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

Using supervised machine-learning approaches to understand abiotic stress tolerance and design resilient crops

Abiotic stresses such as drought, heat, cold, salinity and flooding significantly impact plant growth, development and productivity. As the planet has warmed, these abiotic stresses have increased in frequency and intensity, affecting the global food supply and making it imperative to develop stress-resilient crops. In the past 20 years, the development of omics technologies has contributed to the growth of datasets for plants grown under a wide range of abiotic environments. Integration of these rapidly growing data using machine-learning (ML) approaches can complement existing breeding efforts by providing insights into the mechanisms underlying plant responses to stressful conditions, which can be used to guide the design of resilient crops. In this review, we introduce ML approaches and provide examples of how researchers use these approaches to predict molecular activities, gene functions and genotype responses under stressful conditions. Finally, we consider the potential and challenges of using such approaches to enable the design of crops that are better suited to a changing environment.This article is part of the theme issue 'Crops under stress: can we mitigate the impacts of climate change on agriculture and launch the 'Resilience Revolution'?'.

The selection and application of tiller number QTLs in modern wheat breeding

Tiller number is a key factor influencing wheat plant architecture and yield potential. This study analyzed the variation in tiller number among 323 wheat accessions across nine environmental conditions. The results revealed a significant decrease in tiller number in modern cultivars compared to traditional landraces, indicating a trend in selective breeding for fewer tillers. Genome-wide association studies (GWASs) identified four quantitative trait loci (QTLs) associated with tiller number on chromosomes 2D, 5A, and 6A. Haplotypes linked to reduced tiller number at three of these QTLs have been significantly selected and are predominantly found in various Chinese agroecological zones, positively affecting grain number and/or weight. These findings suggest that contemporary Chinese wheat breeding strategies have prioritized reducing tiller number while improving spike yield. Identifying selected haplotype combinations can further enhance breeding efforts aimed at optimizing the tiller number trait. This study provides insights into the genetic basis of tiller number QTLs and their relevance to wheat breeding practices.

Unravelling consensus genomic regions conferring stem-related traits in wheat via meta-QTL analysis

BACKGROUND

Wheat stem is an important vegetative organ, that is responsible for supporting leaves and spikes, and transporting water, minerals and organic nutrients to spikes through vascular bundles, thereby promoting plant growth and development. Therefore, wheat stems contribute to plant structure and total yield.

RESULTS

We constructed a high-density consensus map and performed a meta-analysis using 306 stem-related QTLs. The results revealed 45 meta-QTLs (MQTLs) with a mean confidence interval (CI) 4.50 times lower than that of the original QTL. A total of 25/31 MQTLs (PI < 100 Mb) overlapped with SNP-trait associations reported in genome-wide association studies (GWASs). By associating MQTL interval genes with multiomics datasets and performing coexpression analysis of common genes, we generated a more reliable and concise list of putative candidate genes involved in stem regulation. Furthermore, we identified an ethylene-responsive transcription factor in the form of TraesCS5B02G236900 haplotype, whose variations may be related to stem-related traits.

CONCLUSION

In this study, comprehensive analysis was used to determine the most stable loci on a high-density consensus map associated with reliable genetic markers, and MQTLs, candidate genes and excellent haplotypes were identified, which laid a foundation for further research on the genetic basis of wheat stem development and promoted the development of wheat molecular breeding.

Genetic dissection of yield and yield-related traits in mungbean based on QTL meta-analysis

As an important food crop and nutritional source, mungbean has prioritized yield improvement as a key objective in breeding programs. In present study, we conducted a QTL meta-analysis to integrate 660 QTLs related to yield and yield-related traits in mungbean published over the past 20 years. A total of 590 initial QTLs were mapped onto a high-density consensus map, resulting in the identification of 72 meta-QTLs (MQTLs). These MQTLs were unevenly distributed across 11 linkage groups (LGs) with an average confidence interval (CI) of 1.21 cM, which was 6.26-fold narrower than the average CI of the initial QTLs. Among these 72 MQTLs, 20 were validated in a genome-wide association study (GWAS) for yield and yield-related traits in mungbean. Orthologous MQTL analysis revealed that 22 mungbean MQTLs were collinear with 19 MQTLs in common bean for yield and yield-related traits. In addition, 20 breeder's MQTLs were screened from the 72 MQTLs, and 339 gene models were identified within the breeder's MQTL regions. Twenty-two mungbean orthologs of yield-related genes such as seed germination, tiller number, and plant height in rice and Arabidopsis were identified in the breeder's MQTL regions using homology analysis. This study contributes to understanding the genetic mechanisms for yield and yield-related traits and provides new ideas for the genetic improvement and breeding of mungbean.

Whole-genome meta-analysis coupled with haplotype analysis reveal new genes and functional haplotypes conferring pre-harvest sprouting in rice

BACKGROUND

Pre-harvest sprouting (PHS), which adversely impacts grain yield and quality, is controlled by seed dormancy genes. However, only a few dormancy-related genes have been characterized, and the effects of allelic variation in genes and the genetic basis of seed dormancy in rice remain largely unknown. Here, we performed a whole-genome meta-quantitative trait loci study to elucidate the genetic basis of seed dormancy in rice.

RESULT

One hundred and sixty-seven QTL were identified for PHS from which 134 were successfully projected onto the reference map yielding 20 consensus regions, meta-QTL (mQTL). The mean confidence interval of the mQTL was narrower (9.56-fold reduction) than that of the initial QTL. Six of the 20 identified mQTL were designated as breeders' mQTL based on their small confidence intervals, large phenotypic variance explained, and the involvement of high number of QTL. Further, we retrieved 559 high-confidence genes from breeders' mQTL regions conferring resistance to PHS. Comparative analysis of genes found in breeders' mQTL loci and an RNA-seq-based transcriptomic dataset discovered 34 common genes. Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis revealed a significant enrichment of the common genes in amino sugar and nucleotide sugar metabolism, carbon metabolism, and carbon fixation in photosynthetic organs. Combined in silico expression profiling and qRT-PCR validation showed that LOC_Os10g18364, LOC_Os10g21940, LOC_Os10g22590, and LOC_Os10g25140 exhibited high fold-change expression in PHS resistant cultivar (23xS-261) than PHS susceptible cultivar (23xS-262). Association analysis of these genes with germination rate index demonstrated that LOC_Os10g18364Hap1, LOC_Os10g21940Hap1, LOC_Os10g22590Hap1, and LOC_Os10g25140Hap1/Hap3 exhibited low germination rate (GR) in cultivars carrying these haplotypes.

CONCLUSION

In summary, this study delineates the genetic basis of PHS and provides a new set of target genes for improving PHS resistance. The natural variants identified in these genes and markers associated with breeders' mQTL serve as potential resources for incorporating PHS resistance in rice.

Large-scale integration of meta-QTL and genome-wide association study identifies genomic regions and candidate genes for photosynthetic efficiency traits in bread wheat

BACKGROUND

Improving photosynthetic efficiency is an essential strategy for advancing wheat breeding progress. Integrating wheat genetic resources provides an opportunity to discover pivotal genomic regions and candidate genes (CGs) for photosynthetic efficiency traits in wheat.

