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

Annual/Termination Reports:

[11/01/2022] [12/18/2023] [01/13/2025] [12/18/2025]

Date of Annual Report: 11/01/2022

Report Information

Annual Meeting Dates: 11/01/2022 - 11/01/2022
Period the Report Covers: 01/01/2022 - 12/31/2022

Participants

Brittney Keel (USMARC)
Mark Thallman (USMARC)
Warren Snelling (USMARC)
Larry Kuehn (USMARC)
John Kuehn (USMARC)
Cedric Gondro (Michigan State Universoty)
Jenny Bormann (Kansas State University)
Daniela Lourenco (University of Georgia)
Ryan Boldt (IGS).
Ignacy Misztal (University of Georgia)
Matt Spangler (University of Nebraska)
Duc Lu (Angus Genetics, Inc)
Andre Garcia (Angus Genetics, Inc,)
Kelli Retallick (Angus Genetics, Inc.)

Brief Summary of Minutes

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

Business Meeting November 1, 2022 via Zoom

Chair: Dr. Brittney Keel

Secretary: Dr. Cedric

The meeting was called to order by chair Brittney Keel at 4:18 PM on Tuesday, November 1, 2022.

Administrative Advisor Dr. Joe Cassady made the announcement that he had been approached by USDA to continue in his supervisory role and that he was happy to do so. He highlighted that objectives for the group should continue to be the same: working together within the group and building additional collaborations.

Dr. Brittney Keel announced that the project has been renewed for another 5 years and that members need to be sure to go to the NIMSS website and update participation. She announced that she would be needing station reports to put together the annual report.

Dr. Cedric Gondro offered to host the NCERA-225 meeting at Michigan State University in 2023, and Dr. Daniela Lourenco volunteered to be secretary. By unanimous decision, Dr. Cedric Gondro was elected chairperson and Dr. Daniela Lourenco was elected secretary.

Attendees present at the business meeting were: Brittney Keel (USMARC), Mark Thallman (USMARC), Warren Snelling (USMARC), Larry Kuehn (USMARC), Cedric Gondro (Michigan State Universoty), Jenny Bormann (Kansas State University), Daniela Lourenco (University of Georgia), and Ryan Boldt (IGS).

Meeting adjourned at 4:37 PM.

Accomplishments

Accomplishments Objective 1: Provide a venue for the discussion and exchange of information for the many disconnected and diverse research activities--biological, genomic, statistical, computational, and economical--that support National Cattle Evaluation (NCE). <ul> <li>The previous NCERA-225 meeting was held virtually in December 2021. The 2022 meeting, held virtually on November 1, 2022, covers the timeframe from the January through December 2022. Efforts were made to hold the meeting in-person, but continued travel restrictions due to COVID-19 did not make that possible for many members. Therefore, the meeting was held virtually to ensure adequate attendance.</li> <li>During the reporting period, members of the group were active in NBCEC, the NBCEC Brown Bagger Series, and the BIF board and annual meeting.</li> <li>This committee’s meeting is one of the only places where leaders in the area of genetic evaluation in the beef industry meet on an annual basis. The most pioneering methods and technologies are discussed and debated. Ideas from the meeting help inform research priorities and activities for the coming year.</li> <li>Training of the next generation of producers and scientists is critical for advancements in the field of genetic evaluation. Committee members were involved in teaching activities such as undergraduate curriculum and graduate student training in addition to outreach to producer groups.</li> </ul> Objective 2: Develop through this exchange new tools for delivery and use of beef cattle genetic research, including genomic information, to beef breed associations and beef cattle producers. <ul> <li>The 2022 NCERA-225 annual meeting provided in-depth discussion on new avenues for development of new traits and tools for selection.</li> <li>Continued collaborations between committee members as well as beef breed associations and genetic evaluation groups have led to enhanced single breed evaluations as well as development of prototype multi-breed genetic evaluations with genomic enhancement. Specific examples include:</li> <li>USMARC and UNL members collaborate on applying results from low-pass genomic sequencing to problems in cattle evaluation programs.</li> <li>USMARC and UNL members estimated genetic parameters, heterosis, and breed effects for body condition score and mature cow weight to parameterize a multi-breed evaluation for these traits. IGS is currently prototyping this evaluation.</li> <li>CSU and USMARC members collaborated on pooled genotyping for late feedlot death due to heart failure to determine genetic susceptibility and inheritance.</li> <li>USMARC and CSU collaborated in initial stages to develop methods for recording variable methane emissions and grazing behaviors in beef cattle.</li> <li>UNL and USMARC members collaborated to test a multi-variate framework for genetic evaluation using pooled data.</li> <li>Committee members continue to collaborate on the development of genetic understanding and/or EPDs for novel traits, including early indicators for longevity; muscle and meat quality; biomarkers for traits such as BRD susceptibility, RFI, and temperament; software and data pipelines for improved genomic investigations; characterization of major histology complexes in cattle; effects of climate on genetic evaluations; among other traits.</li> </ul> Objective 3: Update the beef cattle industry on current developments in beef breeding and genetics research including changes in genomics tools and analyses. <ul> <li>Committee members R. Weaber and M. Spangler led the 2022 Brown Brown Bagger Series of webinars to provide education content to county, district, regional and state extension educators and breed association technical staff on new and emerging issues/developments in beef cattle genetic evaluation. This series is nationally known for its educational content. The 2022 series featured committee member S. Speidel. The series recordings and agenda is available here: <a href="http://www.nbcec.org/professionals/brownbag.html">http://www.nbcec.org/professionals/brownbag.html</a></li> <li>Committee members M. Spangler, R. Weaber, M. Rolf, and J. Decker collaborate with other researchers to maintain and expand the availability of beef genetics extension materials available online at eBEEF.org .</li> <li>A wide range of committee members interface beef producers and various organization involved in selection and genetic improvement via presentations, symposia or trade publications. A list of those are included is included with the annual report (see publications – abstracts and proceedings as well as presentations sections).</li> </ul> Objective 4: Collaborate with appropriate groups (e.g. BIF and USDA/NIFA funded integrated projects) on research and outreach. <ul> <li>A number of committee members are routinely engaged in genetic evaluation system development and deployment for various breed associations. This engagement leads to the direct incorporation of novel research and development into national cattle evaluation systems.</li> <li>Several committee members serve on the BIF board of directors: M. Rolf, M. Enns, M. Spangler, R. Weaber, and W. Snelling.</li> <li>Committee members B. Golden, L. Kuehn, M. Spangler, M. Thallman, R. Weaber, and W. Snelling completed testing of iGENDEC software to enable customizable economic index construction and are now working with BIF to implement the package.</li> </ul> Impacts <ol> <li>Committee members published 33 peer reviewed articles as part of their own research or as co-authors by supporting research stations outside this group.</li> <li>Research efforts were communicated through 37 published abstracts or proceedings at various conferences, domestically and internationally, thereby supporting graduate education and/or disseminating their own expertise to stations outside the group on topics of cattle evaluation.</li> <li>Continued and new collaborations among committee members as well as stations outside of this group will result in enhanced knowledge for breed associations to implement genetic evaluations.</li> <li>Graduate and undergraduate training relative to quantitative genetics and beef cattle evaluations will ensure future research and improvements for cattle producers can occur in genetic evaluations.</li> </ol>

