NCERA225: Implementation and Strategies for National Beef Cattle Genetic Evaluation

(Multistate Research Coordinating Committee and Information Exchange Group)

Status: Active

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SAES-422 Reports

11/01/2022

Publications


Peer reviewed



  1. Abdollahi-Arpanahi, R., D. Lourenco, and I. Misztal. (2022) A comprehensive study on core size and type on prediction accuracy of algorithm of proven and young in single-step GBLUP evaluation. Genet Sel Evol 54:34. doi: 10.1186/s12711-022-00726-6.

  2. Aherin, D.G., R.L. Weaber, D.L. Pendell, J.L. Heier Stamm and R.L. Larson. (2022) Stochastic, individual-based systems model of beef cow-calf production: model development and validation. Transl Anim Sci. doi: 10.1093/tas/txac155.

  3. Baller, J.L., S.D. Kachman, L.A. Kuehn, and M.L. Spangler. (2022) Using pooled data for genomic prediction in a bivariate framework with missing data. J Anim Breed Genet 139(5):489-501. doi: 10.1111/jbg.12727.

  4. Bermann, M., D. Lourenco, and I. Misztal. (2022) Efficient approximation of reliabilities for single-step genomic BLUP models with the Algorithm for Proven and Young. J Anim Sci 100(1):skab353. doi: 10.1093/jas/skab353.

  5. Bermann, M., D. Lourenco, N. Forneris, A. Legarra, and I. Misztal. (2022) On the equivalence between marker effect models and breeding value models and direct genomic values with the Algorithm for Proven and Young. Genet Sel Evol 54:52. doi: 10.1186/s12711-022-00741-7.

  6. Bermann, M., A. Cesarani, I. Misztal, and D. Lourenco. (2022) Past, present, and future developments in single-step genomic models. Italian J Anim Sci 21(1):673-685. doi: 10.1080/1828051X.2022.2053366.

  7. Butler, M., A.R. Hartman, J. Bormann, R.L. Weaber, D. Grieger, and M.M. Rolf. (2021) Genetic parameter estimation of beef bull semen attributes. J Anim Sci 99(2):skab013. doi: 10.1093/jas/skab013.

  8. Butler, M., A.R. Hartman, J. Bormann, R.L. Weaber, D. Grieger, and M.M. Rolf. (2022) Genome wide association study of beef bull semen attributes. BMC Genomics 23:74. doi: 10.1186/s12864-021-08256-z.

  9. Campos, G.S., F.F. Cardoso, C.C.G. Gomes, R. Domingues, L.C.A. Regitano, M.C.S. Oliveira, H.N. Oliveira, R. Carvalheiro, L.G. Albuquerque, S. Miller, I. Misztal, and D. Lourenco. (2022) Development of genomic predictions for Angus cattle in Brazil incorporating genotypes from related American sires. J Anim Sci 100(2):skac009. doi: 10.1093/jas/skac009.

  10. Flesch, E., T. Graves, J. Thomson, K. Proffitt, and R. Garrott. (2022) Average kinship within bighorn sheep populations is associated with connectivity, augmentation, and bottlenecks. Ecosphere 13(3):e3972. doi: 10.1002/ecs2.3972.

  11. Garcia, A., I. Aguilar, A. Legarra, S. Miller, S. Tsuruta, I. Misztal, and D. Lourenco. (2021) Theoretical accuracy for indirect predictions based on SNP effects from single-step GBLUP. Genet Sel Evol 54:66. doi: 10.1186/s12711-022-00752-4.

  12. Giess, L.K., B.R. Jensen, J.M. Bormann, M.M. Rolf, and R.L. Weaber. (2021) Genetic parameter estimates for feet and leg traits in Red Angus cattle. J Anim Sci 99(11): skab256. doi: 10.1093/jas/skab256.

  13. Han, J., J. Siegford, G. de los Campos, R.J. Tempelman, C. Gondro, and J.P. Steibel. (2022) Analysis of social interactions in group-housed animals using dyadic linear models. Appl Anim Behav Sci 256:105747. doi: 10.1016/j.applanim.

  14. Hay, E.A., S. Toghiani, A.J. Roberts, T. Paim, L.A. Kuehn, and H.D. Blackburn. (2022)


Genetic architecture of a composite beef cattle population. J Anim Sci 2022. Article 230. doi: 10.1093/jas/skac230.



  1. Heaton, M.P., G.P. Harhay, A.S. Bassett, H.J. Clark, J.M. Carlson, E.E. Jobman,


H.R. Sadd, M.C. Pelster, A.M. Workman, L.A. Kuehn, T.S. Kalbfleisch, H. Piscatelli, M. Carrie, G.M. Krafsur, D.M. Grotelueschen, and B.L. Vander Ley. (2022) Association


of ARRDC3 and NFIA variants with bovine congestive heart failure in feedlot cattle.


F1000Research. 11. Article 385. doi: 10.12688/f1000research.109488.1.



  1. Hille, M.M., M.L. Spangler, M.L. Clawson, K.D. Heath, H.L.X. Vu, R.E.S. Rogers, and J.D. Loy. (2022) A five year randomized controlled trial to asses the efficacy and antibody responses to a commercial and autogenous vaccine for the prevention of bovine keratoconjunctivitis. Vaccines 10:916. doi: 10.3390/vaccines10060916.

  2. Holder, A.L., M.A. Gross, A.N. Moehlenpah, C.L. Goad, M.M. Rolf, R.S. Walker, J.K. Rogers, and D.L. Lalman. (2022) Effects of diet on feed intake, weight change, and gas emissions in mature Angus cows. J Anim Sci 100(10):1-9. doi: 10.1093/jas/skac257.

  3. Hollifield, M.K., M. Bermann, D. Lourenco, and I. Misztal. (2022) Impact of blending the genomic relationship matrix with different levels of pedigree relationships or the identity matrix on genetic evaluations. J Dairy Sci Comm. doi: 10.3168/jdsc.2022-0229.

  4. Jang, S., D. Lourenco, S. Miller. (2022) Inclusion of Sire by Herd interaction effect in the genomic evaluation for weaning weight of American Angus. J Anim Sci 100(3):skac057. doi: 10.1093/jas/skac057.

  5. Junqueira, V.S., D. Lourenco, Y. Masuda, F.F. Cardoso, P.S. Lopes, F.F. Silva, and I. Misztal. (2022) Is single-step genomic REML with algorithm for proven and young more efficient when less generations of data are present? J Anim Sci 100(5):skac082. doi: 10.1093/jas/skac082.

  6. LaKamp, A.D., R.L. Weaber, J.M. Bormann, and M.M. Rolf. (2022) Relationships between enteric methane production and economically important traits in beef cattle. Livest Sci 265 (2022):105102. doi: 10.1016/j.livsci.2022.105102.

  7. McWhorter, T.M., M. Bermann, A.L.S. Garcia, A. Legarra, I. Aguilar, I. Misztal, and D. Lourenco. (2022) Implication of the order of blending and tuning when computing the genomic relationship matrix in single-step GBLUP. J Anim Breed Genet 140(1):60-78. doi: 10.1111/jbg.12734.

  8. Misztal, I., Y. Steyn, and D. Lourenco. (2022) Genomic evaluation with multibreed and crossbred data. JDS Comm 3(2):156-159. doi: 10.3168/jdsc.2021-0177.

  9. Nawaz, M.Y., P.A. Bernardes, R.P. Savegnago, D. Lim, S.H. Lee, and C. Gondro. (2022) Evaluation of whole-genome sequence imputation strategies in Korean Hanwoo cattle. Animals 12:2265. doi: 10.3390/ani12172265.

  10. Ribeiro, A.M.F., L.P. Sanglard, W.M. Snelling, R.M. Thallman, L.A. Kuehn, and M.L. Spangler. (2022) Genetic parameters, heterosis, and breed effects for body condition score and mature cow weight in beef cattle. J Anim Sci 100:skac017. doi: 10.1093/jas/skac017.

  11. Ribeiro, A.M.F., L.P. Sanglard, H.R. Wijesena, D.C. Ciobanu, S. Horvath, and M.L. Spangler. (2022) DNA methylation profile in beef cattle is influenced by additive genetics and age. Sci Reports 12:12016. doi: 10.1038/s41598-022-16350-9.

  12. Sanglard, L.P., L.A. Kuehn, W.M. Snelling, and M.L. Spangler. (2022) Influence of environmental factors and genetic variation on mitochondrial DNA copy number. J Anim Sci 100(5):skac059. doi: 10.1093/jas/skac059.

  13. Schumacher, M., H. DelCurto-Wyffels, J. Thomson, and J. Boles. (2022) Fat deposition and fat effects on meat quality—a review. Animals 12(12):1550. doi: 10.3390/ani12121550.

  14. See, G.M., J.S. Fix, C.R. Schwab, and M.L. Spangler. (2022) Imputation of non-genotyped F1 dams to improve genetic gain in swine crossbreeding programs. J Anim Sci 100:skac148. doi: 10.1093/jas/skac148.

  15. Seo D., D.H. Lee, S. Jin, J.I. Won, D. Lim, M. Park, T.H. Kim, H.K. Lee,S. Kim, I. Choi, J.H. Lee, C. Gondro, and S.H. Lee. (2022) Long-term artificial selection of Hanwoo (Korean) cattle left genetic signatures for the breeding traits and has altered the genomic structure. Sci Reports 12:6438. doi: 10.1038/s41598-022-09425-0.

  16. Silva, T.L., C. Gondro, P.A.S. Fonseca, D.A. da Silva, G. Vargas, H.H.R. Neves, I. Carvalho Filho, C.S. Teixeira, L.G. Albuquerque, and R. Carhalheiro. (2022) Testicular hypoplasia in Nellora cattle: genetic analysis and functional analysis of genome-wide association study results. J Anim Breed Genet. doi: 10.1111/jbg.12747.

  17. Singh, A., A. Kumar, C. Gondro, A.K. Pandey, T. Dutt, and B.P. Mishra. (2022) Genome wide scan to identify potential genomic regions associated with milk protein and minerals in Vrindavani cattle. Front Vet Sci 9:760364. doi: 10.3389/fvets.2022.760364.

  18. Snelling, W.M., R.M. Thallman, M.L. Spangler, and L.A. Kuehn. (2022) Breeding sustainable beef cows. Animals 12:1745. doi: 10.3390/ani12141745.


