{"paper_id":"494e69f5-321e-4051-90dc-7cfda80fb2c6","body_text":"Can genetic similarity based on genetic values be used as a mating tool in a Nellore cattle population? | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Can genetic similarity based on genetic values be used as a mating tool in a Nellore cattle population? Milena Aparecida Ferreira Campos, Tiago do Prado Paim, Josiel Ferreira, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7002381/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 11 Nov, 2025 Read the published version in Tropical Animal Health and Production → Version 1 posted 4 You are reading this latest preprint version Abstract Closed cattle herds face daily the tradeoff between genetic progress speed and long term maintenance of genetic diversity. Using pedigree and genetic merit information coupled with multivariate statistics can yield a mating tool to produce balanced and efficient animals for the traits of interest, minimizing genetic diversity losses. The present study aimed to identify genetically similar groups in a closed herd and explore it as a mating system with minimal reduction in genetic diversity. The database came from a single commercial cattle herd (n = 6,579 individuals with complete information). All statistical traits were performed using the R Core Team®. The principal component analysis was used to verify the relationships between the genetic values for several traits and to choose the variables that would be considered for the grouping according to their genetic similarity. Clustering identified four groups as the best option for this population. The inbreeding rate within clusters was 0.60%, 0.61%, 0.72% and 0.71% for cluster 1, 2, 3 and 4, respectively. Grouping animals according to genetic similarity allows the selection of individuals for the formation of specialized lines. The use of basic statistics can be a simple way of guiding mating in closed herds, which sometimes did not have access to more sophisticated genetic tools as genotyping or mating software. beef cattle closed herd hierarchical grouping principal components analysis genetics Figures Figure 1 Figure 2 1. Introduction Brazilian beef and the dairy herds consist of approximately 187.5 million heads (ABIEC, 2021). According to the Association of Nellore Breeders in Brazil (ACNB, 2021), approximately 80% of these herds are composed of Nellore or Nellore crosses. The Nellore breed has been bred for efficient reproduction, feed efficiency, adaptability, and temperament (Fernandes Júnior et al., 2020 ; de Goes et al., 2025). Artificial selection decreased the frequency of undesirable genes for traits of interest in the population and fix certain attributes within the herd (Cardoso et al., 2018 ). However, with the intense use of a reduced number of bulls, maintaining genetic diversity becomes a challenge due to lower gamete variation. Closed herds, although offering greater control over the selection process, pose significant challenges to the maintenance of genetic variability, particularly under intense selection pressure. The lack of external genetic input can increase the risk of inbreeding and limit long-term genetic progress. Nevertheless, simple and accessible strategies based on statistical tools applied to existing genetic merit data can provide viable alternatives for guiding mating decisions, especially in production systems that lack the resources to adopt advanced technologies such as genomic analyses or commercial mating software. Matings between different lines allow the manifestation of heterosis, or hybrid vigor, which is expressed as the superior performance of the progenies compared to the average of the parents. This is caused by the restoration of the loss of adaptive vigor due inbreeding (Oliveira et al., 2011 ). Studying the herd structure and genetic variability is crucial to establish and maintain selection systems with higher selection intensity and genetic gain (Barros et al., 2018 ). In closed herds, knowing the families with greater herd representation becomes essential, as selection and mating tools rely on the differences between the animals. Utilizing genetic values from these animals and their families can help determine the best mating system for each herd using basic statistical tools. The present study aimed to identify genetically similar groups in a closed herd and to propose the use of the clusters as a mating system avoiding genetic diversity losses. 2. Material and methods The database used in this study originates from a single commercial Nelore cattle herd maintained by Genética Aditiva®, a company recognized for its structured genetic selection program and long-standing contribution to the improvement of the Nelore breed, with records and research efforts dating back to 1944. All animals included had genetic values from the Brazilian Nelore genetic improvement program. The data used refer to the genetic evaluation conducted in February 2021. The original dataset contained genetic merit information for twelve traits—MW120 (maternal weight adjusted at 120 days), MW210 (maternal weight adjusted at 210 days), W210 (body weight at 210 days), W365 (body weight at 365 days), W450 (body weight at 450 days), REA (ribeye area), BFT (backfat thickness), PPC (probability of precocious calving), IMF (intramuscular fat), RFI (residual feed intake), DMI (dry matter intake), and APM (age at puberty of males)—for a total of 30,419 animals, along with pedigree, sex, and birth date. Records with missing pedigree or missing genetic evaluation for any of the analyzed traits were excluded from the analysis. The final database comprised 6,579 individuals with complete information. A descriptive summary of the analyzed population is presented in Table 1 . Statistical analyses were performed using the R Core Team® (2021). To reduce the dimensionality of the data and facilitate the interpretation of relationships among traits, a principal component analysis (PCA) was conducted using the FactoMineR package (Husson et al., 2020 ). Clustering was then performed through the Hierarchical Clustering on Principal Components (HCPC) function (Husson et al., 2010 ), based on Euclidean distances calculated from the principal components, and applying Ward’s minimum variance method (Ward, 1963 ). To define the optimal number of clusters, the reduction in total within-cluster inertia was examined. The decrease in inertia from five to four clusters was substantially smaller than the reduction from four to three clusters, indicating that four clusters were sufficient to group the animals without significant information loss. This approach enabled the identification of genetically distinct groups, with predicted differences in performance across categories, highlighting sets of animals with specialized profiles for certain traits. The kinship matrix and the average inbreeding coefficient were calculated according to the method of Meuwissen and Luo ( 1992 ), using the GeneticsPed package (Gorjanc and Henderson, 2021 ). 