QTL mapping for quality traits using a high-density genetic map of wheat

Pleiotropic Quantitative Trait Loci (QTL) Mining for Regulating Wheat Processing Quality- and Yield-Related Traits

To investigate the genetic basis of processing quality- and yield-related traits in bread wheat (Triticum aestivum L., AABBDD), a systematic analysis of wheat processing quality- and yield-related traits based on genome-wide association studies (GWASs) of 285 regional test lines of wheat from Hebei province, China, was conducted. A total of 87 quantitative trait loci (QTL), including twenty-one for water absorption (WA), four for wet gluten content, eight for grain protein content, seventeen for dough stability time (DST), thirteen for extension area (EA), twelve for maximum resistance (MR), five for thousand-grain weight (TGW), one for grain length, and six for grain width were identified. These QTL harbored 188 significant single-nucleotide polymorphisms (SNPs). Twenty-five SNPs were simultaneously associated with multiple traits. Notably, the SNP AX-111015470 on chromosome 1A was associated with DST, EA, and MR. SNPs AX-111917292 and AX-109124553 on chromosome 5D were associated with wheat WA and TGW. Most processing quality-related QTL and seven grain yield-related QTL identified in this study were newly discovered. Among the surveyed accessions, 18 rare superior alleles were identified. This study identified significant QTL associated with quality-related and yield-related traits in wheat, and some of them showed pleiotropic effects. This study will facilitate molecular designs that seek to achieve synergistic improvements of wheat quality and yield.

A Genome-Wide Association Study Approach to Identify Novel Major-Effect Quantitative Trait Loci for End-Use Quality Traits in Soft Red Winter Wheat

Wheat is used for making many food products due to its diverse quality profile among different wheat classes. Since laboratory analysis of these end-use quality traits is costly and time-consuming, genetic dissection of the traits is preferential. This study used a genome-wide association study (GWAS) of ten end-use quality traits, including kernel protein, flour protein, flour yield, softness equivalence, solvent's retention capacity, cookie diameter, and top-grain, in soft red winter wheat (SRWW) adapted to US southeast. The GWAS included 266 SRWW genotypes that were evaluated in two locations over two years (2020-2022). A total of 27,466 single nucleotide markers were used, and a total of 80 significant marker-trait associations were identified. There were 13 major-effect quantitative trait loci (QTLs) explaining >10% phenotypic variance, out of which, 12 were considered to be novel. Five of the major-effect QTLs were found to be stably expressed across multiple datasets, and four showed associations with multiple traits. Candidate genes were identified for eight of the major-effect QTLs, including genes associated with starch biosynthesis and nutritional homeostasis in plants. These findings increase genetic comprehension of these end-use quality traits and could potentially be used for improving the quality of SRWW.

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.

Dissection of a novel major stable QTL on chromosome 7D for grain hardness and its breeding value estimation in bread wheat

Grain hardness (Gh) is important for wheat processing and end-product quality. Puroindolines polymorphism explains over 60% of Gh variation and the novel genetic factors remain to be exploited. In this study, a total of 153 quantitative trait loci (QTLs), clustered into 12 genomic intervals (C1-C12), for 13 quality-related traits were identified using a recombinant inbred line population derived from the cross of Zhongkemai138 (ZKM138) and Chuanmai44 (CM44). Among them, C7 (harboring eight QTLs for different quality-related traits) and C8 (mainly harboring QGh.cib-5D.1 for Gh) were attributed to the famous genes, Rht-D1 and Pina, respectively, indicating that the correlation of involved traits was supported by the pleotropic or linked genes. Notably, a novel major stable QTL for Gh was detected in the C12, QGh.cib-7D, with ZKM138-derived allele increasing grain hardness, which was simultaneously mapped by the BSE-Seq method. The geographic pattern and transmissibility of this locus revealed that the increasing-Gh allele is highly frequently present in 85.79% of 373 worldwide wheat varieties and presented 99.31% transmissibility in 144 ZKM138-derivatives, indicating the non-negative effect on yield performance and that its indirect passive selection has happened during the actual breeding process. Thus, the contribution of this new Gh-related locus was highlighted in consideration of improving the efficiency and accuracy of the soft/hard material selection in the molecular marker-assisted process. Further, TraesCS7D02G099400, TraesCS7D02G098000, and TraesCS7D02G099500 were initially deduced to be the most potential candidate genes of QGh.cib-7D. Collectively, this study provided valuable information of elucidating the genetic architecture of Gh for wheat quality improvement.

