Malting quality and preharvest sprouting traits are genetically correlated in spring malting barley.

KEY MESSAGE: Malt for craft "all-malt" brewing can have high quality, PHS resistance, and malted in normal timeframes. Canadian style adjunct malt is associated with PHS susceptibility. Expansion of malting barley production into non-traditional growing regions and erratic weather has increased the demand for preharvest sprouting (PHS) resistant, high quality malting barley cultivars. This is hindered by the relatively unknown relationships between PHS resistance and malting quality. Here we present a three-year study of malting quality and germination at different after-ripening durations post physiological maturity. Malting quality traits alpha amylase (AA) and free amino nitrogen (FAN) and germination rate at six days post PM shared a common association with a SNP in HvMKK3 on chromosome 5H in the Seed Dormancy 2 (SD2) region responsible for PHS susceptibility. Soluble protein (SP) and soluble over total protein (S/T) both shared a common association with a marker in the SD2 region. Significant genetic correlations between PHS resistance and the malting quality traits AA, FAN, SP, S/T were detected across and within HvMKK3 allele groups. High adjunct malt quality was related to PHS susceptibility. Selection for PHS resistance led to a correlated response in malting quality traits. Results strongly suggest pleiotropy of HvMKK3 on malting quality traits and that the classic "Canadian-style" malt is caused by a PHS susceptible allele of HvMKK3. PHS susceptibility appears to benefit the production of malt intended for adjunct brewing, while PHS resistance is compatible with all-malt brewing specifications. Here we present our analysis on the effect of combining complexly inherited and correlated traits with contrasting goals to inform breeding practice in malting barley, the general principles of which can be extended to other breeding programs.

A SNP-based genetic dissection of versatile traits in bread wheat (Triticum aestivum L.).

The continuous increase in global population prompts increased wheat production. Future wheat (Triticum aestivum L.) breeding will heavily rely on dissecting molecular and genetic bases of wheat yield and related traits which is possible through the discovery of quantitative trait loci (QTLs) in constructed populations, such as recombinant inbred lines (RILs). Here, we present an evaluation of 92 RILs in a bi-parental RIL mapping population (the International Triticeae Mapping Initiative Mapping Population [ITMI/MP]) using newly generated phenotypic data in 3-year experiments (2015), older phenotypic data (1997-2009), and newly created single nucleotide polymorphism (SNP) marker data based on 92 of the original RILs to search for novel and stable QTLs. Our analyses of more than 15 unique traits observed in multiple experiments included analyses of 46 traits in three environments in the USA, 69 traits in eight environments in Germany, 149 traits in 10 environments in Russia, and 28 traits in four environments in India (292 traits in 25 environments) with 7584 SNPs (292 × 7584 = 2 214 528 data points). A total of 874 QTLs were detected with limit of detection (LOD) scores of 2.01-3.0 and 432 QTLs were detected with LOD > 3.0. Moreover, 769 QTLs could be assigned to 183 clusters based on the common markers and relative proximity of related QTLs, indicating gene-rich regions throughout the A, B, and D genomes of common wheat. This upgraded genotype-phenotype information of ITMI/MP can assist breeders and geneticists who can make crosses with suitable RILs to improve or investigate traits of interest.

Developing high-quality value-added cereals for organic systems in the US Upper Midwest: hard red winter wheat (Triticum aestivum L.) breeding.

There is an increased demand for food-grade grains grown sustainably. Hard red winter wheat has comparative advantages for organic farm rotations due to fall soil cover, weed competition, and grain yields. However, limitations of currently available cultivars such as poor disease resistance, winter hardiness, and baking quality, challenges its adoption and use. Our goal was to develop a participatory hard red winter wheat breeding program for the US Upper Midwest involving farmers, millers, and bakers. Specifically, our goals include (1) an evaluation of genotype-by-environment interaction (GEI) and genotypic stability for both agronomic and quality traits, and (2) the development of on-farm trials as well as baking and sensory evaluations of genotypes to include farmers, millers, and bakers' perspectives in the breeding process. Selection in early generations for diseases and protein content was followed by multi-environment evaluations for agronomic, disease, and quality traits in three locations during five years, on-farm evaluations, baking trials, and sensory evaluations. GEI was substantial for most traits, but no repeatable environmental conditions were significant contributors to GEI making selection for stability a critical trait. Breeding lines had similar performance in on-station and on-farm trials compared to commercial checks, but some breeding lines were more stable than the checks for agronomic, quality traits, and baking performance. These results suggest that stable lines can be developed using a participatory breeding approach under organic management. Crop improvement explicitly targeting sustainable agriculture practices for selection with farm to table participatory perspectives are critical to achieve long-term sustainable crop production. KEY MESSAGE: We describe a hard red winter wheat breeding program focused on developing genotypes adapted to organic systems in the US Upper Midwest for high-end artisan baking quality using participatory approaches.

