Theoretical and Applied Genetics

Scald, caused by the fungus Rhynchosporium graminicola Heinsen 1897, is a major foliar disease in winter malting barley (Hordeum vulgare L). Resistance to scald in winter malting barley is controlled by major and minor resistance genes. We used a large population of lines derived from biparental crosses among five winter malting barley parents to analyze resistance to scald and associated agronomic traits. Increased winter survival and later heading dates were negatively correlated with increased resistance, whereas increased height was positively correlated with resistance. A genome-wide association study (GWAS) for resistance to scald was analyzed with multiple models, using 15,463 SNPs. The similarities and differences between the models were identified in SNP trait associations and phenotypic effect sizes. SNP associations identified a large region on chromosome 3H across models. FarmCPU identified additional associations on chromosomes 2H, 3H, and 4H. Linkage disequilibrium on chromosome 3H and GWAS for resistance to scald using the Rrs1-linked marker, HVS3, as a covariate confirmed Rrs1 was segregating in this population. GWAS for winter survival, heading date and plant height identified associations across the genome, with chromosome 2H showing SNP-trait colocalizations between resistance to scald, winter survival, heading date and plant height. Breeding for durable resistance to scald in winter malting barley can include pyramiding major resistance loci, such as Rrs1, as well as QTL for disease resistance and agronomic traits. PLAIN LANGUAGE SUMMARYO_ST_ABSGenetic architecture of resistance to scald in winter malting barleyC_ST_ABSScald is an important foliar pathogen in winter malting barley, affecting both grain yield and quality. While resistance to scald is controlled by major and minor resistance genes, agronomic traits are also known to limit the spread of scald in barley. We determined the genetic architecture using a large multiparent population of winter malting barley. The FarmCPU genome-wide association model proved optimal for defining the resistance genes, with the major resistance gene, Rrs1, conferring 27% of the variation in this population. Fewer days to heading and taller plants contributed to plant avoidance of scald. Reduced canopy coverage in plants with low winter survival led to less scald severity. A region of the genome contributing a minor resistance effect was co-localized with a region for plant height, heading date and winter survival. Core IdeasO_LIResistance to scald in a large multiparent population was derived from a major resistance gene (Rrs1) and several smaller effect QTLs C_LIO_LIRrs1 resistance was derived from Lightning and is located within a large linkage block on Chromosome 3H C_LIO_LIFewer days to heading and taller plants were correlated with less disease in a large multiparent winter malting barley population in NY state C_LIO_LIA QTL for resistance to scald co-localized on chromosome 2H with winter survival, heading date, and plant height C_LIO_LIFarmCPU was an optimal model for association analysis for resistance to scald in the large unbalanced diallel population. C_LI

The genetics of interspecific groups remains largely unexplored, despite the central role of social (or indirect) genetic effects in shaping phenotypic expression within communities. Intercropping, i.e. the simultaneous cultivation of multiple crop species in the same field, offers a powerful model to harness these interspecific social effects. Such species mixtures provide well-documented agricultural benefits, yet few breeding frameworks have integrated the genetics of social interactions. Here, we address this gap by extending quantitative genetic theory to interspecific groups, with intercropping as a concrete and applied model case. We propose a quantitative genetic model that jointly analyzes intra and interspecific interactions within a unifying framework. Breeding values are decomposed into a direct component, shared in mono and mixed-crops, an interspecific social component corresponding to the effect of one species on another, and an intraspecific component that captures the social effects within a mono-genotypic stand of cloned plants. Statistically, this consists in simultaneously fitting several linear mixed models, one per stand type, all having direct breeding values in common. As no open-source software can fit such a complex mixed model, we provide such an implementation in R/C++. Simulations across various genetic (co)variance structures and sparse experimental designs showed accurate estimation of all genetic (co)variances and breeding values. With an incomplete, yet balanced design combining sole crops and intercrops, genetic gains in both systems were achievable simultaneously, enabling breeding strategies that progressively integrate intercropping into existing, sole-crop-only schemes. More broadly, this framework allows dissecting direct and social genetic effects when genotypes are observed in mono- and mixed-species situations, cultivated or not.