Publications

Publications Peer reviewed <ol> <li>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.</li> <li>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.</li> <li>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.</li> <li>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.</li> <li>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.</li> <li>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.</li> <li>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.</li> <li>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.</li> <li>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.</li> <li>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.</li> <li>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.</li> <li>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.</li> <li>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.</li> <li>Hay, E.A., S. Toghiani, A.J. Roberts, T. Paim, L.A. Kuehn, and H.D. Blackburn. (2022)</li> </ol> Genetic architecture of a composite beef cattle population. J Anim Sci 2022. Article 230. doi: 10.1093/jas/skac230. <ol start="15"> <li>Heaton, M.P., G.P. Harhay, A.S. Bassett, H.J. Clark, J.M. Carlson, E.E. Jobman,</li> </ol> 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. <ol start="16"> <li>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.</li> <li>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.</li> <li>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.</li> <li>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.</li> <li>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.</li> <li>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.</li> <li>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.</li> <li>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.</li> <li>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.</li> <li>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.</li> <li>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.</li> <li>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.</li> <li>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.</li> <li>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.</li> <li>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.</li> <li>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.</li> <li>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.</li> <li>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.</li> </ol>   Abstracts and proceedings <ol> <li>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.</li> <li>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.</li> <li>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.</li> <li>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.</li> <li>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.</li> <li>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.</li> <li>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.</li> <li>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.</li> <li>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.</li> <li>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.</li> <li>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.</li> <li>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.</li> <li>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.</li> <li>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.</li> <li>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.</li> <li>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.</li> <li>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.</li> <li>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).</li> <li>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.</li> <li>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.</li> <li>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.</li> <li>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.</li> <li>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.</li> <li>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.</li> <li>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.</li> <li>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.</li> <li>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.</li> <li>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.</li> <li>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.</li> <li>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.</li> <li>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.</li> <li>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.</li> <li>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.</li> <li>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.</li> <li>Tsuruta S., D. Lourenco, and I. Misztal. 2022. Genetic gain for quantitative traits by genomic selection – a simulation study. Aquaculture 2022, San Diego, CA.</li> <li>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).</li> <li>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.</li> </ol>   Presentations <ol> <li>Lourenco, D. Single-step GWAS: association mapping accounting for phenotypes on genotyped and non-genotyped individuals. Plant and Animal Genome Conference XXIX. Vitrual, 2022.</li> <li>Lourenco, D. Leveraging genomics to reshape animal improvement. Advances in Genome Biology and Technology in Agriculture, San Diego, CA, 2022.</li> <li>Lourenco, D. BLUPF90: updates and best practices. University of Florida, 2022.</li> <li>Lourenco, D. Are threshold models feasible for routine genetic evaluations? Purdue University, 2022.</li> <li>Lourenco, D. Single-step GWAS: accounting for the data structure of farm animal populations. Iowa State University, 2022.</li> <li>Lourenco, D. Recent updates in the BLUPF90 software suite. 12th World Congress on Genetics Applied to Livestock Production, Rotterdam, The Netherlands, 2022.</li> <li>Lourenco, D. Advances in beef cattle genomic evaluations in the US. Daejon University, South Korea, 2022.</li> <li>Lourenco, D. Experiences with genomic selection across species. 19th Asian-Australian Association of Animal Production, South Korea, 2022.</li> <li>Lourenco, D. Experiences with genomic selection across species. INRAE, 2022.</li> <li>Lourenco, D. Positive and negative aspects of genomic selection. SimMelhor, Vicosa, Brazil, 2022.</li> <li>Weaber, R.L. Emerging Technologies and Focus on the Future. Allied Genetic Resources Producer Conference. Manhattan, KS, 2022.</li> <li>Weaber, R.L. Building a Profitable Cow Herd: Cow Size-A Measurement of Herd Efficiency. LRF Stockman’s School. Aldam, South Africa, 2022.</li> <li>Weaber, R.L. Building better cattle: heritability and selection for improved feet and leg structure. Purina Genetic Summit. Grey Summit, MO, 2022.</li> <li>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.</li> <li>Weaber, R.L. Using commercial Beef on Dairy data to drive genetic improvement. Beef X Dairy Symposium. Texas Tech University, Lubbock, TX, 2022.</li> <li>Weaber, R.L. Using commercial Beef on Dairy data to drive genetic improvement. Beef X Dairy Symposium. Texas Tech University, Lubbock, TX, 2022.</li> </ol>   Book chapters   <ol> <li>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.</li> </ol>     Extension   <ol> <li>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.</li> </ol>

Impact Statements

Date of Annual Report: 12/18/2023

Report Information

Annual Meeting Dates: 12/18/2023 - 12/20/2023
Period the Report Covers: 01/01/2023 - 12/31/2023

Participants

Brittney Keel (USMARC)
Mark Thallman (USMARC)
Warren Snelling (USMARC)
Larry Kuehn (USMARC)
Cedric Gondro (Michigan State Universoty)
Ryan Boldt (IGS).
Ignacy Misztal (University of Georgia)
Matt Spangler (University of Nebraska)
Scott Speidel (Colorado State University)
Andre Garcia (Angus Genetics, Inc,)
Kelli Retallick (Angus Genetics, Inc.)
Troy Rowan (University of Tennessee)

Brief Summary of Minutes

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

Business Meeting December 18, Embassy Suites Airport, Kansas City, Missouri

Chair: Dr. Brittney Keel

Secretary: Dr. Cedric Gondro

The meeting was called to order by chair Brittney Keel Decebmer 18 at 6:00 pm

Administrative Advisor Dr. Joe Cassady announced that he was willing to allow a different administrator for the committee if anyone is interested. The group discussed options and will be approaching possible candidates.

Dr. Cedric Gondro offered to host the NCERA-225 meeting at Michigan State University in 2024 if cleared by Dr. Daniela :piremco who had volunteered to host in 2024 previously. Dr. Gondro and Dr. Lourenco were set as the new chairperson and secretary, respecitvely.

Attendees present at the business meeting were: Brittney Keel (USMARC), Scott Speidel (CSU), Mark Thallman (USMARC), Warren Snelling (USMARC), Larry Kuehn (USMARC), Cedric Gondro (Michigan State Universoty), and Ryan Boldt (IGS).

Meeting adjourned at 6:30 pm.