 


Abstracts and proceedings



  1. Abdollahi Arpanahi, R., D. Lourenco, and I. Misztal. 2022. Investigating the impact of APY core size and definition in single-step GBLUP evaluations. Proc. 12th World Congress on Genetics Applied to Livestock Production, Rotterdam, NL.

  2. Bermann, M., A. Cesarani, D. Lourenco, and I. Misztal. 2022. Young Scholar Award Talk: Computing Strategies for National Beef Cattle Evaluations. American Society of Animal Science Annual Meeting, Oklahoma City, OK.

  3. Bermann, M., D. Lourenco, A. Cesarani, and I. Misztal. 2022. ACCF90GS2: software for fast approximation of reliabilities of estimated breeding values in single-step GBLUP. Proc. 12th World Congress on Genetics Applied to Livestock Production, Rotterdam, NL.

  4. Bermann, M., I. Misztal, D. Lourenco, I. Aguilar, and A. Legarra, A. 2022. Definition of reliabilities for models with metafounders. Proc. 12th World Congress on Genetics Applied to Livestock Production, Rotterdam, NL.

  5. Bullock, K.D., M.L. Spangler, R.L. Weaber, T.N. Rowan, M.M. Rolf, J.E. Decker, D.D. Loy, B.L. Golden, J.J. White and A.L. Van Eenennaam. 2022. Conducting a National Beef Cattle Genetics Outreach Program. Proc. 12th World Congress on Genetics Applied to Livestock Production, Rotterdam, NL.

  6. Campos, G.S., D.A. Silva, H.H.R. Neves, D. Lourenco, G.A.F. Júnior, L.F.S. Fonseca, L.G. Albuquerque, and R. Carvalheiro. 2022. Including selected sequence variants in genomic predictions for age at first calving in Nellore cattle. Proc. 12th World Congress on Genetics Applied to Livestock Production, Rotterdam, NL.

  7. Carroll, A.L., M.L. Spangler, D.L. Morris, and P.J. Kononoff. 2022. Estimating between-animal variance of energy utilization in lactating Jersey cows. J Dairy Sci.

  8. Cuyabano, B.C.D., D. Boichard, P. Croiseau, T. Tribout, V. Ducrocq, and C. Gondro. 2022. Measures to quantify the accuracy and the erosion of genomic predicted breeding values. Proc. 12th World Congress on Genetics Applied to Livestock Production, Rotterdam, NL.

  9. Garcia, A., S. Miller, S. Tsuruta, D. Lourenco, I. Misztal, D. Lu, and K. Retallick. 2022. Updating the core animals in the algorithm for proven and young in the American Angus Association national evaluations. Proc. 12th World Congress on Genetics Applied to Livestock Production, Rotterdam, NL.

  10. Guinan, F.L., G.R. Wiggans, J.W. Dürr, H.D. Norman, J.B. Cole, C.P. Van Tassell, I. Misztal, and D. Lourenco. 2022. Changes in genetic trends for dairy cattle in the U.S. since the implementation of genomic selection. Proc. 12th World Congress on Genetics Applied to Livestock Production, Rotterdam, NL.

  11. Hay, E.H.A., S. Toghiani, A.J. Roberts, T. Paim, L.A. Kuehn, and H. Blackburn. 2022. Genetic architecture of a composite beef cattle population. Proc. 12th World Congress on Genetics Applied to Livestock Production, Rotterdam, NL.

  12. Hidalgo, J., I. Misztal, S. Tsuruta, M. Bermann, A. Garcia, K. Retallick, and D. 2022. Decreasing computing cost of categorical data analysis. Proc. 12th World Congress on Genetics Applied to Livestock Production, Rotterdam, NL.

  13. Hollifield, M.K., M. Bermann, D. Lourenco, and I. Misztal. 2022. Exploring the statistical nature of independent chromosome segments. Proc. 12th World Congress on Genetics Applied to Livestock Production, Rotterdam, NL.

  14. Hollifield, M.K., M. Bermann, D. Lourenco, and I. Misztal. 2022. Exploring the Statistical Nature of Independent Chromosome Segments. American Society of Animal Science Annual Meeting, Oklahoma City, OK.

  15. Hollifield, M.K., M. Bermann, D. Lourenco, and I. Misztal. 2022. Impact of blending the genomic relationship matrix with different levels of pedigree relationships or the identity matrix on genetic evaluations. American Dairy Science Association Annual Meeting, Kansas City, MO.

  16. Lakamp, A.D., A.C. Neujahr, M.M. Hille, J.D. Loy, S.C. Fernando, and M.L. Spangler. 2022. Variance component estimation of longitudinal alpha diversity metrics of the ocular microbiome in preweaned beef cattle. Proc. 12th World Congress on Genetics Applied to Livestock Production, Rotterdam, NL.

  17. Lourenco, D., S. Tsuruta, I. Aguilar, Y. Masuda, M. Bermann, A. Legarra, and I. Misztal. 2022. Recent updates in the BLUPF90 software suite. Proc. 12th World Congress on Genetics Applied to Livestock Production, Rotterdam, NL.

  18. Lourenco, D., S. Tsuruta, I. Aguilar, A. Legarra, and I. Misztal. 2022. Single-step GWAS: association mapping accounting for phenotypes on genotyped and non-genotyped individuals. Plant and Animal Genome Conference XXIX, San Diego, CA (Virtual).

  19. Macciotta, N.P.P. C. Dimauro, D. Lourenco, A. Cesarani, L. Degano, and D. Vicario. 2022. Strategies for choosing core animals in APY and their impact on the accuracy of single-step genomic predictions. Proc. 12th World Congress on Genetics Applied to Livestock Production, Rotterdam, NL.

  20. Makanjuola, B.O., G. Rovere, B.C.D. Cuyabanoooooooooo, S.H. le, and Gondro. 2022. Inccludiingg environmental variables into genomic models for carcass traits in Hanwoo beef cattle. Proc. 12th World Congress on Genetics Applied to Livestock Production, Rotterdam, NL.

  21. Nawaz, M.Y., and C. Gondro. 2022. Improving accuracy of genomic prediction in distant populations by collecting sequence data over generations. Proc. 12th World Congress on Genetics Applied to Livestock Production, Rotterdam, NL.

  22. Ostrovski, H., R.P. Savegnago, W. Huang, and C. Gondro. 2022. Investigating new technologies for on-site real-time sequencing for any animal scientist. Proc. 12th World Congress on Genetics Applied to Livestock Production, Rotterdam, NL.

  23. Rovere, G., B.C.D. Cuyabano, B. Makanjuola, S. Kelly, and C. Gondro. 2022. Phenotypic and genetic trends in American Angus associated with climate variability. Proc. 12th World Congress on Genetics Applied to Livestock Production, Rotterdam, NL.

  24. Russell, C.A., L.A.Kuehn, W.M. Snelling, and M.L. Spangler. 2022. Genetic prediction for growth traits in beef cattle using selected variants from imputed low-pass sequence data. Proc. 12th World Congress on Genetics Applied to Livestock Production, Rotterdam, NL.

  25. Sanglard, L.P., L.A.Kuehn, W.M. Snelling, and M.L. Spangler. 2022. Genotype concordance between SNP chip and imputed low-pass whole-genome sequence in beef cattle. J Anim Sci.

  26. Sanglard, L.P., G.M. See, and M.L. Spangler. 2022. Including gene-edited individuals in genetic evaluations can bias estimated breeding values in their progeny. J. Anim Sci.

  27. Sanglard, L.P. L.A. Kuehn, W.M. Snelling, and M.L. Spangler. 2022. Mitochondrial DNA copy number as a potential indicator of growth and carcass traits in beef cattle. Proc. 12th World Congress on Genetics Applied to Livestock Production, Rotterdam, NL.

  28. Schaff, N., J. Dafoe, D.L. Boss, J.M. Thomson, and J.A.Boles. 2022. PSIII-5 Late-Breaking: Genetic evaluation of energy efficiency in Bos taurus cows classified by residual feed intake. J Anim Sci, 100, Supp. 4, 35-36. doi: 10.1093/jas/skac313.051.

  29. See, G.M., J.S. Fix, C.R.Schwab, and M.L. Spangler. 2022. Filling information gaps in swine crossbreeding schemes by imputing non-genotyped F1 animals to improve genetic gain. Proc. 12th World Congress on Genetics Applied to Livestock Production, Rotterdam, NL.

  30. Silva, T.L., C. Gondro, P.A.S. Fonseca, D.A. da Silva, V. Giovana, H.H.R. Neves, I. Carvalho Filho, C.S. Teixeira, L.G. Albuquerque, and R. Carvalheiro. 2022. Genetic mechanisms underlying feet and leg conformation in Nellore cattle: prioritization of GWAS results. Proc. 12th World Congress on Genetics Applied to Livestock Production, Rotterdam, NL.

  31. Spangler, M.L., B.L. Golden, S. Newman, L.A. Kuehn, W.M. Snelling, R.M. Thallman, and R.L. Weaber. 2022. iGENDEC: A web-based decision support tool for economic index construction. Proc. 12th World Congress on Genetics Applied to Livestock Production, Rotterdam, NL.

  32. Spangler, M.L. 2022. An animal breeder’s view of under-utilized tools to improve fertility in beef herds. Proc. Applied Reproductive Strategies in Beef Cattle.

  33. Thomson, J.M., M.L. Schumacher, and J.A. Boles. 2022. 043 Utilizing RNAseq ro investigate molecular mechanisms impacting meat quality and carcass characteristics in beef steers. Animal-science proceedings 13, no. 3, 296-297. doi: 10.1016/j.anscip.2022.07.053.

  34. Tsuruta, S., D.A.L. Lourenco, and I. Misztal. 2022. Efficient genetic progress for quantitative traits through genomic selection. Proc. 12th World Congress on Genetics Applied to Livestock Production, Rotterdam, NL.

  35. Tsuruta S., D. Lourenco, and I. Misztal. 2022. Genetic gain for quantitative traits by genomic selection – a simulation study. Aquaculture 2022, San Diego, CA.

  36. Tsuruta S., D. Lourenco, and I. Misztal. 2022. Genetic progress for quantitative traits by genomic selection – a simulation study. Plant and Animal Genome Conference XXIX, San Diego, CA (Virtual).

