Simultaneous improvement of grain yield and protein content in durum wheat by different phenotypic indices and genomic selection

Key message

Simultaneous improvement of protein content and grain yield by index selection is possible but its efficiency largely depends on the weighting of the single traits. The genetic architecture of these indices is similar to that of the primary traits.

Abstract

Grain yield and protein content are of major importance in durum wheat breeding, but their negative correlation has hampered their simultaneous improvement. To account for this in wheat breeding, the grain protein deviation (GPD) and the protein yield were proposed as targets for selection. The aim of this work was to investigate the potential of different indices to simultaneously improve grain yield and protein content in durum wheat and to evaluate their genetic architecture towards genomics-assisted breeding. To this end, we investigated two different durum wheat panels comprising 159 and 189 genotypes, which were tested in multiple field locations across Europe and genotyped by a genotyping-by-sequencing approach. The phenotypic analyses revealed significant genetic variances for all traits and heritabilities of the phenotypic indices that were in a similar range as those of grain yield and protein content. The GPD showed a high and positive correlation with protein content, whereas protein yield was highly and positively correlated with grain yield. Thus, selecting for a high GPD would mainly increase the protein content whereas a selection based on protein yield would mainly improve grain yield, but a combination of both indices allows to balance this selection. The genome-wide association mapping revealed a complex genetic architecture for all traits with most QTL having small effects and being detected only in one germplasm set, thus limiting the potential of marker-assisted selection for trait improvement. By contrast, genome-wide prediction appeared promising but its performance strongly depends on the relatedness between training and prediction sets.

     

Phenotypic and genetic analysis of spike and kernel characteristics in wheat reveals long-term genetic trends of grain yield components

Key message

Phenotypic and genetic analysis of six spike and kernel characteristics in wheat revealed geographic patterns as well as long-term trends arising from breeding progress, particularly in regard to spikelet fertility, i.e. the number of kernels per spikelet, a grain yield component that appears to underlie the increase in the number of kernels per spike.

Abstract

Wheat is a staple crop of global relevance that faces continuous demands for improved grain yield. In this study, we evaluated a panel of 407 winter wheat cultivars for six characteristics of spike and kernel development. All traits showed a large genotypic variation and had high heritabilities. We observed geographic patterns for some traits in addition to long-term trends showing a continuous increase in the number of kernels per spike. This breeding progress is likely due to the increase in spikelet fertility, i.e. the number of kernels per spikelet. While the number of kernels per spike and spikelet fertility were significantly positively correlated, both traits showed a significant negative correlation with thousand-kernel weight. Genome-wide association mapping identified only small- and moderate-effect QTL and an effect of the phenology loci Rht-D1 and Ppd-D1 on some of the traits. The allele frequencies of some QTL matched the observed geographic patterns. The quantitative inheritance of all traits with contributions of additional small-effect QTL was substantiated by genomic prediction. Taken together, our results suggest that some of the examined traits were already the basis of grain yield progress in wheat in the past decades. A more targeted exploitation of the available variation, potentially coupled with genomic approaches, may assist wheat breeding in continuing to increase yield levels globally.

     

Identification and validation of QTL for grain yield and plant water status under contrasting water treatments in fall-sown spring wheats

Key message

Chromosome regions affecting grain yield, grain yield components and plant water status were identified and validated in fall-sown spring wheats grown under full and limited irrigation.

