Professor Ben Hayes
Professor

Ben Hayes

Email: 
Phone: 
+61 7 334 62173

Background

Professor Hayes has extensive research experience in genetic improvement of livestock, crop, pasture and aquaculture species, with a focus on integration of genomic information into breeding programs, including leading many large scale projects which have successfully implemented genomic technologies in livestock and cropping industries. Author of more than 300 journal papers, including in Nature Genetics, Nature Reviews Genetics, and Science, contributing to statistical methodology for genomic, microbiome and metagenomic profile predictions, quantitative genetics including knowledge of genetic mechanisms underlying complex traits, and development of bioinformatics pipelines for sequence analysis. Thomson Reuters highly cited researcher in 2015, 2016, 2017 and 2018.

Availability

Professor Ben Hayes is:
Available for supervision

Qualifications

  • Doctor of Philosophy, Central Queensland University
  • Certificate of Quantum Information, Computation and Communication, Massachusetts Institute of Technology

Research impacts

Genomic prediction, of which Ben Hayes was a co-inventor, and first described in the seminal publication [Meuwissen THE, Hayes BJ, Goddard ME (2001) Prediction of total genetic value using genome-wide dense marker maps. Genetics 157, 1819-1829] is now being used very widely in livestock and crops to predict future trait outcomes. The technology is also being used increasingly in human disease research.

In dairy cattle, the technology has been actively used by the industry for the past five years, and increases in genetic gain for key economic traits can be demonstrated. Nearly every dairy bull worldwide chosen for widespread use in the industry is now selected on the basis of genomic predictions. Dr Hayes has been an invited speaker at numerous high profile conferences, including the Biology of Genomes conference at Cold Spring Harbor May 2015, and also has a number of youtube videos explaining complex trait prediction for a general audience, one of which has been viewed more than 5000 times (https://www.youtube.com/watch?v=RovnCsda-zQ).

Ben Hayes led the program on animal improvement of the Dairy Futures CRC, which concluded June 2016. The animal improvement program in the Dairy Futures CRC is a successful research and utilisation program which has a very high level of industry input and collaboration. The value of the program was recognised recently when it was awarded a Cooperative Research Centres Association (CRCA) Award for Excellence in Innovation. The Dairy Futures CRC under the leadership of Dr. Hayes successfully developed and implemented a genomic breeding value for feed efficiency, which has been widely adopted by the industry.

Dr. Hayes established the 1000 bull genomes project, a consortium of over 30 institutes across the globe, which has assembled whole genome sequences of 1682 cattle of 55 breeds. The consortium has published a widely cited paper in Nature Genetics (Daetwyler et al. 2014), and has already led to more than 50 companion papers.

Search Professor Ben Hayes’s works on UQ eSpace

399 works between 2000 and 2024
All (399) Journal Article (337) Book Chapter (9) Conference Publication (51) Other Outputs (2)

161 - 180 of 399 works

2018

Journal Article

Variance explained by whole genome sequence variants in coding and regulatory genome annotations for six dairy traits

Koufariotis, Lambros T., Chen, Yi-Ping Phoebe, Stothard, Paul and Hayes, Ben J. (2018). Variance explained by whole genome sequence variants in coding and regulatory genome annotations for six dairy traits. BMC Genomics, 19 (237) 237, 237. doi: 10.1186/s12864-018-4617-x

Variance explained by whole genome sequence variants in coding and regulatory genome annotations for six dairy traits

2018

Journal Article

Responses of dairy cows with divergent residual feed intake as calves to metabolic challenges during midlactation and the nonlactating period

DiGiacomo, K., Norris, E., Dunshea, F. R., Hayes, B. J., Marett, L. C., Wales, W. J. and Leury, B. J. (2018). Responses of dairy cows with divergent residual feed intake as calves to metabolic challenges during midlactation and the nonlactating period. Journal of Dairy Science, 101 (7), 6474-6485. doi: 10.3168/jds.2017-12569

Responses of dairy cows with divergent residual feed intake as calves to metabolic challenges during midlactation and the nonlactating period

2018

Journal Article

A multi-trait Bayesian method for mapping QTL and genomic prediction

Kemper, Kathryn E., Bowman, Philip J., Hayes, Benjamin J., Visscher, Peter M. and Goddard, Michael E. (2018). A multi-trait Bayesian method for mapping QTL and genomic prediction. Genetics Selection Evolution, 50 (1) 10, 10. doi: 10.1186/s12711-018-0377-y

