G3

Genetically identical cells grown in the same environment show variation in gene expression known as expression noise. Expression noise can be heritable and impact fitness, making it subject to natural selection. Increasing expression noise for the Saccharomyces cerevisiae TDH3 gene was shown to be beneficial in glucose-based media when mean TDH3 expression was far from the fitness optimum but deleterious when it was close to this optimum. Here, we show that growth on different carbon sources alters the effects of new mutations on TDH3 expression noise and examine the fitness effects of changing expression noise. In galactose-based media, we observed the same relationship between expression noise and fitness seen in glucose-based media, but in glycerol- and ethanol-based media, we observed the opposite relationship or no significant relationship, respectively. Using simulations of single-cell organisms, we found that these differences were most likely explained by environment-specific relationships between gene expression and fitness. We also found that, far from the optimum, the fitness effects of noise were greatest when expression was highly heritable between mother and daughter cells. The empirical observations and simulations reported in this study show how environments influence both the production of expression noise and its impacts on fitness.

Genomic prediction models that account genotype-by-environment (GxE) have the potential to accelerate the rate of genetic gain for yield and agronomic performance, yet relatively few studies have applied GxE prediction in public soft red winter wheat (Triticum aestivum) breeding programs. In this study, we extended a reaction norm-based genomic prediction framework by integrating weather-based environmental covariates to more effectively capture genotype- environment interactions. Key agronomic traits, including seed yield, plant height, test weight, and heading date, were evaluated across 33 environments (location-year) using over 3,200 breeding lines from the North Carolina State University small grains breeding program. Multiple genomic prediction models were compared using several cross-validation (CV) schemes representing common breeding scenarios. Across traits, the reaction norm M5 model, which incorporates both GxE and genotype-by-environmental covariate interactions (GxO), achieved the highest prediction accuracy (PA) in CV2 (predicting incomplete field trials) and CV1 for yield and test weight (predicting new lines). The highest PA was observed for test weight under CV2 (0.54) and for yield under CV1 (0.41). Under CV0 (predicting new environments), the M3 model incorporating GxE produced highest PA across traits, with the greatest accuracy for plant height (0.45), although differences among M2, M3, and M4 were small. Prediction under CV00 (predicting new lines in new environments) remained more challenging, with PA values 0.10 - 0.20 across traits. Overall, our results demonstrate that integrating environmental covariates into genomic prediction models can improve predictive performance across diverse wheat-growing environments in North Carolina, supporting their utility for applied breeding efforts. CORE IDEASO_LIIntegrating genotype-by-environment (GxE) interactions with environmental covariates improves prediction accuracy across environments. C_LIO_LIModel performance varies by prediction scenario, with different approaches performing best for new lines, incomplete trials, or new environments. C_LIO_LIPrediction of new lines in new environments remains challenging. C_LI PLAIN LANGUAGE SUMMARYThis study explores how adding environmental information to genomic prediction models can improve prediction accuracy in a public winter wheat breeding program. Using data from multi-environment trials conducted across diverse conditions in North Carolina, we evaluated statistical models that capture how different wheat lines respond to changing environments. By incorporating weather data, we improved the ability to predict performance across locations and years. These findings provide practical insights for refining selection strategies and accelerating genetic gain in wheat breeding.

