QTL mapping of 1000-kernel weight, kernel length, and kernel width in bread wheat (Triticum aestivum L.)

Wheat individual grain-size variance originates from crop development and from specific genetic determinism

Wheat grain yield is usually decomposed in the yield components: number of spikes / m2, number of grains / spike, number of grains / m2 and thousand kernel weight (TKW). These are correlated one with another due to yield component compensation. Under optimal conditions, the number of grains per m2 has been identified as the main determinant of yield. However, with increasing occurrences of post-flowering abiotic stress associated with climate change, TKW may become severely limiting and hence a target for breeding. TKW is usually studied at the plot scale as it represents the average mass of a grain. However, this view disregards the large intra-genotypic variance of individual grain mass and its effect on TKW. The aim of this study is to investigate the determinism of the variance of individual grain size. We measured yield components and individual grain size variances of two large genetic wheat panels grown in two environments. We also carried out a genome-wide association study using a dense SNPs array. We show that the variance of individual grain size partly originates from the pre-flowering components of grain yield; in particular it is driven by canopy structure via its negative correlation with the number of spikes per m2. But the variance of final grain size also has a specific genetic basis. The genome-wide analysis revealed the existence of QTL with strong effects on the variance of individual grain size, independently from the other yield components. Finally, our results reveal some interesting drivers for manipulating individual grain size variance either through canopy structure or through specific chromosomal regions.

Dissection of Superior Alleles for Yield-Related Traits and Their Distribution in Important Cultivars of Wheat by Association Mapping

Uncovering the genetic basis of yield-related traits is important for molecular improvement of wheat cultivars. In this study, a genome-wide association study was conducted using the wheat 55K genotyping assay and a diverse panel of 384 wheat genotypes. The accessions used included 18 founder parents and 15 widely grown cultivars with annual maximum acreages of over 667,000 ha, and the remaining materials were elite cultivars and breeding lines from several major wheat ecological areas of China. Field trials were conducted in five major wheat ecological regions of China over three consecutive years. A total of 460 significant loci were detected for eight yield-related traits. Forty-five superior alleles distributed over 31 loci for which differences in phenotypic values grouped by single nucleotide polymorphism (SNP) reached significant levels (P < 0.05) in nine or more environments, were detected; some of these loci were previously reported. Eleven of the 31 superior allele loci on chromosomes 4A, 5A, 3B, 5B, 6B, 7B, 5D, and 7D had pleiotropic effects. For example, AX-95152512 on 5D was simultaneously related to increased grain weight per spike (GWS) and decreased plant height (PH); AX-109860828 on 5B simultaneously led to a high 1,000-kernel weight (TKW) and short PH; and AX-111600193 on 4A was simultaneously linked to a high TKW and GWS, and short PH. The favorable alleles in each accession ranged from 2 to 30 with an average of 16 at the thirty-one loci in the population, and six accessions (Zhengzhou683, Suzhou7829, Longchun7, Ningmai6, Yunmai35 and Zhen7630) contained more than 27 favorable alleles. A significant association between the number of favorable alleles and yield was observed (r = 0.799, p < 0.0001), suggesting that pyramiding multiple QTL with marker-assisted selection may effectively increase yield of wheat. Furthermore, distribution of superior alleles in founder parents and widely grown cultivars was also discussed here. This study is useful for marker-assisted selection for yield improvement and dissecting the genetic mechanism of important cultivars in wheat.

QTL Mapping of Kernel Traits and Validation of a Major QTL for Kernel Length-Width Ratio Using SNP and Bulked Segregant Analysis in Wheat

One RIL population derived from the cross between Dalibao and BYL8 was used to examine the phenotypes of kernel-related traits in four different environments. Six important kernel traits, kernel length (KL), kernel width (KW), kernel perimeter (KP), kernel area (KA), kernel length/width ratio (KLW), and thousand-kernel weight (TKW) were evaluated in Yangling, Shaanxi Province, China (2016 and 2017), Nanyang, Henan Province, China (2017) and Suqian, Jiangsu Province, China (2017). A genetic linkage map was constructed using 205 SSR markers, and a total of 21 significant QTLs for KL, KW, KP, KA, KLW and TKW were located on 10 of the 21 wheat chromosomes, including 1A, 1B, 2A, 2B, 2D, 3D, 4D, 5A, 5B, and 7D, with a single QTL in different environments explaining 3.495–30.130% of the phenotypic variation. There were four loci for KLW, five for KA, five for KL, three for KP, two for KW, and two for TKW among the detected QTLs. We used BSA + 660 K gene chip technology to reveal the positions of major novel QTLs for KLW. A total of 670 out of 5285 polymorphic SNPs were detected on chromosome 2A. The SNPs in 2A are most likely related to the major QTL, and there may be minor QTLs on 5B, 7A, 3A and 4B. SSR markers were developed to verify the chromosome region associated with KLW. A linkage map was constructed with 7 SSR markers, and a major effect QTL was identified within a 21.55 cM interval, corresponding to a physical interval of 10.8 Mb in the Chinese Spring RefSeq v1.0 sequence. This study can provide useful information for subsequent construction of fine mapping and marker-assisted selection breeding.

Dissection of genetic factors underlying grain size and fine mapping of QTgw.cau-7D in common wheat (Triticum aestivum L.)

Thirty environmentally stable QTL controlling grain size and/or plant height were identified, among which QTgw.cau-7D was delimited into the physical interval of approximately 4.4 Mb. Grain size and plant height (PHT) are important agronomic traits in wheat breeding. To dissect the genetic basis of these traits, we conducted a quantitative trait locus (QTL) analysis using recombinant inbred lines (RILs). In total, 30 environmentally stable QTL for thousand grain weight (TGW), grain length (GL), grain width (GW) and PHT were detected. Notably, one major pleiotropic QTL on chromosome arm 3DS explained the highest phenotypic variance for TGW, GL and PHT, and two stable QTL (QGw.cau-4B and QGw.cau-7D) on chromosome arms 4BS and 7DS contributed greater effects for GW. Furthermore, the stable QTL controlling grain size (QTgw.cau-7D and QGw.cau-7D) were delimited into the physical interval of approximately 4.4 Mb harboring 56 annotated genes. The elite NILs of QTgw.cau-7D increased TGW by 12.79-21.75% and GW by 4.10-8.47% across all three environments. Collectively, these results provide further insight into the genetic basis of TGW, GL, GW and PHT, and the fine-mapped QTgw.cau-7D will be an attractive target for positional cloning and marker-assisted selection in wheat breeding programs.

Mapping quantitative trait loci for yield-related traits and predicting candidate genes for grain weight in maize

Quantitative trait loci (QTLs) mapped in different genetic populations are of great significance for marker-assisted breeding. In this study, an F_2:3 population were developed from the crossing of two maize inbred lines SG-5 and SG-7 and applied to QTL mapping for seven yield-related traits. The seven traits included 100-kernel weight, ear length, ear diameter, cob diameter, kernel row number, ear weight, and grain weight per plant. Based on an ultra-high density linkage map, a total of thirty-three QTLs were detected for the seven studied traits with composite interval mapping (CIM) method, and fifty-four QTLs were indentified with genome-wide composite interval mapping (GCIM) methods. For these QTLs, Fourteen were both detected by CIM and GCIM methods. Besides, eight of the thirty QTLs detected by CIM were identical to those previously mapped using a F₂ population (generating from the same cross as the mapping population in this study), and fifteen were identical to the reported QTLs in other recent studies. For the fifty-four QTLs detected by GCIM, five of them were consistent with the QTLs mapped in the F₂ population of SG-5 × SG-7, and twenty one had been reported in other recent studies. The stable QTLs associated with grain weight were located on maize chromosomes 2, 5, 7, and 9. In addition, differentially expressed genes (DEGs) between SG-5 and SG-7 were obtained from the transcriptomic profiling of grain at different developmental stages and overlaid onto the stable QTLs intervals to predict candidate genes for grain weight in maize. In the physical intervals of confirmed QTLs qKW-7, qEW-9, qEW-10, qGWP-6, qGWP-8, qGWP-10, qGWP-11 and qGWP-12, there were 213 DEGs in total. Finally, eight genes were predicted as candidate genes for grain size/weight. In summary, the stable QTLs would be reliable and the candidate genes predicted would be benefit for maker assisted breeding or cloning.