Accomplishments

Accomplishments Objective 1: Provide a venue for the discussion and exchange of information for the many disconnected and diverse research activities--biological, genomic, statistical, computational, and economical--that support National Cattle Evaluation (NCE). <ul> <li>The 2020, 2021, and 2022 NCERA-225 meetings were held virtually due to COVID-19.The 2023 meeting, held in Kansas City, MO on December 18, 2023, in conjunction with the BIF Genetic Improvement Workshop, covers the timeframe from the January through December 2023.</li> <li>During the reporting period, members of the group were active in NBCEC, the NBCEC Brown Bagger Series, and the BIF board and annual meeting.</li> <li>This committee’s meeting is one of the only places where leaders in the area of genetic evaluation in the beef industry meet on an annual basis. The most pioneering methods and technologies are discussed and debated. Ideas from the meeting help inform research priorities and activities for the coming year.</li> <li>Training of the next generation of producers and scientists is critical for advancements in the field of genetic evaluation. Committee members were involved in teaching activities such as undergraduate curriculum and graduate student training in addition to outreach to producer groups.</li> </ul> Objective 2: Develop through this exchange new tools for delivery and use of beef cattle genetic research, including genomic information, to beef breed associations and beef cattle producers. <ul> <li>The 2023 NCERA-225 annual meeting provided in-depth discussion on opportunities and obstacles to enhancing beef cattle evaluation with sequence data.</li> <li>Continued collaborations between committee members as well as beef breed associations and genetic evaluation groups have led to enhanced single breed evaluations as well as development of prototype multi-breed genetic evaluations with genomic enhancement. Specific examples include:</li> <li>USMARC and UNL members collaborate on applying low-pass genomic sequencing to problems in cattle evaluation programs.</li> <li>CSU and USMARC members collaborated on pooled genotyping for Bovine Respiratory Disease and Feedlot Heart Disease to determine genetic susceptibility and inheritance.</li> <li>USMARC and CSU collaborated in initial stages to develop methods for recording variable methane emissions and grazing behaviors in beef cattle.</li> <li>UNL and USMARC began a project, with support of the Red Angus Association of American and the American Simmental Association, to utilize data pooling techniques to model commercial data recovery programs.</li> <li>CSU collaborated with American Simmental Association and IGS in developing a pulmonary arterial pressure EPD and evaluation of genetic variability of pulmonary arterial pressure (PAP) dependent on elevation PAP is measured and the elevation of a bull’s intended use.</li> <li>Committee members continue to collaborate on the development of genetic understanding and/or EPDs for novel traits, including early indicators for longevity; muscle and meat quality; biomarkers for traits such as BRD susceptibility, RFI, and temperament; software and data pipelines for improved genomic investigations; characterization of major histology complexes in cattle; effects of climate on genetic evaluations; among other traits.</li> </ul> Objective 3: Update the beef cattle industry on current developments in beef breeding and genetics research including changes in genomics tools and analyses. <ul> <li>Committee members Spangler, W. Snelling, R. Weaber, C Gondro, R. Thallman, and L. Kueh,planned a genetic prediction workshop, focused on the use of low-pass sequencing for genetic prediction, and the use of commercial data in routine genetic evaluations.</li> <li>Committee member L. Hulsman-Hanna served as the Executive Secretary for the North Dakota Beef Cattle Improvement Association (NDBCIA). This role included organizing, planning, and hosting the annual meeting (January 17, 2023) and board meetings (January 17 and October 5, 2023).</li> <li>Committee members R. Weaber and M. Spangler led the 2023 Brown Brown Bagger Series of webinars to provide education content to county, district, regional and state extension educators and breed association technical staff on new and emerging issues/developments in beef cattle genetic evaluation. This series is nationally known for its educational content. The 2023 series featured committee member M. Spangler. The series recordings and agenda is available here: <a href="http://www.nbcec.org/professionals/brownbag.html">http://www.nbcec.org/professionals/brownbag.html</a></li> <li>Committee members M. Spangler, R. Weaber, M. Rolf, and J. Decker collaborate with other researchers to maintain and expand the availability of beef genetics extension materials available online at eBEEF.org .</li> <li>A wide range of committee members interface beef producers and various organization involved in selection and genetic improvement via presentations, symposia, or trade publications. A list of those are included is included with the annual report (see publications – abstracts and proceedings as well as presentations sections).</li> </ul> Objective 4: Collaborate with appropriate groups (e.g. BIF and USDA/NIFA funded integrated projects) on research and outreach. <ul> <li>A number of committee members are routinely engaged in genetic evaluation system development and deployment for various breed associations. This engagement leads to the direct incorporation of novel research and development into national cattle evaluation systems.</li> <li>Several committee members serve on the BIF board of directors: M. Rolf, M. Enns, M. Spangler, R. Weaber, and W. Snelling.</li> <li>The iGENDEC software, developed by committee members L. Kuehn, M. Spangler, M. Thallman, R. Weaber, and W. Snelling, is supported by BIF. This software currently supports the creation of economic selection indexes of 3 U.S. beef breed associations and has been used by extension personnel for programming in 6 states and in classroom instruction by 2 universities. A beta version of a beef x dairy module was completed in 2023.</li> <li>Committee member C. Gondro developed and delivered a two-week course on Genomic Analysis and Artificial Intelligence Applied to Animal Genetics as part of MSU’s Global Scholars program.</li> </ul> Impacts <ol> <li>Committee members published 32 peer reviewed articles as part of their own research or as co-authors by supporting research stations outside this group.</li> <li>Research efforts were communicated through 45 published abstracts or proceedings at various conferences, domestically and internationally, thereby supporting graduate education and/or disseminating their own expertise to stations outside the group on topics of cattle evaluation.</li> <li>Continued and new collaborations among committee members as well as stations outside of this group will result in enhanced knowledge for breed associations to implement genetic evaluations.</li> <li>Graduate and undergraduate training relative to quantitative genetics and beef cattle evaluations will ensure future research and improvements for cattle producers can occur in genetic evaluations.</li> </ol>

