Analysis of genetic control and QTL mapping of essential wheat grain quality traits in a recombinant inbred population

TaSED interacts with TaSPA synergistically regulating SDS-sedimentation volume in bread wheat

The SDS-sedimentation volume (SSV) is a critical indicator for assessing wheat gluten quality and is widely used when evaluating wheat processing quality. However, the molecular mechanisms regulating SSV remain poorly understood. In this study, we performed an analysis of quantitative trait loci (QTLs) for SSV using a recombinant inbred line (RIL) population derived from a cross between TAA10 and XX329, and identified four environmentally stable QTLs located on chromosomes 1D, 2D, 4D, and 6D. Among them, the effects of Qssv.cau-1D and Qssv.cau-6D were likely to be explained by genome variations at the Glu-D1 and Gli-D2 loci. We fine mapped Qssv.cau-2D to the candidate causal gene TaSED, encoding a nucleolar protein. Gene-edited TaSED knockout mutants (tased) had a lower SSV, while TaSED overexpression lines showed a higher SSV. We demonstrated that TaSED interacted with the transcription factor TaSPA to enhance its transcriptional activation activity of glutenin and gliadin, whose expression was downregulated in tased and upregulated in TaSED-OE plants, with corresponding differences in glutenin and gliadin content compared with the wild-type. A molecular marker sedTX was further developed based on a nonsynonymous mutation of the parents in TaSED that could be used to identify haplotypes with high SSV effectively. Our findings elucidate a molecular mechanism governing SSV and reveal valuable variants with promising applications for improving wheat quality.

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.

Genome-wide association analysis and KASP markers development for protein quality traits in winter wheat

BACKGROUND

Wheat (Triticum aestivum L.) is a significant cereal crop that plays a vital role in global food production. To expedite the breeding of wheat cultivars with high protein quality, it is necessary to genetically analyze the traits related to quality. A genome-wide association study (GWAS) was conducted to identify the genomic regions responsible for protein quality traits in winter wheat.

RESULTS

Six protein quality traits were evaluated across two locations and two years for a total of 341 wheat accessions. Utilizing the wheat 40 K SNP array, GWAS identified 97 significantly stable SNPs at 43 loci for five out of six protein quality traits using a linear mixed model. The 43 loci distribution was four for grain protein content, two for flour protein content, one for wet gluten content, four for gluten index, and thirty-two for Zeleny sedimentation value. The most significant associations were identified on chromosomes 1 A, 1B, and 1D. Haplotype analysis of loci associated with the gluten index in the 412-416 Mb interval on chromosome 1D identified three blocks. Accessions with superior haplotypes showed a significantly higher gluten index than those with inferior haplotypes. Six KASP markers were successfully developed for the gluten index, while five KASP markers were developed for the Zeleny sedimentation value. Additionally, eight candidate genes were identified that may affect protein accumulation during grain development.

CONCLUSIONS

Our study identified 97 SNPs significantly associated with protein quality traits; developed 6 KASP markers for gluten index, and 5 KASP markers for Zeleny sedimentation values; screened 8 candidate genes that may be related to protein quality during grain development. Thise research will offer valuable insights for wheat breeding programs in China and globally.

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.

Elucidation of the genetic architecture of water absorption capacity in hard winter wheat through genome wide association study

Water absorption capacity (WAC) influences various aspects of bread making, such as loaf volume, bread yield, and shelf life. Despite its importance in the baking process and end-product quality, its genetic determinants are less explored. To address this limitation, a genome-wide association study was conducted on 337 hard wheat (Triticum aestivum L.) genotypes evaluated over 5 years in multi-environmental trials. Phenotyping was done using the solvent retention capacity (SRC) test with water (SRC-water), sucrose (SRC-sucrose), lactic acid (SRC-lactic acid), and sodium carbonate (SRC-carbonate) as solvents. Individuals were genotyped using genotyping-by-sequencing to detect single nucleotide polymorphisms across the wheat genome. To detect the genomic regions that underline the SRCs and gluten performance index (GPI), a genome-wide association study was performed using six multi-locus models using the mrMLM package in R. Adjusted means for SRC-water ranged from 54.1% to 66.5%, while SRC-carbonate exhibited a narrow range from 84.9% to 93.9%. Moderate to high genomic heritability values were observed for SRCs and GPI, ranging from h2 = 0.61 to 0.88. The genome-wide association study identified a total of 42 quantitative trait nucleotides (QTNs), of which five explained over 10% of the phenotypic variation (R2 ≥ 10%). Most of the QTNs were detected on chromosomes 1A, 1B, 3B, and 5B. Few QTNs, such as S1A_5190318, S1B_3282665, S4D_472908721, and S7A_37433960, were located near gliadin, glutenin starch synthesis, and galactosyltransferase genes. Overall, these results show WAC to be under polygenic genetic control, with genes involved in the synthesis of key flour components influencing overall water absorption.

Genetic dissection of value-added quality traits and agronomic parameters through genome-wide association mapping in bread wheat (T. aestivum L.)