Identification of quantitative trait loci for kernel traits in a wheat cultivar Chuannong16

Background

Kernel length (KL), kernel width (KW) and thousand-kernel weight (TKW) are key agronomic traits in wheat breeding. Chuannong16 (‘CN16’) is a commercial cultivar with significantly longer kernels than the line ‘20828’. To identify and characterize potential alleles from CN16 controlling KL, the previously developed recombinant inbred line (RIL) population derived from the cross ‘20828’ × ‘CN16’ and the genetic map constructed by the Wheat55K SNP array and SSR markers were used to perform quantitative trait locus/loci (QTL) analyses for kernel traits.

Results

A total of 11 putative QTL associated with kernel traits were identified and they were located on chromosomes 1A (2 QTL), 2B (2 QTL), 2D (3 QTL), 3D, 4A, 6A, and 7A, respectively. Among them, three major QTL, QKL.sicau-2D, QKW.sicau-2D and QTKW.sicau-2D, controlling KL, KW and TKW, respectively, were detected in three different environments. Respectively, they explained 10.88–18.85%, 17.21–21.49% and 10.01–23.20% of the phenotypic variance. Further, they were genetically mapped in the same interval on chromosome 2DS. A previously developed kompetitive allele-specific PCR (KASP) marker KASP-AX-94721936 was integrated in the genetic map and QTL re-mapping finally located the three major QTL in a 1- cM region flanked by AX-111096297 and KASP-AX-94721936. Another two co-located QTL intervals for KL and TKW were also identified. A few predicted genes involved in regulation of kernel growth and development were identified in the intervals of these identified QTL. Significant relationships between kernel traits and spikelet number per spike and anthesis date were detected and discussed.

Conclusions

Three major and stably expressed QTL associated with KL, KW, and TKW were identified. A KASP marker tightly linked to these three major QTL was integrated. These findings provide information for subsequent fine mapping and cloning the three co-localized major QTL for kernel traits.

Frequent intra- and inter-species introgression shapes the landscape of genetic variation in bread wheat

BACKGROUND

Bread wheat is one of the most important and broadly studied crops. However, due to the complexity of its genome and incomplete genome collection of wild populations, the bread wheat genome landscape and domestication history remain elusive.

RESULTS

By investigating the whole-genome resequencing data of 93 accessions from worldwide populations of bread wheat and its diploid and tetraploid progenitors, together with 90 published exome-capture data, we find that the B subgenome has more variations than A and D subgenomes, including SNPs and deletions. Population genetics analyses support a monophyletic origin of domesticated wheat from wild emmer in northern Levant, with substantial introgressed genomic fragments from southern Levant. Southern Levant contributes more than 676 Mb in AB subgenomes and enriched in the pericentromeric regions. The AB subgenome introgression happens at the early stage of wheat speciation and partially contributes to their greater genetic diversity. Furthermore, we detect massive alien introgressions that originated from distant species through natural and artificial hybridizations, resulting in the reintroduction of ~ 709 Mb and ~ 1577 Mb sequences into bread wheat landraces and varieties, respectively. A large fraction of these intra- and inter-introgression fragments are associated with quantitative trait loci of important traits, and selection events are also identified.

CONCLUSION

We reveal the significance of multiple introgressions from distant wild populations and alien species in shaping the genetic components of bread wheat, and provide important resources and new perspectives for future wheat breeding.

QTL mapping for micronutrients concentration and yield component traits in a hexaploid wheat mapping population

Bread wheat is a major staple cereal provides more than 20% of dietary energy and protein supply to global population. However, with increasing population growth, the problem of nutritional deficiencies is increasingly affecting the health of resource people with predominantly cereal-based diet. Therefore, the development of wheat genotypes with micronutrient-dense grains along with high-yield potential is one of the major priorities of wheat biofortification program at CIMMYT. We conducted a QTL mapping study using a recombinant inbred line (RIL) population derived from a cross between a Chinese parental line with highGZnC and a Mexican commercial bread wheat cultivar Roelfs F2007 to identify QTLs that could potentially be integrated in mineral nutrient concentrations and agronomic-related traits breeding. We evaluated 200 RIL lines for mineral nutrient concentrations and agronomic-related traits over two years. A total of 60 QTLs were detected, of which 10 QTLs for GZnC, 9 for GFeC, 5 for GPC and 36 for agronomic-related traits. Moreover, a total of 55 promising candidate genes were identified from the list of associated markers for GFeC and GZnC using the recently annotated wheat genome sequence. We identified the promising genomic regions with high mineral nutrient concentrations and acceptable yield potential, which are good resource for further use in wheat biofortification breeding programs.

Genomic Regions From an Iranian Landrace Increase Kernel Size in Durum Wheat

Kernel size and shape are important parameters determining the wheat profitability, being main determinants of yield and its technological quality. In this study, a segregating population of 118 recombinant inbred lines, derived from a cross between the Iranian durum landrace accession "Iran_249" and the Iranian durum cultivar "Zardak", was used to investigate durum wheat kernel morphology factors and their relationships with kernel weight, and to map the corresponding QTLs. A high density genetic map, based on wheat 90k iSelect Infinium SNP assay, comprising 6,195 markers, was developed and used to perform the QTL analysis for kernel length and width, traits related to kernel shape and weight, and heading date, using phenotypic data from three environments. Overall, a total of 31 different QTLs and 9 QTL interactions for kernel size, and 21 different QTLs and 5 QTL interactions for kernel shape were identified. The landrace Iran_249 contributed the allele with positive effect for most of the QTLs related to kernel length and kernel weight suggesting that the landrace might have considerable potential toward enhancing the existing gene pool for grain shape and size traits and for further yield improvement in wheat. The correlation among traits and co-localization of corresponding QTLs permitted to define 11 clusters suggesting causal relationships between simplest kernel size trait, like kernel length and width, and more complex secondary trait, like kernel shape and weight related traits. Lastly, the recent release of the T. durum reference genome sequence allowed to define the physical interval of our QTL/clusters and to hypothesize novel candidate genes inspecting the gene content of the genomic regions associated to target traits.

QTL identification and KASP marker development for productive tiller and fertile spikelet numbers in two high-yielding hard white spring wheat cultivars

Selecting high-yielding wheat cultivars with more productive tillers per unit area (PTN) combined with more fertile spikelets per spike (fSNS) is difficult. QTL mapping of these traits may aid understanding of this bottleneck and accelerate precision breeding for high yield via marker-assisted selection. PTN and fSNS were assessed in four to five trials from 2015 to 2017 in a doubled haploid population derived from two high-yielding cultivars "UI Platinum" and "SY Capstone." Two QTL for PTN (QPTN.uia-4A and QPTN.uia-6A) and four QTL for fSNS (QfSNS.uia-4A, QfSNS.uia-5A, QfSNS.uia-6A, and QfSNS.uia-7A) were identified. The effects of the QTL were primarily additive and, therefore, pyramiding of multiple QTL may increase PTN and fSNS. However, the two QTL for PTN were positioned in the flanking regions for the two QTL for fSNS on chromosomes 4A and 6A, respectively, suggesting either possible pleiotropic effect of the same QTL or tightly linked QTL and explaining the difficulty of selecting both high PTN and fSNS in phenotypic selection. Kompetitive allele-specific PCR (KASP) markers for all identified QTL were developed and validated in a recombinant inbred line (RIL) population derived from the same two cultivars. In addition, KASP markers for three of the QTL (QPTN.uia-6A, QfSNS.uia-6A, and QfSNS.uia-7A) were further validated in a diverse spring wheat panel, indicating their usefulness under different genetic backgrounds. These KASP markers could be used by wheat breeders to select high PTN and fSNS.

Enhancement of grain number per spike by RNA interference of cytokinin oxidase 2 gene in bread wheat

Background

This study aimed to validate the function of CKX gene on grain numbers in wheat.

Methods

we constructed and transformed a RNA interference expression vector of TaCKX2.4 in bread wheat line NB1. Southern blotting analysis was used to select transgenic plants with single copy. The expression of TaCKX2.4 gene was estimated by Quantitative real-time PCR (qRT-PCR) analysis. Finally, the relation between expression of TaCKX2.4 gene and grain numbers was validated.

Results

Totally, 20 positive independent events were obtained. Homozygous lines from 5 events with a single copy of transformed gene each were selected to evaluate the expression of TaCKX2.4 and grain numbers per spike in T₃ generation. Compared with the control NB1, the average grain numbers per spike significantly increased by 12.6%, 8.3%, 6.5% and 5.8% in the T₃ lines JW39-3A, JW1-2B, JW1-1A and JW5-1A, respectively.

Conclusion

Our study indicated that the expression level of TaCKX2.4 was negatively correlated with the grain number per spike, indicating that the reduced expression of TaCKX2.4 increased grain numbers per spike in wheat.

