Training set optimization is a feasible alternative for perennial orphan crop domestication and germplasm management: an Acrocomia aculeata example.

Orphan perennial native species are gaining importance as sustainability in agriculture becomes crucial to mitigate climate change. Nevertheless, issues related to the undomesticated status and lack of improved germplasm impede the evolution of formal agricultural initiatives. Acrocomia aculeata - a neotropical palm with potential for oil production - is an example. Breeding efforts can aid the species to reach its full potential and increase market competitiveness. Here, we present genomic information and training set optimization as alternatives to boost orphan perennial native species breeding using Acrocomia aculeata as an example. Furthermore, we compared three SNP calling methods and, for the first time, presented the prediction accuracies of three yield-related traits. We collected data for two years from 201 wild individuals. These trees were genotyped, and three references were used for SNP calling: the oil palm genome, de novo sequencing, and the A. aculeata transcriptome. The traits analyzed were fruit dry mass (FDM), pulp dry mass (PDM), and pulp oil content (OC). We compared the predictive ability of GBLUP and BayesB models in cross- and real validation procedures. Afterwards, we tested several optimization criteria regarding consistency and the ability to provide the optimized training set that yielded less risk in both targeted and untargeted scenarios. Using the oil palm genome as a reference and GBLUP models had better results for the genomic prediction of FDM, OC, and PDM (prediction accuracies of 0.46, 0.45, and 0.39, respectively). Using the criteria PEV, r-score and core collection methodology provides risk-averse decisions. Training set optimization is an alternative to improve decision-making while leveraging genomic information as a cost-saving tool to accelerate plant domestication and breeding. The optimized training set can be used as a reference for the characterization of native species populations, aiding in decisions involving germplasm collection and construction of breeding populations.

Genomic prediction of yield-related traits and genome-based establishment of heterotic pattern in maize hybrid breeding of Southwest China.

When genomic prediction is implemented in breeding maize (Zea mays L.), it can accelerate the breeding process and reduce cost to a large extent. In this study, 11 yield-related traits of maize were used to evaluate four genomic prediction methods including rrBLUP, HEBLP|A, RF, and LightGBM. In all the 11 traits, rrBLUP had similar predictive accuracy to HEBLP|A, and so did RF to LightGBM, but rrBLUP and HEBLP|A outperformed RF and LightGBM in 8 traits. Furthermore, genomic prediction-based heterotic pattern of yield was established based on 64620 crosses of maize in Southwest China, and the result showed that one of the parent lines of the top 5% crosses came from temp-tropic or tropic germplasm, which is highly consistent with the actual situation in breeding, and that heterotic pattern (Reid+ × Suwan+) will be a major heterotic pattern of Southwest China in the future.

Genomic-inferred cross-selection methods for multi-trait improvement in a recurrent selection breeding program.

The major drawback to the implementation of genomic selection in a breeding program lies in long-term decrease in additive genetic variance, which is a trade-off for rapid genetic improvement in short term. Balancing increase in genetic gain with retention of additive genetic variance necessitates careful optimization of this trade-off. In this study, we proposed an integrated index selection approach within the genomic inferred cross-selection (GCS) framework to maximize genetic gain across multiple traits. With this method, 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-years period, we evaluated different GCS methods along with other factors, such as the number of parents, crosses, and progeny per cross, that influence genetic gain in a pulse crop breeding program. Across all breeding scenarios, the posterior mean variance consistently enhances genetic gain when compared to other methods, 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 progeny per cross that can potentially maximize short- and long-term genetic gain in a public breeding program.

GWAS Enhances Genomic Prediction Accuracy of Caviar Yield, Caviar Color and Body Weight Traits in Sturgeons Using Whole-Genome Sequencing Data.

