Theoretical and Applied Genetics最新文献

Key message: RNA-seq, bulked segregant analysis, anthocyanin quantification, and WGCNA identified Pl1, Lc1, P1, and Wrky33 as important regulatory factors for enhancing anthocyanin content in maize pericarp. Anthocyanins are plant pigments that can be used as natural food colorants. We developed Midwestern, purple corn lines with enhanced anthocyanin content exclusively in the pericarp tissue layer: two lines near-isogenic to elite inbreds B73 and Mo17 (B73 Color Converted and Mo17 Color Converted, respectively) and two recombinant inbred lines (RILs) with diverse anthocyanin profiles (Amazonas and Maize Morado). In Experiment 1, a time-series, RNA-sequencing (RNA-seq) analysis of whole pericarp tissue was conducted on three pigmented genotypes (B73 Color Converted, Amazonas, and Maize Morado). Ultra-High-Pressure Liquid Chromatography (UHPLC) identified a dramatic increase in anthocyanin accumulation between 15 and 20 days after pollination (DAP) in pigmented genotypes. Bulk segregant analysis discovered Leaf color1 (Lc1) and Purple plant1 (Pl1) as the major contributors to pericarp pigmentation in B73 Color Converted. Additional loci Bronze2 (Bz2) and Pericarp color1 (P1) were also donated by the purple parent. In Experiment 2, RNA-seq was performed on 18 DAP kernels of four pigmented maize lines (B73 Color Converted, Mo17 Color Converted, Amazonas, and Maize Morado), comparing pigmented and unpigmented pericarp fractions from bulked individual kernels. Upregulation of Lc1, Pl1, and P1 suggests a distinct MBW protein complex in pigmented pericarp. Correlational analyses of 18 DAP pigmented pericarp fractions revealed enriched expression of anthocyanin transporters, Bz2 and Multidrug resistance-associated protein3 (Mrpa3), and a candidate transcription factor, WRKY-transcription factor 33 (Wrky33). These candidate genes can be used in breeding programs as a source of natural food and beverage colorants and improve our understanding of the mechanisms underlying maize pericarp pigmentation.

The peduncle stem plays an essential role in resource allocation and water transport to spike grains under normal conditions. Therefore, exploring peduncle traits and their relationships with spike production under drought stress may provide insights into the mechanisms that mitigate the effects of drought stress on grain yield in wheat. To address this challenge, a panel of 198 highly homozygous and diverse spring wheat varieties was evaluated under normal and drought conditions across two growing seasons. Peduncle traits, namely, length (PL), diameter (PD), and weight (PW), as well as spike traits, namely, spike length (SPL), number of spikelets/spike (NSPS), grain number/spike (GNPS), and grain yield per spike (GYPS), and thousand-kernel weight (TKW), were assessed. We revealed that PW and PD, unlike PL, were significantly and strongly associated with spike traits and grain weight under both conditions. Genome-wide association study (GWAS) revealed that spike and peduncle traits were controlled by different genetic mechanisms, as no stable markers were shared between these two groups. Distinct SNPs found between genotypes with contrasting peduncle traits led to the identification of a key SNP marker in a gene model encoding a protein highly expressed in the peduncle and spike of wheat. Comparing cultivars with low peduncle trait values to cultivars with high peduncle trait values, particularly PW and PD, high peduncle trait cultivars had greater yield-related trait values under both drought and normal conditions. The results of this study shed light on the importance of peduncle traits in enhancing wheat spike productivity under normal and drought stress conditions.

Key message: Four novel and stable disease resistance QTL associated with Gibberella stalk rot were identified and validated. A major QTL qGSR_8.08 was fine-mapped to a 1.34 Mb region. Gibberella stalk rot (GSR) is a highly destructive fungal disease that threatens global maize production significantly. Although many quantitative trait loci (QTL) associated with maize stalk rot resistance have been identified, only a few have been validated and fine-mapped. KA105 is an elite maize inbred line widely utilized in arid and semi-arid areas in China, which shows high level of resistance to multiple diseases. Here, a recombinant inbred line (RIL) population derived from a cross between KA105 and KB204 was evaluated for GSR resistance in four environments and genotyped using 61,282 single-nucleotide polymorphism (SNP) markers. Twenty GSR QTL were identified: 11 are novel and stable QTL; 13 QTL contain favorable alleles from KA105. Phenotypic effects of four novel and stable QTL with relatively large effects, qGSR_5.05, qGSR_5.09, qGSR_8.08, and qGSR_10.06, were validated in field trials using near-isogenic lines (NILs) developed from heterogeneous inbred families (HIFs). KA105-derived alleles significantly reduced the mean disease severity index (DSI) by 15.20-52.02% compared to lines with the KB204 alleles at these loci. Pyramiding of multiple QTL, specifically qGSR_5.05, qGSR_5.09, qGSR_8.08, and qGSR_10.06, significantly enhanced stalk rot resistance and decreased yield losses in hybrids under field disease conditions. qGSR_8.08 was further fine-mapped to a 1.34 Mb physical region, which conferred multiple disease resistance against Gibberella stalk rot, Gibberella ear rot, and Fusarium ear rot. This study provides valuable insights into the genetic basis of stalk rot resistance and demonstrates the potential of marker-assisted gene pyramiding to enhance maize disease resistance.

