Multi-trait Multi-environment Genomic Prediction Strategies for <i>Miscanthus sacchariflorus</i> Populations

bioRxiv (Cold Spring Harbor Laboratory) · 2026-03-23

Abstract 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 (G×E×T) and (2) two single-trait multi-environment (STME) models (with and without G×E 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.

Optimizing resource allocation in <i>Miscanthus</i> breeding with sparse testing designs for genomic prediction

bioRxiv (Cold Spring Harbor Laboratory) · 2026-03-23

Abstract Phenotyping high-biomass perennial crops is laborious and the rate of genetic gain in perennial crop breeding programs is typically low. So, it is especially important to identify methods that produce efficiency gains in the breeding process. Miscanthus is a C4 perennial grass with favorable characteristics for producing biomass as a feedstock for biofuels and diverse biobased products. Increasing biomass yield will increase profitability and environmental benefits, so is a key target for Miscanthus breeding. In addition, the identification of well-adapted genotypes across a wide range of environmental conditions requires the establishment of multi-environment trials (METs). Sparse testing is a genomic prediction-based strategy that reduces the phenotyping costs in METs by selecting a subset of genotypes to evaluate in a subset of environments and then predicts the performance of the unobserved genotype-environment combinations. A Miscanthus sacchariflorus (MSA) population comprising 336 genotypes observed across three environments was analyzed. Three prediction models considering main effects (environments, genotypes, genomic) and interaction effects (genotype-by-environment; G×E interaction) were implemented for forecasting dry biomass yield (YDY), total culm (TCM), average internode length (AIL), and culm node number (CNN). Multiple calibration sets based on different compositions and sizes were considered to evaluate performance in terms of the predictive ability (PA) and the mean square error (MSE) for a fixed testing set size. The training set size ranged from 52 to 112 to predict a fixed set of 224 unobserved genotypes across all three environments. The results showed that the model accounting for G×E interaction presented the highest PA and the lowest MSE for CNN (PA: ∼0.77, MSE: ∼0.5) and YDY (PA: ∼0.70, MSE: ∼1.3) while for TCM and AIL these ranged from ∼0.28 to 0.41 and ∼1.3 to 4.3, respectively. Overall, varying training sets and allocation strategies did not affect PA and MSE, with 52 non-overlapping and 0 overlapping genotypes per environment as the optimal cost-effective allocation framework. This suggests that implementing sparse testing designs could significantly reduce phenotyping costs by fivefold, without compromising PA in breeding programs for perennial crops such as Miscanthus .

Dissecting genetic variance structure and evaluating genomic prediction models for single-cross hybrids derived from Stiff Stalk and Non-Stiff Stalk maize heterotic groups

bioRxiv (Cold Spring Harbor Laboratory) · 2026-03-13

Abstract The early 20th-century discovery of heterosis and the establishment of heterotic groups transformed maize ( Zea mays L.) into a keystone of global agriculture. However, maize breeding faces two significant challenges: the gradual decline of general combining ability (GCA) variance within heterotic groups and the impracticality of testing all possible single crosses in the early stages of a breeding program. Here, we developed genomic best linear unbiased prediction (GBLUP)-based multi-kernel models, using additive and two alternative non-additive genomic relationship matrices, to estimate the variance components associated with the GCA of Stiff Stalk (SS) and Non-Stiff Stalk (NSS) heterotic groups and the specific combining ability (SCA) arising from their crosses. We further applied these models to predict the performance of untested single-cross combinations under varying levels of parental information. We showed that the SS and NSS groups retained significant GCA variance across traits in both early- and late-maturity groups. The SS group, in contrast, exhibited no detectable GCA variance in grain yield for the intermediate-flowering subset of hybrids, highlighting a limitation for future genetic improvement. Furthermore, our results showed that GBLUP-based multi-kernel models effectively identified superior hybrids when parental information was available. In the absence of this information, however, these models underperformed compared to covariance-based approaches. Both types of non-additive matrices produced similar results, affirming the robustness of the inferred genetic architecture. Overall, this study sheds light on the future use of US maize commercial germplasm and demonstrates how GBLUP-based multi-kernel models can improve the efficiency of hybrid breeding programs.

Do we need to account for genetic architecture when applying genomic prediction to perennialized sorghum?

Euphytica · 2026-02-21

ENHANCING GENOMIC PREDICTION MODELS IN <i>MISCANTHUS</i> POPULATIONS BY INCORPORATING THE GENOTYPE-BY-ENVIRONMENT INTERACTION

bioRxiv (Cold Spring Harbor Laboratory) · 2026-03-18

ABSTRACT Giant Miscanthus giganteus ( Mxg ) is one of the most promising perennial crops to generate biomass feedstock for bioenergy and biobased products. It is derived from the natural inter-species hybridization of Miscanthus sacchariflorus ( Msa ) and Miscanthus sinensis ( Msi ) species, thus population improvement within these species is crucial. Genomic selection (GS) is an attractive option to accelerate breeding of perennial grasses, such as Miscanthus, which requires up to three years of evaluation to produce reliable phenotypic data. Hence, genotypes are observed in multiple years and locations causing inconsistent response patterns from one year to the next, between location, and/or location-by-year combinations. These inconsistencies are known as the genotype-by-environment interaction effect (G×E). Although GS has been successfully implemented in multiple annual crops where straightforward cross-validation schemes exist to assess the levels of predictive ability that can be reached, for perennial crops new cross-validation schemes will help avoid data contamination. Here, we propose a series of cross-validation schemes to evaluate model performance for perennial crops. We perform a case study by analyzing one panel of each species (516 genotypes of Msa , 280 genotypes of Msi ) scored for biomass yield at different locations around the world over several years. The results of the different cross-validation schemes provide insights about the usefulness of GS to accelerate the breeding process of Miscanthus species. In addition, leveraging the G×E effects of different types significantly increases predictive ability (up to 10% in Msa and 30% for Msi ) compared to the conventional approaches based on main effects only.