Bread wheat (T. aestivum) is one of the world's most widely consumed cereals. Since micronutrient deficiencies are becoming more common among people who primarily depend upon cereal-based diets, a need for better-quality wheat varieties has been felt. An association panel of 154 T. aestivum lines was evaluated for the following quality traits: grain appearance (GA) score, grain hardness (GH), phenol reaction (PR) score, protein percent, sodium dodecyl sulfate (SDS) sedimentation value, and test weight (TWt). In addition, the panel was also phenotyped for grain yield and related traits such as days to heading, days to maturity, plant height, and thousand kernel weight for the year 2017-18 at the Borlaug Institute for South Asia (BISA) Ludhiana and Jabalpur sites. We performed a genome-wide association analysis on this panel using 18,351 genotyping-by-sequencing (GBS) markers to find marker-trait associations for quality and grain yield-related traits. We detected 55 single nucleotide polymorphism (SNP) marker trait associations (MTAs) for quality-related traits on chromosomes 7B (10), 1A (9), 2A (8), 3B (6), 2B (5), 7A (4), and 1B (3), with 3A, 4A, and 6D, having two and the rest, 4B, 5A, 5B, and 1D, having one each. Additionally, 20 SNP MTAs were detected for yield-related traits based on a field experiment conducted in Ludhiana on 7D (4) and 4D (3) chromosomes, while 44 SNP MTAs were reported for Jabalpur on chromosomes 2D (6), 7A (5), 2A (4), and 4A (4). Utilizing these loci in marker-assisted selection will benefit from further validation studies for these loci to improve hexaploid wheat for better yield and grain quality.

QTL mapping: insights into genomic regions governing component traits of yield under combined heat and drought stress in wheat

Drought and heat frequently co-occur during crop growth leading to devastating yield loss. The knowledge of the genetic loci governing component traits of yield under combined drought and heat stress is essential for enhancing the climate resilience. The present study employed a mapping population of 180 recombinant inbred lines (RILs) derived from a cross between GW322 and KAUZ to identify quantitative trait loci (QTLs) governing the component traits of yield under heat and combined stress conditions. Phenotypic evaluation was conducted across two consecutive crop seasons (2021-2022 and 2022-2023) under late sown irrigation (LSIR) and late sown restricted irrigation (LSRI) conditions at the Indian Council of Agricultural Research Institute-Indian Agricultural Research Institute (ICAR-IARI), New Delhi. Various physiological and agronomic traits of importance were measured. Genotyping was carried out with 35K SNP Axiom breeder's genotyping array. The linkage map spanned a length of 6769.45 cM, ranging from 2.28 cM/marker in 1A to 14.21 cM/marker in 5D. A total of 35 QTLs were identified across 14 chromosomes with 6B containing the highest (seven) number of QTLs. Out of 35 QTLs, 16 were major QTLs explaining the phenotypic variance greater than 10%. The study identified eight stable QTLs along with two hotspots on chromosomes 6B and 5B. Five QTLs associated with traits thousand-grain weight (TGW), normalized difference vegetation index (NDVI), and plant height (PH) were successfully validated. Candidate genes encoding antioxidant enzymes, transcription factors, and growth-related proteins were identified in the QTL regions. In silico expression analysis highlighted higher expression of transcripts TraesCS2D02G021000.1, TraesCS2D02G031000, TraesCS6A02G247900, and TraesCS6B02G421700 under stress conditions. These findings contribute to a deeper understanding of the genetic architecture underlying combined heat and drought tolerance in wheat, providing valuable insights for wheat improvement strategies under changing climatic conditions.

Association mapping reveals novel genes and genomic regions controlling grain size architecture in mini core accessions of Indian National Genebank wheat germplasm collection

Wheat (Triticum aestivum L.) is a staple food crop for the global human population, and thus wheat breeders are consistently working to enhance its yield worldwide. In this study, we utilized a sub-set of Indian wheat mini core germplasm to underpin the genetic architecture for seed shape-associated traits. The wheat mini core subset (125 accessions) was genotyped using 35K SNP array and evaluated for grain shape traits such as grain length (GL), grain width (GW), grain length, width ratio (GLWR), and thousand grain weight (TGW) across the seven different environments (E₁, E₂, E₃, E₄, E₅, E₅, E₆, and E₇). Marker-trait associations were determined using a multi-locus random-SNP-effect Mixed Linear Model (mrMLM) program. A total of 160 non-redundant quantitative trait nucleotides (QTNs) were identified for four grain shape traits using two or more GWAS models. Among these 160 QTNs, 27, 36, 38, and 35 QTNs were associated for GL, GW, GLWR, and TGW respectively while 24 QTNs were associated with more than one trait. Of these 160 QTNs, 73 were detected in two or more environments and were considered reliable QTLs for the respective traits. A total of 135 associated QTNs were annotated and located within the genes, including ABC transporter, Cytochrome450, Thioredoxin_M-type, and hypothetical proteins. Furthermore, the expression pattern of annotated QTNs demonstrated that only 122 were differentially expressed, suggesting these could potentially be related to seed development. The genomic regions/candidate genes for grain size traits identified in the present study represent valuable genomic resources that can potentially be utilized in the markers-assisted breeding programs to develop high-yielding varieties.

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.

Mapping of the QTLs governing grain micronutrients and thousand kernel weight in wheat (Triticum aestivum L.) using high density SNP markers

Biofortification is gaining importance globally to improve human nutrition through enhancing the micronutrient content, such as vitamin A, iron, and zinc, in staple food crops. The present study aims to identify the chromosomal regions governing the grain iron concentration (GFeC), grain zinc concentration (GZnC), and thousand kernel weight (TKW) using recombinant inbred lines (RILs) in wheat, developed from a cross between HD3086 and HI1500. The experiment was conducted in four different production conditions at Delhi viz., control, drought, heat, and combined heat and drought stress and at Indore under drought stress. Grain iron and zinc content increased under heat and combined stress conditions, while thousand kernel weight decreased. Medium to high heritability with a moderate correlation between grain iron and zinc was observed. Out of 4,106 polymorphic markers between the parents, 3,407 SNP markers were used for linkage map construction which spanned over a length of 14791.18 cm. QTL analysis identified a total of 32 chromosomal regions governing the traits under study, which includes 9, 11, and 12 QTLs for GFeC, GZnC, and TKW, respectively. A QTL hotspot was identified on chromosome 4B which is associated with grain iron, grain zinc, and thousand kernel weight explaining the phenotypic variance of 29.28, 10.98, and 17.53%, respectively. Similarly, common loci were identified on chromosomes 4B and 4D for grain iron, zinc, and thousand kernel weight. In silico analysis of these chromosomal regions identified putative candidate genes that code for proteins such as Inositol 1,3,4-trisphosphate 5/6-kinase, P-loop containing nucleoside triphosphate hydrolase, Pleckstrin homology (PH) domains, Serine-threonine/tyrosine-protein kinase and F-box-like domain superfamily proteins which play role in many important biochemical or physiological process. The identified markers linked to QTLs can be used in MAS once successfully validated.