Current Progress and Future Prospect of Wheat Genetics Research towards an Enhanced Nitrogen Use Efficiency

To improve the yield and quality of wheat is of great importance for food security worldwide. One of the most effective and significant approaches to achieve this goal is to enhance the nitrogen use efficiency (NUE) in wheat. In this review, a comprehensive understanding of the factors involved in the process of the wheat nitrogen uptake, assimilation and remobilization of nitrogen in wheat were introduced. An appropriate definition of NUE is vital prior to its precise evaluation for the following gene identification and breeding process. Apart from grain yield (GY) and grain protein content (GPC), the commonly recognized major indicators of NUE, grain protein deviation (GPD) could also be considered as a potential trait for NUE evaluation. As a complex quantitative trait, NUE is affected by transporter proteins, kinases, transcription factors (TFs) and micro RNAs (miRNAs), which participate in the nitrogen uptake process, as well as key enzymes, circadian regulators, cross-talks between carbon metabolism, which are associated with nitrogen assimilation and remobilization. A series of quantitative genetic loci (QTLs) and linking markers were compiled in the hope to help discover more efficient and useful genetic resources for breeding program. For future NUE improvement, an exploration for other criteria during selection process that incorporates morphological, physiological and biochemical traits is needed. Applying new technologies from phenomics will allow high-throughput NUE phenotyping and accelerate the breeding process. A combination of multi-omics techniques and the previously verified QTLs and molecular markers will facilitate the NUE QTL-mapping and novel gene identification.

Genome-Wide Association Study for Grain Protein, Thousand Kernel Weight, and Normalized Difference Vegetation Index in Bread Wheat (Triticum aestivum L.)

Genomic regions governing grain protein content (GPC), 1000 kernel weight (TKW), and normalized difference vegetation index (NDVI) were studied in a set of 280 bread wheat genotypes. The genome-wide association (GWAS) panel was genotyped using a 35K Axiom array and phenotyped in three environments. A total of 26 marker-trait associations (MTAs) were detected on 18 chromosomes covering the A, B, and D subgenomes of bread wheat. The GPC showed the maximum MTAs (16), followed by NDVI (6), and TKW (4). A maximum of 10 MTAs was located on the B subgenome, whereas, 8 MTAs each were mapped on the A and D subgenomes. In silico analysis suggest that the SNPs were located on important putative candidate genes such as NAC domain superfamily, zinc finger RING-H2-type, aspartic peptidase domain, folylpolyglutamate synthase, serine/threonine-protein kinase LRK10, pentatricopeptide repeat, protein kinase-like domain superfamily, cytochrome P450, and expansin. These candidate genes were found to have different roles including regulation of stress tolerance, nutrient remobilization, protein accumulation, nitrogen utilization, photosynthesis, grain filling, mitochondrial function, and kernel development. The effects of newly identified MTAs will be validated in different genetic backgrounds for further utilization in marker-aided breeding.

5M approach to decipher starch-lipid interaction in minor millets

The 5M approach can be applied to understand genetic complexity underlying nutritional traits of minor millets. It will help to systematically identify genomic regions/candidate genes imprinting metabolite profiles.