Caviar yield, caviar color, and body weight are crucial economic traits in sturgeon breeding. Understanding the molecular mechanisms behind these traits is essential for their genetic improvement. In this study, we performed whole-genome sequencing on 673 Russian sturgeons, renowned for their high-quality caviar. With an average sequencing depth of 13.69×, we obtained approximately 10.41 million high-quality single nucleotide polymorphisms (SNPs). Using a genome-wide association study (GWAS) with a single-marker regression model, we identified SNPs and genes associated with these traits. Our findings revealed several candidate genes for each trait: caviar yield: TFAP2A, RPS6KA3, CRB3, TUBB, H2AFX, morc3, BAG1, RANBP2, PLA2G1B, and NYAP1; caviar color: NFX1, OTULIN, SRFBP1, PLEK, INHBA, and NARS; body weight: ACVR1, HTR4, fmnl2, INSIG2, GPD2, ACVR1C, TANC1, KCNH7, SLC16A13, XKR4, GALR2, RPL39, ACVR2A, ADCY10, and ZEB2. Additionally, using the genomic feature BLUP (GFBLUP) method, which combines linkage disequilibrium (LD) pruning markers with GWAS prior information, we improved genomic prediction accuracy by 2%, 1.9%, and 3.1% for caviar yield, caviar color, and body weight traits, respectively, compared to the GBLUP method. In conclusion, this study enhances our understanding of the genetic mechanisms underlying caviar yield, caviar color, and body weight traits in sturgeons, providing opportunities for genetic improvement of these traits through genomic selection.

Modeling Chickpea Productivity with Artificial Image Objects and Convolutional Neural Network.

The chickpea plays a significant role in global agriculture and occupies an increasing share in the human diet. The main aim of the research was to develop a model for the prediction of two chickpea productivity traits in the available dataset. Genomic data for accessions were encoded in Artificial Image Objects, and a model for the thousand-seed weight (TSW) and number of seeds per plant (SNpP) prediction was constructed using a Convolutional Neural Network, dictionary learning and sparse coding for feature extraction, and extreme gradient boosting for regression. The model was capable of predicting both traits with an acceptable accuracy of 84-85%. The most important factors for model solution were identified using the dense regression attention maps method. The SNPs important for the SNpP and TSW traits were found in 34 and 49 genes, respectively. Genomic prediction with a constructed model can help breeding programs harness genotypic and phenotypic diversity to more effectively produce varieties with a desired phenotype.

Developing genomic tools to assist turnip rape [Brassica rapa (L.) subsp.oleifera (DC.) Metzg.] breeding.

Introduction: Turnip rape is recognized as an oilseed crop contributing to environmentally sustainable agriculture via integration into crop rotation systems. Despite its various advantages, the crop's cultivation has declined globally due to a relatively low productivity, giving way to other crops. The use of genomic tools could enhance the breeding process and accelerate genetic gains. Therefore, the present research investigated 170 turnip rape accessions representing its global gene pool to identify SNP markers associated nine phenological and agro-morphological traits and estimate the genomic breeding values (GEBVs) of the germplasm through GWAS and genomic prediction analyses, respectively. Methods: Field trials were conducted at two sites in northern and southern Sweden to obtain the phenotypic data while genotyping was conducted via the genotyping-by-sequencing (GBS) method. The traits studied include days to flowering (DTF) and maturity (DTM), plant height (PH), seed yield (YLD), thousand seed weight (TSW), silique length (SL), number of siliques (NS), number of seeds per silique (SS), and pod shattering resistance (PSHR). Results and conclusion: Analysis of variance revealed substantial variation among accessions, with significant genotype-by-environment interaction for most traits. A total of 25, 17, 16, 14, 7, 5, 3, and 3 MTAs were identified for TSW, DTF, PH, PSHR, SL, YLD, SS and DTM, respectively. An 80%-20% training-test set genomic prediction analysis was conducted using the ridge regression - BLUP (RR-BLUP) model. The accuracy of genomic prediction for most traits was high, indicating that these tools may assist turnip rape breeders in accelerating genetic gains. The study highlights the potential of genomic tools to significantly advance breeding programs for turnip rape by identifying pivotal SNP markers and effectively estimating genomic breeding values. Future breeding perspectives should focus on leveraging these genomic insights to enhance agronomic traits and productivity, thereby reinstating turnip rape as a competitive and sustainable crop in Sweden and broader global agriculture.

Sparse testing designs for optimizing predictive ability in sugarcane populations.

