NCERA225: Implementation and Strategies for National Beef Cattle Genetic Evaluation

(Multistate Research Coordinating Committee and Information Exchange Group)

Status: Active

SAES-422 Reports

12/28/2017

04/08/2019

2018 NCERA-225 Implementation and Strategies for
National Beef Cattle Genetic Evaluation

Publications and Presentations

Peer Reviewed Manuscripts:

  1. Abdoli, R., S.Z. Mirhoseini, N. Ghavi Hossein-Zadeh, P. Zamani, C. Gondro (2018). Genome-wide association study to identify genomic regions affecting prolificacy in Lori-Bakhtiari sheep. Animal Genetics 49:488-491.
  2. Aherin, D.G., J.M. Bormann, J.L. Heier Stamm, M.D. MacNeil, and R.L. Weaber. 2018. Decision Making Tools: Stochastic simulation model accounting for the impacts of biological variation on success of bovine embryo transfer programs. Translation Animal Science. 2:451-462. doi: 10.1093/tas/txy087.
  3. Ahlberg CM, Allwardt K, Broocks A, Bruno K, McPhillips L, Taylor A, Krehbiel CR, Calvo-Lorenzo MS, Richards CJ, Place SE, DeSilva U, VanOverbeke DL, Mateescu RG, Kuehn LA, Weaber RL, Bormann JM, Rolf MM. Environmental effects on water intake and water intake prediction in growing beef cattle. J Anim Sci. 2018 Sep 29;96(10):4368-4384. doi: 10.1093/jas/sky267.
  4. Ahlberg CM, Allwardt K, Broocks A, Bruno K, McPhillips L, Taylor A, Krehbiel CR, Calvo-Lorenzo M, Richards CJ, Place SE, DeSilva U, VanOverbeke DL, Mateescu RG, Kuehn LA, Weaber RL, Bormann JM, Rolf MM. Test duration for water intake, ADG, and DMI in beef cattle. J Anim Sci. 2018 Jul 28;96(8):3043-3054. doi: 10.1093/jas/sky209.
  5. Ahlberg, C.M., K. Allwardt, A. Broocks, K. Bruno, L. McPhillips, A. Taylor, C.R. Krehbiel, M. Calvo-Lorenzo, C.J. Richards, S.E. Place, U. DeSilva, D.L. VanOverbeke, R.G. Mateescu, L.A. Kuehn, R. Weaber, J. Bormann, M.M. Rolf. 2018. Environmental effects on water intake and water intake prediction in growing beef cattle. J. Anim. Sci. 96:4368-4384. doi: 10.1093/jas/sky267
  6. Ahlberg, C.M., K. Allwardt, A. Broocks, K. Bruno, L. McPhillips, A. Taylor, C.R. Krehbiel, M. Calvo-Lorenzo, C.J. Richards, S.E. Place, U. DeSilva, D.L. Van Overbeke, R.G. Mateescu, L.A. Kuehn, R.L. Weaber, J.M. Bormann, and M.M. Rolf. 2018. Test duration for water intake, ADG, and DMI in beef cattle. Anim. Sci. 96:3043-3054. doi:10.1093/jas/sky209.
  7. Bhuiyan, M.S.A., D. Lim, M. Park, S. Lee, Y. Kim, C. Gondro, B. Park and S. Lee (2018). Functional Partitioning of Genomic Variance and Genome-Wide Association Study for Carcass Traits in Korean Hanwoo Cattle Using Imputed Sequence Level SNP Data. Frontiers in Genetics 9:217.
  8. Boldt, R.J., S.E. Speidel, M.G. Thomas, and R.M. Enns. 2018. Genetic parameters for fertility and production traits in Red Angus cattle. J. Anim. Sci. 96:4100-4111. doi:10.1093/jas/sky294.
  9. Bormann, J.M. and M.M. Rolf.   The use of current events to enhance student learning in agricultural genetics.  NACTA Journal.  62:89-94.
  10. Bourgon, S.L., M. Diel de Amorim, T. Chenier, M. Sargolzaei, S.P. Miller, J.E. Martell and Y.R. Montanholi.   Relationships of nutritional plane and feed efficiency with sexual development and fertility related measures in young beef bulls.  Anim. Reprod. Sci. 198:99-111.
  11. Bruinjé, T.C., P. Ponce-Barajas, A. Dourey, M.G. Colazo, T. Caldwell, Z. Wang, S.P. Miller and D.J. Ambrose.   Morphology, membrane integrity and mitochondrial function in sperm of crossbred beef bulls selected for residual feed intake.  Can. J. Anim. Sci. (accepted 2 October 2018) (https://doi.org/10.1139/CJAS-2018-0103)
  12. Chang, L.Y. S. Toghiani, A. Ling, S.E. Aggrey, and R. Rekaya. 2018. High density marker panels, SNPs prioritizing and accuracy of genomic selection. BMC Genet. 19:4. https://doi.org/10.1186/s12863-017-0595-2
  13. Crawford, N. F., S. J. Coleman, T. N. Holt, S. E. Speidel, R. M. Enns, R. Hamid, and M. G. Thomas. 2018. Allele distribution and candidate polymorphism association of EPAS1variant with mean pulmonary arterial pressures in yearling Angus cattle. Agri Gene. 9:27-31. doi: https://doi.org/10.1016/j.aggene.2018.07.004.
  14. D. J., R. L. Weaber, and W. R. Lamberson. 2018. Genotype by environment interaction for stayability of Red Angus in the United States. J. Anim. Sci. 2018 96:422-429. https://doi.org/10.1093/jas/skx080
  15. Garcia A.L.S., B. Bosworth, G. Waldbieser, I. Misztal, S. Tsuruta, and D.A.L. Lourenco. 2018. Development of genomic predictions for harvest and carcass weight in channel catfish. Gent. Sel. Evol. 50:66. https://doi.org/10.1186/s12711-018-0435-5
  16. Guarini, A.R., D.A.L. Lourenco, L.F. Brito, M. Sargolzaei, C. Baes, F. Miglior, I. Misztal, and F. Schenkel. 2018. Comparison of genomic predictions for lowly heritable traits using multi-step and single-step genomic BLUP in Holstein cattle. J. Dairy Sci. 101:8076-8086. https://doi.org/10.3168/jds.2017-14193
  17. Howard*, J.T., T. A. Rathje, C. E. Bruns, D. F Wilson-Wells, S. D. Kachman, and M. L. Spangler. 2018. The impact of truncating data on predictive ability for single-step genomic best linear unbiased prediction. J. Anim. Breed. and Genetics 135: 251-262.
  18. Howard*, J.T., T. A. Rathje, C. E. Bruns, D. F Wilson-Wells, S. D. Kachman, and M. L. Spangler. 2018. The impact of selective genotyping on the response to selection using single-step genomic best linear unbiased prediction. J. Anim. Sci. 96: 4532-4542.
  19. Id-Lahoucine, A. Cánovas, C. Jaton, F. Miglior, P.A.S Fonseca, M. Sargolzaei, S. Miller, F.S. Schenkel, J.F. Medrano and J. Casellas.   Implementation of Bayesian methods to identify SNP and haplotype regions with transmission ration distortion across the whole genome: TRDscan v.1.0.  J. Dairy Sci. 102(4):3175-3188
  20. Junqueira, V.S., P.S. Lopes, M.D.V. Resende, F.F. Silva, D.A.L. Lourenco, M.J. Yokoo, F.F. Cardoso. 2018. Impact of embryo transfer phenotypic records on large-scale beef cattle genetic evaluations. R. Bras. Zootec. 47:e20170033. https://doi.org/10.1590/rbz4720170033
  21. Karimi, K., A. Esmailizadeh, D.D. Wu and C. Gondro (2018). Mapping of genome-wide copy number variations in the Iranian indigenous cattle using a dense SNP data set. Animal Production Science 58:1192-1200.