The cassava (Manihot esculenta) genome has two large introgressions from its wild relative M. glaziovii on chromosomes 1 and 4 that originate from historical hybridization efforts. The 10 Mbp chromosome 1 introgression has been increasing in frequency in African breeding populations due to its statistical association with higher dry matter content and root number. However, the region also exhibits suppressed recombination, hindering breeders ability to combine favorable glaziovii alleles with the cultivated esculenta background. Since homozygous introgressed lines are rarely selected for advanced trials, dominance effects have not been well-characterized. To analyze the effects of the introgression with higher resolution, we generated a population of over 5000 seedlings from crosses between heterozygous introgressed parents and screened for recombinants using ten KASP markers tagging glaziovii-specific alleles. An optimized subset of 453 lines was then selected and evaluated over two years for yield and vigor traits. Unlike previous studies, composite interval mapping and mixed linear models showed no significant associations between glaziovii alleles and dry matter content or root number. Small, opposing effects on clonal vigor were observed at different ends of the introgression. The region showed significant segregation distortion and enrichment of putative deleterious alleles. Genome alignment of M. esculenta and M. glaziovii assemblies did not show any major structural variants in the introgression region, suggesting that suppressed recombination is likely driven by sequence-level divergence rather than structural rearrangements. These results indicate that the glaziovii introgression does not directly contribute to dry matter, supporting the need for recombination and purging of the glaziovii introgression to aid cassava improvement. Plain language summaryA large chromosome segment from a wild relative of cassava is an important structural aspect in the cassava genome. Since the chromosome segment tends to be inherited as one block, its effects on cassava traits were not well resolved. Through genetic mapping at higher resolution, we identified that the wild segment impacts early vigor and does not appear to impact dry yield, as was previously thought. While there are no major structural differences between the wild and cultivated chromosome segments, their overall divergence seems to suppress the wild chromosome segment from pairing with the cultivated chromosome segment during reproduction. In the apparent absence of any major benefits from the wild segment, removing it from the breeding population may be beneficial. Core ideasO_LIA set of glaziovii allele-specific markers were designed to track the chromosome 1 introgression haplotype. C_LIO_LISegregation distortion suggests the presence of recessive deleterious or lethal alleles in the introgression. C_LIO_LIIncreased recombination is needed to purge deleterious alleles enriched in introgression region. C_LIO_LIThe glaziovii introgression was associated with slightly lower vigor rating and stem diameter. C_LIO_LIThe effects of the previously-identified glaziovii DM QTL were not detected in this population. C_LI

Improving rice (Oryza sativa L.) yield requires a balanced enhancement of both sink size and source capacity. While many QTLs for sink size have been identified, only a few are known for source capacity, which is essential for achieving high yield. Here we identified qHP10 as a major QTL for increased photosynthetic rate by using chromosome segment substitution lines derived from a cross between the high-yielding indica cultivar Takanari and the average-yielding japonica cultivar Koshihikari. High-resolution mapping combined with CRISPR/Cas9-induced mutagenesis revealed that the causative gene underlying qHP10 is Mitogen-Activated Protein Kinase 4 (OsMPK4). A near-isogenic line carrying the OsMPK4Takanari allele (NIL-OsMPK4) had a 15-25% higher photosynthetic rate than Koshihikari. NIL-OsMPK4 also had higher stomatal conductance than Koshihikari but similar stomatal pore size and density, indicating that increased stomatal aperture increases photosynthetic rate. This enhancement is likely attributable to the down-regulation of OsMPK4 expression, which increases stomatal conductance and thus promotes CO2 uptake. Our findings demonstrate that OsMPK4 is a promising genetic target for increasing source capacity and, potentially, rice yield through molecular breeding. (175 words)

The Dense and erect particle 1 (HvDEP1) gene, located on chromosome 5H in barley (Hordeum vulgare L.), encodes a heterotrimeric G-protein {gamma}-subunit that regulates grain size and stem elongation. Multiple alleles of HvDEP1 have been identified, including the widely utilized semi-dwarf allele HvDEP1.GP, caused by an insertion mutation, and a recently discovered variant, HvDEP1.V, characterized by two deletions in the putative cis-regulatory region. In this study, we evaluated the phenotypic effects of HvDEP1.V relative to HvDEP1.GP and the wild-type allele (HvDEP1.WT) using two BC{square}F{square} populations across multi-environment field trials spanning two locations and three years. HvDEP1.V was associated with plants that were 5-14.6 cm taller, had 3-6.7 higher lodging score, and increased head loss compared to HvDEP1.GP. HvDEP1.V showed comparable agronomic attributes to HvDEP1.WT. Substituting HvDEP1.V for HvDEP1.GP significantly increased all physical grain attributes, including grain width (1.44-4.24% in three out of five environments), grain length (4.88-8.69 %), grain area (6.45-11.06%) and thousand-grain weight (6.75-13.8%). Out of five environments, compared to HvDEP1.WT, HvDEP1.V was associated with wider grain in three environments, shorter grain in four environments, and increased grain roundness in four environments. These findings link allelic variation of the HvDEP1 gene to key agronomic and physical grain traits and demonstrate the functional consequences of HvDEP1.V in diverse genetic backgrounds and field conditions, providing valuable insights for barley improvement. Key messageBy evaluating agronomic performance and physical grain traits in two genetically distinct barley populations across multiple environments, we reveal strong environment- and background-dependent effects of HvDEP1 alleles.

