Major genomic regions responsible for wheat yield and its components as revealed by meta-QTL and genotype-phenotype association analyses

GWAS in a Collection of Bulgarian Old and Modern Bread Wheat Accessions Uncovers Novel Genomic Loci for Grain Protein Content and Thousand Kernel Weight

Genetic enhancement of grain production and quality is a priority in wheat breeding projects. In this study, we assessed two key agronomic traits-grain protein content (GPC) and thousand kernel weight (TKW)-across 179 Bulgarian contemporary and historic varieties and landraces across three growing seasons. Significant phenotypic variation existed for both traits among genotypes and seasons, and no discernible difference was evident between the old and modern accessions. To understand the genetic basis of the traits, we conducted a genome-wide association study with MLM using phenotypic data from the crop seasons, best linear unbiased estimators, and genotypic data from the 25K Infinium iSelect array. As a result, we detected 16 quantitative trait nucleotides (QTNs) associated with GPC and 15 associated with TKW, all of which passed the false discovery rate threshold. Seven loci favorably influenced GPC, resulting in an increase of 1.4% to 8.1%, while four loci had a positive impact on TKW with increases ranging from 1.9% to 8.4%. While some loci confirmed previously published associations, four QTNs linked to GPC on chromosomes 2A, 7A, and 7B, as well as two QTNs related to TKW on chromosomes 1B and 6A, may represent novel associations. Annotations for proteins involved in the senescence-associated nutrient remobilization and in the following buildup of resources required for seed germination have been found for selected putative candidate genes. These include genes coding for storage proteins, cysteine proteases, cellulose-synthase, alpha-amylase, transcriptional regulators, and F-box and RWP-RK family proteins. Our findings highlight promising genomic regions for targeted breeding programs aimed at improving grain yield and protein content.

Identifying and Characterizing Candidate Genes Contributing to a Grain Yield QTL in Wheat

The current study focuses on identifying the candidate genes of a grain yield QTL from a double haploid population, Westonia × Kauz. The QTL region spans 20 Mbp on the IWGSC whole-genome sequence flank with 90K SNP markers. The IWGSC gene annotation revealed 16 high-confidence genes and 41 low-confidence genes. Bioinformatic approaches, including functional gene annotation, ontology investigation, pathway exploration, and gene network study using publicly available gene expression data, enabled the short-listing of four genes for further confirmation. Complete sequencing of those four genes demonstrated that only two genes are polymorphic between the parental cultivars, which are the ferredoxin-like protein gene and the tetratricopeptide-repeat (TPR) protein gene. The two genes were selected for downstream investigation. Two SNP variations were observed in the exon for both genes, with one SNP resulting in changes in amino acid sequence. qPCR-based gene expression showed that both genes were highly expressed in the high-yielding double haploid lines along with the parental cultivar Westonia. In contrast, their expression was significantly lower in the low-yielding lines in the other parent. It can be concluded that these two genes are the contributing genes to the grain yield QTL.

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.

Genome wide association in Spanish bread wheat landraces identifies six key genomic regions that constitute potential targets for improving grain yield related traits

KEY MESSAGE

Association mapping conducted in 189 Spanish bread wheat landraces revealed six key genomic regions that constitute stable QTLs for yield and include 15 candidate genes. Genetically diverse landraces provide an ideal population to conduct association analysis. In this study, association mapping was conducted in a collection of 189 Spanish bread wheat landraces whose genomic diversity had been previously assessed. These genomic data were combined with characterization for yield-related traits, including grain size and shape, and phenological traits screened across five seasons. The association analysis revealed a total of 881 significant marker trait associations, involving 434 markers across the genome, that could be grouped in 366 QTLs based on linkage disequilibrium. After accounting for days to heading, we defined 33 high density QTL genomic regions associated to at least four traits. Considering the importance of detecting stable QTLs, 6 regions associated to several grain traits and thousand kernel weight in at least three environments were selected as the most promising ones to harbour targets for breeding. To dissect the genetic cause of the observed associations, we studied the function and in silico expression of the 413 genes located inside these six regions. This identified 15 candidate genes that provide a starting point for future analysis aimed at the identification and validation of wheat yield related genes.