  22. Kluska, S., L.O.C. Silva, F.M. Costa-Maia, D.A.L. Lourenco, T.E. Stivanin, F. Baldi, E. Peripolli, E.N. Martins. 2018. Estimation of genetic parameters for probability of calving up to 39 months of age, stayability, and scrotal circumference in Nelore cattle. Livest. Res. Rural. Dev. 30(5):1-8.
  23. Lam, S., J.C. Munro, M. Zhou, L.L. Guan, F.S. Schenkel, M.A. Steele, S.P. Miller and Y.R. Montanholi.   Associations of rumen parameters with feed efficiency and sampling routine in beef cattle.  Animal 12(7):1442-1450 (https://doi.org/10.1017/S1751731117002750 Published online: 10 Nov. 2017)
  24. Ling A., Hay E.H., Aggrey S.E., Rekaya R. 2018. A Bayesian approach for analysis of ordered categorical responses subject to misclassification. PLoS ONE 13(12): e0208433. https://doi.org/10.1371/journal.pone.0208433
  25. Maiorano, A.M., D.A.L. Lourenco, S. Tsuruta, A.M. Toro, N.B. Stafuzza, Y. Masuda, A. Vercesi Filho, J.N.S.G. Cyrillo, R.A. Curi, J.A.V. Silva. 2018. Assessing genetic architecture and signatures of selection of dual purpose Gir cattle populations using genomic information. PlosONE. 13(8):e0200694. https://doi.org/10.1371/journal.pone.0200694
  26. Marley KB, Kuehn LA, Keele JW, Wileman BW, Gonda MG. Genetic variation in humoral response to an Escherichia coli O157:H7 vaccine in beef cattle. PLoS One. 2018 May 14;13(5):e0197347. doi: 10.1371/journal.pone.0197347. eCollection 2018.
  27. Masuda, Y., P. M. VanRaden, I. Misztal, and T. J. Lawlor. 2018. Differing genetic trend estimates from traditional and genomic evaluations of genotyped animals as evidence of preselection bias in US Holsteins. J. Dairy Sci. 101:5194-5206.
  28. Oliveira, D.P., D.A.L. Lourenco, S. Tsuruta, I. Misztal, D. J.A. Santos, F. R. de Araújo Neto, R. R. Aspilcueta-Borquis, F. Baldi, R. Carvalheiro, G.M.F. de Camargo, L.G. Albuquerque, H. Tonhati. 2018. Reaction norm for yearling weight by single-step methodology in beef cattle. J. Anim. Sci. 96:27–34. https://doi.org/10.1093/jas/skx006
  29. Pauling, R.C., S.E. Speidel, M.G. Thomas, T.N. Holt, and R.M. Enns. 2018. Evaluation of moderate to high elevation on pulmonary arterial pressure measures in Angus cattle. J. Anim. Sci. 96:3599-3605.  doi:10.1093/jas/sky292.
  30. Payton, R.R., L.A. Rispoli, K.A. Nagle, C. Gondro, A.M. Saxton, B.H. Voy and J.L. Edwards (2018). Mitochondrial-related consequences of heat stress exposure during bovine oocyte maturation persist in early embryo development. Journal of Reproduction and Development 64(3): 243–251.
  31. Paz, Henry A., Kristin E. Hales, James E. Wells, Larry A. Kuehn, Harvey C. Freetly, Elaine D. Berry, Michael D. Flythe, Matthew L. Spangler, and Samodha C. Fernando. 2018.  Rumen bacterial community structure impacts feed efficiency in beef cattle. J. Anim. Sci. 96:1045-105.
  32. E. Speidel, B.A. Buckley, R.J. Boldt, R.M. Enns, J. Lee, M.L. Spangler, and M.G. Thomas. 2018. Genome wide association of heifer pregnancy and stayability in Red Angus cattle. J. Anim. Sci. 96:846-853. doi:10.1093/jas/sky041/4869971.
  33. Salem, M., R. Al-Tobasei, A. Ali, D. Lourenco, G. Gao, Y. Palti, B. Kenney, and T. D. Leeds. 2018. Genome-wide association analysis with a 50K transcribed gene SNP-chip identifies QTL affecting muscle yield in rainbow trout. Front. Genet. 9:387. https://doi.org/10.3389/fgene.2018.00387
  34. Schweer KR, Kachman SD, Kuehn LA, Freetly HC, Pollak JE, Spangler ML. Genome-wide association study for feed efficiency traits using SNP and haplotype models. J Anim Sci. 2018 Jun 4;96(6):2086-2098. doi: 10.1093/jas/sky119.
  35. Schweer*, K.R., S.D. Kachman,  L.A. Kuehn, H.C. Freetly, E.J. Pollak, and M.L. Spangler. 218. Genome-wide association study for feed efficiency traits using SNP and haplotype models. J. Anim. Sci. 96:2086-2098.
  36. Snelling WM, Kuehn LA, Thallman RM, Bennett GL, Golden BL. Genetic correlations among weight and cumulative productivity of crossbred beef cows. J Anim Sci. 2019 Jan 1;97(1):63-77. doi: 10.1093/jas/sky420.
  37. Speidel, S.E., B. A. Buckley, R. J. Boldt, R. M. Enns, J. Lee, M. L. Spangler, M. G. Thomas. 2018. Genome wide association study of Stayability and Heifer Pregnancy in Red Angus Cattle. J. Anim. Sci. 96:846-853.
  38. Stafuzza, N.B., R.M.O. Silva, E. Peripolli, L.A.F. Bezerra, R.B. Lôbo, C.U. Magnabosco, F.A. Di Croce, J.B. Osterstock, D.P. Munari, D.A.L. Lourenco, F. Baldi. 2018. Genome-Wide Association Study Provides Insights into Genes Related with Horn Development in Nelore Beef Cattle. PlosONE 13(8): e0202978. https://doi.org/10.1371/journal.pone.0202978
  39. Thallman RM, Kuehn LA, Snelling WM, Retallick KJ, Bormann JM, Freetly HC, Hales KE, Bennett GL, Weaber RL, Moser DW, MacNeil MD. Reducing the period of data collection for intake and gain to improve response to selection for feed efficiency in beef cattle. J Anim Sci. 2018 Apr 3;96(3):854-866. doi: 10.1093/jas/skx077.
  40. Thallman, R.M., L.A. Kuehn, W.M. Snelling, K.J. Retallick, J.M. Bormann, H.C. Freetly, K.E. Hales, G.L. Bennett, R.L. Weaber, D.W. Moser, and M.D. MacNeil. 2018. Reducing the period of data collection for intake and gain to improve response to selection for feed efficiency in beef cattle. Anim. Sci. 96:854-866. doi:10.1093/jas/skx077.
  41. Vallejo R.L., Silva R.M.O., Evenhuis J.P., Gao G., Liu S., Parsons J.E., Martin K.E., Lourenco D.A.L., Leeds T.D. & Y. Palti. 2018. Accurate genomic predictions for BCWD resistance in rainbow trout are achieved using low-density SNP panels: Evidence that long-range LD is a major contributing factor. J. Anim. Breed. Genet. 135:263-274. https://doi.org/10.1111/jbg.12335
  42. Yu, Haipeng, Matthew L. Spangler, Ronald M. Lewis, and Gota Morota. 2018. Do stronger measures of genomic connectedness enhance prediction accuracies across management units? J. Anim. Sci. 96:4490-4500.
  43. Zhang, X., S. Tsuruta, S. Andonov, D.A.L. Lourenco, R.L. Sapp, R.J. Hawken, and I. Misztal. 2018. Relationships among performance traits, mortality, and disorder traits in broiler chickens: a genetic and genomic approach. Poultry Sci. pex431, https://doi.org/10.3382/ps/pex431