Wheat stripe rust, caused by Puccinia striiformis f. sp. tritici (Pst), remains a major global constraint to wheat production. Rapid pathogen evolution, exemplified by the recent breakdown of Yr15 in Europe, underscores the need to identify diverse and durable resistance loci. The A.E. Watkins landrace collection represents a globally diverse pre-breeding resource with substantial untapped variation for stripe rust resistance. In this study, 297 Watkins landraces were evaluated against six diverse Pst isolates (representing six races and three North American lineages) and subjected to genome-wide association analysis using high-density whole-genome resequencing data. Continuous phenotypic variation was observed across isolates, with several accessions displaying stable resistance across all lineages. A total of 87 QTLs were identified across all 21 wheat chromosomes. Ten loci co-localized with designated or cloned Yr genes, including Yr84, Yr85, Yrq1, Yr71, Yr60, Yr62, Yr50, Yr68, Yr34, and Lr34/Yr18/Sr57. An additional 34 loci overlapped previously reported stripe rust QTL, whereas the majority did not coincide with known loci, suggesting potential novel resistance regions. Eighteen QTLs were supported by multiple isolates, and fourteen showed supports across statistical models, indicating robust genomic signals. Several Watkins accessions carried favorable alleles that co-localized with multiple Yr-aligned loci, identifying promising donor candidates for validation and pre-breeding. Key MessageGenome-wide association mapping of 297 Watkins wheat landraces across diverse stripe rust races & genetic lineages identified 87 QTL, including 10 formally designated Yr genes and 46 novel loci, highlighting Watkins landraces as valuable pre-breeding donors for novel all-stage stripe rust resistance.

While high-density SNP genotyping enables genome-wide assays in rice (Oryza sativa L.) and other crops, PCR-based markers--particularly those derived from insertion-deletion (InDel) variations--remain crucial for fine-mapping. Currently, readily available primer information for high-density InDel rice markers is still limited. We present the first version of PrimeInDel (https://rfgb.rmbreeding.cn/search/variation/primeIndel), an online tool integrating three sets of InDel markers: an 8K-set and a 316K-set from the 3K Rice Genomes (3K-RG) project, and a 22K-set compiled from published literatures. In user cases, primers from PrimeInDel proved highly effective, narrowing a cold-tolerance QTL (qSR2) from 2.7 Mb to 200 kb and a heading-date QTL (qDeh1.1) from 239.5 kb to 83.6 kb. Our work provides the community with a robust, PCR-based InDel primer, accessible via an online data set, which will facilitate germplasm genotyping and gene identification in rice breeding.

Host resistance is a critical component of oat crown rust disease management. Pc94 is a qualitative resistance locus derived from diploid Avena strigosa with several independent introgressions into A. sativa that have been used in cultivar deployment. Quantitative trait locus (QTL) analysis combining previously published data for a historic A. strigosa population segregating for Pc94 revealed a large effect QTL on the distal end of A. strigosa chromosome 7A. Genome assembly of the parents identified a cluster of five nucleotide binding site leucine-rich repeat receptor (NLR) candidate genes within the QTL region. A single candidate NLR with an integrated zinc finger BED domain, AstNLR94, was determined as necessary for Pc94 resistance based on map-based cloning and forward mutagenesis. A presence/absence allele specific PCR marker was designed in AstNLR94 and verified for accuracy and specificity in a diverse panel of A. strigosa and A. sativa. Pc94 introgressions in A. sativa ranged in size from 1.7-71 Mbp and two different introgression locations appear to have occurred. In A. sativa Leggett, a 6.3 Mbp Pc94 introgression is located at the end of chromosome 2A, and the same sized introgression was discovered in the OT3098 v2 genome. Finally, a QTL analysis identified an additional minor resistance locus on A. strigosa chromosome 4A, which has complicated previous efforts to characterize the Pc94 locus. This is the first report of an NLR gene underlying disease resistance in Avena spp. and delivers a Pc94 marker for marker assisted selection to produce disease resistant cultivars. Key messageWe mapped a zfBED-NLR encoding gene necessary for Pc94 resistance, developed a diagnostic marker, and revealed diverse introgression sizes, clarifying Pc94s history and utility for durable oat crown rust resistance.