Publications

Publications Peer reviewed <ol> <li>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.</li> <li>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.</li> <li>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.</li> <li>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)</li> <li>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</li> <li>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.</li> <li>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.</li> <li>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.</li> <li>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.</li> <li>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)</li> <li>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.</li> <li>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).</li> <li>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.</li> <li>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.</li> <li>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.</li> <li>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)</li> <li>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.</li> <li>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.</li> <li>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.</li> <li>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.</li> <li>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.</li> <li>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.</li> <li>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.</li> <li>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.</li> <li>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.</li> <li>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.</li> <li>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.</li> <li>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.</li> <li>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.</li> <li>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.</li> <li>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.</li> <li>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).</li> </ol>   Abstracts and proceedings <ol> <li>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.</li> <li>Alvarez Munera, A., D. Lourenco, I. Misztal, I. Aguilar, J. Bauer, J. &Scaron;plíchal and M. Bermann. 2023. Improving the efficiency of genomic evaluations with random regression models. In: 74th EAAP – European Association for Animal Production, Lyon, France.</li> <li>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.</li> <li>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.</li> <li>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.</li> <li>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.</li> <li>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.</li> <li>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.</li> <li>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.</li> <li>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.</li> <li>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.</li> <li>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.</li> <li>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)</li> <li>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.</li> <li>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.</li> <li>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.</li> <li>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. </li> <li>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.</li> <li>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.</li> <li>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.</li> <li>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.</li> <li>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.</li> <li>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.</li> <li>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.</li> <li>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.</li> <li>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.</li> <li>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.</li> <li>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.</li> <li>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.</li> <li>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.</li> <li>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.</li> <li>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.</li> <li>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.</li> <li>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.</li> <li>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.</li> <li>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.</li> <li>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.</li> <li>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.</li> <li>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.</li> <li>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.</li> <li>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.</li> <li>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.</li> <li>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.</li> <li>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.</li> <li>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.</li> </ol>   Presentations <ol> <li>Engle, B. Introduction to the USMARC Germplasm Evaluation Program. Presented to Fort Hayes State University students. Clay Center, NE, 2023.</li> <li>Engle, B. Developing resources to improve genetic selection in beef cattle. University of Nebraska-Lincoln. Lincoln, NE, 2023.</li> <li>Enns, R.M. and S.E. Speidel. Interpreting and using the new PAP EPD. University of Wyoming Bull Sale Dinner, 2023.</li> <li>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.</li> <li>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. </li> <li>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.</li> <li>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.</li> <li>Gondro, C. Imputation and genetic evaluation with sequence data (and a bit of AI). BIF 12th Genetic Prediction Workshop, Kansas City, MO, 2023.</li> <li>Hulsman-Hanna, L.L. Breed and Animal Improvements + Genomics, Heartland Highland Association Annual Meeting, Branson, MO, 2023.</li> <li>Hulsman-Hanna, L.L. Interplay of Cow Size in the Production System, NDSU Dickinson Research Extension Center Field Day, Fargo, ND, 2023.</li> <li>Hulsman-Hanna, L.L. Interplay of Cow Size in the Production System, NDSU Fall Extension Conference, Fargo, ND, 2023.</li> <li>Lourenco, D. Big data analysis in animal breeding. 10th Animal Science Congress of Iran. Tehran, Iran, 2023.</li> <li>Lourenco, D. Single-step GWAS with p-values for large genotyped populations. Animal and Dairy Science Association. Ottawa, Canada, 2023.</li> <li>Lourenco, D. Are there benefits in using sequence data for genomic predictions?. Animal Production Science Congress. Bari, Italy, 2023.</li> <li>Lourenco, D. Updates in the BLUPF90 software suite. Plemdat Workshop. Prague, Czech Republic, 2023.</li> <li>Lourenco, D. Understanding Genomic Selection. Genomic Breeding Workshop. Dunedin, New Zealand, 2023.</li> <li>Lourenco, D. Implementing Genomic Selection. Genomic Breeding Workshop. Dunedin, New Zealand, 2023.</li> <li>Lourenco, D. Large multibreed genomic evaluation in dairy cattle. Genomic Breeding Workshop. Dunedin, New Zealand, 2023.</li> <li>Lourenco, D. Efficient approximation of GEBV reliability (a measure of precision). Genomic Breeding Workshop. Dunedin, New Zealand, 2023.</li> <li>Lourenco, D. Use of sequence data and other sources of information for genomic predictions. Genomic Breeding Workshop. Dunedin, New Zealand, 2023.</li> <li>Lourenco, D. SNP-BLUP vs. GBLUP based models. Genomic Breeding Workshop. Dunedin, New Zealand, 2023.</li> <li>Lourenco, D. Simple vs. Complex models. Genomic Breeding Workshop. Dunedin, New Zealand, 2023.</li> <li>Lourenco, D. Making genomic evaluations for millions of animals possible. University of Queensland Seminar. Brisbane, Australia, 2023.</li> <li>Lourenco, D. Decoding Genomic Predictions in Large Populations. Benchmark Genetics Workshop. Bergen, Norway, 2023.</li> <li>Lourenco, D., and I. Misztal. Decoding Genomic Predictions in Large Populations. Gordon Conference - Quantitative Genetics. Ventura, CA, 2023.</li> <li>Spangler, M.L. Value of Genetic Data Beyond Seedstock and Geneticists, UNL Beef Group meeting, Lincoln, NE, 2023.</li> <li>Spangler, M.L. Why Genotype? National Salers Show and Sale, Oklahoma City, OK, 2023.</li> <li>Spangler, M.L. Genomic EPDs, Eastern NE Cattle Conference, Syracuse, NE, 2023.</li> <li>Spangler, M.L. Impact of Genomics on EPDs (panelist), Cattlemens Conference—Blueprint for the Future, Stillwater, OK, 2023.</li> <li>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.</li> <li>Spangler, M.L. Genetic Selection for Enterprise Profit, American Simmental Association STYLE Conference, Oklahoma City, OK, 2023.</li> <li>Spangler, M.L. Genetic selection for improved profit conditioned on enterprise-specific circumstances, American Society of Animal Science meetings (Invited), Albuquerque, NM, 2023.</li> <li>Spangler, M.L. Tools for selecting U.S. beef genetics, Argentine trade group, Lincoln, NE, 2023.</li> <li>Spangler, M.L. Genetic considerations for the cowherd, UNL Ranch practicum (virtual), 2023.</li> <li>Spangler, M.L. Genomics: Improving the U.S. Cowherd, American Gelbvieh Association webinar series (virtual), 2023.</li> <li>Spangler, M.L. iGENDEC—Next generation decision support, Wulf/Riverview webinar (virtual), 2023.</li> <li>Spangler, M.L. Current and Future Use of Genomics in Beef Cattle, Iowa Vet Med Association annual meeting, Ames, IA, 2023.</li> <li>Spangler, M.L. Profit Focused? Make Sure Your Selection Decisions Are Too, NBCEC Brown Bagger webinar series (virtual), 2023.</li> <li>Spangler, M.L. Here in the middle with you: modern quantitative animal genetics, Complex Biosystems seminar, Lincoln, NE, 2023.</li> <li>Spangler, M.L. Impacting the Quality of EPDs for You and Your Customers, Beef Seedstock Symposium, Lexington, KY, 2023.</li> <li>Spangler, M.L. Putting Selection Tools to Work (EPDs and Indices), Beef Seedstock Symposium, Lexington, KY, 2023.</li> <li>Spangler, M.L. Impacting the Quality of EPDs for You and Your Customers, Beef Seedstock Symposium, Glasgow, KY, 2023.</li> <li>Spangler, M.L. Putting Selection Tools to Work (EPDs and Indices), Beef Seedstock Symposium, Glasgow, KY, 2023.</li> <li>Spangler, M.L. Impacting the Quality of EPDs for You and Your Customers, Beef Seedstock Symposium, Spring Hill, TN, 2023.</li> <li>Spangler, M.L. Putting Selection Tools to Work (EPDs and Indices), Beef Seedstock Symposium, Spring Hill, TN, 2023.</li> <li>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</li> <li>Spangler, M.L. Genetic tools for the cow/calf producer (panel moderator), Nebraska Beef Industry Scholars Beef Summit, Mead, NE, 2023</li> <li>Spangler, M.L. Making genetic progress and why end product quality matters to Seedstock producers, American Gelbvieh Association annual convention, Omaha, NE, 2023.</li> <li>Spangler, M.L. Increasing the accuracy of selection decisions, ISU Genetics Symposium, Ames, IA, 2023.</li> <li>Spangler, M.L. Leveraging commercial data to improve selection and management decisions, BIF Genetic Prediction Workshop, Kansas City, MO, 2023.</li> <li>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.</li> <li>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.</li> <li>Thallman, R.M. Maternal composites for terminal crossing. Presented to representatives from Maddux Ranches. Clay Center, NE, 2023.</li> <li>Thallman, R.M. The Germplasm Evaluation Project. USDA-ARS Grazinglands Research Laboratory. El Reno, OK, 2023.</li> <li>Thallman, R.M. The Germplasm Evaluation Project. Junior American Braunvieh Association. Kearney, NE, 2023.</li> <li>Thallman, R.M. Rewarding high-quality data with higher accuracies, Beef Improvement Federation Annual Research Symposium. Calgary, AB, Canada, 2023.</li> <li>Thallman, R.M. The Germplasm Evaluation Project. Presented to visiting beef producers from New Zealand. Clay Center, NE, 2023.</li> <li>Thallman, R.M. The Germplasm Evaluation Project. American Gelbvieh Association. Clay Center, NE, 2023.</li> <li>Thallman, R.M. Recent developments in the Germplasm Evaluation Project, U.S. Meat Animal Research Center Beef Focus Group. Clay Center, NE, 2023.</li> <li>Thallman, R.M. A vision for the future of low-pass sequencing. Beef Improvement Federation Genetic Prediction Workshop. Kansas City, MO, 2023.</li> </ol>   Extension   <ol> <li>Hulsman Hanna, L.L. Fence Post: Have you thought about cow size lately? North Dakota Stockman Magazine. May/June 2023: 20-21. 2023.</li> <li>Sanglard, L.P., G.M. See, and M.L. Spangler. Including Gene Edited Sires in Genetic Evaluation. NE Beef Report. 2023.</li> <li>Spangler, M.L. The impact of genomics on EPDs. Proc. Beef Cattle Congress—Blueprint for the Future. 2023.</li> <li>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.</li> </ol>

Impact Statements

Date of Annual Report: 01/13/2025

Report Information

Annual Meeting Dates: 11/07/2024 - 11/08/2024
Period the Report Covers: 01/01/2024 - 12/31/2024

Participants

Lauren Hanna
Daniela Lourenco
Jenny Bormann
Matthew Kinghorn
Megan Rolf
Heather Bradford
Brittney Keel
Menzi Benton
Larry Kuehn
Bailey Engle
Jacqueline Borgert
Matthew Spangler
Jorge Hidalgo
Pedro Ramos
Elizabeth Dressler
Danielle Stock
Warren Snelling
Cedric Gondro
John Russel
Mark Thalman
Scott Speidel

Brief Summary of Minutes

Minutes of the NCERA225 Annual Meeting

Implementation and Strategies for National Beef Cattle Genetic Evaluation
November 7-8, 2024
Location: Department of Animal Science, Michigan State University

The NCERA225 annual meeting convened to discuss ongoing research, advancements, and strategies for national beef cattle genetic evaluation. The meeting included a business session, research presentations, and discussions on collaborative efforts. The event included hybrid participation, with remote attendees joining via Zoom.

Business Meeting Summary

During the business meeting, the following key decisions and discussions occurred:

Future Meeting Plans:

The 2025 annual meeting will be hosted by the University of Georgia in late October to better accommodate the group’s schedules.

The chair for the 2025 meeting is Daniela Lourenco.

Secretary for 2025 and Incoming Chair for 2026:

Lauren Hanna from North Dakota State University was elected as the secretary for 2025 and incoming chair for 2026, with all participants unanimously voting in favor.

Proposal Renewal:

Members discussed the NCERA225 proposal renewal, which is due in December 2026.

Work on the renewal will begin at the 2025 meeting at the University of Georgia.

Engagement and Outreach:

The group agreed to actively engage with a broader range of academics and will identify a list of academics who could be interested in joining the group. Members committed to reaching out and inviting these individuals to join.

