Detection of grain protein content QTLs across environments in tetraploid wheats

Starch and storage protein dynamics in the developing and matured grains of durum wheat and diploid progenitor species

Durum wheat, less immunogenically intolerant than bread wheat, originates from diploid progenitors known for nutritional quality and stress tolerance. Present study involves the analysis of major grain parameters, viz. size, weight, sugar, starch, and protein content of Triticum durum (AABB genome) and its diploid progenitors, Triticum monococcum (AA genome) and Aegilops speltoides (BB genome). Samples were collected during 2-5 weeks after anthesis (WAA), and at maturity. The investigation revealed that T. durum displayed the maximum grain size and weight. Expression analysis of Grain Weight 2 (GW2) and Glutamine Synthase (GS2), negative and positive regulators of grain weight and size, respectively, revealed higher GW2 expression in Ae. speltoides and higher GS2 expression in T. durum. Further we explored total starch, sugar and protein content, observing higher levels of starch and sugar in durum wheat while AA genome species exhibited higher protein content dominated by the fractions of albumin/globulin. HPLC profiling revealed unique sub-fractions in all three genome species. Additionally, a comparative transcriptome analysis also corroborated with the starch and protein content in the grains. This study provides valuable insights into the genetic and biochemical distinctions among durum wheat and its diploid progenitors, offering a foundation for their nutritional composition.

Simultaneous improvement of grain yield and grain protein concentration in durum wheat by using association tests and weighted GBLUP

KEY MESSAGE

Simultaneous improvement for GY and GPC by using GWAS and GBLUP suggested a significant application in durum wheat breeding. Despite the importance of grain protein concentration (GPC) in determining wheat quality, its negative correlation with grain yield (GY) is still one of the major challenges for breeders. Here, a durum wheat panel of 200 genotypes was evaluated for GY, GPC, and their derived indices (GPD and GYD), under eight different agronomic conditions. The plant material was genotyped with the Illumina 25 k iSelect array, and a genome-wide association study was performed. Two statistical models revealed dozens of marker-trait associations (MTAs), each explaining up to 30%. phenotypic variance. Two markers on chromosomes 2A and 6B were consistently identified by both models and were found to be significantly associated with GY and GPC. MTAs identified for phenological traits co-mapped to well-known genes (i.e., Ppd-1, Vrn-1). The significance values (p-values) that measure the strength of the association of each single nucleotide polymorphism marker with the target traits were used to perform genomic prediction by using a weighted genomic best linear unbiased prediction model. The trained models were ultimately used to predict the agronomic performances of an independent durum wheat panel, confirming the utility of genomic prediction, although environmental conditions and genetic backgrounds may still be a challenge to overcome. The results generated through our study confirmed the utility of GPD and GYD to mitigate the inverse GY and GPC relationship in wheat, provided novel markers for marker-assisted selection and opened new ways to develop cultivars through genomic prediction approaches.

Consensus genomic regions for grain quality traits in wheat revealed by Meta-QTL analysis and in silico transcriptome integration

Grain quality traits are the key factors that determine the economic value of wheat and are largely influenced by genetics and the environment. In this study, using a meta-analysis of quantitative trait loci (QTLs) and a comprehensive in silico transcriptome assessment, we identified key genomic regions and putative candidate genes for the grain quality traits protein content, gluten content, and test weight. A total of 508 original QTLs were collected from 41 articles on QTL mapping for the three quality traits in wheat published from 2003 to 2021. When these original QTLs were projected onto a high-density consensus map consisting of 14,548 markers, 313 QTLs resulted in the identification of 64 MQTLs distributed across 17 of the 21 chromosomes. Most of the meta-QTLs (MQTLs) were distributed on sub-genomes A and B. Compared with the original QTLs, the confidence interval (CI) of the MQTLs was smaller, with an average CI of 4.47 cM, while the projected QTLs CI was 11.13 cM (2.49-fold lower). The corresponding physical length of the MQTL ranged from 0.45 to 239.01 Mb. Thirty-one of these 64 MQTLs were validated in at least one genome-wide association study. In addition, five of the 64 MQTLs were selected and designated as core MQTLs. The 211 quality-related genes from rice were used to identify wheat homologs in MQTLs. In combination with transcriptional and omics analyses, 135 putative candidate genes were identified from 64 MQTL regions. The findings should contribute to a better understanding of the molecular genetic mechanisms underlying grain quality and the improvement of these traits in wheat breeding.

