Genomic Characterization of Productive and Maternal Traits in Arequipa Fighting Cattle Using Snp-based Igenity Panels

Genomic Characterization of Productive and Maternal Traits in Arequipa Fighting Cattle Using Snp-based Igenity Panels | 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 Article Genomic Characterization of Productive and Maternal Traits in Arequipa Fighting Cattle Using Snp-based Igenity Panels Lizeth A. Heredia-Vilchez, Carlos Scotto, Jerry Valdeiglesias, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9162489/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 10 You are reading this latest preprint version Abstract Genomic technologies based on single nucleotide polymorphisms (SNPs) have become powerful tools for improving genetic evaluation in cattle populations lacking comprehensive phenotypic and pedigree records. The present study aimed to assess the genomic potential for meat and milk production traits in Arequipa fighting cattle, a culturally significant but poorly characterized bovine biotype in Peru. A total of 95 adult animals were sampled, of which 60 and 22 individuals passed quality control for the Igenity Beef and Igenity Basic panels, respectively. Genomic scores were analyzed using descriptive statistics and multivariate approaches to evaluate genetic variability and population structure. The results revealed substantial genomic heterogeneity within the population, enabling the identification of individuals with superior genetic merit for growth performance, carcass yield, meat quality, and maternal traits. Genomic ranking distinguished groups associated with maternal and terminal production objectives, suggesting the presence of differentiated genetic profiles within the herd. Analysis of milk protein markers showed a high frequency of favorable β-casein genotypes associated with improved digestibility and potential value in A2 milk markets, whereas alleles linked to enhanced cheese-making properties were less frequent. Hierarchical clustering further revealed the presence of genetically structured subpopulations, indicating the maintenance of genetic diversity. hese findings provide the first genomic assessment of productive traits in Arequipa fighting cattle using SNP-based commercial panels. They highlight the potential of genomic tools to identify animals with desirable productive and maternal characteristics and support the development of selection, conservation, and breeding strategies adapted to regional production systems. Biological sciences/Computational biology and bioinformatics Biological sciences/Genetics genomic selection Creole cattle SNP markers genetic diversity breeding strategies Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Introduction Genomic tools have become fundamental components of modern animal breeding programs, as they enable the evaluation of economically important traits and improve the accuracy of genetic selection in cattle populations [1]. Their use is particularly relevant in systems where phenotypic and pedigree records are limited or unavailable. In this context, commercial SNP panels, such as Igenity Beef, provide genomic predictions for maternal, growth, and carcass traits with greater accuracy than traditional selection approaches, especially in small or unstructured herds [2,3]. At the same time, the characterization of local cattle populations has gained increasing global attention due to their role in preserving genetic diversity and contributing traits such as adaptability, resilience, and efficiency under challenging environmental conditions. These underrepresented biotypes constitute valuable genetic resources for sustainable livestock production. [4–6]. In Peru, the application of genomic approaches in cattle has expanded in recent years, including studies on genetic diversity using high-density SNP arrays and mitochondrial genome analyses in Creole populations [7]. Among these, Arequipa fighting cattle represent a biotype of cultural and economic importance, traditionally selected for temperament, hardiness, and functional performance in local events[8]. Although commonly classified as Creole cattle due to their heterogeneous origin, some lineages exhibit distinctive phenotypic and genetic characteristics, as well as relatively high levels of genetic variability [9]. Despite its importance, Arequipa fighting cattle remains poorly characterized from a scientific perspective. Previous studies have primarily focused on genetic diversity, reporting high heterozygosity and a large proportion of polymorphic SNPs compared with other cattle populations in the country [7]. Similarly, mitochondrial data have enabled preliminary phylogenetic inferences. However, no peer-reviewed studies have evaluated their productive potential using genomic prediction tools, nor have standardized genetic indices for milk production, growth, or carcass traits been reported [8]. Field observations from local breeders suggest the presence of individuals with notable performance in meat and milk-related traits, even though these attributes have not been the primary targets of selection [10]. This suggests the presence of exploitable genetic variability within the population within this population, highlighting the need for objective genomic evaluation[8]. Therefore, the use of genomic prediction tools such as Igenity Beef offers a valuable opportunity to assess productive potential in populations lacking structured records and to support evidence-based breeding strategies. In this context, commercial SNP panels such as Igenity Beef provide genomic predictions for maternal, growth, and carcass traits with greater accuracy than traditional selection approaches, particularly in small or unstructured herds [10]. Materials and Methods Sample collection and ethical considerations Hair follicles with intact roots were collected from each animal in accordance with the Peruvian Animal Welfare Law No. 30407 (Peru, 2016). This procedure is non-invasive and does not cause harm or distress to the animals. All experimental procedures were reviewed and approved by the Institutional Research Ethics Committee of the Universidad Nacional Toribio Rodríguez de Mendoza (UNTRM; CIEI No. 0070). All methods were carried out in accordance with relevant institutional guidelines and regulations. The animals included in this study were privately owned by local farmers. Prior to sample collection, verbal informed consent was obtained from all owners after explaining the objectives of the study. Farmers voluntarily agreed to the participation of their animals and provided the corresponding information at the time of sampling. During field collection, each sample was recorded in individually labeled paper envelopes containing key information to ensure traceability, including a unique sample code, origin, breed, animal identification, sex, date of birth, sampling date, person responsible for sample collection, and additional observations (Figure S1 ). Information provided by the owners, such as animal name and age, was also recorded. A total of 95 adult cattle (55 males and 40 females) were evaluated. Samples were initially stored in labeled paper envelopes during fieldwork and subsequently transferred to official NEOGEN® cards to ensure proper traceability and sample integrity. Study Design and Animal Population The present study employed a descriptive design aimed at characterizing the genomic potential of cattle from the Arequipa fighting cattle biotype. A total of 95 adult animals (40 females and 55 males) raised in the province of Arequipa, Peru, were sampled between June and September 2024. Although these cattle have traditionally been selected for temperament, hardiness, and functional performance in cultural events, some individuals also exhibit productive traits relevant to meat and milk production, providing a rationale for their scientific evaluation. DNA Extraction and SNP Genotyping All samples were sent to the NEOGEN® laboratory (Lansing, Michigan, USA), where DNA extraction and SNP genotyping were performed. Only samples meeting the required concentration and quality standards were processed: 60 animals for the Igenity Beef panel (28 females and 32 males) and 22 females for the Igenity Basic panel, out of the 29 samples initially submitted. Genotyping was conducted using commercial SNP panels, which served as the basis for genomic predictions generated by NEOGEN® using models from International Genetic Solutions (IGS). Genomic Prediction Using Igenity® Beef The Igenity Beef test provides genomic estimates for 17 traits, grouped into maternal, growth, and carcass characteristics. Each trait is scored on a standardized scale from 1 to 10, where higher values indicate better expected performance. In addition, four selection indices were calculated: the Maternal Index, Terminal Index, Balanced Index, and Top 25% Index, which summarize the overall productive merit of each individual (see Supplementary Table S1 ). Genomic Prediction Using Igenity® Basic The Igenity Basic test, applied exclusively to females, evaluated 14 traits related to milk production, health, and compositional attributes. Scores were expressed on a scale from 1 to 10, with trait-specific interpretation depending on the nature of each trait (see Supplementary Table S2 ). Statistical Analysis All data management and cleaning procedures were performed in R version 4.5.1 [11], using the readxl [12] and dplyr [13] packages for data import and manipulation. Genomic scores provided by the Igenity panels were treated as ordinal variables (scale 1–10), representing relative genetic merit rather than direct phenotypic measurements. Exploratory analyses were conducted to assess the distribution of genomic scores and to detect potential outliers. To examine the multivariate structure of maternal, growth, and carcass traits, a Multiple Correspondence Analysis (MCA) was applied using the FactoMineR [14] and factoextra [15] packages, allowing the identification of associations among variables and the grouping of individuals according to similar genomic profiles. In addition, hierarchical cluster analyses were performed using base R functions (dist and hclust), and heatmaps were generated with the pheatmap [16] package to visualize similarity patterns among animals while preserving their real individual identification. Finally, customized plots were created using ggplot2 [17] and ggrepel [18] to highlight the observed patterns and the distribution of genomic scores. Results Population Characteristics of the Studied Cattle The studied population consisted of 60 Creole cattle, with a slight predominance of males (32; 53.3%) over females (28; 46.7%). According to the genomic ranking, 15 individuals (25.0%) were classified in the Bottom 25% category, 15 (25.0%) in the Maternal group, 16 (26.7%) in the Terminal group, and 14 (23.3%) in the Top 25% category. Each animal was identified using a unique code to