Fine mapping of the region on wheat chromosome 7D controlling grain weight

Mining of QTLs for Spring Bread Wheat Spike Productivity by Comparing Spring Wheat Cultivars Released in Different Decades of the Last Century

Genome-wide association studies (GWAS) are among the genetic tools for the mining of genomic loci associated with useful agronomic traits. The study enabled us to find new genetic markers associated with grain yield as well as quality. The sample under study consisted of spring wheat cultivars developed in different decades of the last century. A panel of 186 accessions was evaluated at VIR's experiment station in Pushkin across a 3-year period of field trials. In total, 24 SNPs associated with six productivity characteristics were revealed. Along with detecting significant markers for each year of the field study, meta-analyses were conducted. Loci associated with useful yield-related agronomic characteristics were detected on chromosomes 4A, 5A, 6A, 6B, and 7B. In addition to previously described regions, novel loci associated with grain yield and quality were identified during the study. We presume that the utilization of contrast cultivars which originated in different breeding periods allowed us to identify new markers associated with useful agronomic characteristics.

Fine mapping of a major QTL, qKl-1BL controlling kernel length in common wheat

KEY MESSAGE

A major stable QTL, qKl-1BL, for kernel length of wheat was narrowed down to a 2.04-Mb interval on chromosome 1BL; the candidate genes were predicated and the genetic effects on yield-related traits were characterized. As a key factor influencing kernel weight, wheat kernel shape is closely related to yield formation, and in turn affects both wheat processing quality and market value. Fine mapping of the major quantitative trait loci (QTL) for kernel shape could provide genetic resources and a theoretical basis for the genetic improvement of wheat yield-related traits. In this study, a major QTL for kernel length (KL) on 1BL, named qKl-1BL, was identified from the recombinant inbred lines (RIL) in multiple environments based on the genetic map and physical map, with 4.76-21.15% of the phenotypic variation explained. To fine map qKl-1BL, the map-based cloning strategy was used. By using developed InDel markers, the near-isogenic line (NIL) pairs and eight key recombinants were identified from a segregating population containing 3621 individuals derived from residual heterozygous lines (RHLs) self-crossing. In combination with phenotype identification, qKl-1BL was finely positioned into a 2.04-Mb interval, KN1B:698.15-700.19 Mb, with eight differentially expressed genes enriched at the key period of kernel elongation. Based on transcriptome analysis and functional annotation information, two candidate genes for qKl-1BL controlling kernel elongation were identified. Additionally, genetic effect analysis showed that the superior allele of qKl-1BL from Jing411 could increase KL, thousand kernel weight (TKW), and yield per plant (YPP) significantly, as well as kernel bulk density and stability time. Taken together, this study identified a QTL interval for controlling kernel length with two possible candidate genes, which provides an important basis for qKl-1BL cloning, functional analysis, and application in molecular breeding programs.

Wheat end-use quality: State of art, genetics, genomics-assisted improvement, future challenges, and opportunities

Wheat is the most important source of food, feed, and nutrition for humans and livestock around the world. The expanding population has increasing demands for various wheat products with different quality attributes requiring the development of wheat cultivars that fulfills specific demands of end-users including millers and bakers in the international market. Therefore, wheat breeding programs continually strive to meet these quality standards by screening their improved breeding lines every year. However, the direct measurement of various end-use quality traits such as milling and baking qualities requires a large quantity of grain, traits-specific expensive instruments, time, and an expert workforce which limits the screening process. With the advancement of sequencing technologies, the study of the entire plant genome is possible, and genetic mapping techniques such as quantitative trait locus mapping and genome-wide association studies have enabled researchers to identify loci/genes associated with various end-use quality traits in wheat. Modern breeding techniques such as marker-assisted selection and genomic selection allow the utilization of these genomic resources for the prediction of quality attributes with high accuracy and efficiency which speeds up crop improvement and cultivar development endeavors. In addition, the candidate gene approach through functional as well as comparative genomics has facilitated the translation of the genomic information from several crop species including wild relatives to wheat. This review discusses the various end-use quality traits of wheat, their genetic control mechanisms, the use of genetics and genomics approaches for their improvement, and future challenges and opportunities for wheat breeding.

A Haplotype-Based GWAS Identified Trait-Improving QTL Alleles Controlling Agronomic Traits under Contrasting Nitrogen Fertilization Treatments in the MAGIC Wheat Population WM-800

The multi-parent-advanced-generation-intercross (MAGIC) population WM-800 was developed by intercrossing eight modern winter wheat cultivars to enhance the genetic diversity present in breeding populations. We cultivated WM-800 during two seasons in seven environments under two contrasting nitrogen fertilization treatments. WM-800 lines exhibited highly significant differences between treatments, as well as high heritabilities among the seven agronomic traits studied. The highest-yielding WM-line achieved an average yield increase of 4.40 dt/ha (5.2%) compared to the best founder cultivar Tobak. The subsequent genome-wide-association-study (GWAS), which was based on haplotypes, located QTL for seven agronomic traits including grain yield. In total, 40, 51, and 46 QTL were detected under low, high, and across nitrogen treatments, respectively. For example, the effect of QYLD_3A could be associated with the haplotype allele of cultivar Julius increasing yield by an average of 4.47 dt/ha (5.2%). A novel QTL on chromosome 2B exhibited pleiotropic effects, acting simultaneously on three-grain yield components (ears-per-square-meter, grains-per-ear, and thousand-grain-weight) and plant-height. These effects may be explained by a member of the nitrate-transporter-1 (NRT1)/peptide-family, TaNPF5.34, located 1.05 Mb apart. The WM-800 lines and favorable QTL haplotypes, associated with yield improvements, are currently implemented in wheat breeding programs to develop advanced nitrogen-use efficient wheat cultivars.

Quick mapping and characterization of a co-located kernel length and thousand-kernel weight-related QTL in wheat

A co-located KL and TKW-related QTL with no negative effect on PH and AD was rapidly identified using BSA and wheat 660 K SNP array. Its effect was validated in a panel of 218 wheat accessions. Kernel length (KL) and thousand-kernel weight (TKW) of wheat (Triticum aestivum L.) contribute significantly to kernel yield. In the present study, a recombinant inbred line (RIL) population derived from the cross between the wheat line S849-8 with larger kernels and more spikelets per spike and the line SY95-71 was developed. Further, of both the bulked segregant analysis (BSA) and the wheat 660 K single nucleotide polymorphism (SNP) array were used to rapidly identify genomic regions for kernel-related traits from this RIL population. Kompetitive Allele Specific PCR markers were further developed in the SNP-enriched region on the 2D chromosome to construct a genetic map. Both QKL.sicau-SSY-2D for KL and QTKW.sicau-SSY-2D for TKW were identified at multiple environments on chromosome arm 2DL. These two QTLs explained 9.68-23.02% and 6.73-18.32% of the phenotypic variation, respectively. The effects of this co-located QTL were successfully verified in a natural population consisting of 218 Sichuan wheat accessions. Interestingly, the major QTL was significantly and positively correlated with spike length, but did not negatively affect spikelet number per spike (SNS), plant height, or anthesis date. These results indicated that it is possible to synchronously improve kernel weight and SNS by using this QTL. Additionally, several genes associated with kernel development and filling rate were predicted and sequenced in the QTL-containing physical intervals of reference genomes of 'Chinese spring' and Aegilops tauschii. Collectively, these results provide a QTL with great breeding potential and its linked markers which should be helpful for fine mapping and molecular breeding.

