Owen Powell
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View article: Ensemble AnalySis with Interpretable Genomic Prediction (EasiGP): Computational tool for interpreting ensembles of genomic prediction models
Ensemble AnalySis with Interpretable Genomic Prediction (EasiGP): Computational tool for interpreting ensembles of genomic prediction models Open
An ensemble of multiple genomic prediction models has grown in popularity due to consistent prediction performance improvements in crop breeding. However, technical tools that analyze the predictive behavior at the genome level are lacking…
View article: Ensemble-based genomic prediction for maize flowering time reveals novel insights into trait genetic architecture and improves prediction for breeding applications
Ensemble-based genomic prediction for maize flowering time reveals novel insights into trait genetic architecture and improves prediction for breeding applications Open
While many genomic prediction models have been evaluated for their potential to accelerate genetic gain for multiple traits, no individual genomic prediction model has outperformed others across all applications. This result aligns with th…
View article: Ensemble-based genomic prediction for maize flowering time reveals novel insights into trait genetic architecture and improves prediction for breeding applications
Ensemble-based genomic prediction for maize flowering time reveals novel insights into trait genetic architecture and improves prediction for breeding applications Open
While many genomic prediction models have been evaluated for their potential to accelerate genetic gain for multiple traits, no individual genomic prediction model has outperformed others across all applications. This problem aligns with t…
View article: Breeding perspectives on tackling trait genome-to-phenome (G2P) dimensionality using ensemble-based genomic prediction
Breeding perspectives on tackling trait genome-to-phenome (G2P) dimensionality using ensemble-based genomic prediction Open
Key message Trait Genome-to-Phenome (G2P) dimensionality and “breeding context” combine to influence the realised prediction skill of different whole genome prediction (WGP) methods. Theory and empirical evidence both suggest there is like…
View article: Toward a general framework for AI-enabled prediction in crop improvement
Toward a general framework for AI-enabled prediction in crop improvement Open
A theoretical framework for AI and ensembled prediction for crop improvement is introduced and demonstrated using the logistic map. Symbolic/sub-symbolic AI-based prediction can increase predictive skill with increase in system complexity.…
View article: Ensemble AnalySis with Interpretable Genomic Prediction (EasiGP): Computational Tool for Interpreting Ensembles of Genomic Prediction Models
Ensemble AnalySis with Interpretable Genomic Prediction (EasiGP): Computational Tool for Interpreting Ensembles of Genomic Prediction Models Open
Ensemble of multiple genomic prediction models have grown in popularity due to consistent prediction performance improvements in crop breeding. However, technical tools that analyse the predictive behaviour at the genome level are lacking.…
View article: Improved genomic prediction performance with ensembles of diverse models
Improved genomic prediction performance with ensembles of diverse models Open
The improvement of selection accuracy of genomic prediction is a key factor in accelerating genetic gain for crop breeding. Traditionally, efforts have focused on developing superior individual genomic prediction models. However, this appr…
View article: The impact of genotype transformations and aneuploidy on genomic prediction accuracy in polyploid species: a simulation study
The impact of genotype transformations and aneuploidy on genomic prediction accuracy in polyploid species: a simulation study Open
The accuracy of genomic prediction for key traits in sugarcane remains low relative to other crops, even with growing reference sets. This is potentially due to high-level polyploidy, frequent aneuploidy and complex genetic architectures. …
View article: Genomic prediction for sugarcane diseases including hybrid Bayesian-machine learning approaches
Genomic prediction for sugarcane diseases including hybrid Bayesian-machine learning approaches Open
Sugarcane smut and Pachymetra root rots are two serious diseases of sugarcane, with susceptible infected crops losing over 30% of yield. A heritable component to both diseases has been demonstrated, suggesting selection could improve disea…
