The University of Queensland

Advancing artificial intelligence to help feed the world

Hayes, Ben J., Chen, Chensong, Powell, Owen, Dinglasan, Eric, Villiers, Kira, Kemper, Kathryn E. and Hickey, Lee T. (2023). Advancing artificial intelligence to help feed the world. Nature Biotechnology, 41 (9), 1-2. doi: 10.1038/s41587-023-01898-2

Investigations into the emergent properties of gene-to-phenotype networks across cycles of selection: a case study of shoot branching in plants

Powell, Owen M., Barbier, Francois, Voss-Fels, Kai P., Beveridge, Christine and Cooper, Mark (2022). Investigations into the emergent properties of gene-to-phenotype networks across cycles of selection: a case study of shoot branching in plants. in silico Plants, 4 (1) diac006, 1-9. doi: 10.1093/insilicoplants/diac006

Designing breeding programs in the genomic era

Owen Powell (2021). Designing breeding programs in the genomic era. PhD Thesis, The Roslin Institute, The University of Edinburgh . doi: 10.7488/era/1463

Perspectives on applications of hierarchical gene-to-phenotype (G2P) maps to capture non-stationary effects of alleles in genomic prediction

Powell, Owen M., Voss-Fels, Kai P., Jordan, David R., Hammer, Graeme and Cooper, Mark (2021). Perspectives on applications of hierarchical gene-to-phenotype (G2P) maps to capture non-stationary effects of alleles in genomic prediction. Frontiers in Plant Science, 12 663565, 663565. doi: 10.3389/fpls.2021.663565/full

Predicting Genotype × Environment × Management (G × E × M) interactions for the design of crop improvement strategies: Integrating breeder, agronomist, and farmer perspectives

Cooper, Mark, Messina, Carlos D., Tang, Tom, Gho, Carla, Powell, Owen M., Podlich, Dean W., Technow, Frank and Hammer, Graeme L. (2023). Predicting Genotype × Environment × Management (G × E × M) interactions for the design of crop improvement strategies: Integrating breeder, agronomist, and farmer perspectives. Plant breeding reviews. (pp. 467-585) edited by Irwin Goldman. Hoboken, NJ, United States: Wiley Blackwell. doi: 10.1002/9781119874157.ch8

Genomic prediction for sugarcane diseases including hybrid Bayesian-machine learning approaches

Chen, Chensong, Bhuiyan, Shamsul A., Ross, Elizabeth, Powell, Owen, Dinglasan, Eric, Wei, Xianming, Atkin, Felicity, Deomano, Emily and Hayes, Ben (2024). Genomic prediction for sugarcane diseases including hybrid Bayesian-machine learning approaches. Frontiers in Plant Science, 15 1398903. doi: 10.3389/fpls.2024.1398903

Adaptation and plasticity of yield in hybrid and inbred sorghum

Otwani, Daniel, Hunt, Colleen, Cruickshank, Alan, Powell, Owen, Koltunow, Anna, Mace, Emma and Jordan, David (2024). Adaptation and plasticity of yield in hybrid and inbred sorghum. Crop Science, 64 (2), 560-570. doi: 10.1002/csc2.21160

Use of continuous genotypes for genomic prediction in sugarcane

Yadav, Seema, Ross, Elizabeth M., Wei, Xianming, Liu, Shouye, Nguyen, Loan To, Powell, Owen, Hickey, Lee T., Deomano, Emily, Atkin, Felicity, Voss‐Fels, Kai P. and Hayes, Ben J. (2023). Use of continuous genotypes for genomic prediction in sugarcane. The Plant Genome, 17 (1) e20417, e20417. doi: 10.1002/tpg2.20417

Optimising clonal performance in sugarcane: leveraging non-additive effects via mate-allocation strategies

Yadav, Seema, Ross, Elizabeth M., Wei, Xianming, Powell, Owen, Hivert, Valentin, Hickey, Lee T., Atkin, Felicity, Deomano, Emily, Aitken, Karen S., Voss-Fels, Kai P. and Hayes, Ben J. (2023). Optimising clonal performance in sugarcane: leveraging non-additive effects via mate-allocation strategies. Frontiers in Plant Science, 14 1260517, 1260517. doi: 10.3389/fpls.2023.1260517

Comparison of genomic prediction models for general combining ability in early stages of hybrid breeding programs

de Jong, Guilherme, Powell, Owen, Gorjanc, Gregor, Hickey, John M. and Gaynor, R. Chris (2023). Comparison of genomic prediction models for general combining ability in early stages of hybrid breeding programs. Crop Science, 63 (6), 3338-3355. doi: 10.1002/csc2.21105