Genetic Diversity and Selection Signatures in Synthetic-Derived Wheats and Modern Spring Wheat

Synthetic hexaploid wheats and their derived advanced lines were subject to empirical selection in developing genetically superior cultivars. To investigate genetic diversity, patterns of nucleotide diversity, population structure, and selection signatures during wheat breeding, we tested 422 wheat accessions, including 145 synthetic-derived wheats, 128 spring wheat cultivars, and 149 advanced breeding lines from Pakistan. A total of 18,589 high-quality GBS-SNPs were identified that were distributed across the A (40%), B (49%), and D (11%) genomes. Values of population diversity parameters were estimated across chromosomes and genomes. Genome-wide average values of genetic diversity and polymorphic information content were estimated to be 0.30 and 0.25, respectively. Neighbor-joining (NJ) tree, principal component analysis (PCA), and kinship analyses revealed that synthetic-derived wheats and advanced breeding lines were genetically diverse. The 422 accessions were not separated into distinct groups by NJ analysis and confirmed using the PCA. This conclusion was validated with both relative kinship and Rogers' genetic distance analyses. EigenGWAS analysis revealed that 32 unique genome regions had undergone selection. We found that 50% of the selected regions were located in the B-genome, 29% in the D-genome, and 21% in the A-genome. Previously known functional genes or QTL were found within the selection regions associated with phenology-related traits such as vernalization, adaptability, disease resistance, and yield-related traits. The selection signatures identified in the present investigation will be useful for understanding the targets of modern wheat breeding in Pakistan.

Identifying Quantitative Trait Loci for Thousand Grain Weight in Eggplant by Genome Re-Sequencing Analysis

Eggplant (Solanum melongena L.; 2n = 24) is one of the most important Solanaceae vegetables and is primarily cultivated in China (approximately 42% of world production) and India (approximately 39%). Thousand-grain weight (TGW) is an important trait that affects eggplant breeding cost and variety promotion. This trait is controlled by quantitative trait loci (QTLs); however, no quantitative trait loci (QTL) has been reported for TGW in eggplant so far, and its potential genetic basis remain unclear. In this study, two eggplant lines, 17C01 (P1, wild resource, small seed) and 17C02 (P2, cultivar, large seed), were crossed to develop F1, F2 (308 lines), BC1P1 (44 lines), and BC1P2 (44 lines) populations for quantitative trait association analysis. The TGWs of P1, P2 and F1 were determined as 3.00, 3.98 and 3.77 g, respectively. The PG-ADI (polygene-controlled additive-dominance-epistasis) genetic model was identified as the optimal model for TGW and the polygene heritability value in the F2 generation was as high as 80.87%. A high-quality genetic linkage bin map was constructed with resequencing analysis. The map contained 3,918 recombination bins on 12 chromosomes, and the total length was 1,384.62 cM. A major QTL (named as TGW9.1) located on chromosome 9 was identified to be strongly associated with eggplant TGW, with a phenotypic variance explanation of 20.51%. A total of 45 annotated genes were identified in the genetic region of TGW9.1. Based on the annotation of Eggplant genome V3 and orthologous genes in Arabidopsis thaliana, one candidate gene SMEL_009g329850 (SmGTS1, encoding a putative ubiquitin ligase) contains 4 SNPs and 2 Indels consecutive intron mutations in the flank of the same exon in P1. SmGTS1 displayed significantly higher expression in P1 and was selected as a potential candidate gene controlling TGW in eggplant. The present results contribute to shed light on the genetic basis of the traits exploitable in future eggplant marker-assisted selection (MAS) breeding.

Fine mapping KT1 on wheat chromosome 5A that conditions kernel dimensions and grain weight

KT1 was validated as a novel thickness QTL with major effects on wheat kernel dimensions and weight and fine mapped to a 0.04 cM interval near the chromosome-5A centromere. Kernel size, the principal grain weight determining factor of wheat and a target trait for both domestication and artificial breeding, is mainly defined by kernel length (KL), kernel width (KW) and kernel thickness (KT), of which KW and KT have been shown to be positively related to grain weight (GW). Qkt.nau-5A, a major QTL for KT, was validated using the QTL near-isogenic lines (NILs) in three genetic backgrounds. Genetic analysis using two F₂ populations derived from the NILs showed that Qkt.nau-5A was dominant for thicker kernel and inherited like a single gene and therefore was designated as Kernel Thickness 1 (KT1). With 77 recombinant lines identified from a total of 19,160 F₂ plants from the two NIL-derived F₂ populations, KT1 was mapped to the 0.04 cM Xwgrb1356-Xwgrb1619 interval, which was near the centromere and displayed strong recombination suppression. The KT1 interval showed positive correlation with KW and GW and negative correlation with KL and therefore could be used in breeding for cultivars with round-shaped kernels that are beneficial to higher flour yield. KT1 candidate identification could be achieved through combination of sequence variation analysis with expression profiling of the annotated genes in the interval.

GWAS for main effects and epistatic interactions for grain morphology traits in wheat

In the present study in wheat, GWAS was conducted for identification of marker trait associations (MTAs) for the following six grain morphology traits: (1) grain cross-sectional area (GCSA), (2) grain perimeter (GP), (3) grain length (GL), (4) grain width (GWid), (5) grain length-width ratio (GLWR) and (6) grain form-density (GFD). The data were recorded on a subset of spring wheat reference set (SWRS) comprising 225 diverse genotypes, which were genotyped using 10,904 SNPs and phenotyped for two consecutive years (2017-2018, 2018-2019). GWAS was conducted using five different models including two single-locus models (CMLM, SUPER), one multi-locus model (FarmCPU), one multi-trait model (mvLMM) and a model for Q x Q epistatic interactions. False discovery rate (FDR) [P value -log10(p) ≥ 5] and Bonferroni correction [P value -log10(p) ≥ 6] (corrected p value < 0.05) were applied to eliminate false positives due to multiple testing. This exercise gave 88 main effect and 29 epistatic MTAs after FDR and 13 main effect and 6 epistatic MTAs after Bonferroni corrections. MTAs obtained after Bonferroni corrections were further utilized for identification of 55 candidate genes (CGs). In silico expression analysis of CGs in different tissues at different parts of the seed at different developmental stages was also carried out. MTAs and CGs identified during the present study are useful addition to available resources for MAS to supplement wheat breeding programmes after due validation and also for future strategic basic research.

Supplementary Information

The online version contains supplementary material available at 10.1007/s12298-022-01164-w.

Introducing Beneficial Alleles from Plant Genetic Resources into the Wheat Germplasm

Wheat (Triticum sp.) is one of the world's most important crops, and constantly increasing its productivity is crucial to the livelihoods of millions of people. However, more than a century of intensive breeding and selection processes have eroded genetic diversity in the elite genepool, making new genetic gains difficult. Therefore, the need to introduce novel genetic diversity into modern wheat has become increasingly important. This review provides an overview of the plant genetic resources (PGR) available for wheat. We describe the most important taxonomic and phylogenetic relationships of these PGR to guide their use in wheat breeding. In addition, we present the status of the use of some of these resources in wheat breeding programs. We propose several introgression schemes that allow the transfer of qualitative and quantitative alleles from PGR into elite germplasm. With this in mind, we propose the use of a stage-gate approach to align the pre-breeding with main breeding programs to meet the needs of breeders, farmers, and end-users. Overall, this review provides a clear starting point to guide the introgression of useful alleles over the next decade.