3. Results The first four principal components explained 54.4% of the total variation, with 36.91% attributed to dimensions 1 and 2, and 19.56% to dimensions 3 and 4. It is important to interpret the results presented in Fig. 1 and Fig. 2 together, as this comparison allows for the characterization of the groups based on their defining traits. According to the hierarchical clustering dendrogram (Fig. 2 A), four distinct clusters can be identified. Cluster 1 (black) is characterized by lower dry matter intake (DMI) and consequently more negative residual feed intake (RFI). Cluster 2 (pink, lower region of Fig. 2 B) is defined by traits related to marbling (IMF) and precocity (PPC). Cluster 3 (green, upper region) is associated with maternal traits (MW120, MW210) and carcass fatness (BFT). Finally, Cluster 4 (blue, right side) stands out for higher body weight traits, particularly weaning and yearling weights (W210, W365). The color-coded groups are visualized in the two-dimensional principal component plot (Fig. 2 B), and their corresponding trait profiles can be related to the loading plots shown in Fig. 1 A and Fig. 1 B. Additionally, Fig. 2 .C presents the EPDs for each trait within the four identified clusters and reforce the unique genetic profiles of each group. Cluster 1 showed the lowest average values for DMI and RFI, reinforcing its association with superior feed efficiency. Cluster 2 displayed intermediate and balanced EPD values for most traits, with slight emphasis on IMF and PPC. Cluster 3 had the highest mean values for maternal traits and BFT, indicating its potential for breeding females with superior maternal ability and fat deposition. Cluster 4 stood out with the highest means for growth-related traits, REA, and IMF, suggesting its relevance for meat yield and carcass quality. These profiles reflect the practical implications of clustering as a mating tool. 4. Discussion and conclusion The application of multivariate statistical methods — such as principal component analysis and hierarchical clustering — proved effective in identifying genetically distinct groups within a closed Nellore cattle herd. Studies using this type of analysis are common for Nelore cattle populations (Utsunomiya et al., 2022 ; dos Santos et al., 2024). These clusters revealed differentiated profiles related to maternal ability, growth potential, carcass quality, and feed efficiency, providing a strategic foundation for the development of targeted mating plans. The findings confirm that genetic similarity, based on estimated breeding values, can serve as a practical and powerful tool for guiding mating strategies aimed at balancing performance and preserving genetic diversity. Cluster profiles defined in this study offer valuable insights for breeding decisions. Cluster 1 grouped animals with superior feed efficiency, characterized by lower DMI and more negative RFI values. These individuals, which combine good performance with lower feed intake, are aligned with sustainable and cost-effective production goals (Fernandes Júnior et al., 2020 ; Silveira et al., 2023 ). Cluster 2 represented a balanced group with moderate EPDs across most traits, with a slight emphasis on IMF and probability of PPC, making it a potentially useful group for maintaining genetic stability and overall performance. Cluster 3 exhibited the highest averages for maternal traits and BFT, indicating its usefulness for producing females with superior maternal capacity and early reproductive maturity. On the other hand, Cluster 4 included animals with the highest EPDs for body weight at different ages, REA, and IMF, which are key indicators for carcass quality and marketable yield. These groupings support the design of complementary matings between clusters to exploit heterosis and optimize antagonistic traits in closed herds, as suggested by Guerrero et al. ( 2024 ). For example, while growth and carcass traits may be improved by cluster 4, combining it with feed-efficient individuals from cluster 1 could yield progeny that balance productivity with sustainability. This strategy is particularly important in closed herds, where managing genetic diversity and minimizing inbreeding is essential. As demonstrated, the average inbreeding coefficients across the four clusters remained below critical thresholds, suggesting that the mating strategies employed — using EPDs and pedigree data—were effective in maintaining genetic variability. It is worth noting that the current approach relies solely on EPDs derived from traditional pedigree-based genetic evaluations. While effective, this may not capture the full spectrum of genomic relationships or Mendelian sampling effects. Future studies incorporating genomic information could improve the precision of clustering and enhance mating strategies through more accurate kinship estimations and relationship matrices. Furthermore, evaluations across different herds and production environments would help assess the robustness and applicability of this strategy under variable conditions. The results also emphasize the potential for creating specialized lines within the herd, each tailored to specific production goals—such as maternal lines, terminal lines, or lines focused on feed efficiency. The identification of these groups provides a low-cost and accessible alternative to commercial mating software or genotyping, particularly for herds with limited resources. When combined with selection indices, which synthesize multiple trait EPDs into a single value, this approach becomes even more powerful for supporting breeder decision-making (Portes et al., 2024 ). In summary, the clustering of animals based on genetic similarity is an effective, low-complexity mating tool that can be adopted in commercial herds to optimize genetic gains, reduce inbreeding risk, and align animal performance with market demands and environmental constraints. These results underscore the importance of integrating data-driven tools in practical breeding programs, reinforcing the long-term sustainability and competitiveness