Genomic prediction with machine learning in sugarcane, a complex highly polyploid clonally propagated crop with substantial non‐additive variation for key traits

Chen, Chensong, Powell, Owen, Dinglasan, Eric, Ross, Elizabeth M., Yadav, Seema, Wei, Xianming, Atkin, Felicity, Deomano, Emily and Hayes, Ben J. (2023). Genomic prediction with machine learning in sugarcane, a complex highly polyploid clonally propagated crop with substantial non‐additive variation for key traits. The Plant Genome, 16 (4) e20390, 1-13. doi: 10.1002/tpg2.20390

Advancing artificial intelligence to help feed the world

Hayes, Ben J., Chen, Chensong, Powell, Owen, Dinglasan, Eric, Villiers, Kira, Kemper, Kathryn E. and Hickey, Lee T. (2023). Advancing artificial intelligence to help feed the world. Nature Biotechnology, 41 (9), 1-2. doi: 10.1038/s41587-023-01898-2

Extending the breeder’s equation to take aim at the target population of environments

Cooper, Mark, Powell, Owen, Gho Brito, Carla, Tang, Tom and Messina, Carlos (2023). Extending the breeder’s equation to take aim at the target population of environments. Frontiers in Plant Science, 14 1129591, 1-10. doi: 10.3389/fpls.2023.1129591

Genomic mate-allocation strategies exploiting additive and non-additive genetic effects to maximise total clonal performance in sugarcane

Yadav, Seema, Ross, Elizabeth, Wei, Xianming, Powell, Owen, Hivert, Valentin, Hickey, Lee T., Atkin, Felicity, Deomano, Emily, Aitken, Karen S., Voss-Fels, Kai P. and Hayes, Ben J. (2022). Genomic mate-allocation strategies exploiting additive and non-additive genetic effects to maximise total clonal performance in sugarcane.

Investigations into the emergent properties of gene-to-phenotype networks across cycles of selection: a case study of shoot branching in plants

Powell, Owen M., Barbier, Francois, Voss-Fels, Kai P., Beveridge, Christine and Cooper, Mark (2022). Investigations into the emergent properties of gene-to-phenotype networks across cycles of selection: a case study of shoot branching in plants. in silico Plants, 4 (1) diac006, 1-9. doi: 10.1093/insilicoplants/diac006

Crop improvement for circular bioeconomy systems

Messina, Carlos D., van Eeuwijk, Fred, Tang, Tom, Truong, Sandra K., McCormick, Ryan F., Technow, Frank, Powell, Owen, Mayor, Laura, Gutterson, Neal, Jones, James W., Hammer, Graeme and Cooper, Mark (2022). Crop improvement for circular bioeconomy systems. Journal of the ASABE, 65 (3), 491-504. doi: 10.13031/ja.14912

A linkage disequilibrium-based approach to position unmapped SNPs in crop species

Yadav, Seema, Ross, Elizabeth M., Aitken, Karen S., Hickey, Lee T., Powell, Owen, Wei, Xianming, Voss-Fels, Kai P. and Hayes, Ben J. (2021). A linkage disequilibrium-based approach to position unmapped SNPs in crop species. BMC Genomics, 22 (1) 773, 1-9. doi: 10.1186/s12864-021-08116-w

Genomic evaluations using data recorded on smallholder dairy farms in low- to middle-income countries

Owen Powell, Raphael Mrode, R. Chris Gaynor, Martin Johnsson, Gregor Gorjanc and John M.Hickey (2021). Genomic evaluations using data recorded on smallholder dairy farms in low- to middle-income countries. JDS Communications, 2 (6), 366-370. doi: 10.3168/jdsc.2021-0092

Perspectives on applications of hierarchical gene-to-phenotype (G2P) maps to capture non-stationary effects of alleles in genomic prediction

Powell, Owen M., Voss-Fels, Kai P., Jordan, David R., Hammer, Graeme and Cooper, Mark (2021). Perspectives on applications of hierarchical gene-to-phenotype (G2P) maps to capture non-stationary effects of alleles in genomic prediction. Frontiers in Plant Science, 12 663565, 663565. doi: 10.3389/fpls.2021.663565/full

Modelling selection response in plant breeding programs using crop models as mechanistic gene-to-phenotype (CGM-G2P) multi-trait link functions