Unravelling the genetic framework associated with grain quality and yield-related traits in maize (Zea mays L.)

Maize serves as a crucial nutrient reservoir for a significant portion of the global population. However, to effectively address the growing world population's hidden hunger, it is essential to focus on two key aspects: biofortification of maize and improving its yield potential through advanced breeding techniques. Moreover, the coordination of multiple targets within a single breeding program poses a complex challenge. This study compiled mapping studies conducted over the past decade, identifying quantitative trait loci associated with grain quality and yield related traits in maize. Meta-QTL analysis of 2,974 QTLs for 169 component traits (associated with quality and yield related traits) revealed 68 MQTLs across different genetic backgrounds and environments. Most of these MQTLs were further validated using the data from genome-wide association studies (GWAS). Further, ten MQTLs, referred to as breeding-friendly MQTLs (BF-MQTLs), with a significant phenotypic variation explained over 10% and confidence interval less than 2 Mb, were shortlisted. BF-MQTLs were further used to identify potential candidate genes, including 59 genes encoding important proteins/products involved in essential metabolic pathways. Five BF-MQTLs associated with both quality and yield traits were also recommended to be utilized in future breeding programs. Synteny analysis with wheat and rice genomes revealed conserved regions across the genomes, indicating these hotspot regions as validated targets for developing biofortified, high-yielding maize varieties in future breeding programs. After validation, the identified candidate genes can also be utilized to effectively model the plant architecture and enhance desirable quality traits through various approaches such as marker-assisted breeding, genetic engineering, and genome editing.

Comprehensive meta-analysis of QTL and gene expression studies identify candidate genes associated with Aspergillus flavus resistance in maize

Aflatoxin (AF) contamination, caused by Aspergillus flavus, compromises the food safety and marketability of commodities, such as maize, cotton, peanuts, and tree nuts. Multigenic inheritance of AF resistance impedes conventional introgression of resistance traits into high-yielding commercial maize varieties. Several AF resistance-associated quantitative trait loci (QTLs) and markers have been reported from multiple biparental mapping and genome-wide association studies (GWAS) in maize. However, QTLs with large confidence intervals (CI) explaining inconsistent phenotypic variance limit their use in marker-assisted selection. Meta-analysis of published QTLs can identify significant meta-QTLs (MQTLs) with a narrower CI for reliable identification of genes and linked markers for AF resistance. Using 276 out of 356 reported QTLs controlling resistance to A. flavus infection and AF contamination in maize, we identified 58 MQTLs on all 10 chromosomes with a 66.5% reduction in the average CI. Similarly, a meta-analysis of maize genes differentially expressed in response to (a)biotic stresses from the to-date published literature identified 591 genes putatively responding to only A. flavus infection, of which 14 were significantly differentially expressed (-1.0 ≤ Log2Fc ≥ 1.0; p ≤ 0.05). Eight MQTLs were validated by their colocalization with 14 A. flavus resistance-associated SNPs identified from GWAS in maize. A total of 15 genes were physically close between the MQTL intervals and SNPs. Assessment of 12 MQTL-linked SSR markers identified three markers that could discriminate 14 and eight cultivars with resistance and susceptible responses, respectively. A comprehensive meta-analysis of QTLs and differentially expressed genes led to the identification of genes and makers for their potential application in marker-assisted breeding of A. flavus-resistant maize varieties.