of beef cattle production systems. Thus, such an approach may represent an accessible genetic management strategy for medium- and small-scale breeders aiming to optimize selection and mating without relying on genomic tools. Declarations Ethical approval Not applicable. Consent for publication Not applicable. Data availability The data that support this study will be shared upon reasonable request to the corresponding author. CRediT authorship contribution statement M. A. F. Campos: Writing – original draft, Visualization, Methodology, Investigation, Formal analysis, Conceptualization. T.P. Paim and J. Ferreira: Writing – review & editing, Final revision. A.L. Bocchi and Collao-Saenz: Writing – review & editing, Investigation, Conceptualization, Supervision. Conflicts of Interest The authors declare no conflicts of interest Declaration of Funding Coordination for the Improvement of Higher Education Personnel - Brazil (CAPES) - Financing Code 001, through a scholarship. Acknowledgments Appreciate to the companies Genética Aditiva ® and Melhora Mais Consultoria Genética ® for making the data available for the research. References Araújo LAP, Silveira IDB, Restle J, Menezes LFG, Souza JS, Farinatti LHE, Fluck AC, Menezes de Sá HAO, Vaz RZ (2004) Genetic group and heterosis in the behavioural evolution of steers during finishing in confinement. Livestock Science 281, 105440. https://doi.org/10.1016/j.livsci.2024.105440 Association of Nelore Breeders of Brazil (2006) [Online]. Available at: http://www.nelore.org.br/Institucional/ACNB (Verified 20 July 2021) Brazilian Association of Meat Export Industries (ABIEC) (2021) Beef report: profile of livestock in Brazil. [s.l.: s.n.]. Available at: http://abiec.com.br/publicacoes/beef-report-2021/ (Verified 20 July 2021) Barros IC, Mota RR, da Silva LP, Carneiro PLS, Filho RM, Malhado CHM (2018) Genetic evaluation of the growth of Polled Nellore via multi-trait models. Ceres 65, 402–406. Available at: https://doi.org/10.1590/0034-737X201865050004 Cardoso DF, Albuquerque LG, Reimer C, Qambari S, Erbe M, Nascimento AV, Venturini GC, Scalez DCB, Baldi F, Camargo GMF, Mercadante MEZ, Cyrillo JNSG, Simianer H, Tonhati H (2018) Genome-wide scan reveals population stratification and footprints of recent selection in Nellore cattle. Genetics Selection Evolution 50, 22. Available at: https://doi.org/10.1186/s12711-018-0381-2 dos Santos RM, Aganete IA, Botrel BD, Menezes GRdO, Martin Nieto L, de Souza Jr, MD, Toral, FL B (2025). Multivariate analysis of herd structure and genetic resource indicators in seedstock beef cattle herds. 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Tropical Animal Health and Production, 56, 258. https://doi.org/10.1007/s11250-024-04011-0 Husson AF, Josse J, Le S, Mazet J, Husson MF (2020) Package ‘FactoMineR’. [s.l.: s.n.] Husson F, Josse J, Pagès J (2010) Main component methods - hierarchical clustering - partitional clustering: why would we need to choose for visualizing data? Formalized Mathematics 18, 17–26. Available at: https://doi.org/10.2478/v10037-010-0003-0 Meuwissen THE, Luo Z (1992) Computing inbreeding coefficients in large populations. Genetics Selection Evolution 24, 305–313. Available at: https://doi.org/10.1186/1297-9686-24-4-305 Oliveira PS, Júnior MLS, Pedrosa VB, de Oliveira ECM, Eler JP, Ferraz JBS (2011) Population structure of a closed herd of the Nelore breed of the Lemgruber lineage. Brazilian Agricultural Research 46, 639–647. Available at: https://doi.org/10.1590/S0100-204X2011000600010 Portes JV, Rodrigues GRD, de Vasconcellos Silva JAI, Mercadante MEZ, Bonilha SFN, Canesin RC, Valente JPS, Cyrillo JNSG (2024) Effects of inbreeding on production traits and genetic evaluations in Guzerá beef cattle raised under tropical conditions. Tropical Animal Health and Production, 56, 132. https://doi.org/10.1007/s11250-024-03987-z R Core Team (2021) R: A language and environment for statistical computing. R Foundation for Statistical Computing. Available at: https://www.R-project.org/ Silveira RMF, Façanha DAE, McManus CM, Ferreira J, Silva IJO (2023) Machine intelligence applied to sustainability: A systematic methodological proposal to identify sustainable animals. Journal of Cleaner Production 420, 138292. https://doi.org/10.1016/j.jclepro.2023.138292 Twomey AJ, Cromie AR, McHugh N, Berry DP (2020) Validation of a beef cattle maternal breeding objective based on a cross-sectional analysis of a large national cattle database. Journal of Animal Science 98, 1–15. Available at: https://doi.org/10.1093/jas/skaa322 Utsunomiya YT, Fortunato AAAD, Milanesi M, Trigo BB, Alves NF, Sonstegard TS, Garcia JF (2022) Bos taurus haplotypes segregating in Nellore ( Bos indicus ) cattle. Animal Genetics, 53, e13164. https://doi.org/10.1111/age.13164 Ward JH (1963) Hierarchical grouping to optimize an objective function. Journal of the American Statistical Association 58, 236–244. Available at: https://doi.org/10.1080/01621459.1963.10500845 Wright S (1922) Coefficients of inbreeding and relationship. The American Naturalist 56, 330–338. Wright, S. (1969). Evolution and the genetics of populations, Vol. 2. Thetheory of gene frequencies. Chicago, IL: University of Chicago Press Table Table 1. Descriptive analysis of the expected progeny differences of the analyzed population Traits Average SD Var Min Max MW120 2.308 1.828 3.340 -5.930 9.150 MW210 3.037 2.488 6.190 -8.660 12.340 W210 11.302 4.098 16.791 -1.630 27.940 W365 20.513 6.041 36.494 -1.530 44.020 W450 21.591 6.536 42.720 -4.230 47.290 REA 2.220 1.548 2.396 -3.120 8.870 BFT 0.395 0.332 0.110 -0.870 1.950 PPC 76.271 7.712 59.479 38.950 95.770 IMF 0.059 0.137 0.019 -0.480 0.500 RFI -0.013 0.079 0.006 -0.357 0.332 DMI 0.036 0.162 0.026 -0.665 0.626 APM -0.506 0.527 0.277 -2.524 1.771 SD standard deviation; Var variance; Min minimum; Max maximum; MW120 maternal weight adjusted at 120 days; MW210 maternal weight adjusted at 210 days; W210 body weight adjusted at 210 days of age; W365 body weight adjusted at 365 days of age; W450 body weight adjusted at 450 days of age; REA ribeye area; BFT backfat thickness; PPC probability of precocious calving; IMF intramuscular fat; RFI residual food intake; DMI dry matter intake; APM age at puberty of males Cite Share Download PDF Status: Published Journal Publication published 11 Nov, 2025 Read the published version in Tropical Animal Health and Production → Version 1 posted Reviewers agreed at journal 24 Aug, 2025 Reviewers invited by journal 09 Jul, 2025 Editor assigned by journal 02 Jul, 2025 First submitted to journal 29 Jun, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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MW210 maternal weight adjusted at 210 days; REA ribeye area; BFT backfat thickness; IMF Intramuscular\\u0026nbsp;fat; RFI residual food intake; APM age at puberty of males; PPC probability of precocious calving; W210 body weight adjusted at 210 days of age; W365 body weight adjusted at 365 days of age; W450 body weight adjusted at 450 days of age; DMI dry matter intake\\u003c/em\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003ePrincipal component analysis (PCA) of phenotypic and performance traits in Nelore beef cattle, where A represents the first 2 dimensions and B represents the 3\\u003csup\\u003erd\\u003c/sup\\u003e and 4\\u003csup\\u003eth\\u003c/sup\\u003e dimensions\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"1.