 

Abstracts:

  1. Cantrell, B. S, Liu, N. Jebbett, RC Switzer III, E. Delay, S. Zinn, S. Aborn, J. O’Neil, R. Funston, R. L. Weaber, G. Liu and S. McKay. 2018. A Neuroepigenomic Investigation of DNA methylation in Cattle with Extreme Measures of Docility. 7th International Symposium on Animal Functional Genomics, Adelaide, Australia.
  2. Cantrell, B., N. Jebbett, R. C. Switzer III, E. Delay, S. Zinn, S. Aborn, J. O’Neil, R. Funston, R. Weaber and S.D. McKay. 2018. Prevalence of DNA Methylation in the Bovine Brain Methylome. University of Vermont Student Research Conference, Burlington, Vermont
  3. Cantrell, B., N. Jebbett, R. C. Switzer III, E. Delay, S. Zinn, S. Aborn, J. O’Neil, R. Funston, R. Weaber and S.D. McKay. 2018. Facilitation of the Bovine Epigenome in the Limbic System: An Atlas of the Bovine Brain. Eighth Annual Neuroscience, Behavior and Health Research Forum, Burlington, VT
  4. Cantrell, B., N. Jebbett, R. C. Switzer III, E. Delay, S. Zinn, S. Aborn, J. O’Neil, R. Funston, R. Weaber and S.D. McKay. 2018. Prevalence of DNA Methylation in the Bovine Brain Methylome. International Plant & Animal Genome XXVI, San Diego, California.
  5. Crawford, N. F., S. J. Coleman, R. M. Enns, S. E. Speidel, F. B. Garry, T. N. Holt, and M. G. Thomas. 2018. Pathway analyses revealed up-regulation of calcium-dependent genes in the cardiac right ventricle of Angus steers fed at high altitude. J. Anim. Sci. 96(Suppl 3):61-62.  https://doi.org/10.1093/jas/sky404.136.
  6. Culbertson, M.M., R.M. Enns, and S.E. Speidel.   Estimation of variance components due to direct and maternal effects for feed intake in Gelbvieh cattle.  J. Anim. Sci.  96(Suppl 3):109.  https://doi.org/10.1093/jas/sky404.239.
  7. Delgadillo-Liberona, J. S., J. M. Langdon, D. G. Riley, H. D. Blackburn, S. E. Speidel, B. C. Krehbiel, S. Sanders, A. D. Herring.   Heritability Estimations for Intramuscular Fat in Hereford Cattle Using Random Regressions.  J. Anim. Sci.  96(Suppl 1):2-3.  https://doi.org/10.1093/jas/sky027.004.
  8. Foxworthy, H.M., R.M. Enns, M.G. Thomas, S.E. Speidel and T.N. Holt.   Gestation length is a weak predictor of yearling pulmonary arterial pressure and risk of developing high altitude disease in Angus cattle at high elevation. J. Anim. Sci.  96(Suppl 3):106-107. https://doi.org/10.1093/jas/sky404.235.
  9. Gannon, M. R., T. J. Mercado, D.W. Bailey, M. G. Thomas, S.E. Speidel, and R. M. Enns. 2018. Sampling Period Length Needed to Characterize Cattle Terrain Use on Rugged Rangeland. Proc. 71st Soc. Range Mgmt. Annual Meeting, Sparks, Nevada.
  10. Jennings, K.J., G.M. Krafsur, R.D. Brown, T.N. Holt, S.J. Coleman, R.M. Enns, S.E. Speidel, K.R. Stenmark and M.G. Thomas.   Pulmonary hypertension in Angus steers: influence of finishing systems and altitudes.  J. Anim. Sci.  96(Suppl 3):87.  https://doi.org/10.1093/jas/sky404.192.
  11. Langdon, J. M., J. S. Delgadillo-Liberona, A. D. Herring, H. D. Blackburn, S. E. Speidel, S. Sanders, B. C. Krehbiel, D. G. Riley.   Comparison of Linear Mixed Model and Random Regression Model for Weaning Weight in Hereford Cattle over a United States Longitudinal Gradient.  J. Anim. Sci. 96(Suppl 1):1. https://doi.org/10.1093/jas/sky027.001.
  12. Mercado, T. J., D.W. Bailey, M. G. Thomas, R. M. Enns, and S.E. Speidel. 2018. Repeatability of Terrain Use by Cattle in Rugged Rangeland Pastures. Proc. 71st Soc. Range Mgmt. Annual Meeting, Sparks, Nevada
  13. Pierce, C. F., M. M. Barbero, D. W. Bailey, J. F. Medrano, A. Cánovas, S. E. Speidel, S. J. Coleman, R. M. Enns, and M. G. Thomas. 2018. Studying Grazing Distribution of Beef Cattle Using DNA Technology. Proc. 71st Soc. Range Mgmt. Annual Meeting, Sparks, Nevada.
  14. Pierce, C.F, D.W. Bailey, J. Medrano, A. Cánovas, S E. Speidel, S. Coleman, R.M. Enns, M.G. Thomas.  Validation of Quantitative Trait Loci Associated with Grazing Distribution Traits in Beef Cattle Using Bayes C.  J. Anim. Sci.  96(Suppl 3):105. https://doi.org/10.1093/jas/sky404.232.
  15. Sánchez-Castro, M. A., T. N. Holt, M. G. Thomas, R. M. Enns, and S. E. Speidel. 2018. Use of reaction norms to evaluate high altitude disease susceptibility in Angus sires. Anim. Sci.  96(Suppl 3):110-111. https://doi.org/10.1093/jas/sky404.239.
  16. Spangler, M.L. 2018. Genomic Selection: Historical perspective and development of methods in use today (invited). J. Anim. Sci. 96: Suppl 3.
  17. Spangler, M.L. 2018. Harlan Ritchie Symposium: Current trends in beef cattle genetic evaluation (invited). J. Anim. Sci. 96: Suppl. 2.
  18. Speidel, S.E., M. M. Culbertson, M. A. Sánchez-Castro, K. Sellins, T. E. Engle, M. G. Thomas and R. M. Enns. 2018. Factors influencing blood urea nitrogen concentration in Angus cattle. J. Anim. Sci. 96(Suppl 3):384.  https://doi.org/10.1093/jas/sky404.842.
  19. Yu, Haipeng, Matthew L. Spangler, Ronald M. Lewis, and Gota Morota. 2018. An Assessment of Genomic Relatedness across Management Units. J. Anim. Sci. 96: Suppl. 2.

 

Conference Papers:

  1. Baller, J.L., S. D. Kachman, and M.L. Spangler. 2018. The impact of different clustering methods on the estimated accuracy of genomic predictors. Proc. 11th World Congress on Genetics Applied to Livestock Production.
  2. Bernardes, P. A., H. A. Al-Mamun, M. Suarez, D. Lim, B. Park and C. Gondro (2018). Imputation accuracy of whole-genome sequence data in Hanwoo cattle. Proceedings of the 10th World Congress on Genetics Applied to Livestock Production.
  3. Bhuiyan, M. S. A. D. Lim, C. Gondro, I. C. Choi, J. H. Lee and S. H. Lee (2018). Partitioning of Genomic Variance Captured by Imputed Sequence Level SNP Data for Carcass and Meat Quality Traits in Korean Hanwoo Cattle. Proceedings of the 10th World Congress on Genetics Applied to Livestock Production.
  4. F. Pierce, D.W. Bailey, J.F. Medrano, A. Cánovas, S.E. Speidel, S.J. Coleman, R.M. Enns, and M.G. Thomas. 2018. Characteristics of grazing distribution traits in beef cattle for genotype-phenotypes associations. Proc. 11th World Congr. Genet. Appl. Livest. Prod. 11:246. February 15, 2018, Auckland, NZ.
  5. de las Heras-Saldana, S., H. Kim, G. Gondro and K.Y. Chung (2018). Markers for Marbling development. Proceedings of the 10th World Congress on Genetics Applied to Livestock Production.
  6. Duijvesteijn, N., S. Bolormaa, C. Gondro, S. Clark, M. Khansefid, N. Moghaddar, A.A. Swan, P. Stothard, H.D. Daetwyler, J.H.J. van der Werf and I.M. MacLeod (2018). Genome-wide association study of meat quality traits using whole-genome sequence data in a multi-breed sheep population. Proceedings of the 10th World Congress on Genetics Applied to Livestock Production.
  7. Ekine-Dzivenu, C., E.C. Akanno, L. Chen, L. McKeown, B. Irving, L. Baker, M. Vinsky, S. Miller, Z. Wang, J. Crowley, M. Colazo, D. Ambrose, M. Juarez, H. Bruce, M.D. MacNeil, G. Plastow, J. Basarab, C. Li and C. Fitzsimmons.   Improvement of cow feed efficiency using molecular breeding values for residual feed intake – The “Kinsella Breeding Project”.  Proc. 11th World Congr. Genet. Appl. Livest. Prod., Auckland, New Zealand
  8. Golden, B.L., M. L. Spangler, W. M. Snelling, D. J. Garrick. 2018. Current single-step national beef cattle evaluation models used by the American Hereford Association
  9. Herrera, J. R. V., E.B. Flores, N. Duijvesteijn, C. Gondro and J. H.J. van der Werf (2018). Genome-wide association study for milk traits in Philippine dairy buffaloes. Proceedings of the 10th World Congress on Genetics Applied to Livestock Production.
  10. Howard, J.T., S. D. Kachman, and M. L. Spangler. 2018. The impact of utilizing previous generations of genotyped animals in genomic selection. Proc. 11th World Congress on Genetics Applied to Livestock Production.
  11. Id-Lahoucine, S., J. Casellas, P.A. Fonseca, F. Miglior, M. Sargolzaei, L. Brito, S. Miller, J. Chesnais, M. Lohuis, F. Schenkel, J.F. Medrano and A. Canovas. 2018. Genome scan for regions with transmission ratio distortion in cattle. Proc. 11th World Congr. Genet. Appl. Livest. Prod., Auckland, New ZealandAl-Mamun, H.A. S., Shahinfar, B. Park, S. Kim and C. Gondro (2018). Prediction of marbling score and carcass traits in Hanwoo Korean beef cattle using machine learning methods. Proceedings of the 10th World Congress on Genetics Applied to Livestock Production.
  12. Lourenco, D.A.L., S. Tsuruta, B.O. Fragomeni, Y. Masuda, I. Aguilar, A. Legarra, S.P. Miller, D.W. Moser and I. Misztal.   Single-step genomic BLUP for national beef cattle evaluation in US: from initial developments to final implementation.  Proc. 11th World Congr. Genet. Appl. Livest. Prod., Auckland, New Zealand
  13. Miller, S.P., L. Wang, K.J. Retallick and D.W. Moser. 2018. Developments in the genetic evaluation of American Angus cattle.  11th World Congr. Genet. Appl. Livest. Prod., Auckland, New Zealand
  14. Pariacote, F.A., E.J. Pollak, and M. L. Spangler. 2018. Genetic Parameter Estimates between Maternal and Terminal Traits in Composite Beef Cattle Population. Proc. 11th World Congress on Genetics Applied to Livestock Production.
  15. Souza, G.M., S. de las Heras-Saldana, C. Gondro, A.L.J. Ferraz, G.L.D. Feijó and E.F Delgado (2018). Impact of weight recovery on transcriptome and meat phenotypes of adult Nellore cows. Proceedings of the 10th World Congress on Genetics Applied to Livestock Production.
  16. Spangler, M.L. 2018. Genetic Selection for Efficiency. In Proc. State of Beef Conference, North Platte, NE.
  17. Spangler, M.L. 2018. Use of genomics in bovine genetic selection and utilization of genomics in other ag sectors. American Association of Bovine Practitioners.
  18. Spangler, M.L., B.L. Golden, L.A. Kuehn, W.M. Snelling, R.M. Thallman, R.L. Weaber. 2018. Decision support using customizable indices across breeds. Proc. Beef Improv.
  19. Thomas, M.G., J.M. Neary, G.M. Krafsur, T.N. Holt, R.M. Enns, S.E. Speidel, F.B. Garry, A. Canovas, J.F. Medrano, R.D. Brown, and K.R. Stenmark. 2018. Pulmonary hypertension in beef cattle: a complicated threat to health and productivity in multiple beef industry segments. White Paper for Certified Angus Beef. http://www.cabpartners.com/news/research.php. Accepted 5/9/2018.
  20. Ventura, R.V., L.F. Brito, G. Vandervoort, M. Sargolzaei, A. Hess, M. McMorris, T. Caldwell, A. Canovas, K.G. Dodds, S. Clarke and S.P. Miller.   The potential of genotyping pooled DNA to leverage commercial phenotypes for genetic improvement of beef cattle.  Proc. 11th World Congr. Genet. Appl. Livest. Prod., Auckland, New Zealand
  21. Weaber, R. L. 2018. Genetic Evaluation and Use of Genomic Data in Beef Cattle. Am. Assoc. Bovine Practitioners Genetics Webinar Series. August 7, 2018.
  22. Weerasinghe, W.M.S.P, C. Gondro, A.M. Okeyo, J. Ojango, J. Rao, T. Dessie, F. D. Mujibi, J. E. O. Rege and J. P. Gibson (2018). Genetic diversity of the indigenous cattle of Kenya, Uganda, Ethiopia and Tanzania using high-density SNP data. Proceedings of the 10th World Congress on Genetics Applied to Livestock Production.
  23. Yu, Haipeng, Matthew L. Spangler, Ronald M. Lewis, and Gota Morota. 2018. Stronger measures of genomic connectedness enhance prediction accuracies across management units. Proc. 11th World Congress on Genetics Applied to Livestock Production.
  24. Zimmermann, M.J., L. A. Kuehn, M. L. Spangler, R. M. Thallman, W. M. Snelling, and R. M. Lewis. 2018. Modelling Growth from Weaning to Maturity in Beef Cattle Breeds. Proc. 11th World Congress on Genetics Applied to Livestock Production.