Allelic variation of TaWD40-4B.1 contributes to drought tolerance by modulating catalase activity in wheat

Drought drastically restricts wheat production, so to dissect allelic variations of drought tolerant genes without imposing trade-offs between tolerance and yield is essential to cope with the circumstance. Here, we identify a drought tolerant WD40 protein encoding gene TaWD40-4B.1 of wheat via the genome-wide association study. The full-length allele TaWD40-4B.1^C but not the truncated allele TaWD40-4B.1^T possessing a nonsense nucleotide variation enhances drought tolerance and grain yield of wheat under drought. TaWD40-4B.1^C interacts with canonical catalases, promotes their oligomerization and activities, and reduces H₂O₂ levels under drought. The knock-down of catalase genes erases the role of TaWD40-4B.1^C in drought tolerance. TaWD40-4B.1^C proportion in wheat accessions is negatively correlative with the annual rainfall, suggesting this allele may be selected during wheat breeding. The introgression of TaWD40-4B.1^C enhances drought tolerance of the cultivar harboring TaWD40-4B.1^T. Therefore, TaWD40-4B.1^C could be useful for molecular breeding of drought tolerant wheat.

Genome-wide association study to identify genomic loci associated with early vigor in bread wheat under simulated water deficit complemented with quantitative trait loci meta-analysis

A genome-wide association study (GWAS) was used to identify associated loci with early vigor under simulated water deficit and grain yield under field drought in a diverse collection of Iranian bread wheat landraces. In addition, a meta-quantitative trait loci (MQTL) analysis was used to further expand our approach by retrieving already published quantitative trait loci (QTL) from recombinant inbred lines, double haploids, back-crosses, and F2 mapping populations. In the current study, around 16%, 14%, and 16% of SNPs were in significant linkage disequilibrium (LD) in the A, B, and D genomes, respectively, and varied between 5.44% (4A) and 21.85% (6A). Three main subgroups were identified among the landraces with different degrees of admixture, and population structure was further explored through principal component analysis. Our GWAS identified 54 marker-trait associations (MTAs) that were located across the wheat genome but with the highest number found in the B sub-genome. The gene ontology (GO) analysis of MTAs revealed that around 75% were located within or closed to protein-coding genes. In the MQTL analysis, 23 MQTLs, from a total of 215 QTLs, were identified and successfully projected onto the reference map. MQT-YLD4, MQT-YLD9, MQT-YLD13, MQT-YLD17, MQT-YLD18, MQT-YLD19, and MQTL-RL1 contributed to the highest number of projected QTLs and were therefore regarded as the most reliable and stable QTLs under water deficit conditions. These MQTLs greatly facilitate the identification of putative candidate genes underlying at each MQTL interval due to the reduced confidence of intervals associated with MQTLs. These findings provide important information on the genetic basis of early vigor traits and grain yield under water deficit conditions and set the foundation for future investigations into adaptation to water deficit in bread wheat.

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.

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.

QTL mapping and KASP marker development for seed vigor related traits in common wheat

Seed vigor is an important parameter of seed quality, and identification of seed vigor related genes can provide an important basis for highly efficient molecular breeding in wheat. In the present study, a doubled haploid (DH) population with 174 lines derived from a cross between Yangmai16 and Zhongmai 895 was used to evaluate 10 seed vigor related traits in Luoyang during the 2018-2019 cropping season and in Mengjin and Luoning Counties during 2019-2020 cropping season for three environments. Quantitative trait locus (QTL) mapping of 10 seed vigor related traits in the DH population resulted in the discovery/identification of 28 QTLs on chromosomes 2B, 3D, 4B, 4D, 5A, 5B, 6A, 6B, 6D, 7A and 7D, explaining 3.6-23.7% of the phenotypic variances. Among them, one QTL cluster for shoot length, root length and vigor index was mapped between AX-89421921 and Rht-D1_SNP on chromosome 4D in the physical intervals of 18.78-19.29 Mb (0.51 Mb), explaining 9.2-20.5% of the phenotypic variances. Another QTL for these traits was identified at the physical position 185.74 Mb on chromosome 5B, which was flanked by AX-111465230 and AX-109519938 and accounted for 8.0-13.3% of the phenotypic variances. Two QTLs for shoot length, shoot fresh weight and shoot dry weight were identified in the marker intervals of AX-109384026-AX-111120402 and AX-111651800-AX-94443918 on chromosomes 6A and 6B, explaining 8.2-11.7% and 3.6-10.3% of the phenotypic variance, respectively; both alleles for increasing phenotypic values were derived from Yangmai 16. We also developed the KASP markers for the QTL cluster QVI.haust-4D.1/QSL.haust-4D/QRL.haust-4D, and validated in an international panel of 135 wheat accessions. The germplasm, genes and KASP markers were developed for breeders to improve wheat varieties with seed vigor related traits.