RESULTS

A large-scale meta-QTL (MQTL) analysis was performed with 1363 initial quantitative trait loci (QTLs) for photosynthetic efficiency traits extracted from 66 independent QTL mapping studies over the past decades. Consequently, 718 initial QTLs were refined into 74 MQTLs, which were distributed on all wheat chromosomes except 1D, 3 A, 4B, and 5B. Compared with the confidence interval (CI) of the initial QTL, the CI of the identified MQTL was 0.03 to 10.97 cM, with an average of 1.46 cM, which was 20.46 times narrower than that of the original QTL. The maximum explained phenotypic variance (PVE) of the MQTL ranged from 7.43 to 20.42, with an average of 11.97, which was 1.07 times higher than that of the original QTL. Of these, 54 MQTLs were validated using genome-wide association study (GWAS) data from different natural populations in previous research. A total of 3,102 CGs were identified within the MQTL intervals, where 342 CGs share homology with rice, and 1,043 CGs are highly expressed in leaves, spikes, and stems. These CGs were mainly involved in porphyrin metabolism, glyoxylate, dicarboxylate metabolism, carbon metabolism and photosynthesis antenna proteins metabolism pathways by the in silico transcriptome assessment. For the key CG TaGGR-6A (TraesCS6A02G307700) involved in the porphyrin metabolism pathway, a functional kompetitive allele-specific PCR (KASP) marker was developed at 2464 bp (A/G) position within the 3' untranslated region, successfully distinguishing two haplotypes: TaGGR-6A-Hap I (type AA) and TaGGR-6A-Hap II (type GG). Varieties with the TaGGR-6A-Hap II allele exhibited approximately 13.42% and 11.45% higher flag leaf chlorophyll content than those carrying the TaGGR-6A-Hap I allele. The elite haplotype TaGGR-6A-Hap II was positively selected during wheat breeding, as evidenced by the geographical and annual frequency distributions of the two TaGGR-6A haplotypes.

CONCLUSION

The findings will give further insights into the genetic determinants of photosynthetic efficiency traits and provide some reliable MQTLs and putative CGs for the genetic improvement of photosynthetic efficiency 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.

Single- and multi-locus genome-wide association study reveals genomic regions of thirteen yield-related traits in common wheat

Genetic dissection of yield-related traits can be used to improve wheat yield through molecular design breeding. In this study, we genotyped 245 wheat varieties and measured 13 yield-related plant height-, grain-, and spike-related traits, in seven environments, and identified 778 loci for these traits by genome-wide association study (GWAS) using single- and multi-locus models. Among these loci, nine were major, of which seven were novel, including Qph/lph.ahau-7A for plant height (PH) and leaf pillow height (LPH), Qngps/sps.ahau-1A for number of grains per spike (NGPS) and spikelet number per spike (SPS), Qsd.ahau-2B.1 and Qsd.ahau-5A.2 for spikelet density (SD), Qlph.ahau-7B.2 for LPH, Qgl.ahau-7B.3 for grain length (GL), and Qsl.ahau-3A.3 for spike length (SL). Through marker development, re-GWAS, gene annotation and cloning, and sequence variation, haplotype, and expression analyses, we confirmed two novel major loci and identified potential candidate genes, TraesCS7A02G118000 (named TaF-box-7A) and TraesCS1A02G190200 (named TaBSK2-1A) underlying Qph/lph.ahau-7A for PH-related traits and Qngps/sps.ahau-1A for spike-related traits. We also reported two favorable haplotypes, including TaF-box-Hap1 associated with low PH and LPH and TaBSK2-Hap3 associated with high NGPS and SPS. In summary, these findings can be applied to improve wheat yield and enrich our understanding of the complex genetic mechanisms of yield-related traits.

Mapping heat tolerance QTLs in Triticum durum-Aegilops speltoides backcross introgression lines to enhance thermotolerance in wheat

Wheat, a major cereal crop, is the most consumed staple food after rice in India. Frequent episodes of heat waves during the past decade have raised concerns about food security under impending global warming and necessitate the development of heat-tolerant wheat cultivars. Wild relatives of crop plants serve as untapped reservoirs of novel genetic variations. In the present study a mapping population comprising 311 BC2F10 backcross introgression lines (BILs) developed by crossing Triticum durum and heat-tolerant diploid wild wheat relative Aegilops speltoides accession pau3809 was used to map QTLs for terminal heat tolerance. The homozygous BILs were evaluated for heat stress tolerance component traits under an optimum environment (OE) and a heat-stressed environment (HE) for the two cropping seasons. Data on spike length, spikelet number per spike, peduncle length, thousand-grain weight, grains per spike, days to heading, days to maturity, grain filling duration, NDVI at heading, plant height and plot yield were recorded. Genotyping-by-sequencing (GBS) of the BILs was carried out, and 2945 high-quality, polymorphic SNPs were obtained. Thirty QTLs were detected for various heat tolerance component traits on chromosomes 1A, IB, 2A, 2B, 3B, 4B, 5A, 5B, 6A and 6B with phenotypic variance ranging from 5 to 11.5%. Several candidate genes reported to play a role in heat stress responses were identified by browsing the 1.85 Mb physical region flanking the stable QTLs detected under the HE. Identified QTL and linked markers can be employed for genomics-assisted breeding for heat tolerance in wheat.

Meta-QTL mapping for wheat thousand kernel weight

Wheat domestication and subsequent genetic improvement have yielded cultivated species with larger seeds compared to wild ancestors. Increasing thousand kernel weight (TKW) remains a crucial goal in many wheat breeding programs. To identify genomic regions influencing TKW across diverse genetic populations, we performed a comprehensive meta-analysis of quantitative trait loci (MQTL), integrating 993 initial QTL from 120 independent mapping studies over recent decades. We refined 242 loci into 66 MQTL, with an average confidence interval (CI) 3.06 times smaller than that of the original QTL. In these 66 MQTL regions, a total of 4,913 candidate genes related to TKW were identified, involved in ubiquitination, phytohormones, G-proteins, photosynthesis, and microRNAs. Expression analysis of the candidate genes showed that 95 were specific to grain and might potentially affect TKW at different seed development stages. These findings enhance our understanding of the genetic factors associated with TKW in wheat, providing reliable MQTL and potential candidate genes for genetic improvement of this trait.

Integrated transcriptomic, proteomic and metabolomic analyses revealing the roles of amino acid and sucrose metabolism in augmenting drought tolerance in Agropyron mongolicum

Drought, a major consequence of climate change, initiates molecular interactions among genes, proteins, and metabolites. Agropyron mongolicum a high-quality perennial grass species, exhibits robust drought resistance. However, the molecular mechanism underlying this resistance remaining largely unexplored. In this study, we performed an integrated analysis of the transcriptome, proteome, and metabolome of A. mongolicum under optimal and drought stress conditions. This combined analysis highlighted the pivotal role of transporters in responding to drought stress. Moreover, metabolite profiling indicated that arginine and proline metabolism, as well as the pentose phosphate pathway, are significantly involved in the drought response of A. mongolicum. Additionally, the integrated analysis suggested that proline metabolism and the pentose phosphate pathway are key elements of the drought resistance strategy in A. mongolicum plants. In summary, our research elucidates the drought adaptation mechanisms of A. mongolicum and identifies potential candidate genes for further study.