Collaborative Grant Application:

The group plans to pursue a collective grant application focusing on:

Data compression methods for large genomic data.

Novel approaches for utilizing sequence data in genomic prediction.

A shared workspace will be created, and an additional meeting will be held in 2025 to prepare for the grant submission process.

Research Presentations

Presentations were delivered by researchers and stakeholders covering various topics, including genomic evaluations, precision livestock technologies, and institutional updates:

Foundational Thinking in Statistics – Jacqueline Borgert.

Current Attempts to Predict Efficiency Traits Using Host and Metagenomic Sequence – Matthew Spangler.

North Dakota State University Station Report – Lauren Hanna.

Research Updates at UGA – Daniela Lourenco.

Genomic Evaluations for Binary Traits: Challenges and Alternatives – Jorge Hidalgo.

Application of Precision Livestock Farming Technologies in Swine and Beef Cattle Production – Brittney Keel.

American Angus Association’s Functional Longevity EPD – Pedro Ramos.

Genetic Prediction of Gas Fluxes and Metabolic Heat Production from Grazing Angus Cattle Using a GreenFeed – Elizabeth Dressler.

Genetic Parameter Estimation for Scrotal Circumference and Semen Characteristics of Angus Bulls – Danielle Stock.

Genomic Heterozygosity in Stable Populations – Warren Snelling.

The day concluded with a closing discussion summarizing the presentations and identifying future directions for collaborative work.

Accomplishments

Objective 1: Provide a venue for the discussion and exchange of information for the many disconnected and diverse research activities – biological, genomic, statistical, computational, and economical – that support National Cattle Evaluation (NCE). <ul> <li>The 2024 NCERA-225 meeting was held in-person and via Zoom on November 7-8, 2024, in the Department of Animal Science at Michigan State University, East Lansing, MI. The meeting fostered productive dialogue among researchers, industry representatives, and other stakeholders.</li> <li>Committee members were active in the National Beef Cattle Evaluation Consortium (NBCEC), participating in the NBCEC Brown Bagger Series and serving on the Beef Improvement Federation (BIF) board and annual meeting.</li> <li>Members shared emerging research methodologies, including approaches for genomic data integration, low-pass sequencing, AI-driven predictions, and advanced statistical models, ensuring that knowledge flows freely among institutions.</li> <li>Efforts to train the next generation of scientists and producers continued through undergraduate and graduate coursework, mentorship programs, short courses (e.g., BLUP, genomic prediction tools and AI methods applied to animal breeding), and outreach presentations worldwide, including the U.S., Mexico, Peru, Canada, Korea and others.</li> </ul> Objective 2: Develop through this exchange new tools for delivery and use of beef cattle genetic research, including genomic information, to beef breed associations and beef cattle producers. <ul> <li>Multiple advancements were made in genomic evaluation methods and computational tools:</li> <ul> <li>Formulas derived to convert breeding values on the observed scale to the liability scale, increasing the utility of categorical trait analysis.</li> <li>Strategies to improve prediction accuracy and reduce bias in multi-breed and crossbred populations, including genomic evaluations for beef-on-dairy cattle.</li> <li>Strategies for incorporation of low-pass genomic sequencing, metagenomics, and multi-omic data to enhance predictive accuracy for economically relevant traits.</li> <li>Development of AI methods for genomic prediction.</li> </ul> <li>Collaborations among stations (e.g., USMARC, UNL, CSU, KSU) and breed associations (e.g., American Simmental Association, International Genetic Solutions) led to prototype multi-breed genetic evaluations, development of pulmonary arterial pressure (PAP) EPDs, and improved understanding of genetic parameters for traits such as feed efficiency, methane emissions, heart score, and disease susceptibility. A collaboration between Angus Genetics and MSU is developing AI prediction models that uses climate variables to estimate phenotypic outcomes of production traits in angus cattle.</li> <li>iGENDEC software, a key decision support tool, now supports economic selection index development for four U.S. beef breed associations; it is used by extension personnel in six states and Canada and is also integrated into undergraduate and graduate curricula at three universities. In 2024, a beef x dairy module was added, expanding the software’s relevance to emerging production systems.</li> <li>Projects addressed sire conception rates in both beef-on-beef and beef-on-dairy systems, multi-breed analyses of bull semen traits, and using GreenFeed technology to genetically evaluate gas fluxes in grazing beef cows. Additional efforts explored candidate genes related to male fertility traits in Angus bulls and evaluation of scrotal circumference, semen motility, and morphology traits.</li> </ul> Objective 3: Update the beef cattle industry on current developments in beef breeding and genetics research including changes in genomics tools and analyses. <ul> <li>Committee members organized and participated in national and international meetings, including the Beef Improvement Federation Symposium, American Society of Animal Science (ASAS) Annual Meeting, and breed association conferences. They presented cutting-edge findings on genomic selection, genetic prediction methodologies, and novel evaluation strategies.</li> <li>The NBCEC Brown Bagger Series, led by committee members, provided timely educational webinars for extension educators and breed association technical staff. Topics included emerging issues in genetic evaluation, low-pass sequencing, and leveraging commercial data.</li> <li>Committee members contributed to eBEEF.org, maintaining and expanding online resources for producers and extension professionals. Presentations at producer-oriented venues (e.g., Colorado Cattlemen’s Association Meeting, Kentucky Cattleman’s Annual Convention) ensured knowledge transfer to end users.</li> <li>Research on emerging traits, such as methane emissions and PAP, and the development of best practices for data sharing, including the use of encryption and blockchain technology, keep the industry informed and future focused.</li> <li>Extension and outreach activities included short courses on mixed models, genomic selection (BLUPF90), and the integration of AI in genomic analyses, as well as invited presentations at international symposia.</li> </ul> Objective 4: Collaborate with appropriate groups (e.g., BIF and USDA/NIFA funded integrated projects) on research and outreach. <ul> <li>Committee members held leadership roles within BIF and engaged in integrated projects that directly influence breed association genetic evaluations. This engagement ensures novel research seamlessly flows into industry applications.</li> <li>The development and refinement of iGENDEC, supported by BIF, exemplifies a successful technology transfer. Expanded capabilities, including a beef x dairy module, and widespread adoption indicate ongoing, impactful collaboration with industry stakeholders.</li> <li>Partnerships with USDA and NIFA-funded projects supported the integration of advanced computational methods, data security measures (e.g., encrypted genotypes, blockchain), and environmentally responsive models into NCE pipelines.</li> <li>Collaborations also extended to global partners, including research institutions in Mexico, Peru, Brazil, Korea and Australia, multi-omic integration workshops, and engagements with breed associations in multiple countries.</li> </ul>

Publications

Impact Statements

1. Publications and Dissemination: Committee members published over 30 peer-reviewed articles, complemented by 45+ abstracts and proceedings, ensuring that novel methodologies and research findings reach the global scientific community.
2. Enhanced Evaluation Methods: New analytical tools and genomic strategies improve accuracy and reduce bias in genetic evaluations. This leads to better selection decisions, increased profitability, and sustainability for producers.
3. Training and Capacity Building: Graduate and undergraduate training, workshops, and international short courses bolster the next generation of animal geneticists, extension specialists, and producers. Students mentored this year included several M.S. and Ph.D. candidates and postdoctoral researchers.
4. Integration of Novel Traits and Data Sources: Incorporating low-pass sequencing, AI modeling of climate effects, metagenomics, and environmental adaptability traits (e.g., methane emissions, PAP) ensures that NCE aligns with future production challenges and consumer demands.
5. Industry-Relevant Tools: The continued development and deployment of decision-support software (iGENDEC) and improved data-sharing protocols (encryption, blockchain) foster industry adoption of cutting-edge genetic evaluation techniques.