  37. Valasek, H.F., B.L. Golden, and M.L. Spangler. 2022. Impact of planning horizon length on the relative emphasis of traits in economic breeding goals. Proc. 12th World Congress on Genetics Applied to Livestock Production, Rotterdam, NL.


 


Presentations



  1. Lourenco, D. Single-step GWAS: association mapping accounting for phenotypes on genotyped and non-genotyped individuals. Plant and Animal Genome Conference XXIX. Vitrual, 2022.

  2. Lourenco, D. Leveraging genomics to reshape animal improvement. Advances in Genome Biology and Technology in Agriculture, San Diego, CA, 2022.

  3. Lourenco, D. BLUPF90: updates and best practices. University of Florida, 2022.

  4. Lourenco, D. Are threshold models feasible for routine genetic evaluations? Purdue University, 2022.

  5. Lourenco, D. Single-step GWAS: accounting for the data structure of farm animal populations. Iowa State University, 2022.

  6. Lourenco, D. Recent updates in the BLUPF90 software suite. 12th World Congress on Genetics Applied to Livestock Production, Rotterdam, The Netherlands, 2022.

  7. Lourenco, D. Advances in beef cattle genomic evaluations in the US. Daejon University, South Korea, 2022.

  8. Lourenco, D. Experiences with genomic selection across species. 19th Asian-Australian Association of Animal Production, South Korea, 2022.

  9. Lourenco, D. Experiences with genomic selection across species. INRAE, 2022.

  10. Lourenco, D. Positive and negative aspects of genomic selection. SimMelhor, Vicosa, Brazil, 2022.

  11. Weaber, R.L. Emerging Technologies and Focus on the Future. Allied Genetic Resources Producer Conference. Manhattan, KS, 2022.

  12. Weaber, R.L. Building a Profitable Cow Herd: Cow Size-A Measurement of Herd Efficiency. LRF Stockman’s School. Aldam, South Africa, 2022.

  13. Weaber, R.L. Building better cattle: heritability and selection for improved feet and leg structure. Purina Genetic Summit. Grey Summit, MO, 2022.

  14. Weaber, R.L. Using Beef on Dairy data to increase accuracy of selection decisions for carcass traits. Beef Improvement Federation Annual Research Symposium. Las Cruces, NM, 2022.

  15. Weaber, R.L. Using commercial Beef on Dairy data to drive genetic improvement. Beef X Dairy Symposium. Texas Tech University, Lubbock, TX, 2022.

  16. Weaber, R.L. Using commercial Beef on Dairy data to drive genetic improvement. Beef X Dairy Symposium. Texas Tech University, Lubbock, TX, 2022.


 


Book chapters


 



  1. Thomson, J.M. 2022. Sustainability of Wild Populations: A Conservation Genetics Perspective. In: Meyers, R.A. (eds) Encyclopedia of Sustainability Science and Technology. Springer, New York, NY.


 


 


Extension


 



  1. Weaber, R.L. and M.L. Spangler. Application of advanced genetic technology in beef cattle. King Ranch Institute of Ranch Management. Kingsville, TX. February 24-25, 2022.

12/18/2023

Publications


Peer reviewed



  1. Bermann, M., I. Aguilar, D. Lourenco, I. Misztal, and A. Legarra. (2023) Reliabilities of breeding values in models with metafounders. Genet. Sel. Evol. doi: 10.1186/s12711-023-00778-2.

  2. Berry, D.P., and L. Spangler. (2023) Animal Board Invited Review: Practical applications of genomic information in livestock. Animal. 17(11):100996. doi: 10.1016/j.animal.2023.100996.

  3. Bhowmik, N., T. Seaborn, K.A. Ringwall, C.R. Dahlen, K.C. Swanson, and L.L. Hulsman Hanna. (2023) Genetic distinctness and diversity of American Aberdeen cattle compared to common beef breeds in the United States. Genes. 14(10):1842. doi: 10.3390/genes14101842.

  4. Boldt, R.J., J.W. Keele, L.A. Kuehn, T.G. McDaneld, S.E. Speidel, and R.M. Enns. (2023) Comparison of genomic relationship matrices using differing number of SNP in pooled DNA analyses. J. Anim. Sci. (Submitted)

  5. Cesarani, A., M. Bermann, C. Dimauro, L. Dagano, D. Vicario, D. Lourenco, and N.P.P. Macciotta. (2023) Strategies for choosing core animals in APY and their impact on the accuracy of single-step genomic predictions. Animal. 100766. doi: 10.1016/j.animal.2023.100766

  6. Cesarani, A., D. Lourenco, M. Bermann, E.L. Nicolazzi, P.M. VanRaden, and I. Misztal. (2023) Single-step genomic predictions for crossbred Holstein and Jersey cows in the US. J. Dairy Sci. Comm. doi: 10.3168/jdsc.2023-0399.

  7. Dressler, E.A., C.M. Ahlberg, K. Allwardt, A. Broocks, K. Bruno, L. McPhillips, A. Taylor, C.R. Krehbiel, M.S. Calvo-Lorenzo, C.J. Richards, S.E. Place, U. DeSilva, L.A. Kuehn, R.L. Weaber, J.M. Bormann, and M.M. Rolf. (2023) Heritability and variance component estimation for feed and water intake behaviors.  Anim. Sci. 101:1-19.  doi: 10.1093/jas/skad386.

  8. Dressler, E.A., J.M. Bormann, R.L. Weaber, and M.M. Rolf. (2023)  Technical note: Characterization of the number of spot samples required for quantification of gas fluxes and metabolic heat production from grazing beef cows using a GreenFeed.  Anim. Sci. 101:1-9. doi: 10.1093/jas/skad176.

  9. Dressler, E.A., R.L. Weaber, J.M. Bormann, and M.M. Rolf. (2023) Use of methane production data for genetic prediction in beef cattle: A review.  Anim. Sci. txae014. doi: 10.1093/tas/txae014.

  10. Engle, B., R.M. Thallman, W.M. Snelling, T.L. Wheeler, S. Shackelford, D.A. King, and L.A. Kuehn. (2023) Breed-specific heterosis for growth and carcass traits in 18 U.S. cattle breeds. J. Anim, Sci. (Submitted)

  11. Freetly, H.C., D.R. Jacobs, R.M. Thallman, W.M. Snelling, and L.A. Kuehn. (2023) Heritability of beef cow metabolizable energy for maintenance. J. Anim. Sci. 101:skad145. doi: 10.1093/jas/skad145.

  12. Gonzalez-Murray, R.A., M.G. Thomas, T.N. Holt, S. Coleman, R.M. Enns, and S.E. Speidel. (2023) Heterosis effects on preweaning traits in a multibreed beef cattle herd in Panama.  Agriculture. (submitted).

  13. Haghani, A. C.Z. Li, T.R. Robeck, J. Zhang, A.T. Lu, J. Ablaeva, et al. (2023) DNA methylation networks underlying mammalian traits. Science. 381(6658):eabq5693. doi: 10.1126/science.abq5693.

  14. Hollifield, M.K., M. Bermann, D. Lourenco, and I. Misztal. (2023) Exploring the statistical nature of independent chromosome segments. Livest. Sci. 105207. doi: 10.1016/j.livsci.2023.105207.

  15. Jang, S., S. Tsuruta, N.G. Leite, I. Misztal, and D. Lourenco. (2023) Dimensionality of genomic information and its impact on GWA and variant selection: a simulation study. Genet. Sel. Evol. 55:49. doi: 10.1186/s12711-023-00823-0.

  16. Keele, J.W., B.A. Foraker, R.J. Boldt, C. Kemp, L.A. Kuehn, and D.R. Woerner. (2023) Genetic parameters for carcass traits of progeny of beef bulls mated to dairy cows. J. Anim. Sci. (Submitted)

  17. LaKamp, A.D., C.M. Ahlberg, K. Allwardt, A. Broocks, K. Bruno, L. McPhillips, A. Taylor, C.R. Krehbiel, M.S. Calvo-Lorenzo, C.J. Richards, S.E. Place, U. DeSilva, L.A. Kuehn, R.L. Weaber, J.M. Bormann, and M.M. Rolf. 2023. Variance component estimation and genome-wide association of predicted methane production in crossbred beef steers.  Anim. Sci. 101:1-12. doi: 10.1093/jas/skad179.

  18. Lee, H.J, J.H. Lee, C. Gondro, Y.J. Koh, and S.H. Lee (2023). deepGBLUP: joint deep learning networks and GBLUP framework for accurate genomic prediction of complex traits in Korean native cattle. Genet. Sel. Evol. 55:56. doi: 10.1186/s12711-023-00825-y.

  19. Lu, A.T., Z. Fei, A. Haghani, T.R. Robeck, J.A. Zoller, C.Z. Li, et al. (2023) Universal DNA methylation age across mammalian tissues. Nat. Aging. 3:1144-1166. doi: 10.1038/s43587-023-00462-6.

  20. McWhorter, T.M., M. Bermann, A.L.S. Garcia, A. Legarra, I. Aguilar, I. Misztal, and D. Lourenco. (2023) Implication of the order of blending and tuning when computing the genomic relationship matrix in single-step GBLUP. J. Anim. Breed. Genet. doi: 10.1111/jbg.12734.

  21. McWhorter, T., M. Sargolzaei, C.G. Sattler, M.D. Utt, S. Tsuruta, I. Misztal, and D. Lourenco. (2023) Single-step genomic predictions for heat tolerance of production yields in U.S. Holsteins and Jerseys. J. Dairy Sci. doi: 10.3168/jds.2022-23144.

  22. Pauling, R.C., S.E. Speidel, M.G. Thomas, T.N. Holt, R.M. Enns. (2023) Genetic parameters for pulmonary arterial pressure, yearling performance, and carcass ultrasound traits in Angus cattle. J. Anim. Sci. 101:skad288. doi:10.1093/jas/skad288.

  23. Ramos, P.V.B., G.R.O. Menezes, D.A. Silva, D. Lourenco, G.G. Santiago, R.A.A. Torres Jr, F.F. Silva, P.S. Lopes, and R. Veroneze. (2023) Genomic analysis of feed efficiency traits in Nellore cattle using random regression models. J. Anim. Breed. Genet. doi: 10.1111/jbg.12840.