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-7002381/v1/fb3c25cea710d503b9c39c8e.png\"},{\"id\":86686518,\"identity\":\"873a8634-d507-4bd7-8802-019f167cac17\",\"added_by\":\"auto\",\"created_at\":\"2025-07-14 13:54:14\",\"extension\":\"png\",\"order_by\":2,\"title\":\"Figure 2\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":159506,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003eHierarchical clustering dendrogram based on estimated breeding values (A), biplot of the principal component analysis showing the distribution of animals grouped into four clusters (B) and mean expected progeny differences values for each trait within each cluster (C)\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cem\\u003eMW120 maternal weight adjustedat 120 days; MW210 maternal weight adjusted at 210 days; REA ribeye area; BFT backfat thickness; IMF Intramuscular fat; RFI residual food intake; APM age at puberty of males; PPC probability of precocious calving; W210 body weight adjusted at 210 days of age; W365 body weight adjusted at 365 days of age; W450 body weight adjusted at 450 days of age; DMI dry matter intake\\u003c/em\\u003e\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"2.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-7002381/v1/44a0de7d9709f92eabb4d8b3.png\"},{\"id\":96106154,\"identity\":\"281e6861-1d33-4b4b-bdd5-df05b789f7da\",\"added_by\":\"auto\",\"created_at\":\"2025-11-17 16:12:42\",\"extension\":\"pdf\",\"order_by\":0,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"manuscript-pdf\",\"size\":640944,\"visible\":true,\"origin\":\"\",\"legend\":\"\",\"description\":\"\",\"filename\":\"manuscript.pdf\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-7002381/v1/a0a9e678-8bc2-4f25-8eeb-ed3136c72658.pdf\"}],\"financialInterests\":\"\",\"formattedTitle\":\"Can genetic similarity based on genetic values be used as a mating tool in a Nellore cattle population?\",\"fulltext\":[{\"header\":\"1. Introduction\",\"content\":\"\\u003cp\\u003eBrazilian beef and the dairy herds consist of approximately 187.5\\u0026nbsp;million heads (ABIEC, 2021). According to the Association of Nellore Breeders in Brazil (ACNB, 2021), approximately 80% of these herds are composed of Nellore or Nellore crosses. The Nellore breed has been bred for efficient reproduction, feed efficiency, adaptability, and temperament (Fernandes J\\u0026uacute;nior et al., \\u003cspan citationid=\\\"CR10\\\" class=\\\"CitationRef\\\"\\u003e2020\\u003c/span\\u003e; de Goes et al., 2025).\\u003c/p\\u003e\\u003cp\\u003eArtificial selection decreased the frequency of undesirable genes for traits of interest in the population and fix certain attributes within the herd (Cardoso et al., \\u003cspan citationid=\\\"CR5\\\" class=\\\"CitationRef\\\"\\u003e2018\\u003c/span\\u003e). However, with the intense use of a reduced number of bulls, maintaining genetic diversity becomes a challenge due to lower gamete variation. Closed herds, although offering greater control over the selection process, pose significant challenges to the maintenance of genetic variability, particularly under intense selection pressure. The lack of external genetic input can increase the risk of inbreeding and limit long-term genetic progress. Nevertheless, simple and accessible strategies based on statistical tools applied to existing genetic merit data can provide viable alternatives for guiding mating decisions, especially in production systems that lack the resources to adopt advanced technologies such as genomic analyses or commercial mating software.\\u003c/p\\u003e\\u003cp\\u003eMatings between different lines allow the manifestation of heterosis, or hybrid vigor, which is expressed as the superior performance of the progenies compared to the average of the parents. This is caused by the restoration of the loss of adaptive vigor due inbreeding (Oliveira et al., \\u003cspan citationid=\\\"CR17\\\" class=\\\"CitationRef\\\"\\u003e2011\\u003c/span\\u003e). Studying the herd structure and genetic variability is crucial to establish and maintain selection systems with higher selection intensity and genetic gain (Barros et al., \\u003cspan citationid=\\\"CR4\\\" class=\\\"CitationRef\\\"\\u003e2018\\u003c/span\\u003e). In closed herds, knowing the families with greater herd representation becomes essential, as selection and mating tools rely on the differences between the animals. Utilizing genetic values from these animals and their families can help determine the best mating system for each herd using basic statistical tools.\\u003c/p\\u003e\\u003cp\\u003eThe present study aimed to identify genetically similar groups in a closed herd and to propose the use of the clusters as a mating system avoiding genetic diversity losses.\\u003c/p\\u003e\"},{\"header\":\"2. Material and methods\",\"content\":\"\\u003cp\\u003eThe database used in this study originates from a single commercial Nelore cattle herd maintained by Gen\\u0026eacute;tica Aditiva\\u0026reg;, a company recognized for its structured genetic selection program and long-standing contribution to the improvement of the Nelore breed, with records and research efforts dating back to 1944. All animals included had genetic values from the Brazilian Nelore genetic improvement program. The data used refer to the genetic evaluation conducted in February 2021. The original dataset contained genetic merit information for twelve traits\\u0026mdash;MW120 (maternal weight adjusted at 120 days), MW210 (maternal weight adjusted at 210 days), W210 (body weight at 210 days), W365 (body weight at 365 days), W450 (body weight at 450 days), REA (ribeye area), BFT (backfat thickness), PPC (probability of precocious calving), IMF (intramuscular fat), RFI (residual feed intake), DMI (dry matter intake), and APM (age at puberty of males)\\u0026mdash;for a total of 30,419 animals, along with pedigree, sex, and birth date. Records with missing pedigree or missing genetic evaluation for any of the analyzed traits were excluded from the analysis. The final database comprised 6,579 individuals with complete information. A descriptive summary of the analyzed population is presented in Table\\u0026nbsp;\\u003cspan refid=\\\"Tab1\\\" class=\\\"InternalRef\\\"\\u003e1\\u003c/span\\u003e.