Brief Summary of Minutes of Annual Meeting

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.

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).

• 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.
• 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.
• 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.
• 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.

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.

• Multiple advancements were made in genomic evaluation methods and computational tools:
• Formulas derived to convert breeding values on the observed scale to the liability scale, increasing the utility of categorical trait analysis.
• Methods to estimate variance components and genetic parameters for large genomic datasets.
• Strategies for incorporation of enviromics, metagenomics, and multi-omic data to enhance predictive accuracy for economically relevant traits.
• Development of AI methods for digital phenotyping of hard-to-measure traits.
• Fine-tuning of indirect predictions for commercial, genotyped animals.
• 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.
• 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.
• 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.
• 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.
• 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.
• 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.

Objective 3: Update the beef cattle industry on current developments in beef breeding and genetics research including changes in genomics tools and analyses.

• 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.
• 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.
• 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.
• 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.
• 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.

Objective 4: Collaborate with appropriate groups (e.g., BIF and USDA/NIFA funded integrated projects) on research and outreach.

• 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.
• 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.
• 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.
• 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.

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

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

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

1. Beef Improvement Federation Genetic Prediction Workshop Organizing Committee (including R. L. Weaber). 2025. Beef Improvement Federation Genetic Prediction Workshop: Opportunities and obstacles to enhancing beef cattle evaluation with sequence data. Journal of Animal Science 103: skaf295. https://doi.org/10.1093/jas/skaf295
2. Shaffer, W. R., J. Hidalgo, N. M. Bello, R. S. Noland, J. M. Bormann, R. L. Weaber, C. M. Ahlberg, K. Bruno, C. R. Krehbiel, M. S. Calvo-Lorenzo, C. J. Richards, S. Place, U. DeSilva, L. A. Kuehn, and M. M. Rolf. 2025. Beef cattle phenotypic plasticity and stability of dry matter intake and respiration rate across varying temperature–humidity index levels. Journal of Animal Science 103: skaf115. https://doi.org/10.1093/jas/skaf115
3. Dressler, E. A., J. M. Bormann, R. L. Weaber, R. C. Merkel, and M. M. Rolf. 2024. A review of cashmere fiber phenotypes: Production, heritabilities, and genetic correlations. Small Ruminant Research 240: 107369. https://doi.org/10.1016/j.smallrumres.2024.107369
4. Dressler, E. A., J. M. Bormann, R. L. Weaber, and M. M. Rolf. 2024. Use of methane production data for genetic prediction in beef cattle: A review. Translational Animal Science 8: txae014. https://doi.org/10.1093/tas/txae014
5. Underwood, M., K. J. Starzec, N. Hill-Sullins, and R. L. Weaber. 2024. Print Grades Prime: A quantitative analysis of producer communication preferences of U.S. beef breed association magazines. Journal of Applied Communications 108(1). https://doi.org/10.4148/1051-0834.2501
6. Boldt, R., J. Keele, L. A. Kuehn, T. McDaneld, S. E. Speidel, and R. M. Enns. 2025. Comparison of Random Forest and traditional GWAS models for analysis of pooled DNA data. Genetics Selection Evolution. Submitted.
7. Krafsur, G. M., M. M. Culbertson, R. D. Brown, M. Li, T. N. Holt, S. E. Speidel, R. M. Enns, R. J. Delmore, K. R. Stenmark, and M. G. Thomas. 2025. Reduced feed efficiency, cardiopulmonary remodeling, and increased mortality in feedyard cattle with pulmonary hypertension. Journal of Animal Science. Submitted.
