Theoretical and Applied Genetics最新文献

Key message: Combined single nucleotide polymorphisms (SNPs) and structural chromosome variations (SCVs), genomic regions associated with root angles (RAs), root numbers (RNs), and root dimensions (RDs) at adult stage were detected by multiple analyses, and a novel locus, QRD.sxau.6B.4, was finely mapped, providing valuable insights for assisting in the development of breeding programs for root-related traits in wheat. Roots play a crucial role in absorption of water and minerals, impacting agronomic traits and yield. While direct measurements of root phenotypes in the field are time-consuming and labour-intensive, identifying root phenotypes on a large scale and analysing their genetic basis are essential. The present study investigated RAs, RNs, and RDs at adult stage and integrated association and linkage analyses, using SNPs and SCVs, to dissect root-related traits. The results indicated that three root-related traits exhibited abundant phenotypic variations in both populations. Notably, RNs, RAs, and RDs significantly decreased over the years of release, with decreases of 13.49%, 16.70%, and 50.95%, respectively. Identification of seedling root biomass may provide a reference for determining RA and RD at adult stage. A total of 25 (SNPs) and 15 (SCVs) significant loci in two populations were identified, explaining 3.21% to 16.61% of phenotypic variation. An epistasis analysis revealed an interaction between QRN.sxau.1D.3 and QRN.sxau.2A.1 related to RN, while no epistatic effects were observed in other loci regions. The QRD.sxau.6B.4/PAV.6B overlapped in the same genomic region by association panel and doubled haploid (DH) population, explaining 5.19% to 9.75% of phenotypic variations. In secondary mapping population, fine mapping of QRD.sxau.6B.4 narrowed functional region down to 8.48 Mb region combining RD and genotypes. Two introgression lines were used to demonstrate the significant potential of PAV.6B for root improvement. The results of present study provide novel insights into genetic mechanisms governing root development at adult stage in wheat.

Key message: The findings reveal that BnCLV1 modulates root growth and yield under N-deficient conditions in rapeseed, which may offering a target for breeding varieties with enhanced N use efficiency and improved agricultural sustainability. Nitrogen (N) deficiency is a major limiting factor for rapeseed root growth and yield. Therefore, genetic improvement of rapeseed with N acquisition and utilisation efficiency is a curial strategy for enhancing rapeseed yield and N fertiliser efficiency. Using genome-wide association study (GWAS) and transcriptome analyses, we found that natural variation of leucine-rich repeat receptor-like kinase gene (BnCLV1) regulates N-responsive root system architecture at the seeding stage and yield at the maturity in the natural rapeseed population. N supply inhibited BnCLV1 expression, thereby increasing lateral root number, root surface area, shoot and root biomass, and total root length at the seeding stage. N supply also enhanced yield, pod number and silique length in the maturation stage. GUS assays showed that BnCLV1 signals were negatively regulated in response to N deficiency in primary root, lateral root primordia, leaf veins, and floral primordia tissues. Thus, BnCLV1 modulates lateral root and floral primordia activity, reducing root growth and yield under N deficiency. Overall, these findings reveal that BnCLV1 modulates root growth and yield under N deficiency in rapeseed, providing a target for breeding varieties with enhanced N-use efficiency and improved agricultural sustainability.