  24. Romero, A.R.S., A.V. do Nascimento, M.C.S. Oliveira, C.H. Okino, C.U. Braz, D.C.B. Scalez, D.F Cardoso, F.F. Cardoso, C.C.G. Gomes, A.R. Caetano, H. Tonhati, C. Gondro, and H.N. de Oliveira (2023) Genetic parameters and multi-trait genomic prediction for hemoparasites infection levels in cattle. Livestock Science. 273:105259. doi: 10.1016/j.livsci.2023.105259.

  25. Russell, C.A., L.A. Kuehn, W.M. Snelling, S.D. Kachman, and M.L. Spangler. (2023) Variance component estimates for growth traits in beef cattle using selected variants from imputed low-pass sequence data. J. Anim. Sci. 101:skad274. doi: 10.1093/jas/skad274.

  26. Sanglard, L.P., G.M. See, and M.L. Spangler. (2023) Strategies for accommodating gene-edited sires and their descendants in genetic evaluations. J. Anim. Sci. 101:skad077. doi: 10.1093/jas/skad077.

  27. Sanglard, L.P., W.M. Snelling, L.A. Kuehn, R.M. Thallman, H.C. Freetly, T.L. Wheeler, S.D. Shackelford, D.A. King, and M.L. Spangler. (2023) Genetic and phenotypic associations of mitochondrial DNA copy number, SNP, and haplogroups with growth and carcass traits in beef cattle. J. Anim. Sci. 101:skac415. doi: 10.1093/jas/skac415.

  28. Silva, T.L., C. Gondro, P.A.S. Fonseca, D.A. da Silva, G. Vargas, H.H.R. Neves, I.C. Filho, C.S. Teixeira, L.G. de Albuquerque, and R. Carvalheiro (2023). Feet and legs malformation in Nellore Cattle: genetic analysis and prioritization of GWAS results. Front. Genet. 14:1118308. doi: 10.3389/fgene.2023.1118308.

  29. Silva, T.L., C. Gondro, P.A.S. Fonseca, D.A. da Silva, G. Vargas, H.H.R. Neves, I. Carvalho Filho, C.S. Teixeira, L.G. Albuquerque, and R. Carvalheiro (2023) Testicular hypoplasia in Nellore cattle: genetic analysis and functional analysis of genome-wide association study results. J. Anim. Breed. Genet. 140(2):185-197. doi: 10.1111/jbg.12747.

  30. Steyn, Y., T. Lawlor, D. Lourenco, and I. Misztal. (2023) The importance of historically popular sires on the accuracy of genomic predictions of young animals in the US Holstein population. J. Dairy Sci. Comm. doi: 10.3168/jdsc.2022-0299.

  31. Steyn, Y., T. Lawlor, Y. Masuda, S. Tsuruta, D. Lourenco, and I. Misztal. (2023) Non-parallel genome changes within sub-populations over time contribute to genetic diversity within the U.S. Holstein population. J. Dairy Sci. doi: 10.3168/jds.2022-21914.

  32. Wilson, R.A., B.J. Johnson, J.O. Sarturi, W.L. Crossland, K.E. Hales, R.J. Rathmann, C.L. Bratcher, M.E. Theurer, R.G. Amachawadi, T.G. Nagaraja, S.E. Speidel, R.M. Enns, M.G. Thomas, B.A. Foraker, M.A. Cleveland, and D.R. Woerner. (2023) Identification of blood-based biomarkers for detection of liver abscess in beef x dairy heifers. Applied Animal Science. (submitted).


 


Abstracts and proceedings



  1. Adams, S., N. Aluthge, W. Abbas, M.L. Spangler, J. Wells, K. Hales, L. Kuehn, T. Burkey, P. Miller, and S.C. Fernando. 2023. Microbiomes from the theory to application. J. Anim. Sci. 101: Suppl. 2.

  2. Alvarez Munera, A., D. Lourenco, I. Misztal, I. Aguilar, J. Bauer, J. Šplíchal and M. Bermann. 2023. Improving the efficiency of genomic evaluations with random regression models. In: 74th EAAP – European Association for Animal Production, Lyon, France.

  3. Baty, S.K., R.M. Enns, T.N. Holt, and S.E. Speidel. Genetic correlations between systolic and diastolic pulmonary arterial pressure in Beef Cattle.  J. Anim. Sci.  101:Suppl. 3.

  4. Bermann, M., I. Aguilar, D. Lourenco, and I. Misztal. 2023. Approximation of reliabilities for random-regression single-step genomic best linear unbiased predictor. American Dairy Science Association Annual Meeting, Ottawa, Canada.

  5. Bermann, M., D. Lourenco, and I. Misztal. 2023. Large-scale single-step genome wide association studies with the algorithm for proven and young. PAG XXX– Plant and Animal Genome Conference, San Diego, CA.

  6. Bussiman, F., C. Chen, J. Holl, A. Legarra, I. Misztal and D. Lourenco. 2023. Improving computing performance of genomic evaluations by genotype and phenotype truncation. In: 74th EAAP – European Association for Animal Production, Lyon, France.

  7. Bussiman, F., C.Y. Chen, J. Holl, A. Legarra, I. Misztal, and D. Lourenco. 2023. Data truncation as a tool for increasing computing efficiency in genomic predictions. PAG XXX– Plant and Animal Genome Conference, San Diego, CA.

  8. Croucamp, C., D.M. Stock, M.W. Semler, A.R. Hartman, D.M. Grieger, J.P. Martins, R.L. Weaber, J.M. Bormann, and M.M. Rolf.   The application of GWAS in selection signature analysis of beef bull fertility traits.  Beef Improvement Federation Genetic Prediction Workshop.  Kansas City, MO.

  9. Dressler, E.A., J.M. Bormann, R.L. Weaber, and M.M. Rolf.   Spot sample protocol for gas quantification of grazing beef cattle using a GreenFeed.  American Society of Animal Science Annual meeting. Albuquerque, NM.

  10. Dressler, E.A., J.M. Bormann, R.L. Weaber, and M.M. Rolf.   Characterization of the number of visits required for quantification of gas fluxes and metabolic heat production using a GreenFeed.  Beef Improvement Federation Genetic Prediction Workshop.  Kansas City, MO.

  11. Dressler, E.A., W.R. Shaffer, C.R. Krehbiel, M.S. Calvo-Lorenzo, C.J. Richards, S.E. Place, U. DeSilva, L.A. Kuehn, J.M. Bormann, R.L. Weaber, and M.M. Rolf.   Genetic evaluation of feed and water intake behavior traits for feedlot cattle.  American Society of Animal Science Annual meeting.  Albuquerque, NM.

  12. Dressler, E.A., W.R. Shaffer, C.R. Krehbiel, M.S. Calvo-Lorenzo, C.J. Richards, S.E. Place, U. DeSilva, L.A. Kuehn, J.M. Bormann, R.L. Weaber, and M.M. Rolf.   Genetic evaluation of feed and water intake behavior traits for feedlot cattle.  Beef Improvement Federation Genetic Prediction Workshop.  Kansas City, MO.

  13. Dodds, G., C. Gondro, T. Kendrick, M. Young, and B. Fragomeni 2023. Identifying educational resources and gaps in AG2P data science across plant and animal agriculture genomics. Agricultural Genome to Phenome Initiative Conference. (Virtual)

  14. Engle, B., G. Moser, K.S. Villiers, M. Suarez, E. Grey, B.J. Hayes, and M.J. Kelly. 2023. Using digital twin simulations to optimize genetic selection in an admixed beef herd. American Society of Animal Science Annual meeting. Albuquerque, NM.

  15. Galoro Leite, N., M. Bermann, S. Tsuruta, I. Misztal and D. Lourenco. 2023. Expanding the capabilities of single-step GWAS with P-values for large genotyped populations. In: 74th EAAP – European Association for Animal Production, Lyon, France.

  16. Giess, L.K., S.E. Speidel, R.J. Boldt, W.R. Shafer, M.G. Thomas, and R.M. Enns.   Variance components for age at first calving and yearling weight when accounting for age differences at fixed breeding dates in a contemporary group.  J. Anim. Sci. 101:Suppl. 3.

  17. Gonzalez-Murray, R.A., M.G. Thomas, R.M. Enns, and S.E. Speidel. Heterosis effects on reproductive traits in a multibreed beef cattle herd in Panama.  J. Anim. Sci.  101:Suppl. 3. 

  18. Hess, M., G. Erickson, and M. Spangler. 2023. Genomic analysis of liver abscesses in feedlot beef cattle. Advances in Genome Biology and Technology Agriculture Conference, San Antonio, TX.

  19. Hidalgo, J., S. Tsuruta, D. Gonzalez, G. de Oliveira, M. Sanchez, A. Kulkarni, C. Przybyla, G. Vargas, N. Vukasinovic, I. Misztal, and D. Lourenco. 2023. Converting linear breeding values to probabilities for health traits in dairy cattle. American Dairy Science Association Annual Meeting, Ottawa, Canada.

  20. Hollifield, M.K., J. Hidalgo, F. Bussiman, D. Lourenco, and I. Misztal. 2023. Improving the efficiency of heritability estimation with genomic information—Method R. In: American Dairy Science Association Annual Meeting, Ottawa, Canada.

  21. Hurst, C.W., R.M. Enns, C. Huffhines, K.R. Stackhouse-Lawson, and S.E. Speidel. Sire differences for blood urea nitrogen (BUN) in Hereford cattle.  J. Anim. Sci. 101:Suppl. 3.

  22. Kinghorn, M.S., D.M. Stock, A.R. Hartman, M.W. Semler, G.M. Grieger, J.P. Martins, J.M. Bormann, R.L. Weaber, and M.M. Rolf.   Methods to predict bull fertility:  Connecting the dots.  Beef Improvement Federation Genetic Prediction Workshop.  Kansas City, MO.

  23. Kukor, I., R.M. Enns, T.N. Holt, M.A. Cleveland, B.P. Holland, A.B. Word, G. Ellis, M. Theurer, and S.E. Speidel. Prevalence of right sided heart failure in Angus influenced and beef on dairy fattening feedlot cattle.  J. Anim. Sci. 101:Suppl. 3.

  24. Lakamp, A.D., A.C. Neujahr, M.M. Hille, J.D. Loy, S.C. Fernando, and M.L. Spangler. 2023. Longitudinal heritability of ocular microbiota in preweaned beef cattle. J. Anim Sci. 101: Suppl. 3.

