Theoretical and Applied Genetics

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.

Multi-trait genomic prediction offers a practical route to improve selection for costly, complex traits in clonally propagated crops such as cassava. In a Brazilian breeding panel of 1,078 cassava clones genotyped with 25,923 SNPs and phenotyped for six agronomic traits, we compared single-trait (ST) and multi-trait (MT) GBLUP models. Stage-wise mixed models produced BLUEs that fed into ST and MT-GBLUP. We tested five cross-validation schemes that mimic breeder realities: ST baseline (CV1); naive all-traits MT prediction for unphenotyped candidates (CV2); MT prediction using auxiliary trait phenotypes in the test set (CV3); and two sparse-phenotyping regimes with missingness by trait (CV4) or by clone (CV5) at 25%, 50%, and 75% levels. The main results were that, under the ST baseline (CV1), predictive ability ranged from 0.50 for DMC and 0.45 for FRY down to 0.13 for Le.Dis. A naive full MT model (CV2) performed approximately on par with ST-GBLUP. In contrast, MT designs (CV3) that included informative auxiliary traits, such as shoot yield and combinations with plant vigor and leaf disease severity, yielded small gains for DMC with predictive ability of approximately 0.51 (+2%), while FRY predictive ability increased to approximately 0.65 (+44%), accompanied by RMSE reductions for FRY up to approximately 13.5% (e.g. RMSE approximately 6.2). Sparse-phenotyping simulations (CV4/CV5) demonstrated that MT models sustain or even improve predictive ability under realistic missing-data regimes (PA {approx} 0.62 - 0.65). Selection concordance between MT and ST top-10% sets was generally high (>0.80), and MT configurations produced measurable improvements in expected selection response and genetic gain per cycle for several target traits. These results indicate that strategically implemented MT-GBLUP, using a small set of biologically and operationally informative auxiliary traits and optimized sparse phenotyping, can materially increase predictive accuracy and selection efciency for economically critical cassava traits while reducing phenotyping burden.

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

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.

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.

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 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.

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.

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.

Statistical models that capture the phenotypic plasticity of a genotype across environments are crucial in plant breeding programs to potentially identify parents, generate offspring, and obtain highly productive genotypes for distinct environments. In this study, our aim is to leverage concepts of Bayesian models and probability methods of stability analysis to untangle genotype-by-environment interaction (GEI). The proposed method employs the posterior distribution obtained with the No-U-Turn sampler algorithm to get Monte Carlo estimates of adaptation and stability probabilities. We applied the proposed models in two empirical tropical datasets. Our findings provide a basis to enhance our ability to consider the uncertainty of cultivar recommendation for global or specific adaptation. We further demonstrate that probability methods of stability analysis in a Bayesian framework are a powerful tool for unraveling GEI given a defined intensity of selection that results in a more informed decision-making process towards cultivar recommendation in multi-environment trials.

The major drawback to the implementation of genomic selection in a breeding program is the reduction of additive genetic variance in the long term, primarily due to the Bulmer effect. Increasing genetic gain and retaining additive genetic variance requires optimizing the trade-off between the two competing factors. Our approach integrated index selection in the genomic infer cross-selection (GCS) methods. With this strategy, we identified optimal crosses that simultaneously maximize progeny performance and maintain genetic variance for multiple traits. Using a stochastic simulated recurrent breeding program over a 40-year period, we evaluated different GCS metrics with other factors, such as the number of parents, crosses, and progenies per cross, that influence genetic gain in a breeding program. Across all breeding scenarios, the posterior mean-variance consistently enhances genetic gain when compared to other metrics such as the usefulness criterion, optimal haploid value, mean genomic estimated breeding value, and mean index selection value of the superior parents. In addition, we provide a detailed strategy to optimize the number of parents, crosses, and progenies per cross that maximizes short- and long-term genetic gain in a breeding program.

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.

Bridging the gap between how breeding genotypes perform in trials and how they might fare as commercial varieties or cultivars remains one of the enduring challenges in plant breeding, further compounded by the complexities of genotype-by-environment (GxE) interactions. This study implemented and evaluated an unstructured - uns - bivariate enviromic reaction-norm model capable of predicting breeding genotypes as cultivars by integrating genomic and environmental data. All model components were developed from scratch in R, ensuring full methodological transparency and reproducibility. The model jointly estimated genetic and residual variance-covariance matrices for experimental trials and commercial stands using a derivative-free restricted maximum likelihood (DF-REML) approach with the BOBYQA algorithm. Results demonstrated that employing a SNP-based genomic relationship matrix yielded biologically consistent estimates and enabled the construction of spatial recommendation maps for genotype deployment across diverse environments (e.g., in fields established by farmers or growers). These findings underscore the importance of modeling experimental versus commercial performance as distinct but genetically correlated traits, while also demonstrating the feasibility of implementing complex enviromic models without reliance on specialized software. This approach offers methodological advances for predictive breeding, supporting more efficient selection and reducing reliance on extensive multi-environment trials.