Understanding complex genetic architecture of rice grain weight through QTL-meta analysis and candidate gene identification

Quantitative trait loci (QTL) for rice grain weight identified using bi-parental populations in various environments were found inconsistent and have a modest role in marker assisted breeding and map-based cloning programs. Thus, the identification of a consistent consensus QTL region across populations is critical to deploy in marker aided breeding programs. Using the QTL meta-analysis technique, we collated rice grain weight QTL information from numerous studies done across populations and in diverse environments to find constitutive QTL for grain weight. Using information from 114 original QTL in meta-analysis, we discovered three significant Meta-QTL (MQTL) for grain weight on chromosome 3. According to gene ontology, these three MQTL have 179 genes, 25 of which have roles in developmental functions. Amino acid sequence BLAST of these genes indicated their orthologue conservation among core cereals with similar functions. MQTL3.1 includes the OsAPX1, PDIL, SAUR, and OsASN1 genes, which are involved in grain development and have been discovered to play a key role in asparagine biosynthesis and metabolism, which is crucial for source-sink regulation. Five potential candidate genes were identified and their expression analysis indicated a significant role in early grain development. The gene sequence information retrieved from the 3 K rice genome project revealed the deletion of six bases coding for serine and alanine in the last exon of OsASN1 led to an interruption in the synthesis of α-helix of the protein, which negatively affected the asparagine biosynthesis pathway in the low grain weight genotypes. Further, the MQTL3.1 was validated using linked marker RM7197 on a set of genotypes with extreme phenotypes. MQTL that have been identified and validated in our study have significant scope in MAS breeding and map-based cloning programs for improving rice grain weight.

Genome-wide association mapping of Hagberg falling number, protein content, test weight, and grain yield in U.K. wheat

Association mapping using crop cultivars allows identification of genetic loci of direct relevance to breeding. Here, 150 U.K. wheat (Triticum aestivum L.) cultivars genotyped with 23,288 single nucleotide polymorphisms (SNPs) were used for genome-wide association studies (GWAS) using historical phenotypic data for grain protein content, Hagberg falling number (HFN), test weight, and grain yield. Power calculations indicated experimental design would enable detection of quantitative trait loci (QTL) explaining ≥20% of the variation (PVE) at a relatively high power of >80%, falling to 40% for detection of a SNP with an R² ≥ .5 with the same QTL. Genome-wide association studies identified marker-trait associations for all four traits. For HFN (h ² = .89), six QTL were identified, including a major locus on chromosome 7B explaining 49% PVE and reducing HFN by 44 s. For protein content (h ² = 0.86), 10 QTL were found on chromosomes 1A, 2A, 2B, 3A, 3B, and 6B, together explaining 48.9% PVE. For test weight, five QTL were identified (one on 1B and four on 3B; 26.3% PVE). Finally, 14 loci were identified for grain yield (h ² = 0.95) on eight chromosomes (1A, 2A, 2B, 2D, 3A, 5B, 6A, 6B; 68.1% PVE), of which five were located within 16 Mbp of genetic regions previously identified as under breeder selection in European wheat. Our study demonstrates the utility of exploiting historical crop datasets, identifying genomic targets for independent validation, and ultimately for wheat genetic improvement.

