Theoretical and Applied Genetics

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.

Net blotch, caused by Pyrenophora teres, is a major constraint to barley production worldwide and occurs as two epidemiologically distinct forms: net form net blotch (NFNB) and spot form net blotch (SFNB). Although numerous resistance loci have been reported in recent years, their genetic relationship remains poorly understood, and the effective deployment of resistance is constrained by the complex genetic architecture of net blotch resistance. In this study, we used a haplotype-based mapping approach to dissect the genetic basis of resistance to NFNB and SFNB in a diverse panel of 950 barley accessions from the Australian Grains Genebank (AGG). Disease responses were evaluated across 13 experiments, and a total of 40 quantitative trait loci (QTL) were identified, including 26 associated with NFNB, 29 with SFNB, and 15 common for both diseases. Most loci co-localized with previously reported QTL, while six putative novel haploblocks highlighted untapped genetic diversity within the AGG collection. Correlation analyses across phenotypic, genetic and haploblock levels revealed a partial but incomplete overlap in resistance mechanisms between NFNB and SFNB. Among the 4,497 haploblocks, approximately 60% of them showed positive local genetic correlations between the two diseases, suggesting shared genomic contributions to resistance. Haplotype composition analysis further identified a resistant haplotype group, mainly comprising accessions of Asian origin, that exhibited high levels of resistance to both forms of net blotch. Through in-silico haplotype stacking simulations, we demonstrated the cumulative genetic potential achievable by combining favourable haplotypes. When the breeding objective was to improve resistance to both NFNB and SFNB, dual-disease stacking strategies outperformed single-disease approaches, highlighting the value of prioritising haplotypes with positive pleiotropic effects. Overall, this study provides a comprehensive haplotype-level framework for understanding net blotch resistance and delivers practical insights for breeding barley cultivars with durable and broad-spectrum resistance to both NFNB and SFNB.

In breeding programs, the selection of cultivars with the highest yield potential consisted in the selection of the yield per se, which resulted in cultivars with a higher grain number per spike (GN) and occasionally higher grain weight (GW) (main numerical components of the yield). This task could be facilitated with the use of molecular markers such us single nucleotide polymorphism (SNP). In this study, quantitative trait loci (QTL) for GW, GN and spike fertility traits related to GN determination were mapped using two double haploid (DH) populations (Baguette Premium 11 x BioINTA 2002 and Baguette 19 x BioINTA 2002, BP11xB2002 and B19xB2002). Both populations were genotyped with the iSelect 90K SNP array and evaluated in four (BP11xB19) or five (B19xB2002) environments. We identify a total of 305 QTL for 14 traits, however 28 QTL for 12 traits were considered significant with an R2 > 10% and stable for being present at least in three environments. There were detected eight hotspot regions on chromosomes 1A, 2B, 3A, 5A, 5B, 7A and 7B were at least two major QTL sheared confident intervals. QTL on two of these regions have previously been described, but the other six regions were never observed, suggesting that these regions would be novel. The R5A1 (QSL.perg-5A, QCN.perg-5A,QGN.perg-5A) and R5A.2 (QFFTS.perg-5A, QGW.perg-5A) regions together with the QGW.perg-6B resulted in a final higher yield suggesting them to have high relevance as candidates to be used in MAS to improve yield. Author contribution statement Key message28 stable and major QTL for 12 traits associated to spike fertility, GN and GW were detected. Two regions on 5A Ch., and QGW.perg-6B showed direct pleiotropic effects on yield.

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.

O_LIDespite their substantial therapeutic value, medicinal plants have undergone limited genetic improvement through breeding because of the scarcity of expert breeders. Moreover, quantifying bioactive compounds is expensive. Genomic selection (GS), which leverages genome-wide markers to predict breeding values and assemble favorable alleles, offers a practical way to unlock latent genetic potential. As a model case, we evaluated GS in red perilla (Perilla frutescens). C_LIO_LIBuilding on previous work, we implemented a cross-selection strategy that prioritized segregation variance by selecting crosses based on predicted additive genotypic values of the progeny, and evaluated its effectiveness through actual crossing experiments targeting three key medicinal compounds. C_LIO_LIProgeny from GS-based crosses (Crs1-Crs7) outperformed those from phenotypic selection (Crs8) in the G2 generation, demonstrating a higher mean, greater variance, and superior top individuals. The best G2 individual exhibited nearly twofold higher levels of two target compounds relative to the existing cultivar Sekiho. C_LIO_LIThis study provides the first empirical demonstration that GS can improve multiple medicinal compounds in red perilla and highlights the effectiveness of cross-selection based on predicted progeny performance. In addition, the evidence presented here supports the broader application of GS in underutilized medicinal plants. C_LI