Weather Characterization for Optimizing Genomic Prediction in Miscanthus Sacchariflorus

bioRxiv (Cold Spring Harbor Laboratory) · 2026-03-20

ABSTRACT Environmental factors affect crop growth and development thus their consideration across sites and years become essential for genotypic evaluation. Genomic selection (GS) has been broadly implemented to accelerate breeding cycles by skipping field evaluations thus allowing early identification of outperforming genotypes. In this study, 7,740 phenotypic records corresponding to 516 Miscanthus sacchariflorus genotypes evaluated in five locations across three years were considered for analysis. Additionally, environmental data on six weather covariates was implemented to characterize similarities between locations. Different sets of locations of variable sizes were used for model calibration based on two cross-validations (CV00 and CV0) schemes leaving out one location at a time. Predictive ability across locations of the best model varied between 0.45 and 0.90 for both schemes. These results were compared to associate predictive ability in function of weather patterns between training and testing sets to allow model’s calibration optimization. We found it is feasible to optimize resource allocation by considering environmentally correlated sets. In most cases, the information from only one and, at most, two locations were enough to deliver better results than using all four locations, reducing training sets by up to 75%. The results obtained shed light on helping breeders make informed decisions considering weather data when designing evaluations.

High‐Throughput Screen of <scp>NPQ</scp> in Sorghum Shows Highly Polygenic Architecture of Photoprotection

Plant-Environment Interactions · 2026-01-11

(sorghum) is one of the world's most widely grown crops, yet the genetic basis of photoprotection in sorghum is not well understood. This study examined genetic variation in non-photochemical quenching traits by screening a field-grown panel of 861 genetically diverse natural sorghum accessions across 2 years. Broad-sense heritability ranged between 0.3 and 0.65 across different chlorophyll fluorescence parameters. A combination of genome- and transcriptome-wide (GWAS and TWAS) identification of genetic correlates with the observed trait variation uncovered a complex genetic architecture of many significant small-effect loci. An ensemble approach based on GWAS and TWAS results and the covariance between different fluorescence parameters was used to identify 110 unique candidate genes. The resulting high-confidence candidates reveal novel genetic associations with photoprotection and offer resources for further genetic studies and crop genomic improvement efforts.

Optimizing population simulations to accurately parallel empirical data for digital breeding

Crop Science · 2026-01-01

Abstract The use of computational and data‐driven approaches to accelerate and optimize breeding programs is becoming common practice among plant breeders. Simulations allow breeders to evaluate potential changes in breeding schemes in a time‐ and cost‐efficient manner. However, accurately simulating traits that match empirical trait data remain a challenge. Here we tested if incorporating information about the genetic architecture from genome‐wide association studies (GWASs) of maize ( Zea mays L.) agronomic traits with varying heritabilities into simulations can improve the concordance between simulated and empirical data in a population of hybrids developed from crosses of 333 maize recombinant inbred lines grown in 4–11 environments. Using at least 200 nonredundant top GWAS hits as causative variants, regardless of statistical significance, resulted in mean correlations between simulated and empirical trait data of 0.397–0.616 within environments and 0.610–0.915 across environments. Reducing the GWAS‐estimated marker effect sizes in the simulations further improved concordance with empirical data. This study provides valuable insights into methods for simulating more realistic phenotypes for digital breeding to parallel empirical trait distributions and shows that these simulated traits are highly concordant with observed variance partitioning (i.e., genotype, environment, etc.) and genomic prediction performance.

High‐Throughput Screen of <scp>NPQ</scp> in Sorghum Shows Highly Polygenic Architecture of Photoprotection

Apollo (University of Cambridge) · 2025-07-01

Publication status: Published

High throughput screen of NPQ in sorghum shows highly polygenic architecture of photoprotection

bioRxiv (Cold Spring Harbor Laboratory) · 2025-04-22

Summary Natural genetic variation in photosynthesis and photoprotection within crop germplasm represents an untapped resource for crop improvement. Sorghum bicolor (sorghum) is one of the world’s most widely grown crops, yet the genetic basis of photoprotection in sorghum is not well understood. This study examined genetic variation in non-photochemical quenching traits by screening a field-grown panel of 861 genetically diverse natural sorghum accessions across two years. Broad-sense heritability ranged between 0.3 to 0.65 across different chlorophyll fluorescence parameters. A combination of genome- and transcriptome-wide (GWAS and TWAS) identification of genetic correlates with the observed trait variation uncovered a complex genetic architecture of many significant small-effect loci. An ensemble approach based on GWAS and TWAS results and the covariance between different fluorescence parameters was used to identify 110 unique candidate genes. The resulting high-confidence candidates reveal novel genetic associations with photoprotection and offer resources for further genetic studies and crop genomic improvement efforts.