Identification of QTLs for Grain Protein Content in Russian Spring Wheat Varieties

Most modern breeding programs aim to develop wheat (T. aestivum L.) varieties with a high grain protein content (GPC) due to its greater milling and cooking quality, and improved grain price. Here, we used a genome-wide association study (GWAS) to map single nucleotide polymorphisms (SNPs) associated with GPC in 93 spring bread wheat varieties developed by eight Russian Breeding Centers. The varieties were evaluated for GPC, grain weight per spike (GWS), and thousand-kernel weight (TKW) at six environments, and genotyped with 9351 polymorphic SNPs and two SNPs associated with the NAM-A1 gene. GPC varied from 9.8 to 20.0%, depending on the genotype and environment. Nearly 52% of the genotypes had a GPC > 14.5%, which is the threshold value for entry into high-class wheat varieties. Broad-sense heritability for GPC was moderate (0.42), which is due to the significant effect of environment and genotype × environment interactions. GWAS performed on mean GPC evaluated across six environments identified eleven significant marker-trait associations, of which nine were physically mapped on chromosome 6A. Screening of wheat varieties for allelic variants of the NAM-A1 gene indicated that 60% of the varieties contained the NAM-A1c allele, followed by 33% for NAM-A1d, and 5% for NAM-A1a alleles. Varieties with the NAM-A1d allele showed significantly (p < 0.01) smaller GPC than those with NAM-A1c and NAM-A1a. However, no significant differences between NAM-A1 alleles were observed for both GWS and TKW.

Conditional Mapping Identified Quantitative Trait Loci for Grain Protein Concentration Expressing Independently of Grain Yield in Canadian Durum Wheat

Grain protein concentration (GPC) is an important trait in durum cultivar development as a major determinant of the nutritional value of grain and end-use product quality. However, it is challenging to simultaneously select both GPC and grain yield (GY) due to the negative correlation between them. To characterize quantitative trait loci (QTL) for GPC and understand the genetic relationship between GPC and GY in Canadian durum wheat, we performed both traditional and conditional QTL mapping using a doubled haploid (DH) population of 162 lines derived from Pelissier × Strongfield. The population was grown in the field over 5 years and GPC was measured. QTL contributing to GPC were detected on chromosome 1B, 2B, 3A, 5B, 7A, and 7B using traditional mapping. One major QTL on 3A (QGpc.spa-3A.3) was consistently detected over 3 years accounting for 9.4-18.1% of the phenotypic variance, with the favorable allele derived from Pelissier. Another major QTL on 7A (QGpc.spa-7A) detected in 3 years explained 6.9-14.8% of the phenotypic variance, with the beneficial allele derived from Strongfield. Comparison of the QTL described here with the results previously reported led to the identification of one novel major QTL on 3A (QGpc.spa-3A.3) and five novel minor QTL on 1B, 2B and 3A. Four QTL were common between traditional and conditional mapping, with QGpc.spa-3A.3 and QGpc.spa-7A detected in multiple environments. The QTL identified by conditional mapping were independent or partially independent of GY, making them of great importance for development of high GPC and high yielding durum.

Breeding and biotechnological interventions for trait improvement: status and prospects

Present review describes the molecular tools and strategies deployed in the trait discovery and improvement of major crops. The prospects and challenges associated with these approaches are discussed. Crop improvement relies on modulating the genes and genomic regions underlying key traits, either directly or indirectly. Direct approaches include overexpression, RNA interference, genome editing, etc., while breeding majorly constitutes the indirect approach. With the advent of latest tools and technologies, these strategies could hasten the improvement of crop species. Next-generation sequencing, high-throughput genotyping, precision editing, use of space technology for accelerated growth, etc. had provided a new dimension to crop improvement programmes that work towards delivering better varieties to cope up with the challenges. Also, studies have widened from understanding the response of plants to single stress to combined stress, which provides insights into the molecular mechanisms regulating tolerance to more than one stress at a given point of time. Altogether, next-generation genetics and genomics had made tremendous progress in delivering improved varieties; however, the scope still exists to expand its horizon to other species that remain underutilized. In this context, the present review systematically analyses the different genomics approaches that are deployed for trait discovery and improvement in major species that could serve as a roadmap for executing similar strategies in other crop species. The application, pros, and cons, and scope for improvement of each approach have been discussed with examples, and altogether, the review provides comprehensive coverage on the advances in genomics to meet the ever-growing demands for agricultural produce.

Genome wide genetic dissection of wheat quality and yield related traits and their relationship with grain shape and size traits in an elite × non-adapted bread wheat cross