\\u003c/p\\u003e\\u003cp\\u003eStatistical analyses were performed using the R Core Team\\u0026reg; (2021). To reduce the dimensionality of the data and facilitate the interpretation of relationships among traits, a principal component analysis (PCA) was conducted using the \\u003cem\\u003eFactoMineR\\u003c/em\\u003e package (Husson et al., \\u003cspan citationid=\\\"CR14\\\" class=\\\"CitationRef\\\"\\u003e2020\\u003c/span\\u003e). Clustering was then performed through the Hierarchical Clustering on Principal Components (HCPC) function (Husson et al., \\u003cspan citationid=\\\"CR15\\\" class=\\\"CitationRef\\\"\\u003e2010\\u003c/span\\u003e), based on Euclidean distances calculated from the principal components, and applying Ward\\u0026rsquo;s minimum variance method (Ward, \\u003cspan citationid=\\\"CR23\\\" class=\\\"CitationRef\\\"\\u003e1963\\u003c/span\\u003e). To define the optimal number of clusters, the reduction in total within-cluster inertia was examined. The decrease in inertia from five to four clusters was substantially smaller than the reduction from four to three clusters, indicating that four clusters were sufficient to group the animals without significant information loss. This approach enabled the identification of genetically distinct groups, with predicted differences in performance across categories, highlighting sets of animals with specialized profiles for certain traits. The kinship matrix and the average inbreeding coefficient were calculated according to the method of Meuwissen and Luo (\\u003cspan citationid=\\\"CR16\\\" class=\\\"CitationRef\\\"\\u003e1992\\u003c/span\\u003e), using the \\u003cem\\u003eGeneticsPed\\u003c/em\\u003e package (Gorjanc and Henderson, \\u003cspan citationid=\\\"CR12\\\" class=\\\"CitationRef\\\"\\u003e2021\\u003c/span\\u003e).\\u003c/p\\u003e\"},{\"header\":\"3. Results\",\"content\":\"\\u003cp\\u003eThe first four principal components explained 54.4% of the total variation, with 36.91% attributed to dimensions 1 and 2, and 19.56% to dimensions 3 and 4.\\u003c/p\\u003e\\u003cp\\u003eIt is important to interpret the results presented in Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig1\\\" class=\\\"InternalRef\\\"\\u003e1\\u003c/span\\u003e and Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig2\\\" class=\\\"InternalRef\\\"\\u003e2\\u003c/span\\u003e together, as this comparison allows for the characterization of the groups based on their defining traits. According to the hierarchical clustering dendrogram (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig2\\\" class=\\\"InternalRef\\\"\\u003e2\\u003c/span\\u003eA), four distinct clusters can be identified. Cluster 1 (black) is characterized by lower dry matter intake (DMI) and consequently more negative residual feed intake (RFI). Cluster 2 (pink, lower region of Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig2\\\" class=\\\"InternalRef\\\"\\u003e2\\u003c/span\\u003eB) is defined by traits related to marbling (IMF) and precocity (PPC). Cluster 3 (green, upper region) is associated with maternal traits (MW120, MW210) and carcass fatness (BFT). Finally, Cluster 4 (blue, right side) stands out for higher body weight traits, particularly weaning and yearling weights (W210, W365). The color-coded groups are visualized in the two-dimensional principal component plot (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig2\\\" class=\\\"InternalRef\\\"\\u003e2\\u003c/span\\u003eB), and their corresponding trait profiles can be related to the loading plots shown in Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig1\\\" class=\\\"InternalRef\\\"\\u003e1\\u003c/span\\u003eA and Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig1\\\" class=\\\"InternalRef\\\"\\u003e1\\u003c/span\\u003eB.\\u003c/p\\u003e\\u003cp\\u003eAdditionally, Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig2\\\" class=\\\"InternalRef\\\"\\u003e2\\u003c/span\\u003e.C presents the EPDs for each trait within the four identified clusters and reforce the unique genetic profiles of each group. Cluster 1 showed the lowest average values for DMI and RFI, reinforcing its association with superior feed efficiency. Cluster 2 displayed intermediate and balanced EPD values for most traits, with slight emphasis on IMF and PPC. Cluster 3 had the highest mean values for maternal traits and BFT, indicating its potential for breeding females with superior maternal ability and fat deposition. Cluster 4 stood out with the highest means for growth-related traits, REA, and IMF, suggesting its relevance for meat yield and carcass quality. These profiles reflect the practical implications of clustering as a mating tool.\\u003c/p\\u003e\"},{\"header\":\"4. Discussion and conclusion\",\"content\":\"\\u003cp\\u003eThe application of multivariate statistical methods \\u0026mdash; such as principal component analysis and hierarchical clustering \\u0026mdash; proved effective in identifying genetically distinct groups within a closed Nellore cattle herd. Studies using this type of analysis are common for Nelore cattle populations (Utsunomiya et al., \\u003cspan citationid=\\\"CR22\\\" class=\\\"CitationRef\\\"\\u003e2022\\u003c/span\\u003e; dos Santos et al., 2024). These clusters revealed differentiated profiles related to maternal ability, growth potential, carcass quality, and feed efficiency, providing a strategic foundation for the development of targeted mating plans. The findings confirm that genetic similarity, based on estimated breeding values, can serve as a practical and powerful tool for guiding mating strategies aimed at balancing performance and preserving genetic diversity.