Mapping of quantitative trait loci determining agronomic important characters in hexaploid wheat ( Triticum aestivum L.)

Identification and validation of two major QTL for grain number per spike on chromosomes 2B and 2D in bread wheat (Triticum aestivum L.)

KEY MESSAGE

Major QTL for grain number per spike were identified on chromosomes 2B and 2D. Haplotypes and candidate genes of QGns.cib-2B.1 were analyzed. Grain number per spike (GNS) is one of the main components of wheat yield. Genetic dissection of their regulatory factors is essential to improve the yield potential. In present study, a recombinant inbred line population comprising 180 lines developed from the cross between a high GNS line W7268 and a cultivar Chuanyu12 was employed to identify quantitative trait loci (QTL) associated with GNS across six environments. Two major QTL, QGns.cib-2B.1 and QGns.cib-2D.1, were detected in at least four environments with the phenotypic variations of 12.99-27.07% and 8.50-13.79%, respectively. And significant interactions were observed between the two major QTL. In addition, QGns.cib-2B.1 is a QTL cluster for GNS, grain number per spikelet and fertile tiller number, and they were validated in different genetic backgrounds using Kompetitive Allele Specific PCR (KASP) markers. QGns.cib-2B.1 showed pleotropic effects on other yield-related traits including plant height, spike length, and spikelet number per spike, but did not significantly affect thousand grain weight which suggested that it might be potentially applicable in breeding program. Comparison analysis suggested that QGns.cib-2B.1 might be a novel QTL. Furthermore, haplotype analysis of QGns.cib-2B.1 indicated that it is a hot spot of artificial selection during wheat improvement. Based on the expression patterns, gene annotation, orthologs analysis and sequence variations, the candidate genes of QGns.cib-2B.1 were predicted. Collectively, the major QTL and KASP markers reported here provided a wealth of information for the genetic basis of GNS and grain yield improvement.

Exploring the Drought Tolerant Quantitative Trait Loci in Spring Wheat

Drought-induced stress poses a significant challenge to wheat throughout its growth, underscoring the importance of identifying drought-stable quantitative trait loci (QTLs) for enhancing grain yield. Here, we evaluated 18 yield-related agronomic and physiological traits, along with their drought tolerance indices, in a recombinant inbred line population derived from the XC7 × XC21 cross. These evaluations were conducted under both non-stress and drought-stress conditions. Drought stress significantly reduced grain weight per spike and grain yield per plot. Genotyping the recombinant inbred line population using the wheat 90K single nucleotide polymorphism array resulted in the identification of 131 QTLs associated with the 18 traits. Drought stress also exerted negative impacts on grain formation and filling, directly leading to reductions in grain weight per spike and grain yield per plot. Among the identified QTLs, 43 were specifically associated with drought tolerance across the 18 traits, with 6 showing direct linkages to drought tolerance in wheat. These results provide valuable insights into the genetic mechanisms governing wheat growth and development, as well as the traits contributing to the drought tolerance index. Moreover, they serve as a theoretical foundation for the development of new wheat cultivars having exceptional drought tolerance and high yield potentials under both drought-prone and drought-free conditions.

Genetic dissection of major QTL for grain number per spike on chromosomes 5A and 6A in bread wheat (Triticum aestivum L.)

Grain number per spike (GNS) is a crucial component of grain yield and plays a significant role in improving wheat yield. To identify quantitative trait loci (QTL) associated with GNS, a recombinant inbred line (RIL) population derived from the cross of Zhongkemai 13F10 and Chuanmai 42 was employed to conduct QTL mapping across eight environments. Based on the bulked segregant exome sequencing (BSE-Seq), genomic regions associated with GNS were detected on chromosomes 5A and 6A. According to the constructed genetic maps, two major QTL QGns.cib-5A (LOD = 4.35-8.16, PVE = 8.46-14.43%) and QGns.cib-6A (LOD = 3.82-30.80, PVE = 5.44-12.38%) were detected in five and four environments, respectively. QGns.cib-6A is a QTL cluster for other seven yield-related traits. QGns.cib-5A and QGns.cib-6A were further validated using linked Kompetitive Allele Specific PCR (KASP) markers in different genetic backgrounds. QGns.cib-5A exhibited pleiotropic effects on productive tiller number (PTN), spike length (SL), fertile spikelet number per spike (FSN), and ratio of grain length to grain width (GL/GW) but did not significantly affect thousand grain weight (TGW). Haplotype analysis revealed that QGns.cib-5A and QGns.cib-6A were the targets of artificial selection during wheat improvement. Candidate genes for QGns.cib-5A and QGns.cib-6A were predicted by analyzing gene annotation, spatiotemporal expression patterns, and orthologous and sequence differences. These findings will be valuable for fine mapping and map-based cloning of genes underlying QGns.cib-5A and QGns.cib-6A.

Genetic analyses and prediction for lodging‑related traits in a diverse Iranian hexaploid wheat collection

Lodging is one of the most important limiting environmental factors for achieving the maximum yield and quality of grains in cereals, including wheat. However, little is known about the genetic foundation underlying lodging resistance (LR) in wheat. In this study, 208 landraces and 90 cultivars were phenotyped in two cropping seasons (2018-2019 and 2019-2020) for 19 LR-related traits. A genome-wide association study (GWAS) and genomics prediction were carried out to dissect the genomic regions of LR. The number of significant marker pairs (MPs) was highest for genome B in both landraces (427,017) and cultivars (37,359). The strongest linkage disequilibrium (LD) between marker pairs was found on chromosome 4A (0.318). For stem lodging-related traits, 465, 497, and 478 marker-trait associations (MTAs) and 45 candidate genes were identified in year 1, year 2, and pooled. Gene ontology exhibited genomic region on Chr. 2B, 6B, and 7B control lodging. Most of these genes have key roles in defense response, calcium ion transmembrane transport, carbohydrate metabolic process, nitrogen compound metabolic process, and some genes harbor unknown functions that, all together may respond to lodging as a complex network. The module associated with starch and sucrose biosynthesis was highlighted. Regarding genomic prediction, the GBLUP model performed better than BRR and RRBLUP. This suggests that GBLUP would be a good tool for wheat genome selection. As a result of these findings, it has been possible to identify pivotal QTLs and genes that could be used to improve stem lodging resistance in Triticum aestivum L.

Chromosome genomics facilitates the marker development and selection of wheat-Aegilops biuncialis addition, substitution and translocation lines

The annual goatgrass, Aegilops biuncialis is a rich source of genes with considerable agronomic value. This genetic potential can be exploited for wheat improvement through interspecific hybridization to increase stress resistance, grain quality and adaptability. However, the low throughput of cytogenetic selection hampers the development of alien introgressions. Using the sequence of flow-sorted chromosomes of diploid progenitors, the present study enabled the development of chromosome-specific markers. In total, 482 PCR markers were validated on wheat (Mv9kr1) and Ae. biuncialis (MvGB642) crossing partners, and 126 on wheat-Aegilops additions. Thirty-two markers specific for U- or M-chromosomes were used in combination with GISH and FISH for the screening of 44 Mv9kr1 × Ae. biuncialis BC3F3 genotypes. The predominance of chromosomes 4M and 5M, as well as the presence of chromosomal aberrations, may indicate that these chromosomes have a gametocidal effect. A new wheat-Ae. biuncialis disomic 4U addition, 4M(4D) and 5M(5D) substitutions, as well as several introgression lines were selected. Spike morphology and fertility indicated that the Aegilops 4M or 5M compensated well for the loss of 4D and 5D, respectively. The new cytogenetic stocks represent valuable genetic resources for the introgression of key genes alleles into wheat.

Characterization and fine mapping analysis of a major stable QTL qKnps-4A for kernel number per spike in wheat

KEY MESSAGE

A major stable QTL for kernel number per spike was narrowed down to a 2.19-Mb region containing two potential candidate genes, and its effects on yield-related traits were characterized. Kernel number per spike (KNPS) in wheat is a key yield component. Dissection and characterization of major stable quantitative trait loci (QTLs) for KNPS would be of considerable value for the genetic improvement of yield potential using molecular breeding technology. We had previously reported a major stable QTL controlling KNPS, qKnps-4A. In the current study, primary fine-mapping analysis, based on the primary mapping population, located qKnps-4A to an interval of approximately 6.8-Mb from 649.0 to 655.8 Mb on chromosome 4A refering to 'Kenong 9204' genome. Further fine-mapping analysis based on a secondary mapping population narrowed qKnps-4A to an approximately 2.19-Mb interval from 653.72 to 655.91 Mb. Transcriptome sequencing, gene function annotation analysis and homologous gene related reports showed that TraesKN4A01HG38570 and TraesKN4A01HG38590 were most likely to be candidate genes of qKnps-4A. Phenotypic analysis based on paired near-isogenic lines in the target region showed that qKnps-4A increased KNPS mainly by increasing the number of central florets per spike. We also evaluated the effects of qKnps-4A on other yield-related traits. Moreover, we dissected the QTL cluster of qKnps-4A and qTkw-4A and proved that the phenotypic effects were probably due to close linkage of two or more genes rather than pleiotropic effects of a single gene. This study provides molecular marker resource for wheat molecular breeding designed to improve yield potential, and lay the foundation for gene functional analysis of qKnps-4A.