Abstract

Increases in wheat production are required to feed a growing human population. To understand the genetic basis of grain yield in fall-sown spring wheats, we performed a genome-wide association study (GWAS) including 262 photoperiod-insensitive spring wheat accessions grown under full and limited irrigation treatments. Analysis of molecular variance showed that 4.1% of the total variation in the panel was partitioned among accessions originally developed under fall-sowing or spring-sowing conditions, 11.7% among breeding programs within sowing times and 84.2% among accessions within breeding programs. We first identified QTL for grain yield, yield components and plant water status that were significant in at least three environments in the GWAS, and then selected those that were also significant in at least two environments in a panel of eight biparental mapping populations. We identified and validated 14 QTL for grain yield, 15 for number of spikelets per spike, one for kernel number per spike, 11 for kernel weight and 9 for water status, which were not associated with differences in plant height or heading date. We detected significant correlations among traits and colocated QTL that were consistent with those correlations. Among those, grain yield and plant water status were negatively correlated in all environments, and six QTL for these traits were colocated or tightly linked (<?1 cM). QTL identified and validated in this study provide useful information for the improvement of fall-sown spring wheats under full and limited irrigation.

     

Defeating the Warrior: genetic architecture of triticale resistance against a novel aggressive yellow rust race

Key message

Genome-wide association mapping of resistance against the novel, aggressive ‘Warrior’ race of yellow rust in triticale revealed a genetic architecture with some medium-effect QTL and a quantitative component, which in combination confer high levels of resistance on both leaves and ears.

Abstract

Yellow rust is an important destructive fungal disease in small grain cereals and the exotic ‘Warrior’ race has recently conquered Europe. The aim of this study was to investigate the genetic architecture of yellow rust resistance in hexaploid winter triticale as the basis for a successful resistance breeding. To this end, a diverse panel of 919 genotypes was evaluated for yellow rust infection on leaves and ears in multi-location field trials and genotyped by genotyping-by-sequencing as well as for known Yr resistance loci. Genome-wide association mapping identified ten quantitative trait loci (QTL) for yellow rust resistance on the leaves and seven of these also for ear resistance. The total genotypic variance explained by the QTL amounted to 44.0% for leaf and 26.0% for ear resistance. The same three medium-effect QTL were identified for both traits on chromosomes 1B, 2B, and 7B. Interestingly, plants pyramiding the resistance allele of all three medium-effect QTL were generally most resistant, but constitute less than 5% of the investigated triticale breeding material. Nevertheless, a genome-wide prediction yielded a higher predictive ability than prediction based on these three QTL. Taken together, our results show that yellow rust resistance in winter triticale is genetically complex, including both medium-effect QTL as well as a quantitative resistance component. Resistance to the novel ‘Warrior’ race of this fungal pathogen is consequently best achieved by recurrent selection in the field based on identified resistant lines and can potentially be assisted by genomic approaches.

     

Assessing the variation and genetic architecture of asparagine content in wheat: What can plant breeding contribute to a reduction in the acrylamide precursor?

Key message

A large genetic variation, moderately high heritability, and promising prediction ability for genomic selection show that wheat breeding can substantially reduce the acrylamide forming potential in bread wheat by a reduction in its precursor asparagine.

Abstract

Acrylamide is a potentially carcinogenic substance that is formed in baked products of wheat via the Maillard reaction from carbonyl sources and asparagine. In bread, the acrylamide content increases almost linearly with the asparagine content of the wheat grains. Our objective was, therefore, to investigate the potential of wheat breeding to contribute to a reduction in acrylamide by decreasing the asparagine content in wheat grains. To this end, we evaluated 149 wheat varieties from Central Europe at three locations for asparagine content, as well as for sulfur content, and five important quality traits regularly assessed in bread wheat breeding. The mean asparagine content ranged from 143.25 to 392.75 mg/kg for the different wheat varieties, thus underlining the possibility to reduce the acrylamide content of baked wheat products considerably by selecting appropriate varieties. Furthermore, a moderately high heritability of 0.65 and no negative correlations with quality traits like protein content, sedimentation volume and falling number show that breeding of quality wheat with low asparagine content is feasible. Genome-wide association mapping identified few QTL for asparagine content, the largest explaining 18% of the genotypic variance. Combining these QTL with a genome-wide prediction approach yielded a mean cross-validated prediction ability of 0.62. As we observed a high genotype-by-environment interaction for asparagine content, we recommend the costly and slow laboratory analysis only for late breeding generations, while selection in early generations could be based on marker-assisted or genomic selection.