Exploring seed characteristics and performance through advanced physico-chemical techniques

Simple physico-chemical techniques can be used to evaluate the composition, structure, and characteristics of plant seeds to determine their viability, quality, and possible uses in agriculture. Advanced analytical techniques, including thermo gravimetric analysis (TGA), electron paramagnetic resonance (EPR), and high-pressure liquid chromatography (HPLC), provide completely new insights and more precise information. They can be integrated to build up seed quality profiles, with great advantage to assess water content, organic compounds, and inorganic metals without the need to carry out many extraction procedures, as requested by more conventional methods. In this study, seed lots from three different plant species such as Triticum turgidum L. subsp. durum (wheat), Trigonella foenum graecum L. (trigonella or fenugreek), and Atriplex halimus L. (saltbush or sea orach) have been used to test the potential of TGA, EPR, and HPLC to discriminate between seed-specific features. A key finding of this study is that HPLC is essential in Principal Component Analysis (PCA) because various seeds (from the same species or other species) may contain compounds with varying polarity groups. The reported data confirm the efficacy of this approach. These data, fully available for other users, are coherently constructed and provide a proof of concept for future seed quality control studies.

Pleiotropic phenotypic effects of the TaCYP78A family on multiple yield-related traits in wheat

Increasing crop yield depends on selecting and utilizing pleiotropic genes/alleles to improve multiple yield-related traits (YRTs) during crop breeding. However, synergistic improvement of YRTs is challenging due to the trade-offs between YRTs in breeding practices. Here, the favourable haplotypes of the TaCYP78A family are identified by analysing allelic variations in 1571 wheat accessions worldwide, demonstrating the selection and utilization of pleiotropic genes to improve yield and related traits during wheat breeding. The TaCYP78A family members, including TaCYP78A3, TaCYP78A5, TaCYP78A16, and TaCYP78A17, are organ size regulators expressed in multiple organs, and their allelic variations associated with various YRTs. However, due to the trade-offs between YRTs, knockdown or overexpression of TaCYP78A family members does not directly increase yield. Favourable haplotypes of the TaCYP78A family, namely A3/5/16/17Ap-Hap II, optimize the expression levels of TaCYP78A3/5/16/17-A across different wheat organs to overcome trade-offs and improve multiple YRTs. Different favourable haplotypes have both complementary and specific functions in improving YRTs, and their aggregation in cultivars under strong artificial selection greatly increase yield, even under various planting environments and densities. These findings provide new support and valuable genetic resources for molecular breeding of wheat and other crops in the era of Breeding 4.0.

Genotype selection identified elite lines through quantitative trait loci mapping of agronomically important traits in wheat

Wheat is one of the most important staple foods in the world. Genetic characterization of wheat agronomically important traits is crucial for yield improvement through molecular breeding. In this study, a recombinant inbred line (RIL) population was developed by crossing a local adapted high yield variety Jimai 22 (JM22) with an external variety Cunmai no.1 (CM1). A high-density genetic map containing 7,359 single nucleotide polymorphism (SNP) markers was constructed. Quantitative trait loci (QTL) mapping identified 61 QTL for eight yield-related traits under six environments (years). Among them, 17 QTL affecting spike number per plant, grain number per spike and thousand grain weight showed high predictability for theoretical yield per plant (TYP), of which, 12 QTL alleles positively contributed to TYP. Nine promising candidate genes for seven of the 12 QTL were identified including three known wheat genes and six rice orthologs. Four elite lines with TYP increased by 5.6%-15.2% were identified through genotype selection which carried 7-9 favorable alleles from JM22 and 2-3 favorable alleles from CM1 of the 12 QTL. Moreover, the linked SNPs of the 12 QTL were converted to high-throughput kompetitive allele-specific PCR (KASP) markers and validated in the population. The mapped QTL, identified promising candidate genes, developed elite lines and KASP markers are highly valuable in future genotype selection to improve wheat yield.

Supplementary Information

The online version contains supplementary material available at 10.1007/s11032-024-01496-3.

Unveiling the role of the ERD15 gene in wheat's tolerance to combined drought and salinity stress: a meta-analysis of QTL and RNA-Seq data

The coexistence of drought and salinity stresses in field conditions significantly hinders wheat (Triticum aestivum L.) productivity. Understanding the molecular mechanisms governing response and tolerance to these stresses is crucial for developing resilient wheat varieties. Our research, employing a combination of meta-QTL and meta-RNA-Seq transcriptome analyses, has uncovered the genome functional landscape of wheat in response to drought and salinity. We identified 118 meta-QTLs (MQTLs) distributed across all 21 wheat chromosomes, with ten designated as the most promising. Additionally, we found 690 meta-differentially expressed genes (mDEGs) shared between drought and salinity stress. Notably, our findings highlight the Early Responsive to Dehydration 15 (ERD15) gene, located in one of the most promising MQTLs, as a key gene in the shared gene network of drought and salinity stress. ERD15, differentially expressed between contrasting wheat genotypes under combined stress conditions, significantly regulates water relations, photosynthetic activity, antioxidant activity, and ion homeostasis. These findings not only provide valuable insights into the molecular genetic mechanisms underlying combined stress tolerance in wheat but also hold the potential to contribute significantly to the development of stress-resilient wheat varieties.

Exploring the genetic architecture of powdery mildew resistance in wheat through QTL meta-analysis

Powdery mildew (PM), caused by Blumeria graminis f. sp. tritici, poses a significant threat to wheat production, necessitating the development of genetically resistant varieties for long-term control. Therefore, exploring genetic architecture of PM in wheat to uncover important genomic regions is an important area of wheat research. In recent years, the utilization of meta-QTL (MQTL) analysis has gained prominence as an essential tool for unraveling the complex genetic architecture underlying complex quantitative traits. The aim of this research was to conduct a QTL meta-analysis to pinpoint the specific genomic regions in wheat responsible for governing PM resistance. This study integrated 222 QTLs from 33 linkage-based studies using a consensus map with 54,672 markers. The analysis revealed 39 MQTLs, refined to 9 high-confidence MQTLs (hcMQTLs) with confidence intervals of 0.49 to 12.94 cM. The MQTLs had an average physical interval of 41.00 Mb, ranging from 0.000048 Mb to 380.71 Mb per MQTL. Importantly, 18 MQTLs co-localized with known resistance genes like Pm2, Pm3, Pm8, Pm21, Pm38, and Pm41. The study identified 256 gene models within hcMQTLs, providing potential targets for marker-assisted breeding and genomic prediction programs to enhance PM resistance. These MQTLs would serve as a foundation for fine mapping, gene isolation, and functional genomics studies, facilitating a deeper understanding of molecular mechanisms. The identification of candidate genes opens up exciting possibilities for the development of PM-resistant wheat varieties after validation.

Natural allelic variation confers diversity in the regulation of flag leaf traits in wheat

Flag leaf (FL) dimension has been reported as a key ecophysiological aspect for boosting grain yield in wheat. A worldwide winter wheat panel consisting of 261 accessions was tested to examine the phenotypical variation and identify quantitative trait nucleotides (QTNs) with candidate genes influencing FL morphology. To this end, four FL traits were evaluated during the early milk stage under two growing seasons at the Leibniz Institute of Plant Genetics and Crop Plant Research. The results showed that all leaf traits (Flag leaf length, width, area, and length/width ratio) were significantly influenced by the environments, genotypes, and environments × genotypes interactions. Then, a genome-wide association analysis was performed using 17,093 SNPs that showed 10 novel QTNs that potentially play a role in modulating FL morphology in at least two environments. Further analysis revealed 8 high-confidence candidate genes likely involved in these traits and showing high expression values from flag leaf expansion until its senescence and also during grain development. An important QTN (wsnp_RFL_Contig2177_1500201) was associated with FL width and located inside TraesCS3B02G047300 at chromosome 3B. This gene encodes a major facilitator, sugar transporter-like, and showed the highest expression values among the candidate genes reported, suggesting their positive role in controlling flag leaf and potentially being involved in photosynthetic assimilation. Our study suggests that the detection of novel marker-trait associations and the subsequent elucidation of the genetic mechanism influencing FL morphology would be of interest for improving plant architecture, light capture, and photosynthetic efficiency during grain development.