QTL x environment modeling of malting barley preharvest sprouting.

KEY MESSAGE: HvMKK3 alleles are temperature sensitive and are major contributors to environmental stability of preharvest sprouting in barley. Preharvest sprouting (PHS) can severely damage barley (Hordeum vulgare L.) malting quality, but PHS resistance is often negatively correlated with malting quality. Seed dormancy is closely related to PHS. Increased temperature during grain fill can decrease seed dormancy in barley, but genetic components of seed dormancy temperature sensitivity are poorly understood. Six years of PHS data were used to fit quantitative trait locus (QTL) x environment mixed models incorporating marker data from seed dormancy genes HvAlaAT1, HvGA20ox1, and HvMKK3 and weather covariates in spring and winter two-row malting barley. Variation in winter barley PHS was best modeled by average temperature range during grain fill and spring barley PHS by total precipitation during grain fill. Average high temperature during grain fill also accurately modeled PHS for both datasets. A highly non-dormant HvMKK3 allele determined baseline PHS susceptibility and HvAlaAT1 interactions with multiple HvMKK3 alleles conferred environmental sensitivity. Polygenic variation for PHS within haplotype was detected. Residual genotype and QTL by environment interaction variance indicated additional environmental and genetic factors involved in PHS. These models provide insight into genotype and environmental regulation of barley seed dormancy, a method for PHS forecasting, and a tool for breeders to improve PHS resistance.

Genome-wide association mapping and genomic prediction of yield-related traits and starch pasting properties in cassava.

KEY MESSAGE: GWAS identified eight yield-related, peak starch type of waxy and wild-type starch and 21 starch pasting property-related traits (QTLs). Prediction ability of eight GS models resulted in low to high predictability, depending on trait, heritability, and genetic architecture. Cassava is both a food and an industrial crop in Africa, South America, and Asia, but knowledge of the genes that control yield and starch pasting properties remains limited. We carried out a genome-wide association study to clarify the molecular mechanisms underlying these traits and to explore marker-based breeding approaches. We estimated the predictive ability of genomic selection (GS) using parametric, semi-parametric, and nonparametric GS models with a panel of 276 cassava genotypes from Thai Tapioca Development Institute, International Center for Tropical Agriculture, International Institute of Tropical Agriculture, and other breeding programs. The cassava panel was genotyped via genotyping-by-sequencing, and 89,934 single-nucleotide polymorphism (SNP) markers were identified. A total of 31 SNPs associated with yield, starch type, and starch properties traits were detected by the fixed and random model circulating probability unification (FarmCPU), Bayesian-information and linkage-disequilibrium iteratively nested keyway and compressed mixed linear model, respectively. GS models were developed, and forward predictabilities using all the prediction methods resulted in values of - 0.001-0.71 for the four yield-related traits and 0.33-0.82 for the seven starch pasting property traits. This study provides additional insight into the genetic architecture of these important traits for the development of markers that could be used in cassava breeding programs.

Mapping pre-harvest sprouting resistance loci in AAC Innova × AAC Tenacious spring wheat population.