     

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.

Abstract

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 Axiom^TM 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.

     

Modelling the genetic architecture of flowering time control in barley through nested association mapping

Background

Barley, globally the fourth most important cereal, provides food and beverages for humans and feed for animal husbandry. Maximizing grain yield under varying climate conditions largely depends on the optimal timing of flowering. Therefore, regulation of flowering time is of extraordinary importance to meet future food and feed demands. We developed the first barley nested association mapping (NAM) population, HEB-25, by crossing 25 wild barleys with one elite barley cultivar, and used it to dissect the genetic architecture of flowering time.

Results

Upon cultivation of 1,420 lines in multi-field trials and applying a genome-wide association study, eight major quantitative trait loci (QTL) were identified as main determinants to control flowering time in barley. These QTL accounted for 64% of the cross-validated proportion of explained genotypic variance (p_G). The strongest single QTL effect corresponded to the known photoperiod response gene Ppd-H1. After sequencing the causative part of Ppd-H1, we differentiated twelve haplotypes in HEB-25, whereof the strongest exotic haplotype accelerated flowering time by 11 days compared to the elite barley haplotype. Applying a whole genome prediction model including main effects and epistatic interactions allowed predicting flowering time with an unmatched accuracy of 77% of cross-validated p_G.

Conclusions

The elaborated causal models represent a fundamental step to explain flowering time in barley. In addition, our study confirms that the exotic biodiversity present in HEB-25 is a valuable toolbox to dissect the genetic architecture of important agronomic traits and to replenish the elite barley breeding pool with favorable, trait-improving exotic alleles.

     

QTL mapping and comparative genome analysis of agronomic traits including grain yield in winter rye

Key message

Genetic diversity in elite rye germplasm as well as F _2:3 testcross design enables fast QTL mapping to approach genes controlling grain yield, grain weight, tiller number and heading date in rye hybrids.

Abstract

Winter rye (Secale cereale L.) is a multipurpose cereal crop closely related to wheat, which offers the opportunity for a sustainable production of food and feed and which continues to emerge as a renewable energy source for the production of bioethanol and biomethane. Rye contributes to increase agricultural crop species diversity particularly in Central and Eastern Europe. In contrast to other small grain cereals, knowledge on the genetic architecture of complex inherited, agronomic important traits is yet limited for the outbreeding rye. We have performed a QTL analysis based on a F_2:3 design and testcross performance of 258 experimental hybrids in multi-environmental field trials. A genetic linkage map covering 964.9 cM based on SSR, conserved-orthologous set (COS), and mixed-phase dominant DArT markers allowed to describe 22 QTL with significant effects for grain yield, heading date, tiller number, and thousand grain weight across seven environments. Using rye COS markers, orthologous segments for these traits have been identified in the rice genome, which carry cloned and functionally characterized rice genes. The initial genome scan described here together with the existing knowledge on candidate genes provides the basis for subsequent analyses of the genetic and molecular mechanisms underlying agronomic important traits in rye.

     

Genomic prediction ability for yield-related traits in German winter barley elite material

Key message

Genomic prediction was evaluated in German winter barley breeding lines. In this material, prediction ability is strongly influenced by population structure and main determinant of prediction ability is the close genetic relatedness of the breeding material.