Soil salinization poses a major threat to global agricultural productivity, particularly for salt-sensitive crops such as potato (Solanum tuberosum L.). However, the genetic and metabolic mechanisms underlying salt tolerance in potato remain poorly understood. Here, we conducted hydroponic screening of ten potato varieties under NaCl stress and identified Dongnong 303 (DN303) as a highly salt-tolerant genotype. Physiological analyses revealed that DN303 maintained root growth and exhibited enhanced antioxidant activity and osmoprotectant accumulation under salt stress. Integrated transcriptomic and metabolomic profiling revealed that glycerophospholipid metabolism, carbohydrate metabolism, and redox regulation were central to DN303's stress response. Notably, the StGDPD1 gene, encoding a glycerophosphodiester phosphodiesterase, was strongly upregulated under salt stress and associated with increased levels of glycerol-3-phosphate (G3P), a metabolite involved in membrane lipid remodeling and osmotic regulation. Functional validation showed that StGDPD1 overexpression enhanced salt tolerance, while CRISPR-Cas9 knockout lines were hypersensitive to salt stress, with reduced G3P content and impaired antioxidant defense. These findings establish StGDPD1 as a key positive regulator of salt tolerance in potato and highlight the importance of lipid metabolic pathways in abiotic stress adaptation. This work provides a mechanistic foundation and candidate gene for breeding salt-tolerant potato varieties.

Key message: Several seemingly distinct regression methods are closely related. Environmental covariates delivered improved prediction, and a new approach improves estimation of prediction variance. In plant breeding and variety testing, there is an increasing interest in making use of environmental information to enhance predictions for new environments. Here, we will review linear mixed models that have been proposed for this purpose. The emphasis will be on predictions and on methods to assess the uncertainty of predictions for new environments. Our point of departure is straight-line regression, which may be extended to multiple environmental covariates and genotype-specific responses. When observable environmental covariates are used, this is also known as factorial regression. Early work along these lines can be traced back to Stringfield & Salter (1934) and Yates & Cochran (1938), who proposed a method nowadays best known as Finlay-Wilkinson regression. This method, in turn, has close ties with regression on latent environmental covariates and factor-analytic variance-covariance structures for genotype-environment interaction. Extensions of these approaches - reduced rank regression, kernel- or kinship-based approaches, random coefficient regression, and extended Finlay-Wilkinson regression - will be the focus of this paper. Our objective is to demonstrate how seemingly disparate methods are very closely linked and fall within a common model-based prediction framework. The framework considers environments as random throughout, with genotypes also modeled as random in most cases. We will discuss options for assessing uncertainty of predictions, including cross validation and model-based estimates of uncertainty, the latter one being estimated using our new suggested approach. The methods are illustrated using a long-term rice variety trial dataset from Bangladesh.

Utilizing the diversity preserved in genebank collections is essential for accelerating crop improvement, yet information is often limited to selected core collections. Genome-wide prediction (GWP) offers a promising approach to large-scale phenotypic imputation, with proven utility in practical pre-breeding contexts. In this study, we leveraged GWP to expand the German Federal ex situ barley core collection (core1000) with a focus on resistance to Puccinia hordei, Blumeria graminis hordei, and Rhynchosporium commune. Using the barley core1000 collection, which was originally selected to maximize molecular diversity, we trained genomic prediction models and imputed resistance scores for 20,458 genebank accessions based on sequence data encompassing 306,049 high-quality SNPs. To empirically validate prediction accuracy, we selected 300 spring and winter barley genotypes for field evaluation across four environments, resulting in moderate-to-strong correlations between predicted and observed resistance levels. Genome-wide association mapping in this set revealed five marker-trait associations that were not detected in the original core1000 collection. These results demonstrate that prediction-informed sampling can effectively expand trait-relevant genetic diversity and increase the frequency of resistance-associated alleles, thereby improving the power to detect loci that may be overlooked in conventional panels. Accordingly, GWP supports the targeted inclusion of accessions with trait-relevant variation and enhances the value of genebank resources for trait discovery and pre-breeding applications.

Key message: A genotype-by-environment interaction analysis and haplotype mapping approach identifies novel haplo-blocks that can be combined with Rph20 for enhanced resistance against barley leaf rust. Barley (Hordeum vulgare L.) production worldwide is threatened by different rust diseases, particularly barley leaf rust (BLR) caused by fungus Puccinia hordei. Yet, very limited works have explored BLR resistance mechanism across multiple environments. This study explored genotype-by-environment interactions (GEI) in a BLR disease screening dataset collected over multiple years using a multi-environment trial (MET) analysis followed by iClass method. A haplotype-based approach, using local genomic estimated breeding values (LGEBVs), identified five environmentally stable genomic regions (haplo-blocks: 2HS-b000305, 5HS-b001038, 5HS-b001039, 5HS-b001040 and 5HL-b001125) associated with BLR resistance at adult plant stage. While haplo-block co-locating popular adult plant resistance (APR) gene Rph20 was validated as a key genomic region to drive stability in resistance across multiple environments, other haplo-blocks with high-effect haplotypes were also reported as prospective novel sources of stability. Notably, environmentally specific haplo-blocks offered insights into GEI-driven resistance mechanisms. The study also highlighted the potential of haplo-block stacking to improve adult plant resistance as genotypes with multiple favorable haplotypes demonstrated a linear relationship with enhanced BLR resistance. These findings hold practical implications for barley breeders, paving the way for more resilient cultivars and advancing breeding methodologies for complex traits like disease resistance.

