Wheat kernel shape and size has been under selection since early domestication. Kernel morphology is a major consideration in wheat breeding, as it impacts grain yield and quality. A population of 160 recombinant inbred lines (RIL), developed using an elite (ND 705) and a nonadapted genotype (PI 414566), was extensively phenotyped in replicated field trials and genotyped using Infinium iSelect 90K assay to gain insight into the genetic architecture of kernel shape and size. A high density genetic map consisting of 10,172 single nucleotide polymorphism (SNP) markers, with an average marker density of 0.39 cM/marker, identified a total of 29 genomic regions associated with six grain shape and size traits; ∼80% of these regions were associated with multiple traits. The analyses showed that kernel length (KL) and width (KW) are genetically independent, while a large number (∼59%) of the quantitative trait loci (QTL) for kernel shape traits were in common with genomic regions associated with kernel size traits. The most significant QTL was identified on chromosome 4B, and could be an ortholog of major rice grain size and shape gene GS3 or qGL3 . Major and stable loci also were identified on the homeologous regions of Group 5 chromosomes, and in the regions of TaGW2 (6A) and TaGASR7 (7A) genes. Both parental genotypes contributed equivalent positive QTL alleles, suggesting that the nonadapted germplasm has a great potential for enhancing the gene pool for grain shape and size. This study provides new knowledge on the genetic dissection of kernel morphology, with a much higher resolution, which may aid further improvement in wheat yield and quality using genomic tools.

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.

Among the biotic constraints to wheat (Triticum aestivum L.) production, fusarium head blight (FHB), caused by Fusarium graminearum, leaf rust (LR), caused by Puccinia triticina, and stripe rust (SR) caused by Puccinia striiformis are problematic fungal diseases worldwide. Each can significantly reduce grain yield while FHB causes additional food and feed safety concerns due to mycotoxin contamination of grain. Genetic resistance is the most effective and sustainable approach for managing wheat diseases. In the past 20 years, over 500 quantitative trait loci (QTLs) conferring small to moderate effects for the different FHB resistance types have been reported in wheat. Similarly, 79 Lr-genes and more than 200 QTLs and 82 Yr-genes and 140 QTLs have been reported for seedling and adult plant LR and SR resistance, respectively. Most QTLs conferring rust resistance are race-specific generally conforming to a classical gene-for-gene interaction while resistance to FHB exhibits complex polygenic inheritance with several genetic loci contributing to one resistance type. Identification and deployment of additional genes/QTLs associated with FHB and rust resistance can expedite wheat breeding through marker-assisted and/or genomic selection to combine small-effect QTL in the gene pool. LR disease has been present in the southeast United States for decades while SR and FHB have become increasingly problematic in the past 20 years, with FHB arguably due to increased corn acreage in the region. Currently, QTLs on chromosome 1B from Jamestown, 1A, 1B, 2A, 2B, 2D, 4A, 5A, and 6A from W14, Ning7840, Ernie, Bess, Massey, NC-Neuse, and Truman, and 3B (Fhb1) from Sumai 3 for FHB resistance, Lr9, Lr10, Lr18, Lr24, Lr37, LrA2K, and Lr2K38 genes for LR resistance, and Yr17 and YrR61 for SR resistance have been extensively deployed in southeast wheat breeding programs. This review aims to disclose the current status of FHB, LR, and SR diseases, summarize the genetics of resistance and breeding efforts for the deployment of FHB and rust resistance QTL on soft red winter wheat cultivars, and present breeding strategies to achieve sustainable management of these diseases in the southeast US.

Pre-harvest sprouting (PHS) in wheat (Triticum aestivum L.) occurs when physiologically mature kernels begin germinating in the spike. The objective of this study was to provide fundamental information on physicochemical changes of starch due to PHS in Hard Red Spring (HRS) and Hard White Spring (HWS) wheat. The mean values of α-amylase activity of non-sprouted and sprouted wheat samples were 0.12 CU/g and 2.00 CU/g, respectively. Sprouted samples exhibited very low peak and final viscosities compared to non-sprouted wheat samples. Scanning electron microscopy (SEM) images showed that starch granules in sprouted samples were partially hydrolyzed. Based on High Performance Size Exclusion Chromatography (HPSEC) profiles, the starch from sprouted samples had relatively lower molecular weight than that of non-sprouted samples. Overall, high α-amylase activity caused changes to the physicochemical properties of the PHS damaged wheat.

The genetic gain in yield and quality are two major targets of wheat breeding programs around the world. In this study, a high density genetic map consisting of 10,172 SNP markers identified a total of 43 genomic regions associated with three quality traits, three yield traits and two agronomic traits in hard red spring wheat (HRSW). When compared with six grain shape and size traits, the quality traits showed mostly independent genetic control (~18% common loci), while the yield traits showed moderate association (~53% common loci). Association of genomic regions for grain area (GA) and thousand-grain weight (TGW), with yield suggests that targeting an increase in GA may help enhancing wheat yield through an increase in TGW. Flour extraction (FE), although has a weak positive phenotypic association with grain shape and size, they do not share any common genetic loci. A major contributor to plant height was the Rht8 locus and the reduced height allele was associated with significant increase in grains per spike (GPS) and FE, and decrease in number of spikes per square meter and test weight. Stable loci were identified for almost all the traits. However, we could not find any QTL in the region of major known genes like GPC-B1, Ha, Rht-1, and Ppd-1. Epistasis also played an important role in the genetics of majority of the traits. In addition to enhancing our knowledge about the association of wheat quality and yield with grain shape and size, this study provides novel loci, genetic information and pre-breeding material (combining positive alleles from both parents) to enhance the cultivated gene pool in wheat germplasm. These resources are valuable in facilitating molecular breeding for wheat quality and yield improvement.