  25. Leite, N., M. Bermann, S. Tsuruta, I. Misztal, D. Lourenco. 2023. Marker effect p-value for large genotype populations with the algorithm for proven and young. American Society of Animal Science Annual Meeting, Albuquerque, NM.

  26. Leite, N.G., M. Bermann, S. Tsuruta, I. Misztal, and D. Lourenco. 2023. Single-step genome-wide association analysis with P-values for large genotyped populations. American Dairy Science Association Annual Meeting, Ottawa, Canada.

  27. Lourenco, D., A. Cesarani, S. Tsuruta, A. Legarra, E. Nicolazzi, P. VanRaden, and I. Misztal. 2023. Big Data Genomic Analysis in Dairy Cattle. PAG XXX– Plant and Animal Genome Conference, San Diego, CA.

  28. Lourenco, D., F. Guinan, G. Wiggans, J. Dürr, S. Tsuruta, and I. Misztal. 2023. Sometimes we win, sometimes we lose: the consequences of genomic selection. In: 74th EAAP – European Association for Animal Production, Lyon, France.

  29. Lourenco, D., S. Jang, R. Ros-Freixedes, J. Hickey, C.Y. Chen, J. Holl, W. Herring, and I. Misztal. 2023. Large-Scale Genomic Predictions with Sequence Data. PAG XXX– Plant and Animal Genome Conference, San Diego, CA.

  30. Miztal, I., V. Breen, D. Lourenco. 2023. Using theoretical and realized accuracies to estimate changes in heritabilities. American Society of Animal Science Annual Meeting, Albuquerque, NM.

  31. Misztal, I., A. Cesarani, A. Legarra, D. Lourenco, S. Tsuruta, M. Bermann, E. Nicolazzi, and P. VanRaden. 2023. Integrating foreign information into single-step evaluations in US Holsteins. American Dairy Science Association Annual Meeting, Ottawa, Canada.

  32. Nawaz, M.Y., H. Ostrovski, R.P. Savegnago, L.K. Ackerson, and C. Gondro 2023. Breed identification by mobile sequencing technology. Plant and Animal Genome Conference XXX, San Diego, CA.

  33. Place, S.E., M. Swenson, E.J. Raynor, K.R. Stackhouse-Lawson, S.E. Speidel, R.M. Enns, and P.H.V. Carvalho. 2023. Evaluation of methane emissions predictions from observed methane emissions date in beef steers, heifers, and bulls. Anim. Sci. 101: Suppl. 3.

  34. Ramos, P.V.B., A. Garcia, K. Retallick, M. Bermann, I. Misztal, D. Lourenco. 2023. Comparison of algorithms for approximation of accuracies for single-step genomic best linear unbiased predictor models. American Society of Animal Science Annual Meeting, Albuquerque, NM.

  35. Rosa, G.J.M., D. Lourenco, T.N. Rowan, L.F. Brito, C. Gondro, J. Huang, and S. Valle de Souza 2023. Integrating enviromics, genomics, and machine learning for precision breeding of resilient livestock. American Society of Animal Science Annual Meeting, Albuquerque, NM.

  36. Rovere, G., B.C.D. Cuyabano, B. Makanjuola, and C. Gondro. 2023. Longitudinal study of environmental effects for American Angus beef cattle over 30 years. Joint International Congress on Animal Science Conference, Lyon, France.

  37. Shaffer, W., K. Bruno, C. Krehbiel, M.S. Calvo-Lorenzo, C.J. Richards, S.E. Place, U. DeSilva, L.A. Kuehn, J.M. Bormann, R.L. Weaber, and M.M. Rolf.   Combining GWAS and miRNA analysis.  Beef Improvement Federation Genetic Prediction Workshop.  Kansas City, MO.

  38. Shaffer, W., J.A.H. Moreno, N.M. Bello, R. Noland, K. Bruno, C. Krehbiel, M.S. Calvo-Lorenzo, C.J. Richards, S.E. Place, U. DeSilva, L.A. Kuehn, J.M. Bormann, R.L. Weaber, and M.M. Rolf.   Characterization of Dry Matter Intake Genetic Parameters with Respect to a Temperature Humidity Index: Insights into Environmental Sensitivity and Genetic-by-Environment Interactions.  American Society of Animal Science Annual meeting.  Albuquerque, NM.

  39. Spangler, M.L., B.L. Golden, and S. Newman. 2023. Genetic selection for improved profit conditioned on enterprise-specific circumstances. J. Anim. Sci. 101: Suppl. 3.

  40. Speidel, S.E., R.M. Enns, and C.N. Cadaret. 2023. Preliminary investigations into developmental origins of pulmonary arterial pressure in Beef Cattle. Anim. Sci. 101:Suppl. 3.

  41. Stock, D.M., J.M. Bormann, and M.M. Rolf. Male fertility in beef cattle.  In:  2023 Applied Reproductive Strategies in Beef Cattle Proceedings.  Cheyenne, WY.

  42. Stock, D.M., A.R. Hartman, M.W. Semler, G.M. Grieger, J.P. Martins, J.M. Bormann, R.L. Weaber, and M.M. Rolf.   Genetic parameter estimation for breeding soundness examination traits in Angus bulls.  Beef Improvement Federation Genetic Prediction Workshop.  Kansas City, MO.

  43. Stohlmann, M., M.K. Hess, S. Ference, S.R. Nafziger, J.A. Keane, A. Fuller, S.G. Kurz, M.L. Spangler, J.L. Petersen, A.S. Cupp. 2023. Puberty classifications in beef heifers are moderate to highly heritable with nucleotide polymorphisms (SNPs) from candidate genes highly associated to their cyclicity and timing of puberty. Gil Greenwald Reproductive Symposia, Kansas City, KS.

  44. Torres-Quijada, I.F., S.E. Speidel, M.L. Zuvich, E.J. Raynor, P.H.V. Carvalho, S.E. Place, S.E. Speidel, M.L. Zuvich, E.J. Raynor, P.H.V. Carvalho, S.E. Place, K.R. Stackhouse-Lawson, and R.M. Enns. 2023. The relationship of methane emissions with stayability in Angus cattle. Anim. Sci. 101:Suppl. 3.

  45. Zuvich, M.L., S.E. Speidel, I.F. Torres-Quijada, E.J. Raynor, P.H.V. Carvalho, S.E. Place, K.R. Stackhouse-Lawson, and R.M. Enns. 2023. The relationship between pulmonary arterial expected progeny differences and methane emissions. Anim. Sci. 101:Suppl. 3.


 


Presentations



  1. Engle, B. Introduction to the USMARC Germplasm Evaluation Program. Presented to Fort Hayes State University students. Clay Center, NE, 2023.

  2. Engle, B. Developing resources to improve genetic selection in beef cattle. University of Nebraska-Lincoln. Lincoln, NE, 2023.

  3. Enns, R.M. and S.E. Speidel. Interpreting and using the new PAP EPD. University of Wyoming Bull Sale Dinner, 2023.

  4. Enns, R.M., S.E. Speidel, and T.N. Holt. Genetic prediction for pulmonary arterial pressure. Presented at the American Simmental Association Fall Focus. Denver, CO, 2023.

  5. Enns, R.M., S.E. Speidel, and T.N. Holt. Selecting Sires to use at high elevation. Applied Reproductive Strategies in Beef Cattle (ASRBC). Cheyenne, WY, 2023. 

  6. Enns, R.M., S.E. Speidel, and C. Hurst. Breeding and Genetics Research Update. Presented at the Leachman Cattle of Colorado-URUS CSU Facilities Tour. Fort Collins, CO, 2023.

  7. Enns, R.M., S.E. Speidel, K. Stackhouse-Lawson, S. Place, M.G. Thomas, C. Huffhines, and C. Hurst. Evaluating the genetic components of greenhouse gas emissions and reactive nitrogen produced by Hereford seedstock for deriving systems, selection tools, and documented trends to achieving carbon neutral in the US beef industry. Young Hereford Breeder Tour.  Fort Collins, CO, 2023.

  8. Gondro, C. Imputation and genetic evaluation with sequence data (and a bit of AI). BIF 12th Genetic Prediction Workshop, Kansas City, MO, 2023.

  9. Hulsman-Hanna, L.L. Breed and Animal Improvements + Genomics, Heartland Highland Association Annual Meeting, Branson, MO, 2023.

  10. Hulsman-Hanna, L.L. Interplay of Cow Size in the Production System, NDSU Dickinson Research Extension Center Field Day, Fargo, ND, 2023.

  11. Hulsman-Hanna, L.L. Interplay of Cow Size in the Production System, NDSU Fall Extension Conference, Fargo, ND, 2023.

  12. Lourenco, D. Big data analysis in animal breeding. 10th Animal Science Congress of Iran. Tehran, Iran, 2023.

  13. Lourenco, D. Single-step GWAS with p-values for large genotyped populations. Animal and Dairy Science Association. Ottawa, Canada, 2023.

  14. Lourenco, D. Are there benefits in using sequence data for genomic predictions?. Animal Production Science Congress. Bari, Italy, 2023.

  15. Lourenco, D. Updates in the BLUPF90 software suite. Plemdat Workshop. Prague, Czech Republic, 2023.

  16. Lourenco, D. Understanding Genomic Selection. Genomic Breeding Workshop. Dunedin, New Zealand, 2023.

  17. Lourenco, D. Implementing Genomic Selection. Genomic Breeding Workshop. Dunedin, New Zealand, 2023.

  18. Lourenco, D. Large multibreed genomic evaluation in dairy cattle. Genomic Breeding Workshop. Dunedin, New Zealand, 2023.

  19. Lourenco, D. Efficient approximation of GEBV reliability (a measure of precision). Genomic Breeding Workshop. Dunedin, New Zealand, 2023.

  20. Lourenco, D. Use of sequence data and other sources of information for genomic predictions. Genomic Breeding Workshop. Dunedin, New Zealand, 2023.

  21. Lourenco, D. SNP-BLUP vs. GBLUP based models. Genomic Breeding Workshop. Dunedin, New Zealand, 2023.

  22. Lourenco, D. Simple vs. Complex models. Genomic Breeding Workshop. Dunedin, New Zealand, 2023.

  23. Lourenco, D. Making genomic evaluations for millions of animals possible. University of Queensland Seminar. Brisbane, Australia, 2023.