Key message: Through an integrated approach of genetic mapping, transcriptomics, and functional validation, we identified VrGSTU18 as the primary gene associated with fomesafen resistance in mung bean, providing a genetic resource for breeding herbicide-resistant varieties. Herbicides are widely applied for weed control in mung bean cultivation, and the development of new varieties with herbicide resistance is critical for weed management. In this study, a recombinant inbred line (RIL) population, derived from a cross between the fomesafen-resistant variety LZ177 and susceptible variety LD235, was used to map the genes related to fomesafen herbicide resistance. Genetic segregation analysis indicated that fomesafen resistance is controlled by a single dominant gene, following a 3:1 ratio. Genetic mapping combined BSA-seq revealed a candidate region of 1.17 Mb on chromosome 11. RNA-seq analysis of residual heterozygous line 198-comparing resistant (RHL198-R) and susceptible (RHL198-S) bulks at 0, 12, 24, 48, and 72 h after fomesafen treatment-identified 14,402 herbicide-responsive genes. Weighted gene coexpression network analysis (WGCNA) further identified nine modules highly correlated with fomesafen resistance, of which 13 potential candidate genes were selected within the 1.17 Mb interval. Among these, one-base (A) insertion/deletion in the exon of jg37117, which encode a tau-class glutathione S-transferase U18 (GSTU18), emerged as the most promising candidate gene. Heterologous expression of VrGSTU18 cloned from LZ177 in Arabidopsis conferred enhanced fomesafen resistance in T₁ transgenic seedlings compared to wild-type plants. These findings identified VrGSTU18 as a key candidate gene responsible for fomesafen resistance and provided a theoretical basis for molecular breeding in mung bean.

Key message: In the 440-line Southern Oat Association Panel (SOAP), GWAS detected 17 QTL, with ten in the adult stage, five in the seedling stage, and two expressed at both stages. The chromosome 1A region that aligns with Pg13 was the dominant signal. Effects combined additively, and stacking five stable adult plant loci nearly halved the disease severity Six QTL correspond to known regions, and eleven are newly reported. Stem rust (SR), caused by Puccinia graminis f. sp. avenae, threatens global oat production and remains a priority for resistance breeding in the southeastern USA. We conducted a multi-environment genome-wide association study of 440 Southern Oat Association Panel (SOAP) lines, pairing multi-year, multi-location adult plant phenotyping with seedling evaluations. Adult plant resistance response was measured as severity (SV) and infection response (IR). Seedling resistance was evaluated as infection type (IT) using isolates of four pathogen races (DBD, SGD, TGN, and TJS). In total, we detected 17 quantitative trait loci (QTL), including 10 expressed at the adult plant stage, five at the seedling stage, and two shared across stages. The strongest signal was a four-peak cluster on chromosome 1A (370.8-464.4 Mb) within the Pg13 interval. Conditional analysis showed that these peaks were not explained by a single effect, indicating multiple independent determinants within the interval. Two loci affected both seedling IT to TGN and adult field reactions, consistent with at least one race-specific all-stage component, whereas two others were restricted to adult plants. Additional, smaller-effect loci on chromosomes 1D and 6D contributed additively to adult plant field resistance. In contrast, race-specific seedling resistance loci were detected on chromosomes 2A, 2D, and 3C. Stacking stable adult-stage QTL produced cumulative effects on SV and IR, with reductions only when multiple favorable alleles were combined. Candidate intervals were enriched for nucleotide-binding site leucine-rich repeats and receptor-like kinases. Once, validated these SNPs can support marker-assisted selection for SR resistance in southeastern oat breeding programs.

Cytoplasmic male sterility (CMS) and nucleus-controlled fertility restoration systems are essential tools for hybrid breeding to improve crop productivity. In eggplant (Solanum melongena), CMS lines have been widely developed through interspecific hybridization, but the genetics basis of fertility restoration remains poorly understood. In the present study, a fertility restorer (Rf) introgression line '3-26' was generated through interspecific hybridization between S. aethiopicum and cultivated eggplant. The major Rf locus was fine-mapped to ~ 118.4-kb interval on chromosome 6 using whole-genome resequencing according to '67/3' v3.0. Collinearity analysis revealed that this interval is an introgression fragment corresponding to a ~ 436-kb region in S. aethiopicum genome, which contained a cluster of 17 tandemly arranged pentatricopeptide repeat (PPR) genes. Among them, Solaet3_06g003840 is proposed as the best candidate according to RNA-seq, gene structure, and phylogenetic analysis. Further comparative syntenic analysis revealed that the Rf-PPR cluster is present in four other wild eggplant species, but absent in cultivated varieties. We further developed a Rf-linked KASP maker that is practically used for eggplant CMS/Rf breeding systems. These findings broaden our understanding of the fertility restoration for alloplasmic CMS systems and provide practical tools for MAS hybrid breeding in eggplant.