Sugarcane is a crucial crop for sugar and bioenergy production. Saccharose content and total weight are the two main key commercial traits that compose sugarcane's yield. These traits are under complex genetic control and their response patterns are influenced by the genotype-by-environment (G×E) interaction. An efficient breeding of sugarcane demands an accurate assessment of the genotype stability through multi-environment trials (METs), where genotypes are tested/evaluated across different environments. However, phenotyping all genotype-in-environment combinations is often impractical due to cost and limited availability of propagation-materials. This study introduces the sparse testing designs as a viable alternative, leveraging genomic information to predict unobserved combinations through genomic prediction models. This approach was applied to a dataset comprising 186 genotypes across six environments (6×186=1,116 phenotypes). Our study employed three predictive models, including environment, genotype, and genomic markers as main effects, as well as the G×E to predict saccharose accumulation (SA) and tons of cane per hectare (TCH). Calibration sets sizes varying between 72 (6.5%) to 186 (16.7%) of the total number of phenotypes were composed to predict the remaining 930 (83.3%). Additionally, we explored the optimal number of common genotypes across environments for G×E pattern prediction. Results demonstrate that maximum accuracy for SA ( ρ = 0.611 ) and for TCH ( ρ=0.341 ) was achieved using in training sets few (3) to no common (0) genotype across environments maximizing the number of different genotypes that were tested only once. Significantly, we show that reducing phenotypic records for model calibration has minimal impact on predictive ability, with sets of 12 non-overlapped genotypes per environment (72=12×6) being the most convenient cost-benefit combination.

Linking genetic markers and crop model parameters using neural networks to enhance genomic prediction of integrative traits.

Introduction: Predicting the performance (yield or other integrative traits) of cultivated plants is complex because it involves not only estimating the genetic value of the candidates to selection, the interactions between the genotype and the environment (GxE) but also the epistatic interactions between genomic regions for a given trait, and the interactions between the traits contributing to the integrative trait. Classical Genomic Prediction (GP) models mostly account for additive effects and are not suitable to estimate non-additive effects such as epistasis. Therefore, the use of machine learning and deep learning methods has been previously proposed to model those non-linear effects. Methods: In this study, we propose a type of Artificial Neural Network (ANN) called Convolutional Neural Network (CNN) and compare it to two classical GP regression methods for their ability to predict an integrative trait of sorghum: aboveground fresh weight accumulation. We also suggest that the use of a crop growth model (CGM) can enhance predictions of integrative traits by decomposing them into more heritable intermediate traits. Results: The results show that CNN outperformed both LASSO and Bayes C methods in accuracy, suggesting that CNN are better suited to predict integrative traits. Furthermore, the predictive ability of the combined CGM-GP approach surpassed that of GP without the CGM integration, irrespective of the regression method used. Discussion: These results are consistent with recent works aiming to develop Genome-to-Phenotype models and advocate for the use of non-linear prediction methods, and the use of combined CGM-GP to enhance the prediction of crop performances.

To be or not to be tetraploid-the impact of marker ploidy on genomic prediction and GWAS of potato.

Cultivated potato, Solanum tuberosum L., is considered an autotetraploid with 12 chromosomes with four homologous phases. However, recent evidence found that, due to frequent large phase deletions in the genome, gene ploidy is not constant across the genome. The elite cultivar "Otava" was found to have an average gene copy number of 3.2 across all loci. Breeding programs for elite potato cultivars rely increasingly on genomic prediction tools for selection breeding and elucidation of quantitative trait loci underpinning trait genetic variance. These are typically based on anonymous single nucleotide polymorphism (SNP) markers, which are usually called from, for example, SNP array or sequencing data using a tetraploid model. In this study, we analyzed the impact of using whole genome markers genotyped as either tetraploid or observed allele frequencies from genotype-by-sequencing data on single-trait additive genomic best linear unbiased prediction (GBLUP) genomic prediction (GP) models and single-marker regression genome-wide association studies of potato to evaluate the implications of capturing varying ploidy on the statistical models employed in genomic breeding. A panel of 762 offspring of a diallel cross of 18 parents of elite breeding material was used for modeling. These were genotyped by sequencing and phenotyped for five key performance traits: chipping quality, length/width ratio, senescence, dry matter content, and yield. We also estimated the read coverage required to confidently discriminate between a heterozygous triploid and tetraploid state from simulated data. It was found that using a tetraploid model neither impaired nor improved genomic predictions compared to using the observed allele frequencies that account for true marker ploidy. In genome-wide associations studies (GWAS), very minor variations of both signal amplitude and number of SNPs supporting both minor and major quantitative trait loci (QTLs) were observed between the two data sets. However, all major QTLs were reproducible using both data sets.

Evaluation of deep learning for predicting rice traits using structural and single-nucleotide genomic variants.