Molecular Mapping of Biofortification Traits in Bread Wheat (Triticum aestivum L.) Using a High-Density SNP Based Linkage Map

A set of 188 recombinant inbred lines (RILs) derived from a cross between a high-yielding Indian bread wheat cultivar HD2932 and a synthetic hexaploid wheat (SHW) Synthetic 46 derived from tetraploid Triticum turgidum (AA, BB 2n = 28) and diploid Triticum tauschii (DD, 2n = 14) was used to identify novel genomic regions associated in the expression of grain iron concentration (GFeC), grain zinc concentration (GZnC), grain protein content (GPC) and thousand kernel weight (TKW). The RIL population was genotyped using SNPs from 35K Axiom® Wheat Breeder's Array and 34 SSRs and phenotyped in two environments. A total of nine QTLs including five for GPC (QGpc.iari_1B, QGpc.iari_4A, QGpc.iari_4B, QGpc.iari_5D, and QGpc.iari_6B), two for GFeC (QGfec.iari_5B and QGfec.iari_6B), and one each for GZnC (QGznc.iari_7A) and TKW (QTkw.iari_4B) were identified. A total of two stable and co-localized QTLs (QGpc.iari_4B and QTkw.iari_4B) were identified on the 4B chromosome between the flanking region of Xgwm149-AX-94559916. In silico analysis revealed that the key putative candidate genes such as P-loop containing nucleoside triphosphatehydrolase, Nodulin-like protein, NAC domain, Purine permease, Zinc-binding ribosomal protein, Cytochrome P450, Protein phosphatase 2A, Zinc finger CCCH-type, and Kinesin motor domain were located within the identified QTL regions and these putative genes are involved in the regulation of iron homeostasis, zinc transportation, Fe, Zn, and protein remobilization to the developing grain, regulation of grain size and shape, and increased nitrogen use efficiency. The identified novel QTLs, particularly stable and co-localized QTLs are useful for subsequent use in marker-assisted selection (MAS).

Pinb-D1p is an elite allele for improving end-use quality in wheat (Triticum aestivum L.)

We identified ten QTLs controlling SDS-SV trait in a RIL population derived from ND3331 and Zang1817. Pinb-D1p is an elite allele from Tibetan semi‑wild wheat for good end-use quality. Gluten strength is an important factor for wheat processing and end-product quality and is commonly characterized using the sodium dodecyl sulfate-sedimentation volume (SDS-SV) test. The objective of this study was to identify quantitative trait loci (QTLs) associated with wheat SDS-SV traits using a recombinant inbred line (RIL) population derived from common wheat line NongDa3331 (ND3331) and Tibetan semi-wild wheat accession Zang1817. We detected 10 QTLs controlling SDS-SV on chromosomes 1A, 1B, 3A, 4A, 4B, 5A, 5D, 6B and 7A, with individual QTLs explaining 2.02% to 15.53% of the phenotypic variation. They included four major QTLs, Qsdss-1A, Qsdss-1B.1, Qsdss-1B.2, and Qsdss-5D, whose effects on SDS-SV were due to the Glu-A1 locus encoding the high-molecular-weight glutenin subunit 1Ax1, the 1B/1R translocation, 1Bx7 + 1By8 at the Glu-B1 locus, and the hardness-controlling loci Pina-D1 and Pinb-D1, respectively. We developed KASP markers for the Glu-A1, Glu-B1, and Pinb-D1 loci. Importantly, we showed for the first time that the hardness allele Pinb-D1p positively affects SDS-SV, making it a good candidate for wheat quality improvement. These results broaden our understanding of the genetic characterization of SDS-SV, and the QTLs identified are potential target regions for fine-mapping and marker-assisted selection in wheat breeding programs.

Construction of ultra-high-density genetic linkage map of a sorghum-sudangrass hybrid using whole genome resequencing

The sorghum-sudangrass hybrid is a vital annual gramineous herbage. Few reports exist on its ultra-high-density genetic map. In this study, we sought to create an ultra-high-density genetic linkage map for this hybrid to strengthen its functional genomics research and genetic breeding. We used 150 sorghum-sudangrass hybrid F2 individuals and their parents (scattered ear sorghum and red hull sudangrass) for high-throughput sequencing on the basis of whole genome resequencing. In total, 1,180.66 Gb of data were collected. After identification, filtration for integrity, and partial segregation, over 5,656 single nucleotide polymorphism markers of high quality were detected. An ultra-high-density genetic linkage map was constructed using these data. The markers covered approximately 2,192.84 cM of the map with average marker intervals of 0.39 cM. The length ranged from 115.39 cM to 264.04 cM for the 10 linkage groups. Currently, this represents the first genetic linkage map of this size, number of molecular markers, density, and coverage for sorghum-sudangrass hybrid. The findings of this study provide valuable genome-level information on species evolution and comparative genomics analysis and lay the foundation for further research on quantitative trait loci fine mapping and gene cloning and marker-assisted breeding of important traits in sorghum-sudangrass hybrids.