Genetic dissection of marker trait associations for grain micro-nutrients and thousand grain weight under heat and drought stress conditions in wheat

Introduction

Wheat is grown and consumed worldwide, making it an important staple food crop for both its calorific and nutritional content. In places where wheat is used as a staple food, suboptimal micronutrient content levels, especially of grain iron (Fe) and zinc (Zn), can lead to malnutrition. Grain nutrient content is influenced by abiotic stresses, such as drought and heat stress. The best method for addressing micronutrient deficiencies is the biofortification of food crops. The prerequisites for marker-assisted varietal development are the identification of the genomic region responsible for high grain iron and zinc contents and an understanding of their genetics.

Methods

A total of 193 diverse wheat genotypes were evaluated under drought and heat stress conditions across the years at the Indian Agricultural Research Institute (IARI), New Delhi, under timely sown irrigated (IR), restricted irrigated (RI) and late sown (LS) conditions. Grain iron content (GFeC) and grain zinc content (GZnC) were estimated from both the control and treatment groups. Genotyping of all the lines under study was carried out with the single nucleotide polymorphisms (SNPs) from Breeder's 35K Axiom Array.

Result and Discussion

Three subgroups were observed in the association panel based on both principal component analysis (PCA) and dendrogram analysis. A large whole-genome linkage disequilibrium (LD) block size of 3.49 Mb was observed. A genome-wide association study identified 16 unique stringent marker trait associations for GFeC, GZnC, and 1000-grain weight (TGW). In silico analysis demonstrated the presence of 28 potential candidate genes in the flanking region of 16 linked SNPs, such as synaptotagmin-like mitochondrial-lipid-binding domain, HAUS augmin-like complex, di-copper center-containing domain, protein kinase, chaperonin Cpn60, zinc finger, NUDIX hydrolase, etc. Expression levels of these genes in vegetative tissues and grain were also found. Utilization of identified markers in marker-assisted breeding may lead to the rapid development of biofortified wheat genotypes to combat malnutrition.

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).

Marker-assisted backcross breeding for heat tolerance in bread wheat (Triticum aestivum L.)

Manipulation of flowering time for adaptation through natural or genetic approaches may combat heat-stress damage that occurs at the reproductive stages in production conditions. HD2733, a popular wheat variety of the eastern plains of India, is largely sensitive to heat stress. Therefore, the current study aims to improve heat tolerance of HD2733 by introgression of QTLs associated with early anthesis and high kernel weight linked to markers Xbarc186 and Xgwm190, respectively, through marker-assisted backcross breeding (MABB) from a tolerant donor, WH730. A total of 124 simple sequence repeat (SSR) markers distributed evenly across the genome were used for the background selection. The alleles of Xbarc186 and Xgwm190 were fixed in BC₂F₁ and BC₁F₂ generations by selecting individual plants heterozygous for both marker loci and backcrossed with HD2733 and simultaneously selfed to generate BC₂F₁ and BC₁F₂ populations, respectively. Furthermore, the selected BC₁F₂ were selfed to generate the BC₁F4 population. By background screening, a total of 39 BC₂F₃ and 21 BC₁F₄ families homozygous for the targeted QTLs with 90.9-97.9% and 86.8-88.3% RPG recoveries were selected. The best performing 17 BC₂F₃ and 10 BC₁F₄ lines were evaluated for various morpho-physiological traits. Phenotypic evaluation and multi-location trials of the introgressed lines under late sown conditions led to the selection of three promising lines with early anthesis and higher grain yield. The improved lines will serve as an excellent genetic material for functional genomics and expression studies to understand the molecular mechanisms and pathways underlying the stress tolerance.

Grain quality of spring wheat (<i>Triticum aestivum</i> L.) cultivars developed in Western Siberia under the conditions of Northern Kazakhstan

Background. Environmental testing is the first stage of wheat breeding, the purpose of which is to identify wheat samples suitable for local environments and capable of forming a fairly stable yield and high-quality grain. The proposed study presents the test results for spring bread wheat cultivars of Russian breeding grown in arid environments of Northern Kazakhstan in order to preserve their yield and baking qualities.

Materials and methods. The material of the study included 15 spring bread wheat cultivars. Protein and gluten content and the quality of gluten were determined using an infrared analyzer; the physical properties of the test were assessed using a Chopin alveograph and Brabender farinograph.