Transcriptome Analyses of Near Isogenic Lines Reveal Putative Drought Tolerance Controlling Genes in Wheat

Drought stress, especially at the grain-filling stage, is a major constraint for wheat production. Drought tolerance is a complex trait controlled by a large array of genes and pathways. This study conducted gene expression profiling on two pairs of near-isogenic lines (NILs) for an important qDSI.4B.1 QTL conferring drought tolerance on the short arm of chromosome 4B in wheat. Analysis showed 1,614 genome-wide differentially expressed genes (DEGs) between the tolerant and susceptible isolines in both NIL pairs. Six common DEGs were found between NIL1 and NIL2 at both 7 and 14 days after stress induction, with two of them having single nucleotide polymorphism (SNP) variants. These six genes that were confirmed by quantitative real-time PCR (qRT-PCR) expression analysis are considered candidate genes for drought tolerance mediated by qDSI.4B.1 QTL with their main contributions to gene regulation, cell elongation, protein quality control, secondary metabolism, and hormone signaling. These six candidate genes and the highest number of DEGs and variants (SNPs/indels) were located between 49 and 137 Mbp of 4BS, making this interval the most probable location for the qDSI.4B.1 locus. Additionally, 765 and 84 DEGs were detected as responsive genes to drought stress in tolerant and susceptible isolines, respectively. According to gene ontology (GO), protein phosphorylation, oxidation reduction, and regulation of transcription were top biological processes involved in the drought response and tolerance. These results provide insights into stress responses regulated by the 4BS locus and have identified candidate genes and genetic markers that can be used for fine mapping of the qDSI.4B.1 locus and, ultimately, in wheat breeding programs for drought tolerance.

Identification and Validation of a Chromosome 4D Quantitative Trait Locus Hotspot Conferring Heat Tolerance in Common Wheat (Triticum aestivum L.)

Understanding of the genetic mechanism of heat tolerance (HT) can accelerate and improve wheat breeding in dealing with a warming climate. This study identified and validated quantitative trait loci (QTL) responsible for HT in common wheat. The International Triticeae Mapping Initiative (ITMI) population, recombinant inbreed lines (RILs) derived from a cross between Synthetic W7984 and Opata M85, was phenotyped for shoot length, root length, whole plant length under heat stress and corresponding damage indices (DIs) to compare HT performances of individuals. Wide variations among the RILs were shown for all the traits. A total of 13 QTL including 9 major QTL and 4 minor QTL were identified, distributed on 6 wheat chromosomes. The six major QTL with the highest R² were associated with different traits under heat stress. They were all from Opata M85 background and located within a 2.2 cm interval on chromosome 4D, making up a QTL hotspot conferring HT in common wheat. The QTL hotspot was validated by genotyping-phenotyping association analysis using single-nucleotide-polymorphism (SNP) assays. The QTL, especially the 4D QTL hotspot identified and validated in this study, are valuable for the further fine mapping and identification of key genes and exploring genetic mechanism of HT in wheat.

Genetic Control of Efficient Nitrogen Use for High Yield and Grain Protein Concentration in Wheat: A Review

The increasing global population and the negative effects of nitrogen (N) fertilizers on the environment challenge wheat breeding to maximize yield potential and grain protein concentration (GPC) in an economically and environmentally friendly manner. Understanding the molecular mechanisms for the response of yield components to N availability and assimilates allocation to grains provides the opportunity to increase wheat yield and GPC simultaneously. This review summarized quantitative trait loci/genes which can increase spikes and grain number by enhancing N uptake and assimilation at relative early growth stage, and 1000-grain weight and GPC by increasing post-anthesis N uptake and N allocation to grains.