Construction of a novel Wheat 55 K SNP array-derived genetic map and its utilization in QTL mapping for grain yield and quality related traits

Wheat is one of the most important staple crops for supplying nutrition and energy to people world. A new genetic map based on the Wheat 55 K SNP array was constructed using recombinant inbred lines derived from a cross between Zhongkemai138 and Kechengmai2 to explore the genetic foundation for wheat grain features. This new map covered 2,155.72 cM across the 21 wheat chromosomes with 11,455 markers. And 2,846 specific markers for this genetic map and 148 coincident markers among different maps were documented, which was helpful for improving and updating wheat genetic and genomic information. Using this map, a total of 68 additive QTLs and 82 pairs of epistatic QTLs were detected for grain features including yield, nutrient composition, and quality-related traits by QTLNetwork 2.1 and IciMapping 4.1 software. Fourteen additive QTLs and one pair of epistatic QTLs could be detected by both software programs and thus regarded as stable QTLs here, all of which explained higher phenotypic variance and thus could be utilized for wheat grain improvement. Additionally, thirteen additive QTLs were clustered into three genomic intervals (C4D.2, C5D, and C6D2), each of which had at least two stable QTLs. Among them, C4D.2 and C5D have been attributed to the famous dwarfing gene Rht2 and the hardness locus Pina, respectively, while endowed with main effects on eight grain yield/quality related traits and epistatically interacted with each other to control moisture content, indicating that the correlation of involved traits was supported by the pleotropic of individual genes but also regulated by the gene interaction networks. Additionally, the stable additive effect of C6D2 (QMc.cib-6D2 and QTw.cib-6D2) on moisture content was also highlighted, potentially affected by a novel locus, and validated by its flanking Kompetitive Allele-Specific PCR marker, and TraesCS6D02G109500, encoding aleurone layer morphogenesis protein, was deduced to be one of the candidate genes for this locus. This result observed at the QTL level the possible contribution of grain water content to the balances among yield, nutrients, and quality properties and reported a possible new locus controlling grain moisture content as well as its linked molecular marker for further grain feature improvement.

QTL Analysis for Bread Wheat Seed Size, Shape and Color Characteristics Estimated by Digital Image Processing

The size, shape, and color of wheat seeds are important traits that are associated with yield and flour quality (size, shape), nutritional value, and pre-harvest sprouting (coat color). These traits are under multigenic control, and to dissect their molecular and genetic basis, quantitative trait loci (QTL) analysis is used. We evaluated 114 recombinant inbred lines (RILs) in a bi-parental RIL mapping population (the International Triticeae Mapping Initiative, ITMI/MP) grown in 2014 season. We used digital image analysis for seed phenotyping and obtained data for seven traits describing seed size and shape and 48 traits of seed coat color. We identified 212 additive and 34 pairs of epistatic QTLs on all the chromosomes of wheat genome except chromosomes 1A and 5D. Many QTLs were overlapping. We demonstrated that the overlap between QTL regions was low for seed size/shape traits and high for coat color traits. Using the literature and KEGG data, we identified sets of genes in Arabidopsis and rice from the networks controlling seed size and color. Further, we identified 29 and 14 candidate genes for seed size-related loci and for loci associated with seed coat color, respectively.