Genotype-by-environment interaction (GEI) has been studied to identify environment-stable/favorable genotypes. The GEI simulation could help refine the inference by incorporating tangible factors such as genomic and environmental information. The Bayesian additive main effect and multiplicative interaction (Bayesian AMMI) model captures the genotype-specific responses across environments, reflecting directional relationships between genotypes and environments. Thus, we propose a Bayesian AMMI-based GEI simulation framework that utilizes high-throughput environmental covariance matrices to generate GEI effects with interpretable directional structure. To demonstrate the proposed approach, two simulated phenotypes were assessed under four levels of GEI variance. In the first simulation (Sim1), GEI effects were sampled from a multivariate normal distribution defined by the GEI matrix. In the second simulation (Sim2), GEI effects were generated by extending Sim1 with the Bayesian AMMI model. In both simulations, increasing GEI variance resulted in lower correlations of phenotypes across environments and stronger genotype-specific sensitivity to environmental variation. Across five cross-validation designs, models accounting for GEI consistently outperformed one that did not, with prediction accuracy generally decreasing as GEI variance increased. Clear distinctions between the two simulated phenotypes were evident from biplot analyses: Sim2 successfully captured environmental relatedness and genotype-specific responses, whereas such structure was absent in Sim1. These results demonstrate that the proposed Bayesian AMMI-based GEI simulation framework enables interpretable visualization of GEI and supports genomic selection strategies under complex environmental conditions.

Stripe rust, caused by Puccinia striiformis f. sp. tritici, can cause severe yield losses in wheat (Triticum aestivum L.) during epidemics. Breeding resistant wheat varieties remains the most cost-effective approach to manage this disease; and the identification of new resistance loci is essential for maintaining genetic diversity. The CIMMYT-derived wheat line Kijil was highly resistant to stripe rust in both Mexican and Chinese environments. A population of 153 F recombinant inbred lines (RILs) was derived from a cross between Kijil and the susceptible parent Apav#1. The population was phenotyped for stripe rust resistance across seven environments in two countries and genotyped using a genotyping-by-sequencing (GBS) platform. Inclusive composite interval mapping (ICIM) was uesd to construct a genetic map and identify significant resistance quantitative trait loci (QTLs) using 5,468 polymorphic markers. Mapping revealed the known resistance loci Yr29, Yr30 and QYr.hzau-3AS, along with two novel loci, QYr.hzau-2BS and QYr.hzau-5DL, across both Chinese and Mexican rust environments. Among these, QYr.hzau-2BS accounted for 11.75% to 19.19% of the phenotypic variance. A corresponding KASP marker, KASP_2BS, was developed to facilitate maker-assisted selection. Based on the mapping interval, four candidate genes underlying this locus were predicted. Further analysis revealed that Yr29 showed significant additive effects with other stripe rust resistance genes/loci, and the combination of Yr29, Yr30, and QYr.hzau-2BS reduced disease severity by up to 67.8%. Our findings suggest that Kijil and RILs carrying Yr29, Yr30, and QYr.hzau-2BS can serve as valuable donors for breeding wheat varieties with improved stripe rust resistance. Author summaryStripe rust is an important disease that seriously threatens the yield and quality of wheat. It is crucial to explore new resistant resources and cultivate durable resistant varieties in the current breeding programme. In this study, we analysed the genetic basis of resistance to stripe rust in the wheat line "Kijil", which has broad-spectrum resistance to stripe rust. Through genetic mapping, we identified five quantitative trait loci for stripe rust, including two new resistance loci. A closely linked KASP marker, KASP_2BS, was developed for the QYr.hzau-2BS, which can be used for rapid and accurate screening of resistant plants in early breeding populations. Meanwhile, within this QTL region, we screened four candidate genes based on expression analysis. In addition, it was found that polymerization of QYr.hzau-2BS with known resistance genes, Yr29 and Yr30, significantly enhanced resistance and reduced disease severity to low levels and near immunity. In conclusion, this study provides new genetic resources, practical molecular markers and effective gene polymerization strategies for breeding wheat for stripe rust resistance. Kijil and the lines containing resistance loci have important breeding utilization value.