  24. Lourenco, D. Decoding Genomic Predictions in Large Populations. Benchmark Genetics Workshop. Bergen, Norway, 2023.

  25. Lourenco, D., and I. Misztal. Decoding Genomic Predictions in Large Populations. Gordon Conference - Quantitative Genetics. Ventura, CA, 2023.

  26. Spangler, M.L. Value of Genetic Data Beyond Seedstock and Geneticists, UNL Beef Group meeting, Lincoln, NE, 2023.

  27. Spangler, M.L. Why Genotype? National Salers Show and Sale, Oklahoma City, OK, 2023.

  28. Spangler, M.L. Genomic EPDs, Eastern NE Cattle Conference, Syracuse, NE, 2023.

  29. Spangler, M.L. Impact of Genomics on EPDs (panelist), Cattlemens Conference—Blueprint for the Future, Stillwater, OK, 2023.

  30. Spangler, M.L. Utilizing U.S. Beef Cattle Genetics to Improve Quality in Mexican Beef: A Case Study from Nebraska (given by interpreter), International Meat Congress, Leon, Guanajuato, Mexico, 2023.

  31. Spangler, M.L. Genetic Selection for Enterprise Profit, American Simmental Association STYLE Conference, Oklahoma City, OK, 2023.

  32. Spangler, M.L. Genetic selection for improved profit conditioned on enterprise-specific circumstances, American Society of Animal Science meetings (Invited), Albuquerque, NM, 2023.

  33. Spangler, M.L. Tools for selecting U.S. beef genetics, Argentine trade group, Lincoln, NE, 2023.

  34. Spangler, M.L. Genetic considerations for the cowherd, UNL Ranch practicum (virtual), 2023.

  35. Spangler, M.L. Genomics: Improving the U.S. Cowherd, American Gelbvieh Association webinar series (virtual), 2023.

  36. Spangler, M.L. iGENDEC—Next generation decision support, Wulf/Riverview webinar (virtual), 2023.

  37. Spangler, M.L. Current and Future Use of Genomics in Beef Cattle, Iowa Vet Med Association annual meeting, Ames, IA, 2023.

  38. Spangler, M.L. Profit Focused? Make Sure Your Selection Decisions Are Too, NBCEC Brown Bagger webinar series (virtual), 2023.

  39. Spangler, M.L. Here in the middle with you: modern quantitative animal genetics, Complex Biosystems seminar, Lincoln, NE, 2023.

  40. Spangler, M.L. Impacting the Quality of EPDs for You and Your Customers, Beef Seedstock Symposium, Lexington, KY, 2023.

  41. Spangler, M.L. Putting Selection Tools to Work (EPDs and Indices), Beef Seedstock Symposium, Lexington, KY, 2023.

  42. Spangler, M.L. Impacting the Quality of EPDs for You and Your Customers, Beef Seedstock Symposium, Glasgow, KY, 2023.

  43. Spangler, M.L. Putting Selection Tools to Work (EPDs and Indices), Beef Seedstock Symposium, Glasgow, KY, 2023.

  44. Spangler, M.L. Impacting the Quality of EPDs for You and Your Customers, Beef Seedstock Symposium, Spring Hill, TN, 2023.

  45. Spangler, M.L. Putting Selection Tools to Work (EPDs and Indices), Beef Seedstock Symposium, Spring Hill, TN, 2023.

  46. Kuehn, L.A., and Spangler, M.L. A Genetics Primer and Vision for Fed Cattle Phenotypic Prediction and Data Utilization, Genetic Merit Pricing Task Force meeting, Denver, CO, 2023

  47. Spangler, M.L. Genetic tools for the cow/calf producer (panel moderator), Nebraska Beef Industry Scholars Beef Summit, Mead, NE, 2023

  48. Spangler, M.L. Making genetic progress and why end product quality matters to Seedstock producers, American Gelbvieh Association annual convention, Omaha, NE, 2023.

  49. Spangler, M.L. Increasing the accuracy of selection decisions, ISU Genetics Symposium, Ames, IA, 2023.

  50. Spangler, M.L. Leveraging commercial data to improve selection and management decisions, BIF Genetic Prediction Workshop, Kansas City, MO, 2023.

  51. Speidel, S.E., R.M. Enns, M.G. Thomas, I.M. Kukor, and T.N. Holt. Genetics of heart score and relationships with performance.  Presented in the Cowherd Efficiency Subcommittee meeting at the Annual Beef Improvement Federation Meeting. Calgary, AB, Canada, 2023.

  52. Speidel, S.E., R.M. Enns, M.G. Thomas, I.M. Kukor, and T.N. Holt. Genetic prediction for bovine congestive heart failure. Presented at the American Simmental Fall Focus. Denver, CO, 2023.

  53. Thallman, R.M. Maternal composites for terminal crossing. Presented to representatives from Maddux Ranches. Clay Center, NE, 2023.

  54. Thallman, R.M. The Germplasm Evaluation Project. USDA-ARS Grazinglands Research Laboratory. El Reno, OK, 2023.

  55. Thallman, R.M. The Germplasm Evaluation Project. Junior American Braunvieh Association. Kearney, NE, 2023.

  56. Thallman, R.M. Rewarding high-quality data with higher accuracies, Beef Improvement Federation Annual Research Symposium. Calgary, AB, Canada, 2023.

  57. Thallman, R.M. The Germplasm Evaluation Project. Presented to visiting beef producers from New Zealand. Clay Center, NE, 2023.

  58. Thallman, R.M. The Germplasm Evaluation Project. American Gelbvieh Association. Clay Center, NE, 2023.

  59. Thallman, R.M. Recent developments in the Germplasm Evaluation Project, U.S. Meat Animal Research Center Beef Focus Group. Clay Center, NE, 2023.

  60. Thallman, R.M. A vision for the future of low-pass sequencing. Beef Improvement Federation Genetic Prediction Workshop. Kansas City, MO, 2023.


 


Extension


 



  1. Hulsman Hanna, L.L. Fence Post: Have you thought about cow size lately? North Dakota Stockman Magazine. May/June 2023: 20-21. 2023.

  2. Sanglard, L.P., G.M. See, and M.L. Spangler. Including Gene Edited Sires in Genetic Evaluation. NE Beef Report. 2023.

  3. Spangler, M.L. The impact of genomics on EPDs. Proc. Beef Cattle Congress—Blueprint for the Future. 2023.

  4. Valasek, H.F., B.L. Golden, and M.L. Spangler. Impact of Planning Horizon Length on Breeding Objectives and Resulting Selection Decisions. NE Beef Report. 2023.


 

01/13/2025

12/18/2025

Collectively, these peer-reviewed studies advance the scientific foundation of beef cattle genetic improvement by developing and refining genomic evaluation methodologies, integrating whole-genome sequence and low-coverage data, and addressing key computational challenges associated with large-scale national evaluations. The research expands breeding objectives to include feed efficiency, methane emissions, health, fertility, and resilience traits, while accounting for genotype-by-environment interactions and climate-related stressors. Emerging multi-omics approaches—including microbiome, proteomic, and metabolomic data—demonstrate added value for improving prediction accuracy and biological understanding. Together, this body of work supports USDA/NIFA priorities by enabling more sustainable, efficient, and climate-resilient beef production systems and providing decision-support tools that enhance the competitiveness of the U.S. beef industry.


These studies demonstrate the effective application of single-step GBLUP, whole-genome sequence–enabled models, and related genomic evaluation approaches to a wide range of complex traits in beef cattle. Collectively, they highlight the critical role of environmental and physiological drivers—including feed intake, methane emissions, heat stress, health status, and genotype-by-environment interactions—in shaping genetic predictions. Methodological advances addressing reliability estimation, validation, missing pedigree information, data truncation, and computational scalability provide essential tools for improving the accuracy, robustness, and practical implementation of large-scale genomic evaluations. In addition, research on reproductive performance, growth, carcass, and structural soundness traits broadens breeding objectives to better reflect efficiency, resilience, and long-term productivity.


Together, these publications underscore the highly collaborative and international nature of contemporary beef cattle genetics research, with findings applicable across diverse breeds, production systems, and environments. The integration of multi-omics data, development of computationally efficient software and algorithms, establishment of trait definitions and measurement strategies, and dissemination of results through peer-reviewed journals, workshops, and outreach activities ensure that methodological innovations are translated into actionable tools for industry stakeholders. Overall, this body of work strengthens the scientific and operational foundation of genetic evaluation systems, supporting more sustainable, resilient, and economically competitive beef production.



  1. Beef Improvement Federation Genetic Prediction Workshop Organizing Committee (including R. L. Weaber). 2025. Beef Improvement Federation Genetic Prediction Workshop: Opportunities and obstacles to enhancing beef cattle evaluation with sequence data. Journal of Animal Science 103: skaf295. https://doi.org/10.1093/jas/skaf295

  2. Shaffer, W. R., J. Hidalgo, N. M. Bello, R. S. Noland, J. M. Bormann, R. L. Weaber, C. M. Ahlberg, K. Bruno, C. R. Krehbiel, M. S. Calvo-Lorenzo, C. J. Richards, S. Place, U. DeSilva, L. A. Kuehn, and M. M. Rolf. 2025. Beef cattle phenotypic plasticity and stability of dry matter intake and respiration rate across varying temperature–humidity index levels. Journal of Animal Science 103: skaf115. https://doi.org/10.1093/jas/skaf115

  3. Dressler, E. A., J. M. Bormann, R. L. Weaber, R. C. Merkel, and M. M. Rolf. 2024. A review of cashmere fiber phenotypes: Production, heritabilities, and genetic correlations. Small Ruminant Research 240: 107369. https://doi.org/10.1016/j.smallrumres.2024.107369

  4. Dressler, E. A., J. M. Bormann, R. L. Weaber, and M. M. Rolf. 2024. Use of methane production data for genetic prediction in beef cattle: A review. Translational Animal Science 8: txae014. https://doi.org/10.1093/tas/txae014

  5. Underwood, M., K. J. Starzec, N. Hill-Sullins, and R. L. Weaber. 2024. Print Grades Prime: A quantitative analysis of producer communication preferences of U.S. beef breed association magazines. Journal of Applied Communications 108(1). https://doi.org/10.4148/1051-0834.2501

  6. Boldt, R., J. Keele, L. A. Kuehn, T. McDaneld, S. E. Speidel, and R. M. Enns. 2025. Comparison of Random Forest and traditional GWAS models for analysis of pooled DNA data. Genetics Selection Evolution. Submitted.

