Quantitative Trait Locus (QTLs) Mapping for Quality Traits of Wheat Based on High Density Genetic Map Combined With Bulked Segregant Analysis RNA-seq (BSR-Seq) Indicates That the Basic 7S Globulin Gene Is Related to Falling Number

Identification and validation of quantitative trait loci for seven quality-related traits in common wheat (Triticum aestivum L.)

KEY MESSAGE

QTLs for seven different quality traits were mapped. Six QTLs were considered stable and major QTLs, and the genetic effects of the QTLs were validated. Wheat grain quality traits are the key factors for economic value and are largely influenced by genetics and the environment. In this study, a genetic linkage map consisting of 8329 markers spanning 4131.54 cM was constructed using the Wheat55K SNP Array by genotyping a recombinant inbred line population of 304 lines. The quantitative trait loci (QTLs) for the swelling index of glutenin, SDS sedimentation volume (SDSS), wet gluten content, grain protein content, gluten index, grain starch content, and falling number were mapped for multiple years of experiments using the ICIM-BIP, ICIM-MET, and ICIM-EPI methods, respectively. A total of 92 QTLs, 194 cQTLs, and 117 pairs of eQTLs were mapped. Six QTLs, which were QGPC.sau-4A.1, QWGC.sau-4A, QSDSS.sau-1A.1, QGI.sau-1A, QFN.sau-4D, and QSIG.sau-1A, were considered major and stable QTLs. BLAST results showed that except QFN.sau-4D, the other 5 QTLs were new. Eight QTL clusters that contained 19 QTLs were also detected, and all the major and stable QTLs were located in these QTL clusters. Kompetitive allele-specific PCR markers closely linked to the six QTLs were designed. The genetic effects of the major and stable QTLs were successfully confirmed in different populations. These results provide new resources for breeding of high-quality wheat in the future.

Identification of quantitative trait nucleotides for grain quality in bread wheat under heat stress

Heat stress is a critical factor affecting global wheat production and productivity. In this study, out of 500 studied germplasm lines, a diverse panel of 126 wheat genotypes grown under twelve distinct environmental conditions was analyzed. Using 35 K single-nucleotide polymorphism (SNP) genotyping assays and trait data on five biochemical parameters, including grain protein content (GPC), grain amylose content (GAC), grain total soluble sugars (TSS), grain iron (Fe), and zinc (Zn) content, six multi-locus GWAS (ML-GWAS) models were employed for association analysis. This revealed 67 stable quantitative trait nucleotides (QTNs) linked to grain quality parameters, explaining phenotypic variations ranging from 3 to 44.5% under heat stress conditions. By considering the results in consensus to at least three GWAS models and three locations, the final QTNs were reduced to 16, with 12 being novel findings. Notably, two novel markers, AX-94461119 (chromosome 2A) and AX-95220192 (chromosome 7D), associated with grain Fe and Zn, respectively, were validated through Kompetitive Allele Specific Polymerase Chain Reaction (KASP) approach. Candidate genes, including the P-loop-containing nucleoside triphosphate hydrolases (NTPases), Bowman-Birk type proteinase inhibitors (BBI), and the NPSN13 protein, were identified within associated genomic regions. These genes could serve as potential targets for enhancing quality traits and heat tolerance in future wheat improvement programs.

Identification and validation of major and stable quantitative trait locus for falling number in common wheat (Triticum aestivum L.)

KEY MESSAGE

A major and stable QTL, QFn.sau-1B.2, which can explain 13.6% of the PVE in FN and has a positive effect on resistance in SGR, was mapped and validated. The falling number (FN) is considered one of the most important quality traits of wheat grain and is the most important quality evaluation index for wheat trade worldwide. The quantitative trait loci (QTLs) for FN were mapped in three years of experiments. 23, 30, and 58 QTLs were identified using the ICIM-BIP, ICIM-MET, and ICIM-EPI methods, respectively. Among them, seven QTLs were considered stable. QFn.sau-1B.2, which was mapped to the 1BL chromosome, can explain 13.6% of the phenotypic variation on average and is considered a major and stable QTL for FN. This QTL was mapped in a 1 cM interval and is flanked by the markers AX-110409346 and AX-108743901. Epistatic analysis indicated that QFN.sau-1B.2 has a strong influence on FN through both additive and epistatic effects. The Kompetitive Allele-Specific PCR marker KASP-AX-108743901, which is closely linked to QFn.sau-1B.2, was designed. The genetic effect of QFn.sau-1B.2 on FN was successfully confirmed in Chuannong18 × T1208 and CN17 × CN11 populations. Moreover, the results of the additive effects of favorable alleles for FN showed that the QTLs for FN had significant effects not only on FN but also on the resistance to spike germination. Within the interval of QFn.sau-1B.2, 147 high-confidence genes were found. According to the gene annotation and the transcriptome data, four genes might be associated with FN. QFn.sau-1B.2 may provide a new resource for the high-quality breeding of wheat in the future.