Results.  As a result of biochemical assessment, increased protein and gluten content and grain weight were observed in cvs. ‘Tyumenskaya 30’, ‘Aviada’, ‘Lutescens 585’, ‘Serebrina’, and ‘Tyumenets 2’. Dough deformation energy (W) characteristic of high-quality wheat and the balance in the P/L ratio (elasticity/elongation) were shown by cvs. ‘Tyumenskaya 33’ (290 a.u.; 1.15 P/L), ‘SKENT-3’ (307 a.u.; 0.89 P/L), and ‘Lutescens 585’ (374 a.u., 1.10 P/L). In laboratory baking, the volume of bread ranged from 620 ml (‘Tyumenskaya 27’) to 768 ml (‘Tyumenskaya 29’) with an average value of 707 ml. A baking quality analysis of the cultivars grown in the Northern Trans-Urals and Northern Kazakhstan demonstrated that the conditions in Northern Kazakhstan were more favorable for obtaining bread with an increased volume. On the basis of environmental tests and an assessment of a set of biochemical and technological indicators, cvs. ‘SKENT-3’ and ‘Tyumenskaya 29’ were selected. It makes sense to continue studying wheat cultivars that can be sources and donors of high-quality grain for the development of cultivars for the arid steppe of Northern Kazakhstan.

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.

Genetic dissection of grain iron and zinc, and thousand kernel weight in wheat (Triticum aestivum L.) using genome-wide association study

Genetic biofortification is recognized as a cost-effective and sustainable strategy to reduce micronutrient malnutrition. Genomic regions governing grain iron concentration (GFeC), grain zinc concentration (GZnC), and thousand kernel weight (TKW) were investigated in a set of 280 diverse bread wheat genotypes. The genome-wide association (GWAS) panel was genotyped using 35 K Axiom Array and phenotyped in five environments. The GWAS analysis showed a total of 17 Bonferroni-corrected marker-trait associations (MTAs) in nine chromosomes representing all the three wheat subgenomes. The TKW showed the highest MTAs (7), followed by GZnC (5) and GFeC (5). Furthermore, 14 MTAs were identified with more than 10% phenotypic variation. One stable MTA i.e. AX-95025823 was identified for TKW in both E4 and E5 environments along with pooled data, which is located at 68.9 Mb on 6A chromosome. In silico analysis revealed that the SNPs were located on important putative candidate genes such as Multi antimicrobial extrusion protein, F-box domain, Late embryogenesis abundant protein, LEA-18, Leucine-rich repeat domain superfamily, and C3H4 type zinc finger protein, involved in iron translocation, iron and zinc homeostasis, and grain size modifications. The identified novel MTAs will be validated to estimate their effects in different genetic backgrounds for subsequent use in marker-assisted selection. The identified SNPs will be valuable in the rapid development of biofortified wheat varieties to ameliorate the malnutrition problems.

Genetic architecture of end-use quality traits in soft white winter wheat

Background

Genetic improvement of end-use quality is an important objective in wheat breeding programs to meet the requirements of grain markets, millers, and bakers. However, end-use quality phenotyping is expensive and laborious thus, testing is often delayed until advanced generations. To better understand the underlying genetic architecture of end-use quality traits, we investigated the phenotypic and genotypic structure of 14 end-use quality traits in 672 advanced soft white winter wheat breeding lines and cultivars adapted to the Pacific Northwest region of the United States.

Results

This collection of germplasm had continuous distributions for the 14 end-use quality traits with industrially significant differences for all traits. The breeding lines and cultivars were genotyped using genotyping-by-sequencing and 40,518 SNP markers were used for association mapping (GWAS). The GWAS identified 178 marker-trait associations (MTAs) distributed across all wheat chromosomes. A total of 40 MTAs were positioned within genomic regions of previously discovered end-use quality genes/QTL. Among the identified MTAs, 12 markers had large effects and thus could be considered in the larger scheme of selecting and fixing favorable alleles in breeding for end-use quality in soft white wheat germplasm. We also identified 15 loci (two of them with large effects) that can be used for simultaneous breeding of more than a single end-use quality trait. The results highlight the complex nature of the genetic architecture of end-use quality, and the challenges of simultaneously selecting favorable genotypes for a large number of traits. This study also illustrates that some end-use quality traits were mainly controlled by a larger number of small-effect loci and may be more amenable to alternate selection strategies such as genomic selection.

Conclusions

In conclusion, a breeder may be faced with the dilemma of balancing genotypic selection in early generation(s) versus costly phenotyping later on.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12864-022-08676-5.

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.

Pyramiding of genes for grain protein content, grain quality, and rust resistance in eleven Indian bread wheat cultivars: a multi-institutional effort

Improvement of grain protein content (GPC), loaf volume, and resistance to rusts was achieved in 11 Indian wheat cultivars that are widely grown in four different agro-climatic zones of India. This involved use of marker-assisted backcross breeding (MABB) for introgression and pyramiding of the following genes: (i) the high GPC gene Gpc-B1; (ii) HMW glutenin subunits 5 + 10 at Glu-D1 loci, and (iii) rust resistance genes, Yr36, Yr15, Lr24, and Sr24. GPC increased by 0.8 to 3.3%, although high GPC was generally associated with yield penalty. Further selection among high GPC lines allowed identification of progenies with higher GPC associated with improvement in 1000-grain weight and grain yield in the backgrounds of the following four cultivars: NI5439, UP2338, UP2382, and HUW468. The high GPC progenies (derived from NI5439) were also improved for grain quality using HMW glutenin subunits 5 + 10 at Glu-D1 loci. Similarly, progenies combining high GPC and rust resistance were obtained in the backgrounds of following five cultivars: Lok1, HD2967, PBW550, PBW621, and DBW1. The improved pre-bred lines developed following multi-institutional effort should prove a valuable source for the development of cultivars with improved nutritional quality and rust resistance in the ongoing wheat breeding programmes.

Supplementary Information

The online version contains supplementary material available at 10.1007/s11032-022-01277-w.