QTL mapping for some grain traits in bread wheat (Triticum aestivum L.)

Grain traits are important agronomic attributes with the market value as well as milling yield of bread wheat. In the present study, quantitative trait loci (QTL) regulating grain traits in wheat were identified. Data for grain area size (GAS), grain width (GWid), factor form density (FFD), grain length-width ratio (GLWR), thousand grain weight (TGW), grain perimeter length (GPL) and grain length (GL) were recorded on a recombinant inbred line derived from the cross of NW1014 × HUW468 at Meerut and Varanasi locations. A linkage map of 55 simple sequence repeat markers for 8 wheat chromosomes was used for QTL analysis by Composite interval mapping. Eighteen QTLs distributed on 8 chromosomes were identified for seven grain traits. Of these, five QTLs for GLWR were found on chromosomes 1A, 6A, 2B, and 7B, three QTLs for GPL were located on chromosomes 4A, 5A and 7B and three QTLs for GAS were mapped on 5D and 7D. Two QTLs were identified on chromosomes 4A and 5A for GL and two QTLs for GWid were identified on chromosomes 7D and 6A. Similarly, two QTLs for FFD were found on chromosomes 1A and 5D. A solitary QTL for TGW was identified on chromosome 2B. For several traits, QTLs were also co-localized on chromosomes 2B, 4A, 5A, 6A, 5D, 7B and 7D. The QTLs detected in the present study may be validated for specific crosses and then used for marker-assisted selection to improve grain quality in bread wheat.

Genome-wide linkage mapping of yield-related traits in three Chinese bread wheat populations using high-density SNP markers

We identified 21 new and stable QTL, and 11 QTL clusters for yield-related traits in three bread wheat populations using the wheat 90 K SNP assay. Identification of quantitative trait loci (QTL) for yield-related traits and closely linked molecular markers is important in order to identify gene/QTL for marker-assisted selection (MAS) in wheat breeding. The objectives of the present study were to identify QTL for yield-related traits and dissect the relationships among different traits in three wheat recombinant inbred line (RIL) populations derived from crosses Doumai × Shi 4185 (D × S), Gaocheng 8901 × Zhoumai 16 (G × Z) and Linmai 2 × Zhong 892 (L × Z). Using the available high-density linkage maps previously constructed with the wheat 90 K iSelect single nucleotide polymorphism (SNP) array, 65, 46 and 53 QTL for 12 traits were identified in the three RIL populations, respectively. Among them, 34, 23 and 27 were likely to be new QTL. Eighteen common QTL were detected across two or three populations. Eleven QTL clusters harboring multiple QTL were detected in different populations, and the interval 15.5-32.3 cM around the Rht-B1 locus on chromosome 4BS harboring 20 QTL is an important region determining grain yield (GY). Thousand-kernel weight (TKW) is significantly affected by kernel width and plant height (PH), whereas flag leaf width can be used to select lines with large kernel number per spike. Eleven candidate genes were identified, including eight cloned genes for kernel, heading date (HD) and PH-related traits as well as predicted genes for TKW, spike length and HD. The closest SNP markers of stable QTL or QTL clusters can be used for MAS in wheat breeding using kompetitive allele-specific PCR or semi-thermal asymmetric reverse PCR assays for improvement of GY.

Genetic analysis of multi-environmental spring wheat trials identifies genomic regions for locus-specific trade-offs for grain weight and grain number

GWAS on multi-environment data identified genomic regions associated with trade-offs for grain weight and grain number. Grain yield (GY) can be dissected into its components thousand grain weight (TGW) and grain number (GN), but little has been achieved in assessing the trade-off between them in spring wheat. In the present study, the Wheat Association Mapping Initiative (WAMI) panel of 287 elite spring bread wheat lines was phenotyped for GY, GN, and TGW in ten environments across different wheat growing regions in Mexico, South Asia, and North Africa. The panel genotyped with the 90 K Illumina Infinitum SNP array resulted in 26,814 SNPs for genome-wide association study (GWAS). Statistical analysis of the multi-environmental data for GY, GN, and TGW observed repeatability estimates of 0.76, 0.62, and 0.95, respectively. GWAS on BLUPs of combined environment analysis identified 38 loci associated with the traits. Among them four loci-6A (85 cM), 5A (98 cM), 3B (99 cM), and 2B (96 cM)-were associated with multiple traits. The study identified two loci that showed positive association between GY and TGW, with allelic substitution effects of 4% (GY) and 1.7% (TGW) for 6A locus and 0.2% (GY) and 7.2% (TGW) for 2B locus. The locus in chromosome 6A (79-85 cM) harbored a gene TaGW2-6A. We also identified that a combination of markers associated with GY, TGW, and GN together explained higher variation for GY (32%), than the markers associated with GY alone (27%). The marker-trait associations from the present study can be used for marker-assisted selection (MAS) and to discover the underlying genes for these traits in spring wheat.

ANALYSIS OF BAKERY PROPERTIES OF GRAIN OF NEW VARIETIES AND LINES OF WHEAT SPELTS

There were studied technological properties of grain of different varieties and lines of wheat spelt. There were analyzed differences between the quality of bread of flour of the highest sort and wholemeal, demonstrated the topicality of the differentiated approach to technological properties of flour for its production, elucidated the possibility of using wholemeal of wheat spelt for producing bread of the increased biological value. It was experimentally confirmed that a value of gloss of the bread surface and its general assessment is influenced by the protein content in grain. The gluten content influences bread quality parameters a bit less. At the same time, the index of gluten deformation also influences the crust surface, size of pores, general assessment of the bread quality. Its quality is high in all studied samples. The highest general culinary mark is put to bread, obtained from flour of the variety Zorya of Ukraine, LPP 3132, lines NAK34/12-2 and TV 1100. Based on studied of organoleptic, physical-chemical parameters of bread, there was confirmed the possibility of the promising use of wheat spelt grain in the bakery technology for raising the quality of products and widening the assortment.

QTL Detection for Kernel Size and Weight in Bread Wheat (Triticum aestivum L.) Using a High-Density SNP and SSR-Based Linkage Map

High-density genetic linkage maps are essential for precise mapping quantitative trait loci (QTL) in wheat (Triticum aestivum L.). In this study, a high-density genetic linkage map consisted of 6312 SNP and SSR markers was developed to identify QTL controlling kernel size and weight, based on a recombinant inbred line (RIL) population derived from the cross of Shixin828 and Kenong2007. Seventy-eight putative QTL for kernel length (KL), kernel width (KW), kernel diameter ratio (KDR), and thousand kernel weight (TKW) were detected over eight environments by inclusive composite interval mapping (ICIM). Of these, six stable QTL were identified in more than four environments, including two for KL (qKL-2D and qKL-6B.2), one for KW (qKW-2D.1), one for KDR (qKDR-2D.1) and two for TKW (qTKW-5A and qTKW-5B.2). Unconditional and multivariable conditional QTL mapping for TKW with respect to TKW component (TKWC) revealed that kernel dimensions played an important role in regulating the kernel weight. Seven QTL-rich genetic regions including seventeen QTL were found on chromosomes 1A (2), 2D, 3A, 4B and 5B (2) exhibiting pleiotropic effects. In particular, clusters on chromosomes 2D and 5B possessing significant QTL for kernel-related traits were highlighted. Markers tightly linked to these QTL or clusters will eventually facilitate further studies for fine mapping, candidate gene discovery and marker-assisted selection (MAS) in wheat breeding.

QTL Detection for Kernel Size and Weight in Bread Wheat (Triticum aestivum L.) Using a High-Density SNP and SSR-Based Linkage Map

High-density genetic linkage maps are essential for precise mapping quantitative trait loci (QTL) in wheat (Triticum aestivum L.). In this study, a high-density genetic linkage map consisted of 6312 SNP and SSR markers was developed to identify QTL controlling kernel size and weight, based on a recombinant inbred line (RIL) population derived from the cross of Shixin828 and Kenong2007. Seventy-eight putative QTL for kernel length (KL), kernel width (KW), kernel diameter ratio (KDR), and thousand kernel weight (TKW) were detected over eight environments by inclusive composite interval mapping (ICIM). Of these, six stable QTL were identified in more than four environments, including two for KL (qKL-2D and qKL-6B.2), one for KW (qKW-2D.1), one for KDR (qKDR-2D.1) and two for TKW (qTKW-5A and qTKW-5B.2). Unconditional and multivariable conditional QTL mapping for TKW with respect to TKW component (TKWC) revealed that kernel dimensions played an important role in regulating the kernel weight. Seven QTL-rich genetic regions including seventeen QTL were found on chromosomes 1A (2), 2D, 3A, 4B and 5B (2) exhibiting pleiotropic effects. In particular, clusters on chromosomes 2D and 5B possessing significant QTL for kernel-related traits were highlighted. Markers tightly linked to these QTL or clusters will eventually facilitate further studies for fine mapping, candidate gene discovery and marker-assisted selection (MAS) in wheat breeding.

