diverse germplasm, with accessions of Andean, Mesoamerican, and admixed origin, making it a valuable resource for common bean genetic studies. In this work, we evaluated the response of a Portuguese collection of 146 common bean accessions to Erysiphe diffusa infection, observing a wide range of disease severity and different levels of compatible and incompatible reactions, revealing the presence of different resistance mechanisms. We identified 11 incompletely hypersensitive resistant and 80 partially resistant accessions. We performed a genome-wide association study to clarify its genetic control, resulting in the identification of eight disease severity-associated single-nucleotide polymorphisms, spread across chromosomes Pv03, Pv09, and Pv10. Two of the associations were unique to partial resistance and one to incomplete hypersensitive resistance. The proportion of variance explained by each association varied between 15 and 86%. The absence of a major locus, together with the relatively small number of loci controlling disease severity, suggested an oligogenic inheritance of both types of resistance. Seven candidate genes were proposed, including a disease resistance protein (toll interleukin 1 receptor-nucleotide binding site-leucine-rich repeat class), an NF-Y transcription factor complex component, and an ABC-2 type transporter family protein. This work contributes with new resistance sources and genomic targets valuable to develop selection molecular tools and support powdery mildew resistance precision breeding in common bean.

Preselection of QTL markers enhances accuracy of genomic selection in Norway spruce

Genomic prediction (GP) or genomic selection is a method to predict the accumulative effect of all quantitative trait loci (QTLs) in a population by estimating the realized genomic relationships between the individuals and by capturing the linkage disequilibrium between markers and QTLs. Thus, marker preselection is considered a promising method to capture Mendelian segregation effects. Using QTLs detected in a genome-wide association study (GWAS) may improve GP. Here, we performed GWAS and GP in a population with 904 clones from 32 full-sib families using a newly developed 50 k SNP Norway spruce array. Through GWAS we identified 41 SNPs associated with budburst stage (BB) and the largest effect association explained 5.1% of the phenotypic variation (PVE). For the other five traits such as growth and wood quality traits, only 2 - 13 associations were observed and the PVE of the strongest effects ranged from 1.2% to 2.0%. GP using approximately 100 preselected SNPs, based on the smallest p-values from GWAS showed the greatest predictive ability (PA) for the trait BB. For the other traits, a preselection of 2000-4000 SNPs, was found to offer the best model fit according to the Akaike information criterion being minimized. But PA-magnitudes from GP using such selections were still similar to that of GP using all markers. Analyses on both real-life and simulated data also showed that the inclusion of a large QTL SNP in the model as a fixed effect could improve PA and accuracy of GP provided that the PVE of the QTL was ≥ 2.5%.

Genome-wide association mapping for LLS resistance in a MAGIC population of groundnut (Arachis hypogaea L.)

The identified 30 functional nucleotide polymorphisms or genic SNP markers would offer essential information for marker-assisted breeding in groundnut. A genome-wide association study (GWAS) on component traits of LLS resistance in an eight-way multiparent advance generation intercross (MAGIC) population of groundnut in the field and in a light chamber (controlled conditions) was performed via an Affymetrix 48 K single-nucleotide polymorphism (SNP) 'Axiom Arachis' array. Multiparental populations with high-density genotyping enable the detection of novel alleles. In total, five quantitative trait loci (QTLs) with marker - log10(p value) scores ranging from 4.25 to 13.77 for the incubation period (IP) and six QTLs with marker - log10(p value) scores ranging from 4.33 to 10.79 for the latent period (LP) were identified across the A- and B-subgenomes. A total of 62 markers‒trait associations (MTAs) were identified across the A- and B-subgenomes. Markers for LLS scores and the area under the disease progression curve (AUDPC) recorded for plants in the light chamber and under field conditions presented - log10 (p value) scores ranging from 4.22 to 27.30. The highest number of MTAs (six) was identified on chromosomes A05, B07 and B09. Out of a total of 73 MTAs, 37 and 36 MTAs were detected in subgenomes A and B, respectively. Taken together, these results suggest that both subgenomes have equal potential genomic regions contributing to LLS resistance. A total of 30 functional nucleotide polymorphisms or genic SNP markers were detected, among which eight genes were found to encode leucine-rich repeat (LRR) receptor-like protein kinases and putative disease resistance proteins. These important SNPs can be used in breeding programmes for the development of cultivars with improved disease resistance.