BACKGROUND: Structural genomic variants (SVs) are prevalent in plant genomes and have played an important role in evolution and domestication, as they constitute a significant source of genomic and phenotypic variability. Nevertheless, most methods in quantitative genetics focusing on crop improvement, such as genomic prediction, consider only Single Nucleotide Polymorphisms (SNPs). Deep Learning (DL) is a promising strategy for genomic prediction, but its performance using SVs and SNPs as genetic markers remains unknown. RESULTS: We used rice to investigate whether combining SVs and SNPs can result in better trait prediction over SNPs alone and examine the potential advantage of Deep Learning (DL) networks over Bayesian Linear models. Specifically, the performances of BayesC (considering additive effects) and a Bayesian Reproducible Kernel Hilbert space (RKHS) regression (considering both additive and non-additive effects) were compared to those of two different DL architectures, the Multilayer Perceptron, and the Convolution Neural Network, to explore their prediction ability by using various marker input strategies. We found that exploiting structural and nucleotide variation slightly improved prediction ability on complex traits in 87% of the cases. DL models outperformed Bayesian models in 75% of the studied cases, considering the four traits and the two validation strategies used. Finally, DL systematically improved prediction ability of binary traits against the Bayesian models. CONCLUSIONS: Our study reveals that the use of structural genomic variants can improve trait prediction in rice, independently of the methodology used. Also, our results suggest that Deep Learning (DL) networks can perform better than Bayesian models in the prediction of binary traits, and in quantitative traits when the training and target sets are not closely related. This highlights the potential of DL to enhance crop improvement in specific scenarios and the importance to consider SVs in addition to SNPs in genomic selection.

Genomic Prediction of The Performance of Tropical Doubled Haploid Maize Lines under Artificial Striga hermonthica (Del.) Benth. Infestation.

Striga hermonthica (Del.) Benth., a parasitic weed, causes substantial yield losses in maize production in sub-Saharan Africa (SSA). Breeding for Striga resistance in maize is constrained by limited genetic diversity for Striga resistance within the elite germplasm and phenotyping capacity under artificial Striga infestation. Genomics-enabled approaches have the potential to accelerate identification of Striga resistant lines for hybrid development. The objectives of this study were to evaluate the accuracy of genomic selection for traits associated with Striga resistance and grain yield (GY) and to predict genetic values of tested and untested doubled haploid (DH) maize lines. We genotyped 606 DH lines with 8,439 rAmpSeq markers. A training set of 116 DH lines crossed to two testers was phenotyped under artificial Striga infestation at three locations in Kenya. Heritability for Striga resistance parameters ranged from 0.38‒0.65 while that for GY was 0.54. The prediction accuracies for Striga resistance-associated traits across locations, as determined by cross validation (CV) were 0.24 to 0.53 for CV0 and from 0.20 to 0.37 for CV2. For GY, the prediction accuracies were 0.59 and 0.56 for CV0 and CV2, respectively. The results revealed 300 DH lines with desirable genomic estimated breeding values (GEBVs) for reduced number of emerged Striga plants (STR) at 8, 10, and 12 weeks after planting. The GEBVs of DH lines for Striga resistance associated traits in the training and testing sets were similar in magnitude. These results highlight the potential application of genomic selection in breeding for Striga resistance in maize. The integration of genomic-assisted strategies and DH technology for line development coupled with forward breeding for major adaptive traits will enhance genetic gains in breeding for Striga resistance in maize.

Integrating large-scale meta-analysis of genome-wide association studies improve the genomic prediction accuracy for combined pig populations.

