Utilizing genomic prediction to boost hybrid performance in a sweet corn breeding program

Genetic analysis of predicted vegetative biomass and biomass-related traits from digital phenotyping of strawberry

High-throughput digital phenotyping (DP) has been widely explored in plant breeding to assess large numbers of genotypes with minimal manual labor and reduced cost and time. DP platforms using high-resolution images captured by drones and tractor-based platforms have recently allowed the University of Florida strawberry (Fragaria × ananassa) breeding program to assess vegetative biomass at scale. Biomass has not previously been explored in a strawberry breeding context due to the labor required and the need to destroy the plant. This study aims to understand the genetic basis of predicted vegetative biomass and biomass-related traits and to chart a path for the combined use of DP and genomics in strawberry breeding. Aboveground dry vegetative biomass was estimated by adapting a previously published model using ground-truth data on a subset of breeding germplasm. High-resolution images were collected on clonally replicated trials at different time points during the fruiting season. There was moderate to high heritability (h2 = 0.26-0.56) for predicted vegetative biomass, and genetic correlations between vegetative biomass and marketable yield were mostly positive (rG  = -0.13-0.47). Fruit yield traits scaled on a vegetative biomass basis also had moderate to high heritability (h2 = 0.25-0.64). This suggests that vegetative biomass can be decreased or increased through selection, and that marketable fruit yield can be improved without simultaneously increasing plant size. No consistent marker-trait associations were discovered via genome-wide association studies. On the other hand, predictive abilities from genomic selection ranged from 0.15 to 0.46 across traits and years, suggesting that genomic prediction will be an effective breeding tool for vegetative biomass in strawberry.

Genomic Prediction in a Self-Fertilized Progenies of Eucalyptus spp

Genomic selection in Eucalyptus enables the identification of superior genotypes, thereby reducing breeding cycles and increasing selection intensity. However, its efficiency may be compromised due to the complex structures of breeding populations, which arise from the use of multiple parents from different species. In this context, partial inbred lines have emerged as a viable alternative to enhance efficiency and generate productive clones. This study aimed to apply genomic selection to a self-fertilized population of different Eucalyptus spp. Our objective was to predict the genomic breeding values (GEBVs) of individuals lacking phenotypic information, with a particular focus on inbred line development. The studied population comprised 662 individuals, of which 600 were phenotyped for diameter at breast height (DBH) at 36 months in a field experiment. The remaining 62 individuals were located in a hybridization orchard and lacked phenotypic data. All individuals, including progeny and parents, were genotyped using 10,132 SNP markers. Genomic prediction was conducted using four frequentist models-GBLUP, GBLUP dominant additive, HBLUP, and ABLUP-and five Bayesian models-BRR, BayesA, BayesB, BayesC, and Bayes LASSO-using k-fold cross-validation. Among the GS models, GBLUP exhibited the best overall performance, with a predictive ability of 0.48 and an R2 of 0.21. For mean squared error, the Bayes LASSO presented the lowest error (3.72), and for the other models, the MSE ranged from 3.72 to 15.50. However, GBLUP stood out as it presented better precision in predicting individual performance and balanced performance in the studied parameter. These results highlight the potential of genomic selection for use in the genetic improvement of Eucalyptus through inbred lines. In addition, our model facilitates the identification of promising individuals and the acceleration of breeding cycles, one of the major challenges in Eucalyptus breeding programs. Consequently, it can reduce breeding program production costs, as it eliminates the need to implement experiments in large planted areas while also enhancing the reliability in selection of genotypes.

