Association Mapping Reveals Novel Stem Rust Resistance Loci in Durum Wheat at the Seedling Stage

Wheat stem rust rapidly evolves new virulence to resistance genes. Recently emerged races in East Africa, such as TTKSK (or Ug99), possess broad virulence to durum cultivars, and only a limited number of genes provide resistance. An association mapping (AM) study conducted on 183 durum wheat accessions has allowed us to identify 41 quantitative trait loci (QTLs; determination coefficient [R2] values from 1.1 to 23.1%) for seedling resistance to one or more of four highly virulent stem rust races: TRTTF, TTTTF, TTKSK (Ug99), and JRCQC, two of which (TRTTF and JRCQC) were isolated from Ethiopia. Among these loci, 24 are novel, while the remaining 17 overlapped with loci previously shown to provide field resistance in Ethiopia and/or chromosome regions known to harbor designated stem rust resistance designated loci (Sr). The identified loci were either effective against multiple races or race specific, particularly for race JRCQC. Our results highlight that stem rust resistance in durum wheat is governed in part by loci for resistance across multiple races, and in part by race-specific ones (23 and 18, respectively). Collectively, these results provide useful information to improve the effectiveness of marker-assisted selection towards the release of durum wheat cultivars with durable stem rust resistance

Use of Genomic Estimated Breeding Values Results in Rapid Genetic Gains for Drought Tolerance in Maize

More than 80% of the 19 million ha of maize ( L.) in tropical Asia is rainfed and prone to drought. The breeding methods for improving drought tolerance (DT), including genomic selection (GS), are geared to increase the frequency of favorable alleles. Two biparental populations (CIMMYT-Asia Population 1 [CAP1] and CAP2) were generated by crossing elite Asian-adapted yellow inbreds (CML470 and VL1012767) with an African white drought-tolerant line, CML444. Marker effects of polymorphic single-nucleotide polymorphisms (SNPs) were determined from testcross (TC) performance of F families under drought and optimal conditions. Cycle 1 (C1) was formed by recombining the top 10% of the F families based on TC data. Subsequently, (i) C2[PerSe_PS] was derived by recombining those C1 plants that exhibited superior per se phenotypes (phenotype-only selection), and (ii) C2[TC-GS] was derived by recombining a second set of C1 plants with high genomic estimated breeding values (GEBVs) derived from TC phenotypes of F families (marker-only selection). All the generations and their top crosses to testers were evaluated under drought and optimal conditions. Per se grain yields (GYs) of C2[PerSe_PS] and that of C2[TC-GS] were 23 to 39 and 31 to 53% better, respectively, than that of the corresponding F population. The C2[TC-GS] populations showed superiority of 10 to 20% over C2[PerSe-PS] of respective populations. Top crosses of C2[TC-GS] showed 4 to 43% superiority of GY over that of C2[PerSe_PS] of respective populations. Thus, GEBV-enabled selection of superior phenotypes (without the target stress) resulted in rapid genetic gains for DT

Sparse testing using genomic prediction improves selection for breeding targets in elite spring wheat

Key message: Sparse testing using genomic prediction can be efficiently used to increase the number of testing environments while maintaining selection intensity in the early yield testing stage without increasing the breeding budget. Abstract: Sparse testing using genomic prediction enables expanded use of selection environments in early-stage yield testing without increasing phenotyping cost. We evaluated different sparse testing strategies in the yield testing stage of a CIMMYT spring wheat breeding pipeline characterized by multiple populations each with small family sizes of 1–9 individuals. Our results indicated that a substantial overlap between lines across environments should be used to achieve optimal prediction accuracy. As sparse testing leverages information generated within and across environments, the genetic correlations between environments and genomic relationships of lines across environments were the main drivers of prediction accuracy in multi-environment yield trials. Including information from previous evaluation years did not consistently improve the prediction performance. Genomic best linear unbiased prediction was found to be the best predictor of true breeding value, and therefore, we propose that it should be used as a selection decision metric in the early yield testing stages. We also propose it as a proxy for assessing prediction performance to mirror breeder’s advancement decisions in a breeding program so that it can be readily applied for advancement decisions by breeding programs.1939–195

Wheat breeding with skim-sequencing for genomic selection: a comparison of marker platforms

The promise of predictive genomics-assisted breeding relies on efficient, affordable, and abundant molecular markers. The quantity and quality of markers have greatly expanded, yet plant breeding programs have struggled to fully harness this power mainly using array-based genotyping, targeted amplicon sequencing platforms, or reduced representation, sequence-based genotyping including genotyping-by-sequencing (GBS). Leveraging modern sequencing technology, commercial laboratory products, and open-source software, we demonstrate how ultra-low coverage (skim-seq, 0.05-0.10x) can be a viable marker platform. We genotyped 1,709 wheat lines with GBS, a mid-density DArTAG SNP panel (TaDArTAG vs. 2.0), and skim-seq (0.07x). All skim-seq variants were identified from the pooled skim-seq data and a reference genome without the aid of high-coverage samples. STITCH software was used for imputation followed by filtering to obtain 125,682 markers. Comparing STITCH imputed values to high coverage samples resulted in the correct imputation for more than 96% of the markers. Using phenotypic data, a 5-fold cross validation was implemented for each marker platform. No one marker system performed the best in all test cases, with GBS often resulting in the highest correlation between observed and predicted values. The skim-seq correlations were typically within 0.03 of GBS, suggesting skim-seq can be a viable marker strategy for genomic prediction. As technology and computational pipelines advances, skim-seq appears to be a promising method to bridge the gap between targeted genotyping and whole-genome sequencing. The skim-seq method is highly flexible and can be optimized to a variety of program needs, potentially allowing for wide adoption by the plant breeding community