Phenomic prediction (PP) is a breeding value prediction method using near infrared spectroscopy (NIRS). Spectra pre-processing is a key step in the analysis pipeline of PP and generally involves chemometrics methods. However, there is still little understanding in the genetics community of what pre-processing does and why it increases performances. Consequently, the choice of pre-processing is done either arbitrarily or through a search of the optimal set of methods and associated parameters. In this study, we propose a PCA-based pre-processing method where genetic values of spectra are estimated on a set of principal components instead of individual wavelengths. This way, estimations are based on a few informative and orthogonal features of spectra instead of many correlated, uninformative wavelengths. We tested this new pre-processing method on five data sets representing four plant species (maize, rice, sorghum and grapevine). Results show that it performs as good, or better than the best classical chemometric pre-processing methods in almost all cases. Combining PCA-based and classical chemometric pre-processing methods maximizes predictive ability. Moreover, this pre-processing method opens up possibilities of better understanding and selecting parts of the spectral information that are relevant for the prediction of breeding values. Indeed, components representing together about 1% of spectral variability were found to be responsible for most of PP predictive ability. Plain language summaryCultivated plants are the result of a breeding process during which their genetic values are used to select those to breed. Estimation of breeding values requires heavy experimental means and is time consuming. Phenomic prediction is a low cost and high throughput genetic value estimation method that is increasingly being used. It often uses near infrared spectroscopy measurements as predictors of genetic values that are easy to collect and thus routinely used in many species. However, near infrared spectra generally require pre-processing before being used in prediction. Currently used pre-processing methods arise from the chemometrics community, and still deserve a better in-depth appropriation by geneticists. In this study, we propose a new pre-processing approach that performs as good as or better than the best chemometric pre-processing generally used, reduces computation time, and allows for a better understanding of what parts of spectral information are relevant for prediction. Core IdeasO_LIWorking on principal components of spectra instead of wavelengths increases predictive ability of phenomic prediction and performs as good as or better than classical chemometrics pre-processing C_LIO_LIWorking on principal components of spectra requires less optimization of parameters than chemometrics pre-processing C_LIO_LIAbout 1% of spectral variance is responsible for most of the predictive power of phenomic prediction C_LIO_LIWorking on principal components of spectra pre-processed with classical chemometrics pre-processing can increase predictive ability even more C_LIO_LIPCA-based methods are valuable to optimize predictive ability of phenomic prediction and could be used more widely in the quantitative genetics field C_LI

Multi-environment trials are central to cultivar evaluation because they reveal how candidate cultivars perform across locations, years, management conditions, and stress environments. The resulting yield matrix is a rich source of data on genotype-by-environment interaction, and a wide literature on estimation, decomposition, visualisation, and prediction of yield potential and stability has flourished. However the ultimate question of which cultivar to recommend on the basis of such a matrix is often left implicit. The question is far from trivial, and in this paper we formulate cultivar recommendation as an axiomatic ranking problem. This framework is rich enough to encompass the existing literature on stability indices, as well as any other deterministic ranking procedure. We show that many commonly used stability-based procedures can violate minimal criteria of efficiency or consistency. The result of such violations is that a cultivar with uniformly high yield could be ranked below a cultivar with uniformly low yield, or the relative ranks of two cultivars could depend on whether or not a third cultivar is present in the matrix. Our results prove that under a small number of such criteria the space of admissible rules collapses to the family of power means and their limiting cases. If we further wish to allow multiplication normalisation of yield, we are left with the geometric mean as the unique solution.

Gene-by-environment (GxE) interactions play a major role in shaping both phenotypic and molecular variation, with important implications for human health and disease. In this study, we used the Doxycycline (Dox) regulated, tetracycline-responsive (Tet-Off) promoter system to sequentially reduce or titrate gene expression levels of the essential yeast transcription factor Repressor Activator Protein 1 (RAP1) similar to a hypomorph allele series, across three distinct environments: Yeast Peptone Dextrose (YPD) media, YPD media with Heat Shock (HS), and Yeast Peptone Acetate (YPAC) media. We then performed RNA sequencing (RNA Seq) to assess global transcriptional responses to RAP1 reduction in these different growth environments. Our analysis first focused on the independent effects of varying RAP1 expression levels within and across environments. We then explored GxE interactions, revealing a subset of genes with significant consequences of reduced levels of RAP1 and environment-specific expression patterns. Notably, many genes exhibited opposite effects of RAP1 titration on gene expression when yeast were grown in YPAC media compared to YPD media and/or HS, suggesting environment-dependent regulatory architecture. This design reveals how cells integrate internal transcriptional and regulatory changes with external environmental cues, providing a deeper view of GxE architecture. Using Weighted Gene Co-expression Network Analysis (WGCNA), we identified co-regulated gene modules, and by combining this with transcription factor motif enrichment tests, our study identified candidate regulators driving their dynamics. Our findings demonstrate that gene regulatory networks can vary dramatically depending on the environmental context an organism experiences, which can then influence the specific phenotypes produced by a particular genetic perturbation. This illustrates the complexity of genotype-environment interactions and the importance of studying gene function in multiple environments to gain a truly comprehensive understanding of a genes sometimes numerous and diverse functions.