The strategy of combining reference populations has been widely recognized as an effective way to enhance the accuracy of genomic prediction (GP). This study investigated the efficiency of genomic prediction using prior information and combined reference population. In total, prior information considering trait-associated single nucleotide polymorphisms (SNPs) obtained from meta-analysis of genome-wide association studies (GWAS meta-analysis) was incorporated into three models to assess the performance of GP using combined reference populations. Two different Yorkshire populations with imputed whole genome sequence (WGS) data (9,741,620 SNPs), named as P1 (1259 individuals) and P2 (1018 individuals), were used to predict genomic estimated breeding values for three live carcass traits, including backfat thickness, loin muscle area, and loin muscle depth. A 10 × 5 fold cross-validation was used to evaluate the prediction accuracy of 203 randomly selected candidate pigs from the P2 population and the reference population consisted of the remaining pigs from P2 and the stepwise added pigs from P1. By integrating SNPs with different p-value thresholds from GWAS meta-analysis downloaded from PigGTEx Project, the prediction accuracy of GBLUP, genomic feature BLUP (GFBLUP) and GBLUP given genetic architecture (BLUP|GA) were compared. Moreover, we explored effects of reference population size and heritability enrichment of genomic features on the prediction accuracy improvement of GFBLUP and BLUP|GA relative to GBLUP. The prediction accuracy of GBLUP using all WGS markers showed average improvement of 4.380% using the P1 + P2 reference population compared with the P2 reference population. Using the combined reference population, GFBLUP and BLUP|GA yielded 6.179% and 5.525% higher accuracies than GBLUP using all SNPs based on the single reference population, respectively. Positive regression coefficients were estimated in relation to the improvement in prediction accuracy (between GFBLUP/BLUP|GA and GBLUP) and the size of the reference as well as the heritability enrichment of genomic features. Compared to the classic GBLUP model, GFBLUP and BLUP|GA models integrating GWAS meta-analysis information increase the prediction accuracy and using combined populations with enlarged reference population size further enhances prediction accuracy of the two approaches. The heritability enrichment of genomic features can be used as an indicator to reflect weather prior information is accurately presented.

Optimizing Genomic Parental Selection for Categorical and Continuous-Categorical Multi-Trait Mixtures.

This study presents a novel approach for the optimization of genomic parental selection in breeding programs involving categorical and continuous-categorical multi-trait mixtures (CMs and CCMMs). Utilizing the Bayesian decision theory (BDT) and latent trait models within a multivariate normal distribution framework, we address the complexities of selecting new parental lines across ordinal and continuous traits for breeding. Our methodology enhances precision and flexibility in genetic selection, validated through extensive simulations. This unified approach presents significant potential for the advancement of genetic improvements in diverse breeding contexts, underscoring the importance of integrating both categorical and continuous traits in genomic selection frameworks.

SABO-ILSTSVR: a genomic prediction method based on improved least squares twin support vector regression.

In modern breeding practices, genomic prediction (GP) uses high-density single nucleotide polymorphisms (SNPs) markers to predict genomic estimated breeding values (GEBVs) for crucial phenotypes, thereby speeding up selection breeding process and shortening generation intervals. However, due to the characteristic of genotype data typically having far fewer sample numbers than SNPs markers, overfitting commonly arise during model training. To address this, the present study builds upon the Least Squares Twin Support Vector Regression (LSTSVR) model by incorporating a Lasso regularization term named ILSTSVR. Because of the complexity of parameter tuning for different datasets, subtraction average based optimizer (SABO) is further introduced to optimize ILSTSVR, and then obtain the GP model named SABO-ILSTSVR. Experiments conducted on four different crop datasets demonstrate that SABO-ILSTSVR outperforms or is equivalent in efficiency to widely-used genomic prediction methods. Source codes and data are available at: https://github.com/MLBreeding/SABO-ILSTSVR.

Optimising desired gain indices to maximise selection response.

Introduction: In plant breeding, we often aim to improve multiple traits at once. However, without knowing the economic value of each trait, it is hard to decide which traits to focus on. This is where "desired gain selection indices" come in handy, which can yield optimal gains in each trait based on the breeder's prioritisation of desired improvements when economic weights are not available. However, they lack the ability to maximise the selection response and determine the correlation between the index and net genetic merit. Methods: Here, we report the development of an iterative desired gain selection index method that optimises the sampling of the desired gain values to achieve a targeted or a user-specified selection response for multiple traits. This targeted selection response can be constrained or unconstrained for either a subset or all the studied traits. Results: We tested the method using genomic estimated breeding values (GEBVs) for seven traits in a bread wheat (Triticum aestivum) reference breeding population comprising 3,331 lines and achieved prediction accuracies ranging between 0.29 and 0.47 across the seven traits. The indices were validated using 3,005 double haploid lines that were derived from crosses between parents selected from the reference population. We tested three user-specified response scenarios: a constrained equal weight (INDEX1), a constrained yield dominant weight (INDEX2), and an unconstrained weight (INDEX3). Our method achieved an equivalent response to the user-specified selection response when constraining a set of traits, and this response was much better than the response of the traditional desired gain selection indices method without iteration. Interestingly, when using unconstrained weight, our iterative method maximised the selection response and shifted the average GEBVs of the selection candidates towards the desired direction. Discussion: Our results show that the method is an optimal choice not only when economic weights are unavailable, but also when constraining the selection response is an unfavourable option.