ensure data traceability (Table 1 ). Table 1 Distribution of qualitative variables in the studied population Variable Category Frequency Percentage Gender Female (F) 28 46.67 Male (M) 32 53.33 Breed Creole 60 100 Igenity Rank Bottom 25% 15 25.00 Maternal 15 25.00 Terminal 16 26.67 Top 25% 14 23.33 Genomic profiles of Arequipa cattle based on Igenity® panels Figure 1 shows the distribution of genomic categories assigned to the evaluated animals, grouped according to three functional sets of traits: maternal traits, yield traits, and carcass traits. Within the Carcass Traits group, most individuals exhibit low values for marbling (MARB) and ribeye area (REA), while tenderness (TEND) also shows consistently low values. In the Maternal Traits group, low to moderate values predominate for birth weight (BW), calving ease direct (CED), and milk production (MILK), whereas docility (DOC) shows variability among animals. In contrast, Yield Traits display greater diversity, with some individuals exhibiting higher values for weaning weight (WW) and yearling weight (YW), while residual feed intake (RFI) shows lower values in a subset of animals. Note Maternal Traits: BW = Birth Weight, CED = Calving Ease Direct, CEM = Calving Ease Maternal, DOC = Docility, HPR = Heifer Pregnancy Rate, MILK = Milk Production, STAY = Stayability. Yield Traits: ADG = Average Daily Gain, RFI = Residual Feed Intake, SC = Scrotal Circumference, WW = Weaning Weight, YW = Yearling Weight. Carcass Traits: FAT = Fat Thickness, HCW = Hot Carcass Weight, MARB = Marbling, REA = Ribeye Area, TEND = Tenderness. Figure 1. Distribution of genomic categories by trait group evaluated using the Igenity Beef test. As shown in Table 2 , the AA genotype of κ-casein was the most frequent in the evaluated population (45.5%), followed by AB (36.4%). The AE genotype was observed at a frequency of 9.1%, while BB and BE were less frequent (4.5% each). For β-casein, the AA genotype predominated (90.9%), whereas the remaining genotypes were observed at lower frequencies. Regarding β-lactoglobulin, the AB genotype was the most frequent (59.1%), followed by BB (18.2%) and AA (13.6%). A total of 9.1% of the animals had no recorded genotype (NR). As shown in Fig. 2, the mosaic plot depicts the relationship between κ-casein (CSN3) and β-casein (CSN2) genotypes in the evaluated animals. The AA, AB, and AE genotypes of κ-casein are exclusively associated with the AA genotype of β-casein, whereas the BB and BE genotypes are associated only with the AB genotype of β-casein. The NA category for κ-casein corresponds to missing data in β-casein. The observed distribution shows consistent genotype combinations across the evaluated animals. The chi-square test of independence was not applied due to the low frequency of some genotype combinations Figure 2. Mosaic plot showing the association between κ-casein (CSN3) and β-casein (CSN2) genotypes in the evaluated cattle. Genomic identification of cattle with meat and milk traits As shown in Fig. 3, the heatmap represents the genetic profiles of the animals, organized according to three groups of traits: maternal traits (Maternal Traits), yield traits (Yield Traits), and carcass traits (Carcass Traits). Colors represent the ordinal level assigned to each trait, allowing direct visual comparison among individuals. High variability is observed in maternal traits, particularly for calving ease (CED) and milk production (MILK). Yield traits display more homogeneous patterns, although some individuals exhibit consistently high or low values. In carcass traits, significant contrasts are identified for marbling (MARB) and ribeye area (REA), indicating potential differences in meat quality. Note Maternal Traits: BW = Birth Weight, CED = Calving Ease Direct, CEM = Calving Ease Maternal, DOC = Docility, HPR = Heifer Pregnancy Rate, MILK = Milk Production, STAY = Stayability. Yield Traits: ADG = Average Daily Gain, RFI = Residual Feed Intake, SC = Scrotal Circumference, WW = Weaning Weight, YW = Yearling Weight. Carcass Traits: FAT = Fat Thickness, HCW = Hot Carcass Weight, MARB = Marbling, REA = Ribeye Area, TEND = Tenderness. Figure 3. Heatmap of genomic profiles grouped by trait category. In Fig. 4, the analysis of the 60 animals evaluated using the Igenity Beef test allowed their classification into four performance categories: Top 25%, Maternal, Terminal, and Bottom 25%. It is important to note that the Maternal and Terminal categories identify animals with higher scores in specific indices, but do not exclude their inclusion in the overall Top 25% category. Within the Top 25% category, 14 individuals (11 females and 3 males) were identified, including PBT_ARE_048. The Maternal category included 15 animals (7 females and 8 males), among which PBT_ARE_055 (female) and PBT_ARE_058 (male) were observed. The Terminal category comprised 16 individuals (6 females and 10 males), including PBT_ARE_100, PBT_ARE_008, and PBT_ARE_045. In the Bottom 25% group, 15 animals (4 females and 11 males) were identified. Overall, the population consisted of 28 females (48.3%) and 32 males (51.7%). Females were more represented in the Top 25% and Maternal categories, whereas males were more frequent in the Bottom 25% category. Genomic Index Distribution Across Igenity Rank Categories Figure 5 shows the distribution of genomic indices across Igenity ranking categories. Animals classified within the Top 25% display higher values across the three evaluated indices (maternal, production, and terminal) compared with the remaining groups. In contrast, individuals in the Bottom 25% exhibit lower scores and a relatively narrower distribution. Partial overlap among interquartile ranges is observed across categories. Across most ranking categories, the Production Index shows higher values than the other indices. The Maternal Index presents smaller differences among categories and a relatively homogeneous distribution. In contrast, the Terminal Index displays greater dispersion in some groups, particularly in higher-ranking categories. Overall, the observed overlap among distributions indicates that the ranking categories do not represent genetically discrete groups but rather positions along a continuum within the evaluated population. Provide genomic information to support improvement of cattle quality and productivity in the Arequipa region As shown in Fig. 6, the hierarchical dendrogram generated using Ward’s method groups the animals into four clusters differentiated by colors. Cluster 1 (blue) includes individuals with high genetic similarity and low internal variation, whereas Cluster 2 (yellow) also shows a relatively homogeneous structure, although distinct from Cluster 1. Cluster 3 (gray) is smaller in size and occupies an intermediate position between adjacent groups, and Cluster 4 (red) is the largest and presents greater internal variability. Some animals, such as PBT_ARE_085 and PBT_ARE_059, show distinct positions within their respective clusters. Analyses performed using the Igenity Beef genomic test show variation across the three evaluated trait groups. The heatmap and Multiple Correspondence Analysis (MCA, see Supplementary Figure S2) illustrate differences among individuals, including consistent and contrasting profiles, while the dendrogram indicates the presence of genetically differentiated groups within the evaluated population. Fig. 6. Hierarchical clustering revealing four genetic subpopulations within the herd. Figure 7 shows the hierarchical dendrogram constructed from qualitative data obtained with the Igenity Basic genetic test for 22 animals, using Ward’s method. Each branch represents an individual identified by its code, and the height of the linkage reflects the degree of genetic similarity, with lower linkage heights indicating greater similarity among animals. The analysis identified seven distinct clusters, represented by colors and shaded areas. Cluster 1 (light blue) corresponds to the individual PBT_ARE_003, which shows a distinct genetic profile. Cluster 2 (light yellow) includes only PBT_ARE_013, clearly differentiated from the rest of the population. Cluster 3 (gray) groups PBT_ARE_020, PBT_ARE_023, PBT_ARE_024, PBT_ARE_025, PBT_ARE_033, PBT_ARE_041, and PBT_ARE_042, forming the largest cluster with high internal similarity. Cluster 4 (red) comprises PBT_ARE_044, PBT_ARE_048, PBT_ARE_049, PBT_ARE_052, PBT_ARE_053, PBT_ARE_054, and PBT_ARE_058, which are differentiated from Cluster 3. Cluster 5 (dark blue) consists of PBT_ARE_063, which is positioned close to Cluster 6, formed by PBT_ARE_064 and PBT_ARE_065, both showing high similarity to each other. Finally, Cluster 7 (dark yellow) groups PBT_ARE_068, PBT_ARE_070, and PBT_ARE_096, displaying a distinct pattern relative to the rest of the population. The clustering pattern is consistent with the distribution observed in the Multiple Correspondence Analysis (MCA; see Supplementary Figure S3), which also reflects differentiation among individuals within the population. Figure 7. Hierarchical dendrogram based on Igenity Basic data. Discussion The genomic analyses performed using the Igenity Beef and Igenity Basic panels provided a comprehensive characterization of genetic variability in the evaluated Arequipa fighting cattle population. These tools enabled the simultaneous assessment of multiple productive, reproductive, and milk quality traits, offering an alternative to traditional phenotypic selection, particularly in populations with limited structured records [2,22]. The results obtained from the Igenity Beef panel revealed substantial heterogeneity among individuals, with some animals showing higher genomic merit for growth performance, carcass yield, and meat quality. This variability aligns with previous reports describing high genetic diversity in Peruvian Creole cattle, often associated with traditional management systems and the absence of intensive directional selection. In this context, the identification of superior individuals represents a valuable opportunity to implement more efficient breeding strategies, including early selection based on genomic information, which may reduce generation intervals and accelerate genetic gain [23–26]. Similarly, the Igenity Basic panel allowed the identification of females with favorable genomic profiles for maternal, productive, and reproductive traits. Individuals combining adequate milk production with reproductive efficiency are particularly relevant as replacement candidates. These findings support previous studies highlighting the importance of selecting genotypes associated with productive and reproductive efficiency in Creole cattle populations [29–31]. The integration of genomic approaches into local systems may contribute to improved productivity and sustainability [32,33]. The identification of individuals classified within the top 25% of genomic merit provides a practical basis for establishing elite breeding nuclei. However, the