Owen M. Powell

This paper improves genomic prediction accuracy for maize flowering-time traits using an ensemble approach, revealing novel insights into trait genetic variation.

This study evaluates graph attention networks (GAT) for genomic prediction in crops, finding that ensembles of GAT models outperform single models using gene network structures.

This study evaluates three weight optimisation approaches for genomic prediction ensembles in maize, finding that weighted models improve prediction performance when weights differ from equal weights, but no single method clearly outperforms others.

EasiGP is a tool that interprets genomic prediction models by visualizing marker effects on trait phenotypes, revealing both known and novel genetic regions in maize.

The paper shows that combining multiple genomic prediction models improves accuracy and reduces errors in predicting crop traits, supporting the Diversity Prediction Theorem.

The paper discusses the importance of the MET-TPE relationship in genomic prediction for improving breeding outcomes in target populations of environments.

The paper compares mate-allocation strategies in sugarcane breeding that use additive and non-additive genetic effects to maximize clonal performance and reduce inbreeding.

The paper develops a gene-to-phenotype network for shoot branching in plants to explore how genetic canalization affects selection responses and trait prediction across different contexts.

The paper explores using crop models as gene-to-phenotype link functions to optimize selection in plant breeding programs by integrating quantitative genetic theory and trait modeling.

This study shows spatial modelling improves genetic evaluation in smallholder dairy cattle systems by better separating genetic and environmental effects.

This study shows that a two-part hybrid crop breeding strategy with genomic selection and a maximum avoidance of inbreeding crossing scheme maximizes genetic gain and long-term genetic variance.

This study shows genomic data can improve genetic evaluation accuracy in smallholder dairy farms with small herds and weak genetic connections.