Genome-wide screening of meta-QTL and candidate genes controlling yield and yield-related traits in barley (Hordeum vulgare L.)

Increasing yield is an important goal of barley breeding. In this study, 54 papers published from 2001-2022 on QTL mapping for yield and yield-related traits in barley were collected, which contained 1080 QTLs mapped to the barley high-density consensus map for QTL meta-analysis. These initial QTLs were integrated into 85 meta-QTLs (MQTL) with a mean confidence interval (CI) of 2.76 cM, which was 7.86-fold narrower than the CI of the initial QTL. Among these 85 MQTLs, 68 MQTLs were validated in GWAS studies, and 25 breeder's MQTLs were screened from them. Seventeen barley orthologs of yield-related genes in rice and maize were identified within the hcMQTL region based on comparative genomics strategy and were presumed to be reliable candidates for controlling yield-related traits. The results of this study provide useful information for molecular marker-assisted breeding and candidate gene mining of yield-related traits in barley.

Dissecting the molecular basis of spike traits by integrating gene regulatory networks and genetic variation in wheat

Spike architecture influences both grain weight and grain number per spike, which are the two major components of grain yield in bread wheat (Triticum aestivum L.). However, the complex wheat genome and the influence of various environmental factors pose challenges in mapping the causal genes that affect spike traits. Here, we systematically identified genes involved in spike trait formation by integrating information on genomic variation and gene regulatory networks controlling young spike development in wheat. We identified 170 loci that are responsible for variations in spike length, spikelet number per spike, and grain number per spike through genome-wide association study and meta-QTL analyses. We constructed gene regulatory networks for young inflorescences at the double ridge stage and the floret primordium stage, in which the spikelet meristem and the floret meristem are predominant, respectively, by integrating transcriptome, histone modification, chromatin accessibility, eQTL, and protein-protein interactome data. From these networks, we identified 169 hub genes located in 76 of the 170 QTL regions whose polymorphisms are significantly associated with variation in spike traits. The functions of TaZF-B1, VRT-B2, and TaSPL15-A/D in establishment of wheat spike architecture were verified. This study provides valuable molecular resources for understanding spike traits and demonstrates that combining genetic analysis and developmental regulatory networks is a robust approach for dissection of complex traits.

Meta QTL analysis for dissecting abiotic stress tolerance in chickpea

BACKGROUND

Chickpea is prone to many abiotic stresses such as heat, drought, salinity, etc. which cause severe loss in yield. Tolerance towards these stresses is quantitative in nature and many studies have been done to map the loci influencing these traits in different populations using different markers. This study is an attempt to meta-analyse those reported loci projected over a high-density consensus map to provide a more accurate information on the regions influencing heat, drought, cold and salinity tolerance in chickpea.

RESULTS

A meta-analysis of QTL reported to be responsible for tolerance to drought, heat, cold and salinity stress tolerance in chickpeas was done. A total of 1512 QTL responsible for the concerned abiotic stress tolerance were collected from literature, of which 1189 were projected on a chickpea consensus genetic map. The QTL meta-analysis predicted 59 MQTL spread over all 8 chromosomes, responsible for these 4 kinds of abiotic stress tolerance in chickpea. The physical locations of 23 MQTL were validated by various marker-trait associations and genome-wide association studies. Out of these reported MQTL, CaMQAST1.1, CaMQAST4.1, CaMQAST4.4, CaMQAST7.8, and CaMQAST8.2 were suggested to be useful for different breeding approaches as they were responsible for high per cent variance explained (PVE), had small intervals and encompassed a large number of originally reported QTL. Many putative candidate genes that might be responsible for directly or indirectly conferring abiotic stress tolerance were identified in the region covered by 4 major MQTL- CaMQAST1.1, CaMQAST4.4, CaMQAST7.7, and CaMQAST6.4, such as heat shock proteins, auxin and gibberellin response factors, etc. CONCLUSION: The results of this study should be useful for the breeders and researchers to develop new chickpea varieties which are tolerant to drought, heat, cold, and salinity stresses.

Characterization of a wheat stable QTL for spike length and its genetic effects on yield-related traits

Spike length (SL) is one of the most important agronomic traits affecting yield potential and stability in wheat. In this study, a major stable quantitative trait locus (QTL) for SL, i.e., qSl-2B, was detected in multiple environments in a recombinant inbred line (RIL) mapping population, KJ-RILs, derived from a cross between Kenong 9204 (KN9204) and Jing 411 (J411). The qSl-2B QTL was mapped to the 60.06-73.06 Mb region on chromosome 2B and could be identified in multiple mapping populations. An InDel molecular marker in the target region was developed based on a sequence analysis of the two parents. To further clarify the breeding use potential of qSl-2B, we analyzed its genetic effects and breeding selection effect using both the KJ-RIL population and a natural mapping population, which consisted of 316 breeding varieties/advanced lines. The results showed that the qSl-2B alleles from KN9204 showed inconsistent genetic effects on SL in the two mapping populations. Moreover, in the KJ-RILs population, the additive effects analysis of qSl-2B showed that additive effect was higher when both qSl-2D and qSl-5A harbor negative alleles under LN and HN. In China, a moderate selection utilization rate for qSl-2B was found in the Huanghuai winter wheat area and the selective utilization rate for qSl-2B continues to increase. The above findings provided a foundation for the genetic improvement of wheat SL in the future via molecular breeding strategies.

Meta-Quantitative Trait Loci Analysis and Candidate Gene Mining for Drought Tolerance-Associated Traits in Maize (Zea mays L.)

Drought is one of the major abiotic stresses with a severe negative impact on maize production globally. Understanding the genetic architecture of drought tolerance in maize is a crucial step towards the breeding of drought-tolerant varieties and a targeted exploitation of genetic resources. In this study, 511 quantitative trait loci (QTL) related to grain yield components, flowering time, and plant morphology under drought conditions, as well as drought tolerance index were collected from 27 published studies and then projected on the IBM2 2008 Neighbors reference map for meta-analysis. In total, 83 meta-QTL (MQTL) associated with drought tolerance in maize were identified, of which 20 were determined as core MQTL. The average confidence interval of MQTL was strongly reduced compared to that of the previously published QTL. Nearly half of the MQTL were confirmed by co-localized marker-trait associations from genome-wide association studies. Based on the alignment of rice proteins related to drought tolerance, 63 orthologous genes were identified near the maize MQTL. Furthermore, 583 candidate genes were identified within the 20 core MQTL regions and maize-rice homologous genes. Based on KEGG analysis of candidate genes, plant hormone signaling pathways were found to be significantly enriched. The signaling pathways can have direct or indirect effects on drought tolerance and also interact with other pathways. In conclusion, this study provides novel insights into the genetic and molecular mechanisms of drought tolerance in maize towards a more targeted improvement of this important trait in breeding.