High-quality fungal reference genomes are essential for comparative, functional, and evolutionary studies, yet fungal genome features such as repeats, structural rearrangements, accessory chromosomes, and intron-rich genes can complicate genome assembly and the selection of cost-effective sequencing strategies. Here, we benchmark fungal genome assembly performance using simulated and empirical short- and long-read datasets to evaluate how sequencing depth, assembler choice, and genome characteristics influence contiguity, completeness, accuracy, and computational requirements. Using simulated reads from complete fungal genomes spanning diverse sizes and compositions, we evaluated short-read, long-read, hybrid, and polished long-read assemblies across sequencing depths from 10X to 100X. Key trends were validated using empirical sequencing data from 10 fungal isolates assembled with multiple strategies, including different Flye assembler parameter sensitivity and short-read polishing. Across datasets, long reads produced the largest improvements in contiguity, with most gains achieved at [~]20-40X coverage and diminishing returns beyond moderate depth. Short-read polishing substantially improved base-level accuracy at relatively low cost, with [~]10-20X coverage often sufficient to approach maximal error reduction. Hybrid assemblers showed strong algorithmic variability, with trade-offs between contiguity, error rates, and computational demand. Genome architecture also influenced outcomes, as larger and more feature-dense genomes benefited more from long-read data while GC content had limited impact. Overall, our results suggest that moderate long-read coverage ([~]30-40X) combined with modest short-read polishing ([~]10-20X), particularly using Flye plus Polypolish, provides a strong balance of contiguity, completeness, accuracy, and resource efficiency for generating high-quality fungal genome assemblies. Impact statementFungal genome sequencing is expanding rapidly across ecology, plant pathology, biotechnology, and clinical and veterinary microbiology, yet experimental design decisions regarding sequencing depth, assembler selection, and hybrid workflows are still largely guided by bacterial benchmarking studies or limited single-species comparisons. Because fungal genomes vary widely in size, repeat content, and gene architecture, these assumptions can lead to inefficient sequencing strategies, increased computational costs, and suboptimal assemblies. Here, we develop a reproducible assembly benchmarking framework that combines large-scale simulations from 66 complete fungal genomes spanning plant, animal, and human-associated taxa with newly generated short- and long-read sequencing data from 10 field-collected isolates. This approach enables evaluation of assembler performance across diverse genome architectures and tests whether patterns identified in simulations translate to real biological datasets. Across both simulated and empirical datasets, we show that reliable fungal genome reconstruction can be achieved without excessive sequencing depth by identifying consistent performance thresholds. Assembly contiguity and completeness stabilize at moderate long-read coverage, after which improvements depend more strongly on assembler choice and genome structure than on additional data volume. Hybrid workflows show trade-offs in accuracy, contiguity, and computational demand, whereas targeted short-read polishing provides an efficient strategy for improving base-level accuracy. These findings offer practical guidance for fungal genome assembly and support more robust downstream genomic analyses across non-model microbial systems, including ecologically, agriculturally, and clinically important fungi. Data summaryThe reference fungal genomes used for simulation are available in the NCBI Assembly database under the accession numbers listed in Supplementary Table S1. Empirical raw sequencing data generated for this study are deposited in the NCBI Sequence Read Archive (SRA) under accession PRJNA1474061. All scripts used for read simulation, assembly, polishing, benchmarking, and statistical analyses are available in the project GitHub repository (github.com/bielasilva/fungi_assembly_benchmarking). Software versions, parameters, and workflow configurations are provided within the repository and detailed in the Methods.

