Genetic analysis of late-maturity α-amylase in twelve wheat populations.

MAIN CONCLUSION: Genetic loci, particularly those with an effect in the independent panel, could be utilised to further reduce LMA expression when used with favourable combinations of genes known to affect LMA. Late maturity α-amylase (LMA) is a grain quality defect involving elevated α-amylase within the aleurone of wheat (Triticum aestivum L.) grains. The genes known to affect expression are the reduced height genes Rht-B1 (chromosome 4B) and Rht-D1 (chromosome 4D), and an ent-copalyl diphosphate synthase gene (LMA-1) on chromosome 7B. Other minor effect loci have been reported, but these are poorly characterised and further genetic understanding is needed. In this study, twelve F4-derived populations were created through single seed descent, genotyped and evaluated for LMA. LMA-1 haplotype C and the Rht-D1b allele substantially reduced LMA expression. The alternative dwarfing genes Rht13 and Rht18 had no significant effect on LMA expression. Additional quantitative trait loci (QTL) were mapped at 16 positions in the wheat genome. Effects on LMA expression were detected for four of these QTL in a large independent panel of Australian wheat lines. The QTL detected in mapping populations and confirmed in the large independent panel provide further opportunity for selection against LMA, especially if combined with Rht-D1b and/or favourable haplotypes of LMA-1.

A multi-environment framework to evaluate the adaptation of wheat (Triticum aestivum) to heat stress.

KEY MESSAGE: Assessing adaptation to abiotic stresses such as high temperature conditions across multiple environments presents opportunities for breeders to target selection for broad adaptation and specific adaptation. Adaptation of wheat to heat stress is an important component of adaptation in variable climates such as the cereal producing areas of Australia. However, in variable climates stress conditions may not be present in every season or are present to varying degrees, at different times during the season. Such conditions complicate plant breeders' ability to select for adaptation to abiotic stress. This study presents a framework for the assessment of the genetic basis of adaptation to heat stress conditions with improved relevance to breeders' selection objectives. The framework was applied here with the evaluation of 1225 doubled haploid lines from five populations across six environments (three environments selected for contrasting temperature stress conditions during anthesis and grain fill periods, over two consecutive seasons), using regionally best practice planting times to evaluate the role of heat stress conditions in genotype adaptation. Temperature co-variates were determined for each genotype, in each environment, for the anthesis and grain fill periods. Genome-wide QTL analysis identified performance QTL for stable effects across all environments, and QTL that illustrated responsiveness to heat stress conditions across the sampled environments. A total of 199 QTL were identified, including 60 performance QTL, and 139 responsiveness QTL. Of the identified QTL, 99 occurred independent of the 21 anthesis date QTL identified. Assessing adaptation to heat stress conditions as the combination of performance and responsiveness offers breeders opportunities to select for grain yield stability across a range of environments, as well as genotypes with higher relative yield in stress conditions.

Plant Variety Selection Using Interaction Classes Derived From Factor Analytic Linear Mixed Models: Models With Independent Variety Effects.

A major challenge in the analysis of plant breeding multi-environment datasets is the provision of meaningful and concise information for variety selection in the presence of variety by environment interaction (VEI). This is addressed in the current paper by fitting a factor analytic linear mixed model (FALMM) then using the fundamental factor analytic parameters to define groups of environments in the dataset within which there is minimal crossover VEI, but between which there may be substantial crossover VEI. These groups are consequently called interaction classes (iClasses). Given that the environments within an iClass exhibit minimal crossover VEI, it is then valid to obtain predictions of overall variety performance (across environments) for each iClass. These predictions can then be used not only to select the best varieties within each iClass but also to match varieties in terms of their patterns of VEI across iClasses. The latter is aided with the use of a new graphical tool called an iClass Interaction Plot. The ideas are introduced in this paper within the framework of FALMMs in which the genetic effects for different varieties are assumed independent. The application to FALMMs which include information on genetic relatedness is the subject of a subsequent paper.