Genome-Wide Association Study Reveals the Genetic Basis of Five Quality Traits in Chinese Wheat

Bread wheat is a highly adaptable food crop grown extensively around the world and its quality genetic improvement has received wide attention. In this study, the genetic loci associated with five quality traits including protein content (PC), gluten content (GC), baking value (BV), grain hardness (HA), and sedimentation value (SV) in a population of 253 Chinese wheat grown in Inner Mongolia were investigated through genome wide association mapping. A total of 103 QTL containing 556 SNPs were significantly related to the five quality traits based on the phenotypic data collected from three environments and BLUP data. Of these QTL, 32 QTL were continuously detected under at least two experiments. Some QTL such as qBV3D.2/qHA3D.2 on 3D, qPC5A.3/qGC5A on 5A, qBV5D/qHA5D on 5D, qBV6B.2/qHA6B.3 on 6B, and qBV6D/qHA6D.1 on 6D were associated with multiple traits. In addition, distribution of favorable alleles of the stable QTL in the association panel and their effects on five quality traits were validated. Analysis of existing transcriptome data revealed that 34 genes were specifically highly expressed in grains during reproductive growth stages. The functions of these genes will be characterized in future experiments. This study provides novel insights into the genetic basis of quality traits in wheat.

Whole-exome sequencing of selected bread wheat recombinant inbred lines as a useful resource for allele mining and bulked segregant analysis

Although wheat (Triticum aestivum L.) is the main staple crop in the world and a major source of carbohydrates and proteins, functional genomics and allele mining are still big challenges. Given the advances in next-generation sequencing (NGS) technologies, the identification of causal variants associated with a target phenotype has become feasible. For these reasons, here, by combining sequence capture and target-enrichment methods with high-throughput NGS re-sequencing, we were able to scan at exome-wide level 46 randomly selected bread wheat individuals from a recombinant inbred line population and to identify and classify a large number of single nucleotide polymorphisms (SNPs). For technical validation of results, eight randomly selected SNPs were converted into Kompetitive Allele-Specific PCR (KASP) markers. This resource was established as an accessible and reusable molecular toolkit for allele data mining. The dataset we are making available could be exploited for novel studies on bread wheat genetics and as a foundation for starting breeding programs aimed at improving different key agronomic traits.

Major Genomic Regions for Wheat Grain Weight as Revealed by QTL Linkage Mapping and Meta-Analysis

Grain weight is a key determinant for grain yield potential in wheat, which is highly governed by a type of quantitative genetic basis. The identification of major quantitative trait locus (QTL) and functional genes are urgently required for molecular improvements in wheat grain yield. In this study, major genomic regions and putative candidate genes for thousand grain weight (TGW) were revealed by integrative approaches with QTL linkage mapping, meta-analysis and transcriptome evaluation. Forty-five TGW QTLs were detected using a set of recombinant inbred lines, explaining 1.76-12.87% of the phenotypic variation. Of these, ten stable QTLs were identified across more than four environments. Meta-QTL (MQTL) analysis were performed on 394 initial TGW QTLs available from previous studies and the present study, where 274 loci were finally refined into 67 MQTLs. The average confidence interval of these MQTLs was 3.73-fold less than that of initial QTLs. A total of 134 putative candidate genes were mined within MQTL regions by combined analysis of transcriptomic and omics data. Some key putative candidate genes similar to those reported early for grain development and grain weight formation were further discussed. This finding will provide a better understanding of the genetic determinants of TGW and will be useful for marker-assisted selection of high yield in wheat breeding.

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.

Association analysis for agronomic traits in wheat under terminal heat stress

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Terminal heat stress leads to irreversible damage in wheat.

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Marker assisted selection and gene pyramiding for portrayal of heat tolerance.

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Allelic frequency and polymorphic information showed significant variability.

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Markers xcfa2147 and xwmc671 could be potentail for heat stress tolerance.

Terminal heat stress causes irreversible damage to wheat crop productivity. It reduces the vegetative growth and flowering period that consequently declines the efficiency to capture available stem reserves (carbohydrates) in grains. Markers associated with thermotolerant traits ease in marker assisted selection (MAS) for crop improvement. It identifies the genomic regions associated with thermotolerant traits in wheat, but the scarcity of markers is the major hindrance in crop improvement. Therefore, 158 wheat genotypes were subjected to genotyping with 165 simple sequence repeat markers dispersed on three genomes (A, B and D). Allelic frequency and polymorphic information content values were highest on genome A (5.34 (14% greater than the lowest value at genome D) and 0.715 (3% greater than the lowest value at genome D)), chromosome 4 (5.40 (16% greater than the lowest value at chromosome 2) and 0.725 (5% greater than the lowest value at chromosome 6)) and marker xgwm44 (13.0 (84% greater than the lowest value at marker xbarc148) and 0.916 (46% greater than the lowest value at marker xbarc148)). Bayesian based population structure discriminated the wheat genotypes into seven groups based on genetic similarity indicating their ancestral origin and geographical ecotype. Linkage disequilibrium pattern had highest significant (P < 0.001) linked loci pairs 732 on genome A at r² > 0.1 whereas, 58 on genome B at r² > 0.5. Linkage disequilibrium decay (P < 0.01 and r² > 0.1) had larger LD block (5–10 cM) on genome A. Highly significant MTAs (P < 0.000061) under heat stress conditions were identified for flag leaf area (xwmc336), spikelet per spike (xwmc553), grains per spike (cxfa2147, xwmc418 and xwmc121), biomass (xbarc7) and grain yield (xcfa2147 and xwmc671). The identified markers in this study could facilitate in MAS and gene pyramiding against heat stress in wheat.