Identification of potential light deficiency response regulators in endangered species Magnolia sinostellata

Magnolia sinostellata is one of the endangered species in China and largely suffers light deficiency stress in the understory of forest. However, the weak light response molecular mechanism remains unclear. More importantly, hub genes in the molecular network have not been pinpointed. To explore potential regulators in the mechanism, weighted gene co-expression network analysis (WGCNA) was performed to analysis the trancriptome data of M. sinostellata leaves subjected to weak light with different time points. Gene co-expression analysis illustrated that module 1, 2 and 3 were closely associated with light deficiency treatment, which. Gene ontology and KEGG analyses showed that genes in module 1 mainly participated in amino and nucleotide metabolism, module 2 mostly involved in carbon fixation and module 3 mostly regulated photosynthesis related pathways, among which 6, 7 and 8 hub genes were identified, respectively. Hub genes isoform_107196 in module 1 and isoform_55976 in module 2 were unique to M. sinostellata. This study found that light deficiency inhibited photosynthesis and stress tolerance, while improved carbon metabolism and flowering related pathways in M. sinostellata, which can impact its accumulation reserves of growth and reproduction in the next season. In addition, key shade response regulators identified in this study have laid a firm foundation for further investigation of shade response molecular mechanism and protection of other shade sensitive plants.

QTL and Candidate Genes: Techniques and Advancement in Abiotic Stress Resistance Breeding of Major Cereals

At least 75% of the world's grain production comes from the three most important cereal crops: rice (Oryza sativa), wheat (Triticum aestivum), and maize (Zea mays). However, abiotic stressors such as heavy metal toxicity, salinity, low temperatures, and drought are all significant hazards to the growth and development of these grains. Quantitative trait locus (QTL) discovery and mapping have enhanced agricultural production and output by enabling plant breeders to better comprehend abiotic stress tolerance processes in cereals. Molecular markers and stable QTL are important for molecular breeding and candidate gene discovery, which may be utilized in transgenic or molecular introgression. Researchers can now study synteny between rice, maize, and wheat to gain a better understanding of the relationships between the QTL or genes that are important for a particular stress adaptation and phenotypic improvement in these cereals from analyzing reports on QTL and candidate genes. An overview of constitutive QTL, adaptive QTL, and significant stable multi-environment and multi-trait QTL is provided in this article as a solid framework for use and knowledge in genetic enhancement. Several QTL, such as DRO1 and Saltol, and other significant success cases are discussed in this review. We have highlighted techniques and advancements for abiotic stress tolerance breeding programs in cereals, the challenges encountered in introgressing beneficial QTL using traditional breeding techniques such as mutation breeding and marker-assisted selection (MAS), and the in roads made by new breeding methods such as genome-wide association studies (GWASs), the clustered regularly interspaced short palindromic repeat (CRISPR)/Cas9 system, and meta-QTL (MQTL) analysis. A combination of these conventional and modern breeding approaches can be used to apply the QTL and candidate gene information in genetic improvement of cereals against abiotic stresses.

Genome-wide association mapping of Hagberg falling number, protein content, test weight, and grain yield in U.K. wheat

Association mapping using crop cultivars allows identification of genetic loci of direct relevance to breeding. Here, 150 U.K. wheat (Triticum aestivum L.) cultivars genotyped with 23,288 single nucleotide polymorphisms (SNPs) were used for genome-wide association studies (GWAS) using historical phenotypic data for grain protein content, Hagberg falling number (HFN), test weight, and grain yield. Power calculations indicated experimental design would enable detection of quantitative trait loci (QTL) explaining ≥20% of the variation (PVE) at a relatively high power of >80%, falling to 40% for detection of a SNP with an R2 ≥ .5 with the same QTL. Genome-wide association studies identified marker-trait associations for all four traits. For HFN (h 2 = .89), six QTL were identified, including a major locus on chromosome 7B explaining 49% PVE and reducing HFN by 44 s. For protein content (h 2 = 0.86), 10 QTL were found on chromosomes 1A, 2A, 2B, 3A, 3B, and 6B, together explaining 48.9% PVE. For test weight, five QTL were identified (one on 1B and four on 3B; 26.3% PVE). Finally, 14 loci were identified for grain yield (h 2 = 0.95) on eight chromosomes (1A, 2A, 2B, 2D, 3A, 5B, 6A, 6B; 68.1% PVE), of which five were located within 16 Mbp of genetic regions previously identified as under breeder selection in European wheat. Our study demonstrates the utility of exploiting historical crop datasets, identifying genomic targets for independent validation, and ultimately for wheat genetic improvement.