Development and Identification of a Wheat-Rye Breeding Line for Harmonious Improvement Between Powdery Mildew Resistance and High Yield Potential

Powdery mildew, caused by Blumeria graminis f. sp. tritici, is a devastating disease that seriously threatens wheat yield and quality. To control this disease, host resistance is the preferred measure. However, wheat breeding is a complex process with elusive exchange and recombination of the traits from their parents. Increased resistance often leads to a decline in other key traits, such as yield and quality. Developing breakthrough germplasms with harmonious powdery mildew resistance and other key breeding traits is attractive in wheat breeding. In this study, we developed an ideal wheat breeding line AL46 that pyramided its hexaploid triticale parent-derived desirable yield traits and its wheat parent-derived powdery mildew resistance gene Pm2. Sequential genomic in situ hybridization (GISH), multicolor GISH, multicolor fluorescence in situ hybridization, and molecular marker analyses revealed that AL46 was a wheat-rye T1RS·1BL translocation line. Genetic analysis combined with function marker detection and sequence alignment were used to confirm that AL46 carried the Pm2 gene. Then, we evaluated the powdery mildew resistance and comprehensive traits of AL46, and just as we designed, AL46 showed harmonious powdery mildew resistance with some key breeding traits. This study not only developed an ideal wheat germplasm resource but also provided a successful example for pyramiding breeding, which could be a promising direction for wheat improvement in the future.

Genomics for Yield and Yield Components in Durum Wheat

In recent years, many efforts have been conducted to dissect the genetic basis of yield and yield components in durum wheat thanks to linkage mapping and genome-wide association studies. In this review, starting from the analysis of the genetic bases that regulate the expression of yield for developing new durum wheat varieties, we have highlighted how, currently, the reductionist approach, i.e., dissecting the yield into its individual components, does not seem capable of ensuring significant yield increases due to diminishing resources, land loss, and ongoing climate change. However, despite the identification of genes and/or chromosomal regions, controlling the grain yield in durum wheat is still a challenge, mainly due to the polyploidy level of this species. In the review, we underline that the next-generation sequencing (NGS) technologies coupled with improved wheat genome assembly and high-throughput genotyping platforms, as well as genome editing technology, will revolutionize plant breeding by providing a great opportunity to capture genetic variation that can be used in breeding programs. To date, genomic selection provides a valuable tool for modeling optimal allelic combinations across the whole genome that maximize the phenotypic potential of an individual under a given environment.

Current Progress and Future Prospect of Wheat Genetics Research towards an Enhanced Nitrogen Use Efficiency

To improve the yield and quality of wheat is of great importance for food security worldwide. One of the most effective and significant approaches to achieve this goal is to enhance the nitrogen use efficiency (NUE) in wheat. In this review, a comprehensive understanding of the factors involved in the process of the wheat nitrogen uptake, assimilation and remobilization of nitrogen in wheat were introduced. An appropriate definition of NUE is vital prior to its precise evaluation for the following gene identification and breeding process. Apart from grain yield (GY) and grain protein content (GPC), the commonly recognized major indicators of NUE, grain protein deviation (GPD) could also be considered as a potential trait for NUE evaluation. As a complex quantitative trait, NUE is affected by transporter proteins, kinases, transcription factors (TFs) and micro RNAs (miRNAs), which participate in the nitrogen uptake process, as well as key enzymes, circadian regulators, cross-talks between carbon metabolism, which are associated with nitrogen assimilation and remobilization. A series of quantitative genetic loci (QTLs) and linking markers were compiled in the hope to help discover more efficient and useful genetic resources for breeding program. For future NUE improvement, an exploration for other criteria during selection process that incorporates morphological, physiological and biochemical traits is needed. Applying new technologies from phenomics will allow high-throughput NUE phenotyping and accelerate the breeding process. A combination of multi-omics techniques and the previously verified QTLs and molecular markers will facilitate the NUE QTL-mapping and novel gene identification.

Identification of Novel QTLs Associated with Frost Tolerance in Winter Wheat (Triticum aestivum L.)

Low temperature (cold) and freezing stress is a major problem during winter wheat growth. Low temperature tolerance (LT) is an important agronomic trait in winter wheat and determines the plants' ability to cope with below-freezing temperatures; thus, the development of cold-tolerant cultivars has become a major goal of breeding in various regions of the world. In this study, we sought to identify quantitative trait loci (QTL) using molecular markers related to freezing tolerance in winter. Thirty-four polymorphic markers among 425 SSR markers were obtained for the population, including 180 inbred lines of F₁₂ generation wheat, derived from crosses (Norstar × Zagros) after testing with parents. LT₅₀ is used as an effective selection criterion for identifying frost-tolerance genotypes. The progeny of individual F₁₂ plants were used to evaluate LT50. Several QTLs related to wheat yield, including heading time period, 1000-seed weight, and number of surviving plants after overwintering, were identified. Single-marker analysis illustrated that four SSR markers with a total of 25% phenotypic variance determination were linked to LT50. Related QTLs were located on chromosomes 4A, 2B, and 3B. Common QTLs identified in two cropping seasons based on agronomical traits were two QTLs for heading time period, one QTL for 1000-seed weight, and six QTLs for number of surviving plants after overwintering. The four markers identified linked to LT₅₀ significantly affected both LT₅₀ and yield-related traits simultaneously. This is the first report to identify a major-effect QTL related to frost tolerance on chromosome 4A by the marker XGWM160. It is possible that some QTLs are closely related to pleiotropic effects that control two or more traits simultaneously, and this feature can be used as a factor to select frost-resistant lines in plant breeding programs.

Preliminary Dissection of Grain Yield and Related Traits at Differential Nitrogen Levels in Diverse Pre-Breeding Wheat Germplasm Through Association Mapping

Development of nutrient efficient cultivars depends on effective identification and utilization of genetic variation. We characterized a set of 276 pre-breeding lines (PBLs) for several traits at different levels of nitrogen application. These PBLs originate from synthetic wheats and landraces. We witnessed significant variation in various traits among PBLs to different nitrogen doses. There was ~ 4-18% variation range in different agronomic traits in response to nitrogen application, with the highest variation for the biological yield (BY) and the harvest index. Among various agronomic traits measured, plant height, tiller number, and BY showed a positive correlation with nitrogen applications. GWAS analysis detected 182 marker-trait associations (MTAs) (at p-value < 0.001), out of which 8 MTAs on chromosomes 5D, 4A, 6A, 1B, and 5B explained more than 10% phenotypic variance. Out of all, 40 MTAs observed for differential nitrogen application response were contributed by the synthetic derivatives. Moreover, 20 PBLs exhibited significantly higher grain yield than checks and can be selected as potential donors for improved plant nitrogen use efficiency (pNUE).

Genome-wide association mapping and genomic prediction of agronomical traits and breeding values in Iranian wheat under rain-fed and well-watered conditions

BACKGROUND

The markers detected by genome-wide association study (GWAS) make it possible to dissect genetic structure and diversity at many loci. This can enable a wheat breeder to reveal and used genomic loci controlling drought tolerance. This study was focused on determining the population structure of Iranian 208 wheat landraces and 90 cultivars via genotyping-by-sequencing (GBS) and also on detecting marker-trait associations (MTAs) by GWAS and genomic prediction (GS) of wheat agronomic traits for drought-tolerance breeding. GWASs were conducted using both the original phenotypes (pGWAS) and estimated breeding values (eGWAS). The bayesian ridge regression (BRR), genomic best linear unbiased prediction (gBLUP), and ridge regression-best linear unbiased prediction (rrBLUP) approaches were used to estimate breeding values and estimate prediction accuracies in genomic selection.