Genome-Wide Association Mapping of Major Root Length QTLs Under PEG Induced Water Stress in Wheat

Roots are vital plant organs that determine adaptation to various soil conditions. The present study evaluated a core winter wheat collection for rooting depth under PEG induced early stage water stress and non-stress growing conditions. Analysis of phenotypic data indicated highly significant (p < 0.01) variation among genotypes. Broad sense heritability of 59 and 73% with corresponding genetic gains of 7.6 and 9.7 (5% selection intensity) were found under non-stress and stress conditions, respectively. The test genotypes were grouped in to three distinct clusters using unweighted pair group method with arithmetic mean (UPGMA) clustering based on maximum Euclidian distance. The first three principal components gave optimum mixed linear model for genome wide association study (GWAS). Linkage disequilibrium (LD) analysis showed significant LD (p < 0.05) amongst 15% of total marker pairs (25,125). Nearly 16% of the significant LDs were among inter chromosomal marker pairs. GWAS revealed five significant root length QTLs spread across four chromosomes. None of the identified QTLs were common between the two growing conditions. Stress specific QTLs, combined explaining 31% of phenotypic variation were located on chromosomes 2B (wPt6278) and 3B (wPt1159). Similarly, two of the three QTLs (wPt0021 and wPt8890) identified under the non-stress condition were found on chromosomes 3B and 5B, respectively. The B genome showed significant importance in controlling root growth both under stress and non-stress conditions. The identified markers can potentially be validated and used for marker assisted selection.

Mapping QTLs controlling kernel dimensions in a wheat inter-varietal RIL mapping population

Seven kernel dimension QTLs were identified in wheat, and kernel thickness was found to be the most important dimension for grain weight improvement. Kernel morphology and weight of wheat (Triticum aestivum L.) affect both yield and quality; however, the genetic basis of these traits and their interactions has not been fully understood. In this study, to investigate the genetic factors affecting kernel morphology and the association of kernel morphology traits with kernel weight, kernel length (KL), width (KW) and thickness (KT) were evaluated, together with hundred-grain weight (HGW), in a recombinant inbred line population derived from Nanda2419 × Wangshuibai, with data from five trials (two different locations over 3 years). The results showed that HGW was more closely correlated with KT and KW than with KL. A whole genome scan revealed four QTLs for KL, one for KW and two for KT, distributed on five different chromosomes. Of them, QKl.nau-2D for KL, and QKt.nau-4B and QKt.nau-5A for KT were newly identified major QTLs for the respective traits, explaining up to 32.6 and 41.5% of the phenotypic variations, respectively. Increase of KW and KT and reduction of KL/KT and KW/KT ratios always resulted in significant higher grain weight. Lines combining the Nanda 2419 alleles of the 4B and 5A intervals had wider, thicker, rounder kernels and a 14% higher grain weight in the genotype-based analysis. A strong, negative linear relationship of the KW/KT ratio with grain weight was observed. It thus appears that kernel thickness is the most important kernel dimension factor in wheat improvement for higher yield. Mapping and marker identification of the kernel dimension-related QTLs definitely help realize the breeding goals.

Genetic diversity, population structure and marker-trait associations for agronomic and grain traits in wild diploid wheat Triticum urartu

BACKGROUND

Wild diploid wheat, Triticum urartu (T. urartu) is the progenitor of bread wheat, and understanding its genetic diversity and genome function will provide considerable reference for dissecting genomic information of common wheat.

RESULTS

In this study, we investigated the morphological and genetic diversity and population structure of 238 T. urartu accessions collected from different geographic regions. This collection had 19.37 alleles per SSR locus and its polymorphic information content (PIC) value was 0.76, and the PIC and Nei's gene diversity (GD) of high-molecular-weight glutenin subunits (HMW-GSs) were 0.86 and 0.88, respectively. UPGMA clustering analysis indicated that the 238 T. urartu accessions could be classified into two subpopulations, of which Cluster I contained accessions from Eastern Mediterranean coast and those from Mesopotamia and Transcaucasia belonged to Cluster II. The wide range of genetic diversity along with the manageable number of accessions makes it one of the best collections for mining valuable genes based on marker-trait association. Significant associations were observed between simple sequence repeats (SSR) or HMW-GSs and six morphological traits: heading date (HD), plant height (PH), spike length (SPL), spikelet number per spike (SPLN), tiller angle (TA) and grain length (GL).

CONCLUSIONS

Our data demonstrated that SSRs and HMW-GSs were useful markers for identification of beneficial genes controlling important traits in T. urartu, and subsequently for their conservation and future utilization, which may be useful for genetic improvement of the cultivated hexaploid wheat.

Dissecting the old Mediterranean durum wheat genetic architecture for phenology, biomass and yield formation by association mapping and QTL meta-analysis

Association mapping was used to identify genome regions affecting yield formation, crop phenology and crop biomass in a collection of 172 durum wheat landraces representative of the genetic diversity of ancient local durum varieties from the Mediterranean Basin. The collection was genotyped with 1,149 DArT markers and phenotyped in Spanish northern and southern locations during three years. A total of 245 significant marker trait associations (MTAs) (P<0.01) were detected. Some of these associations confirmed previously identified quantitative trait loci (QTL) and/or candidate genes, and others are reported for the first time here. Eighty-six MTAs corresponded with yield and yield component traits, 70 to phenology and 89 to biomass production. Twelve genomic regions harbouring stable MTAs (significant in three or more environments) were identified, while five and two regions showed specific MTAs for northern and southern environments, respectively. Sixty per cent of MTAs were located on the B genome and 29% on the A genome. The marker wPt-9859 was detected in 12 MTAs, associated with six traits in four environments and the mean across years. To refine QTL positions, a meta-analysis was performed. A total of 477 unique QTLs were projected onto a durum wheat consensus map and were condensed to 71 meta-QTLs and left 13 QTLs as singletons. Sixty-one percent of QTLs explained less than 10% of the phenotypic variance confirming the high genetic complexity of the traits analysed.

Molecular mapping of the grain iron and zinc concentration, protein content and thousand kernel weight in wheat (Triticum aestivum L.)

Genomic regions responsible for accumulation of grain iron concentration (Fe), grain zinc concentration (Zn), grain protein content (PC) and thousand kernel weight (TKW) were investigated in 286 recombinant inbred lines (RILs) derived from a cross between an old Indian wheat variety WH542 and a synthetic derivative (Triticum dicoccon PI94624/Aegilops squarrosa [409]//BCN). RILs were grown in six environments and evaluated for Fe, Zn, PC, and TKW. The population showed the continuous distribution for all the four traits, that for pooled Fe and PC was near normal, whereas, for pooled Zn, RILs exhibited positively skewed distribution. A genetic map spanning 2155.3cM was constructed using microsatellite markers covering the 21 chromosomes and used for QTL analysis. 16 quantitative trait loci (QTL) were identified in this study. Four QTLs (QGFe.iari-2A, QGFe.iari-5A, QGFe.iari-7A and QGFe.iari-7B) for Fe, five QTLs (QGZn.iari-2A, QGZn.iari-4A, QGZn.iari-5A, QGZn.iari-7A and QGZn.iari-7B) for Zn, two QTLs (QGpc.iari-2A and QGpc.iari-3A) for PC, and five QTLs (QTkw.iari-1A, QTkw.iari-2A, QTkw.iari-2B, QTkw.iari-5B and QTkw.iari-7A) for TKW were identified. The QTLs together explained 20.0%, 32.0%, 24.1% and 32.3% phenotypic variation, respectively, for Fe, Zn, PC and TKW. QGpc.iari-2A was consistently expressed in all the six environments, whereas, QGFe.iari-7B and QGZn.iari-2A were identified in two environments each apart from pooled mean. QTkw.iari-2A and QTkw.iari-7A, respectively, were identified in four and three environments apart from pooled mean. A common region in the interval of Xgwm359-Xwmc407 on chromosome 2A was associated with Fe, Zn, and PC. One more QTL for TKW was identified on chromosome 2A but in a different chromosomal region (Xgwm382-Xgwm359). Two more regions on 5A (Xgwm126-Xgwm595) and 7A (Xbarc49-Xwmc525) were found to be associated with both Fe and Zn. A QTL for TKW was identified (Xwmc525-Xbarc222) in a different chromosomal region on the same chromosome (7A). This reflects at least a partly common genetic basis for the four traits. It is concluded that fine mapping of the regions of the three chromosomes of A genome involved in determining the accumulation of Fe, Zn, PC, and TKW in this mapping population may be rewarding.

