Advanced reproductive technologies are powerful tools for accelerating genetic gain because they enable both increased selection intensity and the generation of large numbers of offspring from elite animals. Among these, somatic cell nuclear transfer (SCNT) cloning offers a means to rapidly multiply elite genetics from nucleus herds into commercial populations. Genomic estimated breeding values allow for the accurate assessment of genetic merit in embryos and newborns, creating opportunities to identify elite young stock for cloning. Cloning will also be an important complement to gene-editing as a way to generate animals from cell lines carrying targeted genetic modifications. Nevertheless, the widespread application of cloning remains constrained by low efficiency and high costs, underscoring the need for continued optimisation. Zona-free cloning, also known as handmade cloning, has potential as a simpler and more scalable alternative to conventional micromanipulator-based methods to prepare cloned embryos, and has demonstrated promising improvements in pregnancy and live birth rates across several livestock species. This review summarises advances in zona-free cloning, tracing its development from the microblade to the two-pipette and micropipette methods, and evaluates their relative advantages and limitations. Approaches to improve the health and welfare of clones are examined, and potential applications of cloning in livestock breeding programmes, along with associated regulatory considerations, are discussed. Cloning may become increasingly important as a crucial bridge between lines of genetically elite and possibly gene-edited embryonic cells, and their manifestation as breeding animals in livestock improvement programs.

The complexity of potato genetics, characterised by tetrasomic inheritance, has contributed to slower genetic gain in potato compared to other major crops. Disease resistance genes, often found in large clusters of highly similar paralogs and alleles, further complicate genetic studies. The H1 resistance locus, introgressed into potato cultivars from Solanum tuberosum ssp. andigena, has been successfully used for over 60years to control Globodera rostochiensis in Europe. Although previous genetic studies mapped this resistance to chromosome 5, the complete structure of the locus remained elusive. To reduce genomic complexity, we generated a dihaploid of the cultivar 'Athlete', DH4_Athlete, carrying the H1 resistance locus, and produced a phased haplotype representation of the H1 interval using Oxford Nanopore sequencing. Combined with RenSeq-based association genetics, this approach allowed us to reconstruct the entire H1 locus, including recombination points at both the 5' and 3' ends of the interval.

Although the green revolution adapted a handful of crops to homogeneous and high-input industrialized agriculture, much of the global population still relies on the local production of variable crop cultivars by low-input smallholder farms. This diversity of unhomogenized crops1, like that of the grain and bioenergy crop sorghum2-5, offers raw materials for genetic gain and cultivar improvement. However, breeding efforts can be constrained by highly specialized traits and breeding targets6. Here, to bridge this diversity, we constructed a 33-member pangenome reference and a diversity panel across 1,984 cultivars and landraces. We leveraged these resources to explore the complex interplay among historical contingency, ongoing adaptation and previously uncharacterized structural diversity. Specifically, our analyses conclusively demonstrated multiple nested and deeply diverged structural variants in the domestication gene SHATTERING1, which distinguish the previously established multicentric origin of sorghum. We then applied landscape genomics to reveal how gene flow and secondary contact created the complex genetic mosaic in contemporary breeding networks. As proof of concept for pangenome-accelerated trait discovery, we connected biosynthetic gene cluster structural variation to phenotypic leaf concentration of the cyanogenic glucoside dhurrin. Combined, these approaches will accelerate breedingand trait discovery and provide a framework for similar applications in other crops.