Abstract

To ensure breeding progress under changing environmental conditions the implementation and evaluation of new breeding methods is of crucial importance. Modern breeding approaches like genomic selection may significantly accelerate breeding progress. We assessed the potential of genomic prediction in a training population of 750 genotypes, consisting of multiple six-rowed winter barley (Hordeum vulgare L.) elite material families and old cultivars, which reflect the breeding history of barley in Germany. Crosses of parents selected from the training set were used to create a set of double-haploid families consisting of 750 genotypes. Those were used to confirm prediction ability estimates based on a cross-validation with the training set material using 11 different genomic prediction models. Population structure was inferred with dimensionality reduction methods like discriminant analysis of principle components and the influence of population structure on prediction ability was investigated. In addition to the size of the training set, marker density is of crucial importance for genomic prediction. We used genome-wide linkage disequilibrium and persistence of linkage phase as indicators to estimate that 11,203 evenly spaced markers are required to capture all QTL effects. Although a 9k SNP array does not contain a sufficient number of polymorphic markers for long-term genomic selection, we obtained fairly high prediction accuracies ranging from 0.31 to 0.71 for the traits earing, hectoliter weight, spikes per square meter, thousand kernel weight and yield and show that they result from the close genetic relatedness of the material. Our work contributes to designing long-term genetic prediction programs for barley breeding.

     

Multi-year linkage and association mapping confirm the high number of genomic regions involved in oilseed rape quantitative resistance to blackleg

Key message

A repertoire of the genomic regions involved in quantitative resistance to Leptosphaeria maculans in winter oilseed rape was established from combined linkage-based QTL and genome-wide association (GWA) mapping.

Abstract

Linkage-based mapping of quantitative trait loci (QTL) and genome-wide association studies are complementary approaches for deciphering the genomic architecture of complex agronomical traits. In oilseed rape, quantitative resistance to blackleg disease, caused by L. maculans, is highly polygenic and is greatly influenced by the environment. In this study, we took advantage of multi-year data available on three segregating populations derived from the resistant cv Darmor and multi-year data available on oilseed rape panels to obtain a wide overview of the genomic regions involved in quantitative resistance to this pathogen in oilseed rape. Sixteen QTL regions were common to at least two biparental populations, of which nine were the same as previously detected regions in a multi-parental design derived from different resistant parents. Eight regions were significantly associated with quantitative resistance, of which five on A06, A08, A09, C01 and C04 were located within QTL support intervals. Homoeologous Brassica napus genes were found in eight homoeologous QTL regions, which corresponded to 657 pairs of homoeologous genes. Potential candidate genes underlying this quantitative resistance were identified. Genomic predictions and breeding are also discussed, taking into account the highly polygenic nature of this resistance.

     

Comparative genome-wide mapping versus extreme pool-genotyping and development of diagnostic SNP markers linked to QTL for adult plant resistance to stripe rust in common wheat

Key message

A major stripe rust resistance QTL on chromosome 4BL was localized to a 4.5-Mb interval using comparative QTL mapping methods and validated in 276 wheat genotypes by haplotype analysis.

Abstract

CYMMIT-derived wheat line P10103 was previously identified to have adult plant resistance (APR) to stripe rust in the greenhouse and field. The conventional approach for QTL mapping in common wheat is laborious. Here, we performed QTL detection of APR using a combination of genome-wide scanning and extreme pool-genotyping. SNP-based genetic maps were constructed using the Wheat55 K SNP array to genotype a recombinant inbred line (RIL) population derived from the cross Mingxian 169?×?P10103. Five stable QTL were detected across multiple environments. After comparing SNP profiles from contrasting, extreme DNA pools of RILs six putative QTL were located to approximate chromosome positions. A major QTL on chromosome 4B was identified in F_2:4 contrasting pools from cross Zhengmai 9023?×?P10103. A consensus QTL (LOD?=?26–40, PVE?=?42–55%), named QYr.nwafu-4BL, was defined and localized to a 4.5-Mb interval flanked by SNP markers AX-110963704 and AX-110519862 in chromosome arm 4BL. Based on stripe rust response, marker genotypes, pedigree analysis and mapping data, QYr.nwafu-4BL is likely to be a new APR QTL. The applicability of the SNP-based markers flanking QYr.nwafu-4BL was validated on a diversity panel of 276 wheat lines. The additional minor QTL on chromosomes 4A, 5A, 5B and 6A enhanced the level of resistance conferred by QYr.nwafu-4BL. Marker-assisted pyramiding of QYr.nwafu-4BL and other favorable minor QTL in new wheat cultivars should improve the level of APR to stripe rust.

