Quantitative trait loci (QTL) associated with resistance to Fusarium head blight (FHB), which is mainly caused by Fusarium graminearum Schwabe [telomorph: Gibberella zeae Schw. (Petch)], have been identified in wheat (Triticum aestivum L.) from different countries. Due to the differences of genetic backgrounds and analysis methods, the linked marker and significance levels of QTL are not consistent across studies. Such discrepancies make it difficult to select diagnostic flanking markers. Meta‐analysis has been used to estimate the confidence intervals (CIs) of QTL in plant and animal genomes. The objective of this study was to cluster 249 FHB resistance QTL identified in 46 unique lines from 45 studies based on the estimated QTL CI by meta‐analysis. A total of 209 QTL conditioning FHB resistance type I, II, III and IV were classified into 43 clusters on 21 chromosomes. Among them, 119 QTL were significant and 116 QTL explained more than 10% of phenotypic variation. There are 19 confirmed QTL located on chromosomes 3A, 5A, 7A, 1B, 3BS, 5B, 6B, and 2D. The results provide chromosome locations and linked markers for overlapping and unique QTL. Markers flanking QTL clusters can be used to pyramid diverse QTL more efficiently through marker‐assisted breeding.

Previous studies have shown that there is considerable population structure in cultivated barley (Hordeum vulgare L.), with the strongest structure corresponding to differences in row number and growth habit. U.S. barley breeding programs include six‐row and two‐row types and winter and spring types in all combinations. To facilitate mapping of complex traits in breeding germplasm, 1816 barley lines from 10 U.S. breeding programs were scored with 1536 single nucleotide polymorphism (SNP) genotyping assays. The number of SNPs segregating within breeding programs varied from 854 to 1398. Model‐based analysis of population structure showed the expected clustering by row type and growth habit; however, there was additional structure, some of which corresponded to the breeding programs. The model that fit the data best had seven populations: three two‐row spring, two six‐row spring, and two six‐row winter. Average linkage disequilibrium (LD) within populations decayed over a distance of 20 to 30 cM, but some populations showed long‐range LD suggestive of admixture. Genetic distance (allele‐sharing) between populations varied from 0.11 (six‐row spring vs. six‐row spring) to 0.45 (two‐row spring vs. six‐row spring). Analyses of pairwise LD revealed that the phase of allelic associations was not well correlated between populations, particularly when their allele‐sharing distance was >0.2. These results suggest that pooling divergent barley populations for purposes of association mapping may be inadvisable.

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.

Identity of quantitative trait loci (QTL) governing resistance to fusarium head blight (FHB) initial infection (type I), spread (type II), kernel infection, and deoxynivalenol (DON) accumulation was characterized in Chinese wheat line W14. Ninety-six double-haploid lines derived from a cross of W14 · ÕPion2684Õ were evaluated for FHB resistance in two greenhouse and one field experiment. Two known major QTL were validated on chromosomes 3BS and 5AS in W14 using the composite interval mapping method. The 3BS QTL had a larger effect on resistance than the 5AS QTL in the greenhouse experiments, whereas, the 5AS QTL had a larger effect in the field experiment. These two QTL together explained 33%, 35%, and 31% of the total phenotypic variation for disease spread, kernel infection, and DON concentration in the greenhouse experiments, respectively. In the field experiment, the two QTL explained 34% and 26% of the total phenotypic variation for FHB incidence and severity, respectively. W14 has both QTL, which confer reduced initial infection, disease spread, kernel infection, and DON accumulation. Therefore, marker-assisted selection (MAS) for both QTL should be implemented in incorporating W14 resistance into adapted backgrounds. Flanking markers Xbarc133 and Xgwm493 on 3BS and Xbarc117 and Xbarc56 on 5AS are suggested for MAS.

