QTL mapping for yield-related traits in wheat based on four RIL populations

Dissection of QTLs underlying the genetic basis of drought resistance in wheat: a meta-analysis

Wheat (Triticum aestivum L.) is one of the most important cereal crops, with its grain serving as a predominant staple food source on a global scale. However, there are many biotic and abiotic stresses challenging the stability of wheat production. Among the abiotic stresses, drought is recognized as a significant stress and poses a substantial threat to food production and quality throughout the world. Raising drought tolerance of wheat varieties through genetic regulation is therefore considered as one of the most effective ways to combat the challenges caused by drought stress. Meta-QTL analysis has demonstrated its effectiveness in identifying consensus QTL regions in wheat drought resistance in numerous instances. In this study, we present a comprehensive meta-analysis aimed at unraveling the drought tolerance genetic basis associated with agronomic traits in bread wheat. Extracting data from 34 previously published studies, we aggregated a corpus of 1291 Quantitative Trait Loci (QTL) pertinent to wheat drought tolerance. Then, the translation of the consensus genetic map yielded a comprehensive compendium of 49 distinct MQTLs, each associated with diverse agronomic traits. Prominently featured among the MQTLs were MQTLs 1.1, 1.7, 1.8 (1D), 4.1 (4A), 4.6 (4D), 5.2 (5B), 6.6 (6B), and 7.2 (7B), distinguished as pivotal MQTLs offering significant potential for application in marker-assisted breeding endeavors. Altogether, a total of 66 putative candidate genes (CGs)-related drought tolerance were identified. This work illustrates a translational research approach in transferring information from published mapping studies to genomic regions hosting major QTLs governing key agronomical traits in wheat.

Genetic dissection of flag leaf morphology traits and fine mapping of a novel QTL (Qflw.sxau-6BL) in bread wheat (Triticum aestivum L.)

KEY MESSAGE

Total 60-QRC for FLM traits were detected by meta-genomics analysis, nine major and stable QTL identified by DH population and validated, and a novel QTL  Qflw.sxau-6BL was fine mapped. The flag leaf is an "ideotypic" morphological trait providing photosynthetic assimilates in wheat. Although flag leaf morphology (FLM) traits had been extensively investigated through genetic mapping, there is a desire for FLM-related loci to be validated in multi-environments and fine mapping. In order to identify the stable genomic regions for FLM traits, we conducted a meta-genomic analysis based on reports from 2008 to 2024. Experimentally, a doubled haploid (DH) population was used to assess the genetic regions associated with FLM traits in nine environments. The meta-genomic analysis extracted 60 QTL-rich clusters (QRC), 45 of which were verified in marker-trait association (MTA) study. Nine major and stable QTL were found being associated with FLM traits across three-to-seven environments including BLUP, with phenotypic variance explained (PVE) ranging from 5.05 to 34.95%. The KASP markers of the nine QTL were validated (P < 0.005) in more than three environments using a panel of diverse wheat collections from Shanxi Province in China. Two co-located major and stable QTL viz. Qflw.sxau-6B.5 and Qfla.sxau-6B.4 were found novel and contributed to increase FLW by 12.09-19.21% and FLA by 5.45-13.28%. They also demonstrated high recombination rates in LD analysis based on the resequencing of 145 wheat landmark cultivars. The fine mapping of Qflw.sxau-6BL narrowed it down to a 1.27 Mb region as a result of the combined genotypic and phenotypic analysis for secondary mapping population. Comparing to NIL-ND3338, the NIL-LF5064 showed higher FLW by 20.45-27.37%, thousand-grain weight by 1.88-2.57% and grain length by 0.47-2.30% across all environments. The expression analysis of 11 tissues revealed seven highly expressed genes within the fine map region. This study provides a genetic basis for the FLM traits for further map-based cloning of FLW genes in wheat.

Dissection of major QTLs and candidate genes for seedling stage salt/drought tolerance in tomato

BACKGROUND

As two of the most impactful abiotic stresses, salt and drought strongly affect tomato growth and development, especially at the seedling stage. However, dissection of the genetic basis underlying salt/drought tolerance at seedling stage in tomato remains limited in scope.

RESULTS

Here, we reported an analysis of major quantitative trait locus (QTL) and potential causal genetic variations in seedling stage salt/drought tolerance in recombinant inbred lines (n = 201) of S. pimpinellifolium and S. lycopersicum parents by whole genome resequencing. A total of 5 QTLs on chromosome 1, 3, 5, 7 and 12 for salt tolerance (ST) and 15 QTLs on chromosome 1, 3, 4, 8, 9, 10, 12 for drought tolerance (DT) were identified by linkage mapping. The proportion of phenotypic variation explained (PVE%) by these QTLs ranged from 4.91 to 15.86. Two major QTLs qST7 and qDT1-3 were detected in both two years, for which two candidate genes (methionine sulfoxide reductase SlMSRB1 and brassinosteroid insensitive 1-like receptor SlBRL1) and the potential functional variations were further analyzed. Taking advantage of the tomato population resequencing data, the frequency changes of the potential favorable QTL allele for seedling stage ST/DT during tomato breeding were explored.

CONCLUSIONS

These results will be beneficial for the exploration of salt/drought tolerance genes at seedling stages, laying a foundation for marker-assisted breeding for seedling stage salt/drought tolerance.

Genetic factors of grain cadmium concentration in Polish wheat (Triticum polonicum L.)

Wheat (Triticum aestivum L.) is one of the most important crops worldwide and a major source of human cadmium (Cd) intake. Limiting grain Cd concentration (Gr_Cd_Conc) in wheat is necessary to ensure food safety. However, the genetic factors associated with Cd uptake, translocation and distribution and Gr_Cd_Conc in wheat are poorly understood. Here, we mapped quantitative trait loci (QTLs) for Gr_Cd_Conc and its related transport pathway using a recombinant inbred line (RIL) population derived from 2 Polish wheat varieties (RIL_DT; dwarf Polish wheat [DPW] and tall Polish wheat [TPW]). We identified 29 novel major QTLs for grain and tissue Cd concentration; 14 novel major QTLs for Cd uptake, translocation, and distribution; and 27 major QTLs for agronomic traits. We also analyzed the pleiotropy of these QTLs. Six novel QTLs (QGr_Cd_Conc-1A, QGr_Cd_Conc-3A, QGr_Cd_Conc-4B, QGr_Cd_Conc-5B, QGr_Cd_Conc-6A, and QGr_Cd_Conc-7A) for Gr_Cd_Conc explained 8.16% to 17.02% of the phenotypic variation. QGr_Cd_Conc-3A, QGr_Cd_Conc-6A, and QGr_Cd_Conc-7A pleiotropically regulated Cd transport; 3 other QTLs were organ-specific for Gr_Cd_Conc. We fine-mapped the locus of QGr_Cd_Conc-4B and identified the candidate gene as Cation/Ca exchanger 2 (TpCCX2-4B), which was differentially expressed in DPW and TPW. It encodes an endoplasmic reticulum membrane/plasma membrane-localized Cd efflux transporter in yeast. Overexpression of TpCCX2-4B reduced Gr_Cd_Conc in rice. The average Gr_Cd_Conc was significantly lower in TpCCX2-4BDPW genotypes than in TpCCX2-4BTPW genotypes of the RIL_DT population and 2 other natural populations, based on a Kompetitive allele-specific PCR marker derived from the different promoter sequences between TpCCX2-4BDPW and TpCCX2-4BTPW. Our study reveals the genetic mechanism of Cd accumulation in wheat and provides valuable resources for genetic improvement of low-Cd-accumulating wheat cultivars.

Identification and fine mapping of a QTL-rich region for yield- and quality-related traits on chromosome 4BS in common wheat (Triticum aestivum L.)

