Theoretical and Applied Genetics最新文献

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.

Sucrose transporters (SUTs) are crucial for plant growth, development, and stress responses. Despite sugarcane's importance as a sugar and biofuel crop, genomic data on its SUT genes under abiotic stress are limited. In this study, 37 ShSUT genes were identified through bioinformatic analysis. Phylogenetic classification grouped them into three major clades (I-III), with conserved motifs and gene structures supporting their evolutionary relationships. Promoter analysis revealed 15 key cis-elements related to hormone response, stress, development, and light regulation. All ShSUT genes were mapped on three contig regions and seven chromosomes. Collinearity and gene duplication analysis identified 15 segmentally duplicated gene pairs, indicating evolutionary expansion. Additionally, 7 putative 'sbi-miRNAs' were predicted to target 28 ShSUT genes, with sbi-miR5381 alone targeted 17 ShSUTs. For functional characterization, ShSUT04 was chosen due to its evolutionary significance, crucial role in sucrose transport, and potential involvement in regulating abiotic stress responses. Eighteen potential interactors were identified, with confirmed interactions for ShPsbR, ShRF2a, ShCOPTS.1, and ShSPT, validated through BiFC and Y2H assays. qRT-PCR analysis demonstrated stress-responsive expression patterns. Under cold stress, ShRF2a, ShPsbR, and ShSPT were down-regulated, indicating negative regulatory roles, while ShSUT04 and ShCOPT5.1 were up-regulated at specific time points, and ShSUT01 showed strong induction, suggesting a positive role in defense. Under drought, ShSUT04 and ShPsbR showed significant upregulation, suggesting positive regulatory roles. In salinity stress, while several genes were suppressed, ShSUT01 and ShPsbR were induced, reflecting their potential in stress adaptation. This study reveals the evolutionary and functional roles of sugarcane SUT genes in abiotic stress regulation, with ShSUT04 showing dual roles, positive under drought and negative under salinity and cold stresses.

Message: A QTL from rye chromosome 5R confers resistance to root-lesion nematode in triticale.Root-lesion nematode (Pratylenchus neglectus, RLN) poses a significant threat to global wheat production. High levels of RLN resistance are rare in wheat. Triticale, an amphiploid generated by combining wheat and rye genomes that naturally carries rye-derived defense alleles, offers an untapped reservoir of nematode resistance. Here, we evaluated the response to RLN in 137 recombinant inbred lines (RILs) derived from a cross between two triticale cultivars: Siskiyou (susceptible) and Villax St. Jose (resistant). Genotyping-by-sequencing identified 1054 high-quality single-nucleotide polymorphism (SNP) markers, which, along with seven simple sequence repeat (SSR) markers, were assembled into 21 linkage groups covering the triticale genome. A single quantitative trait locus (QTL) on the rye-derived chromosome 5R was identified that explained approximately 20% of the phenotypic variance across experiments. A high-throughput Kompetitive allele-specific PCR (KASP) assay based on the most significant SNP marker was developed, providing a rapid genotyping platform for selecting the resistance allele and reducing reliance on labor-intensive phenotyping for P. neglectus resistance in triticale. This study reports the first mapped RLN-resistance QTL in triticale, laying the fundamental foundation for introgressing the 5R resistance allele into wheat via marker-assisted selection combined with chromosome engineering, thereby broadening the genetic basis for nematode resistance in cereal crops.

Key message: BrRLP1 positively regulates the resistance to downy mildew in Brassica rapa by interacting with the monodehydroascorbate reductase BrMDAR1. Downy mildew is a devastating disease that severely affects the yield and quality in Brassica rapa. Receptor-like protein (RLP) is important for plants disease-resistant response. Here, a new downy mildew resistance gene, BrRLP1, was identified in Brassica rapa through GWAS analysis and QTL mapping. BrRLP1 encodes a membrane-localized receptor-like protein, and its expression level showed significant differences in the resistant and susceptible materials after inoculation with downy mildew. Transient expression and transgenic functional verification revealed that BrRLP1 is a positive regulator for the downy mildew resistance. All the BrRLP1^R overexpressed plants exhibited a high-resistance phenotype to downy mildew after inoculation. Haplotype analysis revealed that the SNP309 in the LRR domain of BrRLP1 is a key functional site for the resistance difference to downy mildew. Y2H and LCI assays showed that BrRLP1 can interact with the monodehydroascorbate reductase BrMDAR1, which is involved in the ascorbic acid metabolic pathway. Our results revealed the function of BrRLP1 in regulation of downy mildew resistance by interacting with BrMDAR1, which provides new insight into the molecular mechanism underlying disease resistance immune response in Brassica rapa.

Key message: Integrated genome-wide and haplotype-based association analyses identified a key genomic locus governing plant growth habit (PGH) traits in chickpea. Identification of molecular markers governing plant growth habit (PGH) traits that enable mechanical harvestability is pivotal for boosting production efficiency of crops under changing climates and increasing global food demand. With a combinatorial integrated genomics-assisted breeding strategy comprising of association mapping, haplotype-based association, molecular haplotyping and gene expression analysis in a 286 association panel of chickpea (Cicer arietinum), we dissected the genetic basis of PGH traits. This study employed 382,171 genome-wide SNPs (single-nucleotide polymorphisms) obtained from whole-genome sequencing (WGS) of 286 desi and kabuli chickpea accessions and delineated a major genomic locus associated with PGH traits variation, particularly between erect (E)/semi-erect (SE) versus spreading (S)/semi-spreading (SS) types. Within this genomic loci, CaPAR1 (Cicer arietinum PAR1) and its derived natural alleles/haplotypes was identified as the candidate gene. These findings can facilitate generation of high-yielding, erect/semi-erect, mechanically harvestable cultivars through translational genomics and molecular breeding for genetic enhancement of chickpea.