Key message: A genetic algorithm-optimized deep neural network was developed using proximal sensing data to accurately predict wheat yield at field scale, outperforming traditional machine learning models under diverse conditions. Hand held or vehicle-mounted active proximal sensing technologies offer a rapid, non-destructive method for real-time crop monitoring through spectral vegetation indices. This study integrates such proximal sensing data into a deep learning framework for field-scale wheat yield prediction. Specifically, wheat yield is predicted using normalized difference vegetation index (NDVI), canopy temperature (CT), and plant height (PH) through a deep neural network (DNN) optimized using a genetic algorithm (GA). The model is trained on data from 3,350 diverse wheat germplasm grown under irrigated and rainfed conditions at two locations during the 2020-2021 winter season. Comparative analysis demonstrates that the GA-optimized DNN outperforms traditional machine learning models such as Random Forest Regression (RFR), Least Absolute Shrinkage and Selection Operator (LASSO), and Support Vector Regression (SVR). Among individual feature groups, NDVI measured at five wheat growth stages showing strong predictive capability, with R² values ≥ 60% under irrigated and ≥ 50% under rainfed conditions. Additionally, RFR is employed to identify the most influential features for predicting grain yield. This pioneering study introduces the first-ever application of a GA-optimized deep neural network, leveraging handheld or vehicle-mounted proximal sensing data for predicting crop yield, in the context of Indian agriculture. The proposed approach offers a robust and scalable solution for pre-harvest yield estimation, supporting breeders and researchers in efficient genotype selection and contributing to the achievement of sustainable development goals.

Key message: Sparse testcrossing with 3-5 testers enhances genetic gain in hybrid breeding programs, offering a practical balance of simple testcross designs, resource efficiency, and increased prediction accuracy for general combining ability. Sparse testcrossing is an effective strategy for increasing both short- and long-term genetic gain in hybrid breeding programs. Maize hybrid breeding programs aim to develop new hybrid varieties by crossing genetically distinct parents from different heterotic pools, exploiting heterosis for improved performance. The programs typically consist of two main components: population improvement and product development. The population improvement component aims to enhance the heterotic pools through reciprocal recurrent selection based on general combining ability (GCA). However, especially in the early stages of testing, evaluating large numbers of hybrid combinations to estimate GCA is impractical due to considerable logistical challenges and costs. Therefore, breeders often evaluate the initial population of selection candidates using only a single tester to narrow down the candidate pool before further evaluation. Using a single tester, however, may not adequately represent the heterotic pool, leading to inaccurate GCA estimates and suboptimal selection decisions. To address this, we propose sparse testcrossing for early-stage testing, where subsets of candidate genotypes are testcrossed with different testers, connected through a genomic relationship matrix. We conducted stochastic simulations to compare various sparse testcrossing designs with a conventional testcross strategy using a single tester over 15 cycles of reciprocal recurrent genomic selection. Our results show that using 3-5 testers, sparsely distributed among full-sibs, sparse testcrossing offers breeders a practical balance between simple testcross designs, resource efficiency, and increased prediction accuracy for GCA, ultimately resulting in increased rates of genetic gain.