     

Incorporating pleiotropic quantitative trait loci in dissection of complex traits: seed yield in rapeseed as an example

Key message

A comprehensive linkage atlas for seed yield in rapeseed.

Abstract

Most agronomic traits of interest for crop improvement (including seed yield) are highly complex quantitative traits controlled by numerous genetic loci, which brings challenges for comprehensively capturing associated markers/genes. We propose that multiple trait interactions underlie complex traits such as seed yield, and that considering these component traits and their interactions can dissect individual quantitative trait loci (QTL) effects more effectively and improve yield predictions. Using a segregating rapeseed (Brassica napus) population, we analyzed a large set of trait data generated in 19 independent experiments to investigate correlations between seed yield and other complex traits, and further identified QTL in this population with a SNP-based genetic bin map. A total of 1904 consensus QTL accounting for 22 traits, including 80 QTL directly affecting seed yield, were anchored to the B. napus reference sequence. Through trait association analysis and QTL meta-analysis, we identified a total of 525 indivisible QTL that either directly or indirectly contributed to seed yield, of which 295 QTL were detected across multiple environments. A majority (81.5%) of the 525 QTL were pleiotropic. By considering associations between traits, we identified 25 yield-related QTL previously ignored due to contrasting genetic effects, as well as 31 QTL with minor complementary effects. Implementation of the 525 QTL in genomic prediction models improved seed yield prediction accuracy. Dissecting the genetic and phenotypic interrelationships underlying complex quantitative traits using this method will provide valuable insights for genomics-based crop improvement.

     

Genome-wide association study for agronomic and physiological traits in spring wheat evaluated in a range of heat prone environments

Key message

We identified 27 stable loci associated with agronomic traits in spring wheat using genome-wide association analysis, some of which confirmed previously reported studies. GWAS peaks identified in regions where no QTL for grain yield per se has been mapped to date, provide new opportunities for gene discovery and creation of new cultivars with desirable alleles for improving yield and yield stability in wheat.

Abstract

We undertook large-scale genetic analysis to determine marker-trait associations (MTAs) underlying agronomic and physiological performance in spring wheat using genome-wide association studies (GWAS). Field trials were conducted at seven sites in three countries (Sudan, Egypt, and Syria) over 2–3 years in each country. Twenty-five agronomic and physiological traits were measured on 188 wheat genotypes. After correcting for population structure and relatedness, a total of 245 MTAs distributed over 66 loci were associated with agronomic traits in individual and mean performance across environments respectively; some of which confirmed previously reported loci. Of these, 27 loci were significantly associated with days to heading, thousand kernel weight, grain yield, spike length, and leaf rolling for mean performance across environments. Despite strong QTL by environment interactions, eight of the loci on chromosomes 1A, 1D, 5A, 5D, 6B, 7A, and 7B had pleiotropic effects on days to heading and yield components (TKW, SM^?2, and SNS). The winter-type alleles at the homoeologous VRN1 loci significantly increased days to heading and grain yield in optimal environments, but decreased grain yield in heat prone environments. Top 20 high-yielding genotypes, ranked by additive main effects and multiplicative interaction (AMMI), had low kinship relationship and possessed 4–5 favorable alleles for GY MTAs except two genotypes, Shadi-4 and Qafzah-11/Bashiq-1–2. This indicated different yield stability mechanisms due to potentially favorable rare alleles that are uncharacterized. Our results will enable wheat breeders to effectively introgress several desirable alleles into locally adapted germplasm in developing wheat varieties with high yield stability and enhanced heat tolerance.

     

Improving the baking quality of bread wheat by genomic selection in early generations

Key message

Genomic selection shows great promise for pre-selecting lines with superior bread baking quality in early generations, 3 years ahead of labour-intensive, time-consuming, and costly quality analysis.