BACKGROUND: Pre-harvest sprouting (PHS) is a major problem for wheat production due to its direct detrimental effects on wheat yield, end-use quality and seed viability. Annually, PHS is estimated to cause > 1.0 billion USD in losses worldwide. Therefore, identifying PHS resistance quantitative trait loci (QTLs) is crucial to aid molecular breeding efforts to minimize losses. Thus, a doubled haploid mapping population derived from a cross between white-grained PHS susceptible cv AAC Innova and red-grained resistant cv AAC Tenacious was screened for PHS resistance in four environments and utilized for QTL mapping. RESULTS: Twenty-one PHS resistance QTLs, including seven major loci (on chromosomes 1A, 2B, 3A, 3B, 3D, and 7D), each explaining ≥10% phenotypic variation for PHS resistance, were identified. In every environment, at least one major QTL was identified. PHS resistance at most of these loci was contributed by AAC Tenacious except at two loci on chromosomes 3D and 7D where it was contributed by AAC Innova. Thirteen of the total twenty-one identified loci were located to chromosome positions where at least one QTL have been previously identified in other wheat genotype(s). The remaining eight QTLs are new which have been identified for the first time in this study. Pedigree analysis traced several known donors of PHS resistance in AAC Tenacious genealogy. Comparative analyses of the genetic intervals of identified QTLs with that of already identified and cloned PHS resistance gene intervals using IWGSC RefSeq v2.0 identified MFT-A1b (in QTL interval QPhs.lrdc-3A.1) and AGO802A (in QTL interval QPhs.lrdc-3A.2) on chromosome 3A, MFT-3B-1 (in QTL interval QPhs.lrdc-3B.1) on chromosome 3B, and AGO802D, HUB1, TaVp1-D1 (in QTL interval QPhs.lrdc-3D.1) and TaMyb10-D1 (in QTL interval QPhs.lrdc-3D.2) on chromosome 3D. These candidate genes are involved in embryo- and seed coat-imposed dormancy as well as in epigenetic control of dormancy. CONCLUSIONS: Our results revealed the complex PHS resistance genetics of AAC Tenacious and AAC Innova. AAC Tenacious possesses a great reservoir of important PHS resistance QTLs/genes supposed to be derived from different resources. The tracing of pedigrees of AAC Tenacious and other sources complements the validation of QTL analysis results. Finally, comparing our results with previous PHS studies in wheat, we have confirmed the position of several major PHS resistance QTLs and candidate genes.

Multi-omics prediction of oat agronomic and seed nutritional traits across environments and in distantly related populations.

KEY MESSAGE: Integration of multi-omics data improved prediction accuracies of oat agronomic and seed nutritional traits in multi-environment trials and distantly related populations in addition to the single-environment prediction. Multi-omics prediction has been shown to be superior to genomic prediction with genome-wide DNA-based genetic markers (G) for predicting phenotypes. However, most of the existing studies were based on historical datasets from one environment; therefore, they were unable to evaluate the efficiency of multi-omics prediction in multi-environment trials and distantly related populations. To fill those gaps, we designed a systematic experiment to collect omics data and evaluate 17 traits in two oat breeding populations planted in single and multiple environments. In the single-environment trial, transcriptomic BLUP (T), metabolomic BLUP (M), G + T, G + M, and G + T + M models showed greater prediction accuracy than GBLUP for 5, 10, 11, 17, and 17 traits, respectively, and metabolites generally performed better than transcripts when combined with SNPs. In the multi-environment trial, multi-trait models with omics data outperformed both counterpart multi-trait GBLUP models and single-environment omics models, and the highest prediction accuracy was achieved when modeling genetic covariance as an unstructured covariance model. We also demonstrated that omics data can be used to prioritize loci from one population with omics data to improve genomic prediction in a distantly related population using a two-kernel linear model that accommodated both likely casual loci with large-effect and loci that explain little or no phenotypic variance. We propose that the two-kernel linear model is superior to most genomic prediction models that assume each variant is equally likely to affect the trait and can be used to improve prediction accuracy for any trait with prior knowledge of genetic architecture.

Heritable temporal gene expression patterns correlate with metabolomic seed content in developing hexaploid oat seed.

Oat ranks sixth in world cereal production and has a higher content of health-promoting compounds compared with other cereals. However, there is neither a robust oat reference genome nor transcriptome. Using deeply sequenced full-length mRNA libraries of oat cultivar Ogle-C, a de novo high-quality and comprehensive oat seed transcriptome was assembled. With this reference transcriptome and QuantSeq 3' mRNA sequencing, gene expression was quantified during seed development from 22 diverse lines across six time points. Transcript expression showed higher correlations between adjacent time points. Based on differentially expressed genes, we identified 22 major temporal co-expression (TCoE) patterns of gene expression and revealed enriched gene ontology biological processes. Within each TCoE set, highly correlated transcripts, putatively commonly affected by genetic background, were clustered and termed genetic co-expression (GCoE) sets. Seventeen of the 22 TCoE sets had GCoE sets with median heritabilities higher than 0.50, and these heritability estimates were much higher than that estimated from permutation analysis, with no divergence observed in cluster sizes between permutation and non-permutation analyses. Linear regression between 634 metabolites from mature seeds and the PC1 score of each of the GCoE sets showed significantly lower p-values than permutation analysis. Temporal expression patterns of oat avenanthramides and lipid biosynthetic genes were concordant with previous studies of avenanthramide biosynthetic enzyme activity and lipid accumulation. This study expands our understanding of physiological processes that occur during oat seed maturation and provides plant breeders the means to change oat seed composition through targeted manipulation of key pathways.