The genetic gain in yield and quality are two major targets of wheat breeding programs around the world. In this study, a high density genetic map consisting of 10,172 SNP markers identified a total of 43 genomic regions associated with three quality traits, three yield traits and two agronomic traits in hard red spring wheat (HRSW). When compared with six grain shape and size traits, the quality traits showed mostly independent genetic control (~18% common loci), while the yield traits showed moderate association (~53% common loci). Association of genomic regions for grain area (GA) and thousand-grain weight (TGW), with yield suggests that targeting an increase in GA may help enhancing wheat yield through an increase in TGW. Flour extraction (FE), although has a weak positive phenotypic association with grain shape and size, they do not share any common genetic loci. A major contributor to plant height was the Rht8 locus and the reduced height allele was associated with significant increase in grains per spike (GPS) and FE, and decrease in number of spikes per square meter and test weight. Stable loci were identified for almost all the traits. However, we could not find any QTL in the region of major known genes like GPC-B1, Ha, Rht-1, and Ppd-1. Epistasis also played an important role in the genetics of majority of the traits. In addition to enhancing our knowledge about the association of wheat quality and yield with grain shape and size, this study provides novel loci, genetic information and pre-breeding material (combining positive alleles from both parents) to enhance the cultivated gene pool in wheat germplasm. These resources are valuable in facilitating molecular breeding for wheat quality and yield improvement.

Genetic dissection of agronomic and quality traits based on association mapping and genomic selection approaches in durum wheat grown in Southern Spain

Climatic conditions affect the growth, development and final crop production. As wheat is of paramount importance as a staple crop in the human diet, there is a growing need to study its abiotic stress adaptation through the performance of key breeding traits. New and complementary approaches, such as genome-wide association studies (GWAS) and genomic selection (GS), are used for the dissection of different agronomic traits. The present study focused on the dissection of agronomic and quality traits of interest (initial agronomic score, yield, gluten index, sedimentation index, specific weight, whole grain protein and yellow colour) assessed in a panel of 179 durum wheat lines (Triticum durum Desf.), grown under rainfed conditions in different Mediterranean environments in Southern Spain (Andalusia). The findings show a total of 37 marker-trait associations (MTAs) which affect phenotype expression for three quality traits (specific weight, gluten and sedimentation indexes). MTAs could be mapped on the A and B durum wheat subgenomes (on chromosomes 1A, 1B, 2A, 2B and 3A) through the recently available bread wheat reference assembly (IWGSC RefSeqv1). Two of the MTAs found for quality traits (gluten index and SDS) corresponded to the known Glu-B1 and Glu-A1 loci, for which candidate genes corresponding to high molecular weight glutenin subunits could be located. The GS prediction ability values obtained from the breeding materials analyzed showed promising results for traits as grain protein content, sedimentation and gluten indexes, which can be used in plant breeding programs.

The contribution of wheat to human diet and health

Wheat is the most important staple crop in temperate zones and is in increasing demand in countries undergoing urbanization and industrialization. In addition to being a major source of starch and energy, wheat also provides substantial amounts of a number of components which are essential or beneficial for health, notably protein, vitamins (notably B vitamins), dietary fiber, and phytochemicals. Of these, wheat is a particularly important source of dietary fiber, with bread alone providing 20% of the daily intake in the UK, and well-established relationships between the consumption of cereal dietary fiber and reduced risk of cardio-vascular disease, type 2 diabetes, and forms of cancer (notably colo-rectal cancer). Wheat shows high variability in the contents and compositions of beneficial components, with some (including dietary fiber) showing high heritability. Hence, plant breeders should be able to select for enhanced health benefits in addition to increased crop yield.

Combining quantitative trait loci analysis with physiological models to predict genotype-specific transpiration rates

Transpiration is controlled by evaporative demand and stomatal conductance (gs ), and there can be substantial genetic variation in gs . A key parameter in empirical models of transpiration is minimum stomatal conductance (g0 ), a trait that can be measured and has a large effect on gs and transpiration. In Arabidopsis thaliana, g0 exhibits both environmental and genetic variation, and quantitative trait loci (QTL) have been mapped. We used this information to create a genetically parameterized empirical model to predict transpiration of genotypes. For the parental lines, this worked well. However, in a recombinant inbred population, the predictions proved less accurate. When based only upon their genotype at a single g0 QTL, genotypes were less distinct than our model predicted. Follow-up experiments indicated that both genotype by environment interaction and a polygenic inheritance complicate the application of genetic effects into physiological models. The use of ecophysiological or 'crop' models for predicting transpiration of novel genetic lines will benefit from incorporating further knowledge of the genetic control and degree of independence of core traits/parameters underlying gs variation.

A wheat 1-FEH w3 variant underlies enzyme activity for stem WSC remobilization to grain under drought

In wheat stems, the levels of fructan-dominated water-soluble carbohydrates (WSC) do not always correlate well with grain yield. Field drought experiments were carried out to further explain this lack of correlation. Wheat (Triticum aestivum) varieties, Westonia, Kauz and c. 20 genetically diverse double haploid (DH) lines derived from them were investigated. Substantial genotypic differences in fructan remobilization were found and the 1-FEH w3 gene was shown to be the major contributor in the stem fructan remobilization process based on enzyme activity and gene expression results. A single nucleotide polymorphism (SNP) was detected in an auxin response element in the 1-FEH w3 promoter region, therefore we speculated that the mutated Westonia allele might affect gene expression and enzyme activity levels. A cleaved amplified polymorphic (CAP) marker was generated from the SNP. The harvested results showed that the mutated Westonia 1-FEH w3 allele was associated with a higher thousand grain weight (TGW) under drought conditions in 2011 and 2012. These results indicated that higher gene expression of 1-FEH w3 and 1-FEH w3 mediated enzyme activities that favoured stem WSC remobilization to the grains. The CAP marker residing in the 1-FEH w3 promoter region may facilitate wheat breeding by selecting lines with high stem fructan remobilization capacity under terminal drought.