RESULTS

Population structure analysis using 2,174,975 SNPs revealed four genetically distinct sub-populations from wheat accessions. D-Genome harbored the lowest number of significant marker pairs and the highest linkage disequilibrium (LD), reflecting different evolutionary histories of wheat genomes. From pGWAS, BRR, gBLUP, and rrBLUP, 284, 363, 359 and 295 significant MTAs were found under normal and 195, 365, 362 and 302 under stress conditions, respectively. The gBLUP with the most similarity (80.98 and 71.28% in well-watered and rain-fed environments, correspondingly) with the pGWAS method in the terms of discovered significant SNPs, suggesting the potential of gBLUP in uncovering SNPs. Results from gene ontology revealed that 29 and 30 SNPs in the imputed dataset were located in protein-coding regions for well-watered and rain-fed conditions, respectively. gBLUP model revealed genetic effects better than other models, suggesting a suitable tool for genome selection in wheat.

CONCLUSION

We illustrate that Iranian landraces of bread wheat contain novel alleles that are adaptive to drought stress environments. gBLUP model can be helpful for fine mapping and cloning of the relevant QTLs and genes, and for carrying out trait introgression and marker-assisted selection in both normal and drought environments in wheat collections.

Identification of quantitative trait loci of agronomic traits in bread wheat using a Pamyati Azieva × Paragon mapping population harvested in three regions of Kazakhstan

Background

Although genome-wide association studies (GWAS) are an increasingly informative tool in the mining of new quantitative trait loci (QTLs), a classical biparental mapping approach is still a powerful, widely used method to search the unique genetic factors associated with important agronomic traits in bread wheat.

Methods

In this study, a newly constructed mapping population of Pamyati Azieva (Russian Federation) × Paragon (UK), consisting of 94 recombinant inbred lines (RILs), was tested in three different regions of Kazakhstan with the purpose of QTL identification for key agronomic traits. The RILs were tested in 11 environments of two northern breeding stations (Petropavlovsk, North Kazakhstan region, and Shortandy, Aqmola region) and one southeastern station (Almalybak, Almaty region). The following eight agronomic traits were studied: heading days, seed maturation days, plant height, spike length, number of productive spikes, number of kernels per spike, thousand kernel weight, and yield per square meter. The 94 RILs of the PAxP cross were genotyped using Illumina's iSelect 20K single nucleotide polymorphism (SNP) array and resulted in the identification of 4595 polymorphic SNP markers.

Results

The application of the QTL Cartographer statistical package allowed the identification of 53 stable QTLs for the studied traits. A survey of published studies related to common wheat QTL identification suggested that 28 of those 53 QTLs were presumably novel genetic factors. The SNP markers for the identified QTLs of the analyzed agronomic traits of common wheat can be efficiently applied in ongoing breeding activities in the wheat breeding community using a marker-assisted selection approach.

Fine mapping and characterization of a major QTL for grain weight on wheat chromosome arm 5DL

We fine mapped QTL QTKW.caas-5DL for thousand kernel weight in wheat, predicted candidate genes and developed a breeding-applicable marker. Thousand kernel weight (TKW) is an important yield component trait in wheat, and identification of the underlying genetic loci is helpful for yield improvement. We previously identified a stable quantitative trait locus (QTL) QTKW.caas-5DL for TKW in a Doumai/Shi4185 recombinant inbred line (RIL) population. Here we performed fine mapping of QTKW.caas-5DL using secondary populations derived from 15 heterozygous recombinants and delimited the QTL to an approximate 3.9 Mb physical interval from 409.9 to 413.8 Mb according to the Chinese Spring (CS) reference genome. Analysis of genomic synteny showed that annotated genes in the physical interval had high collinearity among CS and eight other wheat genomes. Seven genes with sequence variation and/or differential expression between parents were predicted as candidates for QTKW.caas-5DL based on whole-genome resequencing and transcriptome assays. A kompetitive allele-specific PCR (KASP) marker for QTKW.caas-5DL was developed, and genotyping confirmed a significant association with TKW but not with other yield component traits in a panel of elite wheat cultivars. The superior allele of QTKW.caas-5DL was frequent in a panel of cultivars, suggesting that it had undergone positive selection. These findings not only lay a foundation for map-based cloning of QTKW.caas-5DL but also provide an efficient tool for marker-assisted selection.

Whole-genome analysis of hard winter wheat germplasm identifies genomic regions associated with spike and kernel traits

Genetic dissection of yield component traits including spike and kernel characteristics is essential for the continuous improvement in wheat yield. Genome-wide association studies (GWAS) have been frequently used to identify genetic determinants for spike and kernel-related traits in wheat, though none have been employed in hard winter wheat (HWW) which represents a major class in US wheat acreage. Further, most of these studies relied on assembled diversity panels instead of adapted breeding lines, limiting the transferability of results to practical wheat breeding. Here we assembled a population of advanced/elite breeding lines and well-adapted cultivars and evaluated over four environments for phenotypic analysis of spike and kernel traits. GWAS identified 17 significant multi-environment marker-trait associations (MTAs) for various traits, representing 12 putative quantitative trait loci (QTLs), with five QTLs affecting multiple traits. Four of these QTLs mapped on three chromosomes 1A, 5B, and 7A for spike length, number of spikelets per spike (NSPS), and kernel length are likely novel. Further, a highly significant QTL was detected on chromosome 7AS that has not been previously associated with NSPS and putative candidate genes were identified in this region. The allelic frequencies of important quantitative trait nucleotides (QTNs) were deduced in a larger set of 1,124 accessions which revealed the importance of identified MTAs in the US HWW breeding programs. The results from this study could be directly used by the breeders to select the lines with favorable alleles for making crosses, and reported markers will facilitate marker-assisted selection of stable QTLs for yield components in wheat breeding.

SSR Linkage Maps and Identification of QTL Controlling Morpho-Phenological Traits in Two Iranian Wheat RIL Populations

Wheat is one of the essential grains grown in large areas. Identifying the genetic structure of agronomic and morphological traits of wheat can help to discover the genetic mechanisms of grain yield. In order to map the morpho-phenological traits, an experiment was conducted in the two cropping years of 2020 and 2021 on the university farm of the Faculty of Agriculture, GonbadKavous University. This study used two F8 populations, including 120 lines resulting from Gonbad × Zagros and Gonbad × Kuhdasht. The number of days to physiological maturity, number of days to flowering, number of germinated grains, number of tillers, number of tillers per plant, grain filling periods, plant height, peduncle length, spike length, awn length, spike weight, peduncle diameter, flag leaf length and weight, number of spikelets per spike, number of grains per spike, grain length, grain width, 1000-grain weight, biomass, grain yield, harvest index, straw-weight, and number of fertile spikelets per spike were measured. A total of 21 and 13 QTLs were identified for 11 and 13 traits in 2020 and 2021, respectively. In 2020, qGL-3D and qHI-1A were identified for grain length and harvest index on chromosomes 3D and 1A, explaining over 20% phenotypic variation, respectively. qNT-5B, qNTS-2D, and qSL-1D were identified on chromosomes 5B, 2D, and 1D with the LOD scores of 4.5, 4.13, and 3.89 in 2021, respectively.

Genome-Wide Association Study of Kernel Traits Using a 35K SNP Array in Bread Wheat (Triticum aestivum L.)

Kernel size and weight are crucial components of grain yield in wheat. Deciphering their genetic basis is essential for improving yield potential in wheat breeding. In this study, five kernel traits, including kernel length (KL), kernel width (KW), kernel diameter ratio (KDR), kernel perimeter (KP), and thousand-kernel weight (TKW), were evaluated in a panel consisting of 198 wheat accessions under six environments. Wheat accessions were genotyped using the 35K SNP iSelect chip array, resulting in a set of 13,228 polymorphic SNP markers that were used for genome-wide association study (GWAS). A total of 146 significant marker-trait associations (MTAs) were identified for five kernel traits on 21 chromosomes [-log₁₀(P) ≥ 3], which explained 5.91-15.02% of the phenotypic variation. Of these, 12 stable MTAs were identified in multiple environments, and six superior alleles showed positive effects on KL, KP, and KDR. Four potential candidate genes underlying the associated SNP markers were predicted for encoding ML protein, F-box protein, ethylene-responsive transcription factor, and 1,4-α-glucan branching enzyme. These genes were strongly expressed in grain development at different growth stages. The results will provide new insights into the genetic basis of kernel traits in wheat. The associated SNP markers and predicted candidate genes will facilitate marker-assisted selection in wheat breeding.

