Biology subjects

Aoun, M.

Publications and source records attributed to Aoun, M..

6 recordsLinked to original sources

bioRxiv2026

Identification of septoria nodorum blotch susceptibility genes in hard winter wheat

Key message Characterized and unknown septoria nodorum blotch susceptibility/resistance genes were identified in contemporary U.S. hard winter wheat. The necrotrophic fungus Parastagonospora nodorum is the causal agent of septoria nodorum blotch (SNB) of wheat. To determine the prevalence of SNB sensitivity genes in a contemporary U.S. hard winter wheat (HWW), we evaluated a panel of 619 breeding lines and cultivars against five P. nodorum isolates and five necrotrophic effectors (NEs), SnToxA, SnTox1, SnTox3, SnTox267 and SnTox5, and genotyped the panel using genotyping-by-sequencing (GBS) markers and diagnostic Kompetetive-allele specific PCR (KASP) markers for the sensitivity genes Tsn1-B1, Snn1-B1, and Snn3-B1/B2. GBS analysis identified 34,357 GBS-single nucleotide polymorphism (SNP) markers. Evaluations against P. nodorum isolates showed that 40-67% of the genotypes were susceptible in the panel. Toxin infiltration assays showed that 54%, 2%, 37%, 13%, and 15% of the genotypes were sensitive to SnToxA, SnTox1, SnTox3, SnTox267, and SnTox5, respectively. Diagnostic KASP markers for Tsn1-B1, Snn1-B1, and Snn3-B1/B2 showed prediction accuracies of 98%, 75%, and 92% for the corresponding effectors SnToxA, SnTox1, and SnTox3, respectively. Genome-wide association studies (GWAS) not only confirmed the presence of the previously characterized sensitivity genes Tsn1-B1, Snn1-B1, Snn2, Snn3-B1/B2, and Snn5-B1, but also identified new loci to be associated with responses to P. nodorum isolates and NEs. Of which, Qsnb.osu-2AS on chromosome 2AS was associated with responses to all five isolates. We developed KASP markers KASP_S4B_643615365, KASP_ S2D_16184991, and KASP_S2A_9833162 linked to Snn5-B1, Snn2, and Qsnb.osu-2AS, respectively. These findings should guide breeding for SNB resistance in hard winter wheat.

genetics↗

bioRxiv2026

Mapping of Stripe Rust and Leaf Rust Resistance Genes in the Hard Red Winter Wheat Population Green Hammer/Lonerider

Stripe rust and leaf rust, caused by Puccinia striiformis f. sp. tritici and P. triticina, respectively, are the most destructive wheat diseases in the southern Great Plains. Green Hammer is a hard red winter wheat (HRWW) cultivar released by Oklahoma State University in 2018 and has demonstrated a stable adult plant resistance to stripe rust and race-specific seedling resistance to leaf rust. To identify and map rust resistance loci, 109 doubled haploid (DH) lines derived from the cross between Green Hammer and another HRWW cultivar, Lonerider, were developed. Lonerider showed adult plant resistance to stripe rust but was susceptible to multiple P. triticina races. The DH lines were evaluated for stripe rust at the adult plant stage in greenhouse and field environments across Oklahoma, Kansas, and Washington, and for leaf rust at the seedling stage against seven U.S. P. triticina races and at the adult plant stage in Oklahoma and Texas. Genotyping-by-sequencing generated 6,078 polymorphic single-nucleotide polymorphisms used for genetic mapping. Quantitative trait loci (QTL) analysis identified 14 stripe rust and 8 leaf rust resistance QTL. For stripe rust, a major QTL in Green Hammer, QYr.osughln-2AS, was identified in the proximity of the 2NvS translocation. Three other major stripe rust resistance QTL were identified in Lonerider on chromosomes 2AL (two QTL) and 2BS (one QTL). For leaf rust, QLr.osughln-1DS and QLr.osughln-2DS.1 were the two major QTL identified in Green Hammer and most likely correspond to the all-stage resistance genes Lr21 and Lr39, respectively. In this study, we identified previously characterized genes as well as unknown genes that can be utilized in wheat breeding programs to enhance resistance to leaf rust and stripe rust.

genetics↗

bioRxiv2025

Identification of stripe rust adult plant resistance genes in the hard winter wheat cultivar Bakers Ann

Stripe rust, caused by Puccinia striiformis f. sp. tritici (Pst), is among the most destructive wheat diseases. Identifying resistance genes is crucial for the development of resistant cultivars. "Bakers Ann", a hard winter wheat cultivar developed by Oklahoma State University, has shown durable adult plant resistance to stripe rust. To dissect the genetic basis underlying stripe rust resistance in Bakers Ann, 125 doubled haploid lines, derived from the cross OK12D22004-016 x Bakers Ann, were evaluated at the adult plant stage in the greenhouse and in field environments in Oklahoma, Kansas, and Washington. This population was genotyped using genotyping-by-sequencing, which produced 7,268 single-nucleotide polymorphisms for genetic mapping. Quantitative trait loci (QTL) analysis identified six loci, four from Bakers Ann on chromosomes 2DL, 4BS, 4BL, and 7BL, and two from OK12D22004-016 on chromosomes 2AS and 2AL. Although OK12D22004-016 is susceptible in the US Great Plains, it was found to carry QYr.osu-2AS, which was linked to Yr17 on the 2NvS translocation and explained up to 30% of the phenotypic variation, but was effective in a single location in Washington. Two major QTL were identified in Bakers Ann, QYr.osu-2DL on chromosome 2DL that explained up to 57% of the phenotypic variation and mapped close to Yr54, and QYr.osu-4BL on chromosome 4BL that explained up to 15% of the phenotypic variation and mapped close to Yr62. Resistance in Bakers Ann resulted from additive effects of the four QTL. Two kompetitive allele-specific PCR markers were developed for QYr.osu-2DL to facilitate marker-assisted selection for stripe rust resistance.

