Spike-related traits such as spike length (Sl), fertile spikelet number (Fsn), sterile spikelet number (Ssn), grain number per spike (Gns), and thousand-kernel weight (Tkw) are important factors influencing wheat yield. However, reliably stable markers that can be used for molecular breeding in different environments have not yet been identified. In this study, a double haploid (DH) population was used for quantitative trait locus (QTL) mapping of five spike-related traits under four different nitrogen (N) supply dates in two locations and years. Seventy additive QTLs with phenotypic variation ranging from 4.12 to 34.74% and 10 major epistatic QTLs were identified. Eight important chromosomal regions on five chromosomes (1B, 2B, 2D, 5D, and 6A) were found. Sixteen stable QTLs were detected for which N application had little effect. Among those stable QTLs, QSl.sdau-2D-1, and QSl.sdau-2D-2, with phenotypic variation explained (PVE) of 10.4 and 24.2%, respectively, were flanked by markers Xwmc112 and Xcfd53 in the same order. The QTLs QSsn.sdau-2B-1, QFsn.sdau-2B-1, and QGns.sdau-2B, with PVE ranging from 4.37 to 28.43%, collocated in the Xwmc179-Xbarc373 marker interval. The consistent kernel wheat QTL (QTkw.sdau-6A) on the long arm of chromosome 6A, flanked by SSR markers Xbarc1055 and Xwmc553, showed PVE of 5.87–15.18%. Among these stable QTLs, the two flanking markers Xwmc112 and Xcfd53 have been validated using different varieties and populations for selecting Sl. Therefore, these results will be of great value for marker-assisted selection (MAS) in breeding programs and will accelerate the understanding of the genetic relationships among spike-related traits at the molecular level.

Background Mineral elements are important for maintaining good human health besides heavy metals. Mining genes that control mineral elements are paramount for improving their accumulation in the wheat grain. Although previous studies have reported some loci for beneficial trace elements, they have mainly focused on Zn and Fe content. However, little information is available regarding the genetic loci differences in dissecting synchronous accumulation of multiple mineral elements in wheat grains, including beneficial and heavy elements. Therefore, a genome-wide association study (GWAS) was conducted on 205 wheat accessions with 24,355 single nucleotide polymorphisms (SNPs) to identify important loci and candidate genes for controlling Ca, Fe, Zn, Se, Cu, Mn, Cd, As, and Pb accumulation in wheat grains. Results A total of 101 marker-trait associations (MTAs) (P < 10–5) loci affecting the content of nine mineral elements was identified on chromosomes 1B, 1D, 2A, 2B, 3A, 3B, 3D, 4A, 4B, 5A, 5B, 5D, 6B, 7A, 7B, and 7D. Among these, 17 major MTAs loci for the nine mineral elements were located, and four MTAs loci (P < 10–5) were found on chromosomes 1B, 6B, 7B, and 7D. Eight multi-effect MTAs loci were detected that are responsible for the control of more than one trait, mainly distributed on chromosomes 3B, 7B, and 5A. Furthermore, sixteen candidate genes controlling Ca, Fe, Zn, Se, Cd, and Pb were predicted, whose functions were primarily related to ion binding, including metals, Fe, Ca, Cu, Mg, and Zn, ATP binding, ATPase activity, DNA binding, RNA binding, and protein kinase activity. Conclusions Our study indicated the existence of gene interactions among mineral elements based on multi-effect MTAs loci and candidate genes. Meanwhile this study provided new insights into the genetic control of mineral element concentrations, and the important loci and genes identified may contribute to the rapid development of beneficial mineral elements and a reduced content of harmful heavy metals in wheat grain.