Identification and validation of QTLs for kernel number per spike and spike length in two founder genotypes of wheat

BACKGROUND

Kernel number per spike (KNS) and spike length (SL) are important spike-related traits in wheat variety improvement. Discovering genetic loci controlling these traits is necessary to elucidate the genetic basis of wheat yield traits and is very important for marker-assisted selection breeding.

RESULTS

In the present study, we used a recombinant inbred line population with 248 lines derived from the two founder genotypes of wheat, Bima4 and BainongAK58, to construct a high-density genetic map using wheat 55 K genotyping assay. The final genetic linkage map consists of 2356 bin markers (14,812 SNPs) representing all 21 wheat chromosomes, and the entire map spanned 4141.24 cM. A total of 7 and 18 QTLs were identified for KNS and SL, respectively, and they were distributed on 11 chromosomes. The allele effects of the flanking markers for 12 stable QTLs, including four QTLs for KNS and eight QTLs for SL, were estimated based on phenotyping data collected from 15 environments in a diverse wheat panel including 384 elite cultivars and breeding lines. The positive alleles at seven loci, namely, QKns.his-7D2-1, QKns.his-7D2-2, QSl.his-4A-1, QSl.his-5D1, QSl.his-4D2-2, QSl.his-5B and QSl.his-5A-2, significantly increased KNS or SL in the diverse panel, suggesting they are more universal in their effects and are valuable for gene pyramiding in breeding programs. The transmission of Bima4 allele indicated that the favorite alleles at five loci (QKns.his-7D2-1, QSl.his-5A-2, QSl.his-2D1-1, QSl.his-3A-2 and QSl.his-3B) showed a relatively high frequency or an upward trend following the continuity of generations, suggesting that they underwent rigorous selection during breeding. At two loci (QKns.his-7D2-1 and QSl.his-5A-2) that the positive effects of the Bima4 alleles have been validated in the diverse panel, two and one kompetitive allele-specific PCR (KASP) markers were further developed, respectively, and they are valuable for marker-assisted selection breeding.

CONCLUSION

Important chromosome regions controlling KNS and SL were identified in the founder parents. Our results are useful for knowing the molecular mechanisms of founder parents and future molecular breeding in wheat.

Analysis of Genetic Regions Related to Field Grain Number per Spike From Chinese Wheat Founder Parent Linfen 5064

Wheat founder parents have been important in the development of new wheat cultivars. Understanding the effects of specific genome regions on yield-related traits in founder variety derivatives can enable more efficient use of these genetic resources through molecular breeding. In this study, the genetic regions related to field grain number per spike (GNS) from the founder parent Linfen 5064 were analyzed using a doubled haploid (DH) population developed from a cross between Linfen 5064 and Nongda 3338. Quantitative trait loci (QTL) for five spike-related traits over nine experimental locations/years were identified, namely, total spikelet number per spike (TSS), base sterile spikelet number per spike (BSSS), top sterile spikelet number per spike (TSSS), fertile spikelet number per spike (FSS), and GNS. A total of 13 stable QTL explaining 3.91-19.51% of the phenotypic variation were found. The effect of six of these QTL, Qtss.saw-2B.1, Qtss.saw-2B.2, Qtss.saw-3B, Qfss.saw-2B.2, Qbsss.saw-5A.1, and Qgns.saw-1A, were verified by another DH population (Linfen 5064/Jinmai 47), which showed extreme significance (P < 0.05) in more than three environments. No homologs of reported grain number-related from grass species were found in the physical regions of Qtss.saw-2B.1 and Qtss.saw-3B, that indicating both of them are novel QTL, or possess novel-related genes. The positive alleles of Qtss.saw-2B.2 from Linfen 5064 have the larger effect on TSS (3.30%, 0.62) and have 66.89% in Chinese cultivars under long-term artificial selection. This study revealed three key regions for GNS in Linfen 5064 and provides insights into molecular marker-assisted breeding.

Mapping Resistance to Argentinean Fusarium (Graminearum) Head Blight Isolates in Wheat

Fusarium head blight (FHB) of wheat, caused by Fusarium graminearum (Schwabe), is a destructive disease worldwide, reducing wheat yield and quality. To accelerate the improvement of scab tolerance in wheat, we assessed the International Triticeae Mapping Initiative mapping population (ITMI/MP) for Type I and II resistance against a wide population of Argentinean isolates of F. graminearum. We discovered a total of 27 additive QTLs on ten different (2A, 2D, 3B, 3D, 4B, 4D, 5A, 5B, 5D and 6D) wheat chromosomes for Type I and Type II resistances explaining a maximum of 15.99% variation. Another four and two QTLs for thousand kernel weight in control and for Type II resistance, respectively, involved five different chromosomes (1B, 2D, 6A, 6D and 7D). Furthermore, three, three and five QTLs for kernel weight per spike in control, for Type I resistance and for Type II resistance, correspondingly, involved ten chromosomes (2A, 2D, 3B, 4A, 5A, 5B, 6B, 7A, 7B, 7D). We were also able to detect five and two epistasis pairs of QTLs for Type I and Type II resistance, respectively, in addition to additive QTLs that evidenced that FHB resistance in wheat is controlled by a complex network of additive and epistasis QTLs.

Genetic parameters controlling the inheritance of glaucousness and yield traits in bread wheat

Wheat breeders frequently use generation mean analysis to obtain information on the type of gene action involved in inheriting a trait to choose the helpful breeding procedure for trait improvement. The present study was carried out to study the inter-allelic and intra-allelic gene action and inheritance of glaucousness, earliness and yield traits in a bread wheat cross between divergent parents in glaucousness and yield traits; namely Mut-2 (P1) and Sakha 93 (P2). The experimental material included six populations, i.e. P1, P2, F1, F2, BC1, and BC2 for this wheat cross. A randomized complete block design with three replications was used, and a six parameters model was applied. Additive effects were generally more critical than dominance for all studied traits, except for plant height (PH) and grain yield/plant (GYPP). The duplicate epistasis was observed in spike length; SL, spikes/plant; SPP and days to heading; DTH. All six types of allelic and non-allelic interaction effects controlled SL, GYPP, DTH and glaucousness. All three types of epistasis, i.e. additive x additive, additive x dominance, and dominance x dominance, are essential in determining the inheritance of four traits (SL, GYPP, DTH and glaucousness). Dominance × dominance effects were higher in magnitude than additive × dominance and additive × additive in most traits. The average degree of dominance was minor than unity in six traits (glaucousness, grains/spike, spike weight, days to maturity, 100-grain weight and SL), indicating partial dominance and selection for these traits might be more effective in early generations. Meanwhile, the remaining traits (PH, SPP, GYPP and DTH) had a degree of dominance more than unity, indicating that overdominance gene effects control such traits and it is preferable to postpone selection to later generations. The highest values of narrow-sense heritability and genetic advance were recorded by glaucousness trait followed by SL and SPP, indicating that selection in segregating generations would be more effective than other traits.

A SNP-based GWAS and functional haplotype-based GWAS of flag leaf-related traits and their influence on the yield of bread wheat (Triticum aestivum L.)

The genetic architecture of five flag leaf morphology traits was dissected by the functional haplotype-based GWAS and a standard SNP-based GWAS in a diverse population consisting of 197 varieties. Flag leaf morphology (FLM) is a critical factor affecting plant architecture and grain yield in wheat. The genetic architecture of FLM traits has been extensively studied with QTL mapping in bi-parental populations, while few studies exploited genome-wide association studies (GWAS) in diverse populations. In this study, a panel of 197 elite and historical varieties from China was evaluated for five FLM traits including the length (FLL), width (FLW), ratio (FLR), area (FLA) and angle (FLANG) as well as yield in nine environments. Based on the phenotypic correlation between yield and FLL (-0.43), FLA (- 0.32) and FLW (0.11), an empirical FLM index combining the three FLM traits proved to be a good predictor for yield. Two GWAS approaches were applied to dissect the genetic architecture of five FLM traits with a Wheat660K SNP array. The functional haplotype-based GWAS revealed 6, 5 and 7 QTL for FLANG, FLL and FLR, respectively, whereas two QTL for FLW and one for FLR were identified by the standard SNP-based GWAS. Due to co-localization, there were 18 independent QTL and 10 of them were close to known ones. One co-localized QTL on chromosome 5A was associated with FLL, FLANG and FLR. Moreover, both GWAS approaches identified a novel QTL for FLR on chromosome 6B which was not reported in previous studies. This study provides new insights into the relationship between FLM and yield and broadens our understanding of the genetic architecture of FLM traits in wheat.