Genetic analysis of wheat (Triticum aestivum) adaptation to heat stress.

KEY MESSAGE: Adaptation to abiotic stresses such as high-temperature conditions should be considered as its independent components of total performance and responsiveness. Understanding and identifying improved adaptation to abiotic stresses such as heat stress has been the focus of a number of studies in recent decades. However, confusing and potentially misleading terminology has made progress difficult and hard to apply within breeding programs selecting for improved adaption to heat stress conditions. This study proposes that adaption to heat stress (and other abiotic stresses) be considered as the combination of total performance and responsiveness to heat stress. In this study, 1413 doubled haploid lines from seven populations were screened through a controlled environment assay, subjecting plants to three consecutive eight hour days of an air temperature of 36 °C and a wind speed of 40 km h-1, 10 days after the end of anthesis. QTL mapping identified a total of 96 QTL for grain yield determining traits and anthesis date with nine correlating to responsiveness, 72 for total performance and 15 for anthesis date. Responsiveness QTL were found both collocated with other performance QTL as well as independently. A sound understanding of genomic regions associated with total performance and responsiveness will be important for breeders. Genomic regions of total performance, those that show higher performance that is stable under both stressed and non-stressed conditions, potentially offer significant opportunities to breeders. We propose this as a definition and selection target that has not previously been defined for heat stress adaptation.

Estimating Biomass and Canopy Height With LiDAR for Field Crop Breeding.

Above-ground biomass (AGB) is a trait with much potential for exploitation within wheat breeding programs and is linked closely to canopy height (CH). However, collecting phenotypic data for AGB and CH within breeding programs is labor intensive, and in the case of AGB, destructive and prone to assessment error. As a result, measuring these traits is seldom a priority for breeders, especially at the early stages of a selection program. LiDAR has been demonstrated as a sensor capable of collecting three-dimensional data from wheat field trials, and potentially suitable for providing objective, non-destructive, high-throughput estimates of AGB and CH for use by wheat breeders. The current study investigates the deployment of a LiDAR system on a ground-based high-throughput phenotyping platform in eight wheat field trials across southern Australia, for the non-destructive estimate of AGB and CH. LiDAR-derived measurements were compared to manual measurements of AGB and CH collected at each site and assessed for their suitability of application within a breeding program. Correlations between AGB and LiDAR Projected Volume (LPV) were generally strong (up to r = 0.86), as were correlations between CH and LiDAR Canopy Height (LCH) (up to r = 0.94). Heritability (H2) of LPV (H2 = 0.32-0.90) was observed to be greater than, or similar to, the heritability of AGB (H2 = 0.12-0.78) for the majority of measurements. A similar level of heritability was observed for LCH (H2 = 0.41-0.98) and CH (H2 = 0.49-0.98). Further to this, measurements of LPV and LCH were shown to be highly repeatable when collected from either the same or opposite direction of travel. LiDAR scans were collected at a rate of 2,400 plots per hour, with the potential to further increase throughput to 7,400 plots per hour. This research demonstrates the capability of LiDAR sensors to collect high-quality, non-destructive, repeatable measurements of AGB and CH suitable for use within both breeding and research programs.

High-Throughput Field Imaging and Basic Image Analysis in a Wheat Breeding Programme.