A genome of Pyricularia oryzae (synonym Magnaporthe oryzae), the fungus that causes blast disease on diverse grass species, has seven core chromosomes and may contain supernumerary mini-chromosomes. The P. oryzae Triticum (PoT) pathotype is the phylogenetic lineage responsible for devastating epidemics of wheat blast disease. Genomic analysis of wheat blast field isolates from the initial outbreak in 1985 in Brazil through recent field isolates in South America revealed dynamic presence and structure of mini-chromosomes. Two "earliest" field isolates representing founder lineages for the Triticum pathotype contain similar mini-chromosomes. Another PoT founder isolate from 1986 and 37 out of 39 Triticum field isolates collected between 1986 and 1992 lack mini-chromosomes. Mini-chromosomes present in the founder strains each contain two copies of the PWT7 wheat blast avirulence gene, and PWT7 was lost from subsequent early strains through mini-chromosome loss. Almost all PoT field isolates from 2005 to 2020 have regained mini-chromosomes in which PWT7 sequences have been replaced by other sequences. Telomere-to-telomere assemblies of 11 mini-chromosomes identified two major mini-chromosome types in the South American PoT population, and demonstrated significant within-mini-chromosome sequence alterations as well as recombination with other mini-chromosomes or core chromosome ends. Additionally, our data indicate horizontal mini-chromosome transfer between Pyricularia species, resulting in nearly identical genomic fragments shared between P. oryzae and Pyricularia pennisetigena isolates in the PWT4 avirulence gene region. Our genomic analysis depicts the dynamic mini-chromosome compartment in the diverse South American Triticum field population through time, indicating important roles for mini-chromosomes in pathogen adaptation and pathogenicity.

Fungal genomics has expanded rapidly over the past 30 years, and recently the pace and breath has further quickened for many taxa, although many taxonomic gaps persist. With three decades of rapid growth, fungal genomics now merits a re-examination of its history, progress, and unresolved taxonomic gaps. Here, we review the development of fungal genomics from early efforts such as the Fungal Genome Initiative to current progress driven by third-generation long-read sequencing. We have compiled and summarised publicly available fungal genomes to highlight trends in assembly quality, adoption of long-read technologies, and taxonomic representation. Notably, substantial phylogenetic gaps remain, particularly outside Dikarya, and significant challenges persist for unculturable taxa. This review identifies priorities for the fungal community, including: (1) coordinated efforts to close major taxonomic gaps across the fungal tree of life; (2) improved repository metrics to facilitate identification of high-quality assemblies; and (3) improved and standardised genome annotation which is lacking for most assemblies. Together, these steps will support the development of reliable genomic resources that capture the full breadth of diversity across the fungal kingdom, generating foundational data for comparative genomics, evolutionary biology, functional studies, genetic studies and applied research.

Genetic influences on gene expression are an important source of variation in complex traits. Conflicting results have been reported about the concordance of genetic effects on mRNA abundance vs. protein levels, ranging from high agreement to a predominance of effects that are specific to mRNA or protein. Here, we integrated 13 published datasets of genetic variation in mRNA or protein collected in the same cross of two strains of the yeast Saccharomyces cerevisiae. These highly replicated data allowed us to gauge the overall agreement between the genetics of mRNA and protein and search for individual loci whose effects on these two gene products are reproducibly different. Overall, genetic effects were highly correlated across all datasets. mRNA and protein showed similar genetic architectures. Pairwise agreement between loci from mRNA datasets and loci from protein datasets was indistinguishable from agreement between loci from datasets of the same gene product. Trans-acting hotspots with effects on numerous genes affected mRNA and protein similarly. There were no hotspots that exclusively affected mRNA or protein across datasets. A small number of loci did show reproducibly different effects on mRNA or protein of individual genes. Collectively, these results show that, with a few notable exceptions, genetic effects on mRNA and protein are largely concordant.

The fungal pathogen Zymoseptoria tritici poses a major global threat to wheat production, causing severe yield losses and necessitating intensive and costly fungicide applications. The increasing demand for durable genetic resistance has intensified interest in quantitative resistance loci, particularly those exhibiting multi-stage resistance (MSR), which suppress pathogen development continuously throughout the wheat life cycle. Many previously effective resistance genes are now showing declining efficacy, underscoring the urgent need for novel and long-lasting sources of resistance. In this study, we report the identification and genetic mapping of two quantitative resistance loci that address this need. The first locus, designated Stb23, is a major QTL on chromosome 1DS, with LOD scores exceeding 9 and explaining 6-36% of phenotypic variation at the seedling stage and 2-16% at the adult-plant stage. The second locus, designated Stb24, is a major QTL on chromosome 3DL, with LOD scores of approximately 10 and accounting for 11-30% of seedling-stage variation and 9-23% of adult-plant variation. Furthermore, two tightly linked KASP markers-snp_1D1217527 for Stb23 and snp_3D1077880 for Stb24-were developed and validated across three popular Australian bread wheat cultivars, providing practical tools for deploying these loci in breeding programs targeting improved resistance to Z. tritici. Key messageTwo significant major-effect resistance loci on chromosomes 1DS (proposed as Stb23) and 3DL (proposed as Stb24) were identified and characterized. Two tightly linked KASP markers with these loci were also discovered and validated for molecular-assisted breeding programs.