Lipidomic, Transcriptomic, and BSA-660K Single Nucleotide Polymorphisms Profiling Reveal Characteristics of the Cuticular Wax in Wheat

Plant epidermal wax helps protect plants from adverse environmental conditions, maintains the function of tissues and organs, and ensures normal plant development. However, the constituents of epidermal wax and the regulatory mechanism of their biosynthesis in wheat have not been fully understood. Wheat varieties with different wax content, Jinmai47 and Jinmai84, were selected to comparatively analyze their waxy components and genetic characteristics, using a combination of lipidomic, transcriptomic, and BSA-Wheat 660K chip analysis. Through lipidomic analysis, 1287 lipid molecules were identified representing 31 lipid subclasses. Among these, Diacylglycerols (DG), (O-acyl)-ω-hydroxy fatty acids (OAHFA), wax ester (WE), Triacylglycerols (TG), and Monoradylglycerols (MG) accounted for 96.4% of the total lipids in Jinmai84 and 94.5% in Jinmai47. DG, OAHFA, and WE were higher in Jinmai84 than in Jinmai47 with the content of OAHFA 2.88-fold greater and DG 1.66-fold greater. Transcriptome sequence and bioinformatics analysis revealed 63 differentially expressed genes related to wax biosynthesis. Differentially expressed genes (DEGs) were found to be involved with the OAHFA, DG, and MG of synthesis pathways, which enriched the wax metabolism pathway. Non-glaucous and glaucous bulks from a mapping population were used to identify single nucleotide polymorphisms (SNP) via 660K chip analysis. Two loci centered on chromosomes 2D and 4B were detected and the locus on 4B is likely novel. These data improve understanding of complex lipid metabolism for cuticular wax biosynthesis in wheat and lay the foundation for future detailed investigation of mechanisms regulating wax metabolism.

A SNP-based genetic dissection of versatile traits in bread wheat (Triticum aestivum L.)

The continuous increase in global population prompts increased wheat production. Future wheat (Triticum aestivum L.) breeding will heavily rely on dissecting molecular and genetic bases of wheat yield and related traits which is possible through the discovery of quantitative trait loci (QTLs) in constructed populations, such as recombinant inbred lines (RILs). Here, we present an evaluation of 92 RILs in a bi-parental RIL mapping population (the International Triticeae Mapping Initiative Mapping Population [ITMI/MP]) using newly generated phenotypic data in 3-year experiments (2015), older phenotypic data (1997-2009), and newly created single nucleotide polymorphism (SNP) marker data based on 92 of the original RILs to search for novel and stable QTLs. Our analyses of more than 15 unique traits observed in multiple experiments included analyses of 46 traits in three environments in the USA, 69 traits in eight environments in Germany, 149 traits in 10 environments in Russia, and 28 traits in four environments in India (292 traits in 25 environments) with 7584 SNPs (292 × 7584 = 2 214 528 data points). A total of 874 QTLs were detected with limit of detection (LOD) scores of 2.01-3.0 and 432 QTLs were detected with LOD > 3.0. Moreover, 769 QTLs could be assigned to 183 clusters based on the common markers and relative proximity of related QTLs, indicating gene-rich regions throughout the A, B, and D genomes of common wheat. This upgraded genotype-phenotype information of ITMI/MP can assist breeders and geneticists who can make crosses with suitable RILs to improve or investigate traits of interest.

Mapping QTL for spike fertility and related traits in two doubled haploid wheat (Triticum aestivum L.) populations

BACKGROUND

In breeding programs, the selection of cultivars with the highest yield potential consisted in the selection of the yield per se, which resulted in cultivars with higher grains per spike (GN) and occasionally increased grain weight (GW) (main numerical components of the yield). In this study, quantitative trait loci (QTL) for GW, GN and spike fertility traits related to GN determination were mapped using two doubled haploid (DH) populations (Baguette Premium 11 × BioINTA 2002 and Baguette 19 × BioINTA 2002).

RESULTS

In total 305 QTL were identified for 14 traits, out of which 12 QTL were identified in more than three environments and explained more than 10% of the phenotypic variation in at least one environment. Eight hotspot regions were detected on chromosomes 1A, 2B, 3A, 5A, 5B, 7A and 7B in which at least two major and stable QTL sheared confidence intervals. QTL on two of these regions (R5A.1 and R5A.2) have previously been described, but the other six regions are novel.

CONCLUSIONS

Based on the pleiotropic analysis within a robust physiological model we conclude that two hotspot genomic regions (R5A.1 and R5A.2) together with the QGW.perg-6B are of high relevance to be used in marker assisted selection in order to improve the spike yield potential. All the QTL identified for the spike related traits are the first step to search for their candidate genes, which will allow their better manipulation in the future.

Dissecting the trade-off of grain number and size in wheat

Principal component and meta-QTL analyses identified genetic loci affecting the trade-off of wheat grain number and size, which could provide opportunities to optimize local breeding strategies for further yield improvement. Grain yield of wheat is complex, and its physiological and genetic bases remain largely unknown. Using the Forno/Oberkulmer recombinant inbred lines, this study validated the negative phenotypic relationships between thousand grain weight (TGW) and grain number components. This trade-off might be alleviated at the population level by early anthesis and at the shoot level by higher shoot biomass. Principal component (PC) analysis revealed three useful PCs, of which both PC1 and PC3 were positively associated with grain yield and grains m^-2 through increased spikes m^-2 (for PC1) or grains per spike (for PC3), while PC2 primarily reflected the trade-off of grain number and TGW. Quantitative trait locus (QTL) mapping detected eight and seven loci for PC1 and PC2, respectively, on chromosomes 1D, 2A, 3A, 3B, 4A, 4B, 5A and 7B, individually explaining 11.7‒29.3% of phenotypic variations. Using the 1203 QTLs published previously, a meta-analysis was performed to reveal 12, 21, 37 and 54 genomic regions (MQTLs) affecting grains m^-2, spikes m^-2, grains per spike and TGW, respectively. Moreover, 67 MQTLs (96%) for grain number were coincided with the TGW MQTLs, with reverse phenotypic effects, suggesting intensive genetic trade-off between grain number and size. The AGP2 gene, which encodes ADP-glucose pyrophosphorylase determining TGW, was found by haplotype analysis in the Forno/Oberkulmer population to affect grain number oppositely, indicating this trade-off at the gene level. Appropriate combinations of the QTLs/genes for local breeding targets, such as higher grain number or larger grains, therefore, would be critical to achieve future yield gains.

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.

The physiology and genetics behind fruiting efficiency: a promising spike trait to improve wheat yield potential

Fruiting efficiency (FE, grains per g of spike dry weight at anthesis) was proposed as a promising spike trait to improve wheat yield potential, based on its functional relationship with grain number determination and the evidence of trait variability in elite germplasm. During the last few years, we have witnessed great advances in the understanding of the physiological and genetic basis of this trait. The present review summarizes the recent heritability estimations and the genetic gains obtained when fruiting efficiency was measured at maturity (FEm, grains per g of chaff) and used as selection criterion. In addition, we propose spike ideotypes for contrasting fruiting efficiencies based on the fertile floret efficiency (FFE, fertile florets per g of spike dry weight at anthesis) and grain set (grains per fertile floret), together with other spike fertility-related traits. We also review novel genes and quantitative trait loci available for using marker-assisted selection for fruiting efficiency and other spike fertility traits. The possible trade-off between FE and grain weight and the genes reported to alter this relation are also considered. Finally, we discuss the benefits and future steps towards the use of fruiting efficiency as a selection criterion in breeding programs.

QTL Analysis of Five Morpho-Physiological Traits in Bread Wheat Using Two Mapping Populations Derived from Common Parents

Traits such as plant height (PH), juvenile growth habit (GH), heading date (HD), and tiller number are important for both increasing yield potential and improving crop adaptation to climate change. In the present study, these traits were investigated by using the same bi-parental population at early (F₂ and F₂-derived F₃ families) and late (F₆ and F₇, recombinant inbred lines, RILs) generations to detect quantitative trait loci (QTLs) and search for candidate genes. A total of 176 and 178 lines were genotyped by the wheat Illumina 25K Infinium SNP array. The two genetic maps spanned 2486.97 cM and 3732.84 cM in length, for the F₂ and RILs, respectively. QTLs explaining the highest phenotypic variation were found on chromosomes 2B, 2D, 5A, and 7D for HD and GH, whereas those for PH were found on chromosomes 4B and 4D. Several QTL detected in the early generations (i.e., PH and tiller number) were not detected in the late generations as they were due to dominance effects. Some of the identified QTLs co-mapped to well-known adaptive genes (i.e., Ppd-1, Vrn-1, and Rht-1). Other putative candidate genes were identified for each trait, of which PINE1 and PIF4 may be considered new for GH and TTN in wheat. The use of a large F₂ mapping population combined with NGS-based genotyping techniques could improve map resolution and allow closer QTL tagging.