Wheat Proteins: A Valuable Resources to Improve Nutritional Value of Bread

Triticum aestivum, commonly known as bread wheat, is one of the most cultivated crops globally. Due to its increasing demand, wheat is the source of many nutritious products including bread, pasta, and noodles containing different types of seed storage proteins. Wheat seed storage proteins largely control the type and quality of any wheat product. Among various unique wheat products, bread is the most consumed product around the world due to its fast availability as compared to other traditional food commodities. The production of highly nutritious and superior quality bread is always a matter of concern because of its increasing industrial demand. Therefore, new and more advanced technologies are currently being applied to improve and enrich the bread, having increased fortified nutrients, gluten-free, highly stable with enhanced shelf-life, and long-lasting. This review focused on bread proteins with improving wheat qualities and nutritional properties using modern technologies. We also describe the recent innovations in processing technologies to improve various quality traits of wheat bread. We also highlight some modern forms of bread that are utilized in different industries for various purposes and future directions.

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.

Genome-wide association mapping reveals key genomic regions for physiological and yield-related traits under salinity stress in wheat (Triticum aestivum L.)

A genome-wide association study (GWAS) was conducted using six different multi-locus GWAS models and 35K SNP array to demarcate genomic regions underlying reproductive stage salinity tolerance. Marker-trait association analysis was performed for salt tolerance indices (STI) of 11 morpho-physiological traits, and the actual concentrations of Na⁺ and K⁺, and the Na⁺/K⁺ ratio in flag leaf. A total of 293 significantly associated quantitative trait nucleotides (QTNs) for 14 morpho-physiological traits were identified. Of these 293 QTNs, 12 major QTNs with R² ≥ 10.0% were detected in three or more GWAS models. Novel major QTNs were identified for plant height, number of effective tillers, biomass, grain yield, thousand grain weight, Na⁺ and K⁺ content, and the Na⁺/K⁺ ratio in flag leaf. Moreover, 48 candidate genes were identified from the associated genomic regions. The QTNs identified in this study could potentially be targeted for improving salinity tolerance in wheat.

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.

Decoding the genome of superior chapatti quality Indian wheat variety 'C 306' unravelled novel genomic variants for chapatti and nutrition quality related genes

An Indian wheat variety, 'C 306' has good chapatti quality, which is controlled by multiple genes that have not been explored. We report the high quality de novo assembled genome of 'C 306' by combining short and long read sequencing data. The hybrid assembly covered 93% of gene space and identified about 142 K coding genes, 34% repetitive DNA and ~ 501 K SSR motifs. The phylogenetic analysis of about 83 K orthologous protein groups suggested the closest relationship with T. turgidum, T. aestivum and Ae. tauschii. Genome wide analysis annotated 69,217,536 genomic variants. Out of them, 1423 missense and 117 deleterious variants identified in processing, nutrition, and chapatti quality related genes such as alpha- and beta-gliadin, SSI, SSIII, SUT1, SBEI, CHS, YSL, DMAS, and NAS encoded proteins. These variants may affect quality genes. The genomic data will be potential genomic resources in wheat breeding programs for quality improvement.

QTL mapping of agronomic traits in wheat using the UK Avalon × Cadenza reference mapping population grown in Kazakhstan

Background

The success of wheat production is largely dependent on local breeding projects that focus on the development of high-yielding cultivars with the use of novel molecular tools. One strategy for improving wheat productivity involves the deployment of diverse germplasms with a high potential yield. An important factor for achieving success involves the dissection of quantitative trait loci (QTLs) for complex agronomic traits, such as grain yield components, in targeted environments for wheat growth.

Methods

In this study, we tested the United Kingdom (UK) spring set of the doubled haploid (DH) reference population derived from the cross between two British cultivars, Avalon (winter wheat) and Cadenza (spring wheat), in the Northern, Central, and Southern regions (Karabalyk, Karaganda, Kyzylorda) of Kazakhstan over three years (2013–2015). The DH population has previously been genotyped by UK scientists using 3647 polymorphic DNA markers. The list of tested traits includes the heading time, seed maturation time, plant height, spike length, productive tillering, number of kernels per spike, number of kernels per meter, thousand kernel weight, and yield per square meter. Windows QTL Cartographer was applied for QTL mapping using the composite interval mapping method.

Results

In total, 83 out of 232 QTLs were identified as stable QTLs from at least two environments. A literature survey suggests that 40 QTLs had previously been reported elsewhere, indicating that this study identified 43 QTLs that are presumably novel marker-trait associations (MTA) for these environments. Hence, the phenotyping of the DH population in new environments led to the discovery of novel MTAs. The identified SNP markers associated with agronomic traits in the DH population could be successfully used in local Kazakh breeding projects for the improvement of wheat productivity.

Allelic response of yield component traits to resource availability in spring wheat

Investigation of resource availability on allele effects for four yield component quantitative trait loci provides guidance for the improvement of grain yield in high and low yielding environments. A greater understanding of grain yield (GY) and yield component traits in spring wheat may increase selection efficiency for improved GY in high and low yielding environments. The objective of this study was to determine allelic response of four yield component quantitative trait loci (QTL) to variable resource levels which were manipulated by varying intraspecific plant competition and seeding density. The four QTL investigated in this study had been previously identified as impacting specific yield components. They included QTn.mst-6B for productive tiller number (PTN), WAPO-A1 for spikelet number per spike (SNS), and QGw.mst-3B and TaGW2-A1 for kernel weight (KWT). Near-isogenic lines for each of the four QTL were grown in multiple locations with three competition (border, no-border and space-planted) and two seeding densities (normal 216 seeds m^-2 and low 76 seeds m^-2). Allele response at QTn.mst-6B was driven by changes in resource availability, whereas allele response at WAPO-A1 and TaGW2-A1 was relatively unaffected by resource availability. The QTn.mst-6B.1 allele at QTn.mst-6B conferred PTN plasticity resulting in significant GY increases in high resource environments. The gw2-A1 allele at TaGW2-A1 significantly increased KWT, SNS and GPC offering a source of GY improvement without negatively impacting end-use quality. QGw.mst-3B allelic variation did not significantly impact KWT but did significantly impact SPS. Treatment effects in both experiments often resulted in significant positive impacts on GY and yield component traits when resource availability was increased. Results provide guidance for leveraging yield component QTL to improve GY performance in high- and low-yield environments.