Utilization of a Wheat50K SNP Microarray-Derived High-Density Genetic Map for QTL Mapping of Plant Height and Grain Traits in Wheat

Plant height is significantly correlated with grain traits, which is a component of wheat yield. The purpose of this study is to investigate the main quantitative trait loci (QTLs) that control plant height and grain-related traits in multiple environments. In this study, we constructed a high-density genetic linkage map using the Wheat50K SNP Array to map QTLs for these traits in 198 recombinant inbred lines (RILs). The two ends of the chromosome were identified as recombination-rich areas in all chromosomes except chromosome 1B. Both the genetic map and the physical map showed a significant correlation, with a correlation coefficient between 0.63 and 0.99. However, there was almost no recombination between 1RS and 1BS. In terms of plant height, 1RS contributed to the reduction of plant height by 3.43 cm. In terms of grain length, 1RS contributed to the elongation of grain by 0.11 mm. A total of 43 QTLs were identified, including eight QTLs for plant height (PH), 11 QTLs for thousand grain weight (TGW), 15 QTLs for grain length (GL), and nine QTLs for grain width (GW), which explained 1.36–33.08% of the phenotypic variation. Seven were environment-stable QTLs, including two loci (Qph.nwafu-4B and Qph.nwafu-4D) that determined plant height. The explanation rates of phenotypic variation were 7.39–12.26% and 20.11–27.08%, respectively. One QTL, Qtgw.nwafu-4B, which influenced TGW, showed an explanation rate of 3.43–6.85% for phenotypic variation. Two co-segregating KASP markers were developed, and the physical locations corresponding to KASP_AX-109316968 and KASP_AX-109519968 were 25.888344 MB and 25.847691 MB, respectively. Qph.nwafu-4B, controlling plant height, and Qtgw.nwafu-4B, controlling TGW, had an obvious linkage relationship, with a distance of 7–8 cM. Breeding is based on molecular markers that control plant height and thousand-grain weight by selecting strains with low plant height and large grain weight. Another QTL, Qgw.nwafu-4D, which determined grain width, had an explanation rate of 3.43–6.85%. Three loci that affected grain length were Qgl.nwafu-5A, Qgl.nwafu-5D.2, and Qgl.nwafu-6B, illustrating the explanation rates of phenotypic variation as 6.72–9.59%, 5.62–7.75%, and 6.68–10.73%, respectively. Two QTL clusters were identified on chromosomes 4B and 4D.

Elucidating the genetics of grain yield and stress-resilience in bread wheat using a large-scale genome-wide association mapping study with 55,568 lines

Wheat grain yield (GY) improvement using genomic tools is important for achieving yield breakthroughs. To dissect the genetic architecture of wheat GY potential and stress-resilience, we have designed this large-scale genome-wide association study using 100 datasets, comprising 105,000 GY observations from 55,568 wheat lines evaluated between 2003 and 2019 by the International Maize and Wheat Improvement Center and national partners. We report 801 GY-associated genotyping-by-sequencing markers significant in more than one dataset and the highest number of them were on chromosomes 2A, 6B, 6A, 5B, 1B and 7B. We then used the linkage disequilibrium (LD) between the consistently significant markers to designate 214 GY-associated LD-blocks and observed that 84.5% of the 58 GY-associated LD-blocks in severe-drought, 100% of the 48 GY-associated LD-blocks in early-heat and 85.9% of the 71 GY-associated LD-blocks in late-heat, overlapped with the GY-associated LD-blocks in the irrigated-bed planting environment, substantiating that simultaneous improvement for GY potential and stress-resilience is feasible. Furthermore, we generated the GY-associated marker profiles and analyzed the GY favorable allele frequencies for a large panel of 73,142 wheat lines, resulting in 44.5 million datapoints. Overall, the extensive resources presented in this study provide great opportunities to accelerate breeding for high-yielding and stress-resilient wheat varieties.

Identification and validation of quantitative trait loci for kernel traits in common wheat (Triticum aestivum L.)

BACKGROUND

Kernel weight and morphology are important traits affecting cereal yields and quality. Dissecting the genetic basis of thousand kernel weight (TKW) and its related traits is an effective method to improve wheat yield.

RESULTS

In this study, we performed quantitative trait loci (QTL) analysis using recombinant inbred lines derived from the cross 'PuBing3228 × Gao8901' (PG-RIL) to dissect the genetic basis of kernel traits. A total of 17 stable QTLs related to kernel traits were identified, notably, two stable QTLs QTkw.cas-1A.2 and QTkw.cas-4A explained the largest portion of the phenotypic variance for TKW and kernel length (KL), and the other two stable QTLs QTkw.cas-6A.1 and QTkw.cas-7D.2 contributed more effects on kernel width (KW). Conditional QTL analysis revealed that the stable QTLs for TKW were mainly affected by KW. The QTLs QTkw.cas-7D.2 and QKw.cas-7D.1 associated with TKW and KW were delimited to the physical interval of approximately 3.82 Mb harboring 47 candidate genes. Among them, the candidate gene TaFT-D1 had a 1 bp insertions/deletion (InDel) within the third exon, which might be the reason for diversity in TKW and KW between the two parents. A Kompetitive Allele-Specific PCR (KASP) marker of TaFT-D1 allele was developed and verified by PG-RIL and a natural population consisted of 141 cultivar/lines. It was found that the favorable TaFT-D1 (G)-allele has been positively selected during Chinese wheat breeding. Thus, these results can be used for further positional cloning and marker-assisted selection in wheat breeding programs.

CONCLUSIONS

Seventeen stable QTLs related to kernel traits were identified. The stable QTLs for thousand kernel weight were mainly affected by kernel width. TaFT-D1 could be the candidate gene for QTLs QTkw.cas-7D.2 and QKw.cas-7D.1.

Dissection and validation of a QTL cluster linked to Rht-B1 locus controlling grain weight in common wheat (Triticum aestivum L.) using near-isogenic lines

This study dissected and validated a QTL cluster associated with thousand grain weight on chromosome 4B using multiple near-isogenic lines in common wheat. Grain size and weight are crucial components of wheat yield. Previously, we identified a QTL cluster for thousand grain weight (TGW) on chromosome 4B using the Nongda3338 (ND3338)/Jingdong6 (JD6) doubled haploid population. Here, near-isogenic lines (NILs) in the ND3338 background were developed to dissect and validate the QTL cluster. Based on six independent BC₃F_3:4 heterogeneous inbred families, the 4B QTL cluster was divided into two linked QTL intervals (designated 4B.1 and 4B.2 QTL). For the 4B.1 QTL, the Rht-B1 gene, of which Rht-B1b allele reduces plant height (PH) by 21.18-29.34 cm (34.34-53.71%), was demonstrated to be the most likely candidate gene with pleiotropic effects on grain size and TGW. For the 4B.2 QTL, the NIL^JD6 consistently showed an increase in TGW of 3.51-7.68 g (8.84-22.77%) compared with NIL^ND3338 across different field trials, along with a significant increase in PH of 2.26-6.71 cm (3.92-12.01%). Moreover, both QTL intervals had a larger effect on grain width than on grain length. Additionally, the first significant difference in 100-grain fresh weight and 100-grain dry weight between the NIL pairs of the 4B.1 QTL interval (Rht-B1) was observed at 6 days after pollination (DAP), while the differences were first visible at 30 DAP for the 4B.2 QTL interval. Collectively, our work provides a new example of QTL dissection for grain weight in wheat and lays a foundation for further map-based cloning of the major QTL that have potential applications in wheat molecular breeding for high yield.

Genetic architecture underpinning yield component traits in wheat

Genetic atlas, reliable QTL and candidate genes of yield component traits in wheat were figured out, laying concrete foundations for map-based gene cloning and dissection of regulatory mechanisms underlying yield. Mining genetic loci for yield is challenging due to the polygenic nature, large influence of environment and complex relationship among yield component traits (YCT). Many genetic loci related to wheat yield have been identified, but its genetic architecture and key genetic loci for selection are largely unknown. Wheat yield potential can be determined by three YCT, thousand kernel weight, kernel number per spike and spike number. Here, we summarized the genetic loci underpinning YCT from QTL mapping, association analysis and homology-based gene cloning. The major loci determining yield-associated agronomic traits, such as flowering time and plant height, were also included in comparative analyses with those for YCT. We integrated yield-related genetic loci onto chromosomes based on their physical locations. To identify the major stable loci for YCT, 58 QTL-rich clusters (QRC) were defined based on their distribution on chromosomes. Candidate genes in each QRC were predicted according to gene annotation of the wheat reference genome and previous information on validation of those genes in other species. Finally, a technological route was proposed to take full advantage of the resultant resources for gene cloning, molecular marker-assisted breeding and dissection of molecular regulatory mechanisms underlying wheat yield.