genetics↗

bioRxiv2024

Identification of leaf rust resistance loci in hard winter wheat using genome-wide association mapping

Leaf rust, caused by Puccinia triticina (Pt), is a serious constraint to wheat production. Developing resistant varieties is the best approach to managing this disease. Wheat leaf rust resistance (Lr) genes have been classified into either all-stage resistance (ASR) or adult-plant resistance (APR). The objectives of this study were to identify sources of leaf rust resistance in contemporary U.S. hard winter wheat (HWW) and to dissect the genetic basis underlying leaf rust resistance in HWW. A panel of 732 elite HWW genotypes was evaluated for response to U.S. Pt races at the seedling stage and at the adult plant stage in leaf rust nurseries in Oklahoma, Texas, and Kansas. Further, the panel was genotyped using Multiplex Restriction Amplicon Sequencing (MRA-Seq) and DNA markers linked to the known ASR genes Lr18, Lr19, Lr21, Lr24, Lr37, and Lr42 and APR genes Lr34, Lr46, Lr67, Lr68, Lr77, and Lr78. Single nucleotide polymorphism (SNP) markers derived from MRA-Seq, DNA markers linked to the known Lr genes, and the phenotypic data were used for genome-wide association study (GWAS) to identify markers associated with leaf rust response. Gene postulation based on leaf rust reactions, DNA markers, and GWAS suggested the presence of Lr1, Lr2a, Lr10, Lr14a, Lr16, Lr18, Lr19, Lr21, Lr24, Lr26, Lr34, Lr37, Lr39, Lr42, Lr46, Lr68, Lr77, and Lr78 in the HWW panel. The GWAS identified 59 SNPs significantly associated with leaf rust response, of which 20 were likely associated with novel resistance loci and can be used to enhance wheat leaf rust resistance.

genomics↗

bioRxiv2024

Wheat Enhanced Disease Resistance EMS-Mutants Include Lesion-mimics With Adult Plant Resistance to Stripe Rust

Tetraploid durum wheat Triticum turgidum subsp. durum cv Kronos has extensive genetic variation resources, including a sequenced and cataloged ethyl methanesulfonate (EMS) mutagenized population. To utilize this allelic diversity, we screened over 2,000 mutant lines and identified over 30 enhanced disease resistance (EDR) mutants in a forward genetic field screen against stripe rust. Sixteen of the EDR lines have persistent resistance to stripe rust after four years, and several mutants showed differential disease responses against other fungal pathogens, indicating that the lines possess diverse alleles that affect multiple routes of pathogen suppression. Five of these 16 lines showed spontaneous lesion formation in the absence of pathogens. Only one showed a reduction in kernel weight under pathogen pressure, a testimony to the high mutational density that wheat can tolerate. Phenotypic selection for resistance at the adult stage identified useful EMS alleles for stripe rust resistance. The mutations in the 16 EDR lines were newly mapped to a recently released long-read Kronos genome to enhance their utility in molecular breeding for fungal resistance and for fundamental studies of plant-pathogen interactions.

plant biology↗

bioRxiv2021

Genomic Selection for End-Use Quality and Processing Traits in Soft White Winter Wheat Breeding Program with Machine and Deep Learning Models

Breeding for grain yield, biotic and abiotic stress resistance, and end-use quality are important goals of wheat breeding programs. Screening for end-use quality traits is usually secondary to grain yield due to high labor needs, cost of testing, and large seed requirements for phenotyping. Hence, testing is delayed until later stages in the breeding program. Delayed phenotyping results in advancement of inferior end-use quality lines into the program. Genomic selection provides an alternative to predict performance using genome-wide markers. Due to large datasets in breeding programs, we explored the potential of the machine and deep learning models to predict fourteen end-use quality traits in a winter wheat breeding program. The population used consisted of 666 wheat genotypes screened for five years (2015-19) at two locations (Pullman and Lind, WA, USA). Nine different models, including two machine learning (random forest and support vector machine) and two deep learning models (convolutional neural network and multilayer perceptron), were explored for cross-validation, forward, and across locations predictions. The prediction accuracies for different traits varied from 0.45-0.81, 0.29-0.55, and 0.27-0.50 under cross-validation, forward, and across location predictions. In general, forward prediction accuracies kept increasing over time due to increments in training data size and was more evident for machine and deep learning models. Deep learning models performed superior over the traditional ridge regression best linear unbiased prediction (RRBLUP) and Bayesian models under all prediction scenarios. The high accuracy observed for end-use quality traits in this study support predicting them in early generations, leading to the advancement of superior genotypes to more extensive grain yield trailing. Furthermore, the superior performance of machine and deep learning models strengthen the idea to include them in large scale breeding programs for predicting complex traits.

genetics↗