Genetic variability in anthocyanin composition and nutritional properties of blue, purple, and red bread (Triticum aestivum L.) and durum (Triticum turgidum L. ssp. turgidum convar. durum) wheats

Renewed interest in breeding for high anthocyanins in wheat (Triticum ssp.) is due to their antioxidant potential. A collection of different pigmented wheats was used to investigate the stability of anthocyanins over three crop years. The data show higher anthocyanins in blue-aleurone bread wheat (Triticum aestivum L.), followed by purple- and red-pericarp durum wheat (Triticum turgidum L. ssp. turgidum convar. durum), using cyanidin 3-O-glucoside as standard. HPLC of the anthocyanin components shows five to eight major anthocyanins for blue wheat extracts, compared to three anthocyanins for purple and red wheats. Delphinidin 3-O-rutinoside, delphinidin 3-O-glucoside, and malvidin 3-O-glucoside are predominant in blue wheat, with cyanidin 3-O-glucoside, peonidin 3-O-galactoside, and malvidin 3-O-glucoside in purple wheat. Of the total anthocyanins, 40-70% remain to be structurally identified. The findings confirm the high heritability for anthocyanins, with small genotype × year effects, which will be useful for breeding purposes, to improve the antioxidant potential of cereal-based foods.

Conditional QTL mapping of protein content in wheat with respect to grain yield and its components

Grain protein content in wheat (Triticum aestivum L.) is generally considered a highly heritable character that is negatively correlated with grain yield and yield-related traits. Quantitative trait loci (QTL) for protein content was mapped using data on protein content and protein content conditioned on the putatively interrelated traits to evaluate possible genetic interrelationships between protein content and yield, as well as yield-related traits. Phenotypic data were evaluated in a recombinant inbred line population with 302 lines derived from a cross between the Chinese cultivar Weimai 8 and Luohan 2. Inclusive composite interval mapping using IciMapping 3.0 was employed for mapping unconditional and conditional QTL with additives. A strong genetic relationship was found between protein content and grain yield, and yield-related traits. Unconditional QTL mapping analysis detected seven additive QTL for protein content, with additive effects ranging in absolute size from 0.1898% to 0.3407% protein content, jointly accounting for 43.45% of the trait variance. Conditional QTL mapping analysis indicated two QTL independent from yield, which can be used in marker-assisted selection for increasing yield without affecting grain protein content. Three additional QTL with minor effects were identified in the conditional mapping. Of the three QTLs, two were identified when protein content was conditioned on yield, which had pleiotropic effects on those two traits. Conditional QTL mapping can be used to dissect the genetic interrelationship between two traits at the individual QTL level for closely correlated traits. Further, conditional QTL mapping can reveal additional QTL with minor effects that are undetectable in unconditional mapping.

Modelling the size and composition of fruit, grain and seed by process-based simulation models

Understanding what determines the size and composition of fruit, grain and seed in response to the environment and genotype is challenging, as these traits result from several linked processes controlled at different levels of organization, from the subcellular to the crop level, with subtle interactions occurring at or between the levels of organization. Process-based simulation models (PBSMs) implement algorithms to simulate metabolic and biophysical aspects of cell, tissue and organ behaviour. In this review, fruit, grain and seed PBSMs describing the main phases of growth, development and storage metabolism are discussed. From this concurrent work, it is possible to identify generic storage organ processes which can be modelled similarly for fruit, grain and seed. Spatial heterogeneity at the tissue and whole-plant level is found to be a key consideration in modelling the effects of the environment and genotype on fruit, grain and seed end-use value. In the future, PBSMs may well become the main link between studies at the molecular and whole-plant levels. To bridge this phenotype-to-genotype gap, future models need to remain plastic without becoming overparameterized.

