Key messageHerein, a method has been proposed for selecting optimal cross pairs based on the genetic potential of progeny in interpopulation crosses, considering different genetic effects among populations.In the breeding programs for self-pollinating plants, genetic improvements in multiple traits can be challenging when relying solely on a single biparental population, and interpopulation crosses are employed to integrate favorable alleles from multiple biparental populations to overcome this limitation. In this context, it is crucial to consider distinct genetic effects in different populations. In this study, we used a selection method based on the usefulness criterion (UC) to identify cross pairs suitable for interpopulation crosses. We expanded this approach to enhance breeding programs by accounting for varying genetic backgrounds within the genomic selection framework. Using the medicinal plant perilla as the study material, we performed simulations to compare the efficacy of selection based on the estimated genotypic values with that of selection based on UC. Our findings demonstrate that the proposed method is effective in facilitating the simultaneous improvement of multiple traits, particularly by considerably increasing the genetic gains among the top-performing individuals in the population. Furthermore, we provide guidelines for implementing interpopulation crosses, including recommendations for the optimal generation for crossing and an appropriate reference generation for calculating UC. The results obtained in this study offer valuable insights for small-scale breeding programs aimed at simultaneously enhancing multiple traits through interpopulation crosses and can be applied to a wide range of crops, including neglected and underutilized species.

Among the broad area of artificial intelligence (AI), generative AI algorithms have emerged as a revolutionary technology able to produce highly realistic ‘synthetic’ data, akin to standard simulation but with fewer contraints. The main focus of generative AI has been on phenotypes, but here we argue it can serve as well for generating synthetic environments and genotypes. This data-driven technology may be able to overcome some of the limitations that standard simulations have, such as strong assumptions on the underlying genotype to phenotype map. We discuss key features of popular generative models including autoregressive models, generative adversarial networks, variational autoencoders, diffusion and flow-based models. Several of these methods utilize a latent space, often of lower dimensionality than the raw data, that can help making the models interpretable and can be a link between simulation and generative algorithms. Augmenting data as realistically as possible with genAI can improve inference and predictive performance of genomic prediction models, but symbolic simulation will continue to play a fundamental role in predictive breeding. A hybrid tool that implements both approaches can be extremely powerful to evaluate predictive breeding strategies in silico. One promising direction is to simulate novel genotypes using conventional methods, then apply generative models to produce realistic phenotypes conditional on genotype and environment.

Key messageStrategic resource allocation in breeding programs is key to balancing cost-effectiveness and genetic improvement.This research aimed to understand the critical role of adopting advanced breeding tools and optimizing breeding strategies to ensure the sustainability and success of public breeding programs in meeting future food security challenges. In this context, there are two main objectives: estimate the genetic gains achieved over 110 years in the rice breeding program of Louisiana State University (LSU); evaluate through stochastic simulations the impacts of modern selection tools such as genomic selection (GS) and high-throughput phenotyping (HTP) on future genetic gains. Considering the 110 years, the average increase was 4.55 kg/ha per generation (23 breeding cycles). However, from 1994 to 2018, we observed more substantial trends in genetic gains, particularly for grain yield, which increased by approximately 56.54 kg/ha per year. Based on simulations, integrating GS and HTP demonstrated significant advantages, including shorter breeding cycles, enhanced selection accuracy, and reduced costs. Also, simulation results showed that this approach yielded the highest response to selection (4.68% per year) due to the synergistic effects of combining advanced phenotyping techniques with GS. Finally, we assessed the effects of balancing the number of parents, crosses, and progeny sizes to maximize genetic gains and maintain genetic variability. Variance component analysis indicated that progeny size had the greatest impact on total variance (36%), followed by the number of crosses (23%) and the number of parents (3.4%). The findings highlight the need for strategic resource allocation in breeding programs to balance cost-effectiveness and genetic improvement.

Key messageWe assembled the genome of Mengdou1137 with high quality and revealed the specific disease resistance genes and a large number of genomic variations related to agronomic traits.As a cornerstone in the global agricultural landscape, soybean stands as a pivotal oilseed crop, underpinning both nutritional and industrial applications. The burgeoning development of novel soybean varieties significantly propels the crop’s industrialization, offering enhanced traits that cater to diverse agricultural and commercial needs. In this study, we present the de novo assembly of the genome a high-yield Chinese soybean variety Mengdou1137, employing an integrated approach of both long-read and short-read sequencing technologies to achieve comprehensive genomic insights. Achieving a notable assembly with a genome size of 999.99 Mb, our work features a contig N50 of 14.92 Mb and a scaffold N50 of 50.26 Mb, successfully anchoring 98.24% of sequences across the 20 chromosomes. Through meticulous comparative analysis with existing soybean genomes, our research unveiled 115 Mengdou1137-specific disease resistance genes alongside a substantial array of agronomical trait-associated genomic variants. Among the salient genomic features, we identified a favorable haplotype of the dwarf gene PH13, a critical determinant of plant stature, underscoring its potential for breeding compact soybean varieties with lodging resistance. This high-quality assembly of the Mengdou1137 genome not only enriches the repository of soybean genetic resources but also paves the way for future innovations in soybean breeding and trait improvement, offering valuable insights for the enhancement of this crucial agricultural commodity.