Optimising lamb production necessitates a comprehensive understanding of the relationship between genotype, nutritional inputs, and breeding methodologies in guiding growth performance, carcass traits, and overall productivity. The practice of crossbreeding, utilising heterosis and breed combination, is a reliable method of enhancing average daily gain, feed conversion rates, dressing percentages, and carcass traits. Research findings highlight the positive outcomes of crosses with these advantages further enhanced under high-energy and concentrate-rich feeding regimens. Advancements in reproductive biotechnologies, including in vitro embryo production, multiple ovulation and embryo transfer, and embryo cryopreservation, have significantly contributed to the proliferation of superior genetic lines and the minimisation of generation intervals. The development of genomic selection, marker-assisted breeding, and gene-editing technologies such as CRISPR/Cas9 has enabled advances in traits including prolificacy, carcass traits, wool characteristics, and disease resistance. The integration of genomic data with phenotypic and multi-omics datasets has enhanced selection accuracy, thereby generating opportunities for the development of customized breeding strategies to specific production systems. However, the effective application of these techniques must overcome several limitations. Financial concerns related to genotyping, the limited availability of crossbred reference groups, and the complexity of protecting genetic variation across different breeds and systems are significant difficulties. Also, the relationship between genotype and both nutrition and environmental factors necessitates careful evaluation to ensure the reliable expression of genetic potential in various management practices. A methodical breeding strategy that integrates phenotypic, genomic, and environmental data can lead to significant improvements in genetic gain, enhanced sustainability, and optimized lamb production based on market and welfare standards.

Abstract The Affordable Phenomics special topic in The Plant Phenome Journal showcased recent advances that expand the accessibility, cost‐effectiveness, and scalability of plant phenotyping technologies. This collection of 15 articles presented innovative approaches, ranging from low‐cost sensors and open‐source analytical pipelines to artificial intelligence–driven image analysis and spectroscopy, that address the financial and technical barriers limiting widespread adoption of plant phenomics. In this foreword, we highlight the contributions featured in the special topic. The foreword also serves as an overview of the state of the art in affordable phenomics by summarizing the vision and perspectives presented in the invited review “Affordable phenomics: Expanding access to enhancing genetic gain in plant breeding.”

Genomic selection (GS) is a promising tool to accelerate genetic gain for complex traits. In this study, we evaluated the potential of GS for the improvement of seven lodging-related traits in double-cropping rice in Southern China using 438 rice accessions. The traits examined included the length and bending resistance of the third and fourth internodes (IL3, IL4, BR3, BR4), plant height (PH), and the ratio of internode length to plant height (IL3/PH, IL4/PH). Significant phenotypic differences were observed for all traits between the two seasons. In comparisons of cross-validation and independent prediction, GBLUP and BayesLASSO outperformed LightGBM across all traits in both seasons. Across all evaluated traits, prediction accuracies (Pearson's r) ranged from 0.33 to 0.78 in cross-validation and from 0.28 to 0.75 in independent prediction using the GBLUP model. Bending resistance exhibited lower prediction accuracy due to its lower genomic heritability. Correlation analysis revealed that plant height was not significantly correlated with culm bending resistance, suggesting that these traits are genetically independent. We utilized GBLUP models trained on our experimental data to predict the genomic estimated breeding values (GEBVs) of the 3000 Rice Genomes Project (3kRG) dataset. The results demonstrated that GS can efficiently enrich the proportion of highly lodging-resistant accessions, increasing it from 31.40% in the base 3kRG population to a maximum of 83.00% among the top 200 selected individuals. Furthermore, indirect selection for traits with higher heritability, such as IL and IL/PH, was more effective at screening highly lodging-resistant cultivars than direct selection for BR. Our research demonstrates the feasibility of applying genomic selection for the breeding of lodging-resistant varieties in double-cropping rice and provides a foundation for further applications.

Benefit of including female genotypes in the Lacaune dairy sheep reference population.

This study aimed to evaluate the accuracy of prediction of breeding values in a genomic selection program for behavior traits in a population of Labrador Retrievers used as guide dogs. Implementing genomic selection as a new tool in service dogs has the potential to increase genetic gain, improving the performance of populations. Additionally, genomic predictions may help service dog organizations in identifying training candidates with higher accuracy. Phenotypes for 17 traits on 4,841 Labrador Retrievers collected from 2008 to 2019 from the International Working Dog Registry's (IWDR) behavior checklist were analyzed. The Behavior Checklist (BCL) standardizes a scoring system for a dog's reaction to a variety of environmental stimuli. Data are used to assess a dog's behavior and suitability for training as well as genetic selection using a selection index of prioritized traits with estimated breeding values. Genomic data were available for 1076 individuals from whole genome sequences and reduced to 94K SNPs. Variance components were estimated using AIREML. Genomic information was included under a single-step GBLUP approach. Accuracies were evaluated among a sample of the higher accuracy animals using the linear regression method. Genomic estimates of heritability ranged from 0.08 to 0.21. Accuracies were calculated with the LR method and ranged from 0.30 to 0.58 for pedigree information, with an average of 0.46. Accuracies of genomic predictions ranged from 0.32 to 0.63, with an average of 0.50, and were higher than pedigree predictions for all traits. The gains in accuracy from inclusion of SNP genotype data show that genomic prediction using single-step GBLUP can improve selection by identifying the cohort of young dogs that have the highest genetic merit for the desired traits. Gains in validation accuracy were limited by the small number of genotyped animals and are expected to increase as more animals are genotyped.