Mapping of main and epistatic effect QTLs associated to grain protein and gluten strength using a RIL population of durum wheat

Quality, specifically protein content and gluten strength are among the main objectives of a durum wheat breeding program. The aim of this work was to validate quantitative trait loci (QTLs) associated with grain protein content (GPC) and gluten strength measured by SDS sedimentation volume (SV) and to find additional QTLs expressed in Argentinean environments. Also, epistatic QTL and QTL x environmental interactions were analyzed. A mapping population of 93 RILs derived from the cross UC1113 x Kofa showing extreme values in gluten quality was used. Phenotypic data were collected along six environments (three locations, two years). Main effect QTLs associated with GPC were found in equivalent positions in two environments on chromosomes 3BS (R(2)=21.0-21.6%) and 7BL (R(2)=12.1-13%), and in one environment on chromosomes 1BS, 2AL, 2BS, 3BL, 4AL, 5AS, 5BL and 7AS. The most important and stable QTL affecting SV was located on chromosome 1BL (Glu-B1) consistently detected over the six environments (R(2)=20.9- 54.2%). Additional QTLs were found in three environments on chromosomes 6AL (R(2)=6.4-12.5%), and in two environments on chromosomes 6BL (R(2)=11.5-12.1%), 7AS (R(2)=8.2-10.2%) and 4BS (R(2)=11-16.4%). In addition, pleiotropic effects were found affecting grain yield, test weight, thousand-kernel-weight and days to heading in some of these QTLs. Epistatic QTLs and QTL x environment interactions were found for both quality traits, mostly for GPC. The flanking markers of the QTLs detected in this work could be efficient tools to select superior genotypes for the mentioned traits.

Under what circumstances can process-based simulation models link genotype to phenotype for complex traits? Case-study of fruit and grain quality traits

Detailed information has arisen from research at gene and cell levels, but it is still incomplete in the context of a quantitative understanding of whole plant physiology. Because of their integrative nature, process-based simulation models can help to bridge the gap between genotype and phenotype and assist in deconvoluting genotype-by-environment (GxE) interactions for complex traits. Indeed, GxE interactions are emergent properties of simulation models, i.e. unexpected properties generated by complex interconnections between subsystem components and biological processes. They co-occur in the system with synergistic or antagonistic effects. In this work, different kinds of GxE interactions are illustrated. Approaches to link model parameters to genes or quantitative trait loci (QTL) are briefly reviewed. Then the analysis of GxE interactions through simulation models is illustrated with an integrated model simulation of peach (Prunus persica (L.) Batsch) fruit mass and sweetness, and with a model of wheat (Triticum aestivum L.) grain yield and protein concentration. This paper suggests that the management of complex traits such as fruit and grain quality may become possible, thanks to the increasing knowledge concerning the genetic and environmental regulation of organ size and composition and to the development of models simulating the complex aspects of metabolism and biophysical behaviours at the plant and organ levels.

Localisation of quantitative trait loci for quality attributes in a doubled haploid population of wheat (Triticum aestivum L.)

Selection of wheat germplasm for a range of quality traits has been a challenging exercise because of the cost of testing, the variation within testing data, and a poor understanding of the underlying genetics. The objective of this study was to identify quantitative trait loci (QTLs) underlying quality traits in wheat. A doubled haploid population comprising 190 lines from Chara/WW2449 was grown in two different environments and evaluated for various quality traits. A molecular map comprising 362 markers based upon simple sequence repeat, sequence tagged microsatellite, glutenin, and DArT loci was constructed and subsequently exploited to identify QTLs using a whole-genome approach. Fifteen QTLs that were consistent in the two different environments were identified for thousand kernel mass, grain protein content, milling yield, flour protein content, flour colour, flour water absorption, dough development time, dough strength (extensograph height and resistance at 5 cm), and dough extensibility (extensograph length) using the whole genome average interval mapping approach. The amount of genetic variation explained by individual QTLs ranged from 3% to 49%. A number of QTLs associated with dough strength, dough extensibility, dough development time, and flour water absorption were located close to the glutenin Glu-B1 locus on chromosome 1B. Identification of the chromosomal location and effect of the QTLs influencing wheat quality may hasten the development of superior wheats for target markets via marker-assisted selection.

Identification and validation of quantitative trait loci for grain protein concentration in adapted Canadian durum wheat populations

Grain protein concentration (GPC) is one of the most important factors influencing pasta-making quality. Durum wheat (Triticum turgidum L. var durum) cultivars with high GPC produce pasta with increased tolerance to overcooking and greater cooked firmness. However, the large environmental effect on expression of GPC and the negative correlation with grain yield have slowed genetic improvement of this important trait. Understanding the genetics and identification of molecular markers associated with high GPC would aid durum wheat breeders in trait selection at earlier generations. The objectives of this study were to identify and validate molecular markers associated with quantitative trait loci (QTL) for elevated GPC in durum wheat. A genetic map was constructed using SSR and DArT markers in an F(1)-derived doubled haploid (DH) population derived from the cross DT695 x Strongfield. The GPC data were collected from replicated trials grown in six Canadian environments from 2002 to 2005. QTL associated with variation for GPC were identified on the group 1, 2, and 7 chromosomes and on 5B and 6B, but only QGpc.usw-B3 on 2B and QGpc.usw-A3 on 7A were expressed consistently in four and six environments, respectively. Positive alleles for GPC at these loci were contributed by the high-GPC parent Strongfield. The QGpc.usw-A3 QTL was validated in a second DH population, and depending on environment, selection for the Strongfield allele at barc108 resulted in +0.4% to +1.0% increase in GPC, with little effect on yield in most environments. Given the consistent expression pattern in multiple populations and environments, barc108 could be useful for marker-assisted selection for high GPC.