Two florigen genes, Hd3a and RFT1, are essential for the floral transition in rice. To elucidate the early steps of the transcriptional dynamics during rice floral induction, we compared a set of temporal transcriptome data of SAM (shoot apical meristem)—region samples between the wild-type and a non-flowering line of the hd3a rft1 double mutant during a short-day (SD) treatment after growing under long-day conditions for 42 days, and identified 6,978 DEGs (differentially expressed genes). As expected, FUL-like MADS-box genes were induced just after 4 days SD treatment; meanwhile, SEP-type and AGL-type MADS-box genes were induced after 9 days of SD treatment. We here newly revealed that majority of rhythmic genes including major circadian clock genes were not affected by the florigen genes, implying normal circadian clock phasing at the SAM regions regardless of floral transitions. We found that around two thousands of genes were repressed by Hd3a and RFT1 genes at the SAM regions before the SD treatments and become derepressed and similar to WT expression levels in the double mutants according to the SD treatments. These clearly imply two distinct actions of florigen genes: one for repression of some developmental key genes during vegetative growth possibly by very low level of florigen expression and the other for floral induction by relatively high florigen expressions upon short-day inductions. This repression by low levels of florigens may serve as a maintenance system for vegetative growth before floral induction, which implies a novel role for florigen genes in rice.

Key messageAccurate prediction of genomic breeding values for Timothy was possible using genomic best linear unbiased prediction.Timothy (Phleum pratense L.) is a grass species of great importance for Finnish agricultural production systems. Genotyping-by-sequencing along with genomic prediction methods offer the possibility to develop breeding materials efficiently. In addition, knowledge about the relationships among traits may be used to increase rates of genetic gain. Still, the quality of the genotypes and the validation population may affect the accuracy of predictions. The objectives of the study were (i) to estimate variance components for yield, winter damage and digestibility traits, and (ii) to assess the accuracy of genomic predictions. Variance components were estimated using genomic residual maximum likelihood where the genomic relationship matrix was scaled using a novel approach. Genomic breeding values were estimated using genomic best linear unbiased prediction in single- and multiple-trait settings, and for different marker filtering criteria. Estimates of heritability ranged from 0.13 ± 0.03 to 0.86 ± 0.05 for yield at first cut and organic matter digestibility at second cut, respectively. Genetic correlations ranged from −0.72 ± 0.12 to 0.59 ± 0.04 between yield at first cut and winter damage, and between digestibility at first and second cuts, respectively. Accuracy of prediction was not severely affected by the quality of genotyping. Using family cross-validation and single-trait models, predictive ability ranged from 0.18 to 0.62 for winter damage and digestibility at second cut, respectively. In addition, validation using forward prediction showed that estimated genomic breeding values were moderately accurate with little dispersion. Thus, genomic prediction constitutes a valuable tool for improving Timothy in Finland.

Key messageGenetic variation in stripe rust resistance exists in Australian wheat breeding populations and is environmentally influenced. Stacking multiple resistance haplotypes or using whole-genome approaches will improve resistance stability and environmental specificity.Wheat stripe rust (Puccinia striiformis) is a fungal disease responsible for substantial yield losses globally. To maintain crop productivity in future climates, the identification of genetics offering durable resistance across diverse growing conditions is crucial. To stay one-step ahead of the pathogen, Australian wheat breeders are actively selecting for adult-plant resistance (APR), which is considered more durable than seedling resistance. However, deploying resistance that is stable or effective across environments and years is challenging as expression of underling APR loci often interacts with environmental conditions. To explore the underlying genetics and interactions with the environment for stripe rust resistance, we employ haplotype-based mapping using the local GEBV approach in elite wheat breeding populations. Our multi-environment trial analyses comprising 35,986 inbred lines evaluated across 10 environments revealed significant genotype-by-environment interactions for stripe rust. A total of 32 haploblocks associated with stripe rust resistance were identified, where 23 were unique to a specific environment and nine were associated with stable resistance across environments. Population structure analysis revealed commercial or advanced breeding lines carried desirable resistance haplotypes, highlighting the opportunity to continue to harness and optimise resistance haplotypes already present within elite backgrounds. Further, we demonstrate that in silico stacking of multiple resistance haplotypes through a whole-genome approach has the potential to substantially improve resistance levels. This represents the largest study to date exploring commercial wheat breeding populations for stripe rust resistance and highlights the breeding opportunities to improve stability of resistance across and within target environments.