Using genome-wide associations and host-by-pathogen predictions to identify allelic interactions that control disease resistance

Characterizing the molecular mechanisms underlying disease symptom expression has been used to improve human health and disease resistance in crops and animal breeds. Quantitative trait loci and genome-wide association studies (GWAS) are widely used to identify genomic regions that are involved in disease progression. This study extends traditional GWAS significance tests of host and pathogen marker main effects by utilizing dual-genome reaction norm models to evaluate the importance of host-single nucleotide polymorphism (SNP) by pathogen-SNP interactions. Disease symptom severity data from Fusarium ear rot (FER) on maize (Zea mays L.) is used to demonstrate the use of both genomes in genomic selection models for breeding and the identification of loci that interact across organisms to impact FER disease development. Dual genome prediction models improved heritability estimates, error variances, and model accuracy while providing predictions for host-by-pathogen interactions that may be used to test the significance of SNP-SNP interactions. Independent GWAS for maize and Fusarium populations identified significantly associated loci and predictions that were used to evaluate the importance of interactions using two different association tests. Predictions from dual genome models were used to evaluate the significance of the SNP-SNP interactions that may be associated with population structure or polygenic effects. As well, association tests incorporating host and pathogen markers in models that also included genomic relationship matrices were used to account for population structure. Subsequent evaluation of protein-protein interactions from candidate genes near the interacting SNPs provides a further in silico evaluation method to expedite the identification of interacting genes.

Integrating phenomic selection using single-kernel near-infrared spectroscopy and genomic selection for corn breeding improvement

KEY MESSAGE

Phenomic selection using intact seeds is a promising tool to improve gain and complement genomic selection in corn breeding. Models that combine genomic and phenomic data maximize the predictive ability. Phenomic selection (PS) is a cost-effective method proposed for predicting complex traits and enhancing genetic gain in breeding programs. The statistical procedures are similar to those utilized in genomic selection (GS) models, but molecular markers data are replaced with phenomic data, such as near-infrared spectroscopy (NIRS). However, the use of NIRS applied to PS typically utilized destructive sampling or collected data after the establishment of selection experiments in the field. Here, we explored the application of PS using nondestructive, single-kernel NIRS in a sweet corn breeding program, focusing on predicting future, unobserved field-based traits of economic importance, including ear and vegetative traits. Three models were employed on a diversity panel: genomic and phenomic best linear unbiased prediction models, which used relationship matrices based on SNP and NIRS data, respectively, and a combined model. The genomic relationship matrices were evaluated with varying numbers of SNPs. Additionally, the PS model trained on the diversity panel was used to select doubled haploid (DH) lines for germination before planting, with predictions validated using observed data. The findings indicate that PS generated good predictive ability (e.g., 0.46 for plant height) and distinguished between high and low germination rates in untested DH lines. Although GS generally outperformed PS, the model combining both information yielded the highest predictive ability, with higher accuracies than GS when low marker densities were used. This study highlights NIRS's potential to achieve genetic gain where GS may not be feasible and to maintain/improve accuracy with SNP-based information while reducing genotyping costs.

Use of simulation to optimize a sweet corn breeding program: implementing genomic selection and doubled haploid technology

Genomic selection and doubled haploids hold significant potential to enhance genetic gains and shorten breeding cycles across various crops. Here, we utilized stochastic simulations to investigate the best strategies for optimize a sweet corn breeding program. We assessed the effects of incorporating varying proportions of old and new parents into the crossing block (3:1, 1:1, 1:3, and 0:1 ratio, representing different degrees of parental substitution), as well as the implementation of genomic selection in two distinct pipelines: one calibrated using the phenotypes of testcross parents (GSTC scenario) and another using F1 individuals (GSF1). Additionally, we examined scenarios with doubled haploids, both with (DH) and without (DHGS) genomic selection. Across 20 years of simulated breeding, we evaluated scenarios considering traits with varying heritabilities, the presence or absence of genotype-by-environment effects, and two program sizes (50 vs 200 crosses per generation). We also assessed parameters such as parental genetic mean, average genetic variance, hybrid mean, and implementation costs for each scenario. Results indicated that within a conventional selection program, a 1:3 parental substitution ratio (replacing 75% of parents each generation with new lines) yielded the highest performance. Furthermore, the GSTC model outperformed the GSF1 model in enhancing genetic gain. The DHGS model emerged as the most effective, reducing cycle time from 5 to 4 years and enhancing hybrid gains despite increased costs. In conclusion, our findings strongly advocate for the integration of genomic selection and doubled haploids into sweet corn breeding programs, offering accelerated genetic gains and efficiency improvements.