Quantitative trait loci conferring grain mineral nutrient concentrations in durum wheat x wild emmer wheat RIL population

Mineral nutrient malnutrition, and particularly deficiency in zinc and iron, afflicts over 3 billion people worldwide. Wild emmer wheat, Triticum turgidum ssp. dicoccoides, genepool harbors a rich allelic repertoire for mineral nutrients in the grain. The genetic and physiological basis of grain protein, micronutrients (zinc, iron, copper and manganese) and macronutrients (calcium, magnesium, potassium, phosphorus and sulfur) concentration was studied in tetraploid wheat population of 152 recombinant inbred lines (RILs), derived from a cross between durum wheat (cv. Langdon) and wild emmer (accession G18-16). Wide genetic variation was found among the RILs for all grain minerals, with considerable transgressive effect. A total of 82 QTLs were mapped for 10 minerals with LOD score range of 3.2-16.7. Most QTLs were in favor of the wild allele (50 QTLs). Fourteen pairs of QTLs for the same trait were mapped to seemingly homoeologous positions, reflecting synteny between the A and B genomes. Significant positive correlation was found between grain protein concentration (GPC), Zn, Fe and Cu, which was supported by significant overlap between the respective QTLs, suggesting common physiological and/or genetic factors controlling the concentrations of these mineral nutrients. Few genomic regions (chromosomes 2A, 5A, 6B and 7A) were found to harbor clusters of QTLs for GPC and other nutrients. These identified QTLs may facilitate the use of wild alleles for improving grain nutritional quality of elite wheat cultivars, especially in terms of protein, Zn and Fe.

Genetic control of wheat quality: interactions between chromosomal regions determining protein content and composition, dough rheology, and sponge and dough baking properties

While the genetic control of wheat processing characteristics such as dough rheology is well understood, limited information is available concerning the genetic control of baking parameters, particularly sponge and dough (S&D) baking. In this study, a quantitative trait loci (QTL) analysis was performed using a population of doubled haploid lines derived from a cross between Australian cultivars Kukri x Janz grown at sites across different Australian wheat production zones (Queensland in 2001 and 2002 and Southern and Northern New South Wales in 2003) in order to examine the genetic control of protein content, protein expression, dough rheology and sponge and dough baking performance. The study highlighted the inconsistent genetic control of protein content across the test sites, with only two loci (3A and 7A) showing QTL at three of the five sites. Dough rheology QTL were highly consistent across the 5 sites, with major effects associated with the Glu-B1 and Glu-D1 loci. The Glu-D1 5 + 10 allele had consistent effects on S&D properties across sites; however, there was no evidence for a positive effect of the high dough strength Glu-B1-al allele at Glu-B1. A second locus on 5D had positive effects on S&D baking at three of five sites. This study demonstrated that dough rheology measurements were poor predictors of S&D quality. In the absence of robust predictive tests, high heritability values for S&D demonstrate that direct selection is the current best option for achieving genetic gain in this product category.

Analysis of the wheat endosperm transcriptome

Among the cereals, wheat is the most widely grown geographically and is part of the staple diet in much of the world. Understanding how the cereal endosperm develops and functions will help generate better tools to manipulate grain qualities important to end-users. We used a genomics approach to identify and characterize genes that are expressed in the wheat endosperm. We analyzed the 17,949 publicly available wheat endosperm EST sequences to identify genes involved in the biological processes that occur within this tissue. Clustering and assembly of the ESTs resulted in the identification of 6,187 tentative unique genes, 2,358 of which formed contigs and 3,829 remained as singletons. A BLAST similarity search against the NCBI non-redundant sequence database revealed abundant messages for storage proteins, putative defense proteins, and proteins involved in starch and sucrose metabolism. The level of abundance of the putatively identified genes reflects the physiology of the developing endosperm. Half of the identified genes have unknown functions. Approximately 61% of the endosperm ESTs has been tentatively mapped in the hexaploid wheat genome. Using microarrays for global RNA profiling, we identified endosperm genes that are specifically up regulated in the developing grain.