Pyrenophora teres f. teres (Ptt), the causal agent of net-form net blotch in barley, was studied using a bi-parental mapping population (Pop1) of 305 isolates derived from a cross between two isolates with contrasting virulence on barley cultivars Skiff and Prior. QTL analysis identified virulence loci on chromosomes (Chr) 3 and 10 for Skiff, and on Chr 1, 4, and 5 for Prior. Major QTL on Chr 3 and 5 explained 24% and 40% of phenotypic variation, respectively. A second population (Pop2) was developed by crossing two Pop1 isolates, one carrying major QTL on Chr 3 and 5 and one avirulent. Isolates from Pop2 with single QTL were phenotyped across a Prior/Skiff recombinant inbred line population to identify corresponding host susceptibility/resistance loci. Skiff virulence QTL on Chr 3 corresponded to barley Chr 3H and 6H, while Prior virulence QTL on Chr 5 mapped to Chr 6H. RNA expression analysis of virulent and avirulent Pop2 isolates identified five candidate genes linked to the Chr 5 QTL, including two predicted effectors. These findings suggest both gene-for-gene and inverse gene-for-gene interactions in the Ptt-barley pathosystem and advance the understanding of molecular mechanisms underlying host-pathogen specificity.

AbstractEarly sowing is a key strategy to improve maize productivity and resilience under climate change, but it exposes plants to prolonged chilling stress that can severely compromise seedling establishment. While previous genetic studies have focused on germination or very early stages, tolerance to long-term chilling during the autotrophic transition remains poorly characterized. Here, we combined genome-wide association studies (GWAS) and transcriptome analysis on QTL near-isogenic lines (NILs) to dissect the genetic architecture of early vigor under chilling in maize. We identified a major genomic region on chromosome 4 (LD_COL4), harboring two QTLs within a 2.7 Mb interval, that were consistently associated with early vigor under long-term chilling conditions. Transcriptomic analysis of contrasted NILs revealed a cluster of differentially expressed genes co-localizing with LD_COL4, pointing to two strong candidate genes, Zm00001d048582, an ortholog of the Arabidopsis OPS gene that regulates the brassinosteroid (BR) signaling pathway upstream of the key transcription factors BES1 and BZR1, and Zm00001d048612, a brassinosteroid-signaling kinase (BSK). Multiple orthologs of BES1/BZR1 modulators were differentially expressed between genotypes under chilling, supporting the involvment of brassinosteroid signaling in this response. These findings highlight both genes as promising targets for marker-assisted breeding and gene editing to improve maize adaptation to early sowing.

Starch Synthase 1 (SS1) participates in the synthesis of amylopectin. To determine its role in hexaploid bread wheat, we selected and combined TILLING mutations for each homoeologue to create two independent SS1-deficient lines. The lines, which have different combinations of ss1 mutations, both lacked SS1 protein. Both lines exhibited mild but significant changes to starch phenotype, including fewer short amylopectin chains, a slight increase in B-type starch granules and a modest increase in amylose content. Lack of SS1 also led to changes in the thermal properties of starch measured by differential scanning calorimetry including reduced enthalpy, and increased gelatinization temperature. Despite the changes to starch properties, the starch contents of the mutant lines compared to wild types were within the normal range, as were grain weight and protein content. However, the concentration of total- and water-extractable arabinoxylan, and MLG, were increased in white flour compared to wild-type controls. HighlightLack of SS1 led to changes in starch molecular structure and thermal behavior. Starch content and grain weight were normal but cell wall polysaccharides in white flour were increased.