A multi-trait Bayesian method for mapping QTL and genomic prediction

2018

Journal Article

Multibreed genomic prediction using multitrait genomic residual maximum likelihood and multitask Bayesian variable selection

Calus, M. P. L., Goddard, M. E., Wientjes, Y. C. J., Bowman, P. J. and Hayes, B. J. (2018). Multibreed genomic prediction using multitrait genomic residual maximum likelihood and multitask Bayesian variable selection. Journal of Dairy Science, 101 (5), 4279-4294. doi: 10.3168/jds.2017-13366

Multibreed genomic prediction using multitrait genomic residual maximum likelihood and multitask Bayesian variable selection

2018

Journal Article

Development of genomic prediction in sorghum

Hunt, Colleen H., van Eeuwijk, Fred A., Mace, Emma S., Hayes, Ben J. and Jordan, David R. (2018). Development of genomic prediction in sorghum. Crop Science, 58 (2), 690-700. doi: 10.2135/cropsci2017.08.0469

Development of genomic prediction in sorghum

2018

Journal Article

Mammalian genomic regulatory regions predicted by utilizing human genomics, transcriptomics, and epigenetics data

Nguyen, Quan H., Tellam, Ross L., Naval-Sanchez, Marina, Porto-Neto, Laercio R., Barendse, William, Reverter, Antonio, Hayes, Benjamin, Kijas, James and Dalrymple, Brian P. (2018). Mammalian genomic regulatory regions predicted by utilizing human genomics, transcriptomics, and epigenetics data. Gigascience, 7 (3), 1-17. doi: 10.1093/gigascience/gix136

Mammalian genomic regulatory regions predicted by utilizing human genomics, transcriptomics, and epigenetics data

2018

Journal Article

Meta-analysis of genome-wide association studies for cattle stature identifies common genes that regulate body size in mammals

Bouwman, Aniek C., Daetwyler, Hans D., Chamberlain, Amanda J., Ponce, Carla Hurtado, Sargolzaei, Mehdi, Schenkel, Flavio S., Sahana, Goutam, Govignon-Gion, Armelle, Boitard, Simon, Dolezal, Marlies, Pausch, Hubert, Brøndum, Rasmus F., Bowman, Phil J., Thomsen, Bo, Guldbrandtsen, Bernt, Lund, Mogens S., Servin, Bertrand, Garrick, Dorian J., Reecy, James, Vilkki, Johanna, Bagnato, Alessandro, Wang, Min, Hoff, Jesse L., Schnabel, Robert D., Taylor, Jeremy F., Vinkhuyzen, Anna A. E., Panitz, Frank, Bendixen, Christian, Holm, Lars-Erik ... Hayes, Ben J. (2018). Meta-analysis of genome-wide association studies for cattle stature identifies common genes that regulate body size in mammals. Nature Genetics, 50 (3), 362-367. doi: 10.1038/s41588-018-0056-5

Meta-analysis of genome-wide association studies for cattle stature identifies common genes that regulate body size in mammals

2018

Journal Article

Prospects for increasing yield in macadamia using component traits and genomics

O'Connor, Katie, Hayes, Ben and Topp, Bruce (2018). Prospects for increasing yield in macadamia using component traits and genomics. Tree Genetics & Genomes, 14 (1) 7. doi: 10.1007/s11295-017-1221-1

Prospects for increasing yield in macadamia using component traits and genomics

2018

Conference Publication

Accuracy of multi-trait genomic predictions for age at puberty in Northern Australian beef heifers

Engle, B., Corbet, N., Allen, J., Laing, A., Fordyce, G., McGowan, M., Burns, B. and Hayes, B. (2018). Accuracy of multi-trait genomic predictions for age at puberty in Northern Australian beef heifers. 2018 ASAS-CSAS Annual Meeting and Trade Show, Vancouver, Canada, 8-12 July 2018. Cary, NC, United States: Oxford University Press (OUP). doi: 10.1093/jas/sky404.231

Accuracy of multi-trait genomic predictions for age at puberty in Northern Australian beef heifers

2018

Conference Publication

The use of multi-breed reference populations and multi-omic data to maximize accuracy of genomic prediction

Goddard, M.E., MacLeod, I.M., Kemper, K.E., Xiang, R., van den Berg, I., Khansefid, M., Daetwyler, H.D. and Hayes, B.J. (2018). The use of multi-breed reference populations and multi-omic data to maximize accuracy of genomic prediction. 11th World Congress of Genetics Applied to Livestock Production, Auckland, New Zealand, 11-16 February 2018. Auckland, New Zealand: WCGALP.

