Genomic signatures of selection for resistance to stripe rust in Austrian winter wheat.
Ontology highlight
ABSTRACT: Key message
We combined quantitative and population genetic methods to identify loci under selection for adult plant resistance to stripe rust in an Austrian winter wheat breeding population from 2008 to 2018. Resistance to stripe rust, a foliar disease caused by the fungus P. striiformis f. sp. tritici, in wheat (Triticum aestivum L.) is both qualitatively and quantitatively controlled. Resistance genes confer complete, race-specific resistance but are easily overcome by evolving pathogen populations, while quantitative resistance is controlled by many small- to medium-effect loci that provide incomplete yet more durable protection. Data on resistance loci can be applied in marker-assisted selection and genomic prediction frameworks. We employed genome-wide association to detect loci associated with stripe rust and selection testing to identify regions of the genome that underwent selection for stripe rust resistance in an Austrian winter wheat breeding program from 2008 to 2018. Genome-wide association mapping identified 150 resistance loci, 62 of which showed significant evidence of selection over time. The breeding population also demonstrated selection for resistance at the genome-wide level.
SUBMITTER: Morales L
PROVIDER: S-EPMC8354948 | biostudies-literature |
REPOSITORIES: biostudies-literature
Similar Datasets
Project description:Virulence shifts in populations of Puccinia striiformis f. sp. tritici (Pst), the causal pathogen of wheat stripe rust, are a major challenge to resistance breeding. The majority of known resistance genes are already ineffective against current races of Pst, necessitating the identification and introgression of new sources of resistance. Germplasm core collections that reflect the range of genetic and phenotypic diversity of crop species are ideal platforms for examining the genetic architecture of complex traits such as resistance to stripe rust. We report the results of genetic characterization and genome-wide association analysis (GWAS) for resistance to stripe rust in a core subset of 1175 accessions in the National Small Grains Collection (NSGC) winter wheat germplasm collection, based on genotyping with the wheat 9K single nucleotide polymorphism (SNP) iSelect assay and phenotyping of seedling and adult plants under natural disease epidemics in four environments. High correlations among the field data translated into high heritability values within and across locations. Population structure was evident when accessions were grouped by stripe rust reaction. GWAS identified 127 resistance loci that were effective across at least two environments, including 20 with significant genome-wide adjusted P-values. Based on relative map positions of previously reported genes and QTL, five of the QTL with significant genome-wide adjusted P-values in this study represent potentially new loci. This study provides an overview of the diversity of Pst resistance in the NSGC winter wheat germplasm core collection, which can be exploited for diversification of stripe rust resistance in breeding programs.
| S-EPMC4978880 | biostudies-other
Project description:A recombinant inbred line (RIL) mapping population developed from a cross between winter wheat (Triticum aestivum L.) cultivars Coda and Brundage was evaluated for reaction to stripe rust (caused by Puccinia striiformis f. sp. tritici). Two hundred and sixty eight RIL from the population were evaluated in replicated field trials in a total of nine site-year locations in the U.S. Pacific Northwest. Seedling reaction to stripe rust races PST-100, PST-114 and PST-127 was also examined. A linkage map consisting of 2,391 polymorphic DNA markers was developed covering all chromosomes of wheat with the exception of 1D. Two QTL on chromosome 1B were associated with adult plant and seedling reaction and were the most significant QTL detected. Together these QTL reduced adult plant infection type from a score of seven to a score of two reduced disease severity by an average of 25% and provided protection against race PST-100, PST-114 and PST-127 in the seedling stage. The location of these QTL and the race specificity provided by them suggest that observed effects at this locus are due to a complementation of the previously known but defeated resistances of the cultivar Tres combining with that of Madsen (the two parent cultivars of Coda). Two additional QTL on chromosome 3B and one on 5B were associated with adult plant reaction only, and a single QTL on chromosome 5D was associated with seedling reaction to PST-114. Coda has been resistant to stripe rust since its release in 2000, indicating that combining multiple resistance genes for stripe rust provides durable resistance, especially when all-stage resistance genes are combined in a fashion to maximize the number of races they protect against. Identified molecular markers will allow for an efficient transfer of these genes into other cultivars, thereby continuing to provide excellent resistance to stripe rust.