Genome-wide analyses of the Nodulin-like gene family in bread wheat revealed its potential roles during arbuscular mycorrhizal symbiosis

Nodulin-like (NL) genes are involved in transporting of various substances and may play key roles during the establishment of symbiosis in legumes plants. However, basic biological information of NL genes in the wheat genome is still largely unknown. Here, we identified and characterized NL genes in wheat via integrating genomic information, collinearity analysis, co-expression network analysis (WGCNA) and transcriptome analysis. In addition, we analyzed the polymorphisms and the roles of NL genes during arbuscular mycorrhizal (AM) symbiosis using a large wheat panel consists of 259 wheat genotypes. We identified 181 NL genes in the wheat genome, which were classified into SWEET, Early Nodulin-Like (ENODL), Major Facilitator Superfamily-Nodulin (MFS), Vacuolar Iron Transporter (VIT) and Early nodulin 93 (ENOD93) subfamily. The expansion of NL genes was mainly driven by segmental duplication. The bHLH genes are potential unrecognized transcription factors regulating NL genes. Moreover, two NL genes were more sensitive than other NL genes to AM colonization. The polymorphisms of NL genes are mainly due to random drift, and the natural mutation of NL genes led to significant differences in the mycorrhizal dependence of wheat in phosphorus uptake. The results concluded that NL genes potentially play important roles during AM symbiosis with wheat.

Major Genomic Regions for Wheat Grain Weight as Revealed by QTL Linkage Mapping and Meta-Analysis

Grain weight is a key determinant for grain yield potential in wheat, which is highly governed by a type of quantitative genetic basis. The identification of major quantitative trait locus (QTL) and functional genes are urgently required for molecular improvements in wheat grain yield. In this study, major genomic regions and putative candidate genes for thousand grain weight (TGW) were revealed by integrative approaches with QTL linkage mapping, meta-analysis and transcriptome evaluation. Forty-five TGW QTLs were detected using a set of recombinant inbred lines, explaining 1.76-12.87% of the phenotypic variation. Of these, ten stable QTLs were identified across more than four environments. Meta-QTL (MQTL) analysis were performed on 394 initial TGW QTLs available from previous studies and the present study, where 274 loci were finally refined into 67 MQTLs. The average confidence interval of these MQTLs was 3.73-fold less than that of initial QTLs. A total of 134 putative candidate genes were mined within MQTL regions by combined analysis of transcriptomic and omics data. Some key putative candidate genes similar to those reported early for grain development and grain weight formation were further discussed. This finding will provide a better understanding of the genetic determinants of TGW and will be useful for marker-assisted selection of high yield in wheat breeding.

Meta-QTLs, ortho-MQTLs, and candidate genes for thermotolerance in wheat (Triticum aestivum L.)

Meta-QTL analysis for thermotolerance in wheat was conducted to identify robust meta-QTLs (MQTLs). In this study, 441 QTLs related to 31 heat-responsive traits were projected on the consensus map with 50,310 markers. This exercise resulted in the identification of 85 MQTLs with confidence interval (CI) ranging from 0.11 to 34.9 cM with an average of 5.6 cM. This amounted to a 2.96-fold reduction relative to the mean CI (16.5 cM) of the QTLs used. Seventy-seven (77) of these MQTLs were also compared and verified with the results of recent genome-wide association studies (GWAS). The 85 MQTLs included seven MQTLs that are particularly useful for breeding purposes (we called them breeders' MQTLs). Seven ortho-MQTLs between wheat and rice genomes were also identified using synteny and collinearity. The MQTLs were used for the identification of 1,704 candidate genes (CGs). In silico expression analysis of these CGs permitted identification of 182 differentially expressed genes (DEGs), which included 36 high confidence CGs with known functions previously reported to be important for thermotolerance. These high confidence CGs encoded proteins belonging to the following families: protein kinase, WD40 repeat, glycosyltransferase, ribosomal protein, SNARE associated Golgi protein, GDSL lipase/esterase, SANT/Myb domain, K homology domain, etc. Thus, the present study resulted in the identification of MQTLs (including breeders' MQTLs), ortho-MQTLs, and underlying CGs, which could prove useful not only for molecular breeding for the development of thermotolerant wheat cultivars but also for future studies focused on understanding the molecular basis of thermotolerance.

Supplementary Information

The online version contains supplementary material available at 10.1007/s11032-021-01264-7.