  7. Krafsur, G. M., M. M. Culbertson, R. D. Brown, M. Li, T. N. Holt, S. E. Speidel, R. M. Enns, R. J. Delmore, K. R. Stenmark, and M. G. Thomas. 2025. Reduced feed efficiency, cardiopulmonary remodeling, and increased mortality in feedyard cattle with pulmonary hypertension. Journal of Animal Science. Submitted.

  8. Gonzalez-Murray, R. A., M. G. Thomas, T. N. Holt, S. Coleman, R. M. Enns, and S. E. Speidel. 2025. Heterosis effects on preweaning traits in a multibreed beef cattle herd in Panama. Tropical Agriculture 102(4): 546–561.

  9. Icedo-Nuñez, S., M. G. Thomas, R. M. Enns, S. E. Speidel, J. Hernandez, X. Zeng, M. A. Sanchez-Castro, G. Luna-Nevarez, M. C. Lopez-Gonzalez, C. M. Aguilar-Trejo, and P. Luna-Nevarez. 2025. Validation of polymorphisms associated with immune response after PRRS vaccination in replacement gilts. Veterinary Sciences 12(4): 295. https://doi.org/10.3390/vetsci12040295

  10. Thallman, R. M., J. E. Borgert, B. N. Engle, J. W. Keele, W. M. Snelling, C. Gondro, and L. A. Kuehn. 2025. A vision of how low-coverage sequence data should contribute to genetic evaluation in the future. Journal of Animal Science 103: skaf294.

  11. Zaabza, H. B., M. H. Ferdosi, I. Strandén, B. C. D. Cuyabano, M. Neupane, I. Misztal, D. Lourenco, and C. Gondro. 2025. Opportunities and computational challenges in large-scale whole-genome sequencing data analysis. Journal of Animal Science 103: skaf292. https://doi.org/10.1093/jas/skaf292

  12. Eckhardt, E., A. Luttman, J. R. Daddam, B. H. Keng, W. Kim, C. Gondro, and J. Kim. 2025. Transcriptomic and proteomic responses of bovine myocytes to temporal heat stress. Journal of Thermal Biology 132: 104246.

  13. O’Shea-Stone, G., B. Tripet, J. Thomson, R. Garrott, and V. Copié. 2025. Polar metabolite profiles distinguish early and severe sub-maintenance nutritional states in wild bighorn sheep. Metabolites 15(3).

  14. Phelps, S. L., and M. M. Culbertson. 2025. Genetic parameters for heifer pregnancy and carcass traits in Angus cattle. Journal of Animal Science. Submitted.

  15. Spangler, M. L., D. P. Berry, and L. A. Kuehn. 2025. Leveraging data from commercial cattle for genetic improvement: An international perspective. Journal of Animal Science 103: skaf291. https://doi.org/10.1093/jas/skaf291

  16. Hess, M. K., R. L. McDermott, G. E. Erickson, and M. L. Spangler. 2025. Genetic parameter estimates of liver abscesses in feedlot beef cattle. Journal of Animal Science 103: skaf029. https://doi.org/10.1093/jas/skaf029

  17. Fernando, S. C., S. Adams, A. Lakamp, and M. L. Spangler. 2025. Stochastic and deterministic factors shaping the rumen microbiome. Journal of Dairy Science 108. https://doi.org/10.3168/jds.2024-25797

  18. Lakamp, A. D., A. C. Neujahr, M. M. Hille, J. D. Loy, S. C. Fernando, and M. L. Spangler. 2025. Genetic influence on ocular microbiome composition in preweaned beef calves. Journal of Animal Science 103: skaf153. https://doi.org/10.1093/jas/skaf153

  19. Lakamp, A. D., A. C. Neujahr, S. C. Fernando, W. M. Snelling, and M. L. Spangler. 2025. Imputation accuracy of host genomic data from metagenomic sequence information. Journal of Animal Science 103: skaf175. https://doi.org/10.1093/jas/skaf175

  20. Lakamp, A., S. Adams, L. A. Kuehn, W. M. Snelling, J. Wells, K. Hales, B. Neville, S. C. Fernando, and M. L. Spangler. 2025. Prediction accuracy for feed intake and body-weight gain using host genomic and rumen metagenomic data. Genetics Selection Evolution. https://doi.org/10.1186/s12711-025-01007-8

  21. Campos, M., H. Rojas, H. Mulim, E. da Silva, J. Hidalgo, and R. Bermal. 2025. Comparison of linear and threshold models for genetic evaluation of morphological defects in Nellore cattle. Journal of Animal Science. In press.

  22. Silva Pereira, L., L. Bordin Temp, E. da Silva Oliveira, J. Hidalgo, C. U. Magnabosco, and F. Baldi. 2025. Genomic prediction using linear and threshold approaches for stayability in Nellore females. Journal of Animal Breeding and Genetics. https://doi.org/10.1111/jbg.70033

  23. Mugambe, J. C., M. Schmidtmann, J. Hidalgo, R. Ahmed, and G. Thaller. 2025. Genetic evaluation of beef sires using a beef-on-dairy crossbred reference population. Journal of Animal Breeding and Genetics. https://doi.org/10.1111/jbg.70030

  24. Tabet, J. M., F. Bussiman, J. Hidalgo, M. Bermann, A. Cesarani, I. Misztal, and D. Lourenco. 2025. Approximating reliabilities of indirect predictions using SNP effects from large single-step GBLUP evaluations. Journal of Dairy Science. https://doi.org/10.3168/jds.2025-27089

  25. Abduch, N. G., H. G. Reolon, R. M. O. Silva, F. Baldi, B. O. Fragomeni, D. Lourenco, C. C. P. Paz, and N. B. Stafuzza. 2025. Plasma proteomics identifies biomarkers for tick resistance in tropically adapted beef cattle. BMC Genomics 26: 1030. https://doi.org/10.1186/s12864-025-12245-x

  26. Bermann, M., A. Legarra, I. Aguilar, A. Alvarez-Munera, I. Misztal, and D. Lourenco. 2025. Estimation of (co)variance components for very large datasets in complex single-step genomic models. Genetics Selection Evolution 57: 63. https://doi.org/10.1186/s12711-025-01006-9

  27. Garcia, A. O., A. A. Mikush, J. B. Cole, S. Tsuruta, S. E. F. Guimaraes, I. Misztal, and D. Lourenco. 2025. Genetic background of calving ease in beef-on-dairy systems. Journal of Dairy Science. https://doi.org/10.3168/jds.2025-26503

  28. Fuentes Rojas, L. J., F. Bussiman, T. F. Cardoso, G. A. Colmenarez, L. C. Conteville, B. C. P. Antonio, H. T. Ventura, J. J. Paschoal, D. Lourenco, and L. C. A. Regitano. 2025. Microbiota diversity and associations with performance traits in beef bulls. Journal of Animal Science 103: skaf340. https://doi.org/10.1093/jas/skaf340

  29. Trujano, Z., A. Garcia, K. Retallick, J. Hidalgo, D. Lourenco, and I. Misztal. 2025. Optimizing large genomic evaluations through data truncation in Angus cattle. Journal of Animal Science 103: skaf382. https://doi.org/10.1093/jas/skaf382

  30. Bermann, M., A. Alvarez-Munera, A. Legarra, I. Misztal, and D. Lourenco. 2025. Monte Carlo approximation of log-determinants for large matrices in quantitative genetics. Genetics Selection Evolution 57: 44. https://doi.org/10.1186/s12711-025-00991-1

  31. Bermann, M., A. A. Munera, I. Misztal, and D. Lourenco. 2025. Semi-parametric validation of genomic predictions and polygenic risk scores with BLUPF90. G3: Genes|Genomes|Genetics. https://doi.org/10.1093/g3journal/jkaf136

  32. Londoño-Gil, M., J. Hidalgo, A. Legarra, C. U. Magnabosco, F. Baldi, and D. Lourenco. 2025. Indirect genomic predictions for indicine breeds using SNP effects from multi-breed evaluations. Journal of Animal Breeding and Genetics. https://doi.org/10.1111/jbg.70008

  33. Carvalho Filho, I., G. S. Campos, D. Lourenco, F. S. Schenkel, D. A. Silva, T. L. Silva, C. S. Teixeira, L. F. S. Fonseca, G. A. Fernandes Junior, L. G. Albuquerque, and R. Carvalheiro. 2025. Genotype-by-environment interaction for productive and reproductive traits using imputed whole-genome sequence. Journal of Applied Genetics. https://doi.org/10.1007/s13353-025-00987-z

  34. Tabet, J. M., I. Aguilar, M. Bermann, D. Lourenco, I. Misztal, P. M. VanRaden, Z. G. Vitezica, and A. Legarra. 2025. Correcting overestimation of approximate reliabilities with herd–sire interactions. Genetics Selection Evolution 57: 33. https://doi.org/10.1186/s12711-025-00984-0

  35. Temp, L., G. Gubiani, L. Brunes, C. Magnabosco, F. Bussiman, J. Hidalgo, D. Lourenco, and F. Baldi. 2025. Genomic evaluation of reproductive traits in Nellore cattle accounting for missing pedigrees. Journal of Animal Breeding and Genetics. https://doi.org/10.1111/jbg.12947

  36. Trujano, Z., J. Hidalgo, G. Gowane, K. Retallick, A. Garcia, D. Lourenco, and I. Misztal. 2025. Impact of genomic selection for growth and carcass traits on foot structure in Angus cattle. Journal of Animal Science 103: skaf158. https://doi.org/10.1093/jas/skaf158

  37. Ogunbawo, A. R., J. Hidalgo, H. A. Mulim, E. R. Carrara, H. T. Ventura, N. O. Souza, D. Lourenco, and H. R. Oliveira. 2025. Proven-and-young GWAS reveals high polygenicity for key traits in Nellore cattle. Frontiers in Genetics 16: 1549284. https://doi.org/10.3389/fgene.2025.1549284

  38. Malheiros, J. M., H. G. Reolon, B. G. Bosquini, F. Baldi, D. Lourenco, B. O. Fragomeni, R. M. O. Silva, C. C. P. Paz, and N. B. Stafuzza. 2025. Plasma proteomics identifies pathways and candidate genes for residual feed intake in beef cattle. Journal of Proteomics 312: 105361. https://doi.org/10.1016/j.jprot.2024.105361

  39. Londoño-Gil, M., R. López-Correa, I. Aguilar, C. U. Magnabosco, J. Hidalgo, F. Baldi, and D. Lourenco. 2025. Strategies for genomic prediction in indicine multibreed populations using single-step GBLUP. Journal of Animal Breeding and Genetics. https://doi.org/10.1111/jbg.12882


 


Selected Conference Proceedings, Invited Talks and Producer Meetings



  1. Weaber, R. L. (2025). Selecting for cow maternal performance: Making your breeding and selection systems pay. Stockfarm, 15(10), 43–45.