Mapping and selection of downy mildew resistance in spinach cv. whale by low coverage whole genome sequencing

Spinach (Spinacia oleracea) is a popular leafy vegetable crop and commercial production is centered in California and Arizona in the US. The oomycete Peronospora effusa causes the most important disease in spinach, downy mildew. A total of nineteen races of P. effusa are known, with more than 15 documented in the last three decades, and the regular emergence of new races is continually overcoming the genetic resistance to the pathogen. This study aimed to finely map the downy mildew resistance locus RPF3 in spinach, identify single nucleotide polymorphism (SNP) markers associated with the resistance, refine the candidate genes responsible for the resistance, and evaluate the prediction performance using multiple machine learning genomic prediction (GP) methods. Segregating progeny population developed from a cross of resistant cultivar Whale and susceptible cultivar Viroflay to race 5 of P. effusa was inoculated under greenhouse conditions to determine downy mildew disease response across the panel. The progeny panel and the parents were resequenced at low coverage (1x) to identify genome wide SNP markers. Association analysis was performed using disease response phenotype data and SNP markers in TASSEL, GAPIT, and GENESIS programs and mapped the race 5 resistance loci (RPF3) to 1.25 and 2.73 Mb of Monoe-Viroflay chromosome 3 with the associated SNP in the 1.25 Mb region was 0.9 Kb from the NBS-LRR gene SOV3g001250. The RPF3 locus in the 1.22-1.23 Mb region of Sp75 chromosome 3 is 2.41-3.65 Kb from the gene Spo12821 annotated as NBS-LRR disease resistance protein. This study extended our understanding of the genetic basis of downy mildew resistance in spinach cultivar Whale and mapped the RPF3 resistance loci close to the NBS-LRR gene providing a target to pursue functional validation. Three SNP markers efficiently selected resistance based on multiple genomic selection (GS) models. The results from this study have added new genomic resources, generated an informed basis of the RPF3 locus resistant to spinach downy mildew pathogen, and developed markers and prediction methods to select resistant lines.

Resequencing worldwide spinach germplasm for identification of field resistance QTLs to downy mildew and assessment of genomic selection methods

Downy mildew, commercially the most important disease of spinach, is caused by the obligate oomycete Peronospora effusa. In the past two decades, new pathogen races have repeatedly overcome the resistance used in newly released cultivars, urging the need for more durable resistance. Commercial spinach cultivars are bred with major R genes to impart resistance to downy mildew pathogens and are effective against some pathogen races/isolates. This work aimed to evaluate the worldwide USDA spinach germplasm collections and commercial cultivars for resistance to downy mildew pathogen in the field condition under natural inoculum pressure and conduct genome wide association analysis (GWAS) to identify resistance-associated genomic regions (alleles). Another objective was to evaluate the prediction accuracy (PA) using several genomic prediction (GP) methods to assess the potential implementation of genomic selection (GS) to improve spinach breeding for resistance to downy mildew pathogen. More than four hundred diverse spinach genotypes comprising USDA germplasm accessions and commercial cultivars were evaluated for resistance to downy mildew pathogen between 2017-2019 in Salinas Valley, California and Yuma, Arizona. GWAS was performed using single nucleotide polymorphism (SNP) markers identified via whole genome resequencing (WGR) in GAPIT and TASSEL programs; detected 14, 12, 5, and 10 significantly associated SNP markers with the resistance from four tested environments, respectively; and the QTL alleles were detected at the previously reported region of chromosome 3 in three of the four experiments. In parallel, PA was assessed using six GP models and seven unique marker datasets for field resistance to downy mildew pathogen across four tested environments. The results suggest the suitability of GS to improve field resistance to downy mildew pathogen. The QTL, SNP markers, and PA estimates provide new information in spinach breeding to select resistant plants and breeding lines through marker-assisted selection (MAS) and GS, eventually helping to accumulate beneficial alleles for durable disease resistance.