observed overlap among genomic ranking categories indicates that these groups do not represent discrete genetic clusters, but rather positions along a continuum of genetic merit within the population [34,35]. Regarding milk quality traits, the high frequency of the β-casein A2A2 genotype suggests potential for improved milk digestibility and access to specialized markets [36]. In contrast, the relatively low frequency of the κ-casein B allele—associated with enhanced cheese-making properties—may limit technological quality for cheese production. Additionally, the predominance of heterozygous genotypes at the β-lactoglobulin locus reflects the maintenance of genetic variability within the population [24,26]. Multivariate analyses, including heatmaps, multiple correspondence analysis, and hierarchical clustering, revealed the presence of genetically structured subpopulations within the herd. This structure likely reflects historical management practices, non-systematic selection, and the heterogeneous origin of the population, representing both an opportunity for targeted selection and a resource for conserving genetic diversity. A key limitation of this study is that genomic predictions were derived from commercial SNP panels and could not be directly compared with phenotypic performance data. This is due to the fact that the evaluated population consists of Creole fighting cattle, primarily used for cultural events rather than productive or reproductive purposes, which limits the availability of systematic records. In addition, these populations have been scarcely studied in Peru from a genetic–productive perspective, restricting the availability of reference information. In this context, the identified genetic potential should be interpreted primarily at the individual level, as group-based evaluations may not adequately reflect the variability within the population. Nevertheless, these results represent an initial step toward understanding the genetic potential of this population and provide a foundation for future studies integrating genomic and phenotypic information. Despite their predominant use in fighting events, the evaluated animals may harbor valuable genetic potential that remains underutilized. In this regard, both fighting bulls and cows can be considered strategic resources for future generations, with the potential to contribute to the improvement of cattle herds through their incorporation into appropriately designed selection programs. Conclusions This study presents the first genomic evaluation of productive and maternal traits in Arequipa fighting cattle using SNP-based commercial panels. The results revealed considerable genetic variability and enabled the identification of individuals with superior genomic merit for growth, carcass, and maternal performance. These findings highlight the potential of genomic approaches to support selection strategies in cattle populations lacking structured phenotypic records. In addition, the detection of favorable milk protein genotypes suggests opportunities for diversification into specialized production systems. Despite these promising outcomes, the absence of phenotypic validation remains an important limitation. Future studies integrating genomic predictions with performance data will be essential to confirm the practical applicability of these results. Overall, this study provides a foundation for the implementation of genomic selection in this under-characterized population and underscores the potential of fighting cattle—traditionally not selected for productive purposes—as valuable genetic resources for the development of sustainable breeding strategies in the Arequipa region. Declarations Funding This study was funded by the project “Creation of the Precision Agriculture Service in the Departments of Lambayeque, Huancavelica, Ucayali, and San Martín” (CUI No. 2449640), implemented through the National Institute of Agrarian Innovation (INIA). Additional financial and administrative support was provided by the Vice-Rectorate for Research (VRIN) of the National University Toribio Rodríguez de Mendoza de Amazonas (UNTRM). Acknowledgments The authors would like to thank the project “Creation of the Precision Agriculture Service in the Departments of Lambayeque, Huancavelica, Ucayali, and San Martín” (CUI No. 2449640), implemented by the National Institute of Agrarian Innovation (INIA), for the institutional support provided for the execution of this study. We also acknowledge the support of the Institute of Research in Genetics and Biotechnology (IGBI) of the National University Toribio Rodríguez de Mendoza (UNTRM), whose administrative and logistical assistance was essential for the development of this research. Special thanks are extended to Eng. Deyanira Figueroa and Eng. Anita Corredor for their valuable participation during the initial stages of the project. We also express our sincere appreciation to Mr. Jerry Valdeiglesias for his committed technical collaboration throughout the study. Author Contributions Conceptualization: L.A.H.V., C.A. Data curation: L.A.H.V. Formal analysis: L.A.H.V. Funding acquisition: C.A., N.L.M.V., J.L.M. Investigation: all authors. Project administration: C.A. Resources: C.A., N.L.M.V., J.L.M. Software: L.A.H.V. Supervision: C.J.S.E., C.A. Validation: C.A., C.J.S.E. Visualization: C.A. Methodology: L.A.H.V., C.A., J.V. Writing – original draft: L.A.H.V., C.A., C.J.S.E. Writing – review and editing: all authors. Competing interests The authors declare no conflict of interest. References Dekkers JC. Commercial application of marker- and gene-assisted selection in livestock: strategies and lessons. J Anim Sci. 2004;82:E313-E328. doi:10.2527/2004.8213_supplE313x Van Eenennaam AL, Weaber RL, Drake DJ, Rafiqul I. Benefits of DNA information to improve accuracy and selection response in beef sire selection. J Anim Sci. 2011;89(10):3223-3240. doi:10.2527/jas.2010-3754 Carillier-Jacquin C, Larroque H, Palhière I, Tomas A. Genomic prediction of dairy traits in cattle breeds with limited reference populations. J Dairy Sci. 2018;101(3):2101-2116. doi:10.3168/jds.2017-13512 FAO. The Second Report on the State of the World's Animal Genetic Resources for Food and Agriculture. Rome: FAO; 2015. Available from: https://www.fao.org/3/i4787e/i4787e.pdf Mwai O, Hanotte O, Kwon YJ, Cho S. African indigenous cattle: unique genetic resources in a rapidly changing world. Asian-Australas J Anim Sci. 2015;28(7):911-921. doi:10.5713/ajas.15.0090 Ward JA, et al. Genomic insights into the population history and adaptive traits of Latin American Criollo cattle. R Soc Open Sci. 2024;11:231388. doi:10.1098/rsos.231388 Corredor FA, Figueroa D, Estrada R, Salazar W, Quilcate C, Vásquez HV, et al. Genetic diversity and population structure of a Peruvian cattle herd using SNP data. Front Genet. 2023;14:1073843. doi:10.3389/fgene.2023.1073843 Arbizu J. Complete mitochondrial genome of Peruvian Creole cattle and insights into their phylogenetic relationships. Mitochondr DNA B. 2022;7(1):123-127. doi:10.1080/23802359.2022.2025001 Corredor JA, Bendezú M, Pizarro E. Genetic diversity assessment of Peruvian cattle populations using high-density SNP genotyping. Trop Anim Health Prod. 2023;55(1):34. doi:10.1007/s11250-022-03320-4 Berry DP, Evans RD, McParland S, Dillon P. Genetics of performance and fertility in beef and dairy cattle and opportunities for genomic selection. Anim. 2016;10(2):179-188. doi:10.1017/S1751731115002233 R Core Team. R: A language and environment for statistical computing. Vienna, Austria: R Foundation for Statistical Computing; 2025. Available from: https://www.R-project.org Wickham H, Bryan J. readxl: Read Excel files. R package version 1.5.4; 2023. Available from: https://CRAN.R-project.org/package=readxl Wickham H, François R, Henry L, Müller K. dplyr: A grammar of data manipulation. R package version 1.1.2; 2023. Available from: https://CRAN.R-project.org/package=dplyr Lê S, Josse J, Husson F. FactoMineR: An R package for multivariate analysis. J Stat Softw. 2008;25(1):1-18. doi:10.18637/jss.v025.i01 Kassambara A, Mundt F. factoextra: Extract and visualize the results of multivariate data analyses. R package version 1.0.7; 2020. Available from: https://CRAN.R-project.org/package=factoextra Kolde R. pheatmap: Pretty heatmaps. R package version 1.0.12; 2019. Available from: https://CRAN.R-project.org/package=pheatmap Wickham H. ggplot2: Elegant graphics for data analysis. 2nd edition. New York: Springer-Verlag; 2016. Available from: https://ggplot2.tidyverse.org Slowikowski K. ggrepel: Automatically position non-overlapping text labels with 'ggplot2'. R package version 0.9.4; 2023. Available from: https://CRAN.R-project.org/package=ggrepel Faggion S, et al. ?-Casein A2 affects milk renneting properties, cheese yield before and after ripening, and alters the texture of Caciotta cheese produced in field conditions. Food Res Int. 2025;160:111750. doi:10.1016/j.foodres.2022.111750 Jiménez-Montenegro L, et al. Worldwide research trends on milk containing only A2 ?-casein: A bibliometric analysis. Animals. 2022;12(15):1909. doi:10.3390/ani12151909 Razhina E, et al. Kappa-casein influence on yield and quality of cottage cheese. Atlantis Press; 2019. doi:10.2991/978-94-6239-035-8_47 Pryce JE, Daetwyler HD. Designing dairy cattle breeding schemes under genomic selection: A review. Anim Prod Sci. 2012;52(3):107-114. doi:10.1071/AN11098 Veli RA, Rivas Seoane E. Caracterización genética de Kappa caseínas y Beta lactoglobulinas del bovino criollo de cuatro comunidades andinas del Perú. Anim Genet Resour. 2010;46:67-72. doi:10.1017/S2078633610000718 Veli EA, Rivas Seoane E, Rivas Palma V, Aquino Y, Estrada R. Variabilidad genética del gen de beta-lactoglobulina en bovinos criollos de Perú. Arch Zootec. 2008;57(219):341-344. Available from: https://repositorio.inia.gob.pe/server/api/core/bitstreams/62533dc8-cd4d-48fe-8455-acfefedfa00e/content Veli E, Rivas Seoane E, Rivas Palma V, Verástegui M, Pastor S. Evaluación de la variabilidad de genes de kappa caseína en poblaciones de bovinos criollos de Ticllos y Huaschao, región Ancash. Boletín de Lima. 2004;23(124):22-36. Available from: https://repositorio.inia.gob.pe/handle/20.500.12996/3435 Scotto C, Rosemberg M. Estudio de dos pares de genes en la ?-lactoalbúmina y ?-lactoglobulina en el ganado vacuno Holstein, Brown Swiss y Criollo en la cuenca central del Perú [tesis]. Lima: Universidad Nacional Agraria La Molina; 2000. Available from: https://repositorio.unalm.edu.pe/handle/20.500.12996/3435 Mateescu RG, Garrick DJ, Reecy JM. Network analysis reveals putative genes affecting meat quality in Angus cattle. Front Genet. 2017;8:171. doi:10.3389/fgene.2017.00171 Neogen Corporation. Annual report 2020. Neogen Corporation; 2020. Available from: https://www.annualreports.com/HostedData/AnnualReportArchive/n/NASDAQ_NEOG_2020.pdf Redacción. La raza criollos representa el 64% del ganado vacuno del Perú. Agraria.pe. 2013 Jul 23. Available from: https://agraria.pe/noticias/la-raza-criollos-representa-el-64-del-ganado-vacuno-del-per-4887 Delgado A, García C. El ganado vacuno Criollo: fuente importante de carne en el Perú. Engormix. 