Genomic prediction of regional-scale performance in switchgrass (Panicum virgatum) by accounting for genotype-by-environment variation and yield surrogate traits.

Switchgrass is a potential crop for bioenergy or carbon capture schemes, but further yield improvements through selective breeding are needed to encourage commercialization. To identify promising switchgrass germplasm for future breeding efforts, we conducted multi-site and multi-trait genomic prediction with a diversity panel of 630 genotypes from 4 switchgrass subpopulations (Gulf, Midwest, Coastal, and Texas), which were measured for spaced plant biomass yield across 10 sites. Our study focused on the use of genomic prediction to share information among traits and environments. Specifically, we evaluated the predictive ability of cross-validation (CV) schemes using only genetic data and the training set, (cross validation 1: CV1), a subset of the sites (cross validation 2: CV2), and/or with two yield surrogates (flowering time and fall plant height). We found that genotype-by-environment interactions were largely due to the north-south distribution of sites. The genetic correlations between yield surrogates and biomass yield were generally positive (mean height r=0.85; mean flowering time r=0.45) and did not vary due to subpopulation or growing region (North, Middle, South). Genomic prediction models had cross-validation predictive abilities of -0.02 for individuals using only genetic data (CV1) but 0.55, 0.69, 0.76, 0.81, and 0.84 for individuals with biomass performance data from one, two, three, four and five sites included in the training data (CV2), respectively. To simulate a resource-limited breeding program, we determined the predictive ability of models provided with: one site observation of flowering time (0.39), one site observation of flowering time and fall height (0.51), one site observation of fall height (0.52), one site observation of biomass (0.55), and five site observations of biomass yield (0.84). The ability to share information at a regional scale is very encouraging but further research is required to accurately translate spaced plant biomass to commercial-scale sward biomass performance.

Integrating mRNA transcripts and genomic information into genomic prediction.

Genomic prediction has emerged as a pivotal technology for the genetic evaluation of livestock, crops, and for predicting human disease risks. However, classical genomic prediction methods face challenges in incorporating biological prior information such as the genetic regulation mechanisms of traits. This study introduces a novel approach that integrates mRNA transcript information to predict complex trait phenotypes. To evaluate the accuracy of the new method, we utilized a Drosophila population that is widely employed in quantitative genetics researches globally. Results indicate that integrating mRNA transcript data can significantly enhance the genomic prediction accuracy for certain traits, though it does not improve phenotype prediction accuracy for all traits. Compared with GBLUP, the prediction accuracy for olfactory response to dCarvone in male Drosophila increased from 0.256 to 0.274. Similarly, the accuracy for cafe in male Drosophila rose from 0.355 to 0.401. The prediction accuracy for survival_paraquat in male Drosophila is improved from 0.101 to 0.138. In female Drosophila, the accuracy of olfactory response to 1hexanol increased from 0.147 to 0.210. In conclusion, integrating mRNA transcripts can substantially improve genomic prediction accuracy of certain traits by up to 43%, with range of 7% to 43%. Furthermore, for some traits, considering interaction effects along with mRNA transcript integration can lead to even higher prediction accuracy.

Genomic predictions under different genetic architectures are impacted by mating designs.