Visual assessment of colour-based traits plays a key role within field-crop breeding programmes, though the process is subjective and time-consuming. Digital image analysis has previously been investigated as an objective alternative to visual assessment for a limited number of traits, showing suitability and slight improvement to throughput over visual assessment. However, easily adoptable, field-based high-throughput methods are still lacking. The aim of the current study was to produce a high-throughput digital imaging and analysis pipeline for the assessment of colour-based traits within a wheat breeding programme. This was achieved through the steps of (i) a proof-of-concept study demonstrating basic image analysis methods in a greenhouse, (ii) application of these methods to field trials using hand-held imaging, and (iii) developing a field-based high-throughput imaging infrastructure for data collection. The proof of concept study showed a strong correlation (r = 0.95) between visual and digital assessments of wheat physiological yellowing (PY) in a greenhouse environment, with both scores having similar heritability (H2 = 0.85 and 0.76, respectively). Digital assessment of hand-held field images showed strong correlations to visual scores for PY (r = 0.61 and 0.78), senescence (r = 0.74 and 0.75) and Septoria tritici blotch (STB; r = 0.76), with greater heritability of digital scores, excluding STB. Development of the high-throughput imaging infrastructure allowed for images of field plots to be collected at a rate of 7,400 plots per hour. Images of an advanced breeding trial collected with this system were analysed for canopy cover at two time-points, with digital scores correlating strongly to visual scores (r = 0.88 and 0.86) and having similar or greater heritability. This study details how high-throughput digital phenotyping can be applied to colour-based traits within field trials of a wheat breeding programme. It discusses the logistics of implementing such systems with minimal disruption to the programme, provides a detailed methodology for the basic image analysis methods utilized, and has potential for application to other field-crop breeding or research programmes.

Optimising Genomic Selection in Wheat: Effect of Marker Density, Population Size and Population Structure on Prediction Accuracy.

Genomic selection applied to plant breeding enables earlier estimates of a line's performance and significant reductions in generation interval. Several factors affecting prediction accuracy should be well understood if breeders are to harness genomic selection to its full potential. We used a panel of 10,375 bread wheat (Triticum aestivum) lines genotyped with 18,101 SNP markers to investigate the effect and interaction of training set size, population structure and marker density on genomic prediction accuracy. Through assessing the effect of training set size we showed the rate at which prediction accuracy increases is slower beyond approximately 2,000 lines. The structure of the panel was assessed via principal component analysis and K-means clustering, and its effect on prediction accuracy was examined through a novel cross-validation analysis according to the K-means clusters and breeding cohorts. Here we showed that accuracy can be improved by increasing the diversity within the training set, particularly when relatedness between training and validation sets is low. The breeding cohort analysis revealed that traits with higher selection pressure (lower allelic diversity) can be more accurately predicted by including several previous cohorts in the training set. The effect of marker density and its interaction with population structure was assessed for marker subsets containing between 100 and 17,181 markers. This analysis showed that response to increased marker density is largest when using a diverse training set to predict between poorly related material. These findings represent a significant resource for plant breeders and contribute to the collective knowledge on the optimal structure of calibration panels for genomic prediction.

Increased genomic prediction accuracy in wheat breeding using a large Australian panel.

KEY MESSAGE: Genomic prediction accuracy within a large panel was found to be substantially higher than that previously observed in smaller populations, and also higher than QTL-based prediction. In recent years, genomic selection for wheat breeding has been widely studied, but this has typically been restricted to population sizes under 1000 individuals. To assess its efficacy in germplasm representative of commercial breeding programmes, we used a panel of 10,375 Australian wheat breeding lines to investigate the accuracy of genomic prediction for grain yield, physical grain quality and other physiological traits. To achieve this, the complete panel was phenotyped in a dedicated field trial and genotyped using a custom AxiomTM Affymetrix SNP array. A high-quality consensus map was also constructed, allowing the linkage disequilibrium present in the germplasm to be investigated. Using the complete SNP array, genomic prediction accuracies were found to be substantially higher than those previously observed in smaller populations and also more accurate compared to prediction approaches using a finite number of selected quantitative trait loci. Multi-trait genetic correlations were also assessed at an additive and residual genetic level, identifying a negative genetic correlation between grain yield and protein as well as a positive genetic correlation between grain size and test weight.

The Genetic Control of Grain Protein Content under Variable Nitrogen Supply in an Australian Wheat Mapping Population.