\\u003c/p\\u003e\\u003cp\\u003eCluster profiles defined in this study offer valuable insights for breeding decisions. Cluster 1 grouped animals with superior feed efficiency, characterized by lower DMI and more negative RFI values. These individuals, which combine good performance with lower feed intake, are aligned with sustainable and cost-effective production goals (Fernandes J\\u0026uacute;nior et al., \\u003cspan citationid=\\\"CR10\\\" class=\\\"CitationRef\\\"\\u003e2020\\u003c/span\\u003e; Silveira et al., \\u003cspan citationid=\\\"CR20\\\" class=\\\"CitationRef\\\"\\u003e2023\\u003c/span\\u003e). Cluster 2 represented a balanced group with moderate EPDs across most traits, with a slight emphasis on IMF and probability of PPC, making it a potentially useful group for maintaining genetic stability and overall performance. Cluster 3 exhibited the highest averages for maternal traits and BFT, indicating its usefulness for producing females with superior maternal capacity and early reproductive maturity. On the other hand, Cluster 4 included animals with the highest EPDs for body weight at different ages, REA, and IMF, which are key indicators for carcass quality and marketable yield.\\u003c/p\\u003e\\u003cp\\u003eThese groupings support the design of complementary matings between clusters to exploit heterosis and optimize antagonistic traits in closed herds, as suggested by Guerrero et al. (\\u003cspan citationid=\\\"CR13\\\" class=\\\"CitationRef\\\"\\u003e2024\\u003c/span\\u003e). For example, while growth and carcass traits may be improved by cluster 4, combining it with feed-efficient individuals from cluster 1 could yield progeny that balance productivity with sustainability. This strategy is particularly important in closed herds, where managing genetic diversity and minimizing inbreeding is essential. As demonstrated, the average inbreeding coefficients across the four clusters remained below critical thresholds, suggesting that the mating strategies employed \\u0026mdash; using EPDs and pedigree data\\u0026mdash;were effective in maintaining genetic variability.\\u003c/p\\u003e\\u003cp\\u003eIt is worth noting that the current approach relies solely on EPDs derived from traditional pedigree-based genetic evaluations. While effective, this may not capture the full spectrum of genomic relationships or Mendelian sampling effects. Future studies incorporating genomic information could improve the precision of clustering and enhance mating strategies through more accurate kinship estimations and relationship matrices. Furthermore, evaluations across different herds and production environments would help assess the robustness and applicability of this strategy under variable conditions.\\u003c/p\\u003e\\u003cp\\u003eThe results also emphasize the potential for creating specialized lines within the herd, each tailored to specific production goals\\u0026mdash;such as maternal lines, terminal lines, or lines focused on feed efficiency. The identification of these groups provides a low-cost and accessible alternative to commercial mating software or genotyping, particularly for herds with limited resources. When combined with selection indices, which synthesize multiple trait EPDs into a single value, this approach becomes even more powerful for supporting breeder decision-making (Portes et al., \\u003cspan citationid=\\\"CR18\\\" class=\\\"CitationRef\\\"\\u003e2024\\u003c/span\\u003e).\\u003c/p\\u003e\\u003cp\\u003eIn summary, the clustering of animals based on genetic similarity is an effective, low-complexity mating tool that can be adopted in commercial herds to optimize genetic gains, reduce inbreeding risk, and align animal performance with market demands and environmental constraints. These results underscore the importance of integrating data-driven tools in practical breeding programs, reinforcing the long-term sustainability and competitiveness of beef cattle production systems. Thus, such an approach may represent an accessible genetic management strategy for medium- and small-scale breeders aiming to optimize selection and mating without relying on genomic tools.\\u003c/p\\u003e\"},{\"header\":\"Declarations\",\"content\":\"\\u003cp\\u003e\\u003cstrong\\u003eEthical approval\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eNot applicable. \\u0026nbsp;\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eConsent for publication\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eNot applicable. \\u0026nbsp;\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eData availability\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eThe data that support this study will be shared upon reasonable request to the corresponding author.\\u0026nbsp;\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eCRediT authorship contribution statement\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eM. A. F. Campos: Writing \\u0026ndash; original draft, Visualization, Methodology, Investigation, Formal analysis, Conceptualization. T.P. Paim and J. Ferreira: Writing \\u0026ndash; review \\u0026amp; editing, Final revision. A.L. Bocchi and Collao-Saenz: Writing \\u0026ndash; review \\u0026amp; editing, Investigation, Conceptualization, Supervision.\\u0026nbsp;\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eConflicts of Interest\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eThe authors declare no conflicts of interest\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eDeclaration of Funding\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eCoordination for the Improvement of Higher Education Personnel - Brazil (CAPES) - Financing Code 001, through a scholarship.\\u003cstrong\\u003e\\u0026nbsp;\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eAcknowledgments\\u0026nbsp;\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eAppreciate to the companies \\u003cem\\u003eGen\\u0026eacute;tica Aditiva\\u003c/em\\u003e\\u0026reg; and \\u003cem\\u003eMelhora Mais Consultoria Gen\\u0026eacute;tica\\u003c/em\\u003e\\u0026reg; for making the data available for the research.\\u003c/p\\u003e\"},{\"header\":\"References\",\"content\":\"\\u003col\\u003e\\n\\u003cli\\u003eAra\\u0026uacute;jo LAP, Silveira IDB, Restle J, Menezes LFG, Souza JS, Farinatti LHE, Fluck AC, Menezes de S\\u0026aacute; HAO, Vaz RZ (2004) Genetic group and heterosis in the behavioural evolution of steers during finishing in confinement. Livestock Science 281, 105440. https://doi.org/10.1016/j.livsci.2024.105440 \\u003c/li\\u003e\\n\\u003cli\\u003eAssociation of Nelore Breeders of Brazil (2006) [Online]. Available at: http://www.nelore.org.br/Institucional/ACNB (Verified 20 July 2021)\\u003c/li\\u003e\\n\\u003cli\\u003eBrazilian Association of Meat Export Industries (ABIEC) (2021) Beef report: profile of livestock in Brazil. [s.l.: s.n.]