Organisms cope with environmental changes by modifying gene expression. To understand how regulatory networks controlling expression plasticity evolve, we analyzed RNAseq data from Saccharomyces cerevisiae, Saccharomyces paradoxus, and their F1 hybrids at multiple timepoints after transferring cells from standard laboratory conditions to five environments (low phosphorus, low nitrogen, hydroxyurea shock, heat stress, and cold stress) and during the diauxic shift. In each of the six datasets, we identified genes that changed expression following the transition to the new environment and used hierarchical clustering to identify genes that increased or decreased in expression. We then compared these classifications between orthologs to identify genes with divergent plasticity. For some genes, plasticity was more extreme in one species than the other, and for others, expression of orthologs changed in opposite directions when acclimating to the same environment. Most cases of plasticity divergence were seen only in one environment and were attributable primarily to trans-regulatory divergence. Using environment-specific regulatory networks inferred from data in Yeastract, we found that divergent plasticity of environment-specific transcription factors generally did not predict divergent plasticity of their target genes. We also found that, as a group, genes with conserved plasticity tended to have more regulatory interactions than genes with divergent plasticity. Interesting patterns of expression divergence were also observed for five transcription factors in the pleiotropic drug resistance network and their target genes that might contribute to phenotypic divergence. Together, these findings show how environment-specific trans-regulatory divergence and combinatorial gene regulation shape the evolution of expression plasticity.

In plant breeding, it is often necessary to improve a target trait while maintaining other essential traits within desirable ranges. When genetic relationships exist among these traits, improvements in the target trait may lead to undesirable changes in essential traits, complicating cross selections. In such cases, it is critical to select cross-pairs that are expected to produce progeny that satisfy the requirements for all traits. The progeny distribution of each crossing pair can be predicted using the estimated genotypic values and genetic (co)variances of the target and essential traits. By utilizing this distribution, the probability of generating progeny that satisfy predefined trait requirements can be evaluated, allowing a direct comparison of alternative crosses. In this study, we developed Cross Potential Selection for Multiple Traits (CPS-MT), a breeding strategy designed to improve a target trait while maintaining one or more essential traits within desirable ranges. CPS-MT extends the original Cross Potential Selection (CPS) framework to explicitly handle trade-offs between traits under genetic correlations. We evaluated the performance of CPS-MT through simulations involving four types of genetic relationships and two genetic causal factors between traits, resulting in seven scenarios. Across all scenarios, CPS-MT consistently improved the likelihood of obtaining desirable progeny, indicating that CPS-MT provides a practical and effective framework for cross selection under multi-trait constraints in breeding programs. Article SummaryThis study developed Cross Potential Selection for Multiple Traits (CPS-MT), a new breeding strategy designed to improve a target trait while maintaining one or more essential traits within desirable ranges. CPS-MT evaluates crossing pairs by predicting progeny distributions based on estimated genotypic values and genetic covariances, enabling direct comparison of alternative crosses under multi-trait constraints. Through simulations incorporating four types of genetic relationships and two causal factors (seven scenarios), CPS-MT consistently increased the likelihood of obtaining progeny that satisfied the predefined trait requirement. These results indicate that CPS-MT provides a practical, robust framework for target trait improvement under trait constraints.