The genetics of nitrogen use in hexaploid wheat: N utilisation, development and yield

A genetic study is presented for traits relating to nitrogen use in wheat. Quantitative trait loci (QTLs) were established for 21 traits relating to growth, yield and leaf nitrogen (N) assimilation during grain fill in hexaploid wheat (Triticum aestivum L.) using a mapping population from the cross Chinese Spring x SQ1. Glutamine synthetase (GS) isozymes and estimated locations of 126 genes were placed on the genetic map. QTLs for flag leaf GS activity, soluble protein, extract colour and fresh weight were found in similar regions implying shared control of leaf metabolism and leaf size. Flag leaf traits were negatively associated with days to anthesis both phenotypically and genetically, demonstrating the complex interactions of metabolism with development. One QTL cluster for GS activity co-localised with a GS2 gene mapped on chromosome 2A, and another with the mapped GSr gene on 4A. QTLs for GS activity were invariably co-localised with those for grain N, with increased activity associated with higher grain N, but with no or negative correlations with grain yield components. Peduncle N was positively correlated, and QTLs co-localised, with grain N and flag leaf N assimilatory traits, suggesting that stem N can be indicative of grain N status in wheat. A major QTL for ear number per plant was identified on chromosome 6B which was negatively co-localised with leaf fresh weight, peduncle N, grain N and grain yield. This locus is involved in processes defining the control of tiller number and consequently assimilate partitioning and deserves further examination.

Molecular detection of QTLs for agronomic and quality traits in a doubled haploid population derived from two Canadian wheats (Triticum aestivum L.)

Development of high-yielding wheat varieties with good end-use quality has always been a major concern for wheat breeders. To genetically dissect quantitative trait loci (QTLs) for yield-related traits such as grain yield, plant height, maturity, lodging, test weight and thousand-grain weight, and for quality traits such as grain and flour protein content, gluten strength as evaluated by mixograph and SDS sedimentation volume, an F1-derived doubled haploid (DH) population of 185 individuals was developed from a cross between a Canadian wheat variety "AC Karma" and a breeding line 87E03-S2B1. A genetic map was constructed based on 167 marker loci, consisting of 160 microsatellite loci, three HMW glutenin subunit loci: Glu-A1, Glu-B1 and Glu-D1, and four STS-PCR markers. Data for investigated traits were collected from three to four environments in Manitoba, Canada. QTL analyses were performed using composite interval mapping. A total of 50 QTLs were detected, 24 for agronomic traits and 26 for quality-related traits. Many QTLs for correlated traits were mapped in the same genomic regions forming QTL clusters. The largest QTL clusters, consisting of up to nine QTLs, were found on chromosomes 1D and 4D. HMW glutenin subunits at Glu-1 loci had the largest effect on breadmaking quality; however, other genomic regions also contributed genetically to breadmaking quality. QTLs detected in the present study are compared with other QTL analyses in wheat.

Detection of QTLs for grain protein content in durum wheat

Grain protein content (GPC) of durum wheat (Triticum turgidum L. var. durum) is an important trait for the nutritional value of grain and for influencing the technological property of flour. Protein content is a quantitative trait negatively correlated with grain yield, thus increase in protein quantity usually results in yield reduction. This study was initiated to introgress alleles for high GPC from var. dicoccoides into durum wheat germplasm by the backcross inbred line (BIL) method and to identify molecular markers linked to high GPC alleles not associated with depressing effects on yield. The backcross line 3BIL-85 with high GPC and similar grain yield to the recurrent parent was backcrossed to Latino, and the generations F2, F3 and F4 were evaluated for GPC and yield per spike (GYS) in three field trials. Three QTLs with major effects on GPC were detected on chromosome arms 2AS, 6AS and 7BL, identified by the markers Xcfa2164, XP39M37 (250) and Xgwm577, respectively. Multiple regression analysis indicated that the three QTLs explained all the genetic variances of the trait. The high GPC parental line 3BIL-85 was not significantly different from the recurrent parent Latino for GYS, but the phenotypic correlation coefficient between GPC and GYS had negative values (from -0.02 to -0.28) in each trial, although it was statistically significant only in the F3 progeny trial. No co-located QTL for GYS was detected, excluding the hypothesis that the putative QTLs for GPC were indirect QTLs for low grain yield. The negative protein-yield response could be due to: (a) co-location of grain yield per spike QTLs with reduced phenotypic effects not detectable by the experimental design or statistical procedures, or to (b) opposite pleiotropic gene effects due to the major bio-energetic requirements for synthesis of protein then carbohydrates. Mapping loci by BILs should enable the production of near-isogenic lines in which the individual effects of each QTL can be examined in detail without confounding variations due to other putative QTLs.