Heterosis, the superiority of hybrids over their parents, is a major genetic force associated with plant fitness and crop yield enhancement. We previously discovered and characterized root-mediated yield heterosis (RMYH) in melon (Cucumis melo) using a half-diallel population, derived from 20 diverse parents. In the current study we investigated the genetic architecture of RMYH using a segregating population derived from a selected F1 hybrid (HDA019) that consistently induced RMYH under several melon scion varieties and growing conditions. 78 recombinant inbred lines (RILs) and their test-crosses to both parents were analyzed in yield trials as rootstocks under a common commercial scion variety. The population displayed normal root-mediated yield distribution and transgressive segregation relative to the parents but none of the RILs equaled the superior performance of the F1 hybrid. RMYH of HDA019 was dissected to small effect QTLs showing mostly additive or dominant mode-of-inheritance and favorable QTL-alleles were contributed by both parents. Five consistent QTLs were selected and used to demonstrate the potential of root-mediated yield QTL pyramiding, and 20 combinations of QTL pairs and triplets supported the cumulative model for heterosis. Favorable QTLs alleles were introgressed to generate advanced QTL-backcross lines that were used for validation. This study provides first detailed genetic dissection of yield-related rootstock traits in cucurbits, highlighting rootstock breeding as an important underutilized route for improving yield and stress tolerance of crops. Key messageRoot-mediated yield heterosis in melon was genetically dissected using grafting strategy, revealing additive QTLs from both parents of the mapping population. Rootstock breeding through pyramiding of favorable alleles is proposed as strategy for enhancing crop yield and stress tolerance.

In the breeding programs of self-pollinated plants, achieving genetic improvement in multiple traits can be challenging when relying solely on a single biparental population. Interpopulation crosses are employed to integrate favorable alleles from multiple biparental populations to overcome this limitation. In this context, it is crucial to consider the distinct genetic effects in different populations. In this study, we utilized 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 accounting for varying genetic backgrounds within the genomic selection framework. Using the medicinal plant red perilla as the study material, we conducted simulations to compare the efficacy of selection based on estimated genomic breeding values with that based on UC. Our findings demonstrate that the proposed method is effective in facilitating the simultaneous improvement of multiple traits, particularly by considerably increasing 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 the appropriate reference generation for calculating the UC. The results obtained in this study offer valuable insights for small-scale breeding programs aimed at simultaneously enhancing multiple traits through inter-population crosses and are applicable to a wide range of crops, including neglected and underutilized species. Key MessageHerein, a method has been proposed for selecting optimal cross pairs based on the genetic potential of progeny in inter-population crosses, considering different genetic effects among populations.

Genomic selection (GS) can accelerate genetic gain in crops, but its effectiveness depends on training population design and marker density. Nested association mapping (NAM) populations provide a structured framework that captures broad allelic diversity within a controlled genetic background. Here, we evaluated genomic prediction (GP) and genome-wide association study (GWAS) performance in an expanded aus-NAM population of rice comprising 1,818 recombinant inbred lines across 14 families and 11 agronomic traits, using genotyping-by-sequencing (GBS) markers and projected whole-genome sequence variants. Prediction accuracy plateaued at moderate marker densities ([~]20k SNPs) and with training populations of [~]500 lines ([~]40-60% of the available pool), with trait heritability emerging as the strongest determinant of predictive performance rather than model choice or marker density. In contrast, GWAS resolution continued to improve with increasing marker density, enabling detection of additional loci, including a chromosome 12 locus associated with heading date, while consistently recovering well-characterized genes such as EARLY HEADING DATE 1 (Ehd1) and SEMIDWARF 1 (SD1). These contrasting patterns indicate that GP reaches near-optimal performance once genome-wide variation is adequately represented, whereas GWAS benefits from higher marker density through improved locus resolution. The present study establishes a benchmark for implementing breeding programs involving japonica/indica crosses using GP in a single environment.