Stripe rust and leaf rust, caused by Puccinia striiformis f. sp. tritici and P. triticina, respectively, are the most destructive wheat diseases in the southern Great Plains. Green Hammer is a hard red winter wheat (HRWW) cultivar released by Oklahoma State University in 2018 and has demonstrated a stable adult plant resistance to stripe rust and race-specific seedling resistance to leaf rust. To identify and map rust resistance loci, 109 doubled haploid (DH) lines derived from the cross between Green Hammer and another HRWW cultivar, Lonerider, were developed. Lonerider showed adult plant resistance to stripe rust but was susceptible to multiple P. triticina races. The DH lines were evaluated for stripe rust at the adult plant stage in greenhouse and field environments across Oklahoma, Kansas, and Washington, and for leaf rust at the seedling stage against seven U.S. P. triticina races and at the adult plant stage in Oklahoma and Texas. Genotyping-by-sequencing generated 6,078 polymorphic single-nucleotide polymorphisms used for genetic mapping. Quantitative trait loci (QTL) analysis identified 14 stripe rust and 8 leaf rust resistance QTL. For stripe rust, a major QTL in Green Hammer, QYr.osughln-2AS, was identified in the proximity of the 2NvS translocation. Three other major stripe rust resistance QTL were identified in Lonerider on chromosomes 2AL (two QTL) and 2BS (one QTL). For leaf rust, QLr.osughln-1DS and QLr.osughln-2DS.1 were the two major QTL identified in Green Hammer and most likely correspond to the all-stage resistance genes Lr21 and Lr39, respectively. In this study, we identified previously characterized genes as well as unknown genes that can be utilized in wheat breeding programs to enhance resistance to leaf rust and stripe rust.

Septoria nodorum blotch (SNB) and tan spot (TS) wheat diseases are caused by necrotrophic fungal pathogens Parastagonospora nodorum (Pn) and Pyrenophora tritici-repentis (Ptr), respectively. Although recognised as premier model pathosystems for our understanding of necrotrophic effectors, no resistance mechanism has been reported in both diseases. Here, two SNB and TS resistance wheat lines ( 56:ZWB11 and 105:ZIF14) derived from the Australian national germplasm evaluation programme (CAIGE) were used to develop a double haploid mapping population. Two Pn and Ptr isolates of different pathotypes, their respective culture filtrates and effector SnTox267 were evaluated on the population. Genetic analysis of Ptr conidial inoculation of race 1 and race 2 identified a major resistance quantitative trait locus (QTL) (QTs.cur-1B) on chromosome 1B, while resistance to SNB was explained by several minor QTL. SnTox267 sensitivity was mapped to six locations (2A2, 2A3, 2B1, 2D3, 5B and 7B1) with only one QTL co-localized to known corresponding gene Snn7. Sensitivity loci 5B and 7B1 also conferred SNB resistance at seedling and adult stages. Two QTL on chromosome 2D1 and 7B2 were common in both SNB and TS, associated with disease at seedling stage and culture filtrate bioactivity, respectively. Resistance responses of 56:ZWB11 and 105:ZIF14 were confirmed cytologically, however, distinct responses were observed on wounded leaves. The defence responses were more effective against Ptr, while resistance to Pn infection was likely a combination of lack of susceptibility and effective physical barriers. Overall results demonstrated the distinction between the underlying resistance mechanisms to TS and SNB.

The zebra mussel (Dreissena polymorpha) is an invasive species that causes extensive economic and ecological damage. Here, we identify and characterize the key components of the small RNA (sRNA) and RNA interference (RNAi) pathways in zebra mussels. Like other mollusks, zebra mussels have extensive microRNA (miRNA) and Piwi-interacting RNA (piRNA) machinery but lack or have modified canonical factors needed to produce small interfering RNA (siRNA). Specifically, the zebra mussel Dicer sequence displays substitutions in the conserved DEAD box motif that is required for substrate processivity, and this organism also lacks some attendant accessory factors such as R2D2. We sequenced the small RNA found in both isolated somatic tissue (adductor muscle) and whole animals (including germline), and identified both conserved and novel miRNA and diverse piRNA sequences, but few endogenous siRNAs. To determine whether their remaining sRNA machinery could still be co-opted to initiate gene silencing, we injected dsRNA targeting several genes into zebra mussel adductor muscle. The injected rpn8-targeting dsRNA reduced rpn8 mRNA levels and was processed into sRNA that resemble endogenous miRNAs and piRNAs. The levels of both sRNA types correlated with mRNA knockdown, suggesting that they may act together to initiate RNAi as seen elsewhere. dsRNA targeting other genes produced variable results suggesting that particular criteria may be needed to trigger an RNAi response in this assay. Our results characterize endogenous sRNA pathways in zebra mussels, establish that dsRNA can induce RNAi, and lay the groundwork for further optimizations to establish RNAi-based genetic manipulation tools for this damaging invasive species.