Mapping Quantitative Trait Loci for 1000-Grain Weight in a Double Haploid Population of Common Wheat

Thousand-grain weight (TGW) is a very important yield trait of crops. In the present study, we performed quantitative trait locus (QTL) analysis of TGW in a doubled haploid population obtained from a cross between the bread wheat cultivar "Superb" and the breeding line "M321" using the wheat 55-k single-nucleotide polymorphism (SNP) genotyping assay. A genetic map containing 15,001 SNP markers spanning 2209.64 cM was constructed, and 9 QTLs were mapped to chromosomes 1A, 2D, 4B, 4D, 5A, 5D, 6A, and 6D based on analyses conducted in six experimental environments during 2015-2017. The effects of the QTLs qTgw.nwipb-4DS and qTgw.nwipb-6AL were shown to be strong and stable in different environments, explaining 15.31-32.43% and 21.34-29.46% of the observed phenotypic variance, and they were mapped within genetic distances of 2.609 cM and 5.256 cM, respectively. These novel QTLs may be used in marker-assisted selection in wheat high-yield breeding.

Fine mapping and gene cloning in the post-NGS era: advances and prospects

Improvement in traits of agronomic importance is the top breeding priority of crop improvement programs. Majority of these agronomic traits show complex quantitative inheritance. Identification of quantitative trait loci (QTLs) followed by fine mapping QTLs and cloning of candidate genes/QTLs is central to trait analysis. Advances in genomic technologies revolutionized our understanding of genetics of complex traits, and genomic regions associated with traits were employed in marker-assisted breeding or cloning of QTLs/genes. Next-generation sequencing (NGS) technologies have enabled genome-wide methodologies for the development of ultra-high-density genetic linkage maps in different crops, thus allowing placement of candidate loci within few kbs in genomes. In this review, we compare the marker systems used for fine mapping and QTL cloning in the pre- and post-NGS era. We then discuss how different NGS platforms in combination with advanced experimental designs have improved trait analysis and fine mapping. We opine that efficient genotyping/sequencing assays may circumvent the need for cumbersome procedures that were earlier used for fine mapping. A deeper understanding of the trait architectures of agricultural significance will be crucial to accelerate crop improvement.

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.

Use of near-isogenic lines to precisely map and validate a major QTL for grain weight on chromosome 4AL in bread wheat (Triticum aestivum L.)

This study precisely mapped and validated a major quantitative trait locus (QTL) on chromosome 4AL for thousand-grain weight in wheat using multiple near-isogenic lines. Thousand-grain weight (TGW) is an essential yield component. Following the previous identification of a major QTL for TGW within the interval of 15.7 cM (92.7-108.4 cM) on chromosome 4AL using the Nongda3338 (ND3338)/Jingdong6 (JD6) doubled haploid population, the aim of this study was to perform more precise mapping and validate the genetic effect of the QTL. Multiple near-isogenic lines (NILs) were developed using ND3338 as the recurrent parent through marker-assisted selection. Based on five independent BC₃F_3:4 segregating populations derived from BC₃F₃ plants with different heterozygous segments for the target QTL site and the results of genotyping analysis performed using the Wheat660 K SNP array, it was possible to delimit the QTL region to a physical interval of approximately 6.5 Mb (677.11-683.61 Mb, IWGSC Ref Seq v1.0). Field trials across multiple environments showed that NILs^JD6 had a consistent effect on increasing the TGW by 5.16-27.48% and decreasing the grain number per spike (GNS) by 3.98-32.91% compared to the corresponding NILs^ND3338, which exhibited locus-specific TGW-GNS trade-offs. Moreover, by using RNA sequencing (RNA-Seq) of whole grains at 10 days after pollination stage of multiple NILs, we found that differentially expressed genes between the NIL pairs were significantly enriched for cell cycle and the replication of chromosome-related genes, hence affecting cell division and cell proliferation. Overall, our results provide a basis for map-based cloning of the major QTL and determining the mechanisms underlying TGW-GNS trade-offs in wheat, which would help to fine-tune these two components and maximize the grain yield for breeders.

A reductionist approach to dissecting grain weight and yield in wheat

Grain yield is a highly polygenic trait that is influenced by the environment and integrates events throughout the life cycle of a plant. In wheat, the major grain yield components often present compensatory effects among them, which alongside the polyploid nature of wheat, makes their genetic and physiological study challenging. We propose a reductionist and systematic approach as an initial step to understand the gene networks regulating each individual yield component. Here, we focus on grain weight and discuss the importance of examining individual sub-components, not only to help in their genetic dissection, but also to inform our mechanistic understanding of how they interrelate. This knowledge should allow the development of novel combinations, across homoeologs and between complementary modes of action, thereby advancing towards a more integrated strategy for yield improvement. We argue that this will break barriers in terms of phenotypic variation, enhance our understanding of the physiology of yield, and potentially deliver improved on-farm yield.

Three genomes differentially contribute to the seedling lateral root number in allohexaploid wheat: evidence from phenotype evolution and gene expression

Common wheat is an allohexaploid (BBAADD) that originated from the hybridization and polyploidization of the diploid Aegilops tauschii (DD) with the allotetraploid Triticum turgidum (BBAA). Phenotypic changes often arise with the formation and evolution of allopolyploid wheat, but little is known about the evolution of root traits in different wheat species with varying ploidy levels. Here, we reported that the lateral root number on the primary root (LRNPR) of synthetic and natural allohexaploid wheats (BBAADD) is significantly higher than that of their allotetraploid (BBAA) and diploid (AA and SS) progenitors, but is much lower than that of their diploid (DD) progenitors. The expression of the wheat gene TaLBD16, an ortholog of the Arabidopsis LATERAL ORGAN BOUNDARIES-DOMAIN16/ASYMMETRIC LEAVES2-LIKE18 (LBD16), which is involved in lateral root development in Arabidopsis, was positively correlated with the LRNPR in diploid and allopolyploid wheats. In natural and synthetic allohexaploid wheats, the transcript of the TaLBD16 from the D genome (TaLBD16-D) was relatively more abundant compared with TaLBD16-A and TaLBD16-B. Consistent with the observed variation in LRNPR, the divergence in the expression of TaLBD16 homoeologous genes occurred before the formation of polyploidy wheat. Collectively, our observations indicate that the D genome played a crucial role in the increased lateral root number of allohexaploid wheats compared with their allotetraploid progenitors, and that TaLBD16-D was one of the key genes involved in the formation of lateral root number during wheat evolution.

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.

Genome-Wide Association Studies to Identify Loci and Candidate Genes Controlling Kernel Weight and Length in a Historical United States Wheat Population

Although kernel weight (KW) is a major component of grain yield, its contribution to yield genetic gain during breeding history has been minimal. This highlights an untapped potential for further increases in yield via improving KW. We investigated variation and genetics of KW and kernel length (KL) via genome-wide association studies (GWAS) using a historical and contemporary soft red winter wheat population representing 200 years of selection and breeding history in the United States. The observed changes of KW and KL over time did not show any conclusive trend. The population showed a structure, which was mainly explained by the time and location of germplasm development. Cluster sharing by germplasm from more than one breeding population was suggestive of episodes of germplasm exchange. Using 2 years of field-based phenotyping, we detected 26 quantitative trait loci (QTL) for KW and 27 QTL for KL with -log₁₀(p) > 3.5. The search for candidate genes near the QTL on the wheat genome version IWGSCv1.0 has resulted in over 500 genes. The predicted functions of several of these genes are related to kernel development, photosynthesis, sucrose and starch synthesis, and assimilate remobilization and transport. We also evaluated the effect of allelic polymorphism of genes previously reported for KW and KL by using Kompetitive Allele Specific PCR (KASP) markers. Only TaGW2 showed significant association with KW. Two genes, i.e., TaSus2-2B and TaGS-D1 showed significant association with KL. Further physiological studies are needed to decipher the involvement of these genes in KW and KL development.