Genetic variation has been observed in both protein concentration in wheat grain and total protein content (protein yield). Here we describe the genetic analysis of variation for grain protein in response to nitrogen (N) supply and locate significant genomic regions controlling grain protein components in a spring wheat population. In total, six N use efficiency (NUE) field trials were carried out for the target traits in a sub-population of doubled haploid lines derived from a cross between two Australian varieties, RAC875 and Kukri, in Southern and Western Australia from 2011 to 2013. Twenty-four putative Quantitative Trait Loci (QTL) for protein-related traits were identified at high and low N supply and ten QTL were identified for the response to N for the traits studied. These loci accounted for a significant proportion of the overall effect of N supply. Several of the regions were co-localised with grain yield QTL and are promising targets for further investigation and selection in breeding programs.

Genetic Basis for Variation in Wheat Grain Yield in Response to Varying Nitrogen Application.

Nitrogen (N) is a major nutrient needed to attain optimal grain yield (GY) in all environments. Nitrogen fertilisers represent a significant production cost, in both monetary and environmental terms. Developing genotypes capable of taking up N early during development while limiting biomass production after establishment and showing high N-use efficiency (NUE) would be economically beneficial. Genetic variation in NUE has been shown previously. Here we describe the genetic characterisation of NUE and identify genetic loci underlying N response under different N fertiliser regimes in a bread wheat population of doubled-haploid lines derived from a cross between two Australian genotypes (RAC875 × Kukri) bred for a similar production environment. NUE field trials were carried out at four sites in South Australia and two in Western Australia across three seasons. There was genotype-by-environment-by-treatment interaction across the sites and also good transgressive segregation for yield under different N supply in the population. We detected some significant Quantitative Trait Loci (QTL) associated with NUE and N response at different rates of N application across the sites and years. It was also possible to identify lines showing positive N response based on the rankings of their Best Linear Unbiased Predictions (BLUPs) within a trial. Dissecting the complexity of the N effect on yield through QTL analysis is a key step towards elucidating the molecular and physiological basis of NUE in wheat.

Factor analytic mixed models for the provision of grower information from national crop variety testing programs.

KEY MESSAGE: Factor analytic mixed models for national crop variety testing programs have the potential to improve industry productivity through appropriate modelling and reporting to growers of variety by environment interaction. Crop variety testing programs are conducted in many countries world-wide. Within each program, data are combined across locations and seasons, and analysed in order to provide information to assist growers in choosing the best varieties for their conditions. Despite major advances in the statistical analysis of multi-environment trial data, such methodology has not been adopted within national variety testing programs. The most commonly used approach involves a variance component model that includes variety and environment main effects, and variety by environment (V × E) interaction effects. The variety predictions obtained from such an analysis, and subsequently reported to growers, are typically on a long-term regional basis. In Australia, the variance component model has been found to be inadequate in terms of modelling V × E interaction, and the reporting of information at a regional level often masks important local V × E interaction. In contrast, the factor analytic mixed model approach that is widely used in Australian plant breeding programs, has regularly been found to provide a parsimonious and informative model for V × E effects, and accurate predictions. In this paper we develop an approach for the analysis of crop variety evaluation data that is based on a factor analytic mixed model. The information obtained from such an analysis may well be superior, but will only enhance industry productivity if mechanisms exist for successful technology transfer. With this in mind, we offer a suggested reporting format that is user-friendly and contains far greater local information for individual growers than is currently the case.

Genetic control of grain yield and grain physical characteristics in a bread wheat population grown under a range of environmental conditions.

KEY MESSAGE: Genetic analysis of the yield and physical quality of wheat revealed complex genetic control, including strong effects of photoperiod-sensitivity loci. Environmental conditions such as moisture deficit and high temperatures during the growing period affect the grain yield and grain characteristics of bread wheat (Triticum aestivum L.). The aim of this study was to map quantitative trait loci (QTL) for grain yield and grain quality traits using a Drysdale/Gladius bread wheat mapping population grown under a range of environmental conditions in Australia and Mexico. In general, yield and grain quality were reduced in environments exposed to drought and/or heat stress. Despite large effects of known photoperiod-sensitivity loci (Ppd-B1 and Ppd-D1) on crop development, grain yield and grain quality traits, it was possible to detect QTL elsewhere in the genome. Some of these QTL were detected consistently across environments. A locus on chromosome 6A (TaGW2) that is known to be associated with grain development was associated with grain width, thickness and roundness. The grain hardness (Ha) locus on chromosome 5D was associated with particle size index and flour extraction and a region on chromosome 3B was associated with grain width, thickness, thousand grain weight and yield. The genetic control of grain length appeared to be largely independent of the genetic control of the other grain dimensions. As expected, effects on grain yield were detected at loci that also affected yield components. Some QTL displayed QTL-by-environment interactions, with some having effects only in environments subject to water limitation and/or heat stress.