Cooper, M., Powell, O., Voss-Fels, K. P., Messina, C. D., Gho, C., Podlich, D. W., Technow, F., Chapman, S. C., Beveridge, C. A., Ortiz-Barrientos, D. and Hammer, G. L. (2021). Modelling selection response in plant breeding programs using crop models as mechanistic gene-to-phenotype (CGM-G2P) multi-trait link functions. in silico Plants, 3 (1) diaa016, 1-21. doi: 10.1093/insilicoplants/diaa016

Spatial modelling improves genetic evaluation in smallholder breeding programs

Selle, Maria L., Steinsland, Ingelin, Powell, Owen, Hickey, John M. and Gorjanc, Gregor (2020). Spatial modelling improves genetic evaluation in smallholder breeding programs. Genetics Selection Evolution, 52 (1) 69. doi: 10.1186/s12711-020-00588-w

A two-part strategy using genomic selection in hybrid crop breeding programs

Powell, Owen, Gaynor, R. Chris, Gorjanc, Gregor, Werner, Christian and Hickey, John (2020). A two-part strategy using genomic selection in hybrid crop breeding programs.

In silico simulation of future hybrid performance to evaluate heterotic pool formation in a self-pollinating crop

Cowling, Wallace A., Gaynor, R. Chris, Antolin, Roberto, Gorjanc, Gregor, Edwards, Stefan M., Powell, Owen and Hickey, John M. (2020). In silico simulation of future hybrid performance to evaluate heterotic pool formation in a self-pollinating crop. Scientific Reports, 10 (1) 4037. doi: 10.1038/s41598-020-61031-0

APSIM-WGP: a software platform to predict crop GxExM interactions

Powell, Owen, McLean, Greg, Brider, Jason, Hammer, Graeme and Cooper, Mark (2023). APSIM-WGP: a software platform to predict crop GxExM interactions. GxExM Symposium II, Gainesville, FL USA, 6-7 November 2023.

Random Forest Importance Diagnostics can Capture Quantitative Genetic Properties of Markers for Genomic Prediction

Tomura, Shunichiro, Powell, Owen and Cooper, Mark (2023). Random Forest Importance Diagnostics can Capture Quantitative Genetic Properties of Markers for Genomic Prediction. International Congress of Genetics, Melbourne, VIC Australia, 16-21 July 2023. figShare. doi: 10.6084/m9.figshare.24211230.v1

GPU can Accelerate the Prediction of Complex Phenotypes

Tomura, Shunichiro, Powell, Owen and Cooper, Mark (2023). GPU can Accelerate the Prediction of Complex Phenotypes. Australasian Leadership Computing Symposium, Canberra, ACT Australia, 14-16 June 2023. doi: 10.6084/m9.figshare.24484831.v1

Hierarchical Gene-Phenotype Maps as a Framework to Predict GxExM Interactions

Powell, Owen, McLean, Greg, Brider, Jason, Technow, Frank, Tang, Tom, Messina, Carlos D., Hammer, Graeme and Cooper, Mark (2023). Hierarchical Gene-Phenotype Maps as a Framework to Predict GxExM Interactions. Quantitative Genetics and Genomics Gordon Research Conference, Ventura, CA, United States, 12-17 February 2023.

Integrating Hierarchical G2P Maps with Genomic Prediction

Powell, Owen, McLean, Greg, Brider, Jason, Technow, Frank, Tang, Tom, Messina, Carlos D., Hammer, Graeme and Cooper, Mark (2022). Integrating Hierarchical G2P Maps with Genomic Prediction. Interdrought VII - The 7th Congress on plant production in water-limited environments, Dakar, Senegal, 1 December 2022.

Utilising prior knowledge to improve breeding

Powell, Owen, Barbier, Francois, Voss-Fels, Kai, Beveridge, Christine and Cooper, Mark (2022). Utilising prior knowledge to improve breeding. GxExM Symposium, Brisbane, QLD Australia, 3-4 November 2022.

Transferring prediction models: from model organisms to crops

Powell, Owen, Barbier, Francois, Fichtner, Franziska, Sukumaran, Sivakumar, McLean, Greg, Brider, Jason, Technow, Frank, Tang, Tom, Messina, Carlos D., Jordan, David, Hammer, Graeme, Beveridge, Christine and Cooper, Mark (2022). Transferring prediction models: from model organisms to crops. TropAg International Agriculture Conference, Brisbane, QLD, Australia, 31 October - 2 November 2022.