View article: In Silico Sorghum GxExM Dataset For Prediction Algorithm Comparisons
In Silico Sorghum GxExM Dataset For Prediction Algorithm Comparisons Open
# In Silico Sorghum GxExM Dataset For Prediction Algorithm Comparisons## BackgroundA simulated Multi-Environment Trial (MET) dataset to stimulate the development and comparisons of predictive algorithms to deconvolute genotype-by-environme…
View article: Adaptation and plasticity of yield in hybrid and inbred sorghum
Adaptation and plasticity of yield in hybrid and inbred sorghum Open
Local adaptation and genotype by environment (G×E) interactions affect the expression of phenotypes in crop species. An investigation on the interplay between adaptation and G×E on sorghum heterosis phenotypes is lacking. To address this q…
View article: Use of continuous genotypes for genomic prediction in sugarcane
Use of continuous genotypes for genomic prediction in sugarcane Open
Genomic selection in sugarcane faces challenges due to limited genomic tools and high genomic complexity, particularly because of its high and variable ploidy. The classification of genotypes for single nucleotide polymorphisms (SNPs) beco…
View article: Optimising clonal performance in sugarcane: leveraging non-additive effects via mate-allocation strategies
Optimising clonal performance in sugarcane: leveraging non-additive effects via mate-allocation strategies Open
Mate-allocation strategies in breeding programs can improve progeny performance by harnessing non-additive genetic effects. These approaches prioritise predicted progeny merit over parental breeding value, making them particularly appealin…
View article: Genomic prediction with machine learning in sugarcane, a complex highly polyploid clonally propagated crop with substantial non‐additive variation for key traits
Genomic prediction with machine learning in sugarcane, a complex highly polyploid clonally propagated crop with substantial non‐additive variation for key traits Open
Sugarcane has a complex, highly polyploid genome with multi‐species ancestry. Additive models for genomic prediction of clonal performance might not capture interactions between genes and alleles from different ploidies and ancestral speci…
View article: Extending the breeder’s equation to take aim at the target population of environments
Extending the breeder’s equation to take aim at the target population of environments Open
A major focus for genomic prediction has been on improving trait prediction accuracy using combinations of algorithms and the training data sets available from plant breeding multi-environment trials (METs). Any improvements in prediction …
View article: Genomic prediction with machine learning in sugarcane, a complex highly polyploid clonally propagated crop with substantial non-additive variation for key traits
Genomic prediction with machine learning in sugarcane, a complex highly polyploid clonally propagated crop with substantial non-additive variation for key traits Open
Sugarcane has a complex, highly polyploid genome with multi-species ancestry. Additive models for genomic prediction of clonal performance might not capture interactions between genes and alleles from different ploidies and ancestral speci…
View article: In Silico Sorghum GxExM Dataset For Prediction Algorithm Comparisons
In Silico Sorghum GxExM Dataset For Prediction Algorithm Comparisons Open
# In Silico Sorghum GxExM Dataset For Prediction Algorithm Comparisons## BackgroundA simulated Multi-Environment Trial (MET) dataset to stimulate the development and comparisons of predictive algorithms to deconvolute genotype-by-environme…
View article: Stochastic Simulation of Divergent Selection Experiment on a Gene-Phenotype Network: A Case Study of Shoot Branching in Plants
Stochastic Simulation of Divergent Selection Experiment on a Gene-Phenotype Network: A Case Study of Shoot Branching in Plants Open
# Stochastic Simulation of Divergent Selection Experiment on a Gene-Phenotype Network: A Case Study of Shoot Branching in Plants ## Background General Background Information on the specification of the Gene-Phenotype Network for Shoot Br…
View article: In Silico Sorghum GxExM Dataset For Prediction Algorithm Comparisons
In Silico Sorghum GxExM Dataset For Prediction Algorithm Comparisons Open
# In Silico Sorghum GxExM Dataset For Prediction Algorithm Comparisons ## Background A simulated Multi-Environment Trial (MET) dataset to stimulate the development and comparisons of predictive algorithms to deconvolute genotype-by-environ…
View article: In Silico Sorghum GxExM Dataset For Prediction Algorithm Comparisons
In Silico Sorghum GxExM Dataset For Prediction Algorithm Comparisons Open