The use of multi-breed reference populations and multi-omic data to maximize accuracy of genomic prediction

2018

Journal Article

METRIC APPROXIMATIONS OF WREATH PRODUCTS

Hayes, Ben and Sale, Andrew W. (2018). METRIC APPROXIMATIONS OF WREATH PRODUCTS. Annales De L Institut Fourier, 68 (1), 423-455. doi: 10.5802/aif.3166

METRIC APPROXIMATIONS OF WREATH PRODUCTS

2017

Journal Article

Rapid Discovery of de Novo Deleterious Mutations in Cattle Enhances the Value of Livestock as Model Species

Bourneuf, E., Otz, P., Pausch, H., Jagannathan, V., Michot, P., Grohs, C., Piton, G., Ammermüller, S., Deloche, M. C., Fritz, S., Leclerc, H., Péchoux, C., Boukadiri, A., Hozé, C., Saintilan, R., Créchet, F., Mosca, M., Segelke, D., Guillaume, F., Bouet, S., Baur, A., Vasilescu, A., Genestout, L., Thomas, A., Allais-Bonnet, A., Rocha, D., Colle, M. A., Klopp, C., Esquerré, D. ... Capitan, A. (2017). Rapid Discovery of de Novo Deleterious Mutations in Cattle Enhances the Value of Livestock as Model Species. Scientific Reports, 7 (1) 11466, 11466. doi: 10.1038/s41598-017-11523-3

Rapid Discovery of de Novo Deleterious Mutations in Cattle Enhances the Value of Livestock as Model Species

2017

Journal Article

Genome-wide association study and annotating candidate gene networks affecting age at first calving in Nellore cattle

Mota, R. R., Guimarães, S. E. F., Fortes, M. R. S., Hayes, B., Silva, F. F., Verardo, L. L., Kelly, M. J., de Campos, C. F., Guimarães, J. D., Wenceslau, R. R., Penitente-Filho, J. M., Garcia, J. F. and Moore, S. (2017). Genome-wide association study and annotating candidate gene networks affecting age at first calving in Nellore cattle. Journal of Animal Breeding and Genetics, 134 (6), 484-492. doi: 10.1111/jbg.12299

Genome-wide association study and annotating candidate gene networks affecting age at first calving in Nellore cattle

2017

Journal Article

Accelerating wheat breeding for end-use quality with multi-trait genomic predictions incorporating near infrared and nuclear magnetic resonance-derived phenotypes

Hayes, B. J., Panozzo, J., Walker, C. K., Choy, A. L., Kant, S., Wong, D., Tibbits, J., Daetwyler, H. D., Rochfort, S., Hayden, M. J. and Spangenberg, G. C. (2017). Accelerating wheat breeding for end-use quality with multi-trait genomic predictions incorporating near infrared and nuclear magnetic resonance-derived phenotypes. Theoretical and Applied Genetics, 130 (12), 2505-2519. doi: 10.1007/s00122-017-2972-7

Accelerating wheat breeding for end-use quality with multi-trait genomic predictions incorporating near infrared and nuclear magnetic resonance-derived phenotypes

2017

Journal Article

Breeding Differently—the Digital Revolution: High-Throughput Phenotyping and Genotyping

Slater, Anthony T., Cogan, Noel O. I., Rodoni, Brendan C., Daetwyler, Hans D., Hayes, Benjamin J., Caruana, Brittney, Badenhorst, Pieter E., Spangenberg, German C. and Forster, John W. (2017). Breeding Differently—the Digital Revolution: High-Throughput Phenotyping and Genotyping. Potato Research, 60 (3-4), 337-352. doi: 10.1007/s11540-018-9388-x

Breeding Differently—the Digital Revolution: High-Throughput Phenotyping and Genotyping

2017

Journal Article

Estimated allele substitution effects underlying genomic evaluation models depend on the scaling of allele counts

Bouwman, Aniek C., Hayes, Ben J. and Calus, Mario P. L. (2017). Estimated allele substitution effects underlying genomic evaluation models depend on the scaling of allele counts. Genetics, Selection, Evolution, 49 (1) 79, 79. doi: 10.1186/s12711-017-0355-9