Increasing Predictive Ability for Crop Improvement: Linking Crop Growth Models with Whole Genome Prediction

Powell, Owen, McLean, Greg, Brider, Jason, Technow, Frank, Sukumaran, Sivakumar, Jordan, David, Hammer, Graeme and Cooper, Mark (2022). Increasing Predictive Ability for Crop Improvement: Linking Crop Growth Models with Whole Genome Prediction. AuSoRGM, Toowoomba, QLD Australia, 18-19 August 2022.

ON the Pulse

Smith, Millicent, Robinson, Hannah and Powell, Owen (2022). ON the Pulse. Protein Futures, Brisbane, QLD, Australia, 15 June 2022.

An Ecophysiology-Inspired Gene-Phenotype Map for Breeding

Powell, Owen, Technow, Frank, Messina, Carlos D., McLean, Greg, Brider, Jason, Van Oosterom, Erik, Wu, Alex, Jordan, David, Hammer, Graeme and Cooper, Mark (2022). An Ecophysiology-Inspired Gene-Phenotype Map for Breeding. Australasian Plant Breeding Conference, Gold Coast, QLD, Australia, 9-11 May 2022.

A Two-Part Strategy for using Genomic Selection in Hybrid Crop Breeding Programs

Powell, Owen, Gaynor, Chris R., Gorjanc, Gregor, Werner, Christian and Hickey, John (2020). A Two-Part Strategy for using Genomic Selection in Hybrid Crop Breeding Programs. The 6th International Conference of Quantitative Genetics, Brisbane, QLD, Australia, 3-13 November 2020.

The impact of physiological non-additivity on variance components for complex traits

Voss-Fels, Kai, Powell, Owen, Jordan, David, Hammer, Graeme, Barbier, Francois, Werner, Christian, Hayes, Ben, Beveridge, Christine and Cooper, Mark (2020). The impact of physiological non-additivity on variance components for complex traits. The 6th International Conference on Quantitative Genetics, Brisbane, QLD Australia, 3-13 November 2020.

Can genomic data enable genetic evaluation with phenotypes recorded on smallholder farms?

Powell, Owen, Jenko, Janez, Gorjanc, Gregor, Mrode, Raphael and Hickey, John M. (2019). Can genomic data enable genetic evaluation with phenotypes recorded on smallholder farms?. Interbull Bulletin, Auckland, New Zealand, 7-11 February 2018. Uppsala, Sweden: International Bull Evaluation Service.

Can Genomics Enable Genetic Evaluations with Phenotypes Recorded on Smallholder Dairy Farms?

Powell, Owen, Jenko, Janez, Gaynor, Chris R., Banos, Georgios, Gorjanc, Gregor and Hickey, John (2018). Can Genomics Enable Genetic Evaluations with Phenotypes Recorded on Smallholder Dairy Farms?. Keystone Symposium, Kampala, Uganda, 25-29 November 2018.

Can genomics enable genetic evaluations with phenotypes recorded on smallholder dairy farms?

Powell, Owen, Jenko, Janez, Gaynor, Chris R., Banos, Georgios, Gorjanc, Gregor and Hickey, John (2018). Can genomics enable genetic evaluations with phenotypes recorded on smallholder dairy farms?. Big Data In Agriculture: DuPont Pioneer Symposia Series, Edinburgh, Scotland, United Kingdom, 14-15 May 2018.

Stochastic Simulation of Divergent Selection Experiment on a Gene-Phenotype Network: A Case Study of Shoot Branching in Plants

Powell, Owen and Cooper, Mark (2023). Stochastic Simulation of Divergent Selection Experiment on a Gene-Phenotype Network: A Case Study of Shoot Branching in Plants. figShare. (Dataset) doi: 10.6084/m9.figshare.23590083

Extending the breeder’s equation to take aim at the Target Population of Environments

Cooper, Mark, Owen Powell, Gho, Carla, Tang, Tom and Messina, Carlos (2022). Extending the breeder’s equation to take aim at the Target Population of Environments.

Investigations into the emergent properties of gene-to-phenotype networks across cycles of selection: a case study of shoot branching in plants

Powell, Owen M., Barbier, Francois, Voss-Fels, Kai P., Beveridge, Christine A. and Cooper, Mark (2022). Investigations into the emergent properties of gene-to-phenotype networks across cycles of selection: a case study of shoot branching in plants.

Designing breeding programs in the genomic era

Owen Powell (2021). Designing breeding programs in the genomic era. PhD Thesis, The Roslin Institute, The University of Edinburgh . doi: 10.7488/era/1463

Breeding with an eye on genes for paddocks

Powell, Owen and Cooper, Mark (2020, 10 02). Breeding with an eye on genes for paddocks Groundcover