. Available at: http://abiec.com.br/publicacoes/beef-report-2021/ (Verified 20 July 2021)\\u003c/li\\u003e\\n\\u003cli\\u003eBarros IC, Mota RR, da Silva LP, Carneiro PLS, Filho RM, Malhado CHM (2018) Genetic evaluation of the growth of Polled Nellore via multi-trait models. Ceres 65, 402\\u0026ndash;406. Available at: https://doi.org/10.1590/0034-737X201865050004\\u003c/li\\u003e\\n\\u003cli\\u003eCardoso DF, Albuquerque LG, Reimer C, Qambari S, Erbe M, Nascimento AV, Venturini GC, Scalez DCB, Baldi F, Camargo GMF, Mercadante MEZ, Cyrillo JNSG, Simianer H, Tonhati H (2018) Genome-wide scan reveals population stratification and footprints of recent selection in Nellore cattle. Genetics Selection Evolution 50, 22. Available at: https://doi.org/10.1186/s12711-018-0381-2 \\u003c/li\\u003e\\n\\u003cli\\u003edos Santos RM, Aganete IA, Botrel BD, Menezes GRdO, Martin Nieto L, de Souza Jr, MD, Toral, FL B (2025). Multivariate analysis of herd structure and genetic resource indicators in seedstock beef cattle herds. Journal of Animal Breeding and Genetics 142, 131\\u0026ndash;144. https://doi.org/10.1111/jbg.12891 \\u003c/li\\u003e\\n\\u003cli\\u003ede Gois RB, do Prado Paim T, Peres RFG, Le\\u0026atilde;o KM, dos Santos CA, Bocchi AL, Ferreira J (2025) Relationship between feed efficiency and reproductive traits in precocious Nelore heifers. Tropical Animal Health and Production 57, 88. https://doi.org/10.1007/s11250-025-04342-6 \\u003c/li\\u003e\\n\\u003cli\\u003eDoekes HP, Bijma P, Windig JJ (2021) How depressing is inbreeding? A meta-analysis of 30 years of research on the effects of inbreeding in livestock. Genes 12. Available at: https://doi.org/10.3390/genes12060926 \\u003c/li\\u003e\\n\\u003cli\\u003eEler J P (2017) Teoria e m\\u0026eacute;todos em melhoramento gen\\u0026eacute;tico animal: Bases do Melhoramento Gen\\u0026eacute;tico Animal. Pirassununga: Universidade de S\\u0026atilde;o Paulo, 2017. https://doi.org/10.11606/9788566404128 \\u003c/li\\u003e\\n\\u003cli\\u003eFernandes J\\u0026uacute;nior GA, de Oliveira HN, Carvalheiro R, Cardoso DF, Fonseca LFS, Ventura RV, Albuquerque LG (2020) Whole-genome sequencing provides new insights into genetic mechanisms of tropical adaptation in Nellore (\\u003cem\\u003eBos primigenius indicus\\u003c/em\\u003e). Scientific Reports 10, 1\\u0026ndash;7. Available at: https://doi.org/10.1038/s41598-020-66272-7 \\u003c/li\\u003e\\n\\u003cli\\u003eFernandes TA, Cerd\\u0026acute; otes L, Vaz RZ., Restle J, Ferreira OGL, 2020. Relationship between heterosis, weight gain, and body measurements of Nellore and Charolais calves. Pesq. Agrop. Bras. 55, e01821. https://doi.org/10.1590/S1678-3921. pab2020.v55.01821 \\u003c/li\\u003e\\n\\u003cli\\u003eGorjanc G, Henderson DA (2021) Calculation of genetic relatedness/relationship between individuals in the pedigree. [s.l.: s.n.] 32, 23\\u0026ndash;24.\\u003c/li\\u003e\\n\\u003cli\\u003eGuerrero LFN, Rogberg-Mu\\u0026ntilde;oz A, Rodr\\u0026iacute;guez N, Herrera LGG (2024) Genomic diversity study of highly crossbred cattle population in a Low and Middle Tropical environment. Tropical Animal Health and Production, 56, 258. https://doi.org/10.1007/s11250-024-04011-0 \\u003c/li\\u003e\\n\\u003cli\\u003eHusson AF, Josse J, Le S, Mazet J, Husson MF (2020) Package \\u0026lsquo;FactoMineR\\u0026rsquo;. [s.l.: s.n.]\\u003c/li\\u003e\\n\\u003cli\\u003eHusson F, Josse J, Pag\\u0026egrave;s J (2010) Main component methods - hierarchical clustering - partitional clustering: why would we need to choose for visualizing data? Formalized Mathematics 18, 17\\u0026ndash;26. Available at: https://doi.org/10.2478/v10037-010-0003-0 \\u003c/li\\u003e\\n\\u003cli\\u003eMeuwissen THE, Luo Z (1992) Computing inbreeding coefficients in large populations. Genetics Selection Evolution 24, 305\\u0026ndash;313. Available at: https://doi.org/10.1186/1297-9686-24-4-305 \\u003c/li\\u003e\\n\\u003cli\\u003eOliveira PS, J\\u0026uacute;nior MLS, Pedrosa VB, de Oliveira ECM, Eler JP, Ferraz JBS (2011) Population structure of a closed herd of the Nelore breed of the Lemgruber lineage. Brazilian Agricultural Research 46, 639\\u0026ndash;647. Available at: https://doi.org/10.1590/S0100-204X2011000600010 \\u003c/li\\u003e\\n\\u003cli\\u003ePortes JV, Rodrigues GRD, de Vasconcellos Silva JAI, Mercadante MEZ, Bonilha SFN, Canesin RC, Valente JPS, Cyrillo JNSG (2024) Effects of inbreeding on production traits and genetic evaluations in Guzer\\u0026aacute; beef cattle raised under tropical conditions. Tropical Animal Health and Production, 56, 132. https://doi.org/10.1007/s11250-024-03987-z \\u003c/li\\u003e\\n\\u003cli\\u003eR Core Team (2021) R: A language and environment for statistical computing. R Foundation for Statistical Computing. Available at: https://www.R-project.org/ \\u003c/li\\u003e\\n\\u003cli\\u003eSilveira RMF, Fa\\u0026ccedil;anha DAE, McManus CM, Ferreira J, Silva IJO (2023) Machine intelligence applied to sustainability: A systematic methodological proposal to identify sustainable animals. Journal of Cleaner Production 420, 138292. https://doi.org/10.1016/j.jclepro.2023.138292 \\u003c/li\\u003e\\n\\u003cli\\u003eTwomey AJ, Cromie AR, McHugh N, Berry DP (2020) Validation of a beef cattle maternal breeding objective based on a cross-sectional analysis of a large national cattle database. Journal of Animal Science 98, 1\\u0026ndash;15. Available at: https://doi.org/10.1093/jas/skaa322 \\u003c/li\\u003e\\n\\u003cli\\u003eUtsunomiya YT, Fortunato AAAD, Milanesi M, Trigo BB, Alves NF, Sonstegard TS, Garcia JF (2022) \\u003cem\\u003eBos taurus\\u003c/em\\u003e haplotypes segregating in Nellore (\\u003cem\\u003eBos indicus\\u003c/em\\u003e) cattle. Animal Genetics, 53, e13164. https://doi.org/10.1111/age.13164 \\u003c/li\\u003e\\n\\u003cli\\u003eWard JH (1963) Hierarchical grouping to optimize an objective function. Journal of the American Statistical Association 58, 236\\u0026ndash;244. Available at: https://doi.org/10.1080/01621459.1963.10500845 \\u003c/li\\u003e\\n\\u003cli\\u003eWright S (1922) Coefficients of inbreeding and relationship. The American Naturalist 56, 330\\u0026ndash;338.\\u003c/li\\u003e\\n\\u003cli\\u003eWright, S. (1969). Evolution and the genetics of populations, Vol. 2. Thetheory of gene frequencies. Chicago, IL: University of Chicago Press\\u003c/li\\u003e\\n\\u003c/ol\\u003e\"},{\"header\":\"Table\",\"content\":\"\\u003cp\\u003eTable 1.