Date of Annual Report: 12/18/2025

Report Information

Annual Meeting Dates: 10/28/2025 - 10/29/2025
Period the Report Covers: 01/01/2025 - 12/31/2025

Participants

In-person:
Guilherme Rosa (UW-Madison)
Anderson Alves (UGA)
Jorge Hidalgo (UGA)
Jeferson Lourenco (UGA)
Fernando Bussiman (UGA)
Randie Culbertson (UGA)
Ryan Boldt (IGS)
Rafael Medeiros (AGI)
Ignacy Misztal (UGA)
Zuleica Trujano (UGA)
Daniela Lourenco (UGA)
Cedric Gondro (MSU)
Online:
Jennifer Bormann (KSU)
Megan Rolf (KSU)
Bob Weaber (KSU)
Jennifer Thomson (Montana State University)
Matt Spangler (UNL)
Lauren Hanna (NDSU)

UGA students and postdocs who participated:
Alejandra Alvarez Munera
Sergio Nicolas Sanchez Sierra
Masum Billah
Marina Cunha Bernardes
Brenda Monis Moreno
Helene Wilmot
Denyus de Oliveira
Heegun Lee
Joe Tabet
John Thomson
Rebeka Magalhaes
Zuleica Trujano
Fernando Bussiman
Fazhir Kayondo
Arielly Garcia
Nedenia Stafuzza
Kaitlyn Scheflen
Pamela Molina
Kevin Moreno
Andre Luis Romeiro de Lima
Suelen Fernandes Padilha

Brief Summary of Minutes

Minutes of the meeting:

October 28:

The group discussed plans for upcoming business meetings, including selecting hosts and secretaries while considering various locations and attendance numbers. They talked about the current meeting attendance, noting that around 35 people were expected, including students and postdocs. They mentioned that some usual attendees, such as those from the USDA Clay Center, were not present this year. The group discussed the renewal of their project, which is due for submission by the end of 2026. They reviewed the history of project renewals, noting that Jenny and Matt were involved in writing the renewal 5 years ago, while Scott and Brittany coordinated the 2022 renewal. Daniela Lourenco and Lauren Hanna will be responsible for the next renewal. The group also discussed the need to engage more participants, particularly those working in beef cattle, and considered inviting individuals to join the group.

Next steps

Ryan Boldt will host the 2027 meeting in Bozeman, Montana and serve as Secretary for 2026.

Daniela and Lauren: Coordinate and write the project renewal document for submission by the end of 2026.

Daniela and Lauren : Prepare and submit the annual report within 90 days after the meeting.

The group came up with some potential new members including Troy, Thiago Bresolin, and others. They will be invited.

Jenny: look up the exact due date for the annual report submission.

Chair and Secretary: Follow up with all participants to collect documents for the annual report

October 29:

The meeting covered presentations on computer vision and deep learning applications for measuring animal traits, genetic parameter models for binary traits, and microbiome research in cattle. Updates were shared on genetic evaluations, including improvements in efficiency and accuracy, as well as discussions on the potential negative effects of genomic selection on secondary traits. The group also addressed challenges in data collection and analysis, with various research projects focusing on methane production, male fertility, and the development of new evaluation models.

IDEAS: Integrating enviromics, genomics, and machine learning for precision breeding of resilient beef cattle

Guilherme Rosa highlighted the growing importance of accounting for genotype-by-environment interactions in beef cattle breeding, particularly under increasing climate variability. He emphasized that traditional genetic evaluations often fail to capture how animals respond differently across environments. To address this, the IDEAS framework integrates environmental information directly into genetic evaluation. He introduced enviromics as the systematic use of high-resolution environmental data, including climate, geography, and management factors. These data are combined with genomic, pedigree, and phenotypic records. The goal is to better characterize environmental heterogeneity affecting cattle performance. This integration enables more accurate prediction of animal performance across diverse conditions. Ultimately, it supports precision breeding of beef cattle resilient to environmental stress.

Computer Vision for novel phenotypes in beef cattle

Anderson Alves presented research on using computer vision and deep learning to measure animal traits, focusing on beef cattle and dairy cows. He demonstrated how cameras can automatically collect data on traits like body condition score, corpus luteum measurements, and morphometric traits. The technology showed promise in predicting pregnancy status and measuring traits that could be used for animal breeding selection. However, challenges remain in animal identification and validating the accuracy of digital phenotypes compared to traditional measurements. The research team is working on addressing these challenges and exploring the potential of computer vision for novel trait measurement in livestock.

Equivalence of genetic parameters on liability and observed scales

Jorge Hidalgo presented on the equivalence of genetic parameters between liability and observed scales for binary traits, focusing on the relationship between threshold models and linear models. He demonstrated that while threshold models provide a more theoretically correct approach, linear models can be used as an approximation, particularly when dealing with large datasets. Jorge also showed how to transform variances and covariances between the two scales, and explained that this approach works even with related individuals. The presentation concluded with a discussion of when to use each model type, noting that linear models may be more practical for certain applications while still providing reasonable results.

Microbiome information for beef cattle efficiency

Jeferson Lourenco presented an overview of microbiome research, focusing on how DNA sequencing has revolutionized the study of microorganisms. He explained the process of obtaining and analyzing microbiome data, including the use of marker genes and shotgun sequencing. Jeferson then discussed a study involving ruminal and fecal samples from beef cattle, which aimed to identify microbial markers associated with feed efficiency. The results showed that microbiome information alone could explain 39% of the variance in residual feed intake, and when combined with dietary information, this increased to 50%. This research suggests that microbiome analysis could be a valuable tool for predicting feed efficiency in beef cattle.

Using microbiome information in genomic evaluations

Fernando Bussiman discussed incorporating microbiome data into genomic evaluations. He explained statistical models for analyzing microbiome data, highlighting the challenges of calculating similarity matrices and the need to consider different levels of microbiome resolution. The discussion concluded with questions about handling zero values in microbiome data and potential approaches to improve accuracy in microbiome-based predictions.

Beef x dairy from an international perspective

Randie Culbertson presented on beef and dairy production from an international perspective. She highlighted the economic incentives driving the use of beef semen in dairy herds, noting that beef on dairy calves currently sell for $1,300 compared to $125 for straight dairy calves. The presentation covered challenges in the U.S. beef industry, including variability in performance and high liver abscess rates. Randie also discussed her experiences presenting on this topic in Europe and Australia, noting differences in beef and dairy production systems across countries. She emphasized the need for better data collection and research on early calf development to address the challenges faced in the beef on dairy sector.

Updates on IGS genomic evaluations

Ryan Boldt from IGS presented an update on their genetic evaluation work, highlighting that they currently have 23 partner associations with over 16 million phenotype records and 793,000 genotyped animals. He discussed their development of a multi-trait model for predicting mature weight at 6 years of age, which showed strong genetic correlations between different ages and across body condition scores. The evaluation includes breed and heterosis effects, with contemporary groups based on birth and weaning information, and has shown a linear increase in genetic estimates for mature weight.

Indirect Predictions and its application in GeneMax Advantage

Rafael Medeiros from AGI presented on the use of indirect prediction (IP) in genetic evaluations, highlighting the Angus GeneMax Advantage product and its application in commercial cattle breeding. He discussed the methodology behind GeneMax, including the use of SNP effect and IP transformed into scores from 0 to 100. The presentation also covered the Angus Link product, which benchmarks animals against industry standards. Rafael raised questions about the optimal frequency of recalibrating the SNP effect and the impact of adding new data on indirect predictions. He presented research findings comparing different approaches to estimating the SNP effect, concluding that using only core animals provides more stable and accurate predictions. The study showed that one year of adding new data may not significantly impact indirect predictions for certain models, suggesting that recalibrations may not be necessary as frequently as currently done.

Formulas for genetic parameters via predictivity (GPP)

Ignacy Misztal discussed the potential negative effects of genomic selection on secondary traits in beef cattle. He presented the GPP formulas to investigate temporal changes in genetic parameters for populations under genomic selection, highlighting the need to update variance components frequently due to genomic selection to identify the potential negative effects before any issues.