A DNA-based method for studying root responses to drought in field-grown wheat genotypes.

Root systems are critical for water and nutrient acquisition by crops. Current methods measuring root biomass and length are slow and labour-intensive for studying root responses to environmental stresses in the field. Here, we report the development of a method that measures changes in the root DNA concentration in soil and detects root responses to drought in controlled environment and field trials. To allow comparison of soil DNA concentrations from different wheat genotypes, we also developed a procedure for correcting genotypic differences in the copy number of the target DNA sequence. The new method eliminates the need for separation of roots from soil and permits large-scale phenotyping of root responses to drought or other environmental and disease stresses in the field.

Quantification of the effects of VRN1 and Ppd-D1 to predict spring wheat (Triticum aestivum) heading time across diverse environments.

Heading time is a major determinant of the adaptation of wheat to different environments, and is critical in minimizing risks of frost, heat, and drought on reproductive development. Given that major developmental genes are known in wheat, a process-based model, APSIM, was modified to incorporate gene effects into estimation of heading time, while minimizing degradation in the predictive capability of the model. Model parameters describing environment responses were replaced with functions of the number of winter and photoperiod (PPD)-sensitive alleles at the three VRN1 loci and the Ppd-D1 locus, respectively. Two years of vernalization and PPD trials of 210 lines (spring wheats) at a single location were used to estimate the effects of the VRN1 and Ppd-D1 alleles, with validation against 190 trials (~4400 observations) across the Australian wheatbelt. Compared with spring genotypes, winter genotypes for Vrn-A1 (i.e. with two winter alleles) had a delay of 76.8 degree days (°Cd) in time to heading, which was double the effect of the Vrn-B1 or Vrn-D1 winter genotypes. Of the three VRN1 loci, winter alleles at Vrn-B1 had the strongest interaction with PPD, delaying heading time by 99.0 °Cd under long days. The gene-based model had root mean square error of 3.2 and 4.3 d for calibration and validation datasets, respectively. Virtual genotypes were created to examine heading time in comparison with frost and heat events and showed that new longer-season varieties could be heading later (with potential increased yield) when sown early in season. This gene-based model allows breeders to consider how to target gene combinations to current and future production environments using parameters determined from a small set of phenotyping treatments.

Genetic mapping and marker development for resistance of wheat against the root lesion nematode Pratylenchus neglectus.

BACKGROUND: The Rlnn1 locus, which resides on chromosome 7A of bread wheat (Triticum aestivum L.) confers moderate resistance against the root lesion nematode Pratylenchus neglectus. Prior to this research, the exact linkage relationships of Rlnn1 with other loci on chromosome 7A were not clear and there were no simple codominant markers available for selection of Rlnn1 in wheat breeding. The objectives of the research reported here were to (1) develop an improved genetic map of the Rlnn1 region of chromosome 7A and (2) develop molecular markers that could be used in marker-assisted selection to improve resistance of wheat against P. neglectus. RESULTS: A large-effect quantitative trait locus (QTL) for resistance against P. neglectus was genetically mapped using a population of Excalibur/Kukri doubled haploid lines. This QTL coincides in position with the rust resistance gene(s) Lr20/Sr15, the phytoene synthase gene Psy-A1 and 10 molecular markers, including five new markers designed using wheat-rice comparative genomics and wheat expressed sequence tags. Two of the new markers are suitable for use as molecular diagnostic tools to distinguish plants that carry Rlnn1 and Lr20/Sr15 from those that do not carry these resistance genes. CONCLUSIONS: The genomic location of Rlnn1 was confirmed to be in the terminal region of the long arm of chromosome 7A. Molecular markers were developed that provide simple alternatives to costly phenotypic assessment of resistance against P. neglectus in wheat breeding. In Excalibur, genetic recombination seems to be completely suppressed in the Rlnn1 region.