8. Gonzalez-Murray, R. A., M. G. Thomas, T. N. Holt, S. Coleman, R. M. Enns, and S. E. Speidel. 2025. Heterosis effects on preweaning traits in a multibreed beef cattle herd in Panama. Tropical Agriculture 102(4): 546–561.
9. Icedo-Nuñez, S., M. G. Thomas, R. M. Enns, S. E. Speidel, J. Hernandez, X. Zeng, M. A. Sanchez-Castro, G. Luna-Nevarez, M. C. Lopez-Gonzalez, C. M. Aguilar-Trejo, and P. Luna-Nevarez. 2025. Validation of polymorphisms associated with immune response after PRRS vaccination in replacement gilts. Veterinary Sciences 12(4): 295. https://doi.org/10.3390/vetsci12040295
10. Thallman, R. M., J. E. Borgert, B. N. Engle, J. W. Keele, W. M. Snelling, C. Gondro, and L. A. Kuehn. 2025. A vision of how low-coverage sequence data should contribute to genetic evaluation in the future. Journal of Animal Science 103: skaf294.
11. Zaabza, H. B., M. H. Ferdosi, I. Strandén, B. C. D. Cuyabano, M. Neupane, I. Misztal, D. Lourenco, and C. Gondro. 2025. Opportunities and computational challenges in large-scale whole-genome sequencing data analysis. Journal of Animal Science 103: skaf292. https://doi.org/10.1093/jas/skaf292
12. Eckhardt, E., A. Luttman, J. R. Daddam, B. H. Keng, W. Kim, C. Gondro, and J. Kim. 2025. Transcriptomic and proteomic responses of bovine myocytes to temporal heat stress. Journal of Thermal Biology 132: 104246.
13. O’Shea-Stone, G., B. Tripet, J. Thomson, R. Garrott, and V. Copié. 2025. Polar metabolite profiles distinguish early and severe sub-maintenance nutritional states in wild bighorn sheep. Metabolites 15(3).
14. Phelps, S. L., and M. M. Culbertson. 2025. Genetic parameters for heifer pregnancy and carcass traits in Angus cattle. Journal of Animal Science. Submitted.
15. Spangler, M. L., D. P. Berry, and L. A. Kuehn. 2025. Leveraging data from commercial cattle for genetic improvement: An international perspective. Journal of Animal Science 103: skaf291. https://doi.org/10.1093/jas/skaf291
16. Hess, M. K., R. L. McDermott, G. E. Erickson, and M. L. Spangler. 2025. Genetic parameter estimates of liver abscesses in feedlot beef cattle. Journal of Animal Science 103: skaf029. https://doi.org/10.1093/jas/skaf029
17. Fernando, S. C., S. Adams, A. Lakamp, and M. L. Spangler. 2025. Stochastic and deterministic factors shaping the rumen microbiome. Journal of Dairy Science 108. https://doi.org/10.3168/jds.2024-25797
18. Lakamp, A. D., A. C. Neujahr, M. M. Hille, J. D. Loy, S. C. Fernando, and M. L. Spangler. 2025. Genetic influence on ocular microbiome composition in preweaned beef calves. Journal of Animal Science 103: skaf153. https://doi.org/10.1093/jas/skaf153
19. Lakamp, A. D., A. C. Neujahr, S. C. Fernando, W. M. Snelling, and M. L. Spangler. 2025. Imputation accuracy of host genomic data from metagenomic sequence information. Journal of Animal Science 103: skaf175. https://doi.org/10.1093/jas/skaf175
20. Lakamp, A., S. Adams, L. A. Kuehn, W. M. Snelling, J. Wells, K. Hales, B. Neville, S. C. Fernando, and M. L. Spangler. 2025. Prediction accuracy for feed intake and body-weight gain using host genomic and rumen metagenomic data. Genetics Selection Evolution. https://doi.org/10.1186/s12711-025-01007-8
21. Campos, M., H. Rojas, H. Mulim, E. da Silva, J. Hidalgo, and R. Bermal. 2025. Comparison of linear and threshold models for genetic evaluation of morphological defects in Nellore cattle. Journal of Animal Science. In press.
22. Silva Pereira, L., L. Bordin Temp, E. da Silva Oliveira, J. Hidalgo, C. U. Magnabosco, and F. Baldi. 2025. Genomic prediction using linear and threshold approaches for stayability in Nellore females. Journal of Animal Breeding and Genetics. https://doi.org/10.1111/jbg.70033
23. Mugambe, J. C., M. Schmidtmann, J. Hidalgo, R. Ahmed, and G. Thaller. 2025. Genetic evaluation of beef sires using a beef-on-dairy crossbred reference population. Journal of Animal Breeding and Genetics. https://doi.org/10.1111/jbg.70030
24. Tabet, J. M., F. Bussiman, J. Hidalgo, M. Bermann, A. Cesarani, I. Misztal, and D. Lourenco. 2025. Approximating reliabilities of indirect predictions using SNP effects from large single-step GBLUP evaluations. Journal of Dairy Science. https://doi.org/10.3168/jds.2025-27089