Lentil cultivation and consumption promote human health and sustainable agriculture, making a significant contribution to the transition toward a plant-based diet. In Europe, lentil yields are still unstable, and the lack of breeding efforts limits the choice of farmers to few varieties. Here, we characterized 46 lentil genotypes, including local cultivars and landraces from diverse geographic origins, in Mediterranean agro-environments for flowering, architectural and production traits in seven field trials, over 3 years (2019-2021), in two localities (central and southern Italy) and during two sowing seasons (autumn and spring). We estimated the genetic merit of each genotype and identified outperforming genotypes for all traits. Indian ILL 11557AGL and Argentinian IL 4605AGL domesticated varieties resulted superior for earliness. Italian landraces and French cultivars achieved the highest values for first pod height, while landraces and breeding materials from Ethiopia, Syria and Iran were the best-yielding. Data from all seven trials were available for 16 genotypes, so we analyzed the genotype, environment and genotype x environment interaction (GEI) to identify specific genotypic adaptations. European cultivars performed well for architectural traits, whereas the best-yielding genotypes were Middle Eastern and Ethiopian landraces. Environmental effect on yield related to sowing season and locality was detected, with an overall higher yield in autumn compared to spring sowing trials and in central rather than southern Italy. By dissecting the GEI structure using additive main effect and multiplicative interaction (AMMI) analysis and Weighted Average of Absolute Scores (WAASB) index, we identified a group of Iranian landraces (PI 431633 AGL and PI 432033 LSP AGL) adapted to both autumn and spring sowing and one Ethiopian landrace (IG 1959 AGL) showing high yield stability across all environmental conditions. These findings provide a foundation to unlock the full potential of lentil cultivation in European and Mediterranean systems by identifying adapted, high-performing genotypes.

The optimization of mating plans, or optimal genomic mating (OGM), is a powerful breeding strategy that balances genetic gain with the preservation of diversity, securing long-term improvement. However, existing OGM implementations neglect the recycling stage, where naive truncation selection dissipates the diversity initially safeguarded. Here, we propose an integrated strategy that couples optimal recycling with genomic mating to better control genetic diversity while delivering competitive genetic gains. Using stochastic simulations of line and hybrid breeding schemes, we show that the integrated strategy retained 1.6-2.0 times more diversity than OGM alone and 3.5-5.0 times more than truncation mating based on family means and the usefulness criterion (UC). These were equivalent to maintaining around 1.7 and 2.4 times less realized inbreeding rates. Additionally, it improved the efficiency of translating variance into gain by 21.6-49.8% and 67.4-108.2% compared to the sole implementation of OGM and truncation mating strategies. We also demonstrate the utility of our newly developed intuitive and standardized metric, proportion of additive standard deviation lost (PropSD), for managing diversity in the crossing and recycling stages. Pareto optimal solutions were achieved at around 2-3% and 4-5% PropSD without and with optimal recycling. Finally, we derive a closed-form expression quantifying the expected advantage of UC over mean-based mating. Modeling within-family variance offered limited additional benefit, mainly due to high family mean-to-standard-deviation variance ratios. Overall, our proposed framework advances genomic selection programs to be sustainable by effectively preserving genetic diversity for future genetic improvement.

The importance of accurate genomic prediction of phenotypes in plant breeding is undeniable, as higher prediction accuracy can increase selection responses. In this study, we investigated the ability of three models to improve prediction accuracy by including phenotypic information from the last growing season. This was done by considering a single biological trait in two growing seasons (2017 and 2018) as separate traits in a multi-trait model. Thus, bivariate variants of the Genomic Best Linear Unbiased Prediction (GBLUP) as an additive model, Epistatic Random Regression BLUP (ERRBLUP) and selective Epistatic Random Regression BLUP (sERRBLUP) as epistasis models were compared with respect to their prediction accuracies for the second year. The results indicate that bivariate ERRBLUP is slightly superior to bivariate GBLUP in predication accuracy, while bivariate sERRBLUP has the highest prediction accuracy in most cases. The average relative increase in prediction accuracy from bivariate GBLUP to maximum bivariate sERRBLUP across eight phenotypic traits and studied dataset from 471/402 doubled haploid lines in the European maize landrace Kemater Landmais Gelb/Petkuser Ferdinand Rot, were 7.61 and 3.47 percent, respectively. We further investigated the genomic correlation, phenotypic correlation and trait heritability as the factors affecting the bivariate models predication accuracy, with genetic correlation between growing seasons being the most important one. For all three considered model architectures results were far worse when using a univariate version of the model, e.g. with an average reduction in prediction accuracy of 0.23/0.14 for Kemater/Petkuser when using univariate GBLUP. Key MassageBivariate models based on selected subsets of pairwise SNP interactions can increase the prediction accuracy by utilizing phenotypic data across years under the assumption of high genomic correlation across years.

The selection of informative markers has been studied massively as an alternative to reduce genotyping costs for the genomic selection (GS) application. Low-density marker panels are attractive for GS because they decrease computational time-consuming and multicollinearity beyond more individuals can be genotyped with the same cost. Nevertheless, these inferences are usually made empirically using "static" training sets and populations, which are adequate only to predict a breeding programs initial cycles but might not for long-term cycles. Moreover, to the best of our knowledge, none of these inferences considered the inclusion of dominance into the GS models, which is particularly important to predict cross-pollinated crops. Therefore, that reveals an important and unexplored topic for allogamous long-term breeding. To achieve this goal, we employed two approaches: the former used empirical maize datasets, and the latter simulations of long-term breeding cycles of phenotypic and genomic recurrent selection (intrapopulation and reciprocal). Then, we observed the reducing marker density effect on populations (mean, the best genotypes performance, accuracy, additive variance) over cycles and models (additive, additive-dominance, specific combining ability (SCA)). Our results indicate that the markers reduction based on different linkage disequili brium (LD) levels is viable only within a cycle and brings a significant decrease in predictive ability over generations. Furthermore, in the long-term, regardless of the selection scheme adopted, the more makers, the better because they buffer LD losses caused by recombination over breeding cycles. Finally, regarding the accuracy, the additive-dominant models tend to outperform the additive ones and perform similar to the SCA.