2018 Jan 19. Available from: https://www.engormix.com/ganaderia/cruzamientos-ganado-carne/ganado-vacuno-criollo-fuente_a41576/ Quispe Coaquira J, Apaza Zúñiga E, Chambilla Carreón P, Sapana Valdivia R. Índices reproductivos y productivos en un hato de bovinos criollo del Altiplano peruano. Rev Investig Altoandinas. 2014;16(2):49-56. Available from: https://repositorio.inia.gob.pe/items/66cba641-abcd-4c5a-90a2-c6cfa4beeca1 Pryce JE, Haile-Mariam M, Goddard ME, Hayes BJ. Identification of genomic regions associated with inbreeding depression in Holstein and Jersey dairy cattle. Genet Sel Evol. 2014;46:71. doi:10.1186/s12711-014-0071-7 García-Ruiz A, Cole JB, VanRaden PM, Wiggans GR, Ruiz-López FJ, Van Tassell CP. Changes in genetic selection differentials and generation intervals in US Holstein dairy cattle as a result of genomic selection. Proc Natl Acad Sci U S A. 2016;113(28):E3995-E4004. doi:10.1073/pnas.1519061113 Hayes BJ, Bowman PJ, Chamberlain AJ, Goddard ME. Genomic prediction in dairy cattle: Progress and challenges. J Dairy Sci. 2013;96(7):4848-4860. doi:10.3168/jds.2012-6120 Figueroa D, Romero Y, Heredia-Vilchez LA, Poemape C, Alvarado W, Quilcate C. Genomic analysis for cattle breeding improvement, progress and future perspectives in Peru: A review. Anim Biotechnol. 2025;36(1):1-12. doi:10.1080/10495398.2025.2547344 Alfonso L, Urrutia O, Mendizabal JA. Conversión de las explotaciones de vacuno de leche a la producción de leche A2 ante una posible demanda del mercado: Posibilidades e implicaciones. ITEA. 2019;115(3):231-251. doi:10.12706/itea.2019.001 Rivas JA, Delgado JV, Gutiérrez JP. Acciones para la caracterización y conservación del bovino criollo peruano (Bos taurus). Anim Genet Resour. 2007;41:65-72. doi:10.1017/S1014233900002261 Sebastiani C, Arcangeli C, Torricelli M, Ciullo M, D'avino N, Cinti G, Fisichella S, Biagetti M. Marker-assisted selection of dairy cows for ?-casein gene A2 variant. Ital J Food Sci. 2022;34(2):21-27. doi:10.15586/ijfs.v34i2.2178 Perú. Ley N.° 30407, Ley de protección y bienestar animal. Diario Oficial El Peruano. 2016 Jan 8;N.° 574725. Available from: https://www.leyes.congreso.gob.pe/documentos/leyes/30407.pdf Neogen Corporation. Neogen attends 2025 Unified Symposium. Neogen NeoCenter. 2025 Aug 8. Available from: https://www.neogen.com/en/neocenter/events/unified-symposium/ Additional Declarations No competing interests reported. Supplementary Files SupplementaryFigureS1S2S3.docx Supplementarypapertable.docx Cite Share Download PDF Status: Under Review Version 1 posted Reviewers agreed at journal 11 May, 2026 Reviews received at journal 04 May, 2026 Reviewers agreed at journal 26 Apr, 2026 Reviewers agreed at journal 25 Apr, 2026 Reviewers agreed at journal 08 Apr, 2026 Reviewers invited by journal 06 Apr, 2026 Editor assigned by journal 06 Apr, 2026 Editor invited by journal 31 Mar, 2026 Submission checks completed at journal 31 Mar, 2026 First submitted to journal 30 Mar, 2026 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. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-9162489","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":619824890,"identity":"249756cf-e057-4392-bc6f-545e37275e25","order_by":0,"name":"Lizeth A. Heredia-Vilchez","email":"","orcid":"","institution":"National University Toribio Rodríguez de Mendoza","correspondingAuthor":false,"prefix":"","firstName":"Lizeth","middleName":"A.","lastName":"Heredia-Vilchez","suffix":""},{"id":619824891,"identity":"8639ab6e-bdff-4c4c-8f52-126f3e09581d","order_by":1,"name":"Carlos Scotto","email":"","orcid":"","institution":"Federico Villarreal National University","correspondingAuthor":false,"prefix":"","firstName":"Carlos","middleName":"","lastName":"Scotto","suffix":""},{"id":619824892,"identity":"f52a41f2-6a42-4190-816b-1db6acda922e","order_by":2,"name":"Jerry Valdeiglesias","email":"","orcid":"","institution":"Independent researcher","correspondingAuthor":false,"prefix":"","firstName":"Jerry","middleName":"","lastName":"Valdeiglesias","suffix":""},{"id":619824893,"identity":"d55f2dfe-48b8-4c73-ad94-15b8e4b0a4c6","order_by":3,"name":"N. Luis. Murga-Valderrama","email":"","orcid":"","institution":"National University Toribio Rodríguez de Mendoza","correspondingAuthor":false,"prefix":"","firstName":"N.","middleName":"Luis.","lastName":"Murga-Valderrama","suffix":""},{"id":619824894,"identity":"cb4e3c07-23ff-4b81-a2a9-b288088f7dea","order_by":4,"name":"Jorge L. Maicelo","email":"","orcid":"","institution":"National University Toribio Rodríguez de Mendoza","correspondingAuthor":false,"prefix":"","firstName":"Jorge","middleName":"L.","lastName":"Maicelo","suffix":""},{"id":619824895,"identity":"47107725-21df-433d-9380-0a9801ff212c","order_by":5,"name":"Carlos Arbizu","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABFElEQVRIiWNgGAWjYHACxgNgSoKxgYGhAibIhl8PkpYzDAw8JGgBWdhGhBb+9rMPDnz4wyBvPru5+TPvvMN59tKnExg+lB1mMGdvwKpF4ky6wcGZbQyGc+4cbDDm3Xa4mIcvdwPjjHOHGSx7DmDVYsCQxnCYt4GBcYZEYkMyUEtiDw/vBmbetsMMBjcSsGvhf8ZwmOcPgz1Iy2HeOVAtf0Fa7j/ArkUCaAsPG0MiUEtjM28DVAsj2Bbs3pe48YwB6BeJ5BkyB5sZ5xxLT+w5w7vhYM+5dB7LHuwO4+9PY3zw4Y+N7Qzp9scf3tRYJ7b38G588KPMWs6cHbv3YZahckFqeQzwacAOyNAyCkbBKBgFwxMAANm6XzNrREnaAAAAAElFTkSuQmCC","orcid":"","institution":"National University Toribio Rodríguez de Mendoza","correspondingAuthor":true,"prefix":"","firstName":"Carlos","middleName":"","lastName":"Arbizu","suffix":""}],"badges":[],"createdAt":"2026-03-18 19:09:08","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9162489/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9162489/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":107479141,"identity":"e2dde523-ecee-4c57-aeb6-5a84331e4303","added_by":"auto","created_at":"2026-04-22 01:20:00","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":2501122,"visible":true,"origin":"","legend":"\u003cp\u003eDistribution of genomic categories by trait group evaluated using the Igenity Beef test.\u003c/p\u003e\n\u003cp\u003eNote: Maternal Traits: BW = Birth Weight, CED = Calving Ease Direct, CEM = Calving Ease Maternal, DOC = Docility, HPR = Heifer Pregnancy Rate, MILK = Milk Production, STAY = Stayability. Yield Traits: ADG = Average Daily Gain, RFI = Residual Feed Intake, SC = Scrotal Circumference, WW = Weaning Weight, YW = Yearling Weight. Carcass Traits: FAT = Fat Thickness, HCW = Hot Carcass Weight, MARB = Marbling, REA = Ribeye Area, TEND = Tenderness.\u003c/p\u003e","description":"","filename":"figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-9162489/v1/888c27e8244fc313a005dbbf.png"},{"id":106622973,"identity":"b37fe1bd-c6a0-465f-af2e-71c34788118c","added_by":"auto","created_at":"2026-04-10 14:22:27","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":1714250,"visible":true,"origin":"","legend":"\u003cp\u003eMosaic plot showing the association between κ-casein (CSN3) and β-casein (CSN2) genotypes in the evaluated cattle.\u003c/p\u003e","description":"","filename":"Figure2.png","url":"https://assets-eu.researchsquare.com/files/rs-9162489/v1/5493846b9dab18b815114f61.png"},{"id":106993560,"identity":"aa5a0c33-0402-45f3-b25a-70e01bd6359e","added_by":"auto","created_at":"2026-04-15 14:37:38","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":7823147,"visible":true,"origin":"","legend":"\u003cp\u003eHeatmap of genomic profiles grouped by trait category.\u003c/p\u003e\n\u003cp\u003eNote. Maternal Traits: BW = Birth Weight, CED = Calving Ease Direct, CEM = Calving Ease Maternal, DOC = Docility, HPR = Heifer Pregnancy Rate, MILK = Milk Production, STAY = Stayability. Yield Traits: ADG = Average Daily Gain, RFI = Residual Feed Intake, SC = Scrotal Circumference, WW = Weaning Weight, YW = Yearling Weight. Carcass Traits: FAT = Fat Thickness, HCW = Hot Carcass Weight, MARB = Marbling, REA = Ribeye Area, TEND = Tenderness.\u003c/p\u003e","description":"","filename":"Figure3.png","url":"https://assets-eu.researchsquare.com/files/rs-9162489/v1/ce3af5506392626d9b978465.png"},{"id":106622977,"identity":"0fbc90fb-f230-4dbb-8c0c-40f2eaee80a9","added_by":"auto","created_at":"2026-04-10 14:22:27","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":246481,"visible":true,"origin":"","legend":"\u003cp\u003eDistribution of evaluated animals by Igenity Beef performance category and sex.\u003c/p\u003e","description":"","filename":"Figure4.png","url":"https://assets-eu.researchsquare.com/files/rs-9162489/v1/5be0cc92586d2bf6f4f619d7.png"},{"id":106726871,"identity":"307aa388-5a35-4a41-9509-0c59da84fee7","added_by":"auto","created_at":"2026-04-12 18:37:28","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":1933358,"visible":true,"origin":"","legend":"\u003cp\u003eGenomic Index Distribution Across Igenity Rank Categories.\u003c/p\u003e","description":"","filename":"Figure5.png","url":"https://assets-eu.researchsquare.com/files/rs-9162489/v1/af2f50e023bf4a28c37a0c7e.png"},{"id":106725942,"identity":"e0a7f28f-d94e-4860-8449-61cfbb627055","added_by":"auto","created_at":"2026-04-12 18:34:35","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":3401786,"visible":true,"origin":"","legend":"\u003cp\u003eHierarchical clustering revealing four genetic subpopulations within the herd.\u003c/p\u003e","description":"","filename":"Figure6.png","url":"https://assets-eu.researchsquare.com/files/rs-9162489/v1/92b89a81f968a9c64f48f84b.png"},{"id":106622978,"identity":"aaa09839-d0b7-45d9-ab03-f67342266f6d","added_by":"auto","created_at":"2026-04-10 14:22:27","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":2503459,"visible":true,"origin":"","legend":"\u003cp\u003eHierarchical dendrogram based on Igenity Basic data.\u003c/p\u003e","description":"","filename":"Figure7.png","url":"https://assets-eu.researchsquare.com/files/rs-9162489/v1/cf2bd069b2be50a0fa837973.png"},{"id":107479152,"identity":"a2868d70-f263-4ac6-9ae9-2dea3935329b","added_by":"auto","created_at":"2026-04-22 01:20:16","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":20344358,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9162489/v1/f9b32350-f824-4865-926f-b13a80ed4713.pdf"},{"id":106726046,"identity":"002ebc5f-0971-4081-b39b-7cb69a7f6fb5","added_by":"auto","created_at":"2026-04-12 18:35:03","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":776684,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryFigureS1S2S3.docx","url":"https://assets-eu.researchsquare.com/files/rs-9162489/v1/f744e8c19265e2ca4da033be.docx"},{"id":106726772,"identity":"0fddff8f-59c2-4807-9067-046b795c91ad","added_by":"auto","created_at":"2026-04-12 18:37:16","extension":"docx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":20328,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementarypapertable.docx","url":"https://assets-eu.researchsquare.com/files/rs-9162489/v1/42908aed4f7d4c64356f9457.