Capturing Wheat Phenotypes at the Genome Level

Recent technological advances in next-generation sequencing (NGS) technologies have dramatically reduced the cost of DNA sequencing, allowing species with large and complex genomes to be sequenced. Although bread wheat (Triticum aestivum L.) is one of the world's most important food crops, efficient exploitation of molecular marker-assisted breeding approaches has lagged behind that achieved in other crop species, due to its large polyploid genome. However, an international public-private effort spanning 9 years reported over 65% draft genome of bread wheat in 2014, and finally, after more than a decade culminated in the release of a gold-standard, fully annotated reference wheat-genome assembly in 2018. Shortly thereafter, in 2020, the genome of assemblies of additional 15 global wheat accessions was released. As a result, wheat has now entered into the pan-genomic era, where basic resources can be efficiently exploited. Wheat genotyping with a few hundred markers has been replaced by genotyping arrays, capable of characterizing hundreds of wheat lines, using thousands of markers, providing fast, relatively inexpensive, and reliable data for exploitation in wheat breeding. These advances have opened up new opportunities for marker-assisted selection (MAS) and genomic selection (GS) in wheat. Herein, we review the advances and perspectives in wheat genetics and genomics, with a focus on key traits, including grain yield, yield-related traits, end-use quality, and resistance to biotic and abiotic stresses. We also focus on reported candidate genes cloned and linked to traits of interest. Furthermore, we report on the improvement in the aforementioned quantitative traits, through the use of (i) clustered regularly interspaced short-palindromic repeats/CRISPR-associated protein 9 (CRISPR/Cas9)-mediated gene-editing and (ii) positional cloning methods, and of genomic selection. Finally, we examine the utilization of genomics for the next-generation wheat breeding, providing a practical example of using in silico bioinformatics tools that are based on the wheat reference-genome sequence.

Unravelling consensus genomic regions associated with quality traits in wheat using meta-analysis of quantitative trait loci

Meta-analysis in wheat for three major quality traits identified 110 meta-QTL (MQTL) with reduced confidence interval (CI). Five GWAS validated MQTL (viz., 1A.1, 1B.2, 3B.4, 5B.2, and 6B.2), each involving more than 20 initial QTL and reduced CI (95%) (< 2 cM), were selected for quality breeding programmes. Functional characterization including candidate gene mining and expression analysis discovered 44 high confidence candidate genes associated with quality traits. A meta-analysis of quantitative trait loci (QTL) associated with dough rheology properties, nutritional traits, and processing quality traits was conducted in wheat. For this purpose, as many as 2458 QTL were collected from 50 interval mapping studies published during 2013-2020. Of the total QTL, 1126 QTL were projected onto the consensus map saturated with 249,603 markers which led to the identification of 110 meta-QTL (MQTL). These MQTL exhibited an 18.84-fold reduction in the average CI compared to the average CI of the initial QTL (ranging from 14.87 to 95.55 cM with an average of 40.35 cM). Of the 110, 108 MQTL were physically anchored to the wheat reference genome, including 51 MQTL verified with marker-trait associations (MTAs) reported from earlier genome-wide association studies. Candidate gene (CG) mining allowed the identification of 2533 unique gene models from the MQTL regions. In-silico expression analysis discovered 439 differentially expressed gene models with > 2 transcripts per million expressions in grains and related tissues, which also included 44 high-confidence CGs involved in the various cellular and biochemical processes related to quality traits. Nine functionally characterized wheat genes associated with grain protein content, high-molecular-weight glutenin, and starch synthase enzymes were also found to be co-localized with some of the MQTL. Synteny analysis between wheat and rice MQTL regions identified 23 wheat MQTL syntenic to 16 rice MQTL associated with quality traits. Furthermore, 64 wheat orthologues of 30 known rice genes were detected in 44 MQTL regions. Markers flanking the MQTL identified in the present study can be used for marker-assisted breeding and as fixed effects in the genomic selection models for improving the prediction accuracy during quality breeding. Wheat orthologues of rice genes and other CGs available from MQTLs can be promising targets for further functional validation and to better understand the molecular mechanism underlying the quality traits in wheat.

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.