Grain mold severely constrains sorghum [Sorghum bicolor (L.) Moench] productivity and grain quality in subhumid environments. Photoperiod-sensitive flowering plays a key role in mold avoidance and yield stability along north-south rainfall gradients. In response to the high susceptibility of elite cultivars in subhumid zones of Senegal, we developed and characterized a recombinant inbred line (RIL) population derived from Nganda (grain mold-susceptible) and Grinkan (photoperiod-sensitive) varieties. The population was evaluated across three distinct agro-ecological zones over two years. Environmental indices derived from genotype-environmental interactions, together with defined growth windows, strongly influenced flag leaf appearance (FLA), a photoperiodic flowering trait. Plasticity parameters (intercept and slope) for environmental indices, FLA, grain mold severity, and yield enabled identification of loci contributing to flowering response, mold resistance, and yield stability. The maturity gene Ma1 and two QTLs for FLA, qFLA6.2 and qFLA6.3, were identified, stable across environments, and colocalized with grain mold and yield QTLs. The wild-type Ma1 allele from Grinkan delayed FLA and reduced grain mold damage but was not associated with increased yield. The Ma1 effect was confirmed using the developed breeder-friendly KASP marker, Sbv3.1_06_40312464K, in 174 F3 three-way cross families. Photoperiod-sensitive lines with intermediate-to-late FLA alleles showed strong negative associations with mold damage. Overall, the identified stable loci and candidate lines provide foundations for effective molecular breeding of climate-resilient varieties. PLAIN LANGUAGE SUMMARYGrain mold is a fungal disease that reduces sorghum grain yield and quality, particularly in subhumid climates. With the limited number of resistant elite varieties, photoperiod-sensitive flowering to day length variation can contribute to grain mold escape at the end of rainy seasons. We characterized 286 sorghum recombinant inbred lines across three contrasting environments over two years along rainfall gradients in Senegal. Using flag leaf appearance (FLA), which is a photoperiodic flowering trait, strong genotype-environment interactions for FLA and genotypic plasticity were revealed. We identified and validated the common genomic locus associated with FLA variation and its plasticity across environments, the canonical maturity gene Ma1, which was influenced by temperature variation across environments. The presence of Ma1 in the background of photoperiod-sensitive lines enhances grain mold avoidance and yield stability along rainfall gradients in Senegal. CORE IDEASO_LIWe investigated photoperiodic flowering plasticity in sorghum as a contributor to grain mold resistance and yield stability along rainfall gradients. C_LIO_LIThe Maturity locus Ma1 (qFLA6.1) is the major contributor of photoperiodic flowering and its plasticity across semi-arid and subhumid environments. C_LIO_LIHybrid genotypes carrying two stable loci qFLA6.1 and qFLA6.2 sustain high grain mold avoidance in diverse environments. C_LIO_LIPhotoperiod-sensitive lines with medium to late flowering times are effective in avoiding grain mold, while maintaining yield stability in subhumid regions. C_LI

Deciphering the genetic architecture of complex quantitative phenotypes remains challenging in quantitative genetics. These traits not only depend of multiple genetic factors but are also established over time and environments. Although quantitative genetics has investigated the genetic determinism of phenotypic plasticity in contrasted environmental conditions, the time related phenotypic plasticity has received less attention. Here we proposed a multivariate Bayesian framework, the Bayesian Varying Coefficient Model, designed for analysing the genetic architecture of the time related phenotypic plasticity by a multilocus approach. We applied the BVCM to time series phenotypes measured at various time scales (daily, monthly, yearly) across a diverse set of biological species. We included in this study: yeast (Saccharomyces cerevisiae), fungi (Fusarium graminearum), eucalyptus (Eucalyptus urophylla x E. grandis), and sweet cherry tree (Prunus avium). The BVCM results were compared with those obtained with a known genome-wide association method carried out time by time. For all species and traits, the BVCM was able to detect the major QTL identified by marker-trait association methods and revealed additional genetic regions of weak effect. It also increased the phenotypic variance explained for most of the phenotypes considered. It revealed dynamic QTLs with transitory, increasing or decreasing effects over time. By considering both the temporal and genetic multivariate structures in a single statistical model, we increased our understanding of the genetic architecture of complex traits notably by reducing the issue of missing heritability. More broadly, this work raises the foundation for extended applications in functional genomics, evolutionary ecology, and crop breeding programs, in which time-related phenotypic plasticity remains crucial for predicting and selecting key quantitative complex traits. Key messageBy capturing the genetic factors influencing the time related phenotypic plasticity, our approach contributes to a deeper understanding of the dynamic nature of genotype-phenotype relationships.