Yield and quality are important for plant breeding. To better understand the genetic basis underlying yield- and quality-related traits in wheat (Triticum aestivum L.), we conducted the quantitative trait locus (QTL) analysis using recombinant inbred lines (RILs) and a high-density genetic linkage map with a 90 K array. In this study, a total of 117 QTLs were detected for spike number per area (SNPA), thousand grain weight (TGW), grain number per spike (GNS), plant height (PH), spike length (SL), total spikelet number (TSN), spikelet density (SD), grain protein content (GPC), and grain starch content (GSC). Among these QTLs, 30 environmentally stable QTLs for yield- and quality-related traits were detected. Notably, five QTL-rich regions (Qrr) for yield- and/or quality-related traits were identified, including the QTL-rich region on chromosome 4BS (QQrr.cau-4B) for eight traits (SNPA, GNS, PH, SL, TSN, SD, GPC, and GSC). The stable QTL-rich region QQrr.cau-4B was delimited into a physical interval of approximately 2.47 Mb. Based on the annotation information of the Chinese spring wheat genome v1.0 and parental re-sequencing results, the interval included twelve genes with sequence variations. Taken together, these results contribute to further understanding of the genetic basis of SNPA, GNS, PH, SL, TSN, SD, GPC, and GSC, and fine mapping of QQrr.cau-4B will be beneficial for gene cloning and marker-assisted selection in the genetic improvement of wheat varieties.

Identification of Quantitative Trait Loci Associated with Plant Adaptation Traits Using Nested Association Mapping Population

This study evaluated 290 recombinant inbred lines (RILs) of the nested association mapping (NAM) population from the UK. The population derived from 24 families, where a common parent was "Paragon," one of the UK's spring wheat cultivar standards. All genotypes were tested in two regions of Kazakhstan at the Kazakh Research Institute of Agriculture and Plant Industry (KRIAPI, Almaty region, Southeast Kazakhstan, 2019-2022 years) and Alexandr Barayev Scientific-Production Center for Grain Farming (SPCGF, Shortandy, Akmola region, Northern Kazakhstan, 2019-2022 years). The studied traits consisted of plant adaptation-related traits, including heading date (HD, days), seed maturation date (SMD, days), plant height (PH, cm), and peduncle length (PL, cm). In addition, the yield per m2 was analyzed in both regions. Based on a field evaluation of the population in northern and southeastern Kazakhstan and using 10,448 polymorphic SNP (single-nucleotide polymorphism) markers, the genome-wide association study (GWAS) allowed for detecting 74 QTLs in four studied agronomic traits (HD, SMD, PH, and PL). The literature survey suggested that 16 of the 74 QTLs identified in our study had also been detected in previous QTL mapping studies and GWASs for all studied traits. The results will be used for further studies related to the adaptation and productivity of wheat in breeding projects for higher grain productivity.

Quantitative Trait Loci Mapping of Heading Date in Wheat under Phosphorus Stress Conditions

Wheat (Triticum aestivum L.) is a crucial cereal crop, contributing around 20% of global caloric intake. However, challenges such as diminishing arable land, water shortages, and climate change threaten wheat production, making yield enhancement crucial for global food security. The heading date (HD) is a critical factor influencing wheat's growth cycle, harvest timing, climate adaptability, and yield. Understanding the genetic determinants of HD is essential for developing high-yield and stable wheat varieties. This study used a doubled haploid (DH) population from a cross between Jinmai 47 and Jinmai 84. QTL analysis of HD was performed under three phosphorus (P) treatments (low, medium, and normal) across six environments, using Wheat15K high-density SNP technology. The study identified 39 QTLs for HD, distributed across ten chromosomes, accounting for 2.39% to 29.52% of the phenotypic variance. Notably, five stable and major QTLs (Qhd.saw-3A.7, Qhd.saw-3A.8, Qhd.saw-3A.9, Qhd.saw-4A.4, and Qhd.saw-4D.3) were consistently detected across varying P conditions. The additive effects of these major QTLs showed that favorable alleles significantly delayed HD. There was a clear trend of increasing HD delay as the number of favorable alleles increased. Among them, Qhd.saw-3A.8, Qhd.saw-3A.9, and Qhd.saw-4D.3 were identified as novel QTLs with no prior reports of HD QTLs/genes in their respective intervals. Candidate gene analysis highlighted seven highly expressed genes related to Ca2+ transport, hormone signaling, glycosylation, and zinc finger proteins, likely involved in HD regulation. This research elucidates the genetic basis of wheat HD under P stress, providing critical insights for breeding high-yield, stable wheat varieties suited to low-P environments.

Characterization of flag leaf morphology identifies a major genomic region controlling flag leaf angle in the US winter wheat (Triticum aestivum L.)

KEY MESSAGE

Multi-environmental characterization of flag leaf morphology traits in the US winter wheat revealed nine stable genomic regions for different flag leaf-related traits including a major region governing flag leaf angle. Flag leaf in wheat is the primary contributor to accumulating photosynthetic assimilates. Flag leaf morphology (FLM) traits determine the overall canopy structure and capacity to intercept the light, thus influencing photosynthetic efficiency. Hence, understanding the genetic control of these traits could be useful for breeding desirable ideotypes in wheat. We used a panel of 272 accessions from the hard winter wheat (HWW) region of the USA to investigate the genetic architecture of five FLM traits including flag leaf length (FLL), width (FLW), angle (FLANG), length-width ratio, and area using multilocation field experiments. Multi-environment GWAS using 14,537 single-nucleotide polymorphisms identified 36 marker-trait associations for different traits, with nine being stable across environments. A novel and major stable region for FLANG (qFLANG.1A) was identified on chromosome 1A accounting for 9-13% variation. Analysis of spatial distribution for qFLANG.1A in a set of 2354 breeding lines from the HWW region showed a higher frequency of allele associated with narrow leaf angle. A KASP assay was developed for allelic discrimination of qFLANG.1A and was used for its independent validation in a diverse set of spring wheat accessions. Furthermore, candidate gene analysis for two regions associated with FLANG identified seven putative genes of interest for each of the two regions. The present study enhances our understanding of the genetic control of FLM in wheat, particularly FLANG, and these results will be useful for dissecting the genes underlying canopy architecture in wheat facilitating the development of climate-resilient wheat varieties.

Genome-wide association study and genomic selection of spike-related traits in bread wheat

KEY MESSAGE

Identification of 337 stable MTAs for wheat spike-related traits improved model accuracy, and favorable alleles of MTA259 and MTA64 increased grain weight and yield per plant. Wheat (Triticum aestivum L.) is one of the three primary global, staple crops. Improving spike-related traits in wheat is crucial for optimizing spike and plant morphology, ultimately leading to increased grain yield. Here, we performed a genome-wide association study using a dataset of 24,889 high-quality unique single-nucleotide polymorphisms (SNPs) and phenotypic data from 314 wheat accessions across eight diverse environments. In total, 337 stable and significant marker-trait associations (MTAs) related to spike-related traits were identified. MTA259 and MTA64 were consistently detected in seven and six environments, respectively. The presence of favorable alleles associated with MTA259 and MTA64 significantly reduced wheat spike exsertion length and spike length, while enhancing thousand kernel weight and yield per plant. Combined gene expression and network analyses identified TraesCS6D03G0692300 and TraesCS6D03G0692700 as candidate genes for MTA259 and TraesCS2D03G0111700 and TraesCS2D03G0112500 for MTA64. The identified MTAs significantly improved the prediction accuracy of each model compared with using all the SNPs, and the random forest model was optimal for genome selection. Additionally, the eight stable and major MTAs, including MTA259, MTA64, MTA66, MTA94, MTA110, MTA165, MTA180, and MTA164, were converted into cost-effective and efficient detection markers. This study provided valuable genetic resources and reliable molecular markers for wheat breeding programs.

Novel PHOTOPERIOD-1 gene variants associate with yield-related and root-angle traits in European bread wheat

KEY MESSAGE

PHOTOPERIOD-1 homoeologous gene copies play a pivotal role in regulation of flowering time in wheat. Here, we show that their influence also extends to spike and shoot architecture and even impacts root development. The sequence diversity of three homoeologous copies of the PHOTOPERIOD-1 gene in European winter wheat was analyzed by Oxford Nanopore amplicon-based multiplex sequencing and molecular markers in a panel of 194 cultivars representing breeding progress over the past 5 decades. A strong, consistent association with an average 8% increase in grain yield was observed for the PpdA1-Hap1 haplotype across multiple environments. This haplotype was found to be linked in 51% of cultivars to the 2NS/2AS translocation, originally introduced from Aegilops ventricosa, which leads to an overestimation of its effect. However, even in cultivars without the 2NS/2AS translocation, PpdA1-Hap1 was significantly associated with increased grain yield, kernel per spike and kernel per m2 under optimal growth conditions, conferring a 4% yield advantage compared to haplotype PpdA1-Hap4. In contrast to Ppd-B1 and Ppd-D1, the Ppd-A1 gene exhibits novel structural variations and a high number of SNPs, highlighting the evolutionary changes that have occurred in this region over the course of wheat breeding history. Additionally, cultivars carrying the photoperiod-insensitive Ppd-D1a allele not only exhibit earlier heading, but also deeper roots compared to those with photoperiod-sensitive alleles under German conditions. PCR and KASP assays have been developed that can be effectively employed in marker-assisted breeding programs to introduce these favorable haplotypes.