Protein, starch, and their components are important for wheat grain yield and end-products, which are affected by wheat grain development. Therefore, QTL mapping and a genome-wide association study (GWAS) of grain protein content (GPC), glutenin macropolymer content (GMP), amylopectin content (GApC), and amylose content (GAsC) were performed on wheat grain development at 7, 14, 21, and 28 days after anthesis (DAA) in two environments using a recombinant inbred line (RIL) population of 256 stable lines and a panel of 205 wheat accessions. A total of 29 unconditional QTLs, 13 conditional QTLs, 99 unconditional marker−trait associations (MTAs), and 14 conditional MTAs significantly associated (p < 10⁻⁴) with four quality traits were found to be distributed on 15 chromosomes, with the phenotypic variation explained (PVE) ranging from 5.35% to 39.86%. Among these genomic variations, three major QTLs [QGPC3B, QGPC2A, and QGPC(S3|S2)3B] and SNP clusters on the 3A and 6B chromosomes were detected for GPC, and the SNP TA005876-0602 was stably expressed during the three periods in the natural population. The QGMP3B locus was detected five times in three developmental stages in two environments with 5.89%–33.62% PVE, and SNP clusters for GMP content were found on the 3A and 3B chromosomes. For GApC, the QGApC3B.1 locus had the highest PVE of 25.69%, and SNP clusters were found on chromosomes 4A, 4B, 5B, 6B, and 7B. Four major QTLs of GAsC were detected at 21 and 28 DAA. Most interestingly, both QTL mapping and GWAS analysis indicated that four chromosomes (3B, 4A, 6B, and 7A) were mainly involved in the development of protein, GMP, amylopectin, and amylose synthesis. Of these, the wPt-5870–wPt-3620 marker interval on chromosome 3B seemed to be most important because it played an important role in the synthesis of GMP and amylopectin before 7 DAA, in the synthesis of protein and GMP from 14 to 21 DAA, and in the development of GApC and GAsC from 21 to 28 DAA. Using the annotation information of IWGSC Chinese Spring RefSeq v1.1 genome assembly, we predicted 28 and 69 candidate genes for major loci from QTL mapping and GWAS, respectively. Most of them have multiple effects on protein and starch synthesis during grain development. These results provide new insights and information for the potential regulatory network between grain protein and starch synthesis.

Tan spot (TS) and Septoria nodorum blotch (SNB) induced by Pyrenophora tritici-repentis and Parastagonospora nodorum, respectively, cause significant yield losses and adversely affect grain quality. The objectives of this study were to decipher the genetics and map the resistance to TS and SNB in the PBW343/Kenya Nyangumi (KN) population comprising 204 F₆ recombinant inbred lines (RILs). Disease screening was performed at the seedling stage under greenhouse conditions. TS was induced by P. tritici-repentis isolate MexPtr1 while SNB by P. nodorum isolate MexSN1. Segregation pattern of the RILs indicated that resistance to TS and SNB in this population was quantitative. Diversity Array Technology (DArTs) and simple sequence repeats (SSRs) markers were used to identify the quantitative trait loci (QTL) for the diseases using inclusive composite interval mapping (ICIM). Seven significant additive QTLs for TS resistance explaining 2.98 to 23.32% of the phenotypic variation were identified on chromosomes 1A, 1B, 5B, 7B and 7D. For SNB, five QTLs were found on chromosomes 1A, 5A, and 5B, explaining 5.24 to 20.87% of the phenotypic variation. The TS QTL on 1B chromosome coincided with the pleiotropic adult plant resistance (APR) gene Lr46/Yr29/Pm39. This is the first report of the APR gene Lr46/Yr29/Pm39 contributing to TS resistance.

Breeding new wheat varieties with salt resistance is one of the best ways to solve a constraint on the sustainability and expansion of wheat cultivation. Therefore, understanding the molecular components or genes related to salt tolerance must contribute to the cultivation of salt-tolerant varieties. The present study used a recombinant inbred line (RIL) population to genetically dissect the effects of different salt stress concentrations on wheat seed germination and seedling traits using two quantitative trait locus (QTL) mapping methods. A total of 31 unconditional and 11 conditional QTLs for salt tolerance were identified on 11 chromosomes explaining phenotypic variation (PVE) ranging from 2.01 to 65.76%. Of these, 15 major QTLs were found accounting for more than 10% PVE. QTL clusters were detected on chromosomes 2A and 3B in the marker intervals ‘wPt-8328 and wPt-2087’ and ‘wPt-666008 and wPt-3620’, respectively, involving more than one salt tolerance trait. QRdw3B and QSfw3B.2 were most consistent in two or more salt stress treatments. 16 candidate genes associated with salt tolerance were predicted in wheat. These results could be useful to improve salt tolerance by marker-assisted selection (MAS) and shed new light on understanding the genetic basis of salt tolerance in wheat.