Genomic selection can increase the rate of genetic gain in crop breeding programs, but its effectiveness depends on the reliability of phenotypic data, the size and composition of the training population (TP), and the statistical model used to estimate genomic breeding values. These design choices are especially important in resource-limited breeding programs, where additional replication, larger TPs, and more extensive genotyping compete for the same resources. Using empirical data from a sweet sorghum [Sorghum bicolor (L.) Moench] breeding population, developed by CHIBAS, we evaluated the effects of phenotyping replication, TP size, training-validation genomic relatedness, and genomic prediction (GP) model on predictive ability (PA). Grain yield, plant height, stem weight, and total soluble solids were evaluated across three field environments. Few studies in sorghum have examined these factors together with comparable empirical rigor. Increasing replication improved genomic heritability and PA for all traits and environments, with the largest gains observed for grain yield. Larger TPs and increased training-validation genomic relatedness also improved PA, but their effects were most significant when phenotype estimates were based on multiple replicates. GP models showed largely comparable PAs across all evaluated traits. Different models produced similar PA, with a few exceptions. These findings provide practical guidance for optimizing genomic selection in resource-limited sorghum breeding programs. ARTICLE SUMMARYGenomic selection can accelerate breeding only when the phenotypes used to train prediction models have high reliability. Using a sweet sorghum breeding population evaluated in three Haitian field environments, we quantified how replication number, training population size, training-validation genomic relatedness, and prediction model affected genomic predictive ability for grain yield, plant height, stem weight, and total soluble solids. Replication increased genomic heritability and predictive ability for all traits, with the strongest effects for grain yield. Larger and more connected training populations improved prediction, mainly when replication was adequate. These results provide practical guidance for resource-limited breeding programs. Core ideasO_LIIn this empirical sweet sorghum breeding population, phenotyping replication was the dominant factor explaining variation in genomic predictive ability across traits and environments. C_LIO_LIThe benefit of larger training populations and greater training-validation genomic relatedness increased when phenotype estimates were based on more replicates. C_LIO_LIGrain yield, the most environmentally sensitive trait evaluated, showed the largest response to improved replication and training-population design. C_LIO_LIBayesian models, rrBLUP, and GBLUP showed similar predictive abilities across traits and environments, suggesting that phenotyping and experimental design may be more important than model complexity. C_LI

Zymoseptoria tritici is the causal agent of Septoria tritici blotch (STB), one of the most destructive diseases of wheat worldwide. Although the Z. tritici genome encodes hundreds of predicted effector proteins, functional characterization through the use of genome-editing techniques has been limited due to low homologous recombination efficiency and extensive effector redundancy. In this study, we established and evaluated a CRISPR/Cas9-based genome editing procedure for targeted effector gene disruption in Z. tritici using in vitro-assembled Cas9-sgRNA ribonucleoprotein (RNP) complexes combined with short (60 bp) homologous donor DNA flanks. Using this approach, we successfully generated knockout mutants for a selected candidate effector gene, the Hce2 domain-containing effector Mycgr3107904. Virulence assays on the susceptible wheat cultivar Taichung 29 revealed that two independent{Delta} Mycgr3107904 mutants exhibited a pronounced delay in symptom development compared to the wild-type strain IPO323, with disease onset and progression delayed by approximately 4-5 days. While mutant strains ultimately followed a similar disease trajectory, wild-type-infected leaves displayed extensive necrosis and pycnidia formation at earlier time points, indicating a significant reduction in virulence upon loss of Mycgr3107904. Together, our results demonstrate the feasibility of CRISPR/Cas9-mediated effector gene knockout in Z. tritici and provide functional evidence that Mycgr3107904 contributes to timely disease progression. This work advances genome editing tools for Z. tritici and facilitates systematic dissection of effector functions underlying fungal virulence.