High-throughput phenotyping platforms enhance genomic selection for wheat grain yield across populations and cycles in early stage.

Genomic selection (GS) models have been validated for many quantitative traits in wheat (Triticum aestivum L.) breeding. However, those models are mostly constrained within the same growing cycle and the extension of GS to the case of across cycles has been a challenge, mainly due to the low predictive accuracy resulting from two factors: reduced genetic relationships between different families and augmented environmental variances between cycles. Using the data collected from diverse field conditions at the International Wheat and Maize Improvement Center, we evaluated GS for grain yield in three elite yield trials across three wheat growing cycles. The objective of this project was to employ the secondary traits, canopy temperature, and green normalized difference vegetation index, which are closely associated with grain yield from high-throughput phenotyping platforms, to improve prediction accuracy for grain yield. The ability to predict grain yield was evaluated reciprocally across three cycles with or without secondary traits. Our results indicate that prediction accuracy increased by an average of 146% for grain yield across cycles with secondary traits. In addition, our results suggest that secondary traits phenotyped during wheat heading and early grain filling stages were optimal for enhancing the prediction accuracy for grain yield.

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

KEY MESSAGE: Genome-wide association mapping in conjunction with population sequencing map and Ensembl plants was used to identify markers/candidate genes linked to leaf rust, stripe rust and tan spot resistance in wheat. 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.

Development and validation of KASP markers for the greenbug resistance gene Gb7 and the Hessian fly resistance gene H32 in wheat.

KEY MESSAGE: Greenbug and Hessian fly are important pests that decrease wheat production worldwide. We developed and validated breeder-friendly KASP markers for marker-assisted breeding to increase selection efficiency. Greenbug (Schizaphis graminum Rondani) and Hessian fly [Mayetiola destructor (Say)] are two major destructive insect pests of wheat (Triticum aestivum L.) throughout wheat production regions in the USA and worldwide. Greenbug and Hessian fly infestation can significantly reduce grain yield and quality. Breeding for resistance to these two pests using marker-assisted selection (MAS) is the most economical strategy to minimize losses. In this study, doubled haploid lines from the Synthetic W7984 × Opata M85 wheat reference population were used to construct linkage maps for the greenbug resistance gene Gb7 and the Hessian fly resistance gene H32 with genotyping-by-sequencing (GBS) and 90K array-based single nucleotide polymorphism (SNP) marker data. Flanking markers were closely linked to Gb7 and H32 and were located on chromosome 7DL and 3DL, respectively. Gb7-linked markers (synopGBS773 and synopGBS1141) and H32-linked markers (synopGBS901 and IWB65911) were converted into Kompetitive Allele Specific PCR (KASP) assays for MAS in wheat breeding. In addition, comparative mapping identified syntenic regions in Brachypodium distachyon, rice (Oryza sativa), and sorghum (Sorghum bicolor) for Gb7 and H32 that can be used for fine mapping and map-based cloning of the genes. The KASP markers developed in this study are the first set of SNPs tightly linked to Gb7 and H32 and will be very useful for MAS in wheat breeding programs and future genetic studies of greenbug and Hessian fly resistance.

Genomic and pedigree-based prediction for leaf, stem, and stripe rust resistance in wheat.

KEY MESSAGE: Genomic 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.

Genome-wide comparative diversity uncovers multiple targets of selection for improvement in hexaploid wheat landraces and cultivars.

Domesticated crops experience strong human-mediated selection aimed at developing high-yielding varieties adapted to local conditions. To detect regions of the wheat genome subject to selection during improvement, we developed a high-throughput array to interrogate 9,000 gene-associated single-nucleotide polymorphisms (SNP) in a worldwide sample of 2,994 accessions of hexaploid wheat including landraces and modern cultivars. Using a SNP-based diversity map we characterized the impact of crop improvement on genomic and geographic patterns of genetic diversity. We found evidence of a small population bottleneck and extensive use of ancestral variation often traceable to founders of cultivars from diverse geographic regions. Analyzing genetic differentiation among populations and the extent of haplotype sharing, we identified allelic variants subjected to selection during improvement. Selective sweeps were found around genes involved in the regulation of flowering time and phenology. An introgression of a wild relative-derived gene conferring resistance to a fungal pathogen was detected by haplotype-based analysis. Comparing selective sweeps identified in different populations, we show that selection likely acts on distinct targets or multiple functionally equivalent alleles in different portions of the geographic range of wheat. The majority of the selected alleles were present at low frequency in local populations, suggesting either weak selection pressure or temporal variation in the targets of directional selection during breeding probably associated with changing agricultural practices or environmental conditions. The developed SNP chip and map of genetic variation provide a resource for advancing wheat breeding and supporting future population genomic and genome-wide association studies in wheat.