Abstract

The genetic improvement of baking quality is one of the grand challenges in wheat breeding as the assessment of the associated traits often involves time-consuming, labour-intensive, and costly testing forcing breeders to postpone sophisticated quality tests to the very last phases of variety development. The prospect of genomic selection for complex traits like grain yield has been shown in numerous studies, and might thus be also an interesting method to select for baking quality traits. Hence, we focused in this study on the accuracy of genomic selection for laborious and expensive to phenotype quality traits as well as its selection response in comparison with phenotypic selection. More than 400 genotyped wheat lines were, therefore, phenotyped for protein content, dough viscoelastic and mixing properties related to baking quality in multi-environment trials 2009–2016. The average prediction accuracy across three independent validation populations was r = 0.39 and could be increased to r = 0.47 by modelling major QTL as fixed effects as well as employing multi-trait prediction models, which resulted in an acceptable prediction accuracy for all dough rheological traits (r = 0.38–0.63). Genomic selection can furthermore be applied 2–3 years earlier than direct phenotypic selection, and the estimated selection response was nearly twice as high in comparison with indirect selection by protein content for baking quality related traits. This considerable advantage of genomic selection could accordingly support breeders in their selection decisions and aid in efficiently combining superior baking quality with grain yield in newly developed wheat varieties.

     

Development of an integrated linkage map of einkorn wheat and its application for QTL mapping and genome sequence anchoring

Key message

An integrated genetic map was constructed for einkorn wheat A genome and provided valuable information for QTL mapping and genome sequence anchoring.

Abstract

Wheat is one of the most widely grown food grain crops in the world. The construction of a genetic map is a key step to organize biologically or agronomically important traits along the chromosomes. In the present study, an integrated linkage map of einkorn wheat was developed using 109 recombinant inbred lines (RILs) derived from an inter sub-specific cross, KT1-1 (T. monococcum ssp. boeoticum) × KT3-5 (T. monococcum ssp. monococcum). The map contains 926 molecular markers assigned to seven linkage groups, and covers 1,377 cM with an average marker interval of 1.5 cM. A quantitative trait locus (QTL) analysis of five agronomic traits identified 16 stable QTL on all seven chromosomes, except 6A. The total phenotypic variance explained by these stable QTL using multiple regressions varied across environments from 8.8 to 87.1 % for days to heading, 24.4–63.0 % for spike length, 48.2–79.6 % for spikelet number per spike, 13.1–48.1 % for plant architecture, and 12.2–26.5 % for plant height, revealing that much of the RIL phenotypic variation had been genetically dissected. Co-localizations of closely linked QTL for different traits were frequently observed, especially on 3A and 7A. The QTL on 3A, 5A and 7A were closely associated with Eps-A ^m 3, Vrn1 and Vrn3 loci, respectively. Furthermore, this genetic map facilitated the anchoring of 237 T. urartu scaffolds onto seven chromosomes with a physical length of 26.15 Mb. This map and the QTL data provide valuable genetic information to dissect important agronomic and developmental traits in diploid wheat and contribute to the genetic ordering of the genome assembly.

     

Unlocking new alleles for leaf rust resistance in the Vavilov wheat collection

Key message

Thirteen potentially new leaf rust resistance loci were identified in a Vavilov wheat diversity panel. We demonstrated the potential of allele stacking to strengthen resistance against this important pathogen.

