Bayesian genomic prediction models perform favorably in maize panel under optimal and deficient phosphorus conditions

Bayesian genomic prediction models generally performed most favorably in a study of 93 maize genotypes phenotyped under optimal phosphorus supply and phosphorus-deficient conditions, according to the available abstract. The study focused on predicting genomic estimated breeding values for agronomic traits associated with biological yield, with Bayes A generally producing the highest correlations between phenotypic values and predicted breeding values. Bayesian Lasso showed comparatively lower performance, while the abstract did not provide model-specific results for every trait.

Researchers phenotyped the maize panel for 34 agronomic traits and used artificial neural network analysis to identify the traits regarded as the most influential contributors to biological yield. Eight traits were selected under optimal phosphorus conditions and five under phosphorus-deficient conditions. The panel was then genotyped with high-density SNP markers, and genomic estimated breeding values were predicted using seven models: Bayes A, Bayes B, Bayes C, Bayesian Lasso, Bayesian Ridge Regression, Genomic Best Linear Unbiased Prediction and Ridge Regression Best Linear Unbiased Prediction.

Phenotypic–genotypic correlations ranged from 0.85 to 0.96 under optimal phosphorus conditions and from 0.61 to 0.95 under phosphorus deficiency. Heritability estimates for the assessed traits were described as moderate to high, although individual estimates were not given in the abstract. Principal component analysis accounted for more than 99% of the total variance, which the authors interpreted as indicating substantial genetic diversity and strong model convergence. Cluster analysis classified the 93 genotypes into three groups. Four genotypes—Ma010, Ma001, Ma011 and Ma013—consistently exhibited favorable genomic estimated breeding values under both phosphorus regimes.

The available evidence supports the relative performance of the evaluated prediction models and identifies promising genotypes within this panel, but it does not demonstrate realized gains in biological yield. The abstract does not identify the trial location, growing conditions, sample sizes for individual traits or performance of each model for each trait. It also does not report an independent validation population, so the stability of the model comparisons and genotype rankings across additional sites, seasons or environments remains unestablished. The authors describe genomic selection combined with statistical and machine-learning analysis as a potential framework for developing maize cultivars adapted to phosphorus-deficient environments; the reported study itself assessed prediction performance rather than the success of a deployed breeding program.

Subject of Research: Genomic prediction of maize traits associated with biological yield under optimal and deficient phosphorus supply

Article Title: Genomic selection for key traits affecting biological yield of maize (Zea mays L.) under optimal phosphorus and phosphorus deficient conditions

Article References: Farhadi, F., Karimzadeh, G., Darvishzadeh, R., Alipour, H., & Razmjoo, M. (2026). Genomic selection for key traits affecting biological yield of maize (Zea mays L.) under optimal phosphorus and phosphorus deficient conditions. BMC Plant Biology. https://doi.org/10.1186/s12870-026-10005-x

Image Credits: AI Generated

DOI: 10.1186/s12870-026-10005-x

Keywords: maize, genomic selection, phosphorus deficiency, genomic prediction, Bayesian models, heritability, biological yield, high-density SNP markers, maize genotypes, Genomic, selection, traits