Based on the estimates of accuracy, genomic selection would be useful for selecting for improved trait values and trait stability for agronomic and quality traits in wheat. Trait values and trait stability estimated by two methods were generally independent indicating a breeder could select for both simultaneously. Genomic selection (GS) is a new marker-assisted selection tool for breeders to achieve higher genetic gain faster and cheaper. Breeders face challenges posed by genotype by environment interaction (GEI) pattern and selecting for trait stability. Obtaining trait stability is costly, as it requires data from multiple environments. There are few studies that evaluate the efficacy of GS for predicting trait stability. A soft winter wheat population of 273 lines was genotyped with 90 K single nucleotide polymorphism markers and phenotyped for four agronomic and seven quality traits. Additive main effect and multiplicative interaction (AMMI) model and  Eberhart and Russell regression (ERR) were used to estimate trait stability. Significant GEI variation was observed and stable lines were identified for all traits in this study. The accuracy of GS ranged from 0.33 to 0.67 for most traits and trait stability. Accuracy of trait stability was greater than trait itself for yield (0.44 using AMMI versus 0.33) and heading date (0.65 using ERR versus 0.56). The opposite trend was observed for the other traits. GS did not predict the stability of the quality traits except for flour protein, lactic acid and softness equivalent. Significant GS accuracy for some trait stability indicated that stability was under genetic control for these traits. The magnitude of GS accuracies for all the traits and most of the trait stability index suggests the possibility of rapid selection for these trait and trait stability in wheat breeding.

In the soft red winter wheat (Triticum aestivum L.) regions of the US, Fusarium head blight (FHB, caused by Fusarium spp.) resistance derived from locally adapted germplasm has been used predominantly. Two soft red winter wheat cultivars, Massey and Ernie, have moderate resistance to FHB. Mapping populations derived from Becker/Massey (B/M) and Ernie/MO 94-317 (E/MO) were evaluated for FHB resistance and other traits in multiple environments. Eight QTL in B/M and five QTL in E/MO were associated with FHB variables including incidence, severity (SEV), index (IND), Fusarium damaged kernels (FDK), deoxynivalenol (DON), and morphological traits flowering time and plant height. Four QTL were common to both populations. Three of them were located at or near known genes: Ppd-D1 on chromosome 2DS, Rht-B1 on 4BS, and Rht-D1 on 4DS. Alleles for dwarf plant height (Rht-B1b and Rht-D1b) and photoperiod insensitivity (Ppd-D1a) had pleiotropic effects in reducing height and increasing FHB susceptibility. The other QTL detected for FHB variables were on 3BL in both populations, 1AS, 1DS, 2BL, and 4DL in B/M, and 5AL (B1) and 6AL in E/MO. The additive effects of FHB variables ranged from 0.4 mg kg−1 of DON to 6.2 % for greenhouse (GH) SEV in B/M and ranged from 0.3 mg kg−1 of DON to 8.3 % for GH SEV in E/MO. The 4DS QTL had epistasis with Ppd-D1, Qdon.umc-6AL, and Qht.umc-4BS, and additive × additive × environment interactions with the 4BS QTL for SEV, IND, and FDK in E/MO. Marker-assisted selection might be used to enhance FHB resistance through selection of favorable alleles of significant QTL, taking into account genotypes at Rht-B1b, Rht-D1a and Ppd-D1a.Electronic supplementary materialThe online version of this article (doi:10.1007/s00122-013-2149-y) contains supplementary material, which is available to authorized users.

Two mapping approaches were use to identify and validate milling and baking quality QTL in soft wheat. Two LG were consistently found important for multiple traits and we recommend the use marker-assisted selection on specific markers reported here. Wheat-derived food products require a range of characteristics. Identification and understanding of the genetic components controlling end-use quality of wheat is important for crop improvement. We assessed the underlying genetics controlling specific milling and baking quality parameters of soft wheat including flour yield, softness equivalent, flour protein, sucrose, sodium carbonate, water absorption and lactic acid, solvent retention capacities in a diversity panel and five bi-parental mapping populations. The populations were genotyped with SSR and DArT markers, with markers specific for the 1BL.1RS translocation and sucrose synthase gene. Association analysis and composite interval mapping were performed to identify quantitative trait loci (QTL). High heritability was observed for each of the traits evaluated, trait correlations were consistent over populations, and transgressive segregants were common in all bi-parental populations. A total of 26 regions were identified as potential QTL in the diversity panel and 74 QTL were identified across all five bi-parental mapping populations. Collinearity of QTL from chromosomes 1B and 2B was observed across mapping populations and was consistent with results from the association analysis in the diversity panel. Multiple regression analysis showed the importance of the two 1B and 2B regions and marker-assisted selection for the favorable alleles at these regions should improve quality.

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