The transition from hulled to free-threshing grain was a pivotal event in wheat domestication, enabling efficient harvesting and processing. Threshability in tetraploid wheat is controlled primarily by the Q locus and two Tenacious glume (Tg) loci on chromosomes 2A and 2B, yet the molecular basis of the major Tg1-B locus remains incompletely characterized. Here, we phenotyped a durum wheat x wild emmer wheat (WEW) recombinant inbred line (RIL) population across two field environments and performed QTL analysis for glume tenacity (TG), threshability ratio (THRR), and seed number per spike (SDNPS). A total of 19 significant QTLs were detected across six chromosomes. The largest-effect loci for both TG and THRR co-localized on chromosome 2B, with LOD scores up to 14.22 and phenotypic variance explained up to 31.2%, corresponding to the previously described Tg1-B locus. To validate this QTL, the donor RIL was backcrossed three times to Svevo to generate a near-isogenic line, NIL-65 (BC3F5), confirmed by whole-genome skim sequencing to carry a homozygous WEW introgression at Tg1-B. A segregating BC4F2 population derived from NIL-65 confirmed that plants homozygous for the dominant Tg1-B allele displayed significantly higher glume tenacity and intact glume morphology compared to tg1-B sister lines, which exhibited basal glume cracking characteristic of the free-threshing phenotype. Genotyping-by-sequencing delimited the causal interval to an approximately 11 Mb introgression on chromosome 2B. These results confirm the major role of Tg1-B in determining glume tenacity in tetraploid wheat, provide a validated near-isogenic germplasm resource, and lay the foundation for fine-mapping and functional characterization of the underlying gene(s).

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.

Wheat production is threatened by numerous fungal diseases, but the potential to breed for multiple disease resistance (MDR) mechanisms is yet to be explored. Here, significant global genetic correlations and underlying local genomic regions were identified in the Vavilov wheat diversity panel for six major fungal diseases, including biotrophic leaf rust (LR), yellow rust (YR), stem rust (SR), hemibiotrophic crown rot (CR), and necrotrophic tan spot (TS) and Septoria nodorum blotch (SNB). By adopting haplotype-based local genomic estimated breeding values, derived from an integrated set of 34,899 SNP and DArT markers, we established a novel haplotype catalogue for resistance to the six diseases in over 20 field experiments across Australia and Ethiopia. Haploblocks with high variances of haplotype effects in all environments were identified for three rusts and pleiotropic haploblocks were identified for at least two diseases, with four haploblocks affecting all six diseases. Through simulation we demonstrated that stacking optimal haplotypes for one disease could improve resistance substantially, but indirectly affected resistance for other five diseases, which varied depending on the genetic correlation with the non-target disease trait. On the other hand, our simulation results combining beneficial haplotypes for all diseases increased resistance to LR, YR, SR, CR, TS and SNB, by up to 48.1%, 35.2%, 29.1%, 12.8%, 18.8% and 32.8%, respectively. Overall, our results highlight the genetic potential to improve MDR in wheat. The haploblock-based catalogue with novel forms of resistance provides a useful resource to guide desirable haplotype stacking for breeding future wheat cultivars with MDR.

Genomic selection has revolutionized breeding by enabling the early identification of superior individuals using genome-wide markers, enhancing breeding efficiency and accelerating variety development. Over the past decade, new selection and mating strategies -- leveraging optimization methods and other approaches -- have been introduced to improve various decision-making processes in breeding programs. However, optimizing breeding remains challenging when the positions and effects of quantitative trait loci are unknown. We developed a framework that optimizes breeding strategies while updating genomic prediction models during breeding schemes. By implementing intermediate model updates, we enabled re-optimization of allocation strategies based on updated predictions. Our simulations compared this approach with equal allocation and optimal cross selection methods across various selection intensities and genetic architectures. Results demonstrated our optimized allocation strategy significantly outperformed the other approaches under moderate to low selection intensities, particularly when combined with model updates. While genetic gains plateaued without updates, our approach enabled continuous improvement through the final generation. The framework showed exceptional robustness across different simulation conditions and better maintained genetic diversity while controlling changes in population structure. This confirms that optimized allocation strategies remain effective when using estimated marker effects rather than true effects, providing a practical framework for improving real-world breeding programs.