Wheat production in Pakistan faces unprecedented challenges from climate change-induced abiotic stresses, including rising temperatures, erratic rainfall, and widespread soil salinity, which collectively threaten national food security. Genomic selection has emerged as a transformative breeding approach that utilizes genome-wide markers to predict breeding values and accelerate genetic gain for complex polygenic traits. This review synthesizes current advancements in genomic selection methodologies and evaluates their applicability within Pakistan's diverse agro-ecological zones. We examine the physiological responses of wheat to drought, heat, and salinity stress, highlighting the polygenic architecture of tolerance mechanisms that make them ideally suited for genomic prediction approaches. Studies demonstrate that genomic selection can achieve prediction accuracies of 0.5–0.6 for grain yield under stressed environments, with multi-trait models incorporating high-throughput phenotyping data improving accuracy by up to 67% compared to univariate approaches. The integration of environmental covariates and genotype-by-environment interactions further enhances predictive ability across variable climatic conditions. Despite promising results, successful implementation in Pakistan requires addressing critical barriers, including limited phenotyping capacity, high genotyping costs, insufficient training population sizes, and the need for robust statistical models adapted to local germplasm. Strategic investments in infrastructure, capacity building, and collaborative networks between national and international research institutions are essential to harness the full potential of genomic selection for developing climate-resilient wheat varieties tailored to Pakistan's vulnerable production systems.

Genomic selection (GS) is critical for accelerating genetic gain in modern plant breeding. Deep learning approaches offer powerful non-linear representation capabilities for modelling non-additive effects. However, their application in GS remains restricted, as high-dimensional, low-sample and noisy data hinder the identification of informative markers. The present study proposes DNAwhisper, a deep learning framework designed for multi-trait prediction and adaptive marker prioritisation. The framework integrates a cascaded architecture, GFIformer, employing shared network parameters across partitioned marker blocks to adaptively compress genetic features within a hierarchical pyramid. Pre-training on population genetic structure regularises feature learning to establish a generalisable latent representation. During trait modelling, importance scores for aggregated genomic regions at multi-resolutions are extracted from the distinct pyramid levels under trait-guided deep supervision, enhancing interpretability and supporting marker prioritisation. DNAwhisper was evaluated on maize, wheat, tomato and grape datasets for marker prioritisation and phenotypic prediction, achieving prediction accuracy approximately 3.0% to 10.0% higher than the baseline model. Furthermore, DNAwhisper identifies major QTLs (e.g., , ) and epistatic signals within the gibberellin metabolic pathway across maize flowering traits. This framework provides a new strategy for dissecting the genetic architecture of complex traits.

Recombination shuffles alleles during meiosis, driving genetic diversity and shaping the outcomes of breeding programs. By breaking the physical links between loci, recombination facilitates the creation of new allelic combinations that can be selected for to improve genetic gain. Increasing recombination rate by methods such as genome editing has become a goal for accelerating breeding. However, the effect of increased recombination rate on a population scale on breeding programs is not fully understood. We therefore carried out simulations to determine the effect of recombination on genetic gain in a breeding program using phenotypic and genomic selection, respectively. We focused on how heritability, number of quantitative trait loci, recombination rate increase factor, marker density and training frequency affect breeding success. We also tested whether it is possible to use historic training sets without changes in recombination rate and merge the pre- and post-recombination populations to improve prediction accuracy and genetic gain in genomic selection. We found that increasing recombination is particularly beneficial for highly quantitative traits with low heritability. However, with genomic selection, increasing recombination requires a higher training frequency as well as an increased marker density to accelerate superiority over phenotypic selection in terms of genetic gain. Furthermore, our simulations show that maintenance of old training sets and merging of training sets with different recombination rate is possible, but a decrease in prediction accuracy is expected, favouring frequent training and high marker density under increased recombination rates.