Identification of a Branch Number Locus in Soybean Using BSA-Seq and GWAS Approaches

The determination of the soybean branch number plays a pivotal role in plant morphogenesis and yield components. This polygenic trait is subject to environmental influences, and despite its significance, the genetic mechanisms governing the soybean branching number remain incompletely understood. To unravel these mechanisms, we conducted a comprehensive investigation employing a genome-wide association study (GWAS) and bulked sample analysis (BSA). The GWAS revealed 18 SNPs associated with the soybean branch number, among which qGBN3 on chromosome 2 emerged as a consistently detected locus across two years, utilizing different models. In parallel, a BSA was executed using an F2 population derived from contrasting cultivars, Wandou35 (low branching number) and Ruidou1 (high branching number). The BSA results pinpointed a significant quantitative trait locus (QTL), designated as qBBN1, located on chromosome 2 by four distinct methods. Importantly, both the GWAS and BSA methods concurred in co-locating qGBN3 and qBBN1. In the co-located region, 15 candidate genes were identified. Through gene annotation and RT-qPCR analysis, we predicted that Glyma.02G125200 and Glyma.02G125600 are candidate genes regulating the soybean branch number. These findings significantly enhance our comprehension of the genetic intricacies regulating the branch number in soybeans, offering promising candidate genes and materials for subsequent investigations aimed at augmenting the soybean yield. This research represents a crucial step toward unlocking the full potential of soybean cultivation through targeted genetic interventions.

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.

Meta-QTL analysis in wheat: progress, challenges and opportunities

Wheat, an important cereal crop globally, faces major challenges due to increasing global population and changing climates. The production and productivity are challenged by several biotic and abiotic stresses. There is also a pressing demand to enhance grain yield and quality/nutrition to ensure global food and nutritional security. To address these multifaceted concerns, researchers have conducted numerous meta-QTL (MQTL) studies in wheat, resulting in the identification of candidate genes that govern these complex quantitative traits. MQTL analysis has successfully unraveled the complex genetic architecture of polygenic quantitative traits in wheat. Candidate genes associated with stress adaptation have been pinpointed for abiotic and biotic traits, facilitating targeted breeding efforts to enhance stress tolerance. Furthermore, high-confidence candidate genes (CGs) and flanking markers to MQTLs will help in marker-assisted breeding programs aimed at enhancing stress tolerance, yield, quality and nutrition. Functional analysis of these CGs can enhance our understanding of intricate trait-related genetics. The discovery of orthologous MQTLs shared between wheat and other crops sheds light on common evolutionary pathways governing these traits. Breeders can leverage the most promising MQTLs and CGs associated with multiple traits to develop superior next-generation wheat cultivars with improved trait performance. This review provides a comprehensive overview of MQTL analysis in wheat, highlighting progress, challenges, validation methods and future opportunities in wheat genetics and breeding, contributing to global food security and sustainable agriculture.

Genetic analysis of iron, zinc and grain yield in wheat-Aegilops derivatives using multi-locus GWAS

BACKGROUND

Wheat is a major staple crop and helps to reduce worldwide micronutrient deficiency. Investigating the genetics that control the concentrations of iron (Fe) and zinc (Zn) in wheat is crucial. Hence, we undertook a comprehensive study aimed at elucidating the genomic regions linked to the contents of Fe and Zn in the grain.

METHODS AND RESULTS

We performed the multi-locus genome-wide association (ML-GWAS) using a panel of 161 wheat-Aegilops substitution and addition lines to dissect the genomic regions controlling grain iron (GFeC), and grain zinc (GZnC) contents. The wheat panel was genotyped using 10,825 high-quality SNPs and phenotyped in three different environments (E1-E3) during 2017-2019. A total of 111 marker-trait associations (MTAs) (at p-value < 0.001) were detected that belong to all three sub-genomes of wheat. The highest number of MTAs were identified for GFeC (58), followed by GZnC (44) and yield (9). Further, six stable MTAs were identified for these three traits and also two pleiotropic MTAs were identified for GFeC and GZnC. A total of 1291 putative candidate genes (CGs) were also identified for all three traits. These CGs encode a diverse set of proteins, including heavy metal-associated (HMA), bZIP family protein, AP2/ERF, and protein previously associated with GFeC, GZnC, and grain yield.

CONCLUSIONS

The significant MTAs and CGs pinpointed in this current study are poised to play a pivotal role in enhancing both the nutritional quality and yield of wheat, utilizing marker-assisted selection (MAS) techniques.

Two QTL regions for spike length showing pleiotropic effects on Fusarium head blight resistance and thousand-grain weight in bread wheat (Triticum aestivum L.)

Spike length (SL) plays an important role in the yield improvement of wheat and is significantly associated with other traits. Here, we used a recombinant inbred line (RIL) population derived from a cross between Yangmai 12 (YM12) and Yanzhan 1 (YZ1) to construct a genetic linkage map and identify quantitative trait loci (QTL) for SL. A total of 5 QTL were identified for SL, among which QSl.yaas-3A and QSl.yaas-5B are two novel QTL for SL. The YZ1 alleles at QSl.yaas-2D and QSl.yaas-5A, and the YM12 alleles at QSl.yaas-2A, QSl.yaas-3A, and QSl.yaas-5B conferred increasing SL effects. Two major QTL QSl.yaas-5A and QSl.yaas-5B explained 9.11-15.85% and 9.01-12.85% of the phenotypic variations, respectively. Moreover, the positive alleles of QSl.yaas-5A and QSl.yaas-5B could significantly increase Fusarium head blight (FHB) resistance (soil surface inoculation and spray inoculation were used) and thousand-grain weight (TGW) in the RIL population. Kompetitive allele-specific PCR (KASP) markers for QSl.yaas-5A and QSl.yaas-5B were developed and validated in an additional panel of 180 wheat cultivars/lines. The cultivars/lines harboring both the positive alleles of QSl.yaas-5A and QSl.yaas-5B accounted for only 28.33% of the validation populations and had the longest SL, best FHB resistance (using spray inoculation), and highest TGW. A total of 358 and 200 high-confidence annotated genes in QSl.yaas-5A and QSl.yaas-5B were identified, respectively. Some of the genes in these two regions were involved in cell development, disease resistance, and so on. The results of this study will provide a basis for directional breeding of longer SL, higher TGW, and better FHB resistance varieties and a solid foundation for fine-mapping QSl.yaas-5A and QSl.yaas-5B in future.

Supplementary Information

The online version contains supplementary material available at 10.1007/s11032-023-01427-8.

An intron-located single nucleotide variation of TaGS5-3D is related to wheat grain size through accumulating intron retention transcripts

KEY MESSAGE

Thirty-three stable QTL for 13 yield-related traits across ten environments were identified in the PD34/MY47 RIL population, and a candidate gene TaGS5-3D in Qmt.nwafu.3D was preliminarily identified to affect grain-related traits through accumulation of specific transcripts. Dissecting the genetic basis of yield-related traits is pivotal for improvement of wheat yield potential. In this study, a recombinant inbred line (RIL) population genotyped by SNP markers was used to detect quantitative trait loci (QTL) related to yield-related traits in ten environments. A total of 102 QTL were detected, including 33 environmentally stable QTL and 69 putative QTL. Among them, Qmt.nwafu.3D was identified as a pleiotropic QTL in the physical interval of 149.77-154.11 Mb containing a potential candidate gene TaGS5-3D. An SNP (T > C) was detected in its ninth intron, and TaGS5-3D-C was validated as a superior allele associated with larger grains using a CAPS marker. Interestingly, we found that TaGS5-3D-C was closely related to significantly up-regulated expression of intron-retained transcript (TaGS5-3D-PD34.1), while TaGS5-3D-T was related to dominant expression of normal splicing transcript (TaGS5-3D-MY47.1). Our results indicated that alternative splicing associated with the SNP T/C could be involved in the regulation of grain-related traits, laying a foundation for the functional analysis of TaGS5-3D and its greater potential application in high-yield wheat breeding.