 

Technical Publications:

  1. Boldt, R.J., S.E. Speidel and R.M. Enns.   Report to the Red Angus Association of America.  Results of the development of a dry matter intake genetic prediction. 5-pages.
  2. Culbertson, M.M., R.M. Enns and S.E. Speidel.   Report to Leachman Cattle of Colorado.  Results of development of a Multivariate PAP EPD.  6-pages.
  3. Enns, R. M., S. E. Speidel.   Report to ABS Global.  Result of Pulmonary Arterial Pressure Tests on Progeny of ABS Global Sires.  6-pages.
  4. Enns, R. M., S. E. Speidel.   Report to Select Sires, Inc.  Result of Pulmonary Arterial Pressure Tests on Progeny of Select Sires, Inc. Bulls.  6-pages.
  5. Giess, L. K.; B. R.Jensen, R. L. Weaber, J. M. Bormann, and W. A. Fiske. 2018 Feet and Leg Traits are Moderately to Lowly Heritable in Red Angus Cattle. Kansas Agricultural Experiment Station Research Reports: Vol. 4: Iss. 1. https://doi.org/10.4148/2378-5977.7533
  6. Leal, W. S.; R. F. Costa, L. L. Cardoso, F. S. Mendonça, F. F. Cardoso, M. J. Yokoo and R. L. Weaber. 2018. Bio-economic Model Predicts Economic Values for Beef Production," Kansas Agricultural Experiment Station Research Reports: Vol. 4: Iss. 1. https://doi.org/10.4148/2378-5977.7534
  7. Saatchi, M., R. L. Fernando, L. Hyde, S. McGuire, W. Shafer, M. L. Spangler, and B. Golden. 2018. Empirical progeny equivalent of genotyped animals in a multi-breed beef cattle genetic evaluation using a single-step Bayesian regression model. Iowa State Beef Cattle Report.
  8. Weaber, R. L. 2018. Weaning weight differences of calves on cows being fed an Altosid IGR mineral supplement versus control. Wellmark Study #5346 Final Report. Submitted to Wellmark International, Dallas, Texas. September 15, 2018.
  9. Weaber, R. L., B. Jensen, L. Giess, W. Fiske, N. Bello and J. Bormann. 2018. Development of a National Genetic Evaluation System for Feet and Leg Conformation in Beef Cattle: Final Report. Red Angus Association of America.

 

Extension publications:

  1. Miller, S. 2018. Parental Influence. By the Numbers - Angus Journal, June:34.
  2. Miller, S. 2018. The power of the database. By the Numbers - Angus Journal, April:59-60.
  3. Miller, S. 2017. Angus GS, a new genomic era for American Angus. By the Numbers - Angus Journal, November:72,74,77.
  4. Spangler, M.L. 2018. Recent changes to National Cattle Evaluation. eBEEF.org


Presentation/Symposia:

  1. Culbertson, M.M, S.E. Speidel and R.M. Enns.   Approaches for evaluating the relationship between feedlot and pasture intake.  Beef Improvement Federation Annual Convention.  Loveland, CO.
  2. Fonseca, P.A., S. Id-Lahoucine, J. Casellas, F. Miglior, A. Reverter, M. R. Fortes, L. T. Nguyen, L.R. Porto-Neto, M. Sargolzaei, L.F. Brito, S.P. Miller, F.S. Schenkel, M. Lohuis, J.F. Medrano and A. Canovas. 2018. Functional Characterization of Genes Mapped in Transmission Ratio Distortion Regions of the Bovine Genome Affecting Reproduction. American Society of Animal Science Midwest section meeting, Omaha NE. J. Anim. Sci. 96(suppl_2):13-14.
  3. Gondro, C. (2018). Feature selection for genomic prediction. 11th BIF Genetic Prediction Workshop.
  4. Gondro, C. (2018). Genomic Technologies, Artificial Intelligence and Livestock Production. International Symposium on future Agricultural Science.
  5. Id-Lahoucine, S., P. Fonseca, F. Miglior, M. Sargolzaei, L.F. Brito, S.P. Miller, F.S. Schenkel, V.H. Asselstine, J.P. Chesnais, M. Lohuis, J.F. Medrano and A. Canovas. 2018. Unravelling genomic regions with transmission ration distortion: identification of candidate lethal alleles in cattle. American Society of Animal Science Midwest section meeting, Omaha NE. J. Anim. Sci. 96(suppl_2):13-14.
  6. Id-Lahoucine, S., J. Casellas, F. Miglior, P.A.S. Fonseca, A. Suarez-Vega, M. Sargolzaei, S. Miller, F. Schenkel, J.F. Medrano and A. Canovas.   Non-Mendelian segregation patterns across the Holstein genome: discovering candidate genomic regions affecting reproduction success.  Southern Ontario Reproductive Biology Conference.  Guelph, ON.
  7. Lourenco, D.A.L. 2018. Big data in breeding and genetics: looking into the dimensionality of genomic information. Invited presentation given at the IX International Symposium on Genetics and Breeding. Viçosa, Brazil.
  8. Lourenco, D.A.L. 2018. Single-step GBLUP in practice: applications in beef and other industries. Invited presentation given at 11th Beef Improvement Federation Genetic Prediction Workshop. Kansas City, Missouri.
  9. Lourenco, D.A.L. 2018. Understanding Animal Breeding and Genetics. Invited presentation given at the Seriola Workshop, La Jolla, CA.
  10. Lourenco, D.A.L. and J. Segers. 2018. Utilizing Technology in Beef Production: Advances in Live Animal Evaluation and Selection. Joint Invited Extension presentation given at the Sunbelt Expo. Moultrie, Georgia. [Extension Talk]
  11. Lourenco, D.A.L., B.O. Fragomeni, S. Tsuruta, and I. Misztal. 2018. Selection to mitigate heat stress in pigs. Invited presentation given at the 2018 American Society of Animal Science Annual Meeting. Vancouver, Canada.
  12. Lourenco, D.A.L., B.O. Fragomeni, S. Tsuruta, and I. Misztal. 2018. How genomics can help to identify resilient animals under environmental stress conditions. Invited presentation given at the 7th International Symposium on Animal Functional Genomics. Adelaide, Australia.
  13. Lourenco, D.A.L., S. Tsuruta, B.O. Fragomeni, Y. Masuda, I. Aguilar, A. Legarra, S. Miller, D. Moser, I. Misztal. 2018. Single-step genomic BLUP for national beef cattle evaluation in US: from initial developments to final implementation. Invited presentation given at the 11th WCGALP, Auckland, New Zealand.
  14. Lourenco, D.A.L., S. Tsuruta, I. Misztal. 2018. Genomic Selection: Benefits, current status, and lessons from commercial implementation. Invited presentation given at the Aquaculture Canada 2018. Quebec City, Canada.
  15. Lourenco, D.A.L., S. Tsuruta, I. Pocrnic, A. Legarra, B.O. Fragomeni, Y. Masuda, I. Aguilar, S. Miller, D. Moser, I. Misztal. 2018. Developments in single-step for beef cattle genomic evaluation in the US. Invited presentation given at the 50th Beef Improvement Federation Research Symposium and Convention. Loveland, Colorado.
  16. Miller, S.P. 2018. Genetic evaluation at Angus Genetics Inc. 11th Beef Improvement Federation Genetic Prediction Workshop. Dec 5, Kansas City, MO.
  17. Miller, S.P. 2018. New breeding goals in beef cattle. Breeding and genetics symposium – new breeding goals in animal breeding.  American Society of Animal Science / Canadian Society of Animal Science Joint Annual Meeting.  Vancouver, BC.
  18. Miller, S.P. 2018. Single Step – Its pretty much what Mendel said, but maybe not what you thought. Beef and Lamb Genetics Sheep Breeders Forum – Dunedin New Zealand, July 3.
  19. Miller, S.P. 2018. Experiences with Single-Step at American Angus – 1 year in.  Beef Improvement Federation Annual Research Symposium. June 22, Loveland CO.
  20. Miller, S.P. 2018. Developing selection tools at American Angus.  New Zealand Angus Breeder’s Seminars – Timaru (Feb 6) and Taupo (Feb 9)
  21. Miller, S.P. 2017. Panel participant: International Genomics Symposium, 2017 Angus Convention, Fort Worth, TX, Nov 4-6.