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"\u003cp\u003eGenomic Characterization of Productive and Maternal Traits in Arequipa Fighting Cattle Using Snp-based Igenity Panels\u003c/p\u003e","fulltext":[{"header":"Introduction","content":"\u003cp\u003eGenomic tools have become fundamental components of modern animal breeding programs, as they enable the evaluation of economically important traits and improve the accuracy of genetic selection in cattle populations [1]. Their use is particularly relevant in systems where phenotypic and pedigree records are limited or unavailable. In this context, commercial SNP panels, such as Igenity Beef, provide genomic predictions for maternal, growth, and carcass traits with greater accuracy than traditional selection approaches, especially in small or unstructured herds [2,3].\u003c/p\u003e \u003cp\u003eAt the same time, the characterization of local cattle populations has gained increasing global attention due to their role in preserving genetic diversity and contributing traits such as adaptability, resilience, and efficiency under challenging environmental conditions. These underrepresented biotypes constitute valuable genetic resources for sustainable livestock production. [4\u0026ndash;6].\u003c/p\u003e \u003cp\u003eIn Peru, the application of genomic approaches in cattle has expanded in recent years, including studies on genetic diversity using high-density SNP arrays and mitochondrial genome analyses in Creole populations [7]. Among these, Arequipa fighting cattle represent a biotype of cultural and economic importance, traditionally selected for temperament, hardiness, and functional performance in local events[8]. Although commonly classified as Creole cattle due to their heterogeneous origin, some lineages exhibit distinctive phenotypic and genetic characteristics, as well as relatively high levels of genetic variability [9].\u003c/p\u003e \u003cp\u003eDespite its importance, Arequipa fighting cattle remains poorly characterized from a scientific perspective. Previous studies have primarily focused on genetic diversity, reporting high heterozygosity and a large proportion of polymorphic SNPs compared with other cattle populations in the country [7]. Similarly, mitochondrial data have enabled preliminary phylogenetic inferences. However, no peer-reviewed studies have evaluated their productive potential using genomic prediction tools, nor have standardized genetic indices for milk production, growth, or carcass traits been reported [8].\u003c/p\u003e \u003cp\u003eField observations from local breeders suggest the presence of individuals with notable performance in meat and milk-related traits, even though these attributes have not been the primary targets of selection [10]. This suggests the presence of exploitable genetic variability within the population within this population, highlighting the need for objective genomic evaluation[8].\u003c/p\u003e \u003cp\u003eTherefore, the use of genomic prediction tools such as Igenity Beef offers a valuable opportunity to assess productive potential in populations lacking structured records and to support evidence-based breeding strategies. In this context, commercial SNP panels such as Igenity Beef provide genomic predictions for maternal, growth, and carcass traits with greater accuracy than traditional selection approaches, particularly in small or unstructured herds [10].\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eSample collection and ethical considerations\u003c/h2\u003e \u003cp\u003eHair follicles with intact roots were collected from each animal in accordance with the Peruvian Animal Welfare Law No. 30407 (Peru, 2016). This procedure is non-invasive and does not cause harm or distress to the animals. All experimental procedures were reviewed and approved by the Institutional Research Ethics Committee of the Universidad Nacional Toribio Rodr\u0026iacute;guez de Mendoza (UNTRM; CIEI No. 0070). All methods were carried out in accordance with relevant institutional guidelines and regulations.\u003c/p\u003e \u003cp\u003eThe animals included in this study were privately owned by local farmers. Prior to sample collection, verbal informed consent was obtained from all owners after explaining the objectives of the study. Farmers voluntarily agreed to the participation of their animals and provided the corresponding information at the time of sampling.\u003c/p\u003e \u003cp\u003eDuring field collection, each sample was recorded in individually labeled paper envelopes containing key information to ensure traceability, including a unique sample code, origin, breed, animal identification, sex, date of birth, sampling date, person responsible for sample collection, and additional observations (Figure \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e). Information provided by the owners, such as animal name and age, was also recorded.\u003c/p\u003e \u003cp\u003eA total of 95 adult cattle (55 males and 40 females) were evaluated. Samples were initially stored in labeled paper envelopes during fieldwork and subsequently transferred to official NEOGEN\u0026reg; cards to ensure proper traceability and sample integrity.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eStudy Design and Animal Population\u003c/h3\u003e\n\u003cp\u003eThe present study employed a descriptive design aimed at characterizing the genomic potential of cattle from the Arequipa fighting cattle biotype. A total of 95 adult animals (40 females and 55 males) raised in the province of Arequipa, Peru, were sampled between June and September 2024. Although these cattle have traditionally been selected for temperament, hardiness, and functional performance in cultural events, some individuals also exhibit productive traits relevant to meat and milk production, providing a rationale for their scientific evaluation.\u003c/p\u003e\n\u003ch3\u003eDNA Extraction and SNP Genotyping\u003c/h3\u003e\n\u003cp\u003eAll samples were sent to the NEOGEN\u0026reg; laboratory (Lansing, Michigan, USA), where DNA extraction and SNP genotyping were performed. Only samples meeting the required concentration and quality standards were processed: 60 animals for the Igenity Beef panel (28 females and 32 males) and 22 females for the Igenity Basic panel, out of the 29 samples initially submitted. Genotyping was conducted using commercial SNP panels, which served as the basis for genomic predictions generated by NEOGEN\u0026reg; using models from International Genetic Solutions (IGS).\u003c/p\u003e\n\u003ch3\u003eGenomic Prediction Using Igenity® Beef\u003c/h3\u003e\n\u003cp\u003eThe Igenity Beef test provides genomic estimates for 17 traits, grouped into maternal, growth, and carcass characteristics. Each trait is scored on a standardized scale from 1 to 10, where higher values indicate better expected performance. In addition, four selection indices were calculated: the Maternal Index, Terminal Index, Balanced Index, and Top 25% Index, which summarize the overall productive merit of each individual (see Supplementary Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e).\u003c/p\u003e\n\u003ch3\u003eGenomic Prediction Using Igenity® Basic\u003c/h3\u003e\n\u003cp\u003eThe Igenity Basic test, applied exclusively to females, evaluated 14 traits related to milk production, health, and compositional attributes. Scores were expressed on a scale from 1 to 10, with trait-specific interpretation depending on the nature of each trait (see Supplementary Table \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003e).\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eStatistical Analysis\u003c/h2\u003e \u003cp\u003eAll data management and cleaning procedures were performed in R version 4.5.1 [11], using the readxl [12] and dplyr [13] packages for data import and manipulation. Genomic scores provided by the Igenity panels were treated as ordinal variables (scale 1\u0026ndash;10), representing relative genetic merit rather than direct phenotypic measurements. Exploratory analyses were conducted to assess the distribution of genomic scores and to detect potential outliers. To examine the multivariate structure of maternal, growth, and carcass traits, a Multiple Correspondence Analysis (MCA) was applied using the FactoMineR [14] and factoextra [15] packages, allowing the identification of associations among variables and the grouping of individuals according to similar genomic profiles.\u003c/p\u003e \u003cp\u003eIn addition, hierarchical cluster analyses were performed using base R functions (dist and hclust), and heatmaps were generated with the pheatmap [16] package to visualize similarity patterns among animals while preserving their real individual identification. Finally, customized plots were created using ggplot2 [17] and ggrepel [18] to highlight the observed patterns and the distribution of genomic scores.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003ePopulation Characteristics of the Studied Cattle\u003c/h2\u003e \u003cp\u003eThe studied population consisted of 60 Creole cattle, with a slight predominance of males (32; 53.3%) over females (28; 46.7%). According to the genomic ranking, 15 individuals (25.0%) were classified in the Bottom 25% category, 15 (25.0%) in the Maternal group, 16 (26.7%) in the Terminal group, and 14 (23.3%) in the Top 25% category. Each animal was identified using a unique code to ensure data traceability (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDistribution of qualitative variables in the studied population\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCategory\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eFrequency\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePercentage\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eGender\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFemale (F)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e46.67\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMale (M)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e53.33\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBreed\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCreole\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e100\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003eIgenity Rank\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBottom 25%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e25.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMaternal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e25.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTerminal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e26.67\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTop 25%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e23.33\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eGenomic profiles of Arequipa cattle based on Igenity\u0026reg; panels\u003c/h2\u003e \u003cp\u003eFigure 1 shows the distribution of genomic categories assigned to the evaluated animals, grouped according to three functional sets of traits: maternal traits, yield traits, and carcass traits. Within the Carcass Traits group, most individuals exhibit low values for marbling (MARB) and ribeye area (REA), while tenderness (TEND) also shows consistently low values.