Identification of Quantitative Trait Loci Hotspots Affecting Agronomic Traits and High-Throughput Vegetation Indices in Rainfed Wheat

Understanding the genetic basis of agronomic traits is essential for wheat breeding programs to develop new cultivars with enhanced grain yield under climate change conditions. The use of high-throughput phenotyping (HTP) technologies for the assessment of agronomic performance through drought-adaptive traits opens new possibilities in plant breeding. HTP together with a genome-wide association study (GWAS) mapping approach can be a useful method to dissect the genetic control of complex traits in wheat to enhance grain yield under drought stress. This study aimed to identify molecular markers associated with agronomic and remotely sensed vegetation index (VI)-related traits under rainfed conditions in bread wheat and to use an in silico candidate gene (CG) approach to search for upregulated CGs under abiotic stress. The plant material consisted of 170 landraces and 184 modern cultivars from the Mediterranean basin. The collection was phenotyped for agronomic and VI traits derived from multispectral images over 3 and 2 years, respectively. The GWAS identified 2,579 marker-trait associations (MTAs). The quantitative trait loci (QTL) overview index statistic detected 11 QTL hotspots involving more than one trait in at least 2 years. A CG analysis detected 12 CGs upregulated under abiotic stress in six QTL hotspots and 46 downregulated CGs in 10 QTL hotspots. The current study highlights the utility of VI to identify chromosome regions that contribute to yield and drought tolerance under rainfed Mediterranean conditions.

Characterisation of a 4A QTL for Metribuzin Resistance in Wheat by Developing Near-Isogenic Lines

Wheat (Triticum aestivum L.) production is constantly affected by weeds in the farming system. Chemical-based weed management is widely practiced; broad-spectrum herbicides such as metribuzin have been successfully used to control weeds in Australia and elsewhere of the world. Breeding metribuzin-resistant wheat through genetic improvement is needed for effective control of weeds. Quantitative trait loci (QTLs) mapping efforts identified a major QTL on wheat chromosome 4A, explaining up to 20% of the phenotypic variance for metribuzin resistance. The quantitative nature of inheritance of this QTL signifies the importance of near-isogenic lines (NILs), which can convert a quantitative trait into a Mendelian factor for better resolution of the QTL. In the current study, NILs were developed using a heterogeneous inbred family method combined with a fast generation-cycling system in a population of Chuan Mai 25 (resistant) and Ritchie (susceptible). Seven pairs of NILs targeting the 4A QTL for metribuzin resistance were confirmed with a molecular marker and phenotyping. The resistant allele from the resistant parent increased metribuzin resistance by 63-85% (average 69%) compared with the susceptible allele from the susceptible parent. Segregation analysis in the NIL pairs for thousand grain weight (TGW) (g), biomass per plant (kg), tillers per plant, plant height (cm), yield per plant, and powdery mildew visual score (0-9) indicated that these traits were linked with metribuzin resistance. Similarly, TGW was observed to co-segregate with metribuzin resistance in most confirmed NILs, signifying that the two traits are controlled by closely linked genes. The most contrasting NILs can be further characterised by transcriptomic and proteomic analyses to identify the candidate genes responsible for metribuzin resistance.

Large-scale integration of meta-QTL and genome-wide association study discovers the genomic regions and candidate genes for yield and yield-related traits in bread wheat

Based on the large-scale integration of meta-QTL and Genome-Wide  Association Study, 76 high-confidence MQTL regions and 237 candidate genes that affected wheat yield and yield-related traits were discovered. Improving yield and yield-related traits are key goals in wheat breeding program. The integration of accumulated wheat genetic resources provides an opportunity to uncover important genomic regions and candidate genes that affect wheat yield. Here, a comprehensive meta-QTL analysis was conducted on 2230 QTL of yield-related traits obtained from 119 QTL studies. These QTL were refined into 145 meta-QTL (MQTL), and 89 MQTL were verified by GWAS with different natural populations. The average confidence interval (CI) of these MQTL was 2.92 times less than that of the initial QTL. Furthermore, 76 core MQTL regions with a physical distance less than 25 Mb were detected. Based on the homology analysis and expression patterns, 237 candidate genes in the MQTL involved in photoperiod response, grain development, multiple plant growth regulator pathways, carbon and nitrogen metabolism and spike and flower organ development were determined. A novel candidate gene TaKAO-4A was confirmed to be significantly associated with grain size, and a CAPS marker was developed based on its dominant haplotype. In summary, this study clarified a method based on the integration of meta-QTL, GWAS and homology comparison to reveal the genomic regions and candidate genes that affect important yield-related traits in wheat. This work will help to lay a foundation for the identification, transfer and aggregation of these important QTL or candidate genes in wheat high-yield breeding.

