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Heat stress adversely affects wheat production in many regions of the world and is particularly detrimental during reproductive development and grainfilling. The objective of this study was to identify quantitative trait loci (QTL) associated with heat susceptibility index (HSI) of yield components in response to a short-term heat shock during early grainfilling in wheat. The HSI was used as an indicator of yield stability and a proxy for heat tolerance. A recombinant inbred line (RIL) population derived from the heat tolerant cultivar 'Halberd' and heat sensitive cultivar 'Cutter' was evaluated for heat tolerance over 2 years in a controlled environment. The RILs and parental lines were grown in the greenhouse and at 10 days after pollination (DAP) half the plants for each RIL received a three-day heat stress treatment at 38°C/ 18°C day/night, while half were kept at control conditions of 20°C/18°C day/night. At maturity, the main spike was harvested and used to determine yield components. A significant treatment effect was observed for most yield components and a HSI was calculated for individual components and used for QTL mapping. QTL analysis identified 15 and 12 QTL associated with HSI in 2005 and 2006, respectively. Five QTL regions were detected in both years, including QTL on chromosomes 1A, 2A, 2B, and 3B. These same regions were commonly associated with QTL for flag leaf length, width, and visual wax content, but not with days to flowering. Pleiotropic trade-offs between the maintenance of kernel number versus increasing single kernel weight under heat stress were present at some QTL regions. The results of this study validate the use of the main spike for detection of QTL for heat tolerance and identify genomic regions associated with improved heat tolerance that can be targeted for future studies.

Rust diseases continue to cause significant losses to wheat production worldwide. Although the life of effective race-specific resistance genes can be prolonged by using gene combinations, an alternative approach is to deploy varieties that posses adult plant resistance (APR) based on combinations of minor, slow rusting genes. When present alone, APR genes do not confer adequate resistance especially under high disease pressure; however, combinations of 4-5 such genes usually result in ''near-immunity'' or a high level of resistance. Although high diversity for APR occurs for all three rusts in improved germplasm, relatively few genes are characterized in detail. Breeding for APR to leaf rust and stripe rust in CIMMYT spring wheats was initiated in the early 1970s by crossing slow rusting parents that lacked effective race-specific resistance genes to prevalent pathogen populations and selecting plants in segregating populations under high disease pressure in field nurseries. Consequently most of the wheat germplasm distributed worldwide now possesses near-immunity or adequate levels of resistance. Some semidwarf wheats such as Kingbird, Pavon 76, Kiritati and Parula show high levels of APR to stem rust race Ug99 and its derivatives based on the Sr2-complex, or a combination of Sr2 with other uncharacterized slow rusting genes. These parents are being utilized in our crossing program and a Mexico-Kenya shuttle breeding scheme is used for selecting resistance to Ug99. High frequencies of lines with near-immunity to moderate levels of resistance are now emerging from these activities. After further yield trials and quality assessments these lines will be distributed internationally through the CIMMYT nursery system.

Key message The optimization of training populations and the use of diagnostic markers as fixed effects increase the predictive ability of genomic prediction models in a cooperative wheat breeding panel. Abstract Plant breeding programs often have access to a large amount of historical data that is highly unbalanced, particularly across years. This study examined approaches to utilize these data sets as training populations to integrate genomic selection into existing pipelines. We used cross-validation to evaluate predictive ability in an unbalanced data set of 467 winter wheat ( Triticum aestivum L.) genotypes evaluated in the Gulf Atlantic Wheat Nursery from 2008 to 2016. We evaluated the impact of different training population sizes and training population selection methods (Random, Clustering, PEVmean and PEVmean1) on predictive ability. We also evaluated inclusion of markers associated with major genes as fixed effects in prediction models for heading date, plant height, and resistance to powdery mildew (caused by Blumeria graminis f. sp. tritici) . Increases in predictive ability as the size of the training population increased were more evident for Random and Clustering training population selection methods than for PEVmean and PEVmean1. The selection methods based on minimization of the prediction error variance (PEV) outperformed the Random and Clustering methods across all the population sizes. Major genes added as fixed effects always improved model predictive ability, with the greatest gains coming from combinations of multiple genes. Maximum predictabilities among all prediction methods were 0.64 for grain yield, 0.56 for test weight, 0.71 for heading date, 0.73 for plant height, and 0.60 for powdery mildew resistance. Our results demonstrate the utility of combining unbalanced phenotypic records with genome-wide SNP marker data for predicting the performance of untested genotypes. Electronic supplementary material The online version of this article (10.1007/s00122-019-03276-6) contains supplementary material, which is available to authorized users.