Genetic mapping and identification of Rht8-B1 that regulates plant height in wheat

BACKGROUND

Plant height (PH) and spike compactness (SC) are important agronomic traits that affect yield improvement in wheat crops. The identification of the loci or genes responsible for these traits is thus of great importance for marker-assisted selection in wheat breeding.

RESULTS

In this study, we used a recombinant inbred line (RIL) population with 139 lines derived from a cross between the mutant Rht8-2 and the local wheat variety NongDa5181 (ND5181) to construct a high-density genetic linkage map by applying the Wheat 40 K Panel. We identified seven stable QTLs for PH (three) and SC (four) in two environments using the RIL population, and found that Rht8-B1 is the causal gene of qPH2B.1 by further genetic mapping, gene cloning and gene editing analyses. Our results also showed that two natural variants from GC to TT in the coding region of Rht8-B1 resulted in an amino acid change from G (ND5181) to V (Rht8-2) at the 175^th position, reducing PH by 3.6%~6.2% in the RIL population. Moreover, gene editing analysis suggested that the height of T₂ generation in Rht8-B1 edited plants was reduced by 5.6%, and that the impact of Rht8-B1 on PH was significantly lower than Rht8-D1. Additionally, analysis of the distribution of Rht8-B1 in various wheat resources suggested that the Rht8-B1b allele has not been widely utilized in modern wheat breeding.

CONCLUSIONS

The combination of Rht8-B1b with other favorable Rht genes might be an alternative approach for developing lodging-resistant crops. Our study provides important information for marker-assisted selection in wheat breeding.

Identification and validation of quantitative trait loci for fertile spikelet number per spike and grain number per fertile spikelet in bread wheat (Triticum aestivum L.)

A major and stable QTL for fertile spikelet number per spike and grain number per fertile spikelet identified in a 4.96-Mb interval on chromosome 2A was validated in different genetic backgrounds. Fertile spikelet number per spike (FSN) and grain number per fertile spikelet (GNFS) contribute greatly to wheat yield improvement. To detect quantitative trait loci (QTL) associated with FSN and GNFS, we used a recombinant inbred line population crossed by Zhongkemai 13F10 and Chuanmai 42 in eight environments. Two Genomic regions associated with FSN were detected on chromosomes 2A and 6A using bulked segregant exome sequencing analysis. After the genetic linkage maps were constructed, four QTL QFsn.cib-2A, QFsn.cib-6A, QGnfs.cib-2A and QGnfs.cib-6A were identified in three or more environments. Among them, two major QTL QFsn.cib-2A (LOD = 4.67-9.34, PVE = 6.66-13.05%) and QGnfs.cib-2A (LOD = 5.27-11.68, PVE = 7.95-16.71%) were detected in seven and six environments, respectively. They were co-located in the same region, namely QFsn/Gnfs.cib-2A. The developed linked Kompetitive Allele Specific PCR (KASP) markers further validated this QTL in a different genetic background. QFsn/Gnfs.cib-2A showed pleiotropic effects on grain number per spike (GNS) and spike compactness (SC), and had no effect on grain weight. Since QFsn/Gnfs.cib-2A might be a new locus, it and the developed KASP markers can be used in wheat breeding. According to haplotype analysis, QFsn/Gnfs.cib-2A was identified as a target of artificial selection during wheat improvement. Based on haplotype analysis, sequence differences, spatiotemporal expression patterns, and gene annotation, the potential candidate genes for QFsn/Gnfs.cib-2A were predicted. These results provide valuable information for fine mapping and cloning gene(s) underlying QFsn/Gnfs.cib-2A.

A Wheat 660 K SNP array-based high-density genetic map facilitates QTL mapping of flag leaf-related traits in wheat

A high-density genetic map containing 122,620 SNP markers was constructed, which facilitated the identification of eight major flag leaf-related QTL in relatively narrow intervals. The flag leaf plays an important role in photosynthetic capacity and yield potential in wheat. In this study, we used a recombinant inbred line population containing 188 lines derived from a cross between 'Lankao86' (LK86) and 'Ermangmai' to construct a genetic map using the Wheat 660 K single-nucleotide polymorphism (SNP) array. The high-density genetic map contains 122,620 SNP markers spanning 5185.06 cM. It shows good collinearity with the physical map of Chinese Spring and anchors multiple sequences of previously unplaced scaffolds onto chromosomes. Based on the high-density genetic map, we identified seven, twelve, and eight quantitative trait loci (QTL) for flag leaf length (FLL), width (FLW), and area (FLA) across eight environments, respectively. Among them, three, one, and four QTL for FLL, FLW, and FLA are major and stably express in more than four environments. The physical distance between the flanking markers for QFll.igdb-3B/QFlw.igdb-3B/QFla.igdb-3B is only 444 kb containing eight high confidence genes. These results suggested that we could directly map the candidate genes in a relatively small region by the high-density genetic map constructed with the Wheat 660 K array. Furthermore, the identification of environmentally stable QTL for flag leaf morphology laid a foundation for the following gene cloning and flag leaf morphology improvement.

QTL cluster analysis and marker development for kernel traits based on DArT markers in spring bread wheat (Triticum aestivum L.)

Genetic dissection of yield component traits including kernel characteristics is essential for the continuous improvement in wheat yield. In the present study, one recombinant inbred line (RIL) F₆ population derived from a cross between Avocet and Chilero was used to evaluate the phenotypes of kernel traits of thousand-kernel weight (TKW), kernel length (KL), and kernel width (KW) in four environments at three experimental stations during the 2018-2020 wheat growing seasons. The high-density genetic linkage map was constructed with the diversity arrays technology (DArT) markers and the inclusive composite interval mapping (ICIM) method to identify the quantitative trait loci (QTLs) for TKW, KL, and KW. A total of 48 QTLs for three traits were identified in the RIL population on the 21 chromosomes besides 2A, 4D, and 5B, accounting for 3.00%-33.85% of the phenotypic variances. Based on the physical positions of each QTL, nine stable QTL clusters were identified in the RILs, and among these QTL clusters, TaTKW-1A was tightly linked to the DArT marker interval 3950546-1213099, explaining 10.31%-33.85% of the phenotypic variances. A total of 347 high-confidence genes were identified in a 34.74-Mb physical interval. TraesCS1A02G045300 and TraesCS1A02G058400 were among the putative candidate genes associated with kernel traits, and they were expressed during grain development. Moreover, we also developed high-throughput kompetitive allele-specific PCR (KASP) markers of TaTKW-1A, validated in a natural population of 114 wheat varieties. The study provides a basis for cloning the functional genes underlying the QTL for kernel traits and a practical and accurate marker for molecular breeding.

Deciphering key genomic regions controlling flag leaf size in wheat via integration of meta-QTL and in silico transcriptome assessment

BACKGROUND

Grain yield is a complex and polygenic trait influenced by the photosynthetic source-sink relationship in wheat. The top three leaves, especially the flag leaf, are considered the major sources of photo-assimilates accumulated in the grain. Determination of significant genomic regions and candidate genes affecting flag leaf size can be used in breeding for grain yield improvement.

RESULTS

With the final purpose of understanding key genomic regions for flag leaf size, a meta-analysis of 521 initial quantitative trait loci (QTLs) from 31 independent QTL mapping studies over the past decades was performed, where 333 loci eventually were refined into 64 meta-QTLs (MQTLs). The average confidence interval (CI) of these MQTLs was 5.28 times less than that of the initial QTLs. Thirty-three MQTLs overlapped the marker trait associations (MTAs) previously reported in genome-wide association studies (GWAS) for flag leaf traits in wheat. A total of 2262 candidate genes for flag leaf size, which were involved in the peroxisome, basal transcription factor, and tyrosine metabolism pathways were identified in MQTL regions by the in silico transcriptome assessment. Of these, the expression analysis of the available genes revealed that 134 genes with > 2 transcripts per million (TPM) were highly and specifically expressed in the leaf. These candidate genes could be critical to affect flag leaf size in wheat.

CONCLUSIONS

The findings will make further insight into the genetic determinants of flag leaf size and provide some reliable MQTLs and putative candidate genes for the genetic improvement of flag leaf size in wheat.