Application of GPP in beef cattle

Zuleica Trujano discussed how intense genomic selection for performance traits like growth and carcass quality can change genetic parameters over time and potentially affect other important traits such as foot structure in Angus cattle. Her study estimated heritabilities and genetic correlations for foot structure traits (foot angle and claw set) alongside performance traits across multiple time periods using both traditional variance component estimation and a predictivity method incorporating genomic information. She found that while heritabilities for performance traits tended to decrease over recent generations, genetic correlations between foot structure and performance traits remained close to zero, indicating that strong selection for performance has not yet adversely impacted foot structure. The results highlighted the importance of regularly updating genetic parameters in breeding programs and selecting on multiple traits together to maintain structural soundness while improving performance.

Estimating variance components for large genomic datasets

Daniela Lourenco presented MC-ssGREML, a new method for estimating variance components in genomic datasets that extends traditional REML to handle very large, complex single-step genomic models. The approach uses Monte Carlo sampling to approximate computationally intensive matrix traces in single-step genomic REML (ssGREML), greatly reducing memory and time requirements compared to exact ssGREML calculations. In tests with both moderate and very large datasets, MC-ssGREML produced similar variance component estimates to exact methods but with substantial savings in computing resources, making it suitable for routine variance components estimation involving millions of animals and genotypes.

Research Station reports

Lauren Hanna provided a research update on factor analysis and computer vision techniques for cattle traits, while Matt Spangler and Megan Rolf shared progress on methane research and male fertility projects. Bob Weaber reminded attendees about the 2026 Beef Improvement Federation Annual Symposium in Boise, Idaho, and Jenny Bormann emphasized the need for group members to submit their publications and evidence of collaboration for the annual report.

Accomplishments

Objective 1: Provide a venue for the discussion and exchange of information for the many disconnected and diverse research activities – biological, genomic, statistical, computational, and economical – that support National Cattle Evaluation (NCE). <ul> <li>The 2025 NCERA-225 meeting was held in-person and via Zoom on October 28-29, 2025, in the Department of Animal and Dairy Science at The University of Georgia, Athens, GA. The meeting fostered productive dialogue among researchers, industry representatives, and other stakeholders.</li> <li>Committee members were active in the National Beef Cattle Evaluation Consortium (NBCEC), participating in the NBCEC Brown Bagger Series and serving on the Beef Improvement Federation (BIF) board and annual meeting.</li> <li>Members shared emerging research methodologies, including approaches for genomic data integration, enviromics, microbiome information, AI-driven predictions, advanced statistical models for genomic predictions and variance components estimation for large datasets, and the uptake of beef-on-dairy, ensuring that knowledge flows freely among institutions.</li> <li>Efforts to train the next generation of scientists and producers continued through undergraduate and graduate coursework, mentorship programs, short courses (e.g., BLUP, genomic prediction tools and AI methods applied to animal breeding), and outreach presentations worldwide, including the U.S., Mexico, Panama, Australia, South Korea, South Africa, Slovenia, and others.</li> </ul> Objective 2: Develop through this exchange new tools for delivery and use of beef cattle genetic research, including genomic information, to beef breed associations and beef cattle producers. <ul> <li>Multiple advancements were made in genomic evaluation methods and computational tools:</li> <ul> <li>Formulas derived to convert breeding values on the observed scale to the liability scale, increasing the utility of categorical trait analysis.</li> <li>Methods to estimate variance components and genetic parameters for large genomic datasets.</li> <li>Strategies for incorporation of enviromics, metagenomics, and multi-omic data to enhance predictive accuracy for economically relevant traits.</li> <li>Development of AI methods for digital phenotyping of hard-to-measure traits.</li> <li>Fine-tuning of indirect predictions for commercial, genotyped animals.</li> </ul> <li>Collaborations among stations (e.g., USMARC, UNL, CSU, KSU) and breed associations (e.g., American Simmental Association, International Genetic Solutions) led to the development of a multi-trait model for predicting mature weight at 6 years of age.</li> <li>A collaboration between Angus Genetics, MSU, UW-Madison, and UGA is developing AI prediction models that uses climate variables to estimate phenotypic outcomes of production traits in angus cattle.</li> <li>Monte-Carlo single-step genomic REML (MC-ssGREML) was developed by UGA in collaboration with Angus Genetics and implemented in the blup90iod3+ software estimate variance components in single-step with millions of genotyped animals.</li> <li>Genetic parameters via predictivity (GPP) was developed by UGA in collaboration with Angus Genetics to investigate the temporal dynamics of genetic correlations and heritabilities and help track changes in traits that can be detrimental to breeding programs.</li> <li>UC Davis led the development of a comprehensive repository housing over 100 publicly accessible agricultural genomic datasets. Each dataset includes standardized metadata, DOIs, and detailed descriptions. The G2P dataset repository is available to the public via GitHub and a Shiny web application at https://ag2p-disc.github.io/, enabling researchers to efficiently access, download, and analyze more than 100 curated agricultural genomic datasets.</li> <li>Projects addressed measuring body condition score, corpus luteum measurements, and morphometric traits using cameras, categorical traits like calving ease and docility, feed efficiency, methane emission, growth traits, and feet and leg scores.</li> </ul> Objective 3: Update the beef cattle industry on current developments in beef breeding and genetics research including changes in genomics tools and analyses. <ul> <li>Committee members organized and participated in national and international meetings, including the Beef Improvement Federation Symposium, American Society of Animal Science (ASAS) Annual Meeting, and breed association conferences. They presented cutting-edge findings on genomic selection, genetic prediction methodologies, and novel evaluation strategies.</li> <li>The NBCEC Brown Bagger Series, led by committee members, provided timely educational webinars for extension educators and breed association technical staff. Topics included maternal trait selection, iGENDEC selection index development tool, across-breed EPD adjustment factors, and the importance of selection for methane emission.</li> <li>Committee members contributed to eBEEF.org, maintaining and expanding online resources for producers and extension professionals. Presentations at producer-oriented venues (e.g., Colorado Cattlemen’s Association Meeting, Kentucky Cattleman’s Annual Convention) ensured knowledge transfer to end users.</li> <li>Research on emerging traits, such as methane emissions and PAP, and the development of best practices for data sharing, including the use of encryption and blockchain technology, keep the industry informed and future focused.</li> <li>Extension and outreach activities included short courses on mixed models, genomic selection (BLUPF90), and the integration of AI in genomic analyses, as well as invited presentations at international symposia.</li> </ul> Objective 4: Collaborate with appropriate groups (e.g., BIF and USDA/NIFA funded integrated projects) on research and outreach. <ul> <li>Committee members held leadership roles within BIF and engaged in integrated projects that directly influence breed association genetic evaluations. This engagement ensures novel research seamlessly flows into industry applications.</li> <li>The development and refinement of iGENDEC, supported by BIF, exemplifies a successful technology transfer. Expanded capabilities, including a beef x dairy module, and widespread adoption indicate ongoing, impactful collaboration with industry stakeholders.</li> <li>Partnerships with USDA and NIFA-funded projects supported the integration of advanced computational methods, new data sources and AI, data security measures (e.g., encrypted genotypes, blockchain), and environmentally responsive models into NCE pipelines.</li> <li>Collaborations also extended to global partners, including research institutions in Mexico, Brazil, South Korea and Australia, multi-omic integration workshops, and engagements with breed associations in multiple countries.</li> </ul>