Isolation and characterization of the TaSnRK2.10 gene and its association with agronomic traits in wheat (Triticum aestivum L.)

Sucrose non-fermenting 1-related protein kinases (SnRKs) comprise a major family of signaling genes in plants and are associated with metabolic regulation, nutrient utilization and stress responses. This gene family has been proposed to be involved in sucrose signaling. In the present study, we cloned three copies of the TaSnRK2.10 gene from bread wheat on chromosomes 4A, 4B and 4D. The coding sequence (CDS) is 1086 bp in length and encodes a protein of 361 amino acids that exhibits functional domains shared with SnRK2s. Based on the haplotypes of TaSnRK2.10-4A (Hap-4A-H and Hap-4A-L), a cleaved amplified polymorphic sequence (CAPS) marker designated TaSnRK2.10-4A-CAPS was developed and mapped between the markers D-1092101 and D-100014232 using a set of recombinant inbred lines (RILs). The TaSnRK2.10-4B alleles (Hap-4B-G and Hap-4B-A) were transformed into allele-specific PCR (AS-PCR) markers TaSnRK2.10-4B-AS1 and TaSnRK2.10-4B-AS2, which were located between the markers D-1281577 and S-1862758. No diversity was found for TaSnRK2.10-4D. An association analysis using a natural population consisting of 128 winter wheat varieties in multiple environments showed that the thousand grain weight (TGW) and spike length (SL) of Hap-4A-H were significantly higher than those of Hap-4A-L, but pant height (PH) was significantly lower.

Interactions between Glu-1 and Glu-3 loci and associations of selected molecular markers with quality traits in winter wheat (Triticum aestivum L.) DH lines

The quality of wheat depends on a large complex of genes and environmental factors. The objective of this study was to identify quantitative trait loci controlling technological quality traits and their stability across environments, and to assess the impact of interaction between alleles at loci Glu-1 and Glu-3 on grain quality. DH lines were evaluated in field experiments over a period of 4 years, and genotyped using simple sequence repeat markers. Lines were analysed for grain yield (GY), thousand grain weight (TGW), protein content (PC), starch content (SC), wet gluten content (WG), Zeleny sedimentation value (ZS), alveograph parameter W (APW), hectolitre weight (HW), and grain hardness (GH). A number of QTLs for these traits were identified in all chromosome groups. The Glu-D1 locus influenced TGW, PC, SC, WG, ZS, APW, GH, while locus Glu-B1 affected only PC, ZS, and WG. Most important marker-trait associations were found on chromosomes 1D and 5D. Significant effects of interaction between Glu-1 and Glu-3 loci on technological properties were recorded, and in all types of this interaction positive effects of Glu-D1 locus on grain quality were observed, whereas effects of Glu-B1 locus depended on alleles at Glu-3 loci. Effects of Glu-A3 and Glu-D3 loci per se were not significant, while their interaction with alleles present at other loci encoding HMW and LMW were important. These results indicate that selection of wheat genotypes with predicted good bread-making properties should be based on the allelic composition both in Glu-1 and Glu-3 loci, and confirm the predominant effect of Glu-D1d allele on technological properties of wheat grains.

Multi-environment QTL analysis of grain morphology traits and fine mapping of a kernel-width QTL in Zheng58 × SK maize population

Sixteen major QTLs regulating maize kernel traits were mapped in multiple environments and one of them, qKW - 9.2 , was restricted to 630 Kb, harboring 28 putative gene models. To elucidate the genetic basis of kernel traits, a quantitative trait locus (QTL) analysis was conducted in a maize recombinant inbred line population derived from a cross between two diverse parents Zheng58 and SK, evaluated across eight environments. Construction of a high-density linkage map was based on 13,703 single-nucleotide polymorphism markers, covering 1860.9 cM of the whole genome. In total, 18, 26, 23, and 19 QTLs for kernel length, width, thickness, and 100-kernel weight, respectively, were detected on the basis of a single-environment analysis, and each QTL explained 3.2-23.7 % of the phenotypic variance. Sixteen major QTLs, which could explain greater than 10 % of the phenotypic variation, were mapped in multiple environments, implying that kernel traits might be controlled by many minor and multiple major QTLs. The major QTL qKW-9.2 with physical confidence interval of 1.68 Mbp, affecting kernel width, was then selected for fine mapping using heterogeneous inbred families. At final, the location of the underlying gene was narrowed down to 630 Kb, harboring 28 putative candidate-gene models. This information will enhance molecular breeding for kernel traits and simultaneously assist the gene cloning underlying this QTL, helping to reveal the genetic basis of kernel development in maize.

QTLs for uniform grain dimensions and germination selected during wheat domestication are co-located on chromosome 4B

A major locus on the long arm of wheat chromosome 4B controls within-spikelet variation in both grain size and seed dormancy, the latter an important survival mechanism likely eliminated from wild wheat during domestication. Seed dormancy can increase the probability of survival of at least some progeny under unstable environmental conditions. In wild emmer wheat, only one of the two grains in a spikelet germinates during the first rainy season following maturation; and this within-plant variation in seed dormancy is associated with both grain dimension differences and position within the spikelet. Here, in addition to characterizing these associations, we elucidate the genetic mechanism controlling differential grain dimensions and dormancy within wild tetraploid wheat spikelets using phenotypic data from a wild emmer × durum wheat population and a high-density genetic map. We show that in wild emmer, the lower grain within the spikelet is about 30 % smaller and more dormant than the larger, upper grain that germinates usually within 3 days. We identify a major locus on the long arm of chromosome 4B that explains >40 % of the observed variation in grain dimensions and seed dormancy within spikelets. This locus, designated QGD-4BL, is validated using an independent set of wild emmer × durum wheat genetic stocks. The domesticated variant of this novel locus on chromosome 4B, likely fixed during the process of wheat domestication, favors spikelets with seeds of uniform size and synchronous germination. The identification of locus QGD-4BL enhances our knowledge of the genetic basis of the domestication syndrome of one of our most important crops.

E-Index for Differentiating Complex Dynamic Traits

While it is a daunting challenge in current biology to understand how the underlying network of genes regulates complex dynamic traits, functional mapping, a tool for mapping quantitative trait loci (QTLs) and single nucleotide polymorphisms (SNPs), has been applied in a variety of cases to tackle this challenge. Though useful and powerful, functional mapping performs well only when one or more model parameters are clearly responsible for the developmental trajectory, typically being a logistic curve. Moreover, it does not work when the curves are more complex than that, especially when they are not monotonic. To overcome this inadaptability, we therefore propose a mathematical-biological concept and measurement, E-index (earliness-index), which cumulatively measures the earliness degree to which a variable (or a dynamic trait) increases or decreases its value. Theoretical proofs and simulation studies show that E-index is more general than functional mapping and can be applied to any complex dynamic traits, including those with logistic curves and those with nonmonotonic curves. Meanwhile, E-index vector is proposed as well to capture more subtle differences of developmental patterns.

Dissection of Genetic Factors underlying Wheat Kernel Shape and Size in an Elite × Nonadapted Cross using a High Density SNP Linkage Map

Wheat kernel shape and size has been under selection since early domestication. Kernel morphology is a major consideration in wheat breeding, as it impacts grain yield and quality. A population of 160 recombinant inbred lines (RIL), developed using an elite (ND 705) and a nonadapted genotype (PI 414566), was extensively phenotyped in replicated field trials and genotyped using Infinium iSelect 90K assay to gain insight into the genetic architecture of kernel shape and size. A high density genetic map consisting of 10,172 single nucleotide polymorphism (SNP) markers, with an average marker density of 0.39 cM/marker, identified a total of 29 genomic regions associated with six grain shape and size traits; ∼80% of these regions were associated with multiple traits. The analyses showed that kernel length (KL) and width (KW) are genetically independent, while a large number (∼59%) of the quantitative trait loci (QTL) for kernel shape traits were in common with genomic regions associated with kernel size traits. The most significant QTL was identified on chromosome 4B, and could be an ortholog of major rice grain size and shape gene or . Major and stable loci also were identified on the homeologous regions of Group 5 chromosomes, and in the regions of (6A) and (7A) genes. Both parental genotypes contributed equivalent positive QTL alleles, suggesting that the nonadapted germplasm has a great potential for enhancing the gene pool for grain shape and size. This study provides new knowledge on the genetic dissection of kernel morphology, with a much higher resolution, which may aid further improvement in wheat yield and quality using genomic tools.