The Middle American rust resistance gene Ur-11 present in common bean (Phaseolus vulgaris L.) confers resistance to all but one known race of the pathogen Uromyces appendiculatus (Pers.) Unger. Even though progress has been made in understanding the host–pathogen interactions between common bean and U. appendiculatus, the causal alleles of the majority of rust resistance loci, including Ur-11, remain unknown. A genome-wide association study (GWAS) was conducted to identify genomic regions associated with resistance to the U. appendiculatus race 31–22, which is avirulent to Ur-11 but virulent to other Middle American rust resistance genes. GWAS using genotypic data consisting of approximately 70,959 SNP markers and phenotypic data based on the median reaction type (1–9 scale) of a panel of 357 Middle American breeding lines and cultivars, plus 5 germplasm lines with the Ur-11 locus derived from PI 181996, located Ur-11 on chromosome Pv11. Twenty-seven SNP markers clustered in the 55.16–55.56 Mb region of the P. vulgaris UI111 reference. Multiple DNA sequence alignments detected a missense mutation [c.1,328A > G] in the PvUI111.11G202400 gene model that encodes a leucine-rich repeat-containing protein in response to race 31–22. A PCR allele competitive extension marker (PACE) was developed and tested across a panel of ~ 700 Middle American dry bean genotypes. No recombination event was observed for the PACE marker among the tested genotypes; suggesting that the polymorphism on which it is based is very close to or in the Ur-11 gene. This PACE marker will be a useful and reliable marker for marker-assisted selection of Ur-11-based resistance to bean rust.

Key messageEarly root traits, particularly total root length, are heritable and show positive genetic correlations with biomass yield in perennial ryegrass; incorporating them into breeding programs can enhance genetic gain.Perennial ryegrass (Lolium perenne L.) is an important forage grass widely used in pastures and lawns, valued for its high nutritive value and environmental benefits. Despite its importance, genetic improvements in biomass yield have been slow, mainly due to its outbreeding nature and the challenges of improving multiple traits simultaneously. This study aims to assess the potential advantages of including early root traits in the perennial ryegrass breeding process. Root traits, including total root length (TRL) and root angle (RA) were phenotyped in a greenhouse using rhizoboxes, and genetic correlations with field yield were estimated across three European locations over two years. Bivariate models estimated significant genetic correlations of 0.40 (SE = 0.14) between TRL and field yield, and a weak but positive correlation to RA of 0.15 (SE = 0.14). Heritability estimates were 0.36 for TRL, 0.39 for RA, and 0.31 for field yield across locations. Incorporating root trait data into selection criteria can improve the efficiency of breeding programs, potentially increasing genetic gain by approximately 10%. This results highlight the potential of early root traits to refine selection criteria in perennial ryegrass breeding programs, contributing to higher yield and efficiency.

Key messageIn a genome-wide association study involving 269 cultivated and wild soybean accessions, potential salt tolerance donors were identified along with significant markers and candidate genes, such as GmKUP6 and GmWRKY33.Salt stress remains a significant challenge in agricultural systems, notably impacting soybean productivity worldwide. A comprehensive genome-wide association study (GWAS) was conducted to elucidate the genetic underpinnings of salt tolerance and identify novel source of salt tolerance among soybean genotypes. A diverse panel comprising 269 wild and cultivated soybean accessions was subjected to saline stress under controlled greenhouse conditions. Phenotypic data revealed that salt tolerance of soybean germplasm accessions was heavily compromised by the accumulation of sodium and chloride, as indicated by highly significant positive correlations of leaf scorching score with leaf sodium/chloride content. The GWAS analysis, leveraging a dataset of 32,832 SNPs, unveiled 32 significant marker-trait associations (MTAs) across seven traits associated with salt tolerance. These markers explained a substantial portion of the phenotypic variation, ranging from 14 to 52%. Notably, 11 markers surpassed Bonferroni’s correction threshold, exhibiting highly significant associations with the respective traits. Gene Ontology enrichment analysis conducted within a 100 Kb range of the identified MTAs highlighted candidate genes such as potassium transporter 6 (GmKUP6), cation hydrogen exchanger (GmCHX15), and GmWRKY33. Expression levels of GmKUP6 and GmWRKY33 significantly varied between salt-tolerant and salt-susceptible soybean accessions under salt stress. The genetic markers and candidate genes identified in this study hold promise for developing soybean varieties resilient to salinity stress, thereby mitigating its adverse effects.