Abstract

Leaf rust (LR) caused by Puccinia triticina is an important disease of wheat (Triticum aestivum L.), and the deployment of genetically resistant cultivars is the most viable strategy to minimise yield losses. In this study, we evaluated a diversity panel of 295 bread wheat accessions from the N. I. Vavilov Institute of Plant Genetic Resources (St Petersburg, Russia) for LR resistance and performed genome-wide association studies (GWAS) using 10,748 polymorphic DArT-seq markers. The diversity panel was evaluated at seedling and adult plant growth stages using three P. triticina pathotypes prevalent in Australia. GWAS was applied to 11 phenotypic data sets which identified a total of 52 significant marker–trait associations representing 31 quantitative trait loci (QTL). Among them, 29 QTL were associated with adult plant resistance (APR). Of the 31 QTL, 13 were considered potentially new loci, whereas 4 co-located with previously catalogued Lr genes and 14 aligned to regions reported in other GWAS and genomic prediction studies. One seedling LR resistance QTL located on chromosome 3A showed pronounced levels of linkage disequilibrium among markers (r ² = 0.7), suggested a high allelic fixation. Subsequent haplotype analysis for this region found seven haplotype variants, of which two were strongly associated with LR resistance at seedling stage. Similarly, analysis of an APR QTL on chromosome 7B revealed 22 variants, of which 4 were associated with resistance at the adult plant stage. Furthermore, most of the tested lines in the diversity panel carried 10 or more combined resistance-associated marker alleles, highlighting the potential of allele stacking for long-lasting resistance.

     

Genetic dissection of sorghum grain quality traits using diverse and segregating populations

Key message

Coordinated association and linkage mapping identified 25 grain quality QTLs in multiple environments, and fine mapping of the Wx locus supports the use of high-density genetic markers in linkage mapping.

Abstract

There is a wide range of end-use products made from cereal grains, and these products often demand different grain characteristics. Fortunately, cereal crop species including sorghum [Sorghum bicolor (L.) Moench] contain high phenotypic variation for traits influencing grain quality. Identifying genetic variants underlying this phenotypic variation allows plant breeders to develop genotypes with grain attributes optimized for their intended usage. Multiple sorghum mapping populations were rigorously phenotyped across two environments (SC Coastal Plain and Central TX) in 2 years for five major grain quality traits: amylose, starch, crude protein, crude fat, and gross energy. Coordinated association and linkage mapping revealed several robust QTLs that make prime targets to improve grain quality for food, feed, and fuel products. Although the amylose QTL interval spanned many megabases, the marker with greatest significance was located just 12 kb from waxy (Wx), the primary gene regulating amylose production in cereal grains. This suggests higher resolution mapping in recombinant inbred line (RIL) populations can be obtained when genotyped at a high marker density. The major QTL for crude fat content, identified in both a RIL population and grain sorghum diversity panel, encompassed the DGAT1 locus, a critical gene involved in maize lipid biosynthesis. Another QTL on chromosome 1 was consistently mapped in both RIL populations for multiple grain quality traits including starch, crude protein, and gross energy. Collectively, these genetic regions offer excellent opportunities to manipulate grain composition and set up future studies for gene validation.

     

Identification and validation of QTL and their associated genes for pre-emergent metribuzin tolerance in hexaploid wheat (<Emphasis Type="Italic">Triticum aestivum</Emphasis> L.)

Background

Herbicide tolerance is an important trait that allows effective weed management in wheat crops. Genetic knowledge of metribuzin tolerance in wheat is needed to develop new cultivars for the industry. Here, we evaluated metribuzin tolerance in a recombinant inbred line (RIL) mapping population derived from Synthetic W7984 and Opata 85 over two consecutive years to identify quantitative trait loci (QTL) contributing to the trait. Herbicide tolerance was measured by two chlorophyll traits, SPAD chlorophyll content index (CCI) and visual senescence score (SNS). The markers associated with major QTL from Synthetic W7984, positively contributing to reduced phytotoxic effects under herbicide treatment were validated in two F_3/4 recombinant inbred populations developed from crosses of Synthetic W7984?×?Westonia and Synthetic W7984?×?Lang.