Training set optimization under population structure in genomic selection.

KEY MESSAGE: Population structure must be evaluated before optimization of the training set population. Maximizing the phenotypic variance captured by the training set is important for optimal performance. The optimization of the training set (TRS) in genomic selection has received much interest in both animal and plant breeding, because it is critical to the accuracy of the prediction models. In this study, five different TRS sampling algorithms, stratified sampling, mean of the coefficient of determination (CDmean), mean of predictor error variance (PEVmean), stratified CDmean (StratCDmean) and random sampling, were evaluated for prediction accuracy in the presence of different levels of population structure. In the presence of population structure, the most phenotypic variation captured by a sampling method in the TRS is desirable. The wheat dataset showed mild population structure, and CDmean and stratified CDmean methods showed the highest accuracies for all the traits except for test weight and heading date. The rice dataset had strong population structure and the approach based on stratified sampling showed the highest accuracies for all traits. In general, CDmean minimized the relationship between genotypes in the TRS, maximizing the relationship between TRS and the test set. This makes it suitable as an optimization criterion for long-term selection. Our results indicated that the best selection criterion used to optimize the TRS seems to depend on the interaction of trait architecture and population structure.

A Grounded Guide to Gluten: How Modern Genotypes and Processing Impact Wheat Sensitivity.

The role of wheat, and particularly of gluten protein, in our diet has recently been scrutinized. This article provides a summary of the main pathologies related to wheat in the human body, including celiac disease, wheat allergy, nonceliac wheat sensitivity, fructose malabsorption, and irritable bowel syndrome. Differences in reactivity are discussed for ancient, heritage, and modern wheats. Due to large variability among species and genotypes, it might be feasible to select wheat varieties with lower amounts and fewer types of reactive prolamins and fructans. Einkorn is promising for producing fewer immunotoxic effects in a number of celiac research studies. Additionally, the impact of wheat processing methods on wheat sensitivity is reviewed. Research indicates that germination and fermentation technologies can effectively alter certain immunoreactive components. For individuals with wheat sensitivity, less-reactive wheat products can slow down disease development and improve quality of life. While research has not proven causation in the increase in wheat sensitivity over the last decades, modern wheat processing may have increased exposure to immunoreactive compounds. More research is necessary to understand the influence of modern wheat cultivars on epidemiological change.

Integrating environmental covariates and crop modeling into the genomic selection framework to predict genotype by environment interactions.

KEY MESSAGE: Development of models to predict genotype by environment interactions, in unobserved environments, using environmental covariates, a crop model and genomic selection. Application to a large winter wheat dataset. Genotype by environment interaction (G*E) is one of the key issues when analyzing phenotypes. The use of environment data to model G*E has long been a subject of interest but is limited by the same problems as those addressed by genomic selection methods: a large number of correlated predictors each explaining a small amount of the total variance. In addition, non-linear responses of genotypes to stresses are expected to further complicate the analysis. Using a crop model to derive stress covariates from daily weather data for predicted crop development stages, we propose an extension of the factorial regression model to genomic selection. This model is further extended to the marker level, enabling the modeling of quantitative trait loci (QTL) by environment interaction (Q*E), on a genome-wide scale. A newly developed ensemble method, soft rule fit, was used to improve this model and capture non-linear responses of QTL to stresses. The method is tested using a large winter wheat dataset, representative of the type of data available in a large-scale commercial breeding program. Accuracy in predicting genotype performance in unobserved environments for which weather data were available increased by 11.1% on average and the variability in prediction accuracy decreased by 10.8%. By leveraging agronomic knowledge and the large historical datasets generated by breeding programs, this new model provides insight into the genetic architecture of genotype by environment interactions and could predict genotype performance based on past and future weather scenarios.

Fine mapping of a preharvest sprouting QTL interval on chromosome 2B in white wheat.