Mating in animal communities must be managed in a way that assures the performance increase in the progenies without increasing the rate of inbreeding. It has currently become possible to identify millions of single nucleotide polymorphisms (SNPs), and it is feasible to select animals based on genome-wide marker profiles. This study aimed to evaluate the impact of five mating designs among individuals (random, positive and negative assortative, minimized and maximized inbreeding) on genomic prediction accuracy. The choice of these five particular mating designs provides a thorough analysis of the way genetic diversity, relatedness, inbreeding, and biological conditions influence the accuracy of genomic predictions. Utilizing a stochastic simulation technique, various marker and quantitative trait loci (QTL) densities were taken into account. The heritabilities of a simulated trait were 0.05, 0.30, and 0.60. A validation population that only had genotypic records was taken into consideration, and a reference population that had both genotypic and phenotypic records was considered for every simulation scenario. By measuring the correlation between estimated and true breeding values, the prediction accuracy was calculated. Computing the regression of true genomic breeding value on estimated genomic breeding value allowed for the examination of prediction bias. The scenario with a positive assortative mating design had the highest accuracy of genomic prediction (0.733 ± 0.003 to 0.966 ± 0.001). In a case of negative assortative mating, the genomic evaluation's accuracy was lowest (0.680 ± 0.011 to 0.899 ± 0.003). Applying the positive assortative mating design resulted in the unbiased regression coefficients of true genomic breeding value on estimated genomic breeding value. Based on the current results, it is suggested to implement positive assortative mating in genomic evaluation programs to obtain unbiased genomic predictions with greater accuracy. This study implies that animal breeding programs can improve offspring performance without compromising genetic health by carefully managing mating strategies based on genetic diversity, relatedness, and inbreeding levels. To maximize breeding results and ensure long-term genetic improvement in animal populations, this study highlights the importance of considering different mating designs when evaluating genomic information. When incorporating positive assortative mating or other mating schemes into genomic evaluation programs, it is critical to consider the complex relationship between gene interactions, environmental influences, and genetic drift to ensure the stability and effectiveness of breeding efforts. Further research and comprehensive analyzes are needed to fully understand the impact of these factors and their possible complex interactions on the accuracy of genomic prediction and to develop strategies that optimize breeding outcomes in animal populations.

Genetic constitution and variability in synthetic populations of intermediate wheatgrass, an outcrossing perennial grain crop.

Intermediate wheatgrass (IWG) is a perennial grass that produces nutritious grain while offering substantial ecosystem services. Commercial varieties of this crop are mostly synthetic panmictic populations that are developed by intermating a few selected individuals. As development and generation advancement of these synthetic populations is a multiyear process, earlier synthetic generations are tested by the breeders and subsequent generations are released to the growers. A comparison of generations within IWG synthetic cultivars is currently lacking. In this study, we used simulation models and genomic prediction to analyze population differences and trends of genetic variance in 4 synthetic generations of MN-Clearwater, a commercial cultivar released by the University of Minnesota. Little to no differences were observed among the 4 generations for population genetic, genetic kinship, and genome-wide marker relationships measured via linkage disequilibrium. A reduction in genetic variance was observed when 7 parents were used to generate synthetic populations while using 20 led to the best possible outcome in determining population variance. Genomic prediction of plant height, free threshing ability, seed mass, and grain yield among the 4 synthetic generations showed a few significant differences among the generations, yet the differences in values were negligible. Based on these observations, we make 2 major conclusions: (1) the earlier and latter synthetic generations of IWG are mostly similar to each other with minimal differences and (2) using 20 genotypes to create synthetic populations is recommended to sustain ample genetic variance and trait expression among all synthetic generations.

Understanding the genetic basis of blueberry postharvest traits to define better breeding strategies.

Blueberry (Vaccinium spp.) is among the most-consumed soft fruit and has been recognized as an important source of health-promoting compounds. Highly perishable and susceptible to rapid spoilage due to fruit softening and decay during postharvest storage, modern breeding programs are looking to maximize the quality and extend the market life of fresh blueberries. However, it is uncertain how genetically controlled postharvest quality traits are in blueberries. This study aimed to investigate the prediction ability and the genetic basis of the main fruit quality traits affected during blueberry postharvest to create breeding strategies for developing cultivars with an extended shelf life. To achieve this goal, we carried out target genotyping in a breeding population of 588 individuals and evaluated several fruit quality traits after 1 day, 1 week, 3 weeks, and 7 weeks of postharvest storage at 1°C. Using longitudinal genome-based methods, we estimated genetic parameters and predicted unobserved phenotypes. Our results showed large diversity, moderate heritability, and consistent predictive accuracies along the postharvest storage for most of the traits. Regarding the fruit quality, firmness showed the largest variation during postharvest storage, with a surprising number of genotypes maintaining or increasing their firmness, even after 7 weeks of cold storage. Our results suggest that we can effectively improve the blueberry postharvest quality through breeding and use genomic prediction to maximize the genetic gains in the long term. We also emphasize the potential of using longitudinal genomic prediction models to predict the fruit quality at extended postharvest periods by integrating known phenotypic data from harvest.