\\u0026nbsp;Descriptive analysis of the expected progeny differences of the analyzed population\\u003c/p\\u003e\\n\\u003cdiv align=\\\"\\\"\\u003e\\n \\u003ctable border=\\\"1\\\" cellspacing=\\\"0\\\" cellpadding=\\\"0\\\" width=\\\"100%\\\"\\u003e\\n \\u003ctbody\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd style=\\\"width: 74px;\\\"\\u003e\\n \\u003cp\\u003eTraits\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 74px;\\\"\\u003e\\n \\u003cp\\u003eAverage\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 74px;\\\"\\u003e\\n \\u003cp\\u003eSD\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 74px;\\\"\\u003e\\n \\u003cp\\u003eVar\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 74px;\\\"\\u003e\\n \\u003cp\\u003eMin\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 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fat; RFI residual food intake; DMI dry matter intake; APM age at puberty of males\\u003c/em\\u003e\\u003c/p\\u003e\"}],\"fulltextSource\":\"\",\"fullText\":\"\",\"funders\":[],\"hasAdminPriorityOnWorkflow\":false,\"hasManuscriptDocX\":true,\"hasOptedInToPreprint\":true,\"hasPassedJournalQc\":\"\",\"hasAnyPriority\":false,\"hideJournal\":false,\"highlight\":\"\",\"institution\":\"\",\"isAcceptedByJournal\":true,\"isAuthorSuppliedPdf\":false,\"isDeskRejected\":\"\",\"isHiddenFromSearch\":false,\"isInQc\":false,\"isInWorkflow\":true,\"isPdf\":false,\"isPdfUpToDate\":true,\"isWithdrawnOrRetracted\":false,\"journal\":{\"display\":true,\"email\":\"info@researchsquare.com\",\"identity\":\"tropical-animal-health-and-production\",\"isNatureJournal\":false,\"hasQc\":true,\"allowDirectSubmit\":false,\"externalIdentity\":\"trop\",\"sideBox\":\"Learn more about [Tropical Animal Health and Production](https://www.springer.com/journal/11250)\",\"snPcode\":\"11250\",\"submissionUrl\":\"https://submission.nature.com/new-submission/11250/3\",\"title\":\"Tropical Animal Health and Production\",\"twitterHandle\":\"\",\"acdcEnabled\":true,\"dfaEnabled\":true,\"editorialSystem\":\"em\",\"reportingPortfolio\":\"Springer Hybrid\",\"inReviewEnabled\":true,\"inReviewRevisionsEnabled\":false},\"keywords\":\"beef cattle, closed herd, hierarchical grouping, principal components analysis, genetics\",\"lastPublishedDoi\":\"10.21203/rs.3.rs-7002381/v1\",\"lastPublishedDoiUrl\":\"https://doi.org/10.21203/rs.3.rs-7002381/v1\",\"license\":{\"name\":\"CC BY 4.0\",\"url\":\"https://creativecommons.org/licenses/by/4.0/\"},\"manuscriptAbstract\":\"\\u003cp\\u003eClosed cattle herds face daily the tradeoff between genetic progress speed and long term maintenance of genetic diversity. Using pedigree and genetic merit information coupled with multivariate statistics can yield a mating tool to produce balanced and efficient animals for the traits of interest, minimizing genetic diversity losses. The present study aimed to identify genetically similar groups in a closed herd and explore it as a mating system with minimal reduction in genetic diversity. The database came from a single commercial cattle herd (n\\u0026thinsp;=\\u0026thinsp;6,579 individuals with complete information). All statistical traits were performed using the R Core Team\\u0026reg;. The principal component analysis was used to verify the relationships between the genetic values for several traits and to choose the variables that would be considered for the grouping according to their genetic similarity. Clustering identified four groups as the best option for this population. The inbreeding rate within clusters was 0.60%, 0.61%, 0.72% and 0.71% for cluster 1, 2, 3 and 4, respectively. Grouping animals according to genetic similarity allows the selection of individuals for the formation of specialized lines. The use of basic statistics can be a simple way of guiding mating in closed herds, which sometimes did not have access to more sophisticated genetic tools as genotyping or mating software.\\u003c/p\\u003e\",\"manuscriptTitle\":\"Can genetic similarity based on genetic values be used as a mating tool in a Nellore cattle population?\",\"msid\":\"\",\"msnumber\":\"\",\"nonDraftVersions\":[{\"code\":1,\"date\":\"2025-07-14 13:53:49\",\"doi\":\"10.21203/rs.3.rs-7002381/v1\",\"editorialEvents\":[{\"type\":\"communityComments\",\"content\":0},{\"type\":\"reviewerAgreed\",\"content\":\"\",\"date\":\"2025-08-25T00:58:57+00:00\",\"index\":0,\"fulltext\":\"\"},{\"type\":\"reviewersInvited\",\"content\":\"\",\"date\":\"2025-07-09T23:47:26+00:00\",\"index\":\"\",\"fulltext\":\"\"},{\"type\":\"editorAssigned\",\"content\":\"\",\"date\":\"2025-07-03T03:55:37+00:00\",\"index\":\"\",\"fulltext\":\"\"},{\"type\":\"submitted\",\"content\":\"Tropical Animal Health and Production\",\"date\":\"2025-06-29T07:28:35+00:00\",\"index\":\"\",\"fulltext\":\"\"}],\"status\":\"published\",\"journal\":{\"display\":true,\"email\":\"info@researchsquare.com\",\"identity\":\"tropical-animal-health-and-production\",\"isNatureJournal\":false,\"hasQc\":true,\"allowDirectSubmit\":false,\"externalIdentity\":\"trop\",\"sideBox\":\"Learn more about [Tropical Animal Health and Production](https://www.springer.com/journal/11250)\",\"snPcode\":\"11250\",\"submissionUrl\":\"https://submission.nature.com/new-submission/11250/3\",\"title\":\"Tropical Animal Health and Production\",\"twitterHandle\":\"\",\"acdcEnabled\":true,\"dfaEnabled\":true,\"editorialSystem\":\"em\",\"reportingPortfolio\":\"Springer Hybrid\",\"inReviewEnabled\":true,\"inReviewRevisionsEnabled\":false}}],\"origin\":\"\",\"ownerIdentity\":\"824a84e1-bd56-4091-895d-c48ee0fe036e\",\"owner\":[],\"postedDate\":\"July 14th, 2025\",\"published\":true,\"recentEditorialEvents\":[],\"rejectedJournal\":[],\"revision\":\"\",\"amendment\":\"\",\"status\":\"published-in-journal\",\"subjectAreas\":[],\"tags\":[],\"updatedAt\":\"2025-11-17T16:11:42+00:00\",\"versionOfRecord\":{\"articleIdentity\":\"rs-7002381\",\"link\":\"https://doi.org/10.1007/s11250-025-04737-5\",\"journal\":{\"identity\":\"tropical-animal-health-and-production\",\"isVorOnly\":false,\"title\":\"Tropical Animal Health and Production\"},\"publishedOn\":\"2025-11-11 15:57:24\",\"publishedOnDateReadable\":\"November 11th, 2025\"},\"versionCreatedAt\":\"2025-07-14 13:53:49\",\"video\":\"\",\"vorDoi\":\"10.1007/s11250-025-04737-5\",\"vorDoiUrl\":\"https://doi.org/10.1007/s11250-025-04737-5\",\"workflowStages\":[]},\"version\":\"v1\",\"identity\":\"rs-7002381\",\"journalConfig\":\"researchsquare\"},\"__N_SSP\":true},\"page\":\"/article/[identity]/[[...version]]\",\"query\":{\"redirect\":\"/article/rs-7002381\",\"identity\":\"rs-7002381\",\"version\":[\"v1\"]},\"buildId\":\"8U1c8b4HqxoKbykW_rLl7\",\"isFallback\":false,\"isExperimentalCompile\":false,\"dynamicIds\":[84888],\"gssp\":true,\"scriptLoader\":[]}","source_license":"CC-BY-4.0","license_restricted":false}