Identification of loci for grain yield (GY) and related traits, and dissection of the genetic architecture are important for yield improvement through marker-assisted selection (MAS). Two genome-wide association study (GWAS) methods were used on a diverse panel of 166 elite wheat varieties from the Yellow and Huai River Valleys Wheat Zone (YHRVWD) of China to detect stable loci and analyze relationships among GY and related traits. A total of 326,570 single nucleotide polymorphism (SNP) markers from the wheat 90 K and 660 K SNP arrays were chosen for GWAS of GY and related traits, generating a physical distance of 14,064.8 Mb. One hundred and twenty common loci were detected using SNP-GWAS and Haplotype-GWAS, among which two were potentially functional genes underpinning kernel weight and plant height (PH), eight were at similar locations to the quantitative trait loci (QTL) identified in recombinant inbred line (RIL) populations in a previous study, and 78 were potentially new. Twelve pleiotropic loci were detected on eight chromosomes; among these the interval 714.4–725.8 Mb on chromosome 3A was significantly associated with GY, kernel number per spike (KNS), kernel width (KW), spike dry weight (SDW), PH, uppermost internode length (UIL), and flag leaf length (FLL). GY shared five loci with thousand kernel weight (TKW) and PH, indicating significantly affected by two traits. Compared with the total number of loci for each trait in the diverse panel, the average number of alleles for increasing phenotypic values of GY, TKW, kernel length (KL), KW, and flag leaf width (FLW) were higher, whereas the numbers for PH, UIL and FLL were lower. There were significant additive effects for each trait when favorable alleles were combined. UIL and FLL can be directly used for selecting high-yielding varieties, whereas FLW can be used to select spike number per unit area (SN) and KNS. The loci and significant SNP markers identified in the present study can be used for pyramiding favorable alleles in developing high-yielding varieties. Our study proved that both GWAS methods and high-density genetic markers are reliable means of identifying loci for GY and related traits, and provided new insight to the genetic architecture of GY.

Genomic selection (GS) is a strategy to predict the genetic merits of individuals using genome-wide markers. However, GS prediction accuracy is affected by many factors, including missing rate and minor allele frequency (MAF) of genotypic data, GS models, trait features, etc. In this study, we used one wheat population to investigate prediction accuracies of various GS models on yield and yield-related traits from various quality control (QC) scenarios, missing genotype imputation, and genome-wide association studies (GWAS)-derived markers. Missing rate and MAF of single nucleotide polymorphism (SNP) markers were two major factors in QC. Five missing rate levels (0%, 20%, 40%, 60%, and 80%) and three MAF levels (0%, 5%, and 10%) were considered and the five-fold cross validation was used to estimate the prediction accuracy. The results indicated that a moderate missing rate level (20% to 40%) and MAF (5%) threshold provided better prediction accuracy. Under this QC scenario, prediction accuracies were further calculated for imputed and GWAS-derived markers. It was observed that the accuracies of the six traits were related to their heritability and genetic architecture, as well as the GS prediction model. Moore–Penrose generalized inverse (GenInv), ridge regression (RidgeReg), and random forest (RForest) resulted in higher prediction accuracies than other GS models across traits. Imputation of missing genotypic data had marginal effect on prediction accuracy, while GWAS-derived markers improved the prediction accuracy in most cases. These results demonstrate that QC on missing rate and MAF had positive impact on the predictability of GS models. We failed to identify one single combination of QC scenarios that could outperform the others for all traits and GS models. However, the balance between marker number and marker quality is important for the deployment of GS in wheat breeding. GWAS is able to select markers which are mostly related to traits, and therefore can be used to improve the prediction accuracy of GS.