Uncovering genomic regions controlling plant architectural traits in hexaploid wheat using different GWAS models

Wheat is a major food crop worldwide. The plant architecture is a complex trait mostly influenced by plant height, tiller number, and leaf morphology. Plant height plays a crucial role in lodging and thus affects yield and grain quality. In this study, a wheat population was genotyped by using Illumina iSelect 90K single nucleotide polymorphism (SNP) assay and finally 22,905 high-quality SNPs were used to perform a genome-wide association study (GWAS) for plant architectural traits employing four multi-locus GWAS (ML-GWAS) and three single-locus GWAS (SL-GWAS) models. As a result, 174 and 97 significant SNPs controlling plant architectural traits were detected by ML-GWAS and SL-GWAS methods, respectively. Among these SNP makers, 43 SNPs were consistently detected, including seven across multiple environments and 36 across multiple methods. Interestingly, five SNPs (Kukri_c34553_89, RAC875_c8121_1490, wsnp_Ex_rep_c66315_64480362, Ku_c5191_340, and tplb0049a09_1302) consistently detected across multiple environments and methods, played a role in modulating both plant height and flag leaf length. Furthermore, candidate SNPs (BS00068592_51, Kukri_c4750_452 and BS00022127_51) constantly repeated in different years and methods associated with flag leaf width and number of tillers. We also detected several SNPs (Jagger_c6772_80, RAC875_c8121_1490, BS00089954_51, Excalibur_01167_1207, and Ku_c5191_340) having common associations with more than one trait across multiple environments. By further appraising these GWAS methods, the pLARmEB and FarmCPU models outperformed in SNP detection compared to the other ML-GWAS and SL-GWAS methods, respectively. Totally, 152 candidate genes were found to be likely involved in plant growth and development. These finding will be helpful for better understanding of the genetic mechanism of architectural traits in wheat.

Marker-based crop model-assisted ideotype design to improve avoidance of abiotic stress in bread wheat

Wheat phenology allows escape from seasonal abiotic stresses including frosts and high temperatures, the latter being forecast to increase with climate change. The use of marker-based crop models to identify ideotypes has been proposed to select genotypes adapted to specific weather and management conditions and anticipate climate change. In this study, a marker-based crop model for wheat phenology was calibrated and tested. Climate analysis of 30 years of historical weather data in 72 locations representing the main wheat production areas in France was performed. We carried out marker-based crop model simulations for 1019 wheat cultivars and three sowing dates, which allowed calculation of genotypic stress avoidance frequencies of frost and heat stress and identification of ideotypes. The phenology marker-based crop model allowed prediction of large genotypic variations for the beginning of stem elongation (GS30) and heading date (GS55). Prediction accuracy was assessed using untested genotypes and environments, and showed median genotype prediction errors of 8.5 and 4.2 days for GS30 and GS55, respectively. Climate analysis allowed the definition of a low risk period for each location based on the distribution of the last frost and first heat days. Clustering of locations showed three groups with contrasting levels of frost and heat risks. Marker-based crop model simulations showed the need to optimize the genotype depending on sowing date, particularly in high risk environments. An empirical validation of the approach showed that it holds good promises to improve frost and heat stress avoidance.

Genetic Mapping by Integration of 55K SNP Array and KASP Markers Reveals Candidate Genes for Important Agronomic Traits in Hexaploid Wheat

Agronomic traits such as heading date (HD), plant height (PH), thousand grain weight (TGW), and spike length (SL) are important factors affecting wheat yield. In this study, we constructed a high-density genetic linkage map using the Wheat55K SNP Array to map quantitative trait loci (QTLs) for these traits in 207 recombinant inbred lines (RILs). A total of 37 QTLs were identified, including 9 QTLs for HD, 7 QTLs for PH, 12 QTLs for TGW, and 9 QTLs for SL, which explained 3.0-48.8% of the phenotypic variation. Kompetitive Allele Specific PCR (KASP) markers were developed based on sequencing data and used for validation of the stably detected QTLs on chromosomes 3A, 4B and 6A using 400 RILs. A QTL cluster on chromosome 4B for PH and TGW was delimited to a 0.8 Mb physical interval explaining 12.2-22.8% of the phenotypic variation. Gene annotations and analyses of SNP effects suggested that a gene encoding protein Photosynthesis Affected Mutant 68, which is essential for photosystem II assembly, is a candidate gene affecting PH and TGW. In addition, the QTL for HD on chromosome 3A was narrowed down to a 2.5 Mb interval, and a gene encoding an R3H domain-containing protein was speculated to be the causal gene influencing HD. The linked KASP markers developed in this study will be useful for marker-assisted selection in wheat breeding, and the candidate genes provide new insight into genetic study for those traits in wheat.

Genetic Mapping and Validation of Loci for Kernel-Related Traits in Wheat (Triticum aestivum L.)

Kernel size (KS) and kernel weight play a key role in wheat yield. Phenotypic data from six environments and a Wheat55K single-nucleotide polymorphism array-based constructed genetic linkage map from a recombinant inbred line population derived from the cross between the wheat line 20828 and the line SY95-71 were used to identify quantitative trait locus (QTL) for kernel length (KL), kernel width (KW), kernel thickness (KT), thousand-kernel weight (TKW), kernel length-width ratio (LWR), KS, and factor form density (FFD). The results showed that 65 QTLs associated with kernel traits were detected, of which the major QTLs QKL.sicau-2SY-1B, QKW.sicau-2SY-6D, QKT.sicau-2SY-2D, and QTKW.sicau-2SY-2D, QLWR.sicau-2SY-6D, QKS.sicau-2SY-1B/2D/6D, and QFFD.sicau-2SY-2D controlling KL, KW, KT, TKW, LWR, KS, and FFD, and identified in multiple environments, respectively. They were located on chromosomes 1BL, 2DL, and 6DS and formed three QTL clusters. Comparison of genetic and physical interval suggested that only QKL.sicau-2SY-1B located on chromosome 1BL was likely a novel QTL. A Kompetitive Allele Specific Polymerase chain reaction (KASP) marker, KASP-AX-109379070, closely linked to this novel QTL was developed and used to successfully confirm its effect in two different genetic populations and three variety panels consisting of 272 Chinese wheat landraces, 300 Chinese wheat cultivars most from the Yellow and Huai River Valley wheat region, and 165 Sichuan wheat cultivars. The relationships between kernel traits and other agronomic traits were detected and discussed. A few predicted genes involved in regulation of kernel growth and development were identified in the intervals of these identified major QTL. Taken together, these stable and major QTLs provide valuable information for understanding the genetic composition of kernel yield and provide the basis for molecular marker-assisted breeding.

Identification and Validation of New Stable QTLs for Grain Weight and Size by Multiple Mapping Models in Common Wheat

Improvement of grain weight and size is an important objective for high-yield wheat breeding. In this study, 174 recombinant inbred lines (RILs) derived from the cross between Jing 411 and Hongmangchun 21 were used to construct a high-density genetic map by specific locus amplified fragment sequencing (SLAF-seq). Three mapping methods, including inclusive composite interval mapping (ICIM), genome-wide composite interval mapping (GCIM), and a mixed linear model performed with forward-backward stepwise (NWIM), were used to identify QTLs for thousand grain weight (TGW), grain width (GW), and grain length (GL). In total, we identified 30, 15, and 18 putative QTLs for TGW, GW, and GL that explain 1.1-33.9%, 3.1%-34.2%, and 1.7%-22.8% of the phenotypic variances, respectively. Among these, 19 (63.3%) QTLs for TGW, 10 (66.7%) for GW, and 7 (38.9%) for GL were consistent with those identified by genome-wide association analysis in 192 wheat varieties. Five new stable QTLs, including 3 for TGW (Qtgw.ahau-1B.1, Qtgw.ahau-4B.1, and Qtgw.ahau-4B.2) and 2 for GL (Qgl.ahau-2A.1 and Qgl.ahau-7A.2), were detected by the three aforementioned mapping methods across environments. Subsequently, five cleaved amplified polymorphic sequence (CAPS) markers corresponding to these QTLs were developed and validated in 180 Chinese mini-core wheat accessions. In addition, 19 potential candidate genes for Qtgw.ahau-4B.2 in a 0.31-Mb physical interval were further annotated, of which TraesCS4B02G376400 and TraesCS4B02G376800 encode a plasma membrane H+-ATPase and a serine/threonine-protein kinase, respectively. These new QTLs and CAPS markers will be useful for further marker-assisted selection and map-based cloning of target genes.