Detection of two major grain yield QTL in bread wheat (Triticum aestivum L.) under heat, drought and high yield potential environments.

A large proportion of the worlds' wheat growing regions suffers water and/or heat stress at some stage during the crop growth cycle. With few exceptions, there has been no utilisation of managed environments to screen mapping populations under repeatable abiotic stress conditions, such as the facilities developed by the International Wheat and Maize Improvement Centre (CIMMYT). Through careful management of irrigation and sowing date over three consecutive seasons, repeatable heat, drought and high yield potential conditions were imposed on the RAC875/Kukri doubled haploid population to identify genetic loci for grain yield, yield components and key morpho-physiological traits under these conditions. Two of the detected quantitative trait loci (QTL) were located on chromosome 3B and had a large effect on canopy temperature and grain yield, accounting for up to 22 % of the variance for these traits. The locus on chromosome arm 3BL was detected under all three treatments but had its largest effect under the heat stress conditions, with the RAC875 allele increasing grain yield by 131 kg ha(-1) (or phenotypically, 7 % of treatment average). Only two of the eight yield QTL detected in the current study (including linkage groups 3A, 3D, 4D 5B and 7A) were previously detected in the RAC875/Kukri doubled haploid population; and there were also different yield components driving grain yield. A number of discussion points are raised to understand differences between the Mexican and southern Australian production environments and explain the lack of correlation between the datasets. The two key QTL detected on chromosome 3B in the present study are candidates for further genetic dissection and development of molecular markers.

Genetic dissection of grain yield and physical grain quality in bread wheat (Triticum aestivum L.) under water-limited environments.

In the water-limited bread wheat production environment of southern Australia, large advances in grain yield have previously been achieved through the introduction and improved understanding of agronomic traits controlled by major genes, such as the semi-dwarf plant stature and photoperiod insensitivity. However, more recent yield increases have been achieved through incremental genetic advances, of which, breeders and researchers do not fully understand the underlying mechanism(s). A doubled haploid population was utilised, derived from a cross between RAC875, a relatively drought-tolerant breeders' line and Kukri, a locally adapted variety more intolerant of drought. Experiments were performed in 16 environments over four seasons in southern Australia, to physiologically dissect grain yield and to detect quantitative trait loci (QTL) for these traits. Two stage multi-environment trial analysis identified three main clusters of experiments (forming distinctive environments, ENVs), each with a distinctive growing season rainfall patterns. Kernels per square metre were positively correlated with grain yield and influenced by kernels per spikelet, a measure of fertility. QTL analysis detected nine loci for grain yield across these ENVs, individually accounting for between 3 and 18% of genetic variance within their respective ENVs, with the RAC875 allele conferring increased grain yield at seven of these loci. These loci were partially dissected by the detection of co-located QTL for other traits, namely kernels per square metre. While most loci for grain yield have previously been reported, their deployment and effect within local germplasm are now better understood. A number of novel loci can be further exploited to aid breeders' efforts in improving grain yield in the southern Australian environment.

Identification of novel quantitative trait loci for days to ear emergence and flag leaf glaucousness in a bread wheat (Triticum aestivum L.) population adapted to southern Australian conditions.