Harnessing adult-plant resistance genes to deploy durable disease resistance in crops

Adult-plant resistance (APR) is a type of genetic resistance in cereals that is effective during the later growth stages and can protect plants from a range of disease-causing pathogens. Our understanding of the functions of APR-associated genes stems from the well-studied wheat-rust pathosystem. Genes conferring APR can offer pathogen-specific resistance or multi-pathogen resistance, whereby resistance is activated following a molecular recognition event. The breeding community prefers APR to other types of resistance because it offers broad-spectrum protection that has proven to be more durable. In practice, however, deployment of new cultivars incorporating APR is challenging because there is a lack of well-characterised APRs in elite germplasm and multiple loci must be combined to achieve high levels of resistance. Genebanks provide an excellent source of genetic diversity that can be used to diversify resistance factors, but introgression of novel alleles into elite germplasm is a lengthy and challenging process. To overcome this bottleneck, new tools in breeding for resistance must be integrated to fast-track the discovery, introgression and pyramiding of APR genes. This review highlights recent advances in understanding the functions of APR genes in the well-studied wheat-rust pathosystem, the opportunities to adopt APR genes in other crops and the technology that can speed up the utilisation of new sources of APR in genebank accessions.

Pseudomonas aeruginosa isolate PM1 effectively controls virus infection and promotes growth in plants

A bacterial isolate PM1 obtained from the rhizosphere of healthy plants was identified as Pseudomonas aeruginosa by biochemical characteristics and 16S rRNA gene sequence (GenBank ID OL321133.1). It induced resistance in Nicotiana tabacum cv. Xanthi-nc and Cyamopsis tetragonoloba, against Tobacco mosaic virus (TMV) and Sunn-hemp rosette virus (SRV), respectively. Foliar treatment with isolate PM1 curbed TMV accumulation in susceptible N. tabacum cv. White Burley. PM1 was more effective as a foliar than a root/soil drench treatment, evident through a comparative decrease in ELISA values, and reduced viral RNA accumulation. Foliar and soil drench treatment with PM1 resulted in a disease index of 48 and 86 per cent, and a control rate of 48.9 and 8.5 per cent, respectively. PM1 exhibited phosphate solubilization, produced siderophores, auxins, HCN, and ammonia, all important plant growth-promoting traits. Foliar treatment with PM1 enhanced growth in tobacco, while its volatiles significantly promoted seedling growth in C. tetragonoloba. Of the several metabolites produced by the isolate, many are known contributors to induction of systemic resistance, antibiosis, and growth promotion in plants. Soluble metabolites of PM1 were less effective in inducing antiviral resistance in N. tabacum cv. Xanthi-nc in comparison with its broth culture. PM1 and its metabolites were antagonistic to Gram-positive Bacillus spizizenii and Staphylococcus aureus, and fungi Fusarium oxysporum, Aspergillus niger, and Rhizopus stolonifer. Its volatiles were inhibitory to F. oxysporum and R. stolonifer. Thus, PM1 exhibited considerable potential for further evaluation in plant virus control and production of diverse metabolites of use in agriculture and medicine.

Genomic Prediction Accuracy of Stripe Rust in Six Spring Wheat Populations by Modeling Genotype by Environment Interaction

Some previous studies have assessed the predictive ability of genome-wide selection on stripe (yellow) rust resistance in wheat, but the effect of genotype by environment interaction (GEI) in prediction accuracies has not been well studied in diverse genetic backgrounds. Here, we compared the predictive ability of a model based on phenotypic data only (M1), the main effect of phenotype and molecular markers (M2), and a model that incorporated GEI (M3) using three cross-validations (CV1, CV2, and CV0) scenarios of interest to breeders in six spring wheat populations. Each population was evaluated at three to eight field nurseries and genotyped with either the DArTseq technology or the wheat 90K single nucleotide polymorphism arrays, of which a subset of 1,058- 23,795 polymorphic markers were used for the analyses. In the CV1 scenario, the mean prediction accuracies of the M1, M2, and M3 models across the six populations varied from -0.11 to -0.07, from 0.22 to 0.49, and from 0.19 to 0.48, respectively. Mean accuracies obtained using the M3 model in the CV1 scenario were significantly greater than the M2 model in two populations, the same in three populations, and smaller in one population. In both the CV2 and CV0 scenarios, the mean prediction accuracies of the three models varied from 0.53 to 0.84 and were not significantly different in all populations, except the Attila/CDC Go in the CV2, where the M3 model gave greater accuracy than both the M1 and M2 models. Overall, the M3 model increased prediction accuracies in some populations by up to 12.4% and decreased accuracy in others by up to 17.4%, demonstrating inconsistent results among genetic backgrounds that require considering each population separately. This is the first comprehensive genome-wide prediction study that investigated details of the effect of GEI on stripe rust resistance across diverse spring wheat populations.