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.

Transcriptome analysis reveals potential mechanisms for different grain size between natural and resynthesized allohexaploid wheats with near-identical AABB genomes

BACKGROUND

Common wheat is a typical allohexaploid species (AABBDD) derived from the interspecific crossing between allotetraploid wheat (AABB) and Aegilops tauschii (DD). Wide variation in grain size and shape observed among Aegilops tauschii can be retained in synthetic allohexaploid wheats, but the underlying mechanism remains enigmatic. Here, the natural and resynthesized allohexaploid wheats with near-identical AB genomes and different D genomes (TAA10 and XX329) were employed for analysis.

RESULTS

Significant differences in grain size and weight between TAA10 and XX329 were observed at the early stages of development, which could be mainly attributed to the higher growth rates of the pericarp and endosperm cells in XX329 compared to TAA10. Furthermore, comparative transcriptome analysis identified that 8891 of 69,711 unigenes (12.75%) were differentially expressed between grains at 6 days after pollination (DAP) of TAA10 and XX329, including 5314 up-regulated and 3577 down-regulated genes in XX329 compared to TAA10. The MapMan functional annotation and enrichment analysis revealed that the differentially expressed genes were significantly enriched in categories of cell wall, carbohydrate and hormone metabolism. Notably, consistent with the up-regulation of sucrose synthase genes in resynthesized relative to natural allohexaploid wheat, the resynthesized allohexaploid wheat accumulated much higher contents of glucose and fructose in 6-DAP grains than those of the natural allohexaploid wheat.

CONCLUSIONS

These data indicated that the genetic variation of the D genome induced drastic alterations of gene expression in grains of the natural and resynthesized allohexaploid wheats, which may contribute to the observed differences in grain size and weight.

Genetic dissection of the relationships between grain yield components by genome-wide association mapping in a collection of tetraploid wheats

Increasing grain yield potential in wheat has been a major target of most breeding programs. Genetic advance has been frequently hindered by negative correlations among yield components that have been often observed in segregant populations and germplasm collections. A tetraploid wheat collection was evaluated in seven environments and genotyped with a 90K SNP assay to identify major and stable quantitative trait loci (QTL) for grain yield per spike (GYS), kernel number per spike (KNS) and thousand-kernel weight (TKW), and to analyse the genetic relationships between the yield components at QTL level. The genome-wide association analysis detected eight, eleven and ten QTL for KNS, TKW and GYS, respectively, significant in at least three environments or two environments and the mean across environments. Most of the QTL for TKW and KNS were found located in different marker intervals, indicating that they are genetically controlled independently by each other. Out of eight KNS QTL, three were associated to significant increases of GYS, while the increased grain number of five additional QTL was completely or partially compensated by decreases in grain weight, thus producing no or reduced effects on GYS. Similarly, four consistent and five suggestive TKW QTL resulted in visible increase of GYS, while seven additional QTL were associated to reduced effects in grain number and no effects on GYS. Our results showed that QTL analysis for detecting TKW or KNS alleles useful for improving grain yield potential should consider the pleiotropic effects of the QTL or the association to other QTLs.

Identification of QTL for Grain Size and Shape on the D Genome of Natural and Synthetic Allohexaploid Wheats with Near-Identical AABB Genomes

Grain size and shape associated with yield and milling quality are important traits in wheat domestication and breeding. To reveal the genetic factors on the D genome that control grain size and shape variation, we conducted analysis of quantitative trait loci (QTL) using the F₂ and F_2:3 populations derived from a common allohexaploid wheat line TAA10 and a synthetic allohexaploid wheat XX329, which have near-identical AABB genomes and different DD genomes. Based on genotyping using wheat 660K single nucleotide polymorphism (SNP) array, TAA10 and XX329 exhibited 96.55, 98.10, and 66.26% genetic similarities of A, B, and D genomes, respectively. Phenotypic evaluation revealed that XX329 had higher thousand grain weight (TGW), grain length, width, area and perimeter than TAA10 across all environments, and the grain yield per plot of XX329 increased by 17.43-30.36% compared with that of TAA10 in two environments. A total of nine environmentally stable QTL associated with grain size and shape were mapped on chromosomes 2D and 7D and verified using near isogenic lines (NILs), with the synthetic allohexaploid wheat XX329 contributing favorable alleles. Notably, a novel QTL QTgw.cau-2D controlling grain weight was first identified from the synthetic allohexaploid wheat, which may be a more desirable target for genetic improvement in wheat breeding. Collectively, these results provide further insights into the genetic factors that shaped the grain morphology during wheat evolution and domestication.

An intercalary translocation from Agropyron cristatum 6P chromosome into common wheat confers enhanced kernel number per spike

This study explored 6P chromosomal translocations in wheat, and determined the effects of 6P intercalary chromosome segments on kernel number per wheat spike. Exploiting and utilising gene(s) from wild relative species has become an essential strategy for wheat crop improvement. In the translocation line Pubing2978, the intercalary 6P chromosome segment from Agropyron cristatum (L.) Gaertn. (2n = 4x = 28, PPPP) carried valuable multi-kernel gene(s) and was selected from the offspring of the common wheat plant Fukuho and the irradiated wheat-A. cristatum 6P disomic substitution line 4844-8. Genomic in situ hybridisation (GISH), dual-colour fluorescence in situ hybridisation (FISH), and molecular markers were used to detect the small segmental 6P chromosome in the wheat background and its translocation breakpoint. Cytological studies demonstrated that Pubing2978 was a T1AS-6PL-1AS·1AL intercalary translocation with 42 chromosomes. The breakpoint was located near the centromeric region on the wheat chromosome 1AS and was flanked by the markers SSR12 and SSR283 based on an F2 linkage map. The genotypic data, combined with the phenotypic information, implied that A. cristatum 6P chromosomal segment plays an important role in regulating the kernel number per spike (KPS). By comparison, the mean value of KPS in plants with translocations was approximately 10 higher than that in plants without translocations in three segregated populations. Moreover, the improvement in KPS was likely achieved by increasing both the spikelet number per spike (SNS) and the kernel number per spikelet. These excellent agronomic traits laid the foundation for further investigation of valuable genes and make the Pubing2978 line a promising germplasm for wheat breeding.

Breeding Value of Primary Synthetic Wheat Genotypes for Grain Yield

To introduce new genetic diversity into the bread wheat gene pool from its progenitor, Aegilops tauschii (Coss.) Schmalh, 33 primary synthetic hexaploid wheat genotypes (SYN) were crossed to 20 spring bread wheat (BW) cultivars at the International Wheat and Maize Improvement Center. Modified single seed descent was used to develop 97 populations with 50 individuals per population using first back-cross, biparental, and three-way crosses. Individuals from each cross were selected for short stature, early heading, flowering and maturity, minimal lodging, and free threshing. Yield trials were conducted under irrigated, drought, and heat-stress conditions from 2011 to 2014 in Ciudad Obregon, Mexico. Genomic estimated breeding values (GEBVs) of parents and synthetic derived lines (SDLs) were estimated using a genomic best linear unbiased prediction (GBLUP) model with markers in each trial. In each environment, there were SDLs that had higher GEBVs than their recurrent BW parent for yield. The GEBVs of BW parents for yield ranged from -0.32 in heat to 1.40 in irrigated trials. The range of the SYN parent GEBVs for yield was from -2.69 in the irrigated to 0.26 in the heat trials and were mostly negative across environments. The contribution of the SYN parents to improved grain yield of the SDLs was highest under heat stress, with an average GEBV for the top 10% of the SDLs of 0.55 while the weighted average GEBV of their corresponding recurrent BW parents was 0.26. Using the pedigree-based model, the accuracy of genomic prediction for yield was 0.42, 0.43, and 0.49 in the drought, heat and irrigated trials, respectively, while for the marker-based model these values were 0.43, 0.44, and 0.55. The SYN parents introduced novel diversity into the wheat gene pool. Higher GEBVs of progenies were due to introgression and retention of some positive alleles from SYN parents.