Integrated meta-analysis and transcriptomics pinpoint genomic loci and novel candidate genes associated with submergence tolerance in rice

BACKGROUND

Due to rising costs, water shortages, and labour shortages, farmers across the globe now prefer a direct seeding approach. However, submergence stress remains a major bottleneck limiting the success of this approach in rice cultivation. The merger of accumulated rice genetic resources provides an opportunity to detect key genomic loci and candidate genes that influence the flooding tolerance of rice.

RESULTS

In the present study, a whole-genome meta-analysis was conducted on 120 quantitative trait loci (QTL) obtained from 16 independent QTL studies reported from 2004 to 2023. These QTL were confined to 18 meta-QTL (MQTL), and ten MQTL were successfully validated by independent genome-wide association studies from diverse natural populations. The mean confidence interval (CI) of the identified MQTL was 3.44 times narrower than the mean CI of the initial QTL. Moreover, four core MQTL loci with genetic distance less than 2 cM were obtained. By combining differentially expressed genes (DEG) from two transcriptome datasets with 858 candidate genes identified in the core MQTL regions, we found 38 common differentially expressed candidate genes (DECGs). In silico expression analysis of these DECGs led to the identification of 21 genes with high expression in embryo and coleoptile under submerged conditions. These DECGs encode proteins with known functions involved in submergence tolerance including WRKY, F-box, zinc fingers, glycosyltransferase, protein kinase, cytochrome P450, PP2C, hypoxia-responsive family, and DUF domain. By haplotype analysis, the 21 DECGs demonstrated distinct genetic differentiation and substantial genetic distance mainly between indica and japonica subspecies. Further, the MQTL7.1 was successfully validated using flanked marker S2329 on a set of genotypes with phenotypic variation.

CONCLUSION

This study provides a new perspective on understanding the genetic basis of submergence tolerance in rice. The identified MQTL and novel candidate genes lay the foundation for marker-assisted breeding/engineering of flooding-tolerant cultivars conducive to direct seeding.

Meta-analysis of identified genomic regions and candidate genes underlying salinity tolerance in rice (Oryza sativa L.)

Rice output has grown globally, yet abiotic factors are still a key cause for worry. Salinity stress seems to have the more impact on crop production out of all abiotic stresses. Currently one of the most significant challenges in paddy breeding for salinity tolerance with the help of QTLs, is to determine the QTLs having the best chance of improving salinity tolerance with the least amount of background noise from the tolerant parent. Minimizing the size of the QTL confidence interval (CI) is essential in order to primarily include the genes responsible for salinity stress tolerance. By considering that, a genome-wide meta-QTL analysis on 768 QTLs from 35 rice populations published from 2001 to 2022 was conducted to identify consensus regions and the candidate genes underlying those regions responsible for the salinity tolerance, as it reduces the confidence interval (CI) to many folds from the initial QTL studies. In the present investigation, a total of 65 MQTLs were extracted with an average CI reduced from 17.35 to 1.66 cM including the smallest of 0.01 cM. Identification of the MQTLs for individual traits and then classifying the target traits into correlated morphological, physiological and biochemical aspects, resulted in more efficient interpretation of the salinity tolerance, identifying the candidate genes and to understand the salinity tolerance mechanism as a whole. The results of this study have a huge potential to improve the rice genotypes for salinity tolerance with the help of MAS and MABC.

Genome-wide association studies reveal stable loci for wheat grain size under different sowing dates

Background

Wheat (Tritium aestivum L.) production is critical for global food security. In recent years, due to climate change and the prolonged growing period of rice varieties, the delayed sowing of wheat has resulted in a loss of grain yield in the area of the middle and lower reaches of the Yangtze River. It is of great significance to screen for natural germplasm resources of wheat that are resistant to late sowing and to explore genetic loci that stably control grain size and yield.

Methods

A collection of 327 wheat accessions from diverse sources were subjected to genome-wide association studies using genotyping-by-sequencing. Field trials were conducted under normal, delayed, and seriously delayed sowing conditions for grain length, width, and thousand-grain weight at two sites. Additionally, the additive main effects and multiplicative interaction (AMMI) model was applied to evaluate the stability of thousand-grain weight of 327 accessions across multiple sowing dates.

Results

Four wheat germplasm resources have been screened, demonstrating higher stability of thousand-grain weight. A total of 43, 35, and 39 significant MTAs were determined across all chromosomes except for 4D under the three sowing dates, respectively. A total of 10.31% of MTAs that stably affect wheat grain size could be repeatedly identified in at least two sowing dates, with PVE ranging from 0.03% to 38.06%. Among these, six were for GL, three for GW, and one for TGW. There were three novel and stable loci (4A_598189950, 4B_307707920, 2D_622241054) located in conserved regions of the genome, which provide excellent genetic resources for pyramid breeding strategies of superior loci. Our findings offer a theoretical basis for cultivar improvement and marker-assisted selection in wheat breeding practices.

Detection of consensus genomic regions and candidate genes for quality traits in barley using QTL meta-analysis

Improving barley grain quality is a major goal in barley breeding. In this study, a total of 35 papers focusing on quantitative trait loci (QTLs) mapping for barley quality traits published since 2000 were collected. Among the 454 QTLs identified in these studies, 349 of them were mapped onto high-density consensus maps, which were used for QTL meta-analysis. Through QTL meta-analysis, the initial QTLs were integrated into 41 meta-QTLs (MQTLs) with an average confidence interval (CI) of 1. 66 cM, which is 88.9% narrower than that of the initial QTLs. Among the 41 identified MQTLs, 25 were subsequently validated in publications using genome-wide association study (GWAS). From these 25 validated MQTLs, ten breeder's MQTLs were selected. Synteny analysis comparing barley and wheat MQTLs revealed orthologous relationships between eight breeder's MQTLs and 45 wheat MQTLs. Additionally, 17 barley homologs associated with rice quality traits were identified within the regions of the breeder's MQTLs through comparative analysis. The findings of this study provide valuable insights for molecular marker-assisted breeding and the identification of candidate genes related to quality traits in barley.

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 Databases and Gene Editing Tools for Enhancing Crop Resistance against Abiotic Stress

Abiotic stresses extensively reduce agricultural crop production globally. Traditional breeding technology has been the fundamental approach used to cope with abiotic stresses. The development of gene editing technology for modifying genes responsible for the stresses and the related genetic networks has established the foundation for sustainable agriculture against environmental stress. Integrated approaches based on functional genomics and transcriptomics are now expanding the opportunities to elucidate the molecular mechanisms underlying abiotic stress responses. This review summarizes some of the features and weblinks of plant genome databases related to abiotic stress genes utilized for improving crops. The gene-editing tool based on clustered, regularly interspaced short palindromic repeats (CRISPR)/CRISPR-associated protein 9 (Cas9) has revolutionized stress tolerance research due to its simplicity, versatility, adaptability, flexibility, and broader applications. However, off-target and low cleavage efficiency hinder the successful application of CRISPR/Cas systems. Computational tools have been developed for designing highly competent gRNA with better cleavage efficiency. This powerful genome editing tool offers tremendous crop improvement opportunities, overcoming conventional breeding techniques' shortcomings. Furthermore, we also discuss the mechanistic insights of the CRISPR/Cas9-based genome editing technology. This review focused on the current advances in understanding plant species' abiotic stress response mechanism and applying the CRISPR/Cas system genome editing technology to develop crop resilience against drought, salinity, temperature, heavy metals, and herbicides.