Genomic Predictions for Common Bunt, FHB, Stripe Rust, Leaf Rust, and Leaf Spotting Resistance in Spring Wheat

Some studies have investigated the potential of genomic selection (GS) on stripe rust, leaf rust, Fusarium head blight (FHB), and leaf spot in wheat, but none of them have assessed the effect of the reaction norm model that incorporated GE interactions. In addition, the prediction accuracy on common bunt has not previously been studied. Here, we investigated within-population prediction accuracies using the baseline M1 model and two reaction norm models (M2 and M3) with three random cross-validation (CV1, CV2, and CV0) schemes. Three Canadian spring wheat populations were evaluated in up to eight field environments and genotyped with 3158, 5732, and 23,795 polymorphic markers. The M3 model that incorporated GE interactions reduced residual variance by an average of 10.2% as compared with the main effect M2 model and increased prediction accuracies on average by 2-6%. In some traits, the M3 model increased prediction accuracies up to 54% as compared with the M2 model. The average prediction accuracies of the M3 model with CV1, CV2, and CV0 schemes varied from 0.02 to 0.48, from 0.25 to 0.84, and from 0.14 to 0.87, respectively. In both CV2 and CV0 schemes, stripe rust in all three populations, common bunt and leaf rust in two populations, as well as FHB severity, FHB index, and leaf spot in one population had high to very high (0.54-0.87) prediction accuracies. This is the first comprehensive genomic selection study on five major diseases in spring wheat.

Classification and Regression Models for Genomic Selection of Skewed Phenotypes: A Case for Disease Resistance in Winter Wheat (Triticum aestivum L.)

Most genomic prediction models are linear regression models that assume continuous and normally distributed phenotypes, but responses to diseases such as stripe rust (caused by Puccinia striiformis f. sp. tritici) are commonly recorded in ordinal scales and percentages. Disease severity (SEV) and infection type (IT) data in germplasm screening nurseries generally do not follow these assumptions. On this regard, researchers may ignore the lack of normality, transform the phenotypes, use generalized linear models, or use supervised learning algorithms and classification models with no restriction on the distribution of response variables, which are less sensitive when modeling ordinal scores. The goal of this research was to compare classification and regression genomic selection models for skewed phenotypes using stripe rust SEV and IT in winter wheat. We extensively compared both regression and classification prediction models using two training populations composed of breeding lines phenotyped in 4 years (2016–2018 and 2020) and a diversity panel phenotyped in 4 years (2013–2016). The prediction models used 19,861 genotyping-by-sequencing single-nucleotide polymorphism markers. Overall, square root transformed phenotypes using ridge regression best linear unbiased prediction and support vector machine regression models displayed the highest combination of accuracy and relative efficiency across the regression and classification models. Furthermore, a classification system based on support vector machine and ordinal Bayesian models with a 2-Class scale for SEV reached the highest class accuracy of 0.99. This study showed that breeders can use linear and non-parametric regression models within their own breeding lines over combined years to accurately predict skewed phenotypes.

Identification and Validation of a QTL for Bacterial Leaf Streak Resistance in Rice (Oryza sativa L.) against Thai Xoc Strains

Rice is one of the most important food crops in the world and is of vital importance to many countries. Various diseases caused by fungi, bacteria and viruses constantly threaten rice plants and cause yield losses. Bacterial leaf streak disease (BLS) caused by Xanthomonas oryzae pv. oryzicola (Xoc) is one of the most devastating rice diseases. However, most modern rice varieties are susceptible to BLS. In this study, we applied the QTL-seq approach using an F2 population derived from the cross between IR62266 and Homcholasit (HSC) to rapidly identify the quantitative trait loci (QTL) that confers resistance to BLS caused by a Thai Xoc isolate, SP7-5. The results showed that a single genomic region at the beginning of chromosome 5 was highly associated with resistance to BLS. The gene xa5 was considered a potential candidate gene in this region since most associated single nucleotide polymorphisms (SNPs) were within this gene. A Kompetitive Allele-Specific PCR (KASP) marker was developed based on two consecutive functional SNPs in xa5 and validated in six F2 populations inoculated with another Thai Xoc isolate, 2NY2-2. The phenotypic variance explained by this marker (PVE) ranged from 59.04% to 70.84% in the six populations. These findings indicate that xa5 is a viable candidate gene for BLS resistance and may help in breeding programs for BLS resistance.