KEY MESSAGE: Fine mapping by recombinant backcross populations revealed that a preharvest sprouting QTL on 2B contained two QTLs linked in coupling with different effects on the phenotype. Wheat preharvest sprouting (PHS) occurs when grain germinates on the plant before harvest, resulting in reduced grain quality. Previous mapping of quantitative trait locus (QTL) revealed a major PHS QTL, QPhs.cnl-2B.1, located on chromosome 2B significant in 16 environments that explained from 5 to 31 % of the phenotypic variation. The objective of this project was to fine map the QPhs.cnl-2B.1 interval. Fine mapping was carried out in recombinant backcross populations (BC1F4 and BC1F5) that were developed by backcrossing selected doubled haploids to a recurrent parent and self-pollinating the BC1F4 and BC1F5 generations. In each generation, three markers in the QPhs.cnl-2B.1 interval were used to screen for recombinants. Fine mapping revealed that the QPhs.cnl-2B.1 interval contained two PHS QTLs linked in coupling. The distal PHS QTL, located between Wmc453c and Barc55, contributed 8 % of the phenotypic variation and also co-located with a major seed dormancy QTL determined by germination index. The proximal PHS QTL, between Wmc474 and CNL415-rCDPK, contributed 16 % of the variation. Several candidate genes including Mg-chelatase H subunit family protein, GTP-binding protein and calmodulin/Ca(2+)-dependent protein kinase were linked to the PHS QTL. Although many recombinant lines were identified, the lack of polymorphism for markers in the QTL interval prevented the localization of the recombination breakpoints and identification of the gene underlying the phenotype.

A consensus map for Ug99 stem rust resistance loci in wheat.

KEY MESSAGE: This consensus map of stem rust genes, QTLs, and molecular markers will facilitate the identification of new resistance genes and provide a resource of in formation for development of new markers for breeding wheat varieties resistant to Ug99. The global effort to identify new sources of resistance to wheat stem rust, caused by Puccinia graminis f. sp. tritici race group Ug99 has resulted in numerous studies reporting both qualitative genes and quantitative trait loci. The purpose of our study was to assemble all available information on loci associated with stem rust resistance from 21 recent studies on Triticum aestivum L. (bread wheat) and Triticum turgidum subsp. durum desf. (durum wheat). The software LPmerge was used to construct a stem rust resistance loci consensus wheat map with 1,433 markers incorporating Single Nucleotide Polymorphism, Diversity Arrays Technology, Genotyping-by-Sequencing as well as Simple Sequence Repeat marker information. Most of the markers associated with stem rust resistance have been identified in more than one population. Several loci identified in these populations map to the same regions with known Sr genes including Sr2, SrND643, Sr25 and Sr57 (Lr34/Yr18/Pm38), while other significant markers were located in chromosome regions where no Sr genes have been previously reported. This consensus map provides a comprehensive source of information on 141 stem rust resistance loci conferring resistance to stem rust Ug99 as well as linked markers for use in marker-assisted selection.

Genomic prediction of seed nutritional traits in biparental families of oat (Avena sativa).

Selection for more nutritious crop plants is an important goal of plant breeding to improve food quality and contribute to human health outcomes. While there are efforts to integrate genomic prediction to accelerate breeding progress, an ongoing challenge is identifying strategies to improve accuracy when predicting within biparental populations in breeding programs. We tested multiple genomic prediction methods for 12 seed fatty acid content traits in oat (Avena sativa L.), as unsaturated fatty acids are a key nutritional trait in oat. Using two well-characterized oat germplasm panels and other biparental families as training populations, we predicted family mean and individual values within families. Genomic prediction of family mean exceeded a mean accuracy of 0.40 and 0.80 using an unrelated and related germplasm panel, respectively, where the related germplasm panel outperformed prediction based on phenotypic means (0.54). Within family prediction accuracy was more variable: training on the related germplasm had higher accuracy than the unrelated panel (0.14-0.16 and 0.05-0.07, respectively), but variability between families was not easily predicted by parent relatedness, segregation of a locus detected by a genome-wide association study in the panel, or other characteristics. When using other families as training populations, prediction accuracies were comparable to the related germplasm panel (0.11-0.23), and families that had half-sib families in the training set had higher prediction accuracy than those that did not. Overall, this work provides an example of genomic prediction of family means and within biparental families for an important nutritional trait and suggests that using related germplasm panels as training populations can be effective.