Accurate modeling of genotype-by-environment (GxE) interaction is critical for genomic prediction in plant breeding but remains challenging due to complex interaction structures. Conventional models often use the Hadamard product of genotype and environment covariance matrices to capture joint similarity, which may not fully represent GxE complexity. Here we propose a novel framework that derives covariance structures from the matrix multiplication of genotype and environment kernels, decomposing these into symmetric components incorporated as random effects in mixed models. Evaluated for 11 wheat and rice multi-environment datasets and across, this approach consistently outperformed the traditional Hadamard-based model, improving prediction accuracy by up to 13.2% in Pearsons correlation and enhancing top-selection accuracy. Combining both methods yielded the highest performance, indicating complementary information capture. This framework offers a flexible, interpretable, and computationally feasible extension for modeling GxE interaction, potentially enhancing genomic selection effectiveness under diverse environmental conditions.

In quantitative genetics, candidate SNPs are identified through genotype-phenotype associations inferred with genome-wide association studies (GWAS). In this study, we explore an alternative approach to detect genetic variants with non-neutral effects by tracking temporal trends in allele frequency in a winter wheat (Triticum aestivum L.) breeding population over an eight-year period, from which signals of selection may be inferred. Selection signatures were inferred with a generalized linear model, where we modeled trends in allele frequency as a function of time (crossing year). These signatures of selection were used to prioritize variants. Associations between phenotypic performance and individual load of prioritized variants were then investigated. Furthermore, we assessed whether incorporating selection information into a genomic best linear unbiased prediction (GBLUP) model improves model performance in terms of quality of fit and prediction ability. Our findings indicate that the inferred signals of selection are effective in identifying non-neutral variants. Variants under strong negative selection were associated with a decrease in protein content adjusted for grain yield (p-value < 0.01), while genetic variants that had been under moderate to high levels of positive selection were associated with increased grain yield (p-value < 0.01). However, incorporating selection information did not improve prediction accuracy. In conclusion, temporal trends in allele frequency can be used to detect non-neutral variants. The proposed approach may hence complement traditional quantitative genetic methods for detecting non-neutral genetic variation. This approach may allow breeders to detect non-neutral variants earlier in the breeding cycle, without resorting to phenotypic data.

Key message Characterized and unknown septoria nodorum blotch susceptibility/resistance genes were identified in contemporary U.S. hard winter wheat. The necrotrophic fungus Parastagonospora nodorum is the causal agent of septoria nodorum blotch (SNB) of wheat. To determine the prevalence of SNB sensitivity genes in a contemporary U.S. hard winter wheat (HWW), we evaluated a panel of 619 breeding lines and cultivars against five P. nodorum isolates and five necrotrophic effectors (NEs), SnToxA, SnTox1, SnTox3, SnTox267 and SnTox5, and genotyped the panel using genotyping-by-sequencing (GBS) markers and diagnostic Kompetetive-allele specific PCR (KASP) markers for the sensitivity genes Tsn1-B1, Snn1-B1, and Snn3-B1/B2. GBS analysis identified 34,357 GBS-single nucleotide polymorphism (SNP) markers. Evaluations against P. nodorum isolates showed that 40-67% of the genotypes were susceptible in the panel. Toxin infiltration assays showed that 54%, 2%, 37%, 13%, and 15% of the genotypes were sensitive to SnToxA, SnTox1, SnTox3, SnTox267, and SnTox5, respectively. Diagnostic KASP markers for Tsn1-B1, Snn1-B1, and Snn3-B1/B2 showed prediction accuracies of 98%, 75%, and 92% for the corresponding effectors SnToxA, SnTox1, and SnTox3, respectively. Genome-wide association studies (GWAS) not only confirmed the presence of the previously characterized sensitivity genes Tsn1-B1, Snn1-B1, Snn2, Snn3-B1/B2, and Snn5-B1, but also identified new loci to be associated with responses to P. nodorum isolates and NEs. Of which, Qsnb.osu-2AS on chromosome 2AS was associated with responses to all five isolates. We developed KASP markers KASP_S4B_643615365, KASP_ S2D_16184991, and KASP_S2A_9833162 linked to Snn5-B1, Snn2, and Qsnb.osu-2AS, respectively. These findings should guide breeding for SNB resistance in hard winter wheat.