| S-EPMC3958369 | biostudies-literature
Project description:All stage resistance to stripe rust races prevalent in India was investigated in the European winter wheat cultivar 'Acienda'. In order to dissect the genetic basis of the resistance, a backcross population was developed between 'Acienda' and the stripe rust susceptible Indian spring wheat cultivar 'HD 2967'. Inheritance studies revealed segregation for a dominant resistant gene. High density SNP genotyping was used to map stripe rust resistance and marker regression analysis located stripe rust resistance to the distal end of wheat chromosome 1A. Interval mapping located this region between the SNP markers AX-95162217 and AX-94540853, at a LOD score of 15.83 with a phenotypic contribution of 60%. This major stripe rust resistance locus from 'Acienda' has been temporarily designated as Yraci. A candidate gene search in the 2.76 Mb region carrying Yraci on chromosome 1A identified 18 NBS-LRR genes based on wheat RefSeqv1.0 annotations. Our results indicate that as there is no major gene reported in the Yraci chromosome region, it is likely to be a novel stripe rust resistance locus and offers potential for deployment, using the identified markers, to confer all stage stripe rust resistance.
| S-EPMC8849526 | biostudies-literature
Project description:Stripe rust, caused by the fungus Puccinia striiformis f. sp. tritici, is a worldwide disease of wheat that causes devastating crop losses. Resistant cultivars have been developed over the last 40 years that have significantly reduced the economic impact of the disease on growers, but in heavy infection years it is mostly controlled through the intensive application of fungicides. The Pacific Northwest of the United States has an ideal climate for stripe rust and has one of the most diverse race compositions in the country. This has resulted in many waves of epidemics that have overcome most of the resistance genes traditionally used in elite germplasm. The best way to prevent high yield losses, reduce production costs to growers, and reduce the heavy application of fungicides is to pyramid multiple stripe rust resistance genes into new cultivars. Using genotyping-by-sequencing, we identified 4662 high quality variant positions in a recombinant inbred line population of 196 individuals derived from a cross between Skiles, a highly resistant winter wheat cultivar, and Goetze, a moderately to highly susceptible winter wheat cultivar, both developed at Oregon State University. A subsequent genome wide association study identified two quantitative trait loci (QTL) on chromosomes 3B and 3D within the predicted locations of stripe rust resistance genes. Resistance QTL, when combined together, conferred high levels of stripe rust resistance above the level of Skiles in some locations, indicating that these QTL would be important additions to future breeding efforts of Pacific Northwest winter wheat cultivars.
| S-EPMC8002966 | biostudies-literature
Project description: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.
| S-EPMC9032109 | biostudies-literature
Project description:Autophagy-related 8 (ATG8) protein has been reported to be involved in plant's innate immune response, but it is not clear whether such genes play a similar role in cereal crops against obligate biotrophic fungal pathogens. Here, we reported an ATG8 gene from wheat (Triticum aestivum), designated TaATG8j. This gene has three copies located in chromosomes 2AS, 2BS, and 2DS. The transcriptions of all three copies were upregulated in plants of the wheat cultivar Suwon 11, inoculated with an avirulent race (CYR23) of Puccinia striiformis f. sp. tritici (Pst), the causal fungal pathogen of stripe rust. The transient expression of TaATG8j in Nicotiana benthamiana showed that TaATG8j proteins were distributed throughout the cytoplasm, but mainly in the nucleus and plasma membrane. The overexpression of TaATG8j in N. benthamiana slightly delayed the cell death caused by the mouse apoptotic protein BAX (BCL2-associated X protein). However, the expression of TaATG8j in yeast (Schizosaccharomyces pombe) induced cell death. The virus-induced gene silencing of all TaATG8j copies rendered Suwon 11 susceptible to the avirulent Pst race CYR23, accompanied by an increased fungal biomass and a decreased necrotic area per infection site. These results indicate that TaATG8j contributes to wheat resistance against stripe rust fungus by regulating cell death, providing information for the understanding of the mechanisms of wheat resistance to the stripe rust pathogen.
| S-EPMC6032272 | biostudies-literature
Project description:Leaf rust, caused by Puccinia triticina (Pt), and stripe rust, caused by P. striiformis f. sp. tritici (Pst), are destructive foliar diseases of wheat worldwide. Breeding for disease resistance is the preferred strategy of managing both diseases. The continued emergence of new races of Pt and Pst requires a constant search for new sources of resistance. Here we report a genome-wide association analysis of 567 winter wheat (Triticum aestivum) landrace accessions using the Infinium iSelect 9K wheat SNP array to identify loci associated with seedling resistance to five races of Pt (MDCL, MFPS, THBL, TDBG, and TBDJ) and one race of Pst (PSTv-37) frequently found in the Northern Great Plains of the United States. Mixed linear models identified 65 and eight significant markers associated with leaf rust and stripe rust, respectively. Further, we identified 31 and three QTL associated with resistance to Pt and Pst, respectively. Eleven QTL, identified on chromosomes 3A, 4A, 5A, and 6D, are previously unknown for leaf rust resistance in T. aestivum.