Identification of quantitative trait loci (QTL) and meta-QTL analysis for kernel size-related traits in wheat (Triticum aestivum L.)

BACKGROUND

Kernel size-related traits, including kernel length (KL), kernel width (KW), kernel diameter ratio (KDR) and kernel thickness (KT), are critical determinants for wheat kernel weight and yield and highly governed by a type of quantitative genetic basis. Genome-wide identification of major and stable quantitative trait loci (QTLs) and functional genes are urgently required for genetic improvement in wheat kernel yield. A hexaploid wheat population consisting of 120 recombinant inbred lines was developed to identify QTLs for kernel size-related traits under different water environments. The meta-analysis and transcriptome evaluation were further integrated to identify major genomic regions and putative candidate genes.

RESULTS

The analysis of variance (ANOVA) revealed more significant genotypic effects for kernel size-related traits, indicating the moderate to high heritability of 0.61-0.89. Thirty-two QTLs for kernel size-related traits were identified, explaining 3.06%-14.2% of the phenotypic variation. Eleven stable QTLs were detected in more than three water environments. The 1103 original QTLs from the 34 previous studies and the present study were employed for the MQTL analysis and refined into 58 MQTLs. The average confidence interval of the MQTLs was 3.26-fold less than that of the original QTLs. The 1864 putative candidate genes were mined within the regions of 12 core MQTLs, where 70 candidate genes were highly expressed in spikes and kernels by comprehensive analysis of wheat transcriptome data. They were involved in various metabolic pathways, such as carbon fixation in photosynthetic organisms, carbon metabolism, mRNA surveillance pathway, RNA transport and biosynthesis of secondary metabolites.

CONCLUSIONS

Major genomic regions and putative candidate genes for kernel size-related traits in wheat have been revealed by an integrative strategy with QTL linkage mapping, meta-analysis and transcriptomic assessment. The findings provide a novel insight into understanding the genetic determinants of kernel size-related traits and will be useful for the marker-assisted selection of high yield in wheat breeding.

Amand P, Bai G, Baker J, Baker S, Liu S. QTL mapping of yield components and kernel traits in wheat cultivars TAM 112 and Duster

In the Southern Great Plains, wheat cultivars have been selected for a combination of outstanding yield and drought tolerance as a long-term breeding goal. To understand the underlying genetic mechanisms, this study aimed to dissect the quantitative trait loci (QTL) associated with yield components and kernel traits in two wheat cultivars `TAM 112' and `Duster' under both irrigated and dryland environments. A set of 182 recombined inbred lines (RIL) derived from the cross of TAM 112/Duster were planted in 13 diverse environments for evaluation of 18 yield and kernel related traits. High-density genetic linkage map was constructed using 5,081 single nucleotide polymorphisms (SNPs) from genotyping-by-sequencing (GBS). QTL mapping analysis detected 134 QTL regions on all 21 wheat chromosomes, including 30 pleiotropic QTL regions and 21 consistent QTL regions, with 10 QTL regions in common. Three major pleiotropic QTL on the short arms of chromosomes 2B (57.5 - 61.6 Mbps), 2D (37.1 - 38.7 Mbps), and 7D (66.0 - 69.2 Mbps) colocalized with genes Ppd-B1, Ppd-D1, and FT-D1, respectively. And four consistent QTL associated with kernel length (KLEN), thousand kernel weight (TKW), plot grain yield (YLD), and kernel spike^-1 (KPS) (Qklen.tamu.1A.325, Qtkw.tamu.2B.137, Qyld.tamu.2D.3, and Qkps.tamu.6A.113) explained more than 5% of the phenotypic variation. QTL Qklen.tamu.1A.325 is a novel QTL with consistent effects under all tested environments. Marker haplotype analysis indicated the QTL combinations significantly increased yield and kernel traits. QTL and the linked markers identified in this study will facilitate future marker-assisted selection (MAS) for pyramiding the favorable alleles and QTL map-based cloning.

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.

Genome-wide meta-analysis of QTL for morphological related traits of flag leaf in bread wheat

Flag leaf is an important organ for photosynthesis of wheat plants, and a key factor affecting wheat yield. In this study, quantitative trait loci (QTL) for flag leaf morphological traits in wheat reported since 2010 were collected to investigate the genetic mechanism of these traits. Integration of 304 QTLs from various mapping populations into a high-density consensus map composed of various types of molecular markers as well as QTL meta-analysis discovered 55 meta-QTLs (MQTL) controlling morphological traits of flag leaves, of which 10 MQTLs were confirmed by GWAS. Four high-confidence MQTLs (MQTL-1, MQTL-11, MQTL-13, and MQTL-52) were screened out from 55 MQTLs, with an average confidence interval of 0.82 cM and a physical distance of 9.4 Mb, according to the definition of hcMQTL. Ten wheat orthologs from rice (7) and Arabidopsis (3) that regulated leaf angle, development and morphogenesis traits were identified in the hcMQTL region using comparative genomics, and were speculated to be potential candidate genes regulating flag leaf morphological traits in wheat. The results from this study provides valuable information for fine mapping and molecular markers assisted selection to improve morphological characters in wheat flag leaf.

Genetic identification of the pleiotropic gene Tasg-D1/2 affecting wheat grain shape by regulating brassinolide metabolism

Improving yield potential is a major goal of wheat breeding that depends on identifying key genetic loci. In this study, two residual heterozygous line RHL351- and RHL78-derived populations were employed for genetic linkage map construction and QTL detection. Two genetic populations indicated a robust grain-size QTL between Marker6 and Marker10. It covered a 95.54-99.38 Mb physical interval and was named Qpleio.nwafu.3D, containing the candidate gene Tasg (TraesCS3D02G137200). Intriguingly, RNA-seq analysis and sequencing revealed two different allelic variants in Tasg, named Tasg-D1 (G>A) and Tasg-D2 (C>G), respectively. Although the relationship between Tasg-D1 and grain size had been demonstrated previously, here we provided the first genetic evidence that C/G allelic variation in Tasg-D2 was associated with grain shape and size through a newly developed dCAPS marker. In addition, transcriptome comparison indicated that Tasg-D1/2 might primarily contribute to significant expression differences in brassinolide (BR) metabolism-related genes rather than those related to BR responses in developing grains and spikes. Our study provided new evidence and a breeder-friendly dCAPS marker for improving grain size through the selection of Tasg, as well as a basis to understand Tasg function in the future.

Identification of genetic loci for flag-leaf-related traits in wheat (Triticum aestivum L.) and their effects on grain yield

Flag-leaf-related traits including length (FLL), width (FLW), area (FLA), thickness (FLT), and volume (FLV) of flag leaves are the most important determinants of plant architecture and yield in wheat. Understanding the genetic basis of these traits could accelerate the breeding of high yield wheat varieties. In this study, we constructed a doubled haploid (DH) population and analyzed flag-leaf-related traits in five experimental locations/years using the wheat 90K single-nucleotide polymorphism array. It's worth noting that a novel method was used to measure FLT and FLV easily. Leaf thickness at two-thirds of the leaf length from tip to collar represented the average leaf thickness as measured with freehand sections and was used to calculate the leaf volume. In addition, flag-leaf-related traits showed positive correlations with yield related traits under two different water regimes. A total of 79 quantitative trait loci (QTL) controlling the five traits were detected among all chromosomes except 4D and 5A, explaining 3.09-14.52% of the phenotypic variation. Among them, 15 stable QTL were identified in more than three environments, including two major QTL for FLT, six for FLW, three for FLA, two for FLT and two for FLV. DH lines with positive alleles at both QTL regions had an average FLL (9.90%), FLW (32.87%), FLT (6.62%), FLA (18.47%), and FLV (20.87%) greater than lines with contrasting alleles. QFLT-2B, QFLV-2A, and QFLV-7D were co-located with yield-related traits. The 15 QTL were validated by tightly linked kompetitive allele specific PCR (KASP) markers in a recombinant inbred line (RIL) population derived from a different cross. QFLL-4A, QFLW-4B, QFLA-5D.1, QFLA-7A, QFLA-7D.1, QFLT-2B, QFLT-6A, QFLV-2A, and QFLV-7D are likely novel loci. These results provide a better understanding of the genetic basis underlying flag-leaf-related traits. Also, target regions for fine mapping and marker-assisted selection were identified and these will be valuable for breeding high yielding bread wheat.