Telomere-to-telomere genome assembly of melon (Cucumis melo L. var. inodorus) provides a high-quality reference for meta-QTL analysis of important traits

Melon is an important horticultural crop with extensive diversity in many horticultural groups. To explore its genomic diversity, it is necessary to assemble more high-quality complete genomes from different melon accessions. Meanwhile, a large number of QTLs have been mapped in several studies. Integration of the published QTLs onto a complete genome can provide more accurate information for candidate gene cloning. To address these problems, a telomere-to-telomere (T2T) genome of the elite melon landrace Kuizilikjiz (Cucumis melo L. var. inodorus) was de novo assembled and all the published QTLs were projected onto it in this study. The results showed that a high-quality Kuizilikjiz genome with the size of 379.2 Mb and N50 of 31.7 Mb was de novo assembled using the combination of short reads, PacBio high-fidelity long reads, Hi-C data, and a high-density genetic map. Each chromosome contained the centromere and telomeres at both ends. A large number of structural variations were observed between Kuizilikjiz and the other published genomes. A total of 1294 QTLs published in 67 studies were collected and projected onto the T2T genome. Several clustered, co-localized, and overlapped QTLs were determined. Furthermore, 20 stable meta-QTLs were identified, which significantly reduced the mapping intervals of the initial QTLs and greatly facilitated identification of the candidate genes. Collectively, the T2T genome assembly together with the numerous projected QTLs will not only broaden the high-quality genome resources but also provide valuable and abundant QTL information for cloning the genes controlling important traits in melon.

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.

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.

Unraveling genomic regions and candidate genes for multiple disease resistance in upland cotton using meta-QTL analysis

Verticillium wilt (VW), Fusarium wilt (FW) and Root-knot nematode (RKN) are the main diseases affecting cotton production. However, many reported quantitative trait loci (QTLs) for cotton resistance have not been used for agricultural practices because of inconsistencies in the cotton genetic background. The integration of existing cotton genetic resources can facilitate the discovery of important genomic regions and candidate genes involved in disease resistance. Here, an improved and comprehensive meta-QTL analysis was conducted on 487 disease resistant QTLs from 31 studies in the last two decades. A consensus linkage map with genetic overall length of 3006.59 cM containing 8650 markers was constructed. A total of 28 Meta-QTLs (MQTLs) were discovered, among which nine MQTLs were identified as related to resistance to multiple diseases. Candidate genes were predicted based on public transcriptome data and enriched in pathways related to disease resistance. This study used a method based on the integration of Meta-QTL, known genes and transcriptomics to reveal major genomic regions and putative candidate genes for resistance to multiple diseases, providing a new basis for marker-assisted selection of high disease resistance in cotton breeding.

Application of potassium nitrate and salicylic acid improves grain yield and related traits by delaying leaf senescence in Gpc-B1 carrying advanced wheat genotypes

Grain protein content (GPC) is an important quality trait that effectively modulates end-use quality and nutritional characteristics of wheat flour-based food products. The Gpc-B1 gene is responsible for the higher protein content in wheat grain. In addition to higher GPC, the Gpc-B1 is also generally associated with reduced grain filling period which eventually causes the yield penalty in wheat. The main aim of the present study was to evaluate the effect of foliar application of potassium nitrate (PN) and salicylic acid (SA) on the physiological characteristics of a set of twelve genotypes, including nine isogenic wheat lines carrying the Gpc-B1 gene and three elite wheat varieties with no Gpc-B1 gene, grown at wheat experimental area of the Department of Plant Breeding and Genetics, PAU, Punjab, India. The PN application significantly increased the number of grains per spike (GPS) by 6.42 grains, number of days to maturity (DTM) by 1.03 days, 1000-grain weight (TGW) by 1.97 g and yield per plot (YPP) by 0.2 kg/plot. As a result of PN spray, the flag leaf chlorophyll content was significantly enhanced by 2.35 CCI at anthesis stage and by 1.96 CCI at 10 days after anthesis in all the tested genotypes. Furthermore, the PN application also significantly increased the flag leaf nitrogen content by an average of 0.52% at booting stage and by 0.35% at both anthesis and 10 days after anthesis in all the evaluated genotypes. In addition, the yellow peduncle colour at 30 days after anthesis was also increased by 19.08% while the straw nitrogen content was improved by 0.17% in all the genotypes. The preliminary experiment conducted using SA demonstrated a significant increase in DTM and other yield component traits. The DTM increased by an average of 2.31 days, GPS enhanced by approximately 3.17 grains, TGW improved by 1.13g, and YPP increased by 0.21 kg/plot. The foliar application of PN and SA had no significant effect on GPC itself. The findings of the present study suggests that applications of PN and SA can effectively mitigate the yield penalty associated with Gpc-B1 gene by extending grain filling period in the wheat.

Genomics for Yield and Yield Components in Durum Wheat

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

Identification and validation of a major quantitative trait locus for spike length and compactness in the wheat (Triticum aestivum L.) line Chuanyu12D7

Spike length (SL) and spike compactness (SC) are crucial traits related to wheat (Triticum aestivum L.) yield potential. In this study, a backcrossed inbred lines (BILs) population segregating for SL/SC was developed by using a commercial variety chuanyu25 as recurrent parent and a backbone parent Chuanyu12D7. Bulked segregant analysis (BSA) combined with the Wheat 660K SNP array was performed to conduct quantitative trait locus (QTL) mapping. A major and stable SL/SC QTL (designated as QSl/Sc.cib-2D.1) was identified on chromosome 2DS, explaining 45.63-59.72% of the phenotypic variation. QSl/Sc.cib-2D.1 was mapped to a 102.29-Kb interval by flanking SNPs AX-110276364 and AX-111593853 using a BC₄F_2:3 population. Since QSl/Sc.cib-2D.1 is linked to the Rht8 gene, their additive effects on plant type and spike type were analysed. Remarkably, the superior allele of QSl/Sc.cib-2D.1 combined with Rht8 can increase SL and TGW, and decrese SC without any apparent trade-offs in other yield-related traits. In addition, the closely linked kompetitive allele-specific PCR (KASP) markers of this locus were developed for marker-assisted selection (MAS) breeding. Four genes within the physical interval were considered as potential candidates based on expression patterns as well as orthologous gene functions. These results laid the foundation for map-based cloning of the gene(s) underlying QSl/Sc.cib-2D.1 and its potential application in wheat ideotype breeding.

Association mapping identifies loci and candidate genes for grain-related traits in spring wheat in response to heat stress

Heat stress is a limiting factor in wheat production along with global warming. Development of heat-tolerant wheat varieties and generation of suitable pre-breeding materials are the major goals in current wheat breeding programs. Our understanding on the genetic basis of thermotolerance remains sparse. In this study, we genotyped a collection of 211 core spring wheat accessions and conducted field trials to evaluate the grain-related traits under heat stress and non-stress conditions in two different locations for three consecutive years. Based on SNP datasets and grain-related traits, we performed genome-wide association study (GWAS) to detect stable loci related to thermotolerance. Thirty-three quantitative trait loci (QTL) were identified, nine of them are the same loci as previous studies, and 24 are potentially novel loci. Functional candidate genes at these QTL are predicted and proved to be relevant to heat stress and grain-related traits such as TaELF3-A1 (1A) for earliness per se (Eps), TaHSFA1-B1 (5B) influencing heat tolerance and TaVIN2-A1 (6A) for grain size. Functional markers of TaELF3-A1 were detected and converted to KASP markers, with their function and genetic diversity being analyzed in the natural populations. In addition, our results unveiled favor alleles controlling agronomic traits and/or heat stress tolerance. In summary, we provide insights into heritable correlation between yield and heat stress tolerance, which will accelerate the development of new cultivars with high and stable yield of wheat in the future.