Modern hybrid maize (Zea mays L.) breeding programs are based on the management of distinct complementary heterotic groups to maximize heterosis in high-performing hybrids. This practice lowers shared genetic segments and increases divergence between groups to limit inbreeding in hybrids. However, most breeding programs have not always enforced strict separation between heterotic groups in the past. Competitor commercial hybrids were notably a common elite germplasm source for inbred development, which would diminish divergence between groups. This study proposes a new haplotype-based approach to assess hybrids residual inbreeding based on parental similarity. The new haplotype method has a stronger significant negative effect on hybrids grain yield than raw SNP data. Evaluation of modern experimental hybrids uncovered related inbreds contributing to superior rates of residual inbreeding. Analysis of these inbreds revealed haplotype transfers between heterotic groups, originating notably from the use of a Stiff Stalk-Iodent commercial hybrid as breeding starts material in both Stiff Stalk and Non-Stiff Stalk breeding populations. The introduction of this intergroup parent generated heterotic-group-specific haplotype migration between crossing pools. These fragments caused significant genome-wide residual inbreeding in experimental hybrids across selection cycles. This study highlights the necessity for accurate evaluation of external sources of diversity to minimize haplotype transfers and admixture between crossing pools. We demonstrate the consequences of using commercial hybrids in inbred development, particularly regarding residual inbreeding, and their effects on hybrid performance. Insights from these results can assist breeders in optimizing the choice of parents for introducing genetic diversity in a reciprocal recurrent selection scheme. KEY MESSAGEHaplotype-based hybrids parental similarity better predicts grain yield than marker-based identity-by-state. Utilization of commercial hybrids as breeding start material resulted in higher hybrid residual inbreeding even after several selection cycles

The bacterial leaf streak (BLS) disease of barley, caused by Xanthomonas translucens pv. translucens (Xtt), has become increasingly important worldwide in recent years. Inefficacy of chemical control methods leaves deployment of host resistance to be the only option to manage this disease. However, current commercial varieties are mainly susceptible to BLS. Therefore, our goal was to identify sources of resistance from diverse barley germplasms and map associated genetic factors. To do so, we evaluated a subset of the World Barley Core Collection (BCC), consisting of 198 accessions, on their reaction to BLS from 2013 to 2016 under natural or artificially inoculated disease pressures. Ten accessions exhibited consistently low disease severities over four years of evaluations. Using genotype data from the T3/Barley database, genome-wide association studies were conducted to identify marker-trait associations (MTAs) in this barley mini-core panel for BLS resistance. Utilizing four mixed-model analyses (MLM, MLMM, FarmCPU, BLINK), five significant MTAs were consistently identified from at least two mixed model analyses including two in chromosome 2H, and one each in chromosomes 5H, and 7H. Associations in chromosomes 2H and 5H appear to be in the same region with loci identified in a previous association study, reinforcing their potential relevance. The identified resistant barley accessions and associated markers will be valuable inbreeding BLS-resistant barley varieties. Core ideas- A subset of world barley collection was used in the identification of sources of resistance and marker-trait association for resistance for bacterial leaf streak. - Ten accessions consistently showed low disease severity across different years, highlighting their value in BLS resistance breeding. - Five MTAs were consistently identified using different models, with two aligning with previously identified resistance QTLs.