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.

Quantitative trait loci identification and breeding value estimation of grain weight-related traits based on a new wheat 50K single nucleotide polymorphism array-derived genetic map

Mining novel and less utilized thousand grain weight (TGW) related genes are useful for improving wheat yield. In this study, a recombinant inbred line population from a cross between Zhongkemai 138 (ZKM138, high TGW) and Chuanmai 44 (CM44, low TGW) was used to construct a new Wheat 50K SNP array-derived genetic map that spanned 1,936.59 cM and contained 4, 139 markers. Based on this map, ninety-one quantitative trait loci (QTL) were detected for eight grain-related traits in six environments. Among 58 QTLs, whose superior alleles were contributed by ZKM138, QTgw.cib-6A was a noticeable major stable QTL and was also highlighted by bulked segregant analysis with RNA sequencing (BSR-Seq). It had a pyramiding effect on TGW enhancement but no significant trade-off effect on grain number per spike or tiller number, with two other QTLs (QTgw.cib-2A.2 and QTgw.cib-6D), possibly explaining the excellent grain performance of ZKM138. After comparison with known loci, QTgw.cib-6A was deduced to be a novel locus that differed from nearby TaGW2 and TaBT1. Seven simple sequence repeat (SSR) and thirty-nine kompetitive allele-specific PCR (KASP) markers were finally developed to narrow the candidate interval of QTgw.cib-6A to 4.1 Mb. Only six genes in this interval were regarded as the most likely candidate genes. QTgw.cib-6A was further validated in different genetic backgrounds and presented 88.6% transmissibility of the ZKM138-genotype and a 16.4% increase of TGW in ZKM138 derivatives. And the geographic pattern of this locus revealed that its superior allele is present in only 6.47% of 433 Chinese modern wheat varieties, indicating its potential contribution to further high-yield 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.

Combining QTL mapping and gene co-expression network analysis for prediction of candidate genes and molecular network related to yield in wheat

BACKGROUND

Wheat (Triticum aestivum L.) is an important cereal crop. Increasing grain yield for wheat is always a priority. Due to the complex genome of hexaploid wheat with 21 chromosomes, it is difficult to identify underlying genes by traditional genetic approach. The combination of genetics and omics analysis has displayed the powerful capability to identify candidate genes for major quantitative trait loci (QTLs), but such studies have rarely been carried out in wheat. In this study, candidate genes related to yield were predicted by a combined use of linkage mapping and weighted gene co-expression network analysis (WGCNA) in a recombinant inbred line population.

RESULTS

QTL mapping was performed for plant height (PH), spike length (SL) and seed traits. A total of 68 QTLs were identified for them, among which, 12 QTLs were stably identified across different environments. Using RNA sequencing, we scanned the 99,168 genes expression patterns of the whole spike for the recombinant inbred line population. By the combined use of QTL mapping and WGCNA, 29, 47, 20, 26, 54, 46 and 22 candidate genes were predicted for PH, SL, kernel length (KL), kernel width, thousand kernel weight, seed dormancy, and seed vigor, respectively. Candidate genes for different traits had distinct preferences. The known PH regulation genes Rht-B and Rht-D, and the known seed dormancy regulation genes TaMFT can be selected as candidate gene. Moreover, further experiment revealed that there was a SL regulatory QTL located in an interval of about 7 Mbp on chromosome 7A, named TaSL1, which also involved in the regulation of KL.

CONCLUSIONS

A combination of QTL mapping and WGCNA was applied to predicted wheat candidate genes for PH, SL and seed traits. This strategy will facilitate the identification of candidate genes for related QTLs in wheat. In addition, the QTL TaSL1 that had multi-effect regulation of KL and SL was identified, which can be used for wheat improvement. These results provided valuable molecular marker and gene information for fine mapping and cloning of the yield-related trait loci in the future.

A wiring diagram to integrate physiological traits of wheat yield potential

As crop yields are pushed closer to biophysical limits, achieving yield gains becomes increasingly challenging and will require more insight into deterministic pathways to yields. Here, we propose a wiring diagram as a platform to illustrate the interrelationships of the physiological traits that impact wheat yield potential and to serve as a decision support tool for crop scientists. The wiring diagram is based on the premise that crop yield is a function of photosynthesis (source), the investment of assimilates into reproductive organs (sinks) and the underlying processes that enable expression of both. By illustrating these linkages as coded wires, the wiring diagram can show connections among traits that may not have been apparent, and can inform new research hypotheses and guide crosses designed to accumulate beneficial traits and alleles in breeding. The wiring diagram can also serve to create an ever-richer common point of reference for refining crop models in the future.

QTL detection for bread wheat processing quality in a nested association mapping population of semi-wild and domesticated wheat varieties

BACKGROUND

Wheat processing quality is an important factor in evaluating overall wheat quality, and dough characteristics are important when assessing the processing quality of wheat. As a notable germplasm resource, semi-wild wheat has a key role in the study of wheat processing quality.

RESULTS

In this study, four dough rheological characteristics were collected in four environments using a nested association mapping (NAM) population consisting of semi-wild and domesticated wheat varieties to identify quantitative trait loci (QTL) for wheat processing quality. A total of 49 QTL for wheat processing quality were detected, explaining 0.36-10.82% of the phenotypic variation. These QTL were located on all wheat chromosomes except for 2D, 3A, 3D, 6B, 6D and 7D. Compared to previous studies, 29 QTL were newly identified. Four novel QTL, QMlPH-1B.4, QMlPH-3B.4, QWdEm-1B.2 and QWdEm-3B.2, were stably identified in three or more environments, among which QMlPH-3B.4 was a major QTL. Moreover, eight important genetic regions for wheat processing quality were identified on chromosomes 1B, 3B and 4D, which showed pleiotropy for dough characteristics. In addition, out of 49 QTL, 15 favorable alleles came from three semi-wild parents, suggesting that the QTL alleles provided by the semi-wild parent were not utilized in domesticated varieties.

CONCLUSIONS

The results show that semi-wild wheat varieties can enrich the existing wheat gene pool and provide broader variation resources for wheat genetic research.

Meta-QTLs, ortho-meta-QTLs and candidate genes for grain yield and associated traits in wheat (Triticum aestivum L.)

In wheat, 2852 major QTLs of 8998 QTLs available for yield and related traits were used for meta-analysis; 141 meta-QTLs were identified, which included 13 breeder's MQTLs and 24 ortho-MQTLs; 1202 candidate genes and 50 homologues of genes for yield from other cereals were also identified. Meta-QTL analysis was conducted using 2852 of the 8998 known QTLs, retrieved from 230 reports published during 1999-2020 (including 19 studies on tetraploid wheat) for grain yield (GY) and the following ten component traits: (i) grain weight (GWei), (ii) grain morphology-related traits (GMRTs), (iii) grain number (GN), (iv) spikes-related traits (SRTs), (v) plant height (PH), (vi) tiller number (TN), (vii) harvest index (HI), (viii) biomass yield (BY), (ix) days to heading/flowering and maturity (DTH/F/M), and (x) grain filling duration (GFD). The study resulted in the identification of 141 meta-QTLs (MQTLs), with an average confidence interval (CI) of 1.4 cM as against a CI of > 12.1 cM (8.8 fold reduction) in the QTLs that were used. The corresponding physical length of CI ranged from 0.01 Mb to 661.9 Mb (mean, 31.5 Mb). Seventy-seven (77) of these 141 MQTLs overlapped marker-trait associations (MTAs) reported in genome-wide association studies. Also, 63 MQTLs (each based on at least 10 QTLs) were considered stable and robust, with 13 MQTLs described as breeder's MQTLs (selected based on small CI, large LOD, and high level of phenotypic variation explained). Thirty-five yield-related genes from rice, barley, and maize were also utilized to identify 50 wheat homologues in MQTLs. Further, the use of synteny and collinearity allowed the identification of 24 ortho-MQTLs which were common among the wheat, barley, rice, and maize. The results of the present study should prove useful for wheat breeding and future basic research in cereals including wheat, barley, rice, and maize.

Identification of quantitative trait loci (QTLs) and candidate genes of seed Iron and zinc content in soybean [Glycine max (L.) Merr.]