DEFECTIVE ENDOSPERM-D1 (Dee-D1) is crucial for endosperm development in hexaploid wheat

Hexaploid wheat (Triticum aestivum L.) is a natural allopolyploid and provides a usable model system to better understand the genetic mechanisms that underlie allopolyploid speciation through the hybrid genome doubling. Here we aimed to identify the contribution of chromosome 1D in the development and evolution of hexaploid wheat. We identified and mapped a novel DEFECTIVE ENDOSPERM–D1 (Dee-D1) locus on 1DL that is involved in the genetic control of endosperm development. The absence of Dee-D1 leads to non-viable grains in distant crosses and alters grain shape, which negatively affects grain number and thousand-grain weight. Dee-D1 can be classified as speciation locus with a positive effect on the function of genes which are involved in endosperm development in hybrid genomes. The presence of Dee-D1 is necessary for the normal development of endosperm, and thus play an important role in the evolution and improvement of grain yield in hexaploid wheat.

Natalia Tikhenko et al. investigate the genetic contribution of the wheat chromosome 1D to its development and evolution. They find a novel locus, DEFECTIVE ENDOSPERM-D1, on the long arm of 1D that is required for normal endosperm development as its absence leads to non-viable grains and altered grain shape.

Mapping genomic regions of moisture deficit stress tolerance using backcross inbred lines in wheat (Triticum aestivum L.)

Identification of markers associated with major physiological and yield component traits under moisture deficit stress conditions in preferred donor lines paves the way for marker-assisted selection (MAS). In the present study, a set of 183 backcross inbred lines (BILs) derived from the cross HD2733/2*C306 were genotyped using 35K Axiom genotyping array and SSR markers. The multi-trait, multi-location field phenotyping of BILs was done at three locations covering two major wheat growing zones of India, north-western plains zone (NWPZ) and central zone (CZ) under varying moisture regimes. A linkage map was constructed using 705 SNPs and 86 SSR polymorphic markers. A total of 43 genomic regions and QTL × QTL epistatic interactions were identified for 14 physiological and yield component traits, including NDVI, chlorophyll content, CT, CL, PH, GWPS, TGW and GY. Chromosomes 2A, 5D, 5A and 4B harbors greater number of QTLs for these traits. Seven Stable QTLs were identified across environment for DH (QDh.iari_6D), GWPS (QGWPS.iari_5B), PH (QPh.iari_4B-2, QPh.iari_4B-3) and NDVI (QNdvi1.iari_5D, QNdvi3.iari_5A). Nine genomic regions identified carrying major QTLs for CL, NDVI, RWC, FLA, PH, TGW and biomass explaining 10.32–28.35% of the phenotypic variance. The co-segregation of QTLs of physiological traits with yield component traits indicate the pleiotropic effects and their usefulness in the breeding programme. Our findings will be useful in dissecting genetic nature and marker-assisted selection for moisture deficit stress tolerance in wheat.

QTL analysis of farinograph and mixograph related traits in spring wheat under heat stress conditions

Farinograph and mixograph-related parameters are key elements in wheat end-products quality. Understanding the genetic control of these traits and the influence of environmental factors such as heat stress, and their interaction are critical for developing cultivars with improved for those traits. To identify QTL for six farinograph and three mixograph traits, two double haploid (DH) populations (Yecora Rojo × Ksu106 and Klasic × Ksu105) were used in experiments conducted at Riyadh and Al Qassim locations under heat stress. Single nucleotide polymorphism (SNP) markers were used to determine the number of QTLs controlling these parameters. The genetic analysis of farinograph and mixograph-related traits showed considerable variation with transgressive segregation regardless of heat stress conditions in both locations. A total of 108 additive QTLs were detected for the six farinograph and three mixograph traits in the Yecora Rojo × Ksu106 population in both locations under heat treatments. These QTLs were distributed over all 21 wheat chromosomes except 3A. Similarly, in Klassic × Ksu105 population, there were an additional 68 QTLs identified over the two locations and were allocated on all chromosomes except 1D, 2A, 6A, and 6D. In population (Yecora Rojo × Ksu106), the QTL on chromosome 7A (Excalibur_c62415_288) showed significant effects for farinograph and mixograph traits (FDDT, FDST, FBD, M × h8, and M × t) under normal and heat stress condition at both locations. Interestingly, several QTLs that are related to farinograph and mixograph traits, which showed stable expression under both locations, were detected on chromosome 7A in population (Klassic × Ksu105). Results from this study show the quantitative nature of the genetic control of the studied traits and constitute a step toward identifying major QTLs that can be sued molecular-marker assisted breeding to develop new improved quality wheat cultivars.

Genetics of yield, abiotic stress tolerance and biofortification in wheat (Triticum aestivum L.)

A review of the available literature on genetics of yield and its component traits, tolerance to abiotic stresses and biofortification should prove useful for future research in wheat in the genomics era. The work reviewed in this article mainly covers the available information on genetics of some important quantitative traits including yield and its components, tolerance to abiotic stresses (heat, drought, salinity and pre-harvest sprouting = PHS) and biofortification (Fe/Zn and phytate contents with HarvestPlus Program) in wheat. Major emphasis is laid on the recent literature on QTL interval mapping and genome-wide association studies, giving lists of known QTL and marker-trait associations. Candidate genes for different traits and the cloned and characterized genes for yield traits along with the molecular mechanism are also described. For each trait, an account of the present status of marker-assisted selection has also been included. The details of available results have largely been presented in the form of tables; some of these tables are included as supplementary files.