Genomic selection holds the potential to serve as a strategic tool to enhance the genetic gain of complex traits in Miscanthus breeding programs. The development of improved cultivars requires their assessment for various traits across diverse environments to ensure suitable overall performance. Hence, the multi-trait multi-environment (MTME) genomic prediction (GP) models offer an opportunity to improve selection accuracy. This study aims to evaluate the potential of five GP models: (1) three MTME models including genotype-by-trait-by-environment interaction (GxExT) and (2) two single-trait multi-environment (STME) models (with and without GxE interaction). A Miscanthus sacchariflorus population comprising 336 genotypes evaluated in three environments and scored for four traits (biomass yield YDY, total culm number TCM, average internode length AIL, and culm node number CNN) was analyzed. The predictive ability of the models was evaluated considering three cross-validation schemes resembling realistic scenarios (CV1: predicting new genotypes, CVP: predicting missing traits in a given environment, and CV2: predicting partially observed genotypes). On average, in all cross-validation schemes compared to the STME the predictive ability of the MTME models was 10% to 70% higher for TCM and AIL. On the other hand, for YDY and CNN, both STME models performed similarly or slightly better (between 5 to 64%) than the MTME models in most environments. While the MTME models were not successful for all traits when compared to their STME counterparts, MTME models improved the prediction of the performance of genotypes that were untested across environments or lacked trait information in a specific environment. Overall, our study suggests that MTME GP models can be implemented in Miscanthus breeding programs to improve the predictive ability of the complex traits, shorten breeding cycles, and accelerate selection decisions.

1Sparse testcrossing is an effective strategy for increasing both short- and long-term genetic gain in hybrid breeding programs. Maize hybrid breeding programs aim to develop new hybrid varieties by crossing genetically distinct parents from different heterotic pools, exploiting heterosis for improved performance. The programs typically consist of two main components: population improvement and product development. The population improvement component aims to enhance the heterotic pools through reciprocal recurrent selection based on general combining ability (GCA). However, especially in the early stages of testing, evaluating large numbers of hybrid combinations to estimate GCA is impractical due to considerable logistical challenges and costs. Therefore, breeders often evaluate the initial population of selection candidates using only a single tester to narrow down the candidate pool before further evaluation. Using a single tester, however, may not adequately represent the heterotic pool, leading to inaccurate GCA estimates and suboptimal selection decisions. To address this, we propose sparse testcrossing for early-stage testing, where subsets of candidate genotypes are testcrossed with different testers, connected through a genomic relationship matrix. We conducted stochastic simulations to compare various sparse testcrossing designs with a conventional testcross strategy using a single tester over 15 cycles of reciprocal recurrent genomic selection. Our results show that using 3-5 testers, sparsely distributed among full-sibs, sparse testcrossing offers breeders a practical balance between simple testcross designs, resource efficiency, and increased prediction accuracy for GCA, ultimately resulting in increased rates of genetic gain. Key messageSparse testcrossing with 3-5 testers enhances genetic gain in hybrid breeding programs, offering a practical balance of simple testcross designs, resource efficiency, and increased prediction accuracy for general combining ability.

Breeders commonly use genetic markers to predict the performance of untested individuals as a way to improve the efficiency of breeding programs. These genomic prediction models have almost exclusively used single nucleotide polymorphisms (SNPs) as their source of genetic information, even though other types of markers exist, such as structural variants (SVs). Given that SVs are associated with environmental adaptation and not all of them are in linkage disequilibrium to SNPs, SVs have the potential to bring additional information to multi-environment prediction models that are not captured by SNPs alone. Here, we evaluated different marker types (SNPs and/or SVs) on prediction accuracy across a range of genetic architectures for simulated traits across multiple environments. Our results show that SVs can improve prediction accuracy by up to 19%, but it is highly dependent on the genetic architecture of the trait. Differences in prediction accuracy across marker types were more pronounced for traits with high heritability, high number of QTLs, and SVs as causative variants. In these scenarios, using SV markers resulted in better prediction accuracies than SNP markers, especially when predicting untested genotypes across environments, likely due to more predictors being in linkage disequilibrium with causative variants. The simulations revealed little impact of different effect sizes between SNPs and SVs as causative variants on prediction accuracy. This study demonstrates the importance of knowing the genetic architecture of a trait in deciding what markers and marker types to use in large scale genomic prediction modeling in a breeding program. Key messageWe demonstrate potential for improved multi-environment genomic prediction accuracy using structural variant markers. However, the degree of observed improvement is highly dependent on the genetic architecture of the trait.

