My research interests centre on using quantitative genetics to drive genetic gain and efficiency in plant and animal breeding programmes.
Previous work at the Roslin Institute focused on using genomic prediction to demonstrate and exploit synergies between plant and animal breeding.
Stochastic simulations were used to quantify the impact of new genomic breeding strategies in a wide variety of settings; from low to middle-income (LMIC) dairy cattle breeding programs to large, well-funded maize breeding programs.
My work at QAAFI will focus on the development of prediction methods that combine biological, environmental and management information under a unifying framework.
Researcher biography
My research interests centre on using quantitative genetics to drive genetic gain and efficiency in plant and animal breeding programmes.
Previous work in the UK focused on using genomic information prediction to demonstrate and exploit synergies between plant and animal breeding. Stochastic simulations were used to quantify the impact of new genomic breeding strategies in a wide variety of settings; from low to middle-income (LMIC) dairy cattle breeding programs to large, well-funded maize breeding programs.
My work at QAAFI and the ARC Centre of Excellence for Plant Success in Nature & Agriculture focuses on the development of prediction methods that combine biological, environmental and management information under a unifying framework, to enhance our ability to identify breeding parents, varieties and genotype-by-agronomic management (GxM) solutions that are best suited for future climates.
Publications
Book Chapter
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
Journal Articles
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
Conference Papers
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 United States, 6-7 November 2023.
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
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
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.
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.
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.
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.
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.
Smith, Millicent, Robinson, Hannah and Powell, Owen (2022). ON the Pulse. Protein Futures, Brisbane, QLD, Australia, 15 June 2022.
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.
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.
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.
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.
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.
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.
Data Collection
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
Thesis
Owen Powell (2021). Designing breeding programs in the genomic era. PhD Thesis, The Roslin Institute, The University of Edinburgh . doi: 10.7488/era/1463
Newspaper Article
Powell, Owen and Cooper, Mark (2020, 10 02). Breeding with an eye on genes for paddocks Groundcover
Preprints
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.