QTL detection for wheat kernel size and quality and the responses of these traits to low nitrogen stress

QTLs for kernel characteristics and tolerance to N stress were identified, and the functions of ten known genes with regard to these traits were specified. Kernel size and quality characteristics in wheat (Triticum aestivum L.) ultimately determine the end use of the grain and affect its commodity price, both of which are influenced by the application of nitrogen (N) fertilizer. This study characterized quantitative trait loci (QTLs) for kernel size and quality and examined the responses of these traits to low-N stress using a recombinant inbred line population derived from Kenong 9204 × Jing 411. Phenotypic analyses were conducted in five trials that each included low- and high-N treatments. We identified 109 putative additive QTLs for 11 kernel size and quality characteristics and 49 QTLs for tolerance to N stress, 27 and 14 of which were stable across the tested environments, respectively. These QTLs were distributed across all wheat chromosomes except for chromosomes 3A, 4D, 6D, and 7B. Eleven QTL clusters that simultaneously affected kernel size- and quality-related traits were identified. At nine locations, 25 of the 49 QTLs for N deficiency tolerance coincided with the QTLs for kernel characteristics, indicating their genetic independence. The feasibility of indirect selection of a superior genotype for kernel size and quality under high-N conditions in breeding programs designed for a lower input management system are discussed. In addition, we specified the functions of Glu-A1, Glu-B1, Glu-A3, Glu-B3, TaCwi-A1, TaSus2, TaGS2-D1, PPO-D1, Rht-B1, and Ha with regard to kernel characteristics and the sensitivities of these characteristics to N stress. This study provides useful information for the genetic improvement of wheat kernel size, quality, and resistance to N stress.

Characterization of three wheat grain weight QTLs that differentially affect kernel dimensions

The QGw.nau - 2D, QGw.nau - 4B and QGw.nau - 5A intervals were investigated for their effects on weight, length, width, and thickness of kernels and their differential roles in determining kernel size and shape were demonstrated. Grain weight (GW) contributes greatly to wheat yield and is directly related to kernel size and shape. Although over 100 quantitative trait loci (QTLs) for GW have been reported in the literatures, few have been well characterized for their association with kernel traits. In this study, three GW QTLs identified in elite cultivar 'Nanda2419' ('Mentana'), including QGw.nau-2D, QGw.nau-4B and QGw.nau-5A, were investigated through near isogenic line (NIL) development and evaluation. NILs for all three QTLs and one NIL with both QGw.nau-4B and QGw.nau-5A were developed with the help of marker-assisted selection after two to three generations of backcross using cultivar 'Wangshuibai' as the recurrent parent. One NIL with QGw.nau-4B in the background of cultivar 'Wenmai6' was also obtained. In four different field trials, these NILs consistently produced heavier kernels than the recurrent parents. QGw.nau-4B showed the largest effect on GW; its presence resulted in 0.4-0.5 g increase of hundred-grain weight, depending on genetic backgrounds. QGw.nau-4B and QGw.nau-5A functioned additively in conditioning GW. These three QTL intervals showed pleiotropic effects on, or close linkage with genes for, spike length, plant height and flag leaf width, respectively, and acted differentially in determining the kernel dimensions that are the major GW determinants. They all conditioned wider kernels with QGw.nau-5A displaying the largest effect. QGw.nau-4B and QGw.nau-5A also conditioned thicker kernels but had opposite effects on kernel length. This study demonstrated that marker-assisted selection is effective for GW improvement. The availability of GW NILs could facilitate cloning of GW genes and unraveling of kernel development mechanisms.

Genome-Wide Association Mapping of Yield and Grain Quality Traits in Winter Wheat Genotypes

The main goal of this study was to investigate the genetic basis of yield and grain quality traits in winter wheat genotypes using association mapping approach, and identify linked molecular markers for marker assisted selection. A total of 120 elite facultative/winter wheat genotypes were evaluated for yield, quality and other agronomic traits under rain-fed and irrigated conditions for two years (2011–2012) at the Tel Hadya station of ICARDA, Syria. The same genotypes were genotyped using 3,051 Diversity Array Technologies (DArT) markers, of which 1,586 were of known chromosome positions. The grain yield performance of the genotypes was highly significant both in rain-fed and irrigated sites. Average yield of the genotypes ranged from 2295 to 4038 kg/ha and 4268 to 7102 kg/ha under rain-fed and irrigated conditions, respectively. Protein content and alveograph strength (W) ranged from 13.6–16.1% and 217.6–375 Jx10-4, respectively. DArT markers wPt731910 (3B), wPt4680 (4A), wPt3509 (5A), wPt8183 (6B), and wPt0298 (2D) were significantly associated with yield under rain-fed conditions. Under irrigated condition, tPt4125 on chromosome 2B was significantly associated with yield explaining about 13% of the variation. Markers wPt2607 and wPt1482 on 5B were highly associated with protein content and alveograph strength explaining 16 and 14% of the variations, respectively. The elite genotypes have been distributed to many countries using ICARDA’s International system for potential direct release and/or use as parents after local adaptation trials by the NARSs of respective countries. The QTLs identified in this study are recommended to be used for marker assisted selection after through validation using bi-parental populations.

Impact of the D genome and quantitative trait loci on quantitative traits in a spring durum by spring bread wheat cross

The impact of the D genome and QTL in the A and B genomes on agronomic performance of hexaploid wheat and tetraploid durum was determined using novel recombinant inbred line populations derived from interploid crosses. Genetic differences between common hexaploid (6X) bread wheat (Triticum aestivum, 2n = 6x = 42, genome, AABBDD) and tetraploid (4X) durum wheat (T. turgidum subsp. durum, 2n = 4x = 28, genome, AABB) may exist due to effects of the D genome and allelic differences at loci in the A and B genomes. Previous work allowed identification of a 6X by 4X cross combination that resulted in a large number of fertile recombinant progeny at both ploidy levels. In this study, interspecific recombinant inbred line populations at both 4X and 6X ploidy with 88 and 117 individuals, respectively, were developed from a cross between Choteau spring wheat (6X) and Mountrail durum wheat (4X). The presence of the D genome in the 6X population resulted in increased yield, tiller number, kernel weight, and kernel size, as well as a decrease in stem solidness, test weight and seed per spike. Similar results were found with a second RIL population containing 152 lines from 18 additional 6X by 4X crosses. Several QTL for agronomic and quality traits were identified in both the 4X and 6X populations. Although negatively impacted by the lack of the D genome, kernel weight in Mountrail (4X) was higher than Choteau (6X) due to positive alleles from Mountrail on chromosomes 3B and 7A. These and other favorable alleles may be useful for introgression between ploidy levels.

Agriproteomics of Bread Wheat: Comparative Proteomics and Network Analyses of Grain Size Variation

Agriproteomics signifies the merging of agriculture research and proteomics systems science and is impacting plant research and societal development. Wheat is a frequently consumed foodstuff, has highly variable grain size that in effect contributes to wheat grain yield and the end-product quality. Very limited information is available on molecular basis of grain size due to complex multifactorial nature of this trait. Here, using liquid chromatography-mass spectrometry, we investigated the proteomics profiles from grains of wheat genotypes, Rye selection 111 (RS111) and Chinese spring (CS), which differ in their size. Significant differences in protein expression were found, including 33 proteins uniquely present in RS111 and 32 only in CS, while 54 proteins were expressed from both genotypes. Among differentially expressed proteins, 22 were upregulated, while 21 proteins were downregulated in RS111 compared to CS. Functional classification revealed their role in energy metabolism, seed storage, stress tolerance and transcription. Further, protein interactive network analysis was performed to predict the targets of identified proteins. Significantly different interactions patterns were observed between these genotypes with detection of proteins such as Cyp450, Sus2, and WRKY that could potentially affect seed size. The present study illustrates the potentials of agriproteomics as a veritable new frontier of plant omics research.

Identification of Novel SNP in Promoter Sequence of TaGW2-6A Associated with Grain Weight and Other Agronomic Traits in Wheat (Triticum aestivum L.)