Evidence for the Accumulation of Nonsynonymous Mutations and Favorable Pleiotropic Alleles During Wheat Breeding

Plant breeding leads to the genetic improvement of target traits by selecting a small number of genotypes from among typically large numbers of candidate genotypes after careful evaluation. In this study, we first investigated how mutations at conserved nucleotide sites normally viewed as deleterious, such as nonsynonymous sites, accumulated in a wheat, Triticum aestivum, breeding lineage. By comparing a 150 year old ancestral and modern cultivar, we found recent nucleotide polymorphisms altered amino acids and occurred within conserved genes at frequencies expected in the absence of purifying selection. Mutations that are deleterious in other contexts likely had very small or no effects on target traits within the breeding lineage. Second, we investigated if breeders selected alleles with favorable effects on some traits and unfavorable effects on others and used different alleles to compensate for the latter. An analysis of a segregating population derived from the ancestral and modern parents provided one example of this phenomenon. The recent cultivar contains the Rht-B1b green revolution semi-dwarfing allele and compensatory alleles that reduce its negative effects. However, improvements in traits other than plant height were due to pleiotropic loci with favorable effects on traits and to favorable loci with no detectable pleiotropic effects. Wheat breeding appears to tolerate mutations at conserved nucleotide sites and to only select for alleles with both favorable and unfavorable effects on traits in exceptional situations.

Identification of modifiers of the plant height in wheat using an induced dwarf mutant controlled by RhtB4c allele

In wheat, 25 Rht genes for dwarfness are known, which include both, GA-insensitive and GA-responsive genes. The GA-insensitive Rht genes have been widely used, although, their suitability under abiotic stress conditions has been questioned. This necessitated a search for alternative GA-responsive, spontaneous and induced dwarfing genes. We earlier reported an induced dwarf mutant ('dwarf mutant-3'; 44 cm), and the mutant allele was named Rht4c allele (2BL). This dwarf mutant was not suitable for cultivation due to its extra dwarf nature. Therefore, we searched for naturally occurring QTLs, which would modify the phenotype of 'dwarf-mutant-3' using 'mutant-assisted gene identification and characterization' (MAGIC) approach. For this purpose, the 'dwarf mutant-3' was crossed with a tall wheat cv. NP114 and homozygous mutant F2 plants (~ 25% of the progeny) were selected, which were phenotyped for plant height and genotyped using SSR markers. The data were utilized for QTL analysis and plant height. Six modifier QTLs were identified on chromosomes 2A, 2B and 4A. Two QTLs each on 2A and 2B were responsible for increase in plant height (described as 'enhancer modifiers'), whereas the remaining two QTLs located on 4A were responsible for reducing the plant height (described as 'suppressor modifiers'). It was hypothesized that the enhancer QTLs could be exploited for the development of semi-dwarf high yielding genotypes containing Rht4c allele. This is the first study of its kind in wheat demontsrating that the MAGIC approach could be used for identification of modifiers of the mutant phenotypes of other traits for wheat improvement.

A GBS-based GWAS analysis of adaptability and yield traits in bread wheat (Triticum aestivum L.)

Wheat is a foremost food grain of Pakistan and occupies a vital position in agricultural policies of the country. Wheat demand will be increased by 60% by 2050 which is a serious concern to meet this demand. Conventional breeding approaches are not enough to meet the demand of growing human population. It is paramount to integrate underutilized genetic diversity into wheat gene pool through efficient and accurate breeding tools and technology. In this study, we present the genetic analysis of a 312 diverse pre-breeding lines using DArT-seq SNPs seeking to elucidate the genetic components of emergence percentage, heading time, plant height, lodging, thousand kernel weight, and yield (Yd) which resulted in detection of 201 significant (p value < 10^-3) and 61 highly significant associations (p value < 1.45 × 10^-4). More importantly, chromosomes 1B and 2A carried loci linked to Yd in two different seasons, and an increase of up to 8.20% is possible in Yd by positive allele mining. We identified seven lines with > 4 positive alleles for Yd whose pedigree carried Aegilops squarrosa as one of the parents providing evidence that Aegilops species, apart from imparting resistance against biotic stresses, may also provide alleles for yield enhancement.

Combining QTL Analysis and Genomic Predictions for Four Durum Wheat Populations Under Drought Conditions

Durum wheat is an important crop for the human diet and its consumption is gaining popularity. In order to ensure that durum wheat production maintains the pace with the increase in demand, it is necessary to raise productivity by approximately 1.5% per year. To deliver this level of annual genetic gain the incorporation of molecular strategies has been proposed as a key solution. Here, four RILs populations were used to conduct QTL discovery for grain yield (GY) and 1,000 kernel weight (TKW). A total of 576 individuals were sown at three locations in Morocco and one in Lebanon. These individuals were genotyped by sequencing with 3,202 high-confidence polymorphic markers, to derive a consensus genetic map of 2,705.7 cM, which was used to impute any missing data. Six QTLs were found to be associated with GY and independent from flowering time on chromosomes 2B, 4A, 5B, 7A and 7B, explaining a phenotypic variation (PV) ranging from 4.3 to 13.4%. The same populations were used to train genomic prediction models incorporating the relationship matrix, the genotype by environment interaction, and marker by environment interaction, to reveal significant advantages for models incorporating the marker effect. Using training populations (TP) in full sibs relationships with the validation population (VP) was shown to be the only effective strategy, with accuracies reaching 0.35–0.47 for GY. Reducing the number of markers to 10% of the whole set, and the TP size to 20% resulted in non-significant changes in accuracies. The QTLs identified were also incorporated in the models as fixed effects, showing significant accuracy gain for all four populations. Our results confirm that the prediction accuracy depends considerably on the relatedness between TP and VP, but not on the number of markers and size of TP used. Furthermore, feeding the model with information on markers associated with QTLs increased the overall accuracy.

A complex network of QTL for thousand-kernel weight in the rye genome

Here, QTL mapping for thousand-kernel weight carried out within a 541 × Ot1-3 population of recombinant inbred lines using high-density DArT-based map and three methods (single-marker analysis with F parametric test, marker analysis with the Kruskal–Wallis K* nonparametric test, and the recently developed analysis named genes interaction assorting by divergent selection with χ² test) revealed 28 QTL distributed over all seven rye chromosomes. The first two methods showed a high level of consistency in QTL detection. Each of 13 QTL revealed in the course of gene interaction assorting by divergent selection analysis coincided with those detected by the two other methods, confirming the reliability of the new approach to QTL mapping. Its unique feature of discriminating QTL classes might help in selecting positively acting QTL and alleles for marker-assisted selection. Also, interaction among seven QTL for thousand-kernel weight was analyzed using gene interaction assorting by the divergent selection method. Pairs of QTL showed a predominantly additive relationship, but epistatic and complementary types of two-loci interactions were also revealed.

Genetics of yield, abiotic stress tolerance and biofortification in wheat (Triticum aestivum L.)

A review of the available literature on genetics of yield and its component traits, tolerance to abiotic stresses and biofortification should prove useful for future research in wheat in the genomics era. The work reviewed in this article mainly covers the available information on genetics of some important quantitative traits including yield and its components, tolerance to abiotic stresses (heat, drought, salinity and pre-harvest sprouting = PHS) and biofortification (Fe/Zn and phytate contents with HarvestPlus Program) in wheat. Major emphasis is laid on the recent literature on QTL interval mapping and genome-wide association studies, giving lists of known QTL and marker-trait associations. Candidate genes for different traits and the cloned and characterized genes for yield traits along with the molecular mechanism are also described. For each trait, an account of the present status of marker-assisted selection has also been included. The details of available results have largely been presented in the form of tables; some of these tables are included as supplementary files.

The semidominant mutation w5 impairs epicuticular wax deposition in common wheat (Triticum aestivum L.)

