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

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.

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.

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

KEY MESSAGE

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

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.

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.

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.