Plant Tolerance to Drought Stress with Emphasis on Wheat

Environmental stresses, such as drought, have negative effects on crop yield. Drought is a stress whose impact tends to increase in some critical regions. However, the worldwide population is continuously increasing and climate change may affect its food supply in the upcoming years. Therefore, there is an ongoing effort to understand the molecular processes that may contribute to improving drought tolerance of strategic crops. These investigations should contribute to delivering drought-tolerant cultivars by selective breeding. For this reason, it is worthwhile to review regularly the literature concerning the molecular mechanisms and technologies that could facilitate gene pyramiding for drought tolerance. This review summarizes achievements obtained using QTL mapping, genomics, synteny, epigenetics, and transgenics for the selective breeding of drought-tolerant wheat cultivars. Synthetic apomixis combined with the msh1 mutation opens the way to induce and stabilize epigenomes in crops, which offers the potential of accelerating selective breeding for drought tolerance in arid and semi-arid regions.

Validation and marker-assisted selection of DArT-genomic regions associated with wheat yield-related traits under normal and drought conditions

Quantitative trait loci (QTL) is one of the most important steps in marker-assisted selection. Few studies have validated quantitative trait loci for marker-assisted selection of yield traits under drought stress conditions in wheat. A set of 138 highly diverse wheat genotypes were tested under normal and drought stress conditions for 2 years. Plant height, heading date, spike length, grain number per spike, grain yield per spike, and 1000-kernel weight were scored. High genetic variation was found among genotypes in all traits scored under both conditions in the 2 years. The same panel was genotyped using a diversity-array technology (DArT) marker, and a genome-wide association study was performed to find alleles associated with yield traits under all conditions. A set of 191 significant DArT markers were identified in this study. The results of the genome-wide association study revealed eight common markers in wheat that were significantly associated with the same traits under both conditions in the 2 years. Out of the eight markers, seven were located on the D genome except one marker. Four validated markers were located on the 3D chromosome and found in complete linkage disequilibrium. Moreover, these four markers were significantly associated with the heading date under both conditions and the grain yield per spike under drought stress condition in the 2 years. This high-linkage disequilibrium genomic region was located within the TraesCS3D02G002400 gene model. Furthermore, of the eight validated markers, seven were previously reported to be associated with yield traits under normal and drought conditions. The results of this study provided very promising DArT markers that can be used for marker-assisted selection to genetically improve yield traits under normal and drought conditions.

Comprehensive meta-QTL analysis for dissecting the genetic architecture of stripe rust resistance in bread wheat

BACKGROUND

Yellow or stripe rust, caused by the fungus Puccinia striiformis f. sp. tritici (Pst) is an important disease of wheat that threatens wheat production. Since developing resistant cultivars offers a viable solution for disease management, it is essential to understand the genetic basis of stripe rust resistance. In recent years, meta-QTL analysis of identified QTLs has gained popularity as a way to dissect the genetic architecture underpinning quantitative traits, including disease resistance.

RESULTS

Systematic meta-QTL analysis involving 505 QTLs from 101 linkage-based interval mapping studies was conducted for stripe rust resistance in wheat. For this purpose, publicly available high-quality genetic maps were used to create a consensus linkage map involving 138,574 markers. This map was used to project the QTLs and conduct meta-QTL analysis. A total of 67 important meta-QTLs (MQTLs) were identified which were refined to 29 high-confidence MQTLs. The confidence interval (CI) of MQTLs ranged from 0 to 11.68 cM with a mean of 1.97 cM. The mean physical CI of MQTLs was 24.01 Mb, ranging from 0.0749 to 216.23 Mb per MQTL. As many as 44 MQTLs colocalized with marker-trait associations or SNP peaks associated with stripe rust resistance in wheat. Some MQTLs also included the following major genes- Yr5, Yr7, Yr16, Yr26, Yr30, Yr43, Yr44, Yr64, YrCH52, and YrH52. Candidate gene mining in high-confidence MQTLs identified 1,562 gene models. Examining these gene models for differential expressions yielded 123 differentially expressed genes, including the 59 most promising CGs. We also studied how these genes were expressed in wheat tissues at different phases of development.

CONCLUSION

The most promising MQTLs identified in this study may facilitate marker-assisted breeding for stripe rust resistance in wheat. Information on markers flanking the MQTLs can be utilized in genomic selection models to increase the prediction accuracy for stripe rust resistance. The candidate genes identified can also be utilized for enhancing the wheat resistance against stripe rust after in vivo confirmation/validation using one or more of the following methods: gene cloning, reverse genetic methods, and omics approaches.

Identification and validation of two major QTLs for spikelet number per spike in wheat (Triticum aestivum L.)

The total number of spikelets (TSPN) and the number of fertile spikelets (FSPN) affect the final number of grains per spikelet in wheat. This study constructed a high-density genetic map using 55K single nucleotide polymorphism (SNP) arrays from a population of 152 recombinant inbred lines (RIL) from crossing the wheat accessions 10-A and B39. Twenty-four quantitative trait loci (QTLs) for TSPN and 18 QTLs for FSPN were localized based on the phenotype in 10 environments in 2019-2021. Two major QTLs, QTSPN/QFSPN.sicau-2D.4 (34.43-47.43 Mb) and QTSPN/QFSPN.sicau-2D.5(32.97-34.43 Mb), explained 13.97%-45.90% of phenotypic variation. Linked kompetitive allele-specific PCR (KASP) markers further validated these two QTLs and revealed that QTSPN.sicau-2D.4 had less effect on TSPN than QTSPN.sicau-2D.5 in 10-A×BE89 (134 RILs) and 10-A×Chuannong 16 (192 RILs) populations, and one population of Sichuan wheat (233 accessions). The alleles combination haplotype 3 with the allele from 10-A of QTSPN/QFSPN.sicau-2D.5 and the allele from B39 of QTSPN.sicau-2D.4 resulted in the highest number of spikelets. In contrast, the allele from B39 for both loci resulted in the lowest number of spikelets. Using bulk-segregant analysis-exon capture sequencing, six SNP hot spots that included 31 candidate genes were identified in the two QTLs. We identified Ppd-D1a from B39 and Ppd-D1d from 10-A and further analyzed Ppd-D1 variation in wheat. These results identified loci and molecular markers with potential utility for wheat breeding and laid a foundation for further fine mapping and cloning of the two loci.