The self-incompatibility, perennial growth habit, large tree size, and long juvenility present challenges in applying traditional breeding approaches in almond (Prunus dulcis Mill. D. A. Webb). Moreover, nut and kernel traits in almond are mainly controlled by a large number of small-effect quantitative trait loci (QTLs) and improving complex traits through conventional breeding approaches is slow and often inefficient. Genome-wide selection represents a promising strategy to enhance the efficiency of cultivar identification and selection of superior parents in almond breeding programs by estimating the breeding values (BVs) at early maturity. The main aim of this study was to implement genomic (GBLUP) and pedigree-based (ABLUP) prediction approaches to estimate BVs to identify the superior parental candidates for improving nut and kernel traits in almond. Here, we estimated BVs for nine traits that are commonly used in the primary evaluation stage of the almond breeding using genomic data from 61 parents and phenotypic data of 15,281 progeny derived from 205 unique families. Breeding values obtained from both approaches showed a strong correlation (r [≥] 0.94) for all traits except shell seal (r = 0.87). The population structure analysis conducted using high-quality 90K single nucleotide polymorphisms (SNPs) indicated clear separation of the Californian, European and some old Australian almond cultivars, with considerable admixture across some cultivars. Following further validation, both prediction approaches could be useful in early identification of superior candidates. The slightly higher breeding values obtained using the GBLUP compared to the ABLUP approach suggest that accounting for within-family variations and realised genomic relationships can enhance prediction accuracy, reliability, and overall genomic prediction performance in almond.

Salinity and sodicity stresses adversely affect rice growth and yield. To overcome yield losses, suitable tolerant rice cultivars can be developed through a marker-assisted breeding (MAB) program. In the present study, genomic regions associated with sodicity stress tolerance at the reproductive stage were identified using a high-density 50kSNP array in a recombinant inbred line (RIL) population derived from the contrasting rice genotypes CSR11 and MI48. A total of 50 QTLs were detected for various yield-related traits; further, 19 QTLs with [≥]15% of phenotypic variance were selected for integrated (omics) analysis. RNA sequencing of leaves and panicles at the reproductive stage under sodic stress conditions was employed to find differentially expressed genes. A total of 1368 and 1410 SNPs; 104 and 144 indels were found for MI48 and CSR11, respectively, within the QTL regions from resequencing. At chromosomes 1 and 6, colocalized QTLs (qPH1-1/qGP1-1 and qGP6-2/qSSI6-2) were discovered. Differentially expressed genes (DEGs) were mapped over the QTL regions selected, and SNP variations and indels were screened for colocalized QTLs. Potential candidate genes, namely Os-pGlcT1 (Os01g0133400), OsHKT2;1 (Os06g0701600) and OsHKT2;4 (Os06g0701700), OsANTH12 (Os06g0699800), and OsPTR2 (Os06g0706400), were identified as being responsible for glucose transport, ion homeostasis, pollen germination, and nitrogen use efficiency, respectively, under salt stress. Finally, our study provides important insights into the genes and potential mechanisms affecting grain yield under sodic stress in rice, which will contribute to the development of molecular markers for rice breeding programs.

Ensembles of multiple genomic prediction models have demonstrated improved prediction performance over the individual models contributing to the ensemble. The outperformance of ensemble models is expected from the Diversity Prediction Theorem, which states that for ensembles constructed with diverse prediction models, the ensemble prediction error becomes lower than the mean prediction error of the individual models. While a naive ensemble-average model provides baseline performance improvement by aggregating all individual prediction models with equal weights, optimising weights for each individual model could further enhance ensemble prediction performance. The weights can be optimised based on their level of informativeness regarding prediction error and diversity. Here, we evaluated weighted ensemble-average models with three possible weight optimisation approaches (linear transformation, Nelder-Mead and Bayesian) using flowering time traits from two maize nested associated mapping (NAM) datasets; TeoNAM and MaizeNAM. The three proposed weighted ensemble-average approaches improved prediction performance in several of the prediction scenarios investigated. In particular, the weighted ensemble models enhanced prediction performance when the adjusted weights differed substantially from the equal weights used by the naive ensemble models. For performance comparisons within the weighted ensembles, there was no clear superiority among the proposed approaches in both prediction accuracy and error across the prediction scenarios. Weight optimisation in ensembles warrants further investigation to explore the opportunities to improve their prediction performance; for example, integration of a weighted ensemble with a simultaneous hyperparameter tuning process may offer a promising direction for further research.

