Stewart Bauck

Email: stewartbauck@gmail.com | Phone: 604-868-7154

 

  1. CURRENT ROLES: Index Genetics, Managing Director
  1. Professional
    1. Manager, Genomics Division and VP Genomics, Neogen Corporation (2012-2020); Head Livestock Production Business Unit, Merial Ltd. (2003-2012); Country Manager Canada, Merial Ltd and MSD AgVet (1995-2002); Executive Director US Livestock Division, MSD AgVet; (1994-1995); Sales and Marketing Director MSD AgVet Canada (1992-1994); Manager Technical Services and Regulatory Affairs (Canada and International) MSD AgVet (1986-
    2. Awards – Beef Improvement Federation Pioneer Award (2023); AllFlex Innovation Award (2019); Chairman’s Award MSD AgVet (1992) – commercialization of ivermectin; Lewis B. Hewitt Award in Epidemiology – WCVM (1979)
    3. Professional Organizations – Canadian Veterinary Medical Association member (1981-1986); Canadian Animal Health Institute including term as President (1992-2002)
    4. Publications – see attached for a selection of references specific to genomic and genetic applications in livestock
  1. Education – DVM (Western College of Veterinary Medicine) 1981; Master of Science (Western College of Veterinary Medicine) 1984
  1. Current Research Projects and Partners
    1. Neogen Corporation – beef genomic testing and genetic prediction
    2. Texas A& M – beef respiratory disease and cow longevity
  1. Hobbies/Interests/Passions – Fishing, golfing, skiing, travel, physical fitness and family
  1. Widowed with two daughters (one veterinarian at U of Florida and one high school Athletic Director in West Vancouver, BC) and three grandchildren

Relevant publications

  1. Pan, J. Michal, C. Gaskins, J. Reeves, S. Bauck, Z. Jiang. SNP-Based parentage assignment with different software/programs in beef cattle.  Plant and Animal Genome Conference.  San Diego, CA.  Jan 9-13, 2010.
  2. Wu, T. Bessinger, S. Bauck, B. Woodward, G. Rosa, K. Weigel, N. Gatti, D. Gianola. A primer on high-throughput computing for genomic selection.  Frontiers in Genetics.  2011.  Vol 2: 4
  3. Saatchi,M. McClure, S. McKay, M. Rolf, J. Kim, J. Decker, T. Taxis, R. Chapple, H. Ramey, S. Northcutt, S. Bauck, B. Woodward, J. Dekkers, R. Fernando, R. Schnabel, D. Garrick* and J. Taylor. Accuracies of genomic breeding values in American Angus beef cattle using K-means clustering for cross-validation. Genetics Selection Evolution 2011, Vol 43:40
  4. Sun. X. Wu, K. Weigel, G. Rosa, S. Bauck, B. Woodward, R. Schnabel, J. Taylor, D. Gianola. An ensemble-based approach to imputation of moderate-density genotypes for genomic selection with application to Angus cattle. Genetics Research.  2012. Vol 94: 133-150.
  5. Decker, D. Vasco, S. McKay, M. McClure, M. Rolf, J. Kim; S. Northcutt, S. Bauck, B. Woodward, R. Schnabel, J. Taylor. A novel analytical method, birth date selection mapping, detects response of the Angus (Bos taurus) genome to selection on complex traits.  BMC Genomics.  2012.  Vol 13: 606
  6. Abo-Ismail, M. Kelly, E. Squires, K. Swanson, S. Bauck, S. Miller. Identification of single nucleotide polymorphisms in genes involved in digestive and metabolic processes associated with feed efficiency and performance traits in beef cattle.  J Anim Sci. 2013 Vol 92: 2512-2529.
  7. Okut, X-L. Wu, G. Rosa, S. Bauck, B. Woodward, R. Schnabel, J. Taylor, D. Gianola. Predicting expected progeny difference for marbling score in Angus cattle using artificial neural networks and Bayesian regression models.  Genetics Selection Evolution. 2013 Vol 45. Article 34.
  8. Akanno, G. Plastow, B. Woodward, S. Bauck, H. Okut, X-L. Wu, C. Sun, J. Aalhus, S. Moore, S. Miller, Z. Wang, J. Basarab.  Reliability of molecular breeding values for Warner-Bratzler shear force and carcass traits of beef cattle – An independent evaluation study.  J. Anim. Sci. 2014. Vol 92(7): 2896-2904.
  9. X-L. Wu, J. Xu, G. Feng, G. Wiggans, J. Taylor, J. He, C. Qian, J. Qiu, B. Simpson, J. Walker, S. Bauck.  Optimal design of low-density SNP arrays for genomic prediction: algorithm and applications.  PLoS ONE.  2016 Vol 11(9): e0161719
  10. He, J. Xu, X-L. Wu, S. Bauck, J. Lee, G. Morota, S. Kachman, M. Spangler.  Comparing strategies for selection of low-density SNPs for imputation-mediated genomic prediction in U.S. Holsteins.  Genetica. 2018 Vol 146: 137-149
  11.  J. He, Y. Guo, J. Xu, H. Li, A. Fuller, R. Tait, X-L. Wu, S. Bauck.  Comparing SNP panels and statistical methods for estimating genomic breed composition of individual animals in ten cattle breeds.  BMC Genetics.  2018 Vol 19. Article 56.
  12. J. He, C. Qian, R. Tait, S. Bauck, X-L. Wu.  Estimating genomic breed composition of individual animals using selected SNPs.  Hereditas.  2018 Vol 40(4): 305-314.
  13. Z. Li, J. He, J. Jian, R. Tait, S. Bauck, W. Guo, X-L. Wu.  Impacts of SNP genotyping call rate and SNP genotyping error rate on imputation accuracy in Holstein cattle.  Hereditas. 2019 Vol 41(7): 644-652.
  14. Y. Wang, X-L. Wu, Z. Li, Z. Bao, R. Tait, S. Bauck, G. Rosa.  Estimation of genomic breed composition for purebred and crossbred animals using sparsely regularized admixture models.  Frontiers in Genetics.  2020 Vol 11. Article 576
  15. J. He, X-L. Wu, Q. Zeng, H. Li, H. Ma, J. Jiang, G. Rosa, D. Gianola, R. Tait, S. Bauck.  Genomic mating as sustainable breeding for Chinese indigenous Ningxiang pigs.  PLoS ONE. 2019 Vol 15(8): e0236629
  16. X-L. Wu, Z. Li, Y. Wang, J. He, G. Rosa, R. Ferretti, J. Genho, R. Tait, J. Parham, T. Schultz, S. Bauck.  A causality perspective of genomic breed composition for composite animals. Frontiers in Genetics. 2020 Vol 11. Article 546052.