Publications

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. <ol> <li>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. <a href="https://doi.org/10.1093/jas/skaf295">https://doi.org/10.1093/jas/skaf295</a></li> <li>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. <a href="https://doi.org/10.1093/jas/skaf115">https://doi.org/10.1093/jas/skaf115</a></li> <li>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. <a href="https://doi.org/10.1016/j.smallrumres.2024.107369">https://doi.org/10.1016/j.smallrumres.2024.107369</a></li> <li>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. <a href="https://doi.org/10.1093/tas/txae014">https://doi.org/10.1093/tas/txae014</a></li> <li>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). <a href="https://doi.org/10.4148/1051-0834.2501">https://doi.org/10.4148/1051-0834.2501</a></li> <li>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.</li> <li>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.</li> <li>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.</li> <li>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</li> <li>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.</li> <li>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</li> <li>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.</li> <li>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).</li> <li>Phelps, S. L., and M. M. Culbertson. 2025. Genetic parameters for heifer pregnancy and carcass traits in Angus cattle. Journal of Animal Science. Submitted.</li> <li>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</li> <li>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</li> <li>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</li> <li>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</li> <li>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</li> <li>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. <a href="https://doi.org/10.1186/s12711-025-01007-8">https://doi.org/10.1186/s12711-025-01007-8</a></li> <li>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.</li> <li>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. <a href="https://doi.org/10.1111/jbg.70033">https://doi.org/10.1111/jbg.70033</a></li> <li>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. <a href="https://doi.org/10.1111/jbg.70030">https://doi.org/10.1111/jbg.70030</a></li> <li>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. <a href="https://doi.org/10.3168/jds.2025-27089">https://doi.org/10.3168/jds.2025-27089</a></li> <li>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</li> <li>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. <a href="https://doi.org/10.1186/s12711-025-01006-9">https://doi.org/10.1186/s12711-025-01006-9</a></li> <li>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. <a href="https://doi.org/10.3168/jds.2025-26503">https://doi.org/10.3168/jds.2025-26503</a></li> <li>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. <a href="https://doi.org/10.1093/jas/skaf340">https://doi.org/10.1093/jas/skaf340</a></li> <li>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. <a href="https://doi.org/10.1093/jas/skaf382">https://doi.org/10.1093/jas/skaf382</a></li> <li>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. <a href="https://doi.org/10.1186/s12711-025-00991-1">https://doi.org/10.1186/s12711-025-00991-1</a></li> <li>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. <a href="https://doi.org/10.1093/g3journal/jkaf136">https://doi.org/10.1093/g3journal/jkaf136</a></li> <li>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. <a href="https://doi.org/10.1111/jbg.70008">https://doi.org/10.1111/jbg.70008</a></li> <li>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. <a href="https://doi.org/10.1007/s13353-025-00987-z">https://doi.org/10.1007/s13353-025-00987-z</a></li> <li>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. <a href="https://doi.org/10.1186/s12711-025-00984-0">https://doi.org/10.1186/s12711-025-00984-0</a></li> <li>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. <a href="https://doi.org/10.1111/jbg.12947">https://doi.org/10.1111/jbg.12947</a></li> <li>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. <a href="https://doi.org/10.1093/jas/skaf158">https://doi.org/10.1093/jas/skaf158</a></li> <li>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. <a href="https://doi.org/10.3389/fgene.2025.1549284">https://doi.org/10.3389/fgene.2025.1549284</a></li> <li>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. <a href="https://doi.org/10.1016/j.jprot.2024.105361">https://doi.org/10.1016/j.jprot.2024.105361</a></li> <li>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. <a href="https://doi.org/10.1111/jbg.12882">https://doi.org/10.1111/jbg.12882</a></li> </ol>   Selected Conference Proceedings, Invited Talks and Producer Meetings <ol> <li>Weaber, R. L. (2025). Selecting for cow maternal performance: Making your breeding and selection systems pay. Stockfarm, 15(10), 43–45.</li> <li>Weaber, R. L. (2025). Selekteer vir koei maternale prestasie: Laat jou teling- en seleksiestelsels vir jou werk. Veeplaas, 16(10), 50–51.</li> <li>Weaber, R. L. (October 14, 2025). Advancing genetic improvement in the United States. Simmentaler Annual General Meeting, Pretoria, South Africa.</li> <li>Weaber, R. L. (October 10, 2025). Selecting for cow maternal performance. Livestock Registering Federation Stockman School, Ventersburg, Free State, South Africa.</li> <li>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.</li> <li>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.</li> <li>Weaber, R. L. (October 7, 2025). iGENDEC selection index tools in the USA. Livestock Registering Federation Breeders Workshop, Ventersburg, Free State, South Africa.</li> <li>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.</li> <li>Weaber, R. L. (April 4, 2025). Using genetics to meet the food demand of 2050. University of Jos, Jos, Plateau State, Nigeria.</li> <li>Weaber, R. L. (2025). iGENDEC-based selection indexes at the North American Limousin Foundation and American Gelbvieh Association. eBEEF Brown Bagger Webinar Series.</li> <li>Weaber, R. L. (September 17–18, 2025). Genetic selection for cow fertility and longevity. Applied Reproductive Strategies in Beef Cattle, North Platte, NE.</li> <li>Weaber, R. L. (May 2, 2025). Getting it right: Proper contemporary grouping strategies. Santa Gertrudis Breeders’ Convention, San Marcos, TX.</li> <li>Weaber, R. L. (April 18, 2025). Bull buying using selection indexes: Are the assumptions correct? 74th Florida Beef Cattle Short Course, Gainesville, FL.</li> <li>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.</li> <li>Speidel, S. E. (November 11, 2025). Pulmonary hypertension: New research and developments. Range Beef Cow Symposium, Cheyenne, WY.</li> <li>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.</li> <li>Speidel, S. E. (August 15, 2025). Pulmonary hypertension in moderate-elevation feedlots: New research and developments. Pulmonary Arterial Pressure Summit, Fort Collins, CO.</li> <li>Speidel, S. E. (August 20, 2025). Pulmonary hypertension in moderate-elevation feedlots: New research and developments. Leachman Cattle, Meriden, WY.</li> <li>Speidel, S. E. (September 5, 2025). Pulmonary hypertension: New research and developments. Hereford Academy, Fort Collins, CO.</li> <li>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.</li> <li>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.</li> <li>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.</li> <li>Culbertson, M. M. (May 2024). Beef on dairy: The U.S. perspective. 46th ICAR and Interbull Conference, Bled, Slovenia.</li> <li>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.</li> <li>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)</li> <li>Hidalgo, J. Developments in single-step GBLUP and single-step GWAS. University of Florida, USA.</li> <li>Hidalgo, J. An introduction to single-step GBLUP. Universidad Autónoma Chapingo, Mexico.</li> <li>Hidalgo, J. Equivalence of (co)variance components on the observed and liability scales. University of Georgia, USA.</li> <li>Lourenco, D. The winners and losers in the genomic selection game. Plant and Animal Genome Conference (PAG), San Diego, CA.</li> <li>Lourenco, D. Large-scale single-step GWAS in beef and dairy cattle. Plant and Animal Genome Conference (PAG), San Diego, CA.</li> <li>Lourenco, D. Optimizing genomic evaluations: Single-step GBLUP and GWAS for large genotyped populations. Macon, GA.</li> <li>Lourenco, D. What it takes to deal with the largest livestock datasets in the world. Milan, Italy.</li> <li>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.</li> <li>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.</li> <li>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).</li> <li>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).</li> <li>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).</li> <li>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).</li> <li>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).</li> <li>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).</li> <li>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.</li> <li>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.</li> <li>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.</li> <li>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).</li> <li>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).</li> <li>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.</li> <li>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.</li> <li>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.</li> </ol>

Impact Statements

1. Publications and Dissemination: Committee members published over 39 peer-reviewed articles, complemented by over 30 abstracts and proceedings, ensuring that novel methodologies and research findings reach the global scientific community.
2. Enhanced Evaluation Methods: New analytical tools and genomic strategies improve accuracy and reduce bias in genetic evaluations and allow the estimation of variance components for very large datasets. This leads to better selection decisions, increased profitability, and sustainability for producers.
3. Training and Capacity Building: Graduate and undergraduate training, workshops, and international short courses bolster the next generation of animal geneticists, extension specialists, and producers. Students mentored this year included several M.S. and Ph.D. candidates and postdoctoral researchers.
4. Integration of Novel Traits and Data Sources: AI modeling of novel traits and climate effects, metagenomics, and environmental adaptability traits (e.g., methane emissions) ensures that NCE aligns with future production challenges and consumer demands.
5. Industry-Relevant Tools: The continued development and deployment of genetic evaluation (BLUPF90) decision-support (iGENDEC) software and improved data-sharing protocols (encryption, blockchain) foster industry adoption of cutting-edge genetic evaluation techniques.