| S-EPMC4468153 | biostudies-literature
Project description:Key messageGenomic prediction for seedling and adult plant resistance to wheat rusts was compared to prediction using few markers as fixed effects in a least-squares approach and pedigree-based prediction. The unceasing plant-pathogen arms race and ephemeral nature of some rust resistance genes have been challenging for wheat (Triticum aestivum L.) breeding programs and farmers. Hence, it is important to devise strategies for effective evaluation and exploitation of quantitative rust resistance. One promising approach that could accelerate gain from selection for rust resistance is 'genomic selection' which utilizes dense genome-wide markers to estimate the breeding values (BVs) for quantitative traits. Our objective was to compare three genomic prediction models including genomic best linear unbiased prediction (GBLUP), GBLUP A that was GBLUP with selected loci as fixed effects and reproducing kernel Hilbert spaces-markers (RKHS-M) with least-squares (LS) approach, RKHS-pedigree (RKHS-P), and RKHS markers and pedigree (RKHS-MP) to determine the BVs for seedling and/or adult plant resistance (APR) to leaf rust (LR), stem rust (SR), and stripe rust (YR). The 333 lines in the 45th IBWSN and the 313 lines in the 46th IBWSN were genotyped using genotyping-by-sequencing and phenotyped in replicated trials. The mean prediction accuracies ranged from 0.31-0.74 for LR seedling, 0.12-0.56 for LR APR, 0.31-0.65 for SR APR, 0.70-0.78 for YR seedling, and 0.34-0.71 for YR APR. For most datasets, the RKHS-MP model gave the highest accuracies, while LS gave the lowest. GBLUP, GBLUP A, RKHS-M, and RKHS-P models gave similar accuracies. Using genome-wide marker-based models resulted in an average of 42% increase in accuracy over LS. We conclude that GS is a promising approach for improvement of quantitative rust resistance and can be implemented in the breeding pipeline.
| S-EPMC5487692 | biostudies-literature
Project description:Stripe rust (Pst) is a major disease of wheat crops leading untreated to severe yield losses. The use of fungicides is often essential to control Pst when sudden outbreaks are imminent. Sensors capable of detecting Pst in wheat crops could optimize the use of fungicides and improve disease monitoring in high-throughput field phenotyping. Now, deep learning provides new tools for image recognition and may pave the way for new camera based sensors that can identify symptoms in early stages of a disease outbreak within the field. The aim of this study was to teach an image classifier to detect Pst symptoms in winter wheat canopies based on a deep residual neural network (ResNet). For this purpose, a large annotation database was created from images taken by a standard RGB camera that was mounted on a platform at a height of 2 m. Images were acquired while the platform was moved over a randomized field experiment with Pst-inoculated and Pst-free plots of winter wheat. The image classifier was trained with 224 × 224 px patches tiled from the original, unprocessed camera images. The image classifier was tested on different stages of the disease outbreak. At patch level the image classifier reached a total accuracy of 90%. To test the image classifier on image level, the image classifier was evaluated with a sliding window using a large striding length of 224 px allowing for fast test performance. At image level, the image classifier reached a total accuracy of 77%. Even in a stage with very low disease spreading (0.5%) at the very beginning of the Pst outbreak, a detection accuracy of 57% was obtained. Still in the initial phase of the Pst outbreak with 2 to 4% of Pst disease spreading, detection accuracy with 76% could be attained. With further optimizations, the image classifier could be implemented in embedded systems and deployed on drones, vehicles or scanning systems for fast mapping of Pst outbreaks.
| S-EPMC8042394 | biostudies-literature
Project description:The two recombinant inbred line (RIL) populations developed by crossing Almaly × Avocet S (206 RILs) and Almaly × Anza (162 RILs) were used to detect the novel genomic regions associated with adult plant resistance (APR) and seedling or all-stage resistance (ASR) to yellow rust (YR) and leaf rust (LR). The quantitative trait loci (QTLs) were detected through multi-year phenotypic evaluations (2018-2020) and using high-throughput DArTseq genotyping technology. RILs exhibited significant genetic variation with p < 0.001, and the coefficient of variation ranged from 9.79% to 47.99% for both LR and YR in all Environments and stages of evaluations. The heritability is quite high and ranged between 0.47 and 0.98. We identified nine stable QTLs for YR APR on chromosomes 1B, 2A, 2B, 3D, and 4D and four stable QTLs for LR APR on chromosomes 2B, 3B, 4A, and 5A. Furthermore, in silico analysis revealed that the key putative candidate genes such as cytochrome P450, protein kinase-like domain superfamily, zinc-binding ribosomal protein, SANT/Myb domain, WRKY transcription factor, nucleotide sugar transporter, and NAC domain superfamily were in the QTL regions and probably involved in the regulation of host response toward pathogen infection. The stable QTLs identified in this study are useful for developing rust-resistant varieties through marker-assisted selection (MAS).
| S-EPMC10702562 | biostudies-literature