TaGW2 is an orthologue of rice gene OsGW2, which encodes E3 RING ubiquitin ligase and controls the grain size in rice. In wheat, three copies of TaGW2 have been identified and mapped on wheat homoeologous group 6 viz. TaGW2-6A, TaGW2-6B and TaGW2-6D. In the present study, using as many as 207 Indian wheat genotypes, we identified four SNPs including two novel SNPs (SNP-988 and SNP-494) in the promoter sequence of TaGW2-6A. All the four SNPs were G/A or A/G substitutions (transitions). Out of the four SNPs, SNP-494 was causal, since it was found associated with grain weight. The mean TGW (41.1 g) of genotypes with the allele SNP-494_A was significantly higher than mean TGW (38.6 g) of genotypes with the allele SNP-494_G. SNP-494 also regulates the expression of TaGW2-6A so that the wheat genotypes with SNP-494_G have higher expression and lower TGW and the genotypes with SNP-494_A have lower expression but higher TGW. Besides, SNP-494 was also found associated with grain length-width ratio, awn length, spike length, grain protein content, peduncle length and plant height. This suggested that gene TaGW2-6A not only controls grain size, but also controls other agronomic traits. In the promoter region, SNP-494 was present in 'CGCG' motif that plays an important role in Ca2+/calmodulin mediated regulation of genes. A user-friendly CAPS marker was also developed to identify the desirable allele of causal SNP (SNP-494) for use in marker-assisted selection for improvement of grain weight in wheat. Using four SNPs, five haplotypes were identified; of these, Hap_5 (G_A_G_A) was found to be a desirable haplotype having significantly higher grain weight (41.13g) relative to other four haplotypes (36.33-39.16 g).

Association and Validation of Yield-Favored Alleles in Chinese Cultivars of Common Wheat (Triticumaestivum L.)

Common wheat is one of the most important crops in China, which is the largest producer in the world. A set of 230 cultivars was used to identify yield-related loci by association mapping. This set was tested for seven yield-related traits, viz. plant height (PH), spike length (SL), spikelet number per spike (SNPS), kernel number per spike (KNPS), thousand-kernel weight (TKW), kernel weight per spike (KWPS), and sterile spikelet number (SSN) per plant in four environments. A total of 106 simple sequence repeat (SSR) markers distributed on all 21 chromosomes were used to screen the set. Twenty-one and 19 of them were associated with KNPS and TKW, respectively. Association mapping detected 73 significant associations across 50 SSRs, and the phenotypic variation explained (R²) by the associations ranged from 1.54 to 23.93%. The associated loci were distributed on all chromosomes except 4A, 7A, and 7D. Significant and potentially new alleles were present on 8 chromosomes, namely1A, 1D, 2A, 2D, 3D, 4B, 5B, and 6B. Further analysis showed that genetic effects of associated loci were greatly influenced by association panels, and the R² of crucial loci were lower in modern cultivars than in the mini core collection, probably caused by strong selection in wheat breeding. In order to confirm the results of association analysis, yield-related favorable alleles Xgwm135-1A₁₃₈, Xgwm337-1D₁₈₆, Xgwm102-2D₁₄₄, and Xgwm132-6B₁₂₈ were evaluated in a double haploid (DH) population derived from Hanxuan10 xLumai14.These favorable alleles that were validated in various populations might be valuable in breeding for high-yield.

Analysis of heterosis and quantitative trait loci for kernel shape related traits using triple testcross population in maize

Kernel shape related traits (KSRTs) have been shown to have important influences on grain yield. The previous studies that emphasize kernel length (KL) and kernel width (KW) lack a comprehensive evaluation of characters affecting kernel shape. In this study, materials of the basic generations (B73, Mo17, and B73 × Mo17), 82 intermated B73 × Mo17 (IBM) individuals, and the corresponding triple testcross (TTC) populations were used to evaluate heterosis, investigate correlations, and characterize the quantitative trait loci (QTL) for six KSRTs: KL, KW, length to width ratio (LWR), perimeter length (PL), kernel area (KA), and circularity (CS). The results showed that the mid-parent heterosis (MPH) for most of the KSRTs was moderate. The performance of KL, KW, PL, and KA exhibited significant positive correlation with heterozygosity but their Pearson’s R values were low. Among KSRTs, the strongest significant correlation was found between PL and KA with R values was up to 0.964. In addition, KW, PL, KA, and CS were shown to be significant positive correlation with 100-kernel weight (HKW). 28 QTLs were detected for KSRTs in which nine were augmented additive, 13 were augmented dominant, and six were dominance × additive epistatic. The contribution of a single QTL to total phenotypic variation ranged from 2.1% to 32.9%. Furthermore, 19 additive × additive digenic epistatic interactions were detected for all KSRTs with the highest total R² for KW (78.8%), and nine dominance × dominance digenic epistatic interactions detected for KL, LWR, and CS with the highest total R² (55.3%). Among significant digenic interactions, most occurred between genomic regions not mapped with main-effect QTLs. These findings display the complexity of the genetic basis for KSRTs and enhance our understanding on heterosis of KSRTs from the quantitative genetic perspective.

High-density genetic linkage map construction and QTL mapping of grain shape and size in the wheat population Yanda1817 × Beinong6

High-density genetic linkage maps are necessary for precisely mapping quantitative trait loci (QTLs) controlling grain shape and size in wheat. By applying the Infinium iSelect 9K SNP assay, we have constructed a high-density genetic linkage map with 269 F ₈ recombinant inbred lines (RILs) developed between a Chinese cornerstone wheat breeding parental line Yanda1817 and a high-yielding line Beinong6. The map contains 2431 SNPs and 128 SSR & EST-SSR markers in a total coverage of 3213.2 cM with an average interval of 1.26 cM per marker. Eighty-eight QTLs for thousand-grain weight (TGW), grain length (GL), grain width (GW) and grain thickness (GT) were detected in nine ecological environments (Beijing, Shijiazhuang and Kaifeng) during five years between 2010–2014 by inclusive composite interval mapping (ICIM) (LOD≥2.5). Among which, 17 QTLs for TGW were mapped on chromosomes 1A, 1B, 2A, 2B, 3A, 3B, 3D, 4A, 4D, 5A, 5B and 6B with phenotypic variations ranging from 2.62% to 12.08%. Four stable QTLs for TGW could be detected in five and seven environments, respectively. Thirty-two QTLs for GL were mapped on chromosomes 1B, 1D, 2A, 2B, 2D, 3B, 3D, 4A, 4B, 4D, 5A, 5B, 6B, 7A and 7B, with phenotypic variations ranging from 2.62% to 44.39%. QGl.cau-2A.2 can be detected in all the environments with the largest phenotypic variations, indicating that it is a major and stable QTL. For GW, 12 QTLs were identified with phenotypic variations range from 3.69% to 12.30%. We found 27 QTLs for GT with phenotypic variations ranged from 2.55% to 36.42%. In particular, QTL QGt.cau-5A.1 with phenotypic variations of 6.82–23.59% was detected in all the nine environments. Moreover, pleiotropic effects were detected for several QTL loci responsible for grain shape and size that could serve as target regions for fine mapping and marker assisted selection in wheat breeding programs.

Using the UK reference population Avalon × Cadenza as a platform to compare breeding strategies in elite Western European bread wheat

Wheat breeders select for qualitative and quantitative traits, the latter often detected as quantitative trait loci (QTL). It is, however, a long procedure from QTL discovery to the successful introduction of favourable alleles into new elite varieties and finally into farmers' crops. As a proof of principle for this process, QTL for grain yield (GY), yield components, plant height (PH), ear emergence (EM), solid stem (SS) and yellow rust resistance (Yr) were identified in segregating UK bread wheat reference population, Avalon × Cadenza. Among the 163 detected QTL were several not reported before: 17 for GY, the major GY QTL on 2D; a major SS QTL on 3B; and Yr6 on 7B. Common QTL were identified on ten chromosomes, most interestingly, grain number (GN) was found to be associated with Rht-D1b; and GY and GN with a potential new allele of Rht8. The interaction of other QTL with GY and yield components was discussed in the context of designing a UK breeding target genotype. Desirable characteristics would be: similar PH and EM to Avalon; Rht-D1b and Vrn-A1b alleles; high TGW and GN; long and wide grains; a large root system, resistance to diseases; and maximum GY. The potential of the identified QTL maximising transgressive segregation to produce a high-yielding and resilient genotype was demonstrated by simulation. Moreover, simulating breeding strategies with F₂ enrichment revealed that the F₂-DH procedure was superior to the RIL and the modified SSD procedure to achieve that genotype. The proposed strategies of parent selection and breeding methodology can be used as guidance for marker-assisted wheat breeding.