Durum wheat [Triticum turgidum L. ssp. durum (Desf.) Husn.] is a staple crop for the pasta and semolina industries, particularly in Mediterranean and semi-arid regions where climate variability poses major challenges to yield stability. This study evaluates the performance of single-environment (SE), multi-trait (MT), multi-environment (ME), and multi-trait–multi-environment (MTME) genomic prediction models across seven key traits, such as grain number per spike, grain weight per spike, number of spikelets per spike, spike length, spike weight, heading date, and plant height. Using genomic (G) and target gene-based (G2) relationship matrices with two cross-validation scenarios (CV1 and CV2), MTME models achieved the highest prediction accuracies, particularly under CV2 and sowing-by-season grouping. Modeling G2 information improved predictions for morpho-phenological traits (i.e. heading date and plant height), confirming the utility of functional allele data for capturing gene effects. MTME models effectively leveraged inter-trait and inter-environment covariance, providing biologically realistic predictions of genotype performance across simulated Mediterranean environments. These findings establish MTME genomic prediction as a powerful and scalable framework for climate-resilient durum wheat improvement, supporting predictive and data-driven breeding pipelines aimed at enhancing genetic gain and stability across years and environments.

ABSTRACT Accelerating breeding cycles through rapid generation turnover is a demonstrated strategy to improve the rate of genetic gain in a range of staple crops. This study presents a vertically stacked accelerated single‐seed descent (aSSD) protocol for faba bean ( Vicia faba L.), aimed at rapid generation cycling while ensuring robust plant development and seed viability. Our aim was to take a tall annual crop and grow it within a vertically stacked controlled environment, with a density of 80 plants/m 2 . Combining an LED‐extended photoperiod of 18 h, small pot size, antigibberellin to reduce plant height, and desiccation to germinate immature seed achieved 4.1 to 5.7 generations per year across phenologically diverse germplasm. Plant height was successfully reduced by up to 60% with flurprimidol without compromising flowering or seed yield. The methodology was validated by advancing 14 Australian faba bean breeding programme recombinant inbred line populations over 3 years. Beyond faba bean, this scalable, genotype‐independent platform offers applications for other tall annual species in breeding and vertical farming systems, enabling year‐round crop production and genetic improvement. The protocol's adaptability to closed‐environment agriculture presents opportunities for plant‐based life‐support systems in extreme environments, including space missions. This integrated approach to speed breeding and vertical farming represents a significant advancement in agricultural innovation.

The conservation and genetic improvement of rare and endangered tree species are crucial for sustainable forest management. Liriodendron chinense, a relict species with limited distribution in China, exhibits high cross-compatibility with Liriodendron tulipifera, providing opportunities for interspecific hybrid breeding. In this study, 29 Liriodendron hybrids were established in a progeny trial plantation in Fujian Province, China, and subjected to multi-year evaluation of tree height, diameter at breast height (DBH), and individual stem volume. Significant differences (p < 0.01) among hybrids and hybrid × replicate interactions were detected for all traits across all assessment years, with individual stem volume showing the highest phenotypic coefficient of variation (35.30-40.56%). The mean annual increment in tree height increased during the early years, peaking at 1.50 m in the fourth year. Broad-sense and narrow-sense heritabilities for growth traits were consistently high (0.4073-0.7253 and 0.3410-0.6501, respectively), and the ratio of narrow-sense to broad-sense heritability ranged from 0.64 to 0.99, supporting the feasibility of early hybrid and individual selection. At a 10% selection intensity, hybrids No. 39, No. 59, and No. 74 were identified as elite, with selection based on individual stem volume providing the highest predictive accuracy and genetic gain (26.54-34.69%). Individual selection at a 1% intensity yielded genetic gains of 95.55-107.12% for stem volume. These results demonstrate substantial potential for early and efficient genetic improvement in Liriodendron hybrids, providing a theoretical foundation for the selection and deployment of elite hybrids and individuals in subtropical forest plantations.