The micronutrients iron (Fe) and zinc (Zn) are not only essential for plant survival and proliferation but are crucial for human health. Increasing Fe and Zn levels in edible parts of plants, known as biofortification, is seen a sustainable approach to alleviate micronutrient deficiency in humans. Wheat, as one of the leading staple foods worldwide, is recognized as a prioritized choice for Fe and Zn biofortification. However, to date, limited molecular and physiological mechanisms have been elucidated for Fe and Zn homeostasis in wheat. The expanding molecular understanding of Fe and Zn homeostasis in model plants is providing invaluable resources to biofortify wheat. Recent advancements in NGS (next generation sequencing) technologies coupled with improved wheat genome assembly and high-throughput genotyping platforms have initiated a revolution in resources and approaches for wheat genetic investigations and breeding. Here, we summarize molecular processes and genes involved in Fe and Zn homeostasis in the model plants Arabidopsis and rice, identify their orthologs in the wheat genome, and relate them to known wheat Fe/Zn QTL (quantitative trait locus/loci) based on physical positions. The current study provides the first inventory of the genes regulating grain Fe and Zn homeostasis in wheat, which will benefit gene discovery and breeding, and thereby accelerate the release of Fe- and Zn-enriched wheats.

Deficiency of micronutrient elements, such as zinc (Zn) and iron (Fe), is called “hidden hunger,” and bio-fortification is the most effective way to overcome the problem. In this study, a high-density Affymetrix 50K single-nucleotide polymorphism (SNP) array was used to map quantitative trait loci (QTL) for grain Zn (GZn) and grain Fe (GFe) concentrations in 254 recombinant inbred lines (RILs) from a cross Jingdong 8/Bainong AK58 in nine environments. There was a wide range of variation in GZn and GFe concentrations among the RILs, with the largest effect contributed by the line × environment interaction, followed by line and environmental effects. The broad sense heritabilities of GZn and GFe were 0.36 ± 0.03 and 0.39 ± 0.03, respectively. Seven QTL for GZn on chromosomes 1DS, 2AS, 3BS, 4DS, 6AS, 6DL, and 7BL accounted for 2.2–25.1% of the phenotypic variances, and four QTL for GFe on chromosomes 3BL, 4DS, 6AS, and 7BL explained 2.3–30.4% of the phenotypic variances. QTL on chromosomes 4DS, 6AS, and 7BL might have pleiotropic effects on both GZn and GFe that were validated on a germplasm panel. Closely linked SNP markers were converted to high-throughput KASP markers, providing valuable tools for selection of improved Zn and Fe bio-fortification in breeding.

Summary: High‐molecular‐weight glutenin subunits (HMW‐GS), a major component of seed storage proteins (SSP) in wheat, largely determine processing quality. HMW‐GS encoded by GLU‐1 loci are mainly controlled at the transcriptional level by interactions between cis‐elements and transcription factors (TFs). We previously identified a conserved cis‐regulatory module CCRM1‐1 as the most essential cis‐element for Glu‐1 endosperm‐specific high expression. However, the TFs targeting CCRM1‐1 remained unknown. Here, we built the first DNA pull‐down plus liquid chromatography‐mass spectrometry platform in wheat and identified 31 TFs interacting with CCRM1‐1. TaB3‐2A1 as proof of concept was confirmed to bind to CCRM1‐1 by yeast one hybrid and electrophoretic mobility shift assays. Transactivation experiments demonstrated that TaB3‐2A1 repressed CCRM1‐1‐driven transcription activity. TaB3‐2A1 overexpression significantly reduced HMW‐GS and other SSP, but enhanced starch content. Transcriptome analyses confirmed that enhanced expression of TaB3‐2A1 down‐regulated SSP genes and up‐regulated starch synthesis‐related genes, such as TaAGPL3, TaAGPS2, TaGBSSI, TaSUS1 and TaSUS5, suggesting that it is an integrator modulating the balance of carbon and nitrogen metabolism. TaB3‐2A1 also had significant effects on agronomic traits, including heading date, plant height and grain weight. We identified two major haplotypes of TaB3‐2A1 and found that TaB3‐2A1‐Hap1 conferred lower seed protein content, but higher starch content, plant height and grain weight than TaB3‐2A1‐Hap2 and was subjected to positive selection in a panel of elite wheat cultivars. These findings provide a high‐efficiency tool to detect TFs binding to targeted promoters, considerable gene resources for dissecting regulatory mechanisms underlying Glu‐1 expression, and a useful gene for wheat improvement.