BACKGROUND

Deciphering the hereditary mechanism of seed iron (Fe) and zinc (Zn) content in soybean is important and sustainable to address the "hidden hunger" that presently affects approximately 2 billion people worldwide. Therefore, in order to detect genomic regions related to soybean seed Fe and Zn content, a recombinant inbred line (RIL) population with 248 lines was assessed in four environments to detect Quantitative Trait Loci (QTLs) related to soybean seed Fe and Zn content.

RESULT

Wide variation was found in seed Fe and Zn content in four environments, and genotype, environment, and genotype × environment interactions had significant influences on both the seed Fe and Zn content. A positive correlation was observed between seed Fe content and seed Zn content, and broad-sense heritability (H2) of seed Fe and Zn content were 0.73 and 0.75, respectively. In this study, five QTLs for seed Fe content were detected with 4.57 - 32.71% of phenotypic variation explained (PVE) and logarithm of odds (LOD) scores ranging from 3.60 to 33.79. Five QTLs controlling the seed Zn content were detected, and they individually explained 3.35 to 26.48% of the phenotypic variation, with LOD scores ranging from 3.64 to 20.4. Meanwhile, 409,541 high-quality single-nucleotide variants (SNVs) and 85,102 InDels (except intergenic regions) between two bi-parental lines were identified by whole genome resequencing. A total of 12 candidate genes were reported in one major QTL for seed Fe content and two major QTLs for seed Zn content, with the help of RNA-Seq analysis, gene ontology (GO) enrichment, gene annotation, and bi-parental whole genome sequencing (WGS) data.

CONCLUSIONS

Limited studies were performed about microelement of soybean, so these results may play an important role in the biofortification of Fe and Zn and accelerate the development of marker-assisted selection (MAS) for breeding soybeans fortified with iron and zinc.

Genetic dissection of quantitative trait loci for grain size and weight by high-resolution genetic mapping in bread wheat (Triticum aestivum L.)

Six major QTLs for wheat grain size and weight were identified on chromosomes 4A, 4B, 5A and 6A across multiple environments, and were validated in different genetic backgrounds. Grain size and weight are crucial components of wheat yield. Dissection of their genetic control is thus essential for the improvement of yield potential in wheat breeding. We used a doubled haploid (DH) population to detect quantitative trait loci (QTLs) for grain width (GW), grain length (GL), and thousand grain weight (TGW) in five environments. Six major QTLs, QGw.cib-4B.2, QGl.cib-4A, QGl.cib-5A.1, QGl.cib-6A, QTgw.cib-4B, and QTgw.cib-5A, were consistently identified in at least three individual environments and in best linear unbiased prediction (BLUP) datasets, and explained 5.65-34.06% of phenotypic variation. QGw.cib-4B.2, QTgw.cib-4B, QGl.cib-5A.1 and QGl.cib-6A had no effect on grain number per spike (GNS). In addition to QGl.cib-4A, the other major QTLs were further validated by using Kompetitive Allele Specific PCR (KASP) markers in different genetic backgrounds. Moreover, significant interactions between the three major GL QTLs and two major TGW QTLs were observed. Comparison analysis showed that QGl.cib-5A.1 and QGl.cib-6A are likely new loci. Notably, QGw.cib-4B.2 and QTgw.cib-4B were co-located on chromosome 4B and improved TGW by increasing only GW, unlike nearby or overlapped loci reported previously. Three genes associated with grain development within the QGw.cib-4B.2/QTgw.cib-4B interval were identified by searches on sequence similarity, spatial expression patterns, and orthologs. The major QTLs and KASP markers reported here will be useful for elucidating the genetic architecture of grain size and weight and for developing new wheat cultivars with high and stable yield.

Raise and characterization of a bread wheat hybrid line (Tulaykovskaya 10 × Saratovskaya 29) with chromosome 6Agi2 introgressed from Thinopyrum intermedium

Wheatgrass Thinopyrum intermedium is a source of agronomically valuable traits for common wheat. Partial wheat–wheatgrass amphidiploids and lines with wheatgrass chromosome substitutions are extensively used as intermediates in breeding programs. Line Agis 1 (6Agi2/6D) is present in the cultivar Tulaykovskaya 10 pedigree. Wheatgrass chromosome 6Agi2 carries multiple resistance to fungal diseases in various ecogeographical zones. In this work, we studied the transfer of chromosome 6Agi2 in hybrid populations Saratovskaya 29 × skaya 10 (S29 × T10) and Tulaykovskaya 10 × Saratovskaya 29 (T10 × S29). Chromosome 6Agi2 was identif ied by PCR with chromosome-specif ic primers and by genomic in situ hybridization (GISH). According to molecular data, 6Agi2 was transmitted to nearly half of the plants tested in the F2 and F3 generations. A new breeding line 49-14 (2n = 42) with chromosome pair 6Agi2 was isolated and characterized in T10 × S29 F5 by GISH. According to the results of our f ield experiment in 2020, the line had high productivity traits. The grain weights per plant (10.04 ± 0.93 g) and the number of grains per plant (259.36 ± 22.49) did not differ signif icantly from the parent varieties. The number of grains per spikelet in the main spike was signif icantly higher than in S29 ( p ≤ 0.001) or T10 ( p ≤ 0.05). Plants were characterized by the ability to set 3.77 ± 0.1 grains per spikelet, and this trait varied among individuals from 2.93 to 4.62. The grain protein content was 17.91 %, and the gluten content, 40.55 %. According to the screening for fungal disease resistance carried out in the f ield in 2018 and 2020, chromosome 6Agi2 makes plants retain immunity to the West Siberian population of brown rust and to dominant races of stem rust. It also provides medium resistant and medium susceptible types of response to yellow rust. The possibility of using lines/varieties of bread wheat with wheatgrass chromosomes 6Agi2 in breeding in order to increase protein content in the grain, to confer resistance to leaf diseases on plants and to create multif lowered forms is discussed.

Yield-Related QTL Clusters and the Potential Candidate Genes in Two Wheat DH Populations

In the present study, four large-scale field trials using two doubled haploid wheat populations were conducted in different environments for two years. Grain protein content (GPC) and 21 other yield-related traits were investigated. A total of 227 QTL were mapped on 18 chromosomes, which formed 35 QTL clusters. The potential candidate genes underlying the QTL clusters were suggested. Furthermore, adding to the significant correlations between yield and its related traits, correlation variations were clearly shown within the QTL clusters. The QTL clusters with consistently positive correlations were suggested to be directly utilized in wheat breeding, including 1B.2, 2A.2, 2B (4.9–16.5 Mb), 2B.3, 3B (68.9–214.5 Mb), 4A.2, 4B.2, 4D, 5A.1, 5A.2, 5B.1, and 5D. The QTL clusters with negative alignments between traits may also have potential value for yield or GPC improvement in specific environments, including 1A.1, 2B.1, 1B.3, 5A.3, 5B.2 (612.1–613.6 Mb), 7A.1, 7A.2, 7B.1, and 7B.2. One GPC QTL (5B.2: 671.3–672.9 Mb) contributed by cultivar Spitfire was positively associated with nitrogen use efficiency or grain protein yield and is highly recommended for breeding use. Another GPC QTL without negatively pleiotropic effects on 2A (50.0–56.3 Mb), 2D, 4D, and 6B is suggested for quality wheat breeding.

Identification and validation of two major QTLs for spike compactness and length in bread wheat (Triticum aestivum L.) showing pleiotropic effects on yield-related traits

Two major and stable QTLs for spike compactness and length were detected and validated in multiple genetic backgrounds and environments, and their pleiotropic effects on yield-related traits were analyzed. Spike compactness (SC) and length (SL) are greatly associated with wheat (Triticum aestivum L.) grain yield. To detect quantitative trait loci (QTL) associated with SC and SL, two biparental populations derived from crosses of Chuanmai42/Kechengmai1 and Chuanmai42/Chuannong16 were employed to perform QTL mapping in five environments. A total of 34 QTLs were identified, in which six major QTLs were repeatedly detected in more than four environments and the best linear unbiased prediction datasets, explaining 7.13-33.6% of phenotypic variation. These major QTLs were co-located in two genomic regions on chromosome 5A and 6A, namely QSc/Sl.cib-5A and QSc/Sl.cib-6A, respectively. By developing kompetitive allele-specific PCR (KASP) markers that linked to them, the two loci were validated in different genetic backgrounds, and their interactions were also analyzed. Comparison analysis showed that QSc/Sl.cib-5A was not Vrn-A1 and Q, and QSc/Sl.cib-6A was likely a new locus for SC and SL. Both QSc/Sl.cib-5A and QSc/Sl.cib-6A had pleiotropic effects on other yield-related traits including plant height, thousand grain weight and grain length. Therefore, the two loci combined with the developed KASP markers might be potentially applicable in wheat breeding. Furthermore, based on the spatiotemporal expression patterns, gene annotation, orthologous search and sequence differences, TraesCS5A01G301400 and TraesCS6A01G090300 were considered as potential candidates for QSc/Sl.cib-5A and QSc/Sl.cib-6A, respectively. These results provided valuable information for fine mapping and cloning of the two loci in the future.