Harnessing speed breeding: a pathway to accelerating genetic gain in crop breeding

ABSTRACT Speed breeding can shorten breeding cycles and, when combined with genomic selection, can accelerate genetic gain. Yet it remains unclear how different integration strategies affect long‐term response, genetic variance and cost‐efficiency in small public programs. Here, we use simulations of a 20‐year breeding pipeline to compare speed breeding strategies with traditional pedigree selection and to identify designs that balance genetic gain, sustainability and cost. We simulated a breeding program in AlphaSimR , and all schemes used single‐seed descent. Three speed breeding scenarios, differing in the generation of genomic selection and in whether population size was recovered after selection, were contrasted with two traditional pedigree schemes that advanced fewer lines per cycle. For each design, we monitored population mean, additive genetic variance and prediction accuracy over 20 years. Speed breeding schemes delivered faster short‐term gains than traditional schemes. The design with genomic selection in F 2 and recovery of population size produced the largest cumulative response (3.55 genetic standard deviations) than the other speed scenarios. However, this design required genotyping and phenotyping 16,000 individuals per cycle, compared with 1200 in the best traditional scheme, resulting in lower efficiency per unit cost under current prices. Sensitivity analyses further showed that reducing the F 2 recovery proportion substantially improves the cost–gain trade‐off while retaining much of the long‐term advantage of the recovered‐population design. Designs without population‐size recovery reached lower plateaus of response (~2.4) but demanded far fewer resources and showed a higher return on investment. Here, we demonstrate that coupling speed breeding and genomic selection with recovery of population size is the most powerful strategy for long‐term gain, but its immediate adoption may be constrained in resource‐limited programs. In the short term, simpler speed breeding designs with smaller populations may be more realistic, whereas falling genotyping costs will favour high‐population designs. These results provide guidance for redesigning breeding pipelines that accelerate cultivar development while preserving sustainable genetic gains.

Optimizing biomass partitioning is essential for achieving sustainable yield improvement in wheat, particularly under increasing environmental stress. Traits such as spike partitioning index (SPI), harvest index (HI), and fruiting efficiency (FE) are central to understanding how assimilates are allocated between vegetative and reproductive organs. However, their complex physiology and the difficulty of manual phenotyping have limited their routine use in breeding programs. This study assessed the potential of unmanned aerial vehicle (UAV)-based hyperspectral reflectance data to predict biomass partitioning traits and related yield components in wheat. Three trials of facultative soft wheat lines (2022-2024) and an independent validation set of advanced breeding lines were used to develop genomic prediction (GP), phenomic prediction (PP), and integrated multi-omic models combining genomic, phenomic, and environmental covariates (ECs). Kernel-based best linear unbiased prediction (BLUP), and machine-learning based, random forest regression and partial least squares regression were implemented to estimate predictive ability (PA). Phenomics-driven models markedly outperformed GP across most traits, achieving PA up to 0.61 for SPI, 0.56 for FE, 0.71 for grains/m2 (GN), and 0.66 for grain yield (GY). Hyperspectral data provided higher accuracy than vegetation indices, and multi-omic integration slightly improved prediction (PA up to 0.73 for GN). These results demonstrate that UAV-based hyperspectral phenotyping can effectively capture canopy-level physiological signals associated with biomass partitioning, offering a scalable and data-driven approach for in-season selections. This can help wheat breeding programs to optimize biomass partitioning in modern wheat cultivars for long-term yield resilience and genetic gain.