Results

Composite interval mapping (CIM) identified four QTL, two on chromosome 4A and one each on chromosomes 2D and 1A. The chromosomal position of the two QTL mapped on 4A within 10 cM intervals was refined and validated by multiple interval mapping (MIM). The major QTL affecting both measures of tolerance jointly explained 42 and 45% of the phenotypic variation by percentage CCI reduction and SNS, respectively. The identified QTL have a pure additive effect. The metribuzin tolerant allele of markers, Xgwm33 and Xbarc343, conferred lower phytotoxicity and explained the maximum phenotypic variation of 28.8 and 24.5%, respectively. The approximate physical localization of the QTL revealed the presence of five candidate genes (ribulose-bisphosphate carboxylase, oxidoreductase (rbcS), glycosyltransferase, serine/threonine-specific protein kinase and phosphotransferase) with a direct role in photosynthesis and/or metabolic detoxification pathways.

Conclusion

Metribuzin causes photo-inhibition by interrupting electron flow in PSII. Consequently, chlorophyll traits enabled the measure of high proportion of genetic variability in the mapping population. The validated molecular markers associated with metribuzin tolerance mediating QTL may be used in marker-assisted breeding to select metribuzin tolerant lines. Alternatively, validated favourable alleles could be introgressed into elite wheat cultivars to enhance metribuzin tolerance and improve grain yield in dryland farming for sustainable wheat production.

     

Translational genomics of grain size regulation in wheat

Key message

Identifying and mapping grain size candidate genes in the wheat genome greatly empowers reverse genetics approaches to improve grain yield potential of wheat.

Abstract

Grain size (GS) or grain weight is believed to be a major driving force for further improvement of wheat yield. Although the large, polyploid genome of wheat poses an obstacle to identifying GS determinants using map-based cloning, a translational genomics approach using GS regulators identified in the model plants rice and Arabidopsis as candidate genes appears to be effective and supports a hypothesis that a conserved genetic network regulates GS in rice and wheat. In this review, we summarize the progress in the studies on GS in the model plants and wheat and identify 45 GS candidate loci in the wheat genome. In silico mapping of these GS loci in the diploid wheat and barley genomes showed (1) several gene families amplified in the wheat lineage, (2) a significant number of the GS genes located in the proximal regions surrounding the centromeres, and (3) more than half of candidate genes to be negative regulators, or their expression negatively related by microRNAs. Identifying and mapping the wheat GS gene homologs will not only facilitate candidate gene analysis, but also open the door to improving wheat yield using reverse genetics approaches by mining desired alleles in landraces and wild ancestors and to developing novel germplasm by TILLING and genome editing technologies.

     

A genome-wide association study of malting quality across eight U.S. barley breeding programs

Key message

We report malt quality QTLs relevant to breeding with greater precision than previous mapping studies. The distribution of favorable alleles suggests strategies for marker-assisted breeding and germplasm exchange.

Abstract

This study leverages the breeding data of 1,862 barley breeding lines evaluated in 97 field trials for genome-wide association study of malting quality traits in barley. The mapping panel consisted of six-row and two-row advanced breeding lines from eight breeding populations established at six public breeding programs across the United States. A total of 4,976 grain samples were subjected to micro-malting analysis and mapping of nine quality traits was conducted with 3,072 SNP markers distributed throughout the genome. Association mapping was performed for individual breeding populations and for combined six-row and two-row populations. Only 16 % of the QTL we report here had been detected in prior bi-parental mapping studies. Comparison of the analyses of the combined two-row and six-row panels identified only two QTL regions that were common to both. In total, 108 and 107 significant marker-trait associations were identified in all six-row and all two-row breeding programs, respectively. A total of 102 and 65 marker-trait associations were specific to individual six-row and two-row breeding programs, respectively indicating that most marker-trait associations were breeding population specific. Combining datasets from different breeding program resulted in both the loss of some QTL that were apparent in the analyses of individual programs and the discovery of new QTL not identified in individual programs. This suggests that simply increasing sample size by pooling samples with different breeding history does not necessarily increase the power to detect associations. The genetic architecture of malting quality and the distribution of favorable alleles suggest strategies for marker-assisted selection and germplasm exchange.