Physical Mapping of QTL in Four Spring Wheat Populations under Conventional and Organic Management Systems. I. Earliness

In previous studies, we reported quantitative trait loci (QTL) associated with the heading, flowering, and maturity time in four hard red spring wheat recombinant inbred line (RIL) populations but the results are scattered in population-specific genetic maps, which is challenging to exploit efficiently in breeding. Here, we mapped and characterized QTL associated with these three earliness traits using the International Wheat Genome Sequencing Consortium (IWGSC) RefSeq v2.0 physical map. Our data consisted of (i) 6526 single nucleotide polymorphisms (SNPs) and two traits evaluated at five conventionally managed environments in the 'Cutler' × 'AC Barrie' population; (ii) 3158 SNPs and two traits evaluated across three organic and seven conventional managements in the 'Attila' × 'CDC Go' population; (iii) 5731 SilicoDArT and SNP markers and the three traits evaluated at four conventional and organic management systems in the 'Peace' × 'Carberry' population; and (iv) 1058 SNPs and two traits evaluated across two conventionally and organically managed environments in the 'Peace' × 'CDC Stanley' population. Using composite interval mapping, the phenotypic data across all environments, and the IWGSC RefSeq v2.0 physical maps, we identified a total of 44 QTL associated with days to heading (11), flowering (10), and maturity (23). Fifteen of the 44 QTL were common to both conventional and organic management systems, and the remaining QTL were specific to either the conventional (21) or organic (8) management systems. Some QTL harbor known genes, including the Vrn-A1, Vrn-B1, Rht-A1, and Rht-B1 that regulate photoperiodism, flowering time, and plant height in wheat, which lays a solid basis for cloning and further characterization.

Characterization of near-isogenic lines confirmed QTL and revealed candidate genes for plant height and yield-related traits in common wheat

Plant height (PH) is closely associated with yield-related traits and environmental adaptation. Seven pairs of near-isogenic lines (NILs) targeting four QTL on 3AL, 4BL, 4AS, and 7AL wheat chromosome arms were assessed for PH and four yield-related traits including yield per plant (Y/P), grain number per spike (G/S), thousand kernel weight (TKW), and biomass per plant (B/P). Significant differences were observed in the NIL pairs for the measured traits. NIL pairs targeting the 3AL QTL differed significantly in PH, G/S, and TKW; NILs targeting the 4BL QTL differed significantly in PH, Y/P, and B/P; NIL pairs targeting the 4AS QTL differed significantly in all the traits; and NIL pairs targeting the 7AL QTL differed significantly in PH. A 90 K SNP genotyping assay of the NILs detected nineteen SNPs associated with fourteen functional genes. Among them, eight candidate genes are related to Rht proteins, four genes are related to hormone pathways and two genes are related to carbohydrate synthesis and transport. By searching the interval marker physical positions, it was found that the four targeted QTL in this study overlapped with eight previously reported QTL for PH, TKW, biomass, and yield. Correlation analysis revealed that PH significantly and positively correlated with B/P and G/S. The SNP and candidate gene information is potentially useful for marker-assisted selection in breeding programs, and the four targeted QTL are proved to be critical genomic regions controlling the investigated agronomic traits, which can be further fine mapped to identify the underlying genes.

Supplementary Information

The online version contains supplementary material available at 10.1007/s11032-020-01196-8.