Effect of leaf rust on grain yield and yield traits of durum wheats with race-specific and slow-rusting resistance to leaf rust

Leaf rust, caused by Puccinia triticina, is an important disease of durum wheat (Triticum turgidum) in many countries. We compared the effectiveness of different types of resistance in International Maize and Wheat Improvement Center-derived durum wheat germ plasm for protecting grain yield and yield traits. In all, 10 durum wheat lines with race-specific resistance, 18 with slow-rusting resistance, and 2 susceptible were included in two yield loss trials sown on different planting dates in Mexico with and without fungicide protection under high disease pressure. Eight genotypes with race-specific resistance were immune to leaf rust. Durum wheat lines with slow-rusting resistance displayed a range of severity responses indicating phenotypic diversity. Mean yield losses for susceptible, race-specific, and slow-rusting genotypes were 51, 5, and 26%, respectively, in the normal sowing date trial and 71, 11, and 44% when sown late. Yield losses were associated mainly with a reduction in biomass, harvest index, and kernels per square meter. Slow-rusting durum wheat lines with low disease levels and low yield losses, as well as genotypes with low yield losses despite moderate disease levels, were identified. Such genotypes can be used for breeding durum wheat genotypes with higher levels of resistance and negligible yield losses by using strategies that previously have been shown to be successful in bread wheat.1065-107

Genomic regions associated with resistance to tan spot of wheat

Abstracts submitted for presentation at the APS 2009 Annual MeetingS12

Comparison of models and whole-genome profiling approaches for genomic-enabled prediction of Septoria Tritici Blotch, Stagonospora Nodorum Blotch, and tan spot resistance in wheat

The leaf spotting diseases in wheat that include Septoria tritici blotch (STB) caused by Zymoseptoria tritici, Stagonospora nodorum blotch (SNB) caused by Parastagonospora nodorum, and tan spot (TS) caused by Pyrenophora tritici-repentis pose challenges to breeding programs in selecting for resistance. A promising approach that could enable selection prior to phenotyping is genomic selection that uses genome-wide markers to estimate breeding values (BVs) for quantitative traits. To evaluate this approach for seedling and/or adult plant resistance (APR) to STB, SNB, and TS, we compared the predictive ability of least-squares (LS) approach with genomic-enabled prediction models including genomic best linear unbiased predictor (GBLUP), Bayesian ridge regression (BRR), Bayes A (BA), Bayes B (BB), Bayes Cp (BC), Bayesian least absolute shrinkage and selection operator (BL), and reproducing kernel Hilbert spaces markers (RKHS-M), a pedigree-based model (RKHS-P) and RKHS markers and pedigree (RKHS-MP). We observed that LS gave the lowest prediction accuracies and RKHS-MP, the highest. The genomic-enabled prediction models and RKHS-P gave similar accuracies. The increase in accuracy using genomic prediction models over LS was 48%. The mean genomic prediction accuracies were 0.45 for STB (APR), 0.55 for SNB (seedling), 0.66 for TS (seedling) and 0.48 for TS (APR). We also compared markers from two wholegenome profiling approaches: genotyping by sequencing (GBS) and diversity arrays technology sequencing (DArTseq) for prediction. While, GBS markers performed slightly better than DArTseq, combining markers from the two approaches did not improve accuracies. We conclude that implementing GS in breeding for these diseases would help to achieve higher accuracies and rapid gains from selection

Genome-wide association mapping for resistance to leaf rust, stripe rust and tan spot in wheat reveals potential candidate genes

Leaf rust (LR), stripe rust (YR) and tan spot (TS) are some of the important foliar diseases in wheat (Triticum aestivum L.). To identify candidate resistance genes for these diseases in CIMMYT?s (International Maize and Wheat Improvement Center) International bread wheat screening nurseries, we used genome-wide association studies (GWAS) in conjunction with information from the population sequencing map and Ensembl plants. Wheat entries were genotyped using genotyping-by-sequencing and phenotyped in replicated trials. Using a mixed linear model, we observed that seedling resistance to LR was associated with 12 markers on chromosomes 1DS, 2AS, 2BL, 3B, 4AL, 6AS and 6AL, and seedling resistance to TS was associated with 14 markers on chromosomes 1AS, 2AL, 2BL, 3AS, 3AL, 3B, 6AS and 6AL. Seedling and adult plant resistance (APR) to YR were associated with several markers at the distal end of chromosome 2AS. In addition, YR APR was also associated with markers on chromosomes 2DL, 3B and 7DS. The potential candidate genes for these diseases included several resistance genes, receptor-like serine/threonine-protein kinases and defense-related enzymes. However, extensive LD in wheat that decays at about 5 × 107 bps, poses a huge challenge for delineating candidate gene intervals and candidates should be further mapped, functionally characterized and validated. We also explored a segment on chromosome 2AS associated with multiple disease resistance and identified seventeen disease resistance linked genes. We conclude that identifying candidate genes linked to significant markers in GWAS is feasible in wheat, thus creating opportunities for accelerating molecular breeding.1405-142