  22. Miller, S.P. 2017. EPD 401.  Merk Angus University, 2017 Angus Convention, Indianapolis, IN., Nov 4-6.
  23. Miller, S.P. 2017. Building better genomic predictions for Angus cattle.  Annual research meeting of the multistate research coordinating committee and information exchange group NCERA225: Implementation and strategies for national beef cattle genetic evaluation.  Manhattan, KS. Oct. 18-19.
  24. Miller, S.P. After 7/7 - Genomic Implementation at AGI.  National Beef Cattle Evaluation Consortium “Brown bagger” webinar series. Oct 4. Misztal, I. (2018). Animal breeding at UGA. In Seminar at China Agricultural University. Beijing, China
  25. Misztal, I. (2018). Computing methods in animal breeding using the DNA information. In Seminar at Warsaw Tech. Warsaw, Poland
  26. Misztal, I. (2018). Extension of single-step ssGBLUP to many genotyped individuals. In World Congress on Genetics Applied to Livestock Production. Auckland, New Zealand
  27. Misztal, I. (2018). International bull evaluation by GBLUP with prediction population(s). In Ignacy Misztal. Dubrovnik, Croatia
  28. Misztal, I. (2018). Possible implications of limited dimensionality of genomic information. In Agricultural Biosciences International Conference. Wifang, China
  29. Misztal, I. (2018). Possible implications of limited dimensionality of genomic information. In EAAP Mtg. Dubrovnik, Croatia
  30. Misztal, I. (2018). Progress in genomic selection. In Workshop on Animal Genetics and Breeding in the Genomic Era. Taian, China
  31. Misztal, I. (2018). Studies in genetics of heat stress in dairy, beef and pigs. In Plant and Animal Genome Mtg. San Diego, CA
  32. Spangler, M. L. 2018. $Indexes 101, American Angus Association annual Convention, Columbus, OH, 2018.
  33. Spangler, M. L. 2018. American Hereford Association Board Discussion: Current and future genetic selection tools, Kansas City, MO, 2018.
  34. Spangler, M. L. 2018. Application of advanced genetic technology in beef cattle. King Ranch Institute for Ranch Management, Kissimmee, FL, 2018.
  35. Spangler, M. L. 2018. Ask Not what your NCE can do you for, ask what you can do for your NCE, American Simmental Association Fall Focus, Bozeman, MT, 2018.
  36. Spangler, M. L. 2018. Beef cattle genetics, Kasakstan delegation, Lincoln, NE, 2018.
  37. Spangler, M. L. 2018. Big data = better beef, Agricultural Economic and Technology summit, Kearney, NE, 2018. 
  38. Spangler, M. L. 2018. Breed differences for growth and carcass traits, Brazil Cochran scholars, Lincoln, NE, 2018.
  39. Spangler, M. L. 2018. Bull selection workshop, Beef Improvement Federation annual meetings, Loveland, CO, 2018
  40. Spangler, M. L. 2018. Bull selection—balancing EPDs, genomics, indexes, performance and structure, National Cattlemens Beef Association Cattlemens College, Phoenix, AZ, 2018.
  41. Spangler, M. L. 2018. Current trends in beef cattle genetic evaluation. Midwest American Society of Animal Science meetings, Omaha, NE, 2018.
  42. Spangler, M. L. 2018. Decision support using customizable indices across breeds, Beef Improvement Federation Genetic Prediction Workshop, Kansas City, MO, 2018.
  43. Spangler, M. L. 2018. EPDs and the use of genomics in beef cattle selection, ABS Global South American Tour, Lincoln, NE, 2018.
  44. Spangler, M. L. 2018. Extension in Animal Science, UNL Animal Science departmental seminar, Lincoln, NE, 2018.
  45. Spangler, M. L. 2018. Genetic considerations for the cowherd, NE Ranch Practicum, Whitman, NE 2018 (via distance).
  46. Spangler, M. L. 2018. Genetic selection for efficiency, State of Beef Conference, North Platte, NE, 2018.
  47. Spangler, M. L. 2018. Genetic selection of livestock: Why it matters to you, Bair Ranch community lecture, Montana State University, Bozeman, MT, 2018.
  48. Spangler, M. L. 2018. Genetic selection principles, American Akaushi convention, San Marcos, TX, 2018.
  49. Spangler, M. L. 2018. Genetic Selection Tools Used in the U.S. beef Industry, Argentina Seedstock Tour, Lincoln, NE, 2018
  50. Spangler, M. L. 2018. Genetic technologies on the horizon, National Cattlemens Beef Association Cattlemens College, Phoenix, AZ, 2018.
  51. Spangler, M. L. 2018. Genomic selection: Historical perspective and development of methods in use today, American Society of Animal Science annual meetings, Vancouver, BC, CA, 2018.
  52. Spangler, M. L. 2018. Genomic selection: Practical lessons learned and mistakes made. Iowa State University Animal Science departmental seminar, Ames, IA, 2018.
  53. Spangler, M. L. 2018. Impact of single-step on selection indices, Beef Improvement Federation annual meetings, Loveland, CO, 2018
  54. Spangler, M. L. 2018. New International Genetic Solutions genetic evaluation (Panel discussion), Allied Genetic Resources meeting, Bozeman, MT, 2018.