Genetic dissection of grain architecture-related traits in a winter wheat population

BACKGROUND

The future productivity of wheat (T. aestivum L.) as the most grown crop worldwide is of utmost importance for global food security. Thousand kernel weight (TKW) in wheat is closely associated with grain architecture-related traits, e.g. kernel length (KL), kernel width (KW), kernel area (KA), kernel diameter ratio (KDR), and factor form density (FFD). Discovering the genetic architecture of natural variation in these traits, identifying QTL and candidate genes are the main aims of this study. Therefore, grain architecture-related traits in 261 worldwide winter accessions over three field-year experiments were evaluated.

RESULTS

Genome-wide association analysis using 90K SNP array in FarmCPU model revealed several interesting genomic regions including 17 significant SNPs passing false discovery rate threshold and strongly associated with the studied traits. Four of associated SNPs were physically located inside candidate genes within LD interval e.g. BobWhite_c5872_589 (602,710,399 bp) found to be inside TraesCS6A01G383800 (602,699,767-602,711,726 bp). Further analysis reveals the four novel candidate genes potentially involved in more than one grain architecture-related traits with a pleiotropic effects e.g. TraesCS6A01G383800 gene on 6A encoding oxidoreductase activity was associated with TKW and KA. The allelic variation at the associated SNPs showed significant differences betweeen the accessions carying the wild and mutated alleles e.g. accessions carying C allele of BobWhite_c5872_589, TraesCS6A01G383800 had significantly higher TKW than the accessions carying T allele. Interestingly, these genes were highly expressed in the grain-tissues, demonstrating their pivotal role in controlling the grain architecture.

CONCLUSIONS

These results are valuable for identifying regions associated with kernel weight and dimensions and potentially help breeders in improving kernel weight and architecture-related traits in order to increase wheat yield potential and end-use quality.

Identification of a major and stable QTL on chromosome 5A confers spike length in wheat (Triticum aestivum L.)

Spike length (SL) is the key determinant of plant architecture and yield potential. In this study, 193 recombinant inbred lines (RILs) derived from a cross between 13F10 and Chuanmai 42 (CM42) were evaluated for spike length in six environments. Sixty RILs consisting of 30 high and 30 low SLs were genotyped using the bulked segregant analysis exome sequencing (BSE-Seq) analysis for preliminary quantitative trait locus (QTL) mapping. A 6.69 Mb (518.43-525.12 Mb) region on chromosome 5AL was found to have a significant effect on the SL trait. Fifteen competitive allele-specific PCR (KASP) markers were successfully converted from the single nucleotide polymorphisms (SNPs) in the SL target region. Combined with four novel simple sequence repeat (SSR) markers, a genetic linkage map spanning 21.159 cM was constructed. The mapping result confirmed the identity of a major and stable QTL named QSl.cib-5A in the targeted region that explained 7.88-26.60% of the phenotypic variation in SL. QSl.cib-5A was narrowed to a region of 4.84 cM interval corresponding to a 4.67 Mb (516.60-521.27 Mb) physical region in the Chinese Spring RefSeq v2.0 containing 17 high-confidence genes with 25 transcripts. In addition, this QTL exhibited pleiotropic effects on spikelet density (SD), with the phenotypic variances proportion ranging from 11.34 to 19.92%. This study provides a foundational step for cloning the QSl.cib-5A, which is involved in the regulation of spike morphology in common wheat.

Supplementary Information

The online version contains supplementary material available at 10.1007/s11032-021-01249-6.

Construction of Consensus Genetic Map With Applications in Gene Mapping of Wheat (Triticum aestivum L.) Using 90K SNP Array

Wheat is one of the most important cereal crops worldwide. A consensus map combines genetic information from multiple populations, providing an effective alternative to improve the genome coverage and marker density. In this study, we constructed a consensus map from three populations of recombinant inbred lines (RILs) of wheat using a 90K single nucleotide polymorphism (SNP) array. Phenotypic data on plant height (PH), spike length (SL), and thousand-kernel weight (TKW) was collected in six, four, and four environments in the three populations, and then used for quantitative trait locus (QTL) mapping. The mapping results obtained using the constructed consensus map were compared with previous results obtained using individual maps and previous studies on other populations. A simulation experiment was also conducted to assess the performance of QTL mapping with the consensus map. The constructed consensus map from the three populations spanned 4558.55 cM in length, with 25,667 SNPs, having high collinearity with physical map and individual maps. Based on the consensus map, 21, 27, and 19 stable QTLs were identified for PH, SL, and TKW, much more than those detected with individual maps. Four PH QTLs and six SL QTLs were likely to be novel. A putative gene called TraesCS4D02G076400 encoding gibberellin-regulated protein was identified to be the candidate gene for one major PH QTL located on 4DS, which may enrich genetic resources in wheat semi-dwarfing breeding. The simulation results indicated that the length of the confidence interval and standard errors of the QTLs detected using the consensus map were much smaller than those detected using individual maps. The consensus map constructed in this study provides the underlying genetic information for systematic mapping, comparison, and clustering of QTL, and gene discovery in wheat genetic study. The QTLs detected in this study had stable effects across environments and can be used to improve the wide adaptation of wheat cultivars through marker-assisted breeding.

Genetic variability assessment of 127 Triticum turgidum L. accessions for mycorrhizal susceptibility-related traits detection

Positive effects of arbuscular mycorrhizal fungi (AMF)-wheat plant symbiosis have been well discussed by research, while the actual role of the single wheat genotype in establishing this type of association is still poorly investigated. In this work, the genetic diversity of Triticum turgidum wheats was exploited to detect roots susceptibility to AMF and to identify genetic markers in linkage with chromosome regions involved in this symbiosis. A tetraploid wheat collection of 127 accessions was genotyped using 35K single-nucleotide polymorphism (SNP) array and inoculated with the AMF species Funneliformis mosseae (F. mosseae) and Rhizoglomus irregulare (R. irregulare), and a genome-wide association study (GWAS) was conducted. Six clusters of genetically related accessions were identified, showing a different mycorrhizal colonization among them. GWAS revealed four significant quantitative trait nucleotides (QTNs) involved in mycorrhizal symbiosis, located on chromosomes 1A, 2A, 2B and 6A. The results of this work enrich future breeding activities aimed at developing new grains on the basis of genetic diversity on low or high susceptibility to mycorrhization, and, possibly, maximizing the symbiotic effects.

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.

Major QTL for Seven Yield-Related Traits in Common Wheat (Triticum aestivum L.)

Flag leaves, plant height (PH), and spike-related traits are key determinants contributing to yield potential in wheat. In this study, we developed a recombinant inbred line (RIL) population with 94 lines derived from the cross between 'AS985472' and 'Sumai 3.' A genetic map spanned 3553.69 cM in length were constructed using 1978 DArT markers. Severn traits including flag leaf width (FLW), flag leaf length (FLL), PH, anthesis date (AD), spike length (SL), spikelet number spike (SNS), and spike density (SD) were evaluated against this RIL population under three different environments. Combined phenotypic data and genetic map, we identified quantitative trait loci (QTL) for each trait. A total of four major and stably expressed QTLs for FLW, PH, and SD were detected on chromosomes 2D and 4B. Of them, the major QTLs individually explained 10.10 - 30.68% of the phenotypic variation. QTLs with pleiotropic effects were identified on chromosomes 4A and 6D as well. Furthermore, the genetic relationships between seven yield-related traits were detected and discussed. A few genes related to leaf growth and development at the interval of a major locus for FLW on chromosome 2D were predicated. Overall, the present study provided useful information for understanding the genetic basis of yield-related traits and will be useful for marker-assisted selection in wheat breeding.

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.