Harnessing Diversity in Wheat to Enhance Grain Yield, Climate Resilience, Disease and Insect Pest Resistance and Nutrition Through Conventional and Modern Breeding Approaches

Current trends in population growth and consumption patterns continue to increase the demand for wheat, a key cereal for global food security. Further, multiple abiotic challenges due to climate change and evolving pathogen and pests pose a major concern for increasing wheat production globally. Triticeae species comprising of primary, secondary, and tertiary gene pools represent a rich source of genetic diversity in wheat. The conventional breeding strategies of direct hybridization, backcrossing and selection have successfully introgressed a number of desirable traits associated with grain yield, adaptation to abiotic stresses, disease resistance, and bio-fortification of wheat varieties. However, it is time consuming to incorporate genes conferring tolerance/resistance to multiple stresses in a single wheat variety by conventional approaches due to limitations in screening methods and the lower probabilities of combining desirable alleles. Efforts on developing innovative breeding strategies, novel tools and utilizing genetic diversity for new genes/alleles are essential to improve productivity, reduce vulnerability to diseases and pests and enhance nutritional quality. New technologies of high-throughput phenotyping, genome sequencing and genomic selection are promising approaches to maximize progeny screening and selection to accelerate the genetic gains in breeding more productive varieties. Use of cisgenic techniques to transfer beneficial alleles and their combinations within related species also offer great promise especially to achieve durable rust resistance.

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.

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).

Genetic dissection of grain size and grain number trade-offs in CIMMYT wheat germplasm

Grain weight (GW) and number per unit area of land (GN) are the primary components of grain yield in wheat. In segregating populations both yield components often show a negative correlation among themselves. Here we use a recombinant doubled haploid population of 105 individuals developed from the CIMMYT varieties Weebill and Bacanora to understand the relative contribution of these components to grain yield and their interaction with each other. Weebill was chosen for its high GW and Bacanora for high GN. The population was phenotyped in Mexico, Argentina, Chile and the UK. Two loci influencing grain yield were indicated on 1B and 7B after QTL analysis. Weebill contributed the increasing alleles. The 1B effect, which is probably caused by to the 1BL.1RS rye introgression in Bacanora, was a result of increased GN, whereas, the 7B QTL controls GW. We concluded that increased in GW from Weebill 7B allele is not accompanied by a significant reduction in grain number. The extent of the GW and GN trade-off is reduced. This makes this locus an attractive target for marker assisted selection to develop high yielding bold grain varieties like Weebill. AMMI analysis was used to show that the 7B Weebill allele appears to contribute to yield stability.

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.

Analysis of main effect QTL for thousand grain weight in European winter wheat (Triticum aestivum L.) by genome-wide association mapping

Grain weight, an essential yield component, is under strong genetic control and at the same time markedly influenced by the environment. Genetic analysis of the thousand grain weight (TGW) by genome-wide association study (GWAS) was performed with a panel of 358 European winter wheat (Triticum aestivum L.) varieties and 14 spring wheat varieties using phenotypic data of field tests in eight environments. Wide phenotypic variations were indicated for the TGW with BLUEs (best linear unbiased estimations) values ranging from 35.9 to 58.2 g with a mean value of 45.4 g and a heritability of H(2) = 0.89. A total of 12 candidate genes for plant height, photoperiodism and grain weight were genotyped on all varieties. Only three candidates, the photoperiodism gene Ppd-D1, dwarfing gene Rht-B1and the TaGW-6A gene were significant explaining up to 14.4, 2.3, and 3.4% of phenotypic variation, respectively. For a comprehensive genome-wide analysis of TGW-QTL genotyping data from 732 microsatellite markers and a set of 7769 mapped SNP-markers genotyped with the 90k iSELECT array were analyzed. In total, 342 significant (-log10 (P-value) ≥ 3.0) marker trait associations (MTAs) were detected for SSR-markers and 1195 MTAs (-log10(P-value) ≥ 3.0) for SNP-markers in all single environments plus the BLUEs. After Bonferroni correction, 28 MTAs remained significant for SSR-markers (-log10 (P-value) ≥ 4.82) and 58 MTAs for SNP-markers (-log10 (P-value) ≥ 5.89). Apart from chromosomes 4B and 6B for SSR-markers and chromosomes 4D and 5D for SNP-markers, MTAs were detected on all chromosomes. The highest number of significant SNP-markers was found on chromosomes 3B and 1B, while for the SSRs most markers were significant on chromosomes 6D and 3D. Overall, TGW was determined by many markers with small effects. Only three SNP-markers had R(2) values above 6%.

Fine mapping of a preharvest sprouting QTL interval on chromosome 2B in white wheat

Fine mapping by recombinant backcross populations revealed that a preharvest sprouting QTL on 2B contained two QTLs linked in coupling with different effects on the phenotype. Wheat preharvest sprouting (PHS) occurs when grain germinates on the plant before harvest, resulting in reduced grain quality. Previous mapping of quantitative trait locus (QTL) revealed a major PHS QTL, QPhs.cnl-2B.1, located on chromosome 2B significant in 16 environments that explained from 5 to 31 % of the phenotypic variation. The objective of this project was to fine map the QPhs.cnl-2B.1 interval. Fine mapping was carried out in recombinant backcross populations (BC1F4 and BC1F5) that were developed by backcrossing selected doubled haploids to a recurrent parent and self-pollinating the BC1F4 and BC1F5 generations. In each generation, three markers in the QPhs.cnl-2B.1 interval were used to screen for recombinants. Fine mapping revealed that the QPhs.cnl-2B.1 interval contained two PHS QTLs linked in coupling. The distal PHS QTL, located between Wmc453c and Barc55, contributed 8 % of the phenotypic variation and also co-located with a major seed dormancy QTL determined by germination index. The proximal PHS QTL, between Wmc474 and CNL415-rCDPK, contributed 16 % of the variation. Several candidate genes including Mg-chelatase H subunit family protein, GTP-binding protein and calmodulin/Ca(2+)-dependent protein kinase were linked to the PHS QTL. Although many recombinant lines were identified, the lack of polymorphism for markers in the QTL interval prevented the localization of the recombination breakpoints and identification of the gene underlying the phenotype.