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Fusarium head blight (FHB) is a disease in wheat (Triticum aestivum L.) caused by the fungal pathogen Fusarium graminearum Schwabe. Fusarium head blight poses potential economic losses and health risks due to the accumulation of the mycotoxin deoxynivalenol (DON) on infected seed heads. The objectives of this study were to identify novel FHB resistance loci using a genome‐wide association study (GWAS) approach and to evaluate two genomic selection (GS) approaches to improve prediction accuracies for FHB traits in a population of 354 soft red winter wheat (SRWW) genotypes. The GS approaches included GS+GWAS, where markers associated with a trait were used as fixed effects, and multivariate GS (MVGS), where correlated traits were used as covariates. The population was evaluated in FHB nurseries in Fayetteville and Newport, AR, and Winnsboro, LA, from 2014 to 2017. Genotypes were phenotyped for DON, Fusarium‐damaged kernels (FDK), incidence (INC), and severity (SEV). Forty‐two single nucleotide polymorphism (SNP) markers were significantly (false discovery rate, q [FDRq] ≤ .10) associated with resistance traits across 17 chromosomes. Ten significant SNPs were identified for DON, notably on chromosomes 2BL and 3BL. Eleven were identified for FDK, notably on chromosomes 4BL, 3AL, 1BL, 5BL, and 5DL. Nine were identified for INC, notably on chromosomes 2BS, 2BL, 7BL, 5DL, 6AS, and 5DS. Twelve were identified for SEV, notably on chromosomes 3BL, 4AL, and 4BL. The naïve GS models significantly outperformed the GS+GWAS model for all traits, whereas MVGS models significantly outperformed the naïve GS models for all traits. Results from this study will facilitate the development of SRWW cultivars with improved FHB resistance.

BackgroundGenomic selection has the potential to increase genetic gains by using molecular markers as predictors of breeding values of individuals. This study evaluated the accuracy of predictions for grain yield, heading date, plant height, and yield components in soft red winter wheat under different prediction scenarios. Response to selection for grain yield was also compared across different selection strategies- phenotypic, marker-based, genomic, combination of phenotypic and genomic, and random selections.ResultsGenomic selection was implemented through a ridge regression best linear unbiased prediction model in two scenarios- cross-validations and independent predictions. Accuracy for cross-validations was assessed using a diverse panel under different marker number, training population size, relatedness between training and validation populations, and inclusion of fixed effect in the model. The population in the first scenario was then trained and used to predict grain yield of biparental populations for independent validations. Using subsets of significant markers from association mapping increased accuracy by 64–70% for grain yield but resulted in lower accuracy for traits with high heritability such as plant height. Increasing size of training population resulted in an increase in accuracy, with maximum values reached when ~ 60% of the lines were used as a training panel. Predictions using related subpopulations also resulted in higher accuracies. Inclusion of major growth habit genes as fixed effect in the model caused increase in grain yield accuracy under a cross-validation procedure. Independent predictions resulted in accuracy ranging between − 0.14 and 0.43, dependent on the grouping of site-year data for the training and validation populations. Genomic selection was “superior” to marker-based selection in terms of response to selection for yield. Supplementing phenotypic with genomic selection resulted in approximately 10% gain in response compared to using phenotypic selection alone.ConclusionsOur results showed the effects of different factors on accuracy for yield and agronomic traits. Among the factors studied, training population size and relatedness between training and validation population had the greatest impact on accuracy. Ultimately, combining phenotypic with genomic selection would be relevant for accelerating genetic gains for yield in winter wheat.