Physical map of the wheat high-grain protein content gene Gpc-B1 and development of a high-throughput molecular marker

Grain protein content (GPC) is important for human nutrition and has a strong influence on pasta and bread quality. A quantitative trait locus, derived from a Triticum turgidum ssp. dicoccoides accession (DIC), with an average increase in GPC of 14 g kg(-1) was mapped on chromosome 6BS. Using the wheat-rice colinearity, a high-density map of the wheat region was developed and the quantitative trait locus was mapped as a simple Mendelian locus designated Gpc-B1. A physical map of approx. 250 kb of the Gpc-B1 region was developed using a tetraploid wheat bacterial artificial chromosome library. The constructed physical map included the two Gpc-B1 flanking markers and one potential candidate gene from the colinear rice region completely linked to Gpc-B1. The relationship between physical and genetic distances and the feasibility of isolating genes by positional cloning in wheat are discussed. A high-throughput codominant marker, Xuhw89, was developed. A 4-bp deletion present in the DIC allele was absent in a collection of 117 cultivated tetraploid and hexaploid wheat germplasm, suggesting that this marker will be useful to incorporate the high GPC allele from the DIC accession studied here into commercial wheat varieties.

Flour yield QTLs in three Australian doubled haploid wheat populations

Flour yield quantitative trait loci (QTLs) were identified in 3 Australian doubled haploid populations, Sunco × Tasman, CD87 × Katepwa, and Cranbrook × Halberd. Trial data from 3 to 4 sites or years were available for each population. QTLs were identified on chromosomes 2BS, 4B, 5AL, and 6BL in the Sunco × Tasman population, on chromosomes 4B, 5AS, and 6DL in the CD87 × Katepwa population, and on chromosomes 4DS, 5DS, and 7AS in the Cranbrook × Halberd population. In the Sunco × Tasman cross the highest genetic variance was detected with the QTL on chromosome 2B (31.3%), in the CD87 × Katepwa cross with the QTL on chromosome 4B (23.8%), and in the Cranbrook × Halberd cross with the QTL on chromosome 5D (18%). Only one QTL occurred in a similar location in more than one population, indicating the complexity of the flour yield character across different backgrounds.

Gene networks in hexaploid wheat: interacting quantitative trait loci for grain protein content

In hexaploid wheat, single-locus and two-locus quantitative trait loci (QTL) analyses for grain protein content (GPC) were conducted using two different mapping populations (PI and PII). Main effect QTLs (M-QTLs), epistatic QTLs (E-QTLs) and QTL x environment interactions (QE, QQE) were detected using two-locus analyses in both the populations. Only a few QTLs were common in both the analyses, and the QTLs and the interactions detected in the two populations differed, suggesting the superiority of two-locus analysis and the need for using several mapping populations for QTL analysis. A sizable proportion of genetic variation for GPC was due to interactions (28.59% and 54.03%), rather than to M-QTL effects (7.24% and 7.22%), which are the only genetic effects often detected in the majority of QTL studies. Even E-QTLs made a marginal contribution to genetic variation (2.68% and 6.04%), thus suggesting that the major part of genetic variation is due to changes in gene networks rather than the presence or absence of specific genes. This is in sharp contrast to the genetic dissection of pre-harvest sprouting tolerance conducted by us earlier, where interaction effects were not substantial, suggesting that the nature of genetic variation also depends on the nature of the trait.

Precise mapping of a locus affecting grain protein content in durum wheat

Grain protein content (GPC) is an important factor in pasta and breadmaking quality, and in human nutrition. It is also an important trait for wheat growers because premium prices are frequently paid for wheat with high GPC. A promising source for alleles to increase GPC was detected on chromosome 6B of Triticum turgidum var. dicoccoides accession FA-15-3 (DIC). Two previous quantitative trait locus (QTL) studies found that the positive effect of DIC-6B was associated to a single locus located between the centromere and the Nor-B2 locus on the short arm of chromosome 6B. Microsatellite markers Xgwm508 and Xgwm193 flanking the QTL region were used in this study to develop 20 new homozygous recombinant substitution lines (RSLs) with crossovers between these markers. These 20 RSLs, plus nine RSLs developed in previous studies were characterized with four new RFLP markers located within this chromosome segment. Grain protein content was determined in three field experiments organized as randomized complete block designs with ten replications each. The QTL peaks for protein content were located in the central region of a 2.7-cM interval between RFLP markers Xcdo365 and Xucw67 in the three experiments. Statistical analyses showed that almost all lines could be classified unequivocally within low- and high- protein groups, facilitating the mapping of this trait as a single Mendelian locus designated Gpc-6B1. The Gpc-6B1 locus was mapped 1.5-cM proximal to Xcdo365 and 1.2-cM distal to Xucw67. These new markers can be used to reduce the size of the DIC chromosome segment selected in marker-assisted selection programs. Markers Nor-B2 and Xucw66 flanking the previous two markers can be used to select against the DIC segment and reduce the linkage drag during the transfer of Gpc-6B1 into commercial bread and pasta wheat varieties. The precise mapping of the high GPC gene, the high frequency of recombinants recovered in the targeted region, and the recent development of a tetraploid BAC library including the Gpc-6B1 DIC allele are the first steps towards the map-based cloning of this gene.