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

Protein- and starch-related quality traits, which are quantitatively inherited and significantly influenced by the environment, are critical determinants of the end-use quality of wheat. We constructed a high-density genetic map containing 10,739 loci (5,399 unique loci) using a set of 184 recombinant inbred lines (RILs) derived from a cross of 'Tainong 18 × Linmai 6' (TL-RILs). In this study, a quantitative trait loci (QTLs) analysis was used to examine the genetic control of grain protein content, sedimentation value, farinograph parameters, falling number and the performance of the starch pasting properties using TL-RILs grown in a field for three years. A total of 106 QTLs for 13 quality traits were detected, distributed on the 21 chromosomes. Of these, 38 and 68 QTLs for protein- and starch-related traits, respectively, were detected in three environments and their average values (AV). Twenty-six relatively high-frequency QTLs (RHF-QTLs) that were detected in more than two environments. Twelve stable QTL clusters containing at least one RHF-QTL were detected and classified into three types: detected only for protein-related traits (type I), detected only for starch-related traits (type II), and detected for both protein- and starch-related traits (type III). A total of 339 markers flanked with 11 QTL clusters (all except C6), were found to be highly homologous with 282 high confidence (HC) and 57 low confidence (LC) candidate genes based on IWGSC RefSeq v 1.0. These stable QTLs and RHF-QTLs, especially those grouped into clusters, are credible and should be given priority for QTL fine-mapping and identification of candidate genes with which to explain the molecular mechanisms of quality development and inform marker-assisted breeding in the future.

High Density Mapping of Quantitative Trait Loci Conferring Gluten Strength in Canadian Durum Wheat

Gluten strength is one of the factors that determine the end-use quality of durum wheat and is an important breeding target for this crop. To characterize the quantitative trait loci (QTL) controlling gluten strength in Canadian durum wheat cultivars, a population of 162 doubled haploid (DH) lines segregating for gluten strength and derived from cv. Pelissier × cv. Strongfield was used in this study. The DH lines, parents, and controls were grown in 3 years and two seeding dates in each year and gluten strength of grain samples was measured by sodium dodecyl sulfate (SDS)-sedimentation volume (SV). With a genetic map created by genotyping the DH lines using the Illumina Infinium iSelect Wheat 90K SNP (single nucleotide polymorphism) chip, QTL contributing to gluten strength were detected on chromosome 1A, 1B, 2B, and 3A. Two major and stable QTL detected on chromosome 1A (QGlu.spa-1A) and 1B (QGlu.spa-1B.1) explaining 13.7-18.7% and 25.4-40.1% of the gluten strength variability respectively were consistently detected over 3 years, with the trait increasing alleles derived from Strongfield. Putative candidate genes underlying the major QTL were identified. Two novel minor QTL (QGlu.spa-3A.1 and QGlu.spa-3A.2) with the trait increasing allele derived from Pelissier were mapped on chromosome 3A explaining up to 8.9% of the phenotypic variance; another three minor QTL (QGlu.spa-2B.1, QGlu.spa-2B.2, and QGlu.spa-2B.3) located on chromosome 2B explained up to 8.7% of the phenotypic variance with the trait increasing allele derived from Pelissier. QGlu.spa-2B.1 is a new QTL and has not been reported in the literature. Multi-environment analysis revealed genetic (QTL) × environment interaction due to the difference of effect in magnitude rather than the direction of the QTL. Eleven pairs of digenic epistatic QTL were identified, with an epistatic effect between the two major QTL of QGlu.spa-1A and QGlu.spa-1B.1 detected in four out of six environments. The peak SNPs and SNPs flanking the QTL interval of QGlu.spa-1A and QGlu.spa-1B.1 were converted to Kompetitive Allele Specific PCR (KASP) markers, which can be deployed in marker-assisted breeding to increase the efficiency and accuracy of phenotypic selection for gluten strength in durum wheat. The QTL that were expressed consistently across environments are of great importance to maintain the gluten strength of Canadian durum wheat to current market standards during the genetic improvement.

Quantitative trait loci (QTL) mapping for physiological and biochemical attributes in a Pasban90/Frontana recombinant inbred lines (RILs) population of wheat (Triticum aestivum) under salt stress condition

Salt stress causes nutritional imbalance and ion toxicity which affects wheat growth and production. A population of recombinant inbred lines (RILs) were developed by crossing Pasban90 (salt tolerant) and Frontana (salt suceptible) for identification of quantitative trait loci (QTLs) for physiological traits including relative water content, membrane stability index, water potential, osmotic potential, total chlorophyll content, chlorophyll a, chlorophyll b and biochemical traits including proline contents, superoxide dismutase, sodium content, potassium content, chloride content and sodium/potassium ratio by tagging 202 polymorphic simple sequence repeats (SSR) markers. Linkage map of RILs comprised of 21 linkage group covering A, B and D genome for tagging and maped a total of 60 QTLs with major and minor effect. B genome contributed to the highest number of QTLs under salt stress condition. Xgwm70 and Xbarc361 mapped on chromosome 6B was linked with Total chlorophyll, water potential and sodium content. The increasing allele for all these QTLs were advanced from parent Pasban90. Current study showed that Genome B and D had more potentially active genes conferring plant tolerance against salinity stress which may be exploited for marker assisted selection to breed salinity tolerant high yielding wheat varieties.