CRISPR/Cas9-Mediated Disruption of Xylanase inhibitor protein (XIP) Gene Improved the Dough Quality of Common Wheat

The wheat dough quality is of great significance for the end-use of flour. Some genes have been cloned for controlling the protein fractions, grain protein content, starch synthase, grain hardness, etc. Using a unigene map of the recombinant inbred lines (RILs) for "TN 18 × LM 6," we mapped a quantitative trait locus (QTL) for dough stability time (ST) and SDS-sedimentation values (SV) on chromosome 6A (QSt/Sv-6A-2851). The peak position of the QTL covered two candidate unigenes, and we speculated that TraesCS6A02G077000 (a xylanase inhibitor protein) was the primary candidate gene (named the TaXip gene). The target loci containing the three homologous genes TaXip-6A, TaXip-6B, and TaXip-6D were edited in the variety "Fielder" by clustered regularly interspaced short palindromic repeats-associated protein 9 (CRISPR/Cas9). Two mutant types in the T_2:3 generation were obtained (aaBBDD and AAbbdd) with about 120 plants per type. The SVs of aaBBDD, AAbbdd, and WT were 31.77, 27.30, and 20.08 ml, respectively. The SVs of the aaBBDD and AAbbdd were all significantly higher than those of the wild type (WT), and the aaBBDD was significantly higher than the AAbbdd. The STs of aaBBDD, AAbbdd, and WT were 2.60, 2.24, and 2.25 min, respectively. The ST for the aaBBDD was significantly higher than that for WT and was not significantly different between WT and AAbbdd. The above results indicated that XIP in vivo can significantly affect wheat dough quality. The selection of TaXip gene should be a new strategy for developing high-quality varieties in wheat breeding programs.

Genome-wide QTL mapping of yield and agronomic traits in two widely adapted winter wheat cultivars from multiple mega-environments

Quantitative trait loci (QTL) analysis could help to identify suitable molecular markers for marker-assisted breeding (MAB). A mapping population of 124 F_5:7recombinant inbred lines derived from the cross ‘TAM 112’/‘TAM 111’ was grown under 28 diverse environments and evaluated for grain yield, test weight, heading date, and plant height. The objective of this study was to detect QTL conferring grain yield and agronomic traits from multiple mega-environments. Through a linkage map with 5,948 single nucleotide polymorphisms (SNPs), 51 QTL were consistently identified in two or more environments or analyses. Ten QTL linked to two or more traits were also identified on chromosomes 1A, 1D, 4B, 4D, 6A, 7B, and 7D. Those QTL explained up to 13.3% of additive phenotypic variations with the additive logarithm of odds (LOD(A)) scores up to 11.2. The additive effect increased yield up to 8.16 and 6.57 g m⁻² and increased test weight by 2.14 and 3.47 kg m⁻³ with favorable alleles from TAM 111 and TAM 112, respectively. Seven major QTL for yield and six for TW with one in common were of our interest on MAB as they explained 5% or more phenotypic variations through additive effects. This study confirmed previously identified loci and identified new QTL and the favorable alleles for improving grain yield and agronomic traits.

Identification and Validation of Stable Quantitative Trait Loci for SDS-Sedimentation Volume in Common Wheat (Triticum aestivum L.)

Sodium dodecyl sulfate-sedimentation volume is an important index to evaluate the gluten strength of common wheat and is closely related to baking quality. In this study, a total of 15 quantitative trait locus (QTL) for sodium dodecyl sulfate (SDS)-sedimentation volume (SSV) were identified by using a high-density genetic map including 2,474 single-nucleotide polymorphism (SNP) markers, which was constructed with a doubled haploid (DH) population derived from the cross between Non-gda3753 (ND3753) and Liangxing99 (LX99). Importantly, four environmentally stable QTLs were detected on chromosomes 1A, 2D, and 5D, respectively. Among them, the one with the largest effect was identified on chromosome 1A (designated as QSsv.cau-1A.1) explaining up to 39.67% of the phenotypic variance. Subsequently, QSsv.cau-1A.1 was dissected into two QTLs named as QSsv.cau-1A.1.1 and QSsv.cau-1A.1.2 by saturating the genetic linkage map of the chromosome 1A. Interestedly, favorable alleles of these two loci were from different parents. Due to the favorable allele of QSsv.cau-1A.1.1 was from the high-value parents ND3753 and revealed higher genetic effect, which explained 25.07% of the phenotypic variation, mapping of this locus was conducted by using BC3F1 and BC3F2 populations. By comparing the CS reference sequence, the physical interval of QSsv.cau-1A.1.1 was delimited into 14.9 Mb, with 89 putative high-confidence annotated genes. SSVs of different recombinants between QSsv.cau-1A.1.1 and QSsv.cau-1A.1 detected from DH and BC3F2 populations showed that these two loci had an obvious additive effect, of which the combination of two favorable loci had the high SSV, whereas recombinants with unfavorable loci had the lowest. These results provide further insight into the genetic basis of SSV and QSsv.cau-1A.1.1 will be an ideal target for positional cloning and wheat breeding programs.