The semidominant EMS-induced mutant w5 affects epicuticular wax deposition and mapped to an approximately 194-kb region on chromosome 7DL. Epicuticular wax is responsible for the glaucous appearance of plants and protects against many biotic and abiotic stresses. In wheat (Triticum aestivum L.), β-diketone is a major component of epicuticular wax in adult plants and contributes to the glaucousness of the aerial organs. In the present study, we identified an ethyl methanesulfonate-induced epicuticular wax-deficient mutant from the elite wheat cultivar Jimai22. Compared to wild-type Jimai22, the mutant lacked β-diketone and failed to form the glaucous coating on all aerial organs. The mutant also had significantly increased in cuticle permeability, based on water loss and chlorophyll efflux. Genetic analysis indicated that the mutant phenotype is controlled by a single, semidominant gene on the long arm of chromosome 7D, which was not allelic to the known wax gene loci W1-W4, and was therefore designated W5. W5 was finely mapped to an ~ 194-kb region (flanked by the molecular markers SSR2 and STARP11) that harbored four annotated genes according to the reference genome of Chinese Spring (RefSeq v1.0). Collectively, these data will broaden the knowledge of the genetic basis underlying epicuticular wax deposition in wheat.

Mapping of QTLs Associated with Yield and Yield Related Traits in Durum Wheat (Triticum durum Desf.) Under Irrigated and Drought Conditions

Global durum wheat consumption (Triticum durum Desf.) is ahead of its production. One reason for this is abiotic stress, e.g., drought. Breeding for resistance to drought is complicated by the lack of fast, reproducible screening techniques and the inability to routinely create defined and repeatable water stress conditions. Here, we report the first analysis of dissection of yield and yield-related traits in durum wheat in Pakistan, seeking to elucidate the genetic components of yield and agronomic traits. Analysis of several traits revealed a total of 221 (160 with logarithm of odds (LOD) > 2 ≤ 3 and 61 with LOD > 3) quantitative trait loci (QTLs) distributed on all fourteen durum wheat chromosomes, of which 109 (78 with LOD > 2 ≤ 3 and 31 with LOD > 3) were observed in 2016-17 (S1) and 112 (82 with LOD > 2 ≤ 3 and 30 with LOD > 3) were observed in 2017-18 (S2). Allelic profiles of yield QTLs on chromosome 2A and 7B indicate that allele A of Xgwm895 and allele B of Xbarc276 can enhance the Yd up to 6.16% in control and 5.27% under drought. Moreover, if combined, a yield gain of up to 11% would be possible.

Refining the genetic architecture of flag leaf glaucousness in wheat

The cuticle is the plant's barrier against abiotic and biotic stresses, and the deposition of epicuticular wax crystals results in the scattering of light, an effect termed glaucousness. Here, we dissect the genetic architecture of flag leaf glaucousness in wheat toward a future targeted design of the cuticle. The cuticle serves as a barrier that protects plants against abiotic and biotic stresses. Differences in cuticle composition can be detected by the scattering of light on epicuticular wax crystals, which causes a phenotype termed glaucousness. In this study, we dissected the genetic architecture of flag leaf glaucousness in a panel of 1106 wheat cultivars of global origin. We observed a large genotypic variation, but the geographic pattern suggests that other wax layer characteristics besides glaucousness may be important in conferring tolerance to abiotic stresses such as heat and drought. Genome-wide association mapping identified two major quantitative trait loci (QTL) on chromosomes 3A and 2B. The latter corresponds to the W1 locus, but further characterization revealed that it is likely to contain additional QTL. The same holds true for the major QTL on 3A, which was also found to show an epistatic interaction with another locus located a few centiMorgan distal to it. Genome-wide prediction and the identification of a few additional putative QTL revealed that small-effect QTL also contribute to the trait. Collectively, our results illustrate the complexity of the genetic control of flag leaf glaucousness, with additive effects and epistasis, and lay the foundation for the cloning of the underlying genes toward a more targeted design of the cuticle by plant breeding.

Genome-wide and SNP network analyses reveal genetic control of spikelet sterility and yield-related traits in wheat

Revealing the genetic factors underlying yield and agronomic traits in wheat are an imperative need for covering the global food demand. Yield boosting requires a deep understanding of the genetic basis of grain yield-related traits (e.g., spikelet fertility and sterility). Here, we have detected much natural variation among ancient hexaploid wheat accessions in twenty-two agronomic traits collected over eight years of field experiments. A genome-wide association study (GWAS) using 15 K single nucleotide polymorphisms (SNPs) was applied to detect the genetic basis of studied traits. Subsequently, the GWAS output was reinforced via other statistical and bioinformatics analyses to detect putative candidate genes. Applying the genome-wide SNP-phenotype network defined the most decisive SNPs underlying the traits. Six pivotal SNPs, co-located physically within the genes encoding enzymes, hormone response, metal ion transport, and response to oxidative stress have been identified. Of these, metal ion transport and Gibberellin 2-oxidases (GA2oxs) genes showed strong involvement in controlling the spikelet sterility, which had not been reported previously in wheat. SNP-gene haplotype analysis confirmed that these SNPs influence spikelet sterility, especially the SNP co-located on the exon of the GA2ox gene. Interestingly, these genes were highly expressed in the grain and spike, demonstrating their pivotal role in controlling the trait. The integrative analysis strategy applied in this study, including GWAS, SNP-phenotype network, SNP-gene haplotype, expression analysis, and genome-wide prediction (GP), empower the identification of functional SNPs and causal genes. GP outputs obtained in this study are encouraging for the implementation of the traits to accelerate yield improvement by making an early prediction of complex yield-related traits in wheat. Our findings demonstrate the usefulness of the ancient wheat material as a valuable resource for yield-boosting. This is the first comprehensive genome-wide analysis for spikelet sterility in wheat, and the results provide insights into yield improvement.

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.

Flag leaf size and posture of bread wheat: genetic dissection, QTL validation and their relationships with yield-related traits

Major and environmentally stable QTL for flag leaf-related traits in wheat were identified and validated across ten environments using six populations with different genetic backgrounds. Flag leaf size and posture are two important factors of "ideotype" in wheat. Despite numerous studies on genetic analysis of flag leaf size including flag leaf length (FLL), width (FLW), area (FLA) and the ratio of length/width (FLR), few have focused on flag leaf posture including flag leaf angle (FLANG), opening angle (FLOA) and bend angle (FLBA). Further, the numbers of major, environmentally stable and verified genetic loci for flag leaf-related traits are limited. In this study, QTL for FLL, FLW, FLA, FLR, FLANG, FLOA and FLBA were identified based on a recombinant inbred line population together with values from up to ten different environments. Totally, eight major and stably expressed QTL were identified. Three co-located chromosomal intervals for seven major QTL were identified. The five major QTL QFll.sicau-5B.3 and QFll.sicau-2D.3 for FLL, QFlr.sicau-5B for FLR, QFlw.sicau-2D for FLW and QFla.sicau-2D for FLA were successfully validated by the tightly linked Kompetitive Allele Specific PCR (KASP) markers in the other five populations with different genetic backgrounds. A few genes related to leaf growth and development in intervals for these major QTL were predicated. Significant relationships between flag leaf- and yield-related traits were evidenced by analyses of Pearson correlations, conditional QTL and genetic mapping. Taken together, these results provide valuable information for understanding flag leaf size and posture of "ideotype" as well as fine mapping and breeding utilization of promising loci in bread wheat.

Agronomic Evaluation of Bread Wheat Varieties from Participatory Breeding: A Combination of Performance and Robustness

Participatory plant breeding (PPB) is based on the decentralization of selection in farmers’ fields and their involvement in decision-making at all steps of the breeding scheme. Despite the evidence of its benefits to develop population varieties adapted to diversified and local practices and conditions, such as organic farming, PPB is still not widely used. There is a need to share more broadly how the different programs have overcome scientific, practical, and organizational issues and produced a large number of positive outcomes. Here, we report on a PPB program that started on bread wheat in France in 2006 and has achieved a range of outcomes, from the emergence of new organization among actors, to specific experimental designs and statistical methods developed, and to populations varieties developed and cultivated by farmers. We present the results of a two-year agronomic evaluation of the first population varieties developed within this PPB program compared to two commercial varieties currently grown in organic agriculture. We found that several PPB varieties were of great agronomic interest, combining relatively good performance even under the most favorable conditions of organic agriculture and good robustness, i.e., the ability to maintain productivity under more constraining conditions. The PPB varieties also tended to show a good temporal dynamic stability and appeared promising for the farmers involved.