# In Silico Sorghum GxExM Dataset For Prediction Algorithm Comparisons## BackgroundA simulated Multi-Environment Trial (MET) dataset to stimulate the development and comparisons of predictive algorithms to deconvolute genotype-by-environme…
View article: In Silico Sorghum GxExM Dataset For Prediction Algorithm Comparisons
In Silico Sorghum GxExM Dataset For Prediction Algorithm Comparisons Open
# In Silico Sorghum GxExM Dataset For Prediction Algorithm Comparisons ## Background A simulated Multi-Environment Trial (MET) dataset to stimulate the development and comparisons of predictive algorithms to deconvolute genotype-by-environ…
View article: Genomic mate-allocation strategies exploiting additive and non-additive genetic effects to maximise total clonal performance in sugarcane
Genomic mate-allocation strategies exploiting additive and non-additive genetic effects to maximise total clonal performance in sugarcane Open
Mate-allocation in breeding programs can improve progeny performance by exploiting non-additive effects. Breeding decisions in the mate-allocation approach are based on predicted progeny merit rather than parental breeding value. This is p…
View article: Extending the breeder’s equation to take aim at the Target Population of Environments
Extending the breeder’s equation to take aim at the Target Population of Environments Open
1) Abstract A major focus for genomic prediction has been on improving trait prediction accuracy using combinations of algorithms and the training data sets available from plant breeding multi-environment trials (METs). Any improvements in…
View article: Investigations into the emergent properties of gene-to-phenotype networks across cycles of selection: A case study of shoot branching in plants
Investigations into the emergent properties of gene-to-phenotype networks across cycles of selection: A case study of shoot branching in plants Open
Predictive breeding is now widely practised in crop improvement programs and has accelerated selection response (i.e., the amount of genetic gain between breeding cycles) for complex traits. However, world food production needs to increase…
View article: Investigations into the emergent properties of gene-to-phenotype networks across cycles of selection: a case study of shoot branching in plants
Investigations into the emergent properties of gene-to-phenotype networks across cycles of selection: a case study of shoot branching in plants Open
Predictive breeding is now widely practised in crop improvement programmes and has accelerated selection response (i.e. the amount of genetic gain between breeding cycles) for complex traits. However, world food production needs to increas…
View article: Crop Improvement for Circular Bioeconomy Systems
Crop Improvement for Circular Bioeconomy Systems Open
Highlights We describe and demonstrate a multidimensional framework to integrate environmental and genomic predictors to enable crop improvement for a circular bioeconomy. A model training procedure based on multiple phenotypes is shown to…
View article: A linkage disequilibrium-based approach to position unmapped SNPs in crop species
A linkage disequilibrium-based approach to position unmapped SNPs in crop species Open
View article: Genomic evaluations using data recorded on smallholder dairy farms in low- to middle-income countries
Genomic evaluations using data recorded on smallholder dairy farms in low- to middle-income countries Open
Breeding has increased genetic gain for dairy cattle in advanced economies but has had limited success in improving dairy cattle in low- to middle-income countries (LMIC). Genetic evaluations are a central component of delivering genetic g…
View article: Designing breeding programs in the genomic era
Designing breeding programs in the genomic era Open
Increasing the rate of genetic gain of breeding programs is one route to achieve sustainable increases in food production. Breeding has been responsible for ~50% of the increases in agricultural productivity over the past 70 years. However…
View article: Perspectives on Applications of Hierarchical Gene-To-Phenotype (G2P) Maps to Capture Non-stationary Effects of Alleles in Genomic Prediction
Perspectives on Applications of Hierarchical Gene-To-Phenotype (G2P) Maps to Capture Non-stationary Effects of Alleles in Genomic Prediction Open
Genomic prediction of complex traits across environments, breeding cycles, and populations remains a challenge for plant breeding. A potential explanation for this is that underlying non-additive genetic (GxG) and genotype-by-environment (…