\u003c/p\u003e \u003cp\u003eIn the Maternal Traits group, low to moderate values predominate for birth weight (BW), calving ease direct (CED), and milk production (MILK), whereas docility (DOC) shows variability among animals.\u003c/p\u003e \u003cp\u003eIn contrast, Yield Traits display greater diversity, with some individuals exhibiting higher values for weaning weight (WW) and yearling weight (YW), while residual feed intake (RFI) shows lower values in a subset of animals.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eNote\u003c/strong\u003e \u003cp\u003eMaternal Traits: BW\u0026thinsp;=\u0026thinsp;Birth Weight, CED\u0026thinsp;=\u0026thinsp;Calving Ease Direct, CEM\u0026thinsp;=\u0026thinsp;Calving Ease Maternal, DOC\u0026thinsp;=\u0026thinsp;Docility, HPR\u0026thinsp;=\u0026thinsp;Heifer Pregnancy Rate, MILK\u0026thinsp;=\u0026thinsp;Milk Production, STAY\u0026thinsp;=\u0026thinsp;Stayability. Yield Traits: ADG\u0026thinsp;=\u0026thinsp;Average Daily Gain, RFI\u0026thinsp;=\u0026thinsp;Residual Feed Intake, SC\u0026thinsp;=\u0026thinsp;Scrotal Circumference, WW\u0026thinsp;=\u0026thinsp;Weaning Weight, YW\u0026thinsp;=\u0026thinsp;Yearling Weight. Carcass Traits: FAT\u0026thinsp;=\u0026thinsp;Fat Thickness, HCW\u0026thinsp;=\u0026thinsp;Hot Carcass Weight, MARB\u0026thinsp;=\u0026thinsp;Marbling, REA\u0026thinsp;=\u0026thinsp;Ribeye Area, TEND\u0026thinsp;=\u0026thinsp;Tenderness.\u003c/p\u003e \u003c/p\u003e \u003cp\u003eFigure 1. Distribution of genomic categories by trait group evaluated using the Igenity Beef test.\u003c/p\u003e \u003cp\u003eAs shown in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, the AA genotype of κ-casein was the most frequent in the evaluated population (45.5%), followed by AB (36.4%). The AE genotype was observed at a frequency of 9.1%, while BB and BE were less frequent (4.5% each). For β-casein, the AA genotype predominated (90.9%), whereas the remaining genotypes were observed at lower frequencies.\u003c/p\u003e \u003cp\u003eRegarding β-lactoglobulin, the AB genotype was the most frequent (59.1%), followed by BB (18.2%) and AA (13.6%). A total of 9.1% of the animals had no recorded genotype (NR).\u003c/p\u003e\u003cp\u003e\u003cimg src=\"data:image/png;base64,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\"\u003e\u003c/p\u003e\n\u003cdiv\u003eAs shown in Fig. 2, the mosaic plot depicts the relationship between κ-casein (CSN3) and β-casein (CSN2) genotypes in the evaluated animals. The AA, AB, and AE genotypes of κ-casein are exclusively associated with the AA genotype of β-casein, whereas the BB and BE genotypes are associated only with the AB genotype of β-casein. The NA category for κ-casein corresponds to missing data in β-casein.\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eThe observed distribution shows consistent genotype combinations across the evaluated animals. The chi-square test of independence was not applied due to the low frequency of some genotype combinations \u003c/p\u003e\n\u003cp\u003eFigure 2. Mosaic plot showing the association between κ-casein (CSN3) and β-casein (CSN2) genotypes in the evaluated cattle.\u003c/p\u003e\n\u003cdiv id=\"Sec12\"\u003e\n \u003ch2\u003eGenomic identification of cattle with meat and milk traits\u003c/h2\u003e\n \u003cp\u003eAs shown in Fig.\u0026nbsp;3, the heatmap represents the genetic profiles of the animals, organized according to three groups of traits: maternal traits (Maternal Traits), yield traits (Yield Traits), and carcass traits (Carcass Traits). Colors represent the ordinal level assigned to each trait, allowing direct visual comparison among individuals. High variability is observed in maternal traits, particularly for calving ease (CED) and milk production (MILK). Yield traits display more homogeneous patterns, although some individuals exhibit consistently high or low values. In carcass traits, significant contrasts are identified for marbling (MARB) and ribeye area (REA), indicating potential differences in meat quality.\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eNote\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eMaternal Traits: BW = Birth Weight, CED = Calving Ease Direct, CEM = Calving Ease Maternal, DOC = Docility, HPR = Heifer Pregnancy Rate, MILK = Milk Production, STAY = Stayability. Yield Traits: ADG = Average Daily Gain, RFI = Residual Feed Intake, SC = Scrotal Circumference, WW = Weaning Weight, YW = Yearling Weight. Carcass Traits: FAT = Fat Thickness, HCW = Hot Carcass Weight, MARB = Marbling, REA = Ribeye Area, TEND = Tenderness.\u003c/p\u003e\n \u003cp\u003eFigure 3. Heatmap of genomic profiles grouped by trait category.\u003c/p\u003e\n \u003cp\u003eIn Fig. 4, the analysis of the 60 animals evaluated using the Igenity Beef test allowed their classification into four performance categories: Top 25%, Maternal, Terminal, and Bottom 25%. It is important to note that the Maternal and Terminal categories identify animals with higher scores in specific indices, but do not exclude their inclusion in the overall Top 25% category.\u003c/p\u003e\n \u003cp\u003eWithin the Top 25% category, 14 individuals (11 females and 3 males) were identified, including PBT_ARE_048. The Maternal category included 15 animals (7 females and 8 males), among which PBT_ARE_055 (female) and PBT_ARE_058 (male) were observed. The Terminal category comprised 16 individuals (6 females and 10 males), including PBT_ARE_100, PBT_ARE_008, and PBT_ARE_045. In the Bottom 25% group, 15 animals (4 females and 11 males) were identified.\u003c/p\u003e\n \u003cp\u003eOverall, the population consisted of 28 females (48.3%) and 32 males (51.7%). Females were more represented in the Top 25% and Maternal categories, whereas males were more frequent in the Bottom 25% category.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec13\"\u003e\n \u003ch2\u003eGenomic Index Distribution Across Igenity Rank Categories\u003c/h2\u003e\n \u003cp\u003eFigure 5 shows the distribution of genomic indices across Igenity ranking categories. Animals classified within the Top 25% display higher values across the three evaluated indices (maternal, production, and terminal) compared with the remaining groups. In contrast, individuals in the Bottom 25% exhibit lower scores and a relatively narrower distribution. Partial overlap among interquartile ranges is observed across categories. Across most ranking categories, the Production Index shows higher values than the other indices. The Maternal Index presents smaller differences among categories and a relatively homogeneous distribution. In contrast, the Terminal Index displays greater dispersion in some groups, particularly in higher-ranking categories.\u003c/p\u003e\n \u003cp\u003eOverall, the observed overlap among distributions indicates that the ranking categories do not represent genetically discrete groups but rather positions along a continuum within the evaluated population.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec14\"\u003e\n \u003ch2\u003eProvide genomic information to support improvement of cattle quality and productivity in the Arequipa region\u003c/h2\u003e\n \u003cp\u003eAs shown in Fig.\u0026nbsp;6, the hierarchical dendrogram generated using Ward’s method groups the animals into\u003c/p\u003e\n \u003cp\u003efour clusters differentiated by colors. Cluster 1 (blue) includes individuals with high genetic similarity and low internal variation, whereas Cluster 2 (yellow) also shows a relatively homogeneous structure, although distinct from Cluster 1. Cluster 3 (gray) is smaller in size and occupies an intermediate position between adjacent groups, and Cluster 4 (red) is the largest and presents greater internal variability. Some animals, such as PBT_ARE_085 and PBT_ARE_059, show distinct positions within their respective clusters.\u003c/p\u003e\n \u003cp\u003eAnalyses performed using the Igenity Beef genomic test show variation across the three evaluated trait groups. The heatmap and Multiple Correspondence Analysis (MCA, see Supplementary Figure S2) illustrate differences among individuals, including consistent and contrasting profiles, while the dendrogram indicates the presence of genetically differentiated groups within the evaluated population. Fig.\u0026nbsp;6. Hierarchical clustering revealing four genetic subpopulations within the herd.\u003c/p\u003e\n \u003cp\u003eFigure 7 shows the hierarchical dendrogram constructed from qualitative data obtained with the Igenity Basic genetic test for 22 animals, using Ward’s method. Each branch represents an individual identified by its code, and the height of the linkage reflects the degree of genetic similarity, with lower linkage heights indicating greater similarity among animals.\u003c/p\u003e\n \u003cp\u003eThe analysis identified seven distinct clusters, represented by colors and shaded areas. Cluster 1 (light blue) corresponds to the individual PBT_ARE_003, which shows a distinct genetic profile. Cluster 2 (light yellow) includes only PBT_ARE_013, clearly differentiated from the rest of the population. Cluster 3 (gray) groups PBT_ARE_020, PBT_ARE_023, PBT_ARE_024, PBT_ARE_025, PBT_ARE_033, PBT_ARE_041, and PBT_ARE_042, forming the largest cluster with high internal similarity. Cluster 4 (red) comprises PBT_ARE_044, PBT_ARE_048, PBT_ARE_049, PBT_ARE_052, PBT_ARE_053, PBT_ARE_054, and PBT_ARE_058, which are differentiated from Cluster 3.\u003c/p\u003e\n \u003cp\u003eCluster 5 (dark blue) consists of PBT_ARE_063, which is positioned close to Cluster 6, formed by PBT_ARE_064 and PBT_ARE_065, both showing high similarity to each other. Finally, Cluster 7 (dark yellow) groups PBT_ARE_068, PBT_ARE_070, and PBT_ARE_096, displaying a distinct pattern relative to the rest of the population.\u003c/p\u003e\n \u003cp\u003eThe clustering pattern is consistent with the distribution observed in the Multiple Correspondence Analysis (MCA; see Supplementary Figure S3), which also reflects differentiation among individuals within the population.\u003c/p\u003e\n \u003cp\u003eFigure 7. Hierarchical dendrogram based on Igenity Basic data.\u003c/p\u003e\n\u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003e The genomic analyses performed using the Igenity Beef and Igenity Basic panels provided a comprehensive characterization of genetic variability in the evaluated Arequipa fighting cattle population. These tools enabled the simultaneous assessment of multiple productive, reproductive, and milk quality traits, offering an alternative to traditional phenotypic selection, particularly in populations with limited structured records [2,22].