  2. Weaber, R. L. (2025). Selekteer vir koei maternale prestasie: Laat jou teling- en seleksiestelsels vir jou werk. Veeplaas, 16(10), 50–51.

  3. Weaber, R. L. (October 14, 2025). Advancing genetic improvement in the United States. Simmentaler Annual General Meeting, Pretoria, South Africa.

  4. Weaber, R. L. (October 10, 2025). Selecting for cow maternal performance. Livestock Registering Federation Stockman School, Ventersburg, Free State, South Africa.

  5. Weaber, R. L. (October 9, 2025). Importance of breed societies to drive genetic change to meet future demands. Livestock Registering Federation Stockman School, Ventersburg, Free State, South Africa.

  6. Weaber, R. L. (October 8, 2025). The future of beef cattle genetics in the beef value chain. Livestock Registering Federation Stockman School, Ventersburg, Free State, South Africa.

  7. Weaber, R. L. (October 7, 2025). iGENDEC selection index tools in the USA. Livestock Registering Federation Breeders Workshop, Ventersburg, Free State, South Africa.

  8. Weaber, R. L. (April 8, 2025). Quality improvements in the U.S. beef supply chain driven by genetic selection.S. Meat Export Federation Trade Mission Conference, Accra, Ghana.

  9. Weaber, R. L. (April 4, 2025). Using genetics to meet the food demand of 2050. University of Jos, Jos, Plateau State, Nigeria.

  10. Weaber, R. L. (2025). iGENDEC-based selection indexes at the North American Limousin Foundation and American Gelbvieh Association. eBEEF Brown Bagger Webinar Series.

  11. Weaber, R. L. (September 17–18, 2025). Genetic selection for cow fertility and longevity. Applied Reproductive Strategies in Beef Cattle, North Platte, NE.

  12. Weaber, R. L. (May 2, 2025). Getting it right: Proper contemporary grouping strategies. Santa Gertrudis Breeders’ Convention, San Marcos, TX.

  13. Weaber, R. L. (April 18, 2025). Bull buying using selection indexes: Are the assumptions correct? 74th Florida Beef Cattle Short Course, Gainesville, FL.

  14. Weaber, R. L. (May 13, 2025). Selection and mating systems to improve profit potential: Role of heterosis in the beef value chain. Kentucky Agricultural Agents Conference, Manhattan, KS.

  15. Speidel, S. E. (November 11, 2025). Pulmonary hypertension: New research and developments. Range Beef Cow Symposium, Cheyenne, WY.

  16. Speidel, S. E. (August 8, 2025). Phenotypic and genetic characterization of bovine congestive heart failure in beef and beef × dairy cattle. Academy of Veterinary Consultants Annual Meeting, Norman, OK.

  17. Speidel, S. E. (August 15, 2025). Pulmonary hypertension in moderate-elevation feedlots: New research and developments. Pulmonary Arterial Pressure Summit, Fort Collins, CO.

  18. Speidel, S. E. (August 20, 2025). Pulmonary hypertension in moderate-elevation feedlots: New research and developments. Leachman Cattle, Meriden, WY.

  19. Speidel, S. E. (September 5, 2025). Pulmonary hypertension: New research and developments. Hereford Academy, Fort Collins, CO.

  20. Enns, R. M., & Speidel, S. E. (July 7, 2025). Building industry–academic collaborations to use genetic technologies for solving challenges in the beef industry. American Society of Animal Science Annual Meeting, Hollywood, FL.

  21. Culbertson, M. M. (August 2025). Use of genetics to improve fertility and carcass characteristics. National Association of Cattle Ranchers (ANAGAN) Technical–Scientific Congress, David, Panama.

  22. Culbertson, M. M. (March 2025). Beef on dairy: Has the future arrived in the U.S. beef-on-dairy market? Herd ’25 Conference, Bendigo, Australia.

  23. Culbertson, M. M. (May 2024). Beef on dairy: The U.S. perspective. 46th ICAR and Interbull Conference, Bled, Slovenia.

  24. Culbertson, M. M. (March 2024). Beef on dairy: The current landscape. 59th National Dairy Herd Improvement Association Leadership and Annual Business Meeting, New Orleans, LA.

  25. Culbertson, M. M. (February 2024). Sustainable strategies for genetic improvement. National Cattlemen’s Beef Association Cattlemen’s College, Orlando, FL. (Joint presentation with E. Stackhouse, PhD)

  26. Hidalgo, J. Developments in single-step GBLUP and single-step GWAS. University of Florida, USA.

  27. Hidalgo, J. An introduction to single-step GBLUP. Universidad Autónoma Chapingo, Mexico.

  28. Hidalgo, J. Equivalence of (co)variance components on the observed and liability scales. University of Georgia, USA.

  29. Lourenco, D. The winners and losers in the genomic selection game. Plant and Animal Genome Conference (PAG), San Diego, CA.

  30. Lourenco, D. Large-scale single-step GWAS in beef and dairy cattle. Plant and Animal Genome Conference (PAG), San Diego, CA.

  31. Lourenco, D. Optimizing genomic evaluations: Single-step GBLUP and GWAS for large genotyped populations. Macon, GA.

  32. Lourenco, D. What it takes to deal with the largest livestock datasets in the world. Milan, Italy.

  33. Olinger, G. H., Z. K. Smith, F. Francis, B. B. Grimes Francis, R. J. Leeson, M. Gonda, R. L. Weaber, and W. C. Rusche. (2025). Effect of extended days on feed on growth performance, efficiency, and carcass characteristics of steers and heifers of varying proportions of Angus and Limousin genetics. Journal of Animal Science, 103(Suppl. 3), 262–263.

  34. Kinghorn, M. G., J. M. Bormann, R. L. Weaber, and M. M. Rolf. (2025). Opportunities and challenges related to water use intensity and its effects on sustainability within the U.S. beef supply chain. Journal of Animal Science, 103(Suppl. 3), 581–582.

  35. Autry, P. A., R. M. Enns, I. Kukor, T. N. Holt, M. A. Cleveland, B. P. Holland, A. B. Word, G. Ellis, M. Theurer, and S. E. Speidel. (2025). Mid-finishing pulmonary arterial pressure compared with late-finishing pulmonary arterial pressure as indicators of heart score. Journal of Animal Science, 103(Suppl. 3).

  36. Enns, R. M., and S. E. Speidel. (2025). Building industry–academic collaborations to use genetic technologies for solving challenges in the beef industry. Journal of Animal Science, 103(Suppl. 3).

  37. Garcia-Benitez, C., R. I. Luna Ramirez, J. F. Medrano, R. M. Enns, S. E. Speidel, R. Zamorano-Algandar, M. A. Sanchez-Castro, G. Luna-Nevarez, J. C. Leyva-Corona, and P. Luna-Nevarez. (2025). Validation of polymorphisms as molecular markers for milk production and thermotolerance in Holstein cows managed under heat stress. Journal of Animal Science, 103(Suppl. 3).

  38. Griffin, M. L., S. E. Speidel, R. M. Enns, S. E. Place, and K. R. Stackhouse-Lawson. (2025). Genetic parameters for blood urea nitrogen, methane emissions, and feed intake in Angus beef cattle. Journal of Animal Science, 103(Suppl. 3).

  39. Vargas, J. J., M. Swenson, M. R. Werner, S. E. Speidel, R. M. Enns, D. Manriquez, P. H. V. Carvalho, K. R. Stackhouse-Lawson, and S. E. Place. (2025). Determination and classification of growing steers according to residual methane emissions. Journal of Animal Science, 103(Suppl. 3).

  40. Zuvich, M. L., S. E. Speidel, T. N. Holt, and R. M. Enns. (2025). Preliminary analysis of the relationship between heart score and carcass value in Angus cattle. Journal of Animal Science, 103(Suppl. 3).

  41. Weaber, R. L. (2025). Genetic selection for cow fertility and longevity. In Proceedings of the Applied Reproductive Strategies in Beef Cattle Conference, September 17–18, North Platte, NE.

  42. Wankowski, J., K. Eager, P. Thomson, C. Gondro, G. Larson, K. Van Damme, I. Tammen, and J. Lehner. (2025). Neolithic mobile pastoralism: Challenges merging diverse datasets for a genomic analysis of cattle dispersal. Proceedings of ISAG 2025.

  43. Eckhardt, E. P., A. M. Luttman, C. Gondro, and J. Kim. (2025). Temporal heat stress impact on gene regulation of skeletal muscle hypertrophy in bovine myocytes. Proceedings of ASAS 2025.

  44. Messina, C., C. Gondro, M. E. Sorrells, B. Reading, N. de Leon, and M. Mueller. (2025). AgSystems: Accelerated breeding for a resilient bioeconomy. Plant and Animal Genome Conference (PAG 2025).

  45. He, Y., M. Adhikari, C. Gondro, M. B. Kantar, C. N. Lee, and R. J. Longman. (2025). Genetic ancestry, admixture, divergence, and evolutionary history of Hawaiian cattle. Plant and Animal Genome Conference (PAG 2025).

  46. Thomson, J., N. Schaff, J. Dafoe, D. Boss, and J. A. Boles. (2025). Indications of inflammation and cytokine activity in response to rapid fat deposition in beef steers. ISEP 2025, Rostock-Warnemünde, Germany.

  47. Phelps, S. L., P. B. Wall, G. R. Dahlke, and M. M. Culbertson. (2025). Identification of carcass ultrasound measurements as predictors of heifer pregnancy. Proceedings of the Midwest Section, American Society of Animal Science, March 9–12, Omaha, NE.

  48. Tarochione, A., and M. M. Culbertson. (2025). Impact of yearling weight on culling age in Angus cattle. Proceedings of the Midwest Section, American Society of Animal Science, March 9–12, Omaha, NE.

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