  55. Spangler, M. L. 2018. Past, present, and future approaches to genomic selection, Bair Ranch technical lecture, Montana State University, Bozeman, MT, 2018.
  56. Spangler, M. L. 2018. Putting the tools to use: Selecting your next bull. National Cattlemens Beef Association webinar. 2018
  57. Spangler, M. L. 2018. Quantitative genetics/genomics research at the University of Nebraska-Lincoln, Departmental seminar, Pirassununga, Brazil.
  58. Spangler, M. L. 2018. Selection indexes 101 and beyond, National Beef Cattle Evaluation Consortium Brown Bagger webinar series, 2018.
  59. Spangler, M. L. 2018. The role of Brahman in crossbreeding and the beef industry, V8 ranch workshop, Wharton, TX, 2018.
  60. Spangler, M. L. 2018. Use of genomic-enhanced EPDs to improve beef quality, Cattlemens workshop, La Grande, OR, 2018.
  61. Spangler, M. L. 2018. Use of genomics in bovine genetic selection and utilization of genomics in other ag sectors, American Association of Bovine Practitioners webinar, 2018.
  62. Spangler, M. L. 2018. Using genomics and genomically enhanced EPDs in your cow-calf operation, University of WI extension webinar, 2018.
  63. Speidel, S.E.   Animal Performance:  EPD vs Phenotype.  Leachman cattle of Colorado, Loma Bull Sale.  Loma, CO.
  64. Speidel, S.E.   Development and Implementation of Pulmonary Arterial Pressure EPD.  50th Beef Improvement Federation Annual Convention.  Loveland, CO.
  65. Speidel, S.E.   Genetic Predictions for Pulmonary Arterial Pressure.  Pulmonary Arterial Pressure Summit, Fort Collins, CO.
  66. Speidel, S.E.   Genetic Predictions for Pulmonary Arterial Pressure.  NBCEC Brown Bagger Series.
  67. Speidel, S.E.   Pulmonary Arterial Pressure EPD:  What drives differences in predictions.  Leachman Cattle of Colorado, Annual Bull Sale.  Wellington, CO.
  68. Suarez-Vega, A., S. Id-Lahoucine, J. Casellas, P.A.S. Fonseca, M. Sargolzaei, S. Miller, F. Schenkel, J.F. Medrano, F. Miglior and A. Canovas.   Evaluation of the biological function of genes linked to regions with distortion of Mendelian segregation and their relation to reproductive traits in cattle.  American Society of Animal Science / Canadian Society of Animal Science joint annual meeting.  Vancouver, BC.
  69. Suarez-Vega, A., F. Miglior, S. Id-Lahoucine, J. Casellas, P.A.S. Fonseca, M. Sargolzaei, S. Miller, F. Schenkel, J.F. Medrano and A. Canovas.   Determining the functional consequences of distortion of Mendelian segregation on reproductive traits by combining results from SNP-by-SNP and haplotype analyses.  Southern Ontario Reproductive Biology Conference.  Guelph, ON
  70. Weaber, R. L. and M. L. Spangler. 2018. Application of advanced genetic technology in beef cattle. King Ranch Institute of Ranch Management. Kissimmee, FL. February 2018
  71. Weaber, R. L. Cattle Structure & Mobility: A Genetics and Management Update. National Cattlemen’s Beef Association, Cattlemen’s College. January 31, 2018. Phoenix, Arizona. (T)
  72. Weaber, R. L. EPDs 101. Texas A&M Beef Cattle Short Course. August 7, 2018. College Station, TX. (T)
  73. Weaber, R. L. Genetic parameter estimates for feet and leg traits of Red Angus cattle. Kansas Red Angus Association Field Day. September 21, 2018. Manhattan KS. (T)
  74. Weaber, R. L. Getting it right-proper contemporary grouping strategies for beef cattle performance programs. Beef Improvement Federation Annual Meeting and Research Symposia, June 20, 2018. Loveland, Colorado. (T)
  75. Weaber, R. L. Heterosis importance to the industry. Am. Shorthorn Association Annual Meeting, December 1, 2017. Kansas City, MO. (K)
  76. Weaber, R. L. Selection for improved feed efficiency across the beef value chain. June 14, 2018. GENEX Argentina Tour Group, Manhattan, KS.
  77. Weaber, R. L. Selection for improved feed efficiency across the beef value chain. January 9, 2018. Suther Feed’s Tech Service/Sales Conference. Manhattan, Kansas (K)
  78. Weaber, R. L. Selection of replacement heifers. Purina National Seedstock Supplier Meeting. July 16, 2018. Grey’s Summit, Missouri. (K)
  79. Weaber, R. L. Strategies for Maximizing EPD IMPACT. Kentucky Seedstock Symposium. April 25, 2018. Shelbyville, KY. (K)
  80. Weaber, R. L. The 4 S’s of Crossbreeding: Simple, Structured, Successful, and Sustainable. National Cattlemen’s Beef Association Genetics Webinar Series. March 14, 2018.
  81. Weaber, R. L. The application of genomics for genetic improvement in commercial and seed stock beef herds. 20th Annual MAC Regional Dairy Extension In-service. May 31, 2018. Wilkes-Barre, PA. (T)
  82. Weaber, R. L. What is Genomics and How Does it Help? Kentucky Seedstock Symposium. April 25, 2018. Shelbyville, KY. (K)

12/10/2019

04/27/2021

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