In recent years, the turnover of plant breeding has substantially increased as the use of genomic information allows for earlier selection and the integration of controlled growing environments reduces time to reach a particular growing stage. However, high generation turnover and intensive selection of lines before own yield trials are performed come at the risk of a drastic reduction of genetic diversity paired with lower prediction accuracies. To this end, we investigate strategies to cope with these challenges in a maize rapid cycle breeding scheme using stochastic simulations using the software MoBPS. We find that genetic gains soon reach a plateau when only the original breeding material is phenotyped. Updating the training data set via additional phenotyping of crosses or doubled haploid lines ensures long-term progress with a gain of 6.80 / 6.95 genetic standard deviations for the performance as a cross / per se after 30 cycles of breeding compared to 3.40 / 4.28 without additional phenotyping. Adding genetic material with comparable genetic level and novel diversity from outside the breeding material led to a further increase to 9.34 / 7.89 genetic standard deviations. In particular, for the management of genetic diversity, further additions to the breeding scheme are analyzed to optimize the number of selected lines per cycle and to account for the relatedness of F2 plants in the selection using the software AlphaMate. Finding a balance between genetic gains and diversity is important for a given time frame. MoBPS provides a tool for the quantification of these effects and provides solutions specific to the respective breeding program.

Genomic prediction (GP) across diverse environments has a potential to accelerate genetic gain in cotton breeding programs. A major challenge in GP is modelling genotype-by-environment interactions (GEI), which is essential for selecting stable and high-performing genotypes under variable production conditions. However, incorporating GEI into GP models increases the dimensionality and computational complexity, risking complex models that are impractical to use on commercial breeding-scale data sets because of run times and computational demands. This study addresses two primary aims. Firstly, we evaluate the practical benefits of GEI-informed GP for predicting economically important cotton traits. Second, advanced statistical modelling strategies are developed and assessed for integrating genomic and environmental data at scale. We propose a dimensionality reduction approach that combines linkage disequilibrium network analysis with principal component techniques to reduce redundancy while preserving informative variation. Using this reduced dataset, we implement Bayesian linear regression models and, for comparison, deep residual neural networks for genomic prediction. Analyses were conducted on a large multi-environment dataset from the CSIRO cotton breeding program, comprising 3,236 breeding lines, 54 environmental covariates, and 8,049 yield and fibre quality phenotype records collected over 10 years and 9 locations representing 41 year-location combinations. Results demonstrate that generally Bayesian linear regression approaches outperform BG-BLUP models, with all three linear/linear mixed methods providing clearly more reliable performance than the deep learning models. These findings highlight the value of using interpretable statistical models for integrating genomic and environmental information to support selection decisions under diverse environmental conditions.

In plant breeding programs, rapid production of novel varieties is highly desirable. Genomic selection allows the selection of superior individuals based on genomic estimated breeding values. However, it is worth noting that superior individuals may not always be superior parents. The choice of the crossing pair significantly influences the genotypic value of the resulting progeny. This study introduced a new strategy for selecting crossing pairs, termed Cross Potential Selection (CPS), designed to expedite the production of novel varieties. The CPS assesses the potential of each crossing pair to generate a novel variety. It considers the segregation of each crossing pair and computes the expected genotypic values of the topperforming individuals, assuming that the progeny distribution of genotypic values follows a normal distribution. We simulated a 10-year breeding program to compare CPS with three other selection strategies. CPS consistently demonstrated the highest genetic improvements among the four strategies in early cycles. In particular, during the middle cycles of the breeding program, CPS exhibited the highest genetic improvement of 73% of the 300 independent breeding simulations. In a long-term breeding scheme, some progeny distributions of genotypic values may deviate from normal distribution, affecting the efficiency of CPS. Nevertheless, compared with the other three strategies, CPS achieved significant short-term genetic improvements. In conclusion, CPS holds substantial promise for enhancing the efficiency of plant breeding programs. Article SummaryThis study introduces a novel plant breeding strategy termed Cross Potential Selection (CPS), which was designed to expedite the production of novel varieties. The CPS evaluates the potential of each crossing pair for the target generation. Through comparative breeding simulations, CPS demonstrated superior performance over the three alternative breeding strategies, particularly in the early cycles. These findings suggest that CPS holds significant promise for enhancing plant breeding efficiency.