25. Abduch, N. G., H. G. Reolon, R. M. O. Silva, F. Baldi, B. O. Fragomeni, D. Lourenco, C. C. P. Paz, and N. B. Stafuzza. 2025. Plasma proteomics identifies biomarkers for tick resistance in tropically adapted beef cattle. BMC Genomics 26: 1030. https://doi.org/10.1186/s12864-025-12245-x
26. Bermann, M., A. Legarra, I. Aguilar, A. Alvarez-Munera, I. Misztal, and D. Lourenco. 2025. Estimation of (co)variance components for very large datasets in complex single-step genomic models. Genetics Selection Evolution 57: 63. https://doi.org/10.1186/s12711-025-01006-9
27. Garcia, A. O., A. A. Mikush, J. B. Cole, S. Tsuruta, S. E. F. Guimaraes, I. Misztal, and D. Lourenco. 2025. Genetic background of calving ease in beef-on-dairy systems. Journal of Dairy Science. https://doi.org/10.3168/jds.2025-26503
28. Fuentes Rojas, L. J., F. Bussiman, T. F. Cardoso, G. A. Colmenarez, L. C. Conteville, B. C. P. Antonio, H. T. Ventura, J. J. Paschoal, D. Lourenco, and L. C. A. Regitano. 2025. Microbiota diversity and associations with performance traits in beef bulls. Journal of Animal Science 103: skaf340. https://doi.org/10.1093/jas/skaf340
29. Trujano, Z., A. Garcia, K. Retallick, J. Hidalgo, D. Lourenco, and I. Misztal. 2025. Optimizing large genomic evaluations through data truncation in Angus cattle. Journal of Animal Science 103: skaf382. https://doi.org/10.1093/jas/skaf382
30. Bermann, M., A. Alvarez-Munera, A. Legarra, I. Misztal, and D. Lourenco. 2025. Monte Carlo approximation of log-determinants for large matrices in quantitative genetics. Genetics Selection Evolution 57: 44. https://doi.org/10.1186/s12711-025-00991-1
31. Bermann, M., A. A. Munera, I. Misztal, and D. Lourenco. 2025. Semi-parametric validation of genomic predictions and polygenic risk scores with BLUPF90. G3: Genes|Genomes|Genetics. https://doi.org/10.1093/g3journal/jkaf136
32. Londoño-Gil, M., J. Hidalgo, A. Legarra, C. U. Magnabosco, F. Baldi, and D. Lourenco. 2025. Indirect genomic predictions for indicine breeds using SNP effects from multi-breed evaluations. Journal of Animal Breeding and Genetics. https://doi.org/10.1111/jbg.70008
33. Carvalho Filho, I., G. S. Campos, D. Lourenco, F. S. Schenkel, D. A. Silva, T. L. Silva, C. S. Teixeira, L. F. S. Fonseca, G. A. Fernandes Junior, L. G. Albuquerque, and R. Carvalheiro. 2025. Genotype-by-environment interaction for productive and reproductive traits using imputed whole-genome sequence. Journal of Applied Genetics. https://doi.org/10.1007/s13353-025-00987-z
34. Tabet, J. M., I. Aguilar, M. Bermann, D. Lourenco, I. Misztal, P. M. VanRaden, Z. G. Vitezica, and A. Legarra. 2025. Correcting overestimation of approximate reliabilities with herd–sire interactions. Genetics Selection Evolution 57: 33. https://doi.org/10.1186/s12711-025-00984-0
35. Temp, L., G. Gubiani, L. Brunes, C. Magnabosco, F. Bussiman, J. Hidalgo, D. Lourenco, and F. Baldi. 2025. Genomic evaluation of reproductive traits in Nellore cattle accounting for missing pedigrees. Journal of Animal Breeding and Genetics. https://doi.org/10.1111/jbg.12947
36. Trujano, Z., J. Hidalgo, G. Gowane, K. Retallick, A. Garcia, D. Lourenco, and I. Misztal. 2025. Impact of genomic selection for growth and carcass traits on foot structure in Angus cattle. Journal of Animal Science 103: skaf158. https://doi.org/10.1093/jas/skaf158
37. Ogunbawo, A. R., J. Hidalgo, H. A. Mulim, E. R. Carrara, H. T. Ventura, N. O. Souza, D. Lourenco, and H. R. Oliveira. 2025. Proven-and-young GWAS reveals high polygenicity for key traits in Nellore cattle. Frontiers in Genetics 16: 1549284. https://doi.org/10.3389/fgene.2025.1549284
38. Malheiros, J. M., H. G. Reolon, B. G. Bosquini, F. Baldi, D. Lourenco, B. O. Fragomeni, R. M. O. Silva, C. C. P. Paz, and N. B. Stafuzza. 2025. Plasma proteomics identifies pathways and candidate genes for residual feed intake in beef cattle. Journal of Proteomics 312: 105361. https://doi.org/10.1016/j.jprot.2024.105361
39. Londoño-Gil, M., R. López-Correa, I. Aguilar, C. U. Magnabosco, J. Hidalgo, F. Baldi, and D. Lourenco. 2025. Strategies for genomic prediction in indicine multibreed populations using single-step GBLUP. Journal of Animal Breeding and Genetics. https://doi.org/10.1111/jbg.12882