The content and composition of fatty acids are crucial determinants of soybean nutritional quality. In this study, we conducted an expression genome-wide association analysis (eGWAS) using 298 soybean germplasm accessions. We identified 904,984 high-quality SNP markers (MAF > 0.02, missing data ≤ 10%). Thirty-three association signals were identified that correlated with to the expression levels of very long chain fatty acid (VLCFA) genes. Integrating KEGG pathway enrichment analysis with gene haplotype analysis, we identified GmLACS11 as the candidate gene that potentially involved in regulating long-chain fatty acid biosynthesis. We performed subcellular localization, bioinformatics analysis, and functional validation of the GmLACS11 gene. The fatty acid content was measured following GmLACS11 gene expression in Saccharomyces cerevisiae eukaryotic expression, Arabidopsis thaliana, and in soybean overexpression and knockout lines. The results demonstrated that both overexpression and knockout of GmLACS11 gene altered soybean fatty acid composition. Overexpression significantly increased the levels of the polyunsaturated fatty acids, linoleic acid and linolenic acid, and a corresponding rise in the total fatty acid content was observed. These findings provide insights into the regulation of soybean very long chain fatty acids biosynthesis and the genetic mechanisms underlying soybean fatty acids composition.

Anthracnose is an important fungal disease in cucurbits, caused by the pathogen Colletotrichum orbiculare, which negatively affects all the aboveground parts of the plant. Race 2 anthracnose causes severe economic damage in watermelon. The objective of the study was to identify race 2 anthracnose resistance QTL in a biparental mapping population and association mapping panel. For the F₂ biparental population (N = 188), resistant and susceptible parents were PI 189225 (C. amarus) and 'New Hampshire Midget' (C. lanatus), respectively. The association mapping panel consisted of 1,008 watermelon germplasm accessions (C. amarus (N = 72), C. lanatus (N = 894), and C. mucosospermus (N = 42)). The biparental mapping population identified a significant QTL for race 2 anthracnose resistance, Qar2-3 (LOD = 4.53), on chromosome 3 from the resistant parent, PI 189225. In the association mapping panel, MLM and BLINK models identified a significant marker S06_9279285 and S08_ 28493121 (LOD > 5) on chromosomes 6, Qar2-6, and 8, Qar2-8, respectively, conferring resistance to anthracnose race 2. Three receptor kinase genes (CaUC03G056690, CaUC03G056730, and CaUC03G056740) were close to the Qar2-3. Similarly, leucine-rich receptor-like protein kinase family protein (ClCG06G007520) and serine/threonine protein kinase (ClCG08G016080) genes were close to the Qar2-6 and Qar2-8, respectively. Inconsistent results on QTL locations between the biparental and association mapping populations could be due to various factors including selected germplasm, minor allele frequency, linkage disequilibrium (LD), LD decay, and genotyping. Future research should focus on identifying and understanding the roles of LRR-RLKs genes in governing resistance.

Soil salinization severely limits plant growth and development, posing a significant threat to agriculture. NAC transcription factors are widely involved in the regulation of various abiotic stresses. In this study, we discovered that SlNAC63 responds to both saline-alkali and jasmonic acid (JA) signaling and enhances saline-alkali tolerance in tomato (Solanum lycopersicum L.) by improving the reactive oxygen species (ROS) scavenging capacity. The experiments of Y1H, EMSA, and ChIP-qPCR confirmed that SlNAC63 directly targets and regulates the expression of tomato SlAOS1 and superoxide dismutase SlSOD4. This, in turn, promotes JA biosynthesis and enhances ROS scavenging ability, thereby positively regulating saline-alkali tolerance in tomato. Phenotypic analysis demonstrated that overexpressing SlAOS1 indeed increases JA accumulation, while overexpressing SlSOD4 significantly improves ROS scavenging under saline-alkali stress. Through Y2H, pull-down, and Co-IP assays, we found that SlNAC63 interacts with SlbHLH71. Furthermore, SlbHLH71 enhances the regulatory effects of SlNAC63 on SlAOS1 and SlSOD4 by interacting with SlNAC63 to strengthen its binding affinity to the promoters of SlAOS1 and SlSOD4, thereby promoting JA accumulation and ROS scavenging, which ultimately strengthens saline-alkali tolerance in tomato. This study unveils the central role of the SlNAC63-SlbHLH71 module in the regulation of saline-alkali stress and clarifies the molecular mechanism by which this module participates in the response of tomato to saline-alkali stress through the regulation of JA accumulation and ROS scavenging.