Estimated allele substitution effects underlying genomic evaluation models depend on the scaling of allele counts

2017

Journal Article

Multi-breed genomic prediction using Bayes R with sequence data and dropping variants with a small effect

Van Den Berg, Irene , Bowman, Phil J, MacLeod, Iona M., Hayes, Ben J., Wang, Tingting, Bolormaa, Sunduimijid and Goddard, Mike E. (2017). Multi-breed genomic prediction using Bayes R with sequence data and dropping variants with a small effect. Genetics Selection Evolution, 49 (1) 70, 70. doi: 10.1186/s12711-017-0347-9

Multi-breed genomic prediction using Bayes R with sequence data and dropping variants with a small effect

2017

Journal Article

Short communication: implementation of a breeding value for heat tolerance in Australian dairy cattle

Nguyen, Thuy T. T., Bowman, Phil J., Haile-Mariam, Mekonnen, Nieuwhof, Gert J., Hayes, Benjamin J. and Pryce, Jennie E. (2017). Short communication: implementation of a breeding value for heat tolerance in Australian dairy cattle. Journal of Dairy Science, 100 (9), 7362-7367. doi: 10.3168/jds.2017-12898

Short communication: implementation of a breeding value for heat tolerance in Australian dairy cattle

2017

Journal Article

Exploring and Harnessing Haplotype Diversity to Improve Yield Stability in Crops

Qian, Lunwen, Hickey, Lee T., Stahl, Andreas, Werner, Christian R., Hayes, Ben, Snowdon, Rod J. and Voss-Fels, Kai P. (2017). Exploring and Harnessing Haplotype Diversity to Improve Yield Stability in Crops. Frontiers in Plant Science, 8 1534, 1534. doi: 10.3389/fpls.2017.01534

Exploring and Harnessing Haplotype Diversity to Improve Yield Stability in Crops

2017

Journal Article

Application of a Bayesian non-linear model hybrid scheme to sequence data for genomic prediction and QTL mapping

Wang, Tingting, Chen, Yi-Ping Phoebe, MacLeod, Iona M., Pryce, Jennie E., Goddard, Michael E. and Hayes, Ben J. (2017). Application of a Bayesian non-linear model hybrid scheme to sequence data for genomic prediction and QTL mapping. BMC Genomics, 18 (1) 618, 618. doi: 10.1186/s12864-017-4030-x

Application of a Bayesian non-linear model hybrid scheme to sequence data for genomic prediction and QTL mapping

Current funding

  • 2024 - 2029
    ARC Training Centre in Predictive Breeding for Agricultural Futures
    ARC Industrial Transformation Training Centres
    Open grant
  • 2024 - 2028
    Program 2 - Resistance Sources: Using 'FastStack' to develop effective durable net blotch resistance gene stacks
    Grains Research & Development Corporation
    Open grant
  • 2024 - 2029
    Achieving improved genetic gain for yield in chickpea, faba bean and lentil using genetic diversity (GRDC grant administered by Murdoch University)
    Murdoch University
    Open grant
  • 2024 - 2028
    Fast tracking deployment of chickpea heat tolerance
    GRDC - PROC-9176886 - Fast tracking deployment of chickpea heat tolerance to develop chickpea varieties with improved high temperature tolerance
    Open grant
  • 2023 - 2027
    RustHapSelect: Fast-tracking rust resistance haplotypes into high-yielding germplasm (GRDC grant administered by CSIRO)
    CSIRO
    Open grant
  • 2022 - 2025
    Northern Genomics commercialisation scoping study
    Meat & Livestock Australia
    Open grant
  • 2022 - 2027
    Reducing methane emissions and improving profitability in Northern Australian beef
    Meat & Livestock Australia
    Open grant
  • 2022 - 2026
    Scaling Genomic Selection Across the Indian Smallholder Dairy Sector (BMGF grant administered by BAIF Development Research Foundation)
    BAIF Development Research Foundation
    Open grant
  • 2022 - 2027
    Facilitating Innovations for Resilient Livestock Farming Systems (Re-Livestock) (Horizon Europe grant administered by the Spanish National Research Council)
    Spanish National Research Council (Agencia Estatal Consejo Superior de Investigaciones Científicas)
    Open grant
  • 2022 - 2026
    Balancing polled and profit: demonstration of breeding strategies to replace dehorning in a large integrated beef and cattle operation
    A. A. COMPANY PTY. LTD.
    Open grant
  • 2022 - 2026
    On-farm genomics: genomic solutions for Northern beef cattle management and breeding
    Meat & Livestock Australia
    Open grant
  • 2022 - 2026
    Digging deeper to improve yield stability
    ARC Linkage Projects
    Open grant
  • 2022 - 2026
    LESTR Low Emission Saliva Test for Ruminants
    Meat & Livestock Australia
    Open grant
  • 2021 - 2026
    ARC Research Hub for Supercharging Tropical Aquaculture Through Genetic Solutions - externally administered by James Cook University (JCU)
    James Cook University
    Open grant
  • 2021 - 2026
    NB2: Assessing practical interventions to reduce calf wastage and herd mortality in northern systems
    Meat & Livestock Australia
    Open grant