Genomics-driven breeding for local adaptation of durum wheat is enhanced by farmers' traditional knowledge

In the smallholder, low-input farming systems widespread in sub-Saharan Africa, farmers select and propagate crop varieties based on their traditional knowledge and experience. A data-driven integration of their knowledge into breeding pipelines may support the sustainable intensification of local farming. Here, we combine genomics with participatory research to tap into traditional knowledge in smallholder farming systems, using durum wheat (Triticum durum Desf.) in Ethiopia as a case study. We developed and genotyped a large multiparental population, called the Ethiopian NAM (EtNAM), that recombines an elite international breeding line with Ethiopian traditional varieties maintained by local farmers. A total of 1,200 EtNAM lines were evaluated for agronomic performance and farmers' appreciation in three locations in Ethiopia, finding that women and men farmers could skillfully identify the worth of wheat genotypes and their potential for local adaptation. We then trained a genomic selection (GS) model using farmer appreciation scores and found that its prediction accuracy over grain yield (GY) was higher than that of a benchmark GS model trained on GY. Finally, we used forward genetics approaches to identify marker-trait associations for agronomic traits and farmer appreciation scores. We produced genetic maps for individual EtNAM families and used them to support the characterization of genomic loci of breeding relevance with pleiotropic effects on phenology, yield, and farmer preference. Our data show that farmers' traditional knowledge can be integrated in genomics-driven breeding to support the selection of best allelic combinations for local adaptation.

Meta-QTLs, ortho-meta QTLs and related candidate genes for yield and its component traits under water stress in wheat (Triticum aestivum L.)

Meta-QTLs (MQTLs), ortho-MQTLs, and related candidate genes (CGs) for yield and its seven component traits evaluated under water deficit conditions were identified in wheat. For this purpose, a high density consensus map and 318 known QTLs were used for identification of 56 MQTLs. Confidence intervals (CIs) of the MQTLs were narrower (0.7-21 cM; mean = 5.95 cM) than the CIs of the known QTLs (0.4-66.6 cM; mean = 12.72 cM). Forty-seven MQTLs were co-located with marker trait associations reported in previous genome-wide association studies. Nine selected MQTLs were declared as 'breeders MQTLs' for use in marker-assisted breeding (MAB). Utilizing known MQTLs and synteny/collinearity among wheat, rice and maize, 12 ortho-MQTLs were also identified. A total of 1497 CGs underlying MQTLs were also identified, which were subjected to in-silico expression analysis, leading to identification of 64 differentially expressed CGs (DECGs) under normal and water deficit conditions. These DECGs encoded a variety of proteins, including the following: zinc finger, cytochrome P450, AP2/ERF domain-containing proteins, plant peroxidase, glycosyl transferase, glycoside hydrolase. The expression of 12 CGs at seedling stage (3 h stress) was validated using qRT-PCR in two wheat genotypes, namely Excalibur (drought tolerant) and PBW343 (drought sensitive). Nine of the 12 CGs were up-regulated and three down-regulated in Excalibur. The results of the present study should prove useful for MAB, for fine mapping of promising MQTLs and for cloning of genes across the three cereals studied.

Supplementary Information

The online version contains supplementary material available at 10.1007/s12298-023-01301-z.

Genome-Wide Meta-Analysis of QTLs Associated with Root Traits and Implications for Maize Breeding

Root system architecture (RSA), also known as root morphology, is critical in plant acquisition of soil resources, plant growth, and yield formation. Many QTLs associated with RSA or root traits in maize have been identified using several bi-parental populations, particularly in response to various environmental factors. In the present study, a meta-analysis of QTLs associated with root traits was performed in maize using 917 QTLs retrieved from 43 mapping studies published from 1998 to 2020. A total of 631 QTLs were projected onto a consensus map involving 19,714 markers, which led to the prediction of 68 meta-QTLs (MQTLs). Among these 68 MQTLs, 36 MQTLs were validated with the marker-trait associations available from previous genome-wide association studies for root traits. The use of comparative genomics approaches revealed several gene models conserved among the maize, sorghum, and rice genomes. Among the conserved genomic regions, the ortho-MQTL analysis uncovered 20 maize MQTLs syntenic to 27 rice MQTLs for root traits. Functional analysis of some high-confidence MQTL regions revealed 442 gene models, which were then subjected to in silico expression analysis, yielding 235 gene models with significant expression in various tissues. Furthermore, 16 known genes viz., DXS2, PHT, RTP1, TUA4, YUC3, YUC6, RTCS1, NSA1, EIN2, NHX1, CPPS4, BIGE1, RCP1, SKUS13, YUC5, and AW330564 associated with various root traits were present within or near the MQTL regions. These results could aid in QTL cloning and pyramiding in developing new maize varieties with specific root architecture for proper plant growth and development under optimum and abiotic stress conditions.

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.

Wheat Omics: Advancements and Opportunities

Plant omics, which includes genomics, transcriptomics, metabolomics and proteomics, has played a remarkable role in the discovery of new genes and biomolecules that can be deployed for crop improvement. In wheat, great insights have been gleaned from the utilization of diverse omics approaches for both qualitative and quantitative traits. Especially, a combination of omics approaches has led to significant advances in gene discovery and pathway investigations and in deciphering the essential components of stress responses and yields. Recently, a Wheat Omics database has been developed for wheat which could be used by scientists for further accelerating functional genomics studies. In this review, we have discussed various omics technologies and platforms that have been used in wheat to enhance the understanding of the stress biology of the crop and the molecular mechanisms underlying stress tolerance.

Meta-QTL Analysis for Yield Components in Common Bean (Phaseolus vulgaris L.)

Common bean is one of the most important legumes produced and consumed worldwide because it is a highly valuable food for the human diet. However, its production is mainly carried out by small farmers, who obtain average grain yields below the potential yield of the species. In this sense, numerous mapping studies have been conducted to identify quantitative trait loci (QTL) associated with yield components in common bean. Meta-QTL (MQTL) analysis is a useful approach to combine data sets and for creating consensus positions for the QTL detected in independent studies. Consequently, the objective of this study was to perform a MQTL analysis to identify the most reliable and stable genomic regions associated with yield-related traits of common bean. A total of 667 QTL associated with yield-related traits reported in 21 different studies were collected. A total of 42 MQTL associated with yield-related traits were identified, in which the average confidence interval (CI) of the MQTL was 3.41 times lower than the CIs of the original QTL. Most of the MQTL (28) identified in this study contain QTL associated with yield and phenological traits; therefore, these MQTL can be useful in common bean breeding programs. Finally, a total of 18 candidate genes were identified and associated with grain yield within these MQTL, with functions related to ubiquitin ligase complex, response to auxin, and translation elongation factor activity.

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.

Genetic structure and molecular mechanism underlying the stalk lodging traits in maize (Zea mays L.)

Stalk lodging seriously affects yield and quality of crops, and it can be caused by several factors, such as environments, developmental stages, and internal chemical components of plant stalks. Breeding of stalk lodging-resistant varieties is thus an important task for maize breeders. To better understand the genetic basis underlying stalk lodging resistance, several methods such as quantitative trait locus (QTL) mapping and genome-wide association study (GWAS) have been used to mine potential gene resources. Based on different types of genetic populations and mapping methods, many significant loci associated with stalk lodging resistance have been identified so far. However, few work has been performed to compare and integrate these reported genetic loci. In this study, we first collected hundreds of QTLs and quantitative trait nucleotides (QTNs) related to stalk lodging traits in maize. Then we mapped and integrated the QTLs and QTNs in maize genome to identify overlapped hotspot regions. Based on the genomic confidence intervals harboring these overlapped hotspot regions, we predicted candidate genes related to stalk lodging traits. Meanwhile, we mapped reported genes to these hotspot regions. Finally, we constructed molecular regulatory networks underlying stalk lodging resistance in maize. Collectively, this study provides not only useful genetic loci for deeply exploring molecular mechanisms of stalk lodging resistance traits, but also potential candidate genes and targeted strategies for improving stalk lodging resistance to increase crop yields in future.

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.