Identification of Novel Genomic Regions for Biofortification Traits Using an SNP Marker-Enriched Linkage Map in Wheat (Triticum aestivum L.)

Micronutrient and protein malnutrition is recognized among the major global health issues. Genetic biofortification is a cost-effective and sustainable strategy to tackle malnutrition. Genomic regions governing grain iron concentration (GFeC), grain zinc concentration (GZnC), grain protein content (GPC), and thousand kernel weight (TKW) were investigated in a set of 163 recombinant inbred lines (RILs) derived from a cross between cultivated wheat variety WH542 and a synthetic derivative (Triticum dicoccon PI94624/Aegilops tauschii [409]//BCN). The RIL population was genotyped using 100 simple-sequence repeat (SSR) and 736 single nucleotide polymorphism (SNP) markers and phenotyped in six environments. The constructed genetic map had a total genetic length of 7,057 cM. A total of 21 novel quantitative trait loci (QTL) were identified in 13 chromosomes representing all three genomes of wheat. The trait-wise highest number of QTL was identified for GPC (10 QTL), followed by GZnC (six QTL), GFeC (three QTL), and TKW (two QTL). Four novel stable QTL (QGFe.iari-7D.1, QGFe.iari-7D.2, QGPC.iari-7D.2, and QTkw.iari-7D) were identified in two or more environments. Two novel pleiotropic genomic regions falling between Xgwm350-AX-94958668 and Xwmc550-Xgwm350 in chromosome 7D harboring co-localized QTL governing two or more traits were also identified. The identified novel QTL, particularly stable and co-localized QTL, will be validated to estimate their effects on different genetic backgrounds for subsequent use in marker-assisted selection (MAS). Best QTL combinations were identified by the estimation of additive effects of the stable QTL for GFeC, GZnC, and GPC. A total of 11 RILs (eight for GZnC and three for GPC) having favorable QTL combinations identified in this study can be used as potential donors to develop bread wheat varieties with enhanced micronutrients and protein.

Candidate Genes and Quantitative Trait Loci for Grain Yield and Seed Size in Durum Wheat

Grain yield (YLD) is affected by thousand kernel weight (TKW) which reflects the combination of grain length (GL), grain width (GW) and grain area (AREA). Grain weight is also influenced by heading time (HT) and plant height (PH). To detect candidate genes and quantitative trait loci (QTL) of yield components, a durum wheat recombinant inbred line (RIL) population was evaluated in three field trials. The RIL was genotyped with a 90K single nucleotide polymorphism (SNP) array and a high-density genetic linkage map with 5134 markers was obtained. A total of 30 QTL were detected including 23 QTL grouped in clusters on 1B, 2A, 3A, 4B and 6B chromosomes. A QTL cluster on 2A chromosome included a major QTL for HT co-located with QTL for YLD, TKW, GL, GW and AREA, respectively. The photoperiod sensitivity (Ppd-A1) gene was found in the physical position of this cluster. Serine carboxypeptidase, Big grain 1 and β-fructofuranosidase candidate genes were mapped in clusters containing QTL for seed size. This study showed that yield components and phenological traits had higher inheritances than grain yield, allowing an accurate QTL cluster detection. This was a requisite to physically map QTL on durum genome and to identify candidate genes affecting grain yield.