Selected Conference Proceedings, Invited Talks and Producer Meetings

1. Weaber, R. L. (2025). Selecting for cow maternal performance: Making your breeding and selection systems pay. Stockfarm, 15(10), 43–45.
2. Weaber, R. L. (2025). Selekteer vir koei maternale prestasie: Laat jou teling- en seleksiestelsels vir jou werk. Veeplaas, 16(10), 50–51.
3. Weaber, R. L. (October 14, 2025). Advancing genetic improvement in the United States. Simmentaler Annual General Meeting, Pretoria, South Africa.
4. Weaber, R. L. (October 10, 2025). Selecting for cow maternal performance. Livestock Registering Federation Stockman School, Ventersburg, Free State, South Africa.
5. Weaber, R. L. (October 9, 2025). Importance of breed societies to drive genetic change to meet future demands. Livestock Registering Federation Stockman School, Ventersburg, Free State, South Africa.
6. Weaber, R. L. (October 8, 2025). The future of beef cattle genetics in the beef value chain. Livestock Registering Federation Stockman School, Ventersburg, Free State, South Africa.
7. Weaber, R. L. (October 7, 2025). iGENDEC selection index tools in the USA. Livestock Registering Federation Breeders Workshop, Ventersburg, Free State, South Africa.
8. Weaber, R. L. (April 8, 2025). Quality improvements in the U.S. beef supply chain driven by genetic selection.S. Meat Export Federation Trade Mission Conference, Accra, Ghana.
9. Weaber, R. L. (April 4, 2025). Using genetics to meet the food demand of 2050. University of Jos, Jos, Plateau State, Nigeria.
10. Weaber, R. L. (2025). iGENDEC-based selection indexes at the North American Limousin Foundation and American Gelbvieh Association. eBEEF Brown Bagger Webinar Series.
11. Weaber, R. L. (September 17–18, 2025). Genetic selection for cow fertility and longevity. Applied Reproductive Strategies in Beef Cattle, North Platte, NE.
12. Weaber, R. L. (May 2, 2025). Getting it right: Proper contemporary grouping strategies. Santa Gertrudis Breeders’ Convention, San Marcos, TX.
13. Weaber, R. L. (April 18, 2025). Bull buying using selection indexes: Are the assumptions correct? 74th Florida Beef Cattle Short Course, Gainesville, FL.
14. Weaber, R. L. (May 13, 2025). Selection and mating systems to improve profit potential: Role of heterosis in the beef value chain. Kentucky Agricultural Agents Conference, Manhattan, KS.
15. Speidel, S. E. (November 11, 2025). Pulmonary hypertension: New research and developments. Range Beef Cow Symposium, Cheyenne, WY.
16. Speidel, S. E. (August 8, 2025). Phenotypic and genetic characterization of bovine congestive heart failure in beef and beef × dairy cattle. Academy of Veterinary Consultants Annual Meeting, Norman, OK.
17. Speidel, S. E. (August 15, 2025). Pulmonary hypertension in moderate-elevation feedlots: New research and developments. Pulmonary Arterial Pressure Summit, Fort Collins, CO.
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