Past funding

  • 2023 - 2024
    Genomic prediction of ratoon yield robustness
    Sugar Research Australia Limited
    Open grant
  • 2022 - 2024
    Northern Beef Information Nucleus (Spyglass) Phase 4 Extension
    Australian Brahman Breeders' Association Limited
    Open grant
  • 2022 - 2024
    Development of genomic multi-breed eating quality trait estimates using shared global data (Meat & Livestock Australia project administered by University of New England)
    University of New England
    Open grant
  • 2021 - 2022
    DNA as the ultimate identifier in blockchain traceability systems
    Innovation Connections
    Open grant
  • 2020 - 2024
    Screening of diverse barley germplasm for rapid discovery and utilisation of novel disease resistance in barley using R-HapSelect : A haplotype-based toolkit
    Grains Research & Development Corporation
    Open grant
  • 2020 - 2023
    FY20 Postgraduate Scholarships - Harrison Lamb
    Meat & Livestock Australia
    Open grant
  • 2019 - 2021
    Bull fertility update: historical data, new cohort and advanced genomics (Meat & Livestock Australia grant administered by CSIRO)
    CSIRO
    Open grant
  • 2018 - 2023
    India Dairy Genetic Gain: Array designs for Indian Cattle and Buffalo
    Bill & Melinda Gates Foundation
    Open grant
  • 2018 - 2024
    National Tree Genomics Program - Genomics Toolbox
    Horticulture Innovation Australia Limited
    Open grant
  • 2018 - 2021
    Predicting age of livestock from DNA samples
    Meat & Livestock Australia
    Open grant
  • 2018 - 2023
    National Tree Genomics Program - Phenotype Prediction
    Horticulture Innovation Australia Limited
    Open grant
  • 2018 - 2023
    FastStack - evolutionary computing to stack desirable alleles in wheat
    ARC Linkage Projects
    Open grant
  • 2018 - 2021
    Female Reproduction Phenobank and Validation Herds
    Meat & Livestock Australia
    Open grant
  • 2017 - 2020
    Novel genomic technologies to improve fertility in Northern Beef Cattle
    ARC Linkage Projects
    Open grant
  • 2017 - 2024
    Improving fertility in northern cattle through host and pathogen molecular diagnosis
    Meat & Livestock Australia
    Open grant
  • 2017 - 2023
    Implementing and validating genomic selection in SRA breeding programs to accelerate improvements in yield, commercial cane sugar, and other key traits
    Sugar Research Australia Limited
    Open grant
  • 2017 - 2019
    Genetics R&D: Characterisation of the Brahman Genome
    Meat & Livestock Australia
    Open grant
  • 2017 - 2023
    Improving bovine respiratory disease control through the characterisation of pathogen genomics and host interactions
    Meat & Livestock Australia
    Open grant
  • 2017 - 2018
    Expression of genes and proteins related to meat quality in Bos indicus cattle
    UQ-FAPESP Strategic Research Fund SPRINT
    Open grant
  • 2017 - 2022
    Accelerating genetic gain for productivity and profitability in Northern beef cattle with genomic technologies
    Meat & Livestock Australia
    Open grant
  • 2017 - 2024
    Cattle tick and Buffalo fly host genetics, susceptibility to buffalo fly lesions and biomarkers for resistance
    Meat & Livestock Australia
    Open grant

Availability

Professor Ben Hayes is:
Available for supervision

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Supervision history

Current supervision

Completed supervision

Enquiries

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