Genetic dissection of yield-related traits in a recombinant inbred line population created using a key breeding parent in China's wheat breeding

Understanding the genetics underlying yield formation of wheat is important for increasing wheat yield potential in breeding programs. Nanda2419 was a widely used cultivar for wheat production and breeding in China. In this study, we evaluated yield components and a few yield-related traits of a recombinant inbred line (RIL) population created by crossing Nanda2419 with the indigenous cultivar Wangshuibai in three to four trials at different geographical locations. Negative and positive correlations were found among some of these evaluated traits. Five traits had over 50 % trial-wide broad sense heritability. Using a framework marker map of the genome constructed with this population, quantitative trait loci (QTL) were identified for all traits, and epistatic loci were identified for seven of them. Our results confirmed some of the previously reported QTLs in wheat and identified several new ones, including QSn.nau-6D for effective tillers, QGn.nau-4B.2 for kernel number, QGw.nau-4D for kernel weight, QPh.nau-4B.2 and QPh.nau-4A for plant height, and QFlw.nau-5A.1 for flag leaf width. In the investigated population, Nanda2419 contributed all QTLs associated with higher kernel weight, higher leaf chlorophyll content, and a major QTL associated with wider flag leaf. Seven chromosome regions were related to more than one trait. Four QTL clusters contributed positively to breeding goal-based trait improvement through the Nanda2419 alleles and were detected in trials set in different ecological regions. The findings of this study are relevant to the molecular improvement of wheat yield and to the goal of screening cultivars for better breeding parents.

Down-regulation of the TaGW2 gene by RNA interference results in decreased grain size and weight in wheat

For important food crops such as wheat and rice, grain yield depends on grain number and size. In rice (Oryza sativa), GW2 was isolated from a major quantitative trait locus for yield and encodes an E3 RING ligase that negatively regulates grain size. Wheat (Triticum aestivum) has TaGW2 homologues in the A, B, and D genomes, and polymorphisms in TaGW2-A were associated with grain width. Here, to investigate TaGW2 function, RNA interference (RNAi) was used to down-regulate TaGW2 transcript levels. Transgenic wheat lines showed significantly decreased grain size-related dimensions compared with controls. Furthermore, TaGW2 knockdown also caused a significant reduction in endosperm cell number. These results indicate that TaGW2 regulates grain size in wheat, possibly by controlling endosperm cell number. Wheat and rice GW2 genes thus seem to have divergent functions, with rice GW2 negatively regulating grain size and TaGW2 positively regulating grain size. Analysis of transcription of TaGW2 homoeologues in developing grains suggested that TaGW2-A and -D act in both the division and late grain-filling phases. Furthermore, biochemical and molecular analyses revealed that TaGW2-A is a functional E3 RING ubiquitin ligase with nucleocytoplasmic subcellular partitioning. A functional nuclear export sequence responsible for TaGW2-A export from the nucleus to the cytosol and retention in the nucleolus was identified. Therefore, these results show that TaGW2 acts in the regulation of grain size and may provide an important tool for enhancement of grain yield.

QTL mapping of terminal heat tolerance in hexaploid wheat (T. aestivum L.)

High temperature (>30 °C) at the time of grain filling is one of the major causes of yield reduction in wheat in many parts of the world, especially in tropical countries. To identify quantitative trait loci (QTL) for heat tolerance under terminal heat stress, a set of 148 recombinant inbred lines was developed by crossing a heat-tolerant hexaploid wheat (Triticum aestivum L.) cultivar (NW1014) and a heat-susceptible (HUW468) cultivar. The F(5), F(6), and F(7) generations were evaluated in two different sowing dates under field conditions for 2 years. Using the trait values from controlled and stressed trials, four different traits (1) heat susceptibility index (HSI) of thousand grain weight (HSITGW); (2) HSI of grain fill duration (HSIGFD); (3) HSI of grain yield (HSIYLD); and (4) canopy temperature depression (CTD) were used to determine heat tolerance. Days to maturity was also investigated. A linkage map comprising 160 simple sequence repeat markers was prepared covering the whole genome of wheat. Using composite interval mapping, significant genomic regions on 2B, 7B and 7D were found to be associated with heat tolerance. Of these, two (2B and 7B) were co-localized QTL and explained more than 15 % phenotypic variation for HSITGW, HSIGFD and CTD. In pooled analysis over three trials, QTL explained phenotypic variation ranging from 9.78 to 20.34 %. No QTL × trial interaction was detected for the identified QTL. The three major QTL obtained can be used in marker-assisted selection for heat stress in wheat.

Wheat kernel dimensions: how do they contribute to kernel weight at an individual QTL level?

Kernel dimensions (KD) contribute greatly to thousand-kernel weight (TKW) in wheat. In the present study, quantitative trait loci (QTL) for TKW, kernel length (KL), kernel width (KW) and kernel diameter ratio (KDR) were detected by both conditional and unconditional QTL mapping methods. Two related F(8:9) recombinant inbred line (RIL) populations, comprising 485 and 229 lines, respectively, were used in this study, and the trait phenotypes were evaluated in four environments. Unconditional QTL mapping analysis detected 77 additive QTL for four traits in two populations. Of these, 24 QTL were verified in at least three trials, and five of them were major QTL, thus being of great value for marker assisted selection in breeding programmes. Conditional QTL mapping analysis, compared with unconditional QTL mapping analysis, resulted in reduction in the number of QTL for TKW due to the elimination of TKW variations caused by its conditional traits; based on which we first dissected genetic control system involved in the synthetic process between TKW and KD at an individual QTL level. Results indicated that, at the QTL level, KW had the strongest influence on TKW, followed by KL, and KDR had the lowest level contribution to TKW. In addition, the present study proved that it is not all-inclusive to determine genetic relationships of a pairwise QTL for two related/causal traits based on whether they were co-located. Thus, conditional QTL mapping method should be used to evaluate possible genetic relationships of two related/causal traits.

Identifying loci influencing 1,000-kernel weight in wheat by microsatellite screening for evidence of selection during breeding

Chinese wheat mini core collection (262 accessions) was genotyped at 531 microsatellite loci representing a mean marker density of 5.1 cM. One-thousand-kernel weights (TKW) of lines were measured in five trials (three environments in four growing seasons). Structure analysis based on 42 unlinked SSR loci indicated that the materials formed two sub-populations, viz., landraces and modern varieties. A large difference in TKW (7.08 g, P<0.001) was found between the two sub-groups. Therefore, TKW is a major yield component that was improved in the past 6 decades; it increased from a mean 31.5 g in the 1940s to 44.64 g in the 2000s, representing a 2.19 g increase in each decade. Analyses based on a mixed linear model (MLM), population structure (Q) and relative kinship (K) revealed 22 SSR loci that were significantly associated with mean TKW (MTKW) of the five trials estimated by the best linear unbiased predictor (BLUP) method. They were mainly distributed on chromosomes of homoeologous groups 1, 2, 3, 5 and 7. Six loci, cfa2234-3A, gwm156-3B, barc56-5A, gwm234-5B, wmc17-7A and cfa2257-7A individually explained more than 11.84% of the total phenotypic variation. Favored alleles for breeding at the 22 loci were inferred according to their estimated effects on MTKW based on mean difference of varieties grouped by genotypes. Statistical simulation showed that these favored alleles have additive genetic effects. Frequency changes of alleles at loci associated with TKW are much more dramatic than those at neutral loci between the sub-groups. The numbers of favored alleles in modern varieties indicate there is still considerable genetic potential for their use as markers for genome selection of TKW in wheat breeding. Alleles that can be used globally to increase TKW were inferred according to their distribution by latitude and frequency of changes between landraces and the modern varieties.

High-Resolution Genotyping of Wild Barley Introgression Lines and Fine-Mapping of the Threshability Locus thresh-1 Using the Illumina GoldenGate Assay

Genetically well-characterized mapping populations are a key tool for rapid and precise localization of quantitative trait loci (QTL) and subsequent identification of the underlying genes. In this study, a set of 73 introgression lines (S42ILs) originating from a cross between the spring barley cultivar Scarlett (Hordeum vulgare ssp. vulgare) and the wild barley accession ISR42-8 (H. v. ssp. spontaneum) was subjected to high-resolution genotyping with an Illumina 1536-SNP array. The array enabled a precise localization of the wild barley introgressions in the elite barley background. Based on 636 informative SNPs, the S42IL set represents 87.3% of the wild barley genome, where each line contains on average 3.3% of the donor genome. Furthermore, segregating high-resolution mapping populations (S42IL-HRs) were developed for 70 S42ILs in order to facilitate QTL fine-mapping and cloning. As a case study, we used the developed genetic resources to rapidly identify and fine-map the novel locus thresh-1 on chromosome 1H that controls grain threshability. Here, the recessive wild barley allele confers a difficult to thresh phenotype, suggesting that thresh-1 played an important role during barley domestication. Using a S42IL-HR population, thresh-1 was fine-mapped within a 4.3cM interval that was predicted to contain candidate genes involved in regulation of plant cell wall composition. The set of wild barley introgression lines and derived high-resolution populations are ideal tools to speed up the process of mapping and further dissecting QTL, which ultimately clears the way for isolating the genes behind QTL effects.