Genetic Basis of Gluten Aggregation Properties in Wheat (Triticum aestivum L.) Dissected by QTL Mapping of GlutoPeak Parameters

Bread wheat is one of the most important crops worldwide, supplying approximately one-fifth of the daily protein and the calories for human consumption. Gluten aggregation properties play important roles in determining the processing quality of wheat (Triticum aestivum L.) products. Nevertheless, the genetic basis of gluten aggregation properties has not been reported so far. In this study, a recombinant inbred line (RIL) population derived from the cross between Luozhen No. 1 and Zhengyumai 9987 was used to identify quantitative trait loci (QTL) underlying gluten aggregation properties with GlutoPeak parameters. A linkage map was constructed based on 8,518 SNPs genotyped by specific length amplified fragment sequencing (SLAF-seq). A total of 33 additive QTLs on 14 chromosomes were detected by genome-wide composite interval mapping (GCIM), four of which accounted for more than 10% of the phenotypic variation across three environments. Two major QTL clusters were identified on chromosomes 1DS and 1DL. A premature termination of codon (PTC) mutation in the candidate gene (TraesCS1D02G009900) of the QTL cluster on 1DS was detected between Luozhen No. 1 and Zhengyumai 9987, which might be responsible for the difference in gluten aggregation properties between the two varieties. Subsequently, two KASP markers were designed based on SNPs in stringent linkage with the two major QTL clusters. Results of this study provide new insights into the genetic architecture of gluten aggregation properties in wheat, which are helpful for future improvement of the processing quality in wheat breeding.

Enhancing the Nutritional Quality of Major Food Crops Through Conventional and Genomics-Assisted Breeding

Nutritional stress is making over two billion world population malnourished. Either our commercially cultivated varieties of cereals, pulses, and oilseed crops are deficient in essential nutrients or the soils in which these crops grow are becoming devoid of minerals. Unfortunately, our major food crops are poor sources of micronutrients required for normal human growth. To overcome the problem of nutritional deficiency, greater emphasis should be laid on the identification of genes/quantitative trait loci (QTLs) pertaining to essential nutrients and their successful deployment in elite breeding lines through marker-assisted breeding. The manuscript deals with information on identified QTLs for protein content, vitamins, macronutrients, micro-nutrients, minerals, oil content, and essential amino acids in major food crops. These QTLs can be utilized in the development of nutrient-rich crop varieties. Genome editing technologies that can rapidly modify genomes in a precise way and will directly enrich the nutritional status of elite varieties could hold a bright future to address the challenge of malnutrition.

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

Numerous quantitative trait loci (QTLs) have been identified for wheat quality; however, most are confined to low-density genetic maps. In this study, based on specific-locus amplified fragment sequencing (SLAF-seq), a high-density genetic map was constructed with 193 recombinant inbred lines derived from Chuanmai 42 and Chuanmai 39. In total, 30 QTLs with phenotypic variance explained (PVE) up to 47.99% were identified for falling number (FN), grain protein content (GPC), grain hardness (GH), and starch pasting properties across three environments. Five NAM genes closely adjacent to QGPC.cib-4A probably have effects on GPC. QGH.cib-5D was the only one detected for GH with high PVE of 33.31-47.99% across the three environments and was assumed to be related to the nearest pina-D1 and pinb-D1genes. Three QTLs were identified for FN in at least two environments, of which QFN.cib-3D had relatively higher PVE of 16.58-25.74%. The positive effect of QFN.cib-3D for high FN was verified in a double-haploid population derived from Chuanmai 42 × Kechengmai 4. The combination of these QTLs has a considerable effect on increasing FN. The transcript levels of Basic 7S globulin and Basic 7S globulin 2 in QFN.cib-3D were significantly different between low FN and high FN bulks, as observed through bulk segregant RNA-seq (BSR). These QTLs and candidate genes based on the high-density genetic map would be beneficial for further understanding of the genetic mechanism of quality traits and molecular breeding of wheat.