CONTEXTRapeseed is a globally significant oil crop, exhibiting highly plastic responses among seed yield components (seed number and weight). However, there remains a notable gap in knowing the distribution of quality traits among seed size categories and understanding how seed size and source-sink (S-S) ratio influence comprehensive seed quality traits. OBJECTIVEThis study investigated the effects of seed size and S-S ratio reduction on the quality traits of winter and spring rapeseed genotypes. METHODSThe experiments were carried out at field conditions in Valdivia, Chile, where seed yield, yield components, oil, protein, and element concentrations (P, K, S, Ca, Mg, B, Cu, Fe, Mn, Zn, and Na) were evaluated across five seed size categories; very small (< 1.4 mm), small (1.4-1.7 mm), medium (1.7-2.0 mm), large (2.0-2.36 mm), and very large (> 2.36 mm). Treatments included a control and a reduced S-S ratio (75% shading), which significantly increased seed weight (P < 0.05). RESULTSBoth genotype and seed size affected (P< 0.050) the quality traits. Larger seeds exhibited higher Mg and B concentrations, as well as lower K, Ca, Fe and Na. Shading affected seed size distribution, favouring a higher proportion of large seeds. Under the shading treatment, the small seed category reached 5% lower oil concentration, while protein seed concentration increases 6% in both genotypes. Principal component analysis highlighted the complex interaction between yield, yield components, and quality traits, since there was no clear separation between different seed size categories and S-S ratio treatments. CONCLUSIONThese results provide insights into the plasticity of rapeseed quality traits, highlighting their collective impact on nutrient profiles. SIGNIFICANCEThis information is helpful for optimising cultivation practices and informing breeding programmes aimed at improving seed quality, particularly in high-yielding environments susceptible to environmental stresses. Graphical abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=90 SRC="FIGDIR/small/707178v1_ufig1.gif" ALT="Figure 1"> View larger version (34K): org.highwire.dtl.DTLVardef@19d16eforg.highwire.dtl.DTLVardef@4cc16forg.highwire.dtl.DTLVardef@12f741borg.highwire.dtl.DTLVardef@6fa37a_HPS_FORMAT_FIGEXP M_FIG C_FIG

Accurate projections of crop adaptation to climate change require accounting for the spatial heterogeneity of soils, which modulates both water availability and the effectiveness of genetic adaptation. Using the process-based crop model Sirius, we investigated how intra-regional variability in soil available water capacity (AWC) influences wheat yields and the adaptive value of stress-avoidant ideotypes under future climates in central France (Limagne plain). Detailed soil databases were aggregated across five representative sites and combined with multiple climate projections (CMIP6), two emission pathways (SSP2-4.5 and SSP5-8.5), and three time horizons (2031-2050, 2051-2070 and 2071-2090). Variance decomposition revealed that soil AWC accounted for 23% of the simulated yield variability, significantly exceeding the contribution of local climate contrasts (10%), a pattern consistent across current and future periods. Deep soils (>80 mm AWC) buffered drought effects whereas yields stagnated in shallow soils (<80 mm AWC) where water deficits persisted despite phenology hastening. On average, the reference cultivar showed earlier anthesis by 8-21 days under future climates, leading to higher yields mainly in deep soils. Optimization of flowering timing through stress-avoidant ideotypes provided mean yield gains of +6.33 dt{middle dot}ha-1 in deep soils, but limited benefits (+1.71 dt.ha-1) in shallow ones, highlighting pedological dependence of breeding efficiency. Advancing anthesis also increased exposure to early-spring frost: frost probability rose from <0.1 to >0.4 when flowering occurred more than 250 {degrees}C.days earlier, particularly in the frost-prone part of the study area. Hence, frost risk remains a critical constraint for early ideotypes, even under strong warming. Overall, our results demonstrate that intra-regional soil heterogeneity remains a dominant driver of wheat yield variability and adaptation potential under climate change. Designing stress-avoidant ideotypes without explicit consideration of local soil AWC could lead to maladaptation, especially in regions with shallow soils represent a significant portion of cropped areas. In such situation, breeding for terminal stress avoidance may offer only limited benefit. We advocate that breeding and modeling frameworks integrate high-resolution soil data to refine regional ideotype design, reconcile terminal-stress avoidance with frost tolerance, and better capture the spatial realism required for sustainable crop adaptation strategies. Highlights- Local soil water capacity limits wheat adaptation to climate change. - Deep soils favor earlier, stress-avoidant ideotypes. - Shallow soils restrict the benefits of phenological adjustment for stress avoidance. - Frost exposure remains a key risk when shifting phenology toward earliness.