Key message: This study presents a Bayesian neural networks framework with LASSO regularization and the GSMeSP interpretability tool, enabling accurate, uncertainty-aware, and biologically interpretable genomic prediction. Deep learning offers significant potential for genomic prediction by modeling complex, nonlinear genotype-phenotype relationships. However, its application in plant breeding has been constrained by limited model interpretability and a lack of uncertainty quantification. To address these challenges, we developed a Bayesian neural networks (BNNs) framework incorporating least absolute shrinkage and selection operator (LASSO) regularization for multi-trait genomic prediction with credible uncertainty estimation. In parallel, we introduce GSMeSP, a novel interpretability framework that integrates SHapley Additive exPlanations (SHAP) with genome-wide association study (GWAS) signals to prioritize trait-associated single nucleotide polymorphisms (SNPs) from both statistical and biological perspectives. We applied this framework to a diverse panel of 1385 upland cotton (Gossypium hirsutum) accessions genotyped with over 12,000 SNPs, evaluating performance across multiple fiber-related traits. The BNNs model consistently outperformed conventional and deep learning benchmarks, achieving 0.46-47.85% improvements in predictive accuracy. Moreover, it generated trait- and sample-specific 95% credible intervals, enabling robust uncertainty quantification and more informed selection decisions. Using GSMeSP, we identified biologically meaningful loci, with a substantial proportion of top-ranked SNPs located in the D-subgenome. Notably, chromosome D05 emerged as a genomic hotspot enriched for SNPs associated with fiber length, lint percentage, and uniformity. By integrating high predictive performance, credible uncertainty estimation, and biologically grounded interpretability, our framework provides a transparent and robust deep learning approach to accelerate genomic selection in crop breeding programs.

Many nascent breeding programs aim to achieve genetic gain by crossing locally-elite germplasm, but a lack of systematic approaches to develop elite gene pools from locally adapted varieties hinders their progress. Motivated by the observation of undesirable transgressive segregation in presumed elite crosses in Senegalese cereal breeding programs, we designed approaches for de novo development of elite gene pools from locally adapted landrace-derived germplasm. We first define two types of "elite" germplasm: iso-elite, phenotypically similar and genetically homogeneous for locally adapted traits ("attained traits"); versus allo-elite, phenotypically similar, but genetically heterogeneous for attained traits. Next, we defined two genomic approaches for de novo inference of elite gene pools: population-based genotypic inference (PGI) and QTL-based genotypic inference (QGI), and compared to a family-based phenotypic inference (FPI) approach. Using simulations that trace the evolution from locally adapted landraces to elite breeding lines, we evaluate the effectiveness of these strategies in nascent forward breeding programs. QGI accurately and cost-effectively identifies both iso- and allo-elite pairs, regardless of the underlying trait architecture, while PGI is less sensitive when trait architecture is oligogenic. Over ten cycles of phenotypic recurrent selection, programs based on iso-elite crosses consistently outperformed those based on allo-elite crosses for genetic gain. The findings highlight the value of trait genetic architecture knowledge for elite gene pool development and provide a practical roadmap for elite germplasm development in modernizing breeding programs.

Rice cultivation in the rainfed lowland ecosystem during the rainy season is prone to encounter substantial flooding challenges in the form of complete submergence or prolonged stagnant flooding. While the Sub1 gene enables rice plants to survive the momentary complete submergence, stagnant flooding, defined by incomplete submergence for extended periods, necessitates moderate stem elongation for survival. In this study, we characterized 60 lowland NERICA varieties under stagnant flooding (SF) conditions, identify tolerant germplasm and detect genomic regions associated with key traits to aid breeding efforts. Phenotypic evaluations revealed significant genetic variability among the NERICA varieties, with some accessions showing 20-60% yield reduction under SF stress. The derived NERICA L-19/IR64 Sub1 RIL population showed improved grain yield under SF compared to both parents and submergence-tolerant checks. A total 27 QTLs were identified associated with plant height, tiller number, panicle number, days to flowering and grain yield. Stable and major-effect QTLs, such as qPH1.1, qPH3.1 and qDTF3.1, were consistent across environments, explaining up to 48% of the phenotypic variation. Several QTLs co-localized, indicating potential pleiotropy or tight linkage. Positional candidate genes associated with these regions include regulators of gibberellin signaling, flowering time and other developmental processes. This study highlights the potential of lowland NERICAs as a genetic resource and provides QTL, donor lines, molecular resources that form a practical basis for marker-assisted selection and pre-breeding of dual-tolerant rice cultivars adapted to climate-induced flooding scenarios in sub-Saharan Africa.