\u003c/p\u003e \u003cp\u003e The results obtained from the Igenity Beef panel revealed substantial heterogeneity among individuals, with some animals showing higher genomic merit for growth performance, carcass yield, and meat quality. This variability aligns with previous reports describing high genetic diversity in Peruvian Creole cattle, often associated with traditional management systems and the absence of intensive directional selection. In this context, the identification of superior individuals represents a valuable opportunity to implement more efficient breeding strategies, including early selection based on genomic information, which may reduce generation intervals and accelerate genetic gain [23\u0026ndash;26].\u003c/p\u003e \u003cp\u003eSimilarly, the Igenity Basic panel allowed the identification of females with favorable genomic profiles for maternal, productive, and reproductive traits. Individuals combining adequate milk production with reproductive efficiency are particularly relevant as replacement candidates. These findings support previous studies highlighting the importance of selecting genotypes associated with productive and reproductive efficiency in Creole cattle populations [29\u0026ndash;31]. The integration of genomic approaches into local systems may contribute to improved productivity and sustainability [32,33].\u003c/p\u003e \u003cp\u003eThe identification of individuals classified within the top 25% of genomic merit provides a practical basis for establishing elite breeding nuclei. However, the observed overlap among genomic ranking categories indicates that these groups do not represent discrete genetic clusters, but rather positions along a continuum of genetic merit within the population [34,35].\u003c/p\u003e \u003cp\u003eRegarding milk quality traits, the high frequency of the β-casein A2A2 genotype suggests potential for improved milk digestibility and access to specialized markets [36]. In contrast, the relatively low frequency of the κ-casein B allele\u0026mdash;associated with enhanced cheese-making properties\u0026mdash;may limit technological quality for cheese production. Additionally, the predominance of heterozygous genotypes at the β-lactoglobulin locus reflects the maintenance of genetic variability within the population [24,26].\u003c/p\u003e \u003cp\u003eMultivariate analyses, including heatmaps, multiple correspondence analysis, and hierarchical clustering, revealed the presence of genetically structured subpopulations within the herd. This structure likely reflects historical management practices, non-systematic selection, and the heterogeneous origin of the population, representing both an opportunity for targeted selection and a resource for conserving genetic diversity.\u003c/p\u003e \u003cp\u003eA key limitation of this study is that genomic predictions were derived from commercial SNP panels and could not be directly compared with phenotypic performance data. This is due to the fact that the evaluated population consists of Creole fighting cattle, primarily used for cultural events rather than productive or reproductive purposes, which limits the availability of systematic records. In addition, these populations have been scarcely studied in Peru from a genetic\u0026ndash;productive perspective, restricting the availability of reference information. In this context, the identified genetic potential should be interpreted primarily at the individual level, as group-based evaluations may not adequately reflect the variability within the population.\u003c/p\u003e \u003cp\u003eNevertheless, these results represent an initial step toward understanding the genetic potential of this population and provide a foundation for future studies integrating genomic and phenotypic information. Despite their predominant use in fighting events, the evaluated animals may harbor valuable genetic potential that remains underutilized. In this regard, both fighting bulls and cows can be considered strategic resources for future generations, with the potential to contribute to the improvement of cattle herds through their incorporation into appropriately designed selection programs.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eThis study presents the first genomic evaluation of productive and maternal traits in Arequipa fighting cattle using SNP-based commercial panels. The results revealed considerable genetic variability and enabled the identification of individuals with superior genomic merit for growth, carcass, and maternal performance.\u003c/p\u003e \u003cp\u003eThese findings highlight the potential of genomic approaches to support selection strategies in cattle populations lacking structured phenotypic records. In addition, the detection of favorable milk protein genotypes suggests opportunities for diversification into specialized production systems.\u003c/p\u003e \u003cp\u003eDespite these promising outcomes, the absence of phenotypic validation remains an important limitation. Future studies integrating genomic predictions with performance data will be essential to confirm the practical applicability of these results.\u003c/p\u003e \u003cp\u003eOverall, this study provides a foundation for the implementation of genomic selection in this under-characterized population and underscores the potential of fighting cattle\u0026mdash;traditionally not selected for productive purposes\u0026mdash;as valuable genetic resources for the development of sustainable breeding strategies in the Arequipa region.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was funded by the project \u0026ldquo;Creation of the Precision Agriculture Service in the Departments of Lambayeque, Huancavelica, Ucayali, and San Mart\u0026iacute;n\u0026rdquo; (CUI No. 2449640), implemented through the National Institute of Agrarian Innovation (INIA). Additional financial and administrative support was provided by the Vice-Rectorate for Research (VRIN) of the National University Toribio Rodr\u0026iacute;guez de Mendoza de Amazonas (UNTRM).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors would like to thank the project \u0026ldquo;Creation of the Precision Agriculture Service in the Departments of Lambayeque, Huancavelica, Ucayali, and San Mart\u0026iacute;n\u0026rdquo; (CUI No. 2449640), implemented by the National Institute of Agrarian Innovation (INIA), for the institutional support provided for the execution of this study. We also acknowledge the support of the Institute of Research in Genetics and Biotechnology (IGBI) of the National University Toribio Rodr\u0026iacute;guez de Mendoza (UNTRM), whose administrative and logistical assistance was essential for the development of this research.\u003c/p\u003e\n\u003cp\u003eSpecial thanks are extended to Eng. Deyanira Figueroa and Eng. Anita Corredor for their valuable participation during the initial stages of the project. We also express our sincere appreciation to Mr. Jerry Valdeiglesias for his committed technical collaboration throughout the study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor Contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eConceptualization: L.A.H.V., C.A.\u003cbr\u003e\u0026nbsp;Data curation: L.A.H.V.\u003cbr\u003e\u0026nbsp;Formal analysis: L.A.H.V.\u003cbr\u003e\u0026nbsp;Funding acquisition: C.A., N.L.M.V., J.L.M.\u003cbr\u003e\u0026nbsp;Investigation: all authors.\u003cbr\u003e\u0026nbsp;Project administration: C.A.\u003cbr\u003e\u0026nbsp;Resources: C.A., N.L.M.V., J.L.M.\u003cbr\u003e\u0026nbsp;Software: L.A.H.V.\u003cbr\u003e\u0026nbsp;Supervision: C.J.S.E., C.A.\u003cbr\u003e\u0026nbsp;Validation: C.A., C.J.S.E.\u003cbr\u003e\u0026nbsp;Visualization: C.A.\u003cbr\u003e\u0026nbsp;Methodology: L.A.H.V., C.A., J.V.\u003cbr\u003e\u0026nbsp;Writing \u0026ndash; original draft: L.A.H.V., C.A., C.J.S.E.\u003cbr\u003e\u0026nbsp;Writing \u0026ndash; review and editing: all authors.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;The authors declare no conflict of interest.\u0026nbsp;\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eDekkers JC. Commercial application of marker- and gene-assisted selection in livestock: strategies and lessons. J Anim Sci. 2004;82:E313-E328. doi:10.2527/2004.8213_supplE313x\u003c/li\u003e\n\u003cli\u003eVan Eenennaam AL, Weaber RL, Drake DJ, Rafiqul I. Benefits of DNA information to improve accuracy and selection response in beef sire selection. J Anim Sci. 2011;89(10):3223-3240. doi:10.2527/jas.2010-3754\u003c/li\u003e\n\u003cli\u003eCarillier-Jacquin C, Larroque H, Palhi\u0026egrave;re I, Tomas A. Genomic prediction of dairy traits in cattle breeds with limited reference populations. J Dairy Sci. 2018;101(3):2101-2116. doi:10.3168/jds.2017-13512\u003c/li\u003e\n\u003cli\u003eFAO. The Second Report on the State of the World\u0026apos;s Animal Genetic Resources for Food and Agriculture. Rome: FAO; 2015. Available from: https://www.fao.org/3/i4787e/i4787e.pdf\u003c/li\u003e\n\u003cli\u003eMwai O, Hanotte O, Kwon YJ, Cho S. African indigenous cattle: unique genetic resources in a rapidly changing world. Asian-Australas J Anim Sci. 2015;28(7):911-921. doi:10.5713/ajas.15.0090\u003c/li\u003e\n\u003cli\u003eWard JA, et al. Genomic insights into the population history and adaptive traits of Latin American Criollo cattle. R Soc Open Sci. 2024;11:231388. doi:10.1098/rsos.231388\u003c/li\u003e\n\u003cli\u003eCorredor FA, Figueroa D, Estrada R, Salazar W, Quilcate C, V\u0026aacute;squez HV, et al. Genetic diversity and population structure of a Peruvian cattle herd using SNP data. Front Genet. 2023;14:1073843. doi:10.3389/fgene.2023.1073843\u003c/li\u003e\n\u003cli\u003eArbizu J. Complete mitochondrial genome of Peruvian Creole cattle and insights into their phylogenetic relationships. Mitochondr DNA B. 2022;7(1):123-127. doi:10.1080/23802359.2022.2025001\u003c/li\u003e\n\u003cli\u003eCorredor JA, Bendez\u0026uacute; M, Pizarro E. Genetic diversity assessment of Peruvian cattle populations using high-density SNP genotyping. Trop Anim Health Prod. 2023;55(1):34. doi:10.1007/s11250-022-03320-4\u003c/li\u003e\n\u003cli\u003eBerry DP, Evans RD, McParland S, Dillon P. Genetics of performance and fertility in beef and dairy cattle and opportunities for genomic selection. Anim. 2016;10(2):179-188. doi:10.1017/S1751731115002233\u003c/li\u003e\n\u003cli\u003eR Core Team. R: A language and environment for statistical computing. Vienna, Austria: R Foundation for Statistical Computing; 2025. Available from: https://www.R-project.org\u003c/li\u003e\n\u003cli\u003eWickham H, Bryan J. readxl: Read Excel files. R package version 1.5.4; 2023. Available from: https://CRAN.R-project.org/package=readxl\u003c/li\u003e\n\u003cli\u003eWickham H, Fran\u0026ccedil;ois R, Henry L, M\u0026uuml;ller K. dplyr: A grammar of data manipulation. R package version 1.1.2; 2023. Available from: https://CRAN.R-project.org/package=dplyr\u003c/li\u003e\n\u003cli\u003eL\u0026ecirc; S, Josse J, Husson F. FactoMineR: An R package for multivariate analysis. J Stat Softw. 