Selective breeding is a fundamental tool for improving growth performance in aquaculture species. This study evaluated the realized response to selection, heritability, and genetic gain for three economically important traits consisting of shell length, soft tissue weight, and whole weight in the commercial edible Portuguese oyster ( Crassostrea angulata ) across three consecutive selection generations (S4, S5, and S6) and their respective control lines (C4, C5, and C6). A family‐based breeding design was implemented, and growth traits were measured at five time points during the grow‐out period (120, 180, 210, 270, and 365 days). The results revealed consistent genetic improvement in all three traits, with whole weight exhibiting the highest realized heritabilities (0.35–0.90) and genetic gains (up to 11.13%). Shell length and soft tissue weight also showed moderate realized heritabilities (0.11–0.79), with substantial genetic gains achieved over successive generations (7.69%–19.22%). Comparisons between the selected and control lines confirmed the effectiveness of the selection protocol. At 120 days, the whole weight of the S4 and S5 lines exceeded their respective controls by 8.7% and 11.1%. Moreover, at subsequent time points (180, 210, 270, and 365 days), genetic gains in the selected lines were significantly greater than in the control lines, with all improvements surpassing 8.5%. These findings demonstrate the efficacy of family‐based selection in C. angulata and highlight the potential for further genetic improvement of the lines under selection.

Selection of individuals based on their estimated breeding values (EBV) aims to maximize response to selection in the next generation under an additive model. However, when the aim does not only include short-term population-wide genetic gain but also genetic gain over multiple generations, an optimal strategy is not as clear-cut, as maintenance of genetic diversity may become an important factor. This study provides an extended comparison of existing selection strategies in a unifying testing pipeline using the simulation software MoBPS. Applying a weighting factor on estimated SNP effects based on the frequency of the beneficial allele resulted in an increase of the long-term genetic gain of 1.6% after 50 generations, while reducing inbreeding rates by 16.2% compared to truncation selection based on EBV. However, this also resulted in short-term losses in genetic gain of 1.2% with the break-even point reached after 25 generations. In contrast, inclusion of the average kinship of an individual with individuals that would be selected based on their EBVs as an additional trait in the selection index with a weight of 17.5% resulted in no short-term losses and increased long-term genetic gain by 4.3%, while reducing inbreeding by 15.8%. Combining multiple diversity management strategies, with weights for each strategy optimized using an evolutionary algorithm, resulted in a breeding scheme with 5.1% greater genetic gain and 37.3% lower inbreeding rates than selection based on EBVs. The proposed combined strategy included the use of optimum contribution selection, weighting of SNP effects based on allele frequency, average kinship as a trait in the selection index, avoiding matings between related individuals, and lowering the proportion of selected individuals. The combination of strategies for the management of genetic diversity in a breeding program was shown to be far superior to the use of any singular method tested in this study. As the use of strategies for management of genetic diversity and inbreeding does not necessarily lead to short-term losses in genetic gain and comes at no extra costs, it is critical for breeding companies to implement such strategies for long-term success.

Early flowering is a key element of the rice speed-breeding protocol that enables improved genetic gain and accelerates the cultivation of new varieties. Although far-red light (FR) is commonly used to modulate plant developmental processes, the mechanisms by which it influences flowering and growth in rice are poorly understood. In this study, the control treatment (CK) consisted of red-blue-green composite light at 300 μmol m-2 s-1, while two additional treatments were applied: one with the photon flux density (PFD) increased to 350 μmol m-2 s-1 (HI-high intensity) under the same light spectrum as CK, and the other supplemented with 50 μmol m-2 s-1 of FR based on CK. The results demonstrated that both elevated PFD and supplemental FR significantly enhanced vegetative growth, as evidenced by increased plant height, tiller number, leaf area, and biomass accumulation, along with improved photosynthetic capacity and chlorophyll fluorescence. Under the FR treatment, flowering occurred 53 days after transplanting, which was 12 days and 9 days earlier than in the CK and HI treatments, respectively. Physiological profiling revealed that FR enrichment significantly increased leaf soluble sugar and starch levels, while simultaneously decreasing chlorophyll and carotenoid concentrations. FR also reshaped the endogenous hormonal profile, which was marked by elevated levels of gibberellin (GA3) and abscisic acid (ABA), and reduced auxin (IAA) content. Transcriptomic profiling revealed that FR enrichment activated the circadian rhythm pathway and upregulated genes associated with photoperiodic flowering and inflorescence development. In summary, FR promotes rice growth and early flowering through the integrated regulation of leaf area expansion, enhanced photosynthetic efficiency, hormonal rebalancing, and activation of flowering gene expression. This study provides a theoretical foundation and technical support for optimizing light environments and improving the economic viability of crop speed breeding systems in controlled environmental facilities.