Mapping QTLs of yield-related traits using RIL population derived from common wheat and Tibetan semi-wild wheat

QTLs controlling yield-related traits were mapped using a population derived from common wheat and Tibetan semi-wild wheat and they provided valuable information for using Tibetan semi-wild wheat in future wheat molecular breeding. Tibetan semi-wild wheat (Triticum aestivum ssp tibetanum Shao) is a kind of primitive hexaploid wheat and harbors several beneficial traits, such as tolerance to biotic and abiotic stresses. And as a wild relative of common wheat, heterosis of yield of the progeny between them was significant. This study focused on mapping QTLs controlling yield-related traits using a recombined inbred lines (RILs) population derived from a hybrid between a common wheat line NongDa3331 (ND3331) and the Tibetan semi-wild wheat accession Zang 1817. In nine location-year environments, a total of 148 putative QTLs controlling nine traits were detected, distributed on 19 chromosomes except for 1A and 2D. Single QTL explained the phenotypic variation ranging from 3.12 to 49.95%. Of these QTLs, 56 were contributed by Zang 1817. Some stable QTLs contributed by Zang 1817 were also detected in more than four environments, such as QPh-3A1, QPh-4B1 and QPh-4D for plant height, QSl-7A1 for spike length, QEp-4B2 for ears per plant, QGws-4D for grain weight per spike, and QTgw-4D for thousand grain weight. Several QTL-rich Regions were also identified, especially on the homoeologous group 4. The TaANT gene involved in floral organ development was mapped on chromosome 4A between Xksm71 and Xcfd6 with 0.8 cM interval, and co-segregated with the QTLs controlling floret number per spikelet, explaining 4.96-11.84% of the phenotypic variation. The current study broadens our understanding of the genetic characterization of Tibetan semi-wild wheat, which will enlarge the genetic diversity of yield-related traits in modern wheat breeding program.

Unconditional and conditional QTL analysis of kernel weight related traits in wheat (Triticum aestivum L.) in multiple genetic backgrounds

Wheat thousand kernel weight (TKW) is a complex trait, and is largely controlled by several kernel traits, including kernel length (KL) and kernel width (KW). In order to reveal the genetic relationship between TKW and these kernel traits (KW and KL) as accurate as possible, we applied both unconditional and conditional mapping analyses to three distinct genetic populations, one DH population and two RIL populations. This report describes the identifications of 36 unconditional and conditional additive QTLs and 30 pairs of unconditional and conditional epistatic QTLs, all of which are closely associated with TKW. While the conditional additive locus Qtkw1B, detected in the RIL2 population, exhibited the largest contribution, explaining 14.12 % of TKW variance, the unconditional epistatic QTLs Qtkw3A-2/Qtkw5B.1, detected in the DH population, accounted for 11.95 % of phenotypic variance. This study also showed that, compared with unconditional mapping, conditional mapping resulted in very different numbers and different extent of effects of additive and epistatic QTLs that were associated with TKW when TKW was conditioned on kernel traits (KW and KL). These data strongly suggest that KW and KL indeed play a significant role in determining TKW. Furthermore, we demonstrated that the effects of the 25 additive QTLs for TKW were either entirely or largely determined by KW, while the effects of the other 25 additive QTLs for TKW were either entirely or largely affected by KL. We conclude that the conditional mapping can be useful for a better understanding of the interrelationship between the yield contributing traits at the QTL level.

Identification and independent validation of a stable yield and thousand grain weight QTL on chromosome 6A of hexaploid wheat (Triticum aestivum L.)

BACKGROUND

Grain yield in wheat is a polygenic trait that is influenced by environmental and genetic interactions at all stages of the plant's growth. Yield is usually broken down into three components; number of spikes per area, grain number per spike, and grain weight (TGW). In polyploid wheat, studies have identified quantitative trait loci (QTL) which affect TGW, yet few have been validated and fine-mapped using independent germplasm, thereby having limited impact in breeding.

RESULTS

In this study we identified a major QTL for TGW, yield and green canopy duration on wheat chromosome 6A of the Spark x Rialto population, across 12 North European environments. Using independent germplasm in the form of BC2 and BC4 near isogenic lines (NILs), we validated the three QTL effects across environments. In four of the five experiments the Rialto 6A introgression gave significant improvements in yield (5.5%) and TGW (5.1%), with morphometric measurements showing that the increased grain weight was a result of wider grains. The extended green canopy duration associated with the high yielding/TGW Rialto allele was comprised of two independent effects; earlier flowering and delayed final maturity, and was expressed stably across the five environments. The wheat homologue (TaGW2) of a rice gene associated with increased TGW and grain width was mapped within the QTL interval. However, no polymorphisms were identified in the coding sequence between the parents.

CONCLUSION

The discovery and validation through near-isogenic lines of robust QTL which affect yield, green canopy duration, thousand grain weight, and grain width on chromosome 6A of hexaploid wheat provide an important first step to advance our understanding of the genetic mechanisms regulating the complex processes governing grain size and yield in polyploid wheat.

Transcript suppression of TaGW2 increased grain width and weight in bread wheat

Bread wheat (Triticum aestivum L.) is a major staple crop in the world. Grain weight is a major factor of grain yield in wheat, and the identification of candidate genes associated with grain weight is very important for high-yield breeding of wheat. TaGW2 is an orthologous gene of rice OsGW2 that negatively regulates the grain width and weight in rice. There are three TaGW2 homoeologs in bread wheat, TaGW2A, TaGW2B, and TaGW2D. In this study, a specific TaGW2-RNA interference (RNAi) cassette was constructed and transformed into a Chinese bread wheat variety 'Shi 4185' with small grain. The transcript levels of TaGW2A, TaGW2B, and TaGW2D were simultaneously downregulated in TaGW2-RNAi transgenic wheat lines. Compared with the controls, TaGW2-underexpressing transgenic lines displayed significantly increases in the grain width and weight, suggesting that TaGW2 negatively regulated the grain width and weight in bread wheat. Further transcript analysis showed that in different bread wheat accessions, the transcript abundance of TaGW2A was negatively associated with the grain width.

Genome-wide association for grain morphology in synthetic hexaploid wheats using digital imaging analysis

BACKGROUND

Grain size and shape greatly influence grain weight which ultimately enhances grain yield in wheat. Digital imaging (DI) based phenomic characterization can capture the three dimensional variation in grain size and shape than has hitherto been possible. In this study, we report the results from using digital imaging of grain size and shape to understand the relationship among different components of this trait, their contribution to enhance grain weight, and to identify genomic regions (QTLs) controlling grain morphology using genome wide association mapping with high density diversity array technology (DArT) and allele-specific markers.

RESULTS

Significant positive correlations were observed between grain weight and grain size measurements such as grain length (r = 0.43), width, thickness (r = 0.64) and factor from density (FFD) (r = 0.69). A total of 231 synthetic hexaploid wheats (SHWs) were grouped into five different sub-clusters by Bayesian structure analysis using unlinked DArT markers. Linkage disequilibrium (LD) decay was observed among DArT loci > 10 cM distance and approximately 28% marker pairs were in significant LD. In total, 197 loci over 60 chromosomal regions and 79 loci over 31 chromosomal regions were associated with grain morphology by genome wide analysis using general linear model (GLM) and mixed linear model (MLM) approaches, respectively. They were mainly distributed on homoeologous group 2, 3, 6 and 7 chromosomes. Twenty eight marker-trait associations (MTAs) on the D genome chromosomes 2D, 3D and 6D may carry novel alleles with potential to enhance grain weight due to the use of untapped wild accessions of Aegilops tauschii. Statistical simulations showed that favorable alleles for thousand kernel weight (TKW), grain length, width and thickness have additive genetic effects. Allelic variations for known genes controlling grain size and weight, viz. TaCwi-2A, TaSus-2B, TaCKX6-3D and TaGw2-6A, were also associated with TKW, grain width and thickness. In silico functional analysis predicted a range of biological functions for 32 DArT loci and receptor like kinase, known to affect plant development, appeared to be common protein family encoded by several loci responsible for grain size and shape.

CONCLUSION

Conclusively, we demonstrated the application and integration of multiple approaches including high throughput phenotyping using DI, genome wide association studies (GWAS) and in silico functional analysis of candidate loci to analyze target traits, and identify candidate genomic regions underlying these traits. These approaches provided great opportunity to understand the breeding value of SHWs for improving grain weight and enhanced our deep understanding on molecular genetics of grain weight in wheat.