Bulked Segregant RNA Sequencing Revealed Difference Between Virulent and Avirulent Brown Planthoppers

The brown planthopper (Nilaparvata lugens Stål, BPH) is one of the most devastating insect pests of rice (Oryza sativa L.), but BPH populations have varying degrees of virulence to rice varieties carrying different resistance genes. To help efforts to characterize these variations we applied bulked segregant RNA sequencing (BSR-seq) to identify differentially expressed genes (DEGs) and genetic loci associated with BPH virulence to YHY15 rice plants carrying the resistance gene Bph15. BPHs that are highly virulent or avirulent to these plants were selected from an F2 population to form two contrasting bulks, and BSR-seq identified 751 DEGs between the bulks. Genes associated with carbohydrate, amino acid and nucleotide metabolism, the endocrine system, and signal transduction were upregulated in the avirulent insects when they fed on these plants. The results also indicated that shifts in lipid metabolism and digestive system pathways were crucial for the virulent BPHs' adaptation to the resistant rice. We identified 24 single-nucleotide polymorphisms (SNPs) in 21 genes linked with BPH virulence. Possible roles of genes apparently linked to BPH virulence are discussed. Our results provide potentially valuable information for further studies of BPH virulence mechanisms and development of robust control strategies.

Dissection of the Genetic Architecture for Quantities of Gliadins Fractions in Wheat (Triticum aestivum L.)

Gliadin is a group of grain storage proteins that confers extensibility/viscosity to the dough and are vital to end-use quality in wheat. Moreover, gliadins are one of the important components for nutritional quality because they contain the nutritional unprofitable epitopes that cause chronic immune-mediated intestinal disorder in genetically susceptible individuals designated celiac disease (CD). The main genetic loci encoding the gliadins were revealed by previous studies; however, the genes related to the content of gliadins and their fractions were less elucidated. To illustrate the genetic basis of the content of gliadins and their fractions comprehensively, a recombinant inbred line (RIL) population that consisted of 196 lines was constructed from the two parents, Luozhen No.1 and Zhengyumai 9987. Quantitative trait loci (QTL) controlling the content of total gliadins and their fractions (ω-, α-, and γ-gliadin) were screened genome-widely under four environments across 2 years. Totally, thirty QTL which explained 1.97-12.83% of the phenotypic variation were detected to be distributed on 17 chromosomes and they were gathered into 12 clusters. One hundred and one pairs of epistatic QTL (E-QTL) were revealed, among which five were involved with the total gliadins and its fractions content QTL located on chromosome 1AS, 1DS, 4DS, 1DL, and 6AS. Three Kompetitive Allele-Specific PCR (KASP) markers were developed from three major QTL clusters located on chromosomes 6A, 6D, and 7D, respectively. The present research not only dissects the genetic loci for improving the content of gliadins and their three fractions, but may also contribute to marker-assisted selection of varieties with appropriate gliadin fractions content for end-use quality and health benefit at the early developmental stages and early breeding generations.

Comparative Genomic Analysis of Quantitative Trait Loci Associated With Micronutrient Contents, Grain Quality, and Agronomic Traits in Wheat (Triticum aestivum L.)

Comparative genomics and meta-quantitative trait loci (MQTLs) analysis are important tools for the identification of reliable and stable QTLs and functional genes controlling quantitative traits. We conducted a meta-analysis to identify the most stable QTLs for grain yield (GY), grain quality traits, and micronutrient contents in wheat. A total of 735 QTLs retrieved from 27 independent mapping populations reported in the last 13 years were used for the meta-analysis. The results showed that 449 QTLs were successfully projected onto the genetic consensus map which condensed to 100 MQTLs distributed on wheat chromosomes. This consolidation of MQTLs resulted in a three-fold reduction in the confidence interval (CI) compared with the CI for the initial QTLs. Projection of QTLs revealed that the majority of QTLs and MQTLs were in the non-telomeric regions of chromosomes. The majority of micronutrient MQTLs were located on the A and D genomes. The QTLs of thousand kernel weight (TKW) were frequently associated with QTLs for GY and grain protein content (GPC) with co-localization occurring at 55 and 63%, respectively. The co- localization of QTLs for GY and grain Fe was found to be 52% and for QTLs of grain Fe and Zn, it was found to be 66%. The genomic collinearity within Poaceae allowed us to identify 16 orthologous MQTLs (OrMQTLs) in wheat, rice, and maize. Annotation of promising candidate genes (CGs) located in the genomic intervals of the stable MQTLs indicated that several CGs (e.g., TraesCS2A02G141400, TraesCS3B02G040900, TraesCS4D02G323700, TraesCS3B02G077100, and TraesCS4D02G290900) had effects on micronutrients contents, yield, and yield-related traits. The mapping refinements leading to the identification of these CGs provide an opportunity to understand the genetic mechanisms driving quantitative variation for these traits and apply this information for crop improvement programs.