In southern Australia, where the climate is predominantly Mediterranean, achieving the correct flowering time in bread wheat minimizes the impact of in-season cyclical and terminal drought. Flag leaf glaucousness has been hypothesized as an important component of drought tolerance but its value and genetic basis in locally adapted germplasm is unknown. From a cross between Kukri and RAC875, a doubled-haploid (DH) population was developed. A genetic linkage map consisting of 456 DArT and SSR markers was used to detect QTL affecting time to ear emergence and Zadoks growth score in seven field experiments. While ear emergence time was similar between the parents, there was significant transgressive segregation in the population. This was the result of segregation for the previously characterized Ppd-D1a and Ppd-B1 photoperiod responsive alleles. QTL of smaller effect were also detected on chromosomes 1A, 4A, 4B, 5A, 5B, 7A and 7B. A novel QTL for flag leaf glaucousness of large, repeatable effect was detected in six field experiments, on chromosome 3A (QW.aww-3A) and accounted for up to 52 percent of genetic variance for this trait. QW.aww-3A was validated under glasshouse conditions in a recombinant inbred line population from the same cross. The genetic basis of time to ear emergence in this population will aid breeders' understanding of phenological adaptation to the local environment. Novel loci identified for flag leaf glaucousness and the wide phenotypic variation within the DH population offers considerable scope to investigate the impact and value of this trait for bread wheat production in southern Australia.

Genetic and genomic tools to improve drought tolerance in wheat.

Tolerance to drought is a quantitative trait, with a complex phenotype, often confounded by plant phenology. Breeding for drought tolerance is further complicated since several types of abiotic stress, such as high temperatures, high irradiance, and nutrient toxicities or deficiencies can challenge crop plants simultaneously. Although marker-assisted selection is now widely deployed in wheat, it has not contributed significantly to cultivar improvement for adaptation to low-yielding environments and breeding has relied largely on direct phenotypic selection for improved performance in these difficult environments. The limited success of the physiological and molecular breeding approaches now suggests that a careful rethink is needed of our strategies in order to understand better and breed for drought tolerance. A research programme for increasing drought tolerance of wheat should tackle the problem in a multi-disciplinary approach, considering interaction between multiple stresses and plant phenology, and integrating the physiological dissection of drought-tolerance traits and the genetic and genomics tools, such as quantitative trait loci (QTL), microarrays, and transgenic crops. In this paper, recent advances in the genetics and genomics of drought tolerance in wheat and barley are reviewed and used as a base for revisiting approaches to analyse drought tolerance in wheat. A strategy is then described where a specific environment is targeted and appropriate germplasm adapted to the chosen environment is selected, based on extensive definition of the morpho-physiological and molecular mechanisms of tolerance of the parents. This information was used to create structured populations and develop models for QTL analysis and positional cloning.

Diversity arrays technology (DArT) for high-throughput profiling of the hexaploid wheat genome.

Despite a substantial investment in the development of panels of single nucleotide polymorphism (SNP) markers, the simple sequence repeat (SSR) technology with a limited multiplexing capability remains a standard, even for applications requiring whole-genome information. Diversity arrays technology (DArT) types hundreds to thousands of genomic loci in parallel, as previously demonstrated in a number diploid plant species. Here we show that DArT performs similarly well for the hexaploid genome of bread wheat (Triticum aestivum L.). The methodology previously used to generate DArT fingerprints of barley also generated a large number of high-quality markers in wheat (99.8% allele-calling concordance and approximately 95% call rate). The genetic relationships among bread wheat cultivars revealed by DArT coincided with knowledge generated with other methods, and even closely related cultivars could be distinguished. To verify the Mendelian behaviour of DArT markers, we typed a set of 90 Cranbrook x Halberd doubled haploid lines for which a framework (FW) map comprising a total of 339 SSR, restriction fragment length polymorphism (RFLP) and amplified fragment length polymorphism (AFLP) markers was available. We added an equal number of DArT markers to this data set and also incorporated 71 sequence tagged microsatellite (STM) markers. A comparison of logarithm of the odds (LOD) scores, call rates and the degree of genome coverage indicated that the quality and information content of the DArT data set was comparable to that of the combined SSR/RFLP/AFLP data set of the FW map.