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

Kernel size and morphology influence the market value and milling yield of bread wheat (Triticum aestivum L.). The objective of this study was to identify quantitative trait loci (QTLs) controlling kernel traits in hexaploid wheat. We recorded 1000-kernel weight, kernel length, and kernel width for 185 recombinant inbred lines from the cross Rye Selection 111 &times; Chinese Spring grown in 2 agro-climatic regions in India for many years. Composite interval mapping (CIM) was employed for QTL detection using a linkage map with 169 simple sequence repeat (SSR) markers. For 1000-kernel weight, 10 QTLs were identified on wheat chromosomes 1A, 1D, 2B, 2D, 4B, 5B, and 6B, whereas 6 QTLs for kernel length were detected on 1A, 2B, 2D, 5A, 5B and 5D. Chromosomes 1D, 2B, 2D, 4B, 5B and 5D had 9 QTLs for kernel width. Chromosomal regions with QTLs detected consistently for multiple year-location combinations were identified for each trait. Pleiotropic QTLs were found on chromosomes 2B, 2D, 4B, and 5B. The identified genomic regions controlling wheat kernel size and shape can be targeted during further studies for their genetic dissection.

Characterization of a single recessive yield trait mutant with elevated endogenous ABA concentration and deformed grains, spikelets and leaves

The characterization of yield trait mutants is important for understanding the regulation of grain yield formation in staple food crops. Meh0239 is a yield trait-related mutant identified from a mutant library of the common wheat cultivar Wangshuibai created by ethylmethyl sulfide (EMS) treatment of dry seeds. To shed some light on the nature of this mutation, it was investigated morphologically, physiologically, anatomically and genetically. The mutant plant showed obvious phenotypic differences in comparison with the wild type, starting at the seedling stage, including reduced plant height, wider and shorter leaves, shortened spikes, spikelets and grains and a more compact spikelet distribution. Also, seeds produced in the mutant germinated more slowly. Meh0239 contained a significantly higher level of abscisic acid (ABA) but lower levels of indole-3-acetic acid (IAA), methyl jasmonate (MeJA) and zeatin riboside (ZR) in flag leaves. Cells of all types in the leaf epidermis appeared shorter along the axial direction. The bulliform cells and long cells on the adaxial leaf surface were abnormal in shape. A genetic analysis using two F₂ segregating populations indicated that a single recessive mutation in wheat chromosome 7DS, about 3.1cM distal from Xwmc506, caused these variations. Because of the pleiotropic nature of this gene and its relation with yield trait formation, we named it Yt1 for yield trait related 1.

Characterization and precise mapping of a QTL increasing spike number with pleiotropic effects in wheat

Tiller number (TN) and spike number per plant (SN) are key components of grain yield and/or biomass in wheat. In this study, an introgression line 05210, developed by introgression of chromosomal segments from a synthetic exotic wheat Am3 into an elite cultivar Laizhou953, showed a significantly increased TN and SN, but shorter spike length (SL) and fewer grain number per spike (GNS) than Laizhou953. To investigate the quantitative trait locus (QTL) responsible for these variations, the introgressed segments in 05210 were screened by SSR markers and one follow-up segregation population was developed from the cross 05210/Laizhou953. The population showed 3:1 segregation ratios for SN, SL and GNS, indicating that QTLs for these traits have been dissected into single Mendelian factors. Bulked segregation analysis showed that the markers located on the 4B introgressed segment were polymorphic between the two bulks. Therefore, they were further analyzed in the F(2) population to construct a linkage map. Three new QTLs, QSn.sdau-4B, QSl.sdau-4B and QGns.sdau-4B, were detected for SN, SL and GNS, respectively, which explained a large portion of the phenotypic variation (30.1-67.6%) for these traits with overlapping peaks. Correlation analysis and multiple-trait, multiple-interval mapping (MMIM) suggested pleiotropic effects of the QTL on SN, SL and GNS. Therefore, the QTL was designated as QSn.sdau-4B. By a progeny test based on F(3) families using SN, the QTL was mapped as a Mendelian factor to the proximal region of 4BL. It is a key QTL responsible for variation in spike number and size, which had not been reported previously. Thus, it is an important QTL for wheat to achieve high and stable biomass and grain yield. Dissection and mapping of this QTL as a Mendelian factor laid a solid foundation for map-based cloning of grain yield-related QTLs in wheat.

Diverse approaches to achieving grain yield in wheat

Artificial selection (domestication and breeding) leaves a strong footprint in plant genomes. Second generation high throughput DNA sequencing technologies make it possible to sequence the gene complement of a plant genome within 3 to 5 months, and the costs of doing so are declining very quickly. This makes it practical to identify genomic regions that have undergone very strong selection. Available reference sequences of important crops such as rice, maize, and sorghum will promote the wide use of re-sequencing strategies in these crops. Marker/trait associations, especially haplotype (or haplotype block) association analyses, will help the precise mapping of important genomic regions and location of favored alleles or haplotypes for breeding. This mini-review examines a genomics approach to defining yield traits in wheat.

Identification and development of a functional marker of TaGW2 associated with grain weight in bread wheat (Triticum aestivum L.)

The OsGW2 gene is involved in rice grain development, influencing grain width and weight. Its ortholog in wheat, TaGW2, was considered as a candidate gene related to grain development. We found that TaGW2 is constitutively expressed, with three orthologs expressing simultaneously. The coding sequence (CDS) of TaGW2 is 1,275 bp encoding a protein with 424 amino acids, and has a functional domain shared with OsGW2. No divergence was detected within the CDS sequences in the same locus in ten varieties. Genome-specific primers were designed based on the sequence divergence of the promoter regions in the three orthologous genes, and TaGW2 was located in homologous group 6 chromosomes through CS nulli-tetrasomic (NT). Two SNPs were detected in the promoter region of TaGW2-6A, forming two haplotypes: Hap-6A-A (-593A and -739G) and Hap-6A-G (-593G and -769A). A cleaved amplified polymorphic sequence (CAPS) marker was developed based on the -593 A-G polymorphism to distinguish the two haplotypes in TaGW2-6A. This gene was fine mapped 0.6 cM from marker cfd80.2 near the centromere in a recombinant inbred line (RIL) population. Two hundred sixty-five Chinese wheat varieties were genotyped and association analysis revealed that Hap-6A-A was significantly associated with wider grains and higher one-thousand grain weight (TGW) in two crop seasons. qRT-PCR revealed a negative relationship between TaGW2 expression level and grain width. The Hap-6A-A frequencies in Chinese varieties released at different periods showed that it had been strongly positively selected in breeding. In landraces, Hap-6A-A is mainly distributed in southern Chinese wheat regions. Association analysis also indicated that Hap-6A-A not only increased TGW by more than 3 g, but also had earlier heading and maturity. In contrast to Chinese varieties, Hap-6A-G was the predominant haplotype in European varieties; Hap-6A-A was mainly present in varieties released in the former Yugoslavia, Italy, Bulgaria, Hungary and Portugal.