2008;25(1):1-18. doi:10.18637/jss.v025.i01\u003c/li\u003e\n\u003cli\u003eKassambara A, Mundt F. factoextra: Extract and visualize the results of multivariate data analyses. R package version 1.0.7; 2020. Available from: https://CRAN.R-project.org/package=factoextra\u003c/li\u003e\n\u003cli\u003eKolde R. pheatmap: Pretty heatmaps. R package version 1.0.12; 2019. Available from: https://CRAN.R-project.org/package=pheatmap\u003c/li\u003e\n\u003cli\u003eWickham H. ggplot2: Elegant graphics for data analysis. 2nd edition. New York: Springer-Verlag; 2016. Available from: https://ggplot2.tidyverse.org\u003c/li\u003e\n\u003cli\u003eSlowikowski K. ggrepel: Automatically position non-overlapping text labels with \u0026apos;ggplot2\u0026apos;. R package version 0.9.4; 2023. Available from: https://CRAN.R-project.org/package=ggrepel\u003c/li\u003e\n\u003cli\u003eFaggion S, et al. ?-Casein A2 affects milk renneting properties, cheese yield before and after ripening, and alters the texture of Caciotta cheese produced in field conditions. Food Res Int. 2025;160:111750. doi:10.1016/j.foodres.2022.111750\u003c/li\u003e\n\u003cli\u003eJim\u0026eacute;nez-Montenegro L, et al. Worldwide research trends on milk containing only A2 ?-casein: A bibliometric analysis. Animals. 2022;12(15):1909. doi:10.3390/ani12151909\u003c/li\u003e\n\u003cli\u003eRazhina E, et al. Kappa-casein influence on yield and quality of cottage cheese. Atlantis Press; 2019. doi:10.2991/978-94-6239-035-8_47\u003c/li\u003e\n\u003cli\u003ePryce JE, Daetwyler HD. Designing dairy cattle breeding schemes under genomic selection: A review. Anim Prod Sci. 2012;52(3):107-114. doi:10.1071/AN11098\u003c/li\u003e\n\u003cli\u003eVeli RA, Rivas Seoane E. Caracterizaci\u0026oacute;n gen\u0026eacute;tica de Kappa case\u0026iacute;nas y Beta lactoglobulinas del bovino criollo de cuatro comunidades andinas del Per\u0026uacute;. Anim Genet Resour. 2010;46:67-72. doi:10.1017/S2078633610000718\u003c/li\u003e\n\u003cli\u003eVeli EA, Rivas Seoane E, Rivas Palma V, Aquino Y, Estrada R. Variabilidad gen\u0026eacute;tica del gen de beta-lactoglobulina en bovinos criollos de Per\u0026uacute;. Arch Zootec. 2008;57(219):341-344. Available from: https://repositorio.inia.gob.pe/server/api/core/bitstreams/62533dc8-cd4d-48fe-8455-acfefedfa00e/content\u003c/li\u003e\n\u003cli\u003eVeli E, Rivas Seoane E, Rivas Palma V, Ver\u0026aacute;stegui M, Pastor S. Evaluaci\u0026oacute;n de la variabilidad de genes de kappa case\u0026iacute;na en poblaciones de bovinos criollos de Ticllos y Huaschao, regi\u0026oacute;n Ancash. Bolet\u0026iacute;n de Lima. 2004;23(124):22-36. Available from: https://repositorio.inia.gob.pe/handle/20.500.12996/3435\u003c/li\u003e\n\u003cli\u003eScotto C, Rosemberg M. Estudio de dos pares de genes en la ?-lactoalb\u0026uacute;mina y ?-lactoglobulina en el ganado vacuno Holstein, Brown Swiss y Criollo en la cuenca central del Per\u0026uacute; [tesis]. Lima: Universidad Nacional Agraria La Molina; 2000. Available from: https://repositorio.unalm.edu.pe/handle/20.500.12996/3435\u003c/li\u003e\n\u003cli\u003eMateescu RG, Garrick DJ, Reecy JM. Network analysis reveals putative genes affecting meat quality in Angus cattle. Front Genet. 2017;8:171. doi:10.3389/fgene.2017.00171\u003c/li\u003e\n\u003cli\u003eNeogen Corporation. Annual report 2020. Neogen Corporation; 2020. Available from: https://www.annualreports.com/HostedData/AnnualReportArchive/n/NASDAQ_NEOG_2020.pdf\u003c/li\u003e\n\u003cli\u003eRedacci\u0026oacute;n. La raza criollos representa el 64% del ganado vacuno del Per\u0026uacute;. Agraria.pe. 2013 Jul 23. Available from: https://agraria.pe/noticias/la-raza-criollos-representa-el-64-del-ganado-vacuno-del-per-4887\u003c/li\u003e\n\u003cli\u003eDelgado A, Garc\u0026iacute;a C. El ganado vacuno Criollo: fuente importante de carne en el Per\u0026uacute;. Engormix. 2018 Jan 19. Available from: https://www.engormix.com/ganaderia/cruzamientos-ganado-carne/ganado-vacuno-criollo-fuente_a41576/\u003c/li\u003e\n\u003cli\u003eQuispe Coaquira J, Apaza Z\u0026uacute;\u0026ntilde;iga E, Chambilla Carre\u0026oacute;n P, Sapana Valdivia R. \u0026Iacute;ndices reproductivos y productivos en un hato de bovinos criollo del Altiplano peruano. Rev Investig Altoandinas. 2014;16(2):49-56. Available from: https://repositorio.inia.gob.pe/items/66cba641-abcd-4c5a-90a2-c6cfa4beeca1\u003c/li\u003e\n\u003cli\u003ePryce JE, Haile-Mariam M, Goddard ME, Hayes BJ. Identification of genomic regions associated with inbreeding depression in Holstein and Jersey dairy cattle. Genet Sel Evol. 2014;46:71. doi:10.1186/s12711-014-0071-7\u003c/li\u003e\n\u003cli\u003eGarc\u0026iacute;a-Ruiz A, Cole JB, VanRaden PM, Wiggans GR, Ruiz-L\u0026oacute;pez FJ, Van Tassell CP. Changes in genetic selection differentials and generation intervals in US Holstein dairy cattle as a result of genomic selection. Proc Natl Acad Sci U S A. 2016;113(28):E3995-E4004. doi:10.1073/pnas.1519061113\u003c/li\u003e\n\u003cli\u003eHayes BJ, Bowman PJ, Chamberlain AJ, Goddard ME. Genomic prediction in dairy cattle: Progress and challenges. J Dairy Sci. 2013;96(7):4848-4860. doi:10.3168/jds.2012-6120\u003c/li\u003e\n\u003cli\u003eFigueroa D, Romero Y, Heredia-Vilchez LA, Poemape C, Alvarado W, Quilcate C. Genomic analysis for cattle breeding improvement, progress and future perspectives in Peru: A review. Anim Biotechnol. 2025;36(1):1-12. doi:10.1080/10495398.2025.2547344\u003c/li\u003e\n\u003cli\u003eAlfonso L, Urrutia O, Mendizabal JA. Conversi\u0026oacute;n de las explotaciones de vacuno de leche a la producci\u0026oacute;n de leche A2 ante una posible demanda del mercado: Posibilidades e implicaciones. ITEA. 2019;115(3):231-251. doi:10.12706/itea.2019.001\u003c/li\u003e\n\u003cli\u003eRivas JA, Delgado JV, Guti\u0026eacute;rrez JP. Acciones para la caracterizaci\u0026oacute;n y conservaci\u0026oacute;n del bovino criollo peruano (Bos taurus). Anim Genet Resour. 2007;41:65-72. doi:10.1017/S1014233900002261\u003c/li\u003e\n\u003cli\u003eSebastiani C, Arcangeli C, Torricelli M, Ciullo M, D\u0026apos;avino N, Cinti G, Fisichella S, Biagetti M. Marker-assisted selection of dairy cows for ?-casein gene A2 variant. Ital J Food Sci. 2022;34(2):21-27. doi:10.15586/ijfs.v34i2.2178\u003c/li\u003e\n\u003cli\u003ePer\u0026uacute;. Ley N.\u0026deg; 30407, Ley de protecci\u0026oacute;n y bienestar animal. Diario Oficial El Peruano. 2016 Jan 8;N.\u0026deg; 574725. Available from: https://www.leyes.congreso.gob.pe/documentos/leyes/30407.pdf\u003c/li\u003e\n\u003cli\u003eNeogen Corporation. Neogen attends 2025 Unified Symposium. Neogen NeoCenter. 2025 Aug 8. Available from: https://www.neogen.com/en/neocenter/events/unified-symposium/\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"genomic selection, Creole cattle, SNP markers, genetic diversity, breeding strategies","lastPublishedDoi":"10.21203/rs.3.rs-9162489/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9162489/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eGenomic technologies based on single nucleotide polymorphisms (SNPs) have become powerful tools for improving genetic evaluation in cattle populations lacking comprehensive phenotypic and pedigree records. The present study aimed to assess the genomic potential for meat and milk production traits in Arequipa fighting cattle, a culturally significant but poorly characterized bovine biotype in Peru. A total of 95 adult animals were sampled, of which 60 and 22 individuals passed quality control for the Igenity Beef and Igenity Basic panels, respectively. Genomic scores were analyzed using descriptive statistics and multivariate approaches to evaluate genetic variability and population structure. The results revealed substantial genomic heterogeneity within the population, enabling the identification of individuals with superior genetic merit for growth performance, carcass yield, meat quality, and maternal traits. Genomic ranking distinguished groups associated with maternal and terminal production objectives, suggesting the presence of differentiated genetic profiles within the herd. Analysis of milk protein markers showed a high frequency of favorable β-casein genotypes associated with improved digestibility and potential value in A2 milk markets, whereas alleles linked to enhanced cheese-making properties were less frequent. Hierarchical clustering further revealed the presence of genetically structured subpopulations, indicating the maintenance of genetic diversity. hese findings provide the first genomic assessment of productive traits in Arequipa fighting cattle using SNP-based commercial panels. They highlight the potential of genomic tools to identify animals with desirable productive and maternal characteristics and support the development of selection, conservation, and breeding strategies adapted to regional production systems.\u003c/p\u003e","manuscriptTitle":"Genomic Characterization of Productive and Maternal Traits in Arequipa Fighting Cattle Using Snp-based Igenity Panels","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-04-10 14:22:20","doi":"10.21203/rs.3.rs-9162489/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewerAgreed","content":"321214390614445154115645624360977342743","date":"2026-05-11T22:36:50+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-05-04T10:51:47+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"271817857346517915102463057643397405798","date":"2026-04-26T09:39:30+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"336432813193822532118745144616148142754","date":"2026-04-25T12:27:38+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"7958772289400146345728855308878783725","date":"2026-04-08T17:11:19+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-04-06T16:55:04+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-04-06T16:46:10+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2026-03-31T11:59:26+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-03-31T04:18:38+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2026-03-30T15:34:19+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"6290c630-7b89-4d39-903d-cb67fa248c11","owner":[],"postedDate":"April 10th, 2026","published":true,"recentEditorialEvents":[{"type":"reviewerAgreed","content":"321214390614445154115645624360977342743","date":"2026-05-11T22:36:50+00:00","index":81,"fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-05-04T10:51:47+00:00","index":74,"fulltext":""}],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[{"id":66095063,"name":"Biological sciences/Computational biology and bioinformatics"},{"id":66095064,"name":"Biological sciences/Genetics"}],"tags":[],"updatedAt":"2026-04-10T14:22:20+00:00","versionOfRecord":[],"versionCreatedAt":"2026-04-10 14:22:20","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9162489","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9162489","identity":"rs-9162489","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}