Divergent adaptation to highland and tropical environments in Bolivian Creole cattle

Divergent adaptation to highland and tropical environments in Bolivian Creole cattle | 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 Divergent adaptation to highland and tropical environments in Bolivian Creole cattle Guillermo Giovambattista, Olivia Marcuzzi, Paulo Alvarez Cecco, and 7 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4492487/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Bolivian Creole cattle populations evolved under low levels of breeding management and, during more than 500 years of natural selection, became adapted to various environments such as the contrasting highland and subtropical environments. Recently, highland Creole cattle were crossbred with Holstein to improve dairy production. The aim of this research was to evaluate the divergent adaptation through selection footprints of Bolivian Creole cattle from Andean highland and tropical lowlands, and to evaluate the effect of Holstein introgression in highland Creole. For this purpose, 130 Creole cattle (75 highland, 55 lowland) and 88 Holstein were genotyped using a microarray. The database was used to determine population structure and admixture and detect selection sweeps using F ST , Rsb, XP-EHH and ROH. Ancestry inference suggested that selection peaks were not due to Holstein introgression. The NCBI database was used to retrieve genes from the common regions and then perform gene ontology analysis. The most prominent selection peaks were on BTA20 and BTA23 and included the PRLR (slick phenotype) and Class I and IIa BoLA genes. Other windows contained candidate genes for hypoxia ( ANXA2 , NDUFA4L2 ), angiogenesis, immune response ( IL7R , IL6ST , IL31RA , C6 , C7, STAT6 , NKG2A , IRAK4 , KLR, CLEC ), oxidative stress ( GSTA, HSD17B6 ) and morphological traits ( PLAG1, CHCHD7 , CAP2, ARL15) . GO analysis revealed enrichment terms and pathways related to immune response, glutathione and retinol metabolism and reported QTLs for coat characteristics, immune response, and tick resistance. The results suggest the complex mechanism in the adaptation of Bolivian Creole cattle to the contrasting highland and subtropical environments. Biological sciences/Genetics Biological sciences/Genetics/Agricultural genetics selection footprints heat stress hypoxia slick BoLA SNP Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 INTRODUCTION In 1493, Spanish conquerors brought Creole cattle to the American continent (Primo, 1992). They were initially introduced to the Caribbean islands and then transported to South and North America during the first half of the 16th century. The routes to South America included journeys from the Caribbean to the northern coast (actual Colombia and Venezuela), as well as routes from Central America via the Pacific coast to the Alto Perú, then extending through the todays Bolivia and Paraguay, and further south to the pampa and Patagonia (Wilkins, 1984; Primo, 1992; Felius, 1995). Concurrently, Portuguese cattle were directly shipped to the actual Brazilian territory (De Alba, 1978; Primo, 1992). These animals quickly adapted to the continent’s diverse environmental conditions, leading to an exponential increase in their population. Nowadays, local Creole cattle breeds persist in nearly all American countries (http://www.ansi.okstate.edu/breeds/cattle/). In Bolivia, several Creole cattle populations have adapted to multiple environments such as seasonal floodplains, dry forests, tropical plains, temperate valleys, and highland plains. Highland Bolivian Creoles are predominantly located in the western region of the country, spanning the Departments of Oruro, Potosi and part of La Paz, Cochabamba, and Chuquisaca. These animals are raised at altitudes ranging from 2,500 to 4,200 metres, characterised by a cool and dry environment. In contrast, lowland Creoles inhabit the eastern region, which includes tropical savannahs and subtropical dry forests within the Departments of El Beni, Santa Cruz, and parts of La Paz, Cochabamba, and Chuquisaca, situated at altitudes below 500 metres above sea level (https://senamhi.gob.bo). The adaptation process to a particular environment requires the development of specific physiological and morphological characteristics. For example, high altitude environmental conditions could include low oxygen levels, cold temperatures, high UV exposure and limited food availability (Friedrich and Wiener, 2020). In contrast, the adaptation to warm tropical and subtropical environments primarily requires heat tolerance, where changes in coat features and vascularization of the dermis are beneficial. Additionally, the development of resistance to parasites, especially ticks, and other infectious diseases is crucial (Barendse, 2017). Adaptation is the result of evolutionary and ontogenetic events that contribute to genetic changes over generations. Selection is one of the most important genetic processes that cause changes in specific genomic regions, consequently creates unique genetic patterns or footprints known as selection signatures (Mignon-Grasteau et al. , 2005; Saravanan et al. , 2020; Falchi et al ., 2023). Identifying these signals can be useful for determining genes and beneficial mutations that occur in a given population, including those adapted to different environments (Zhao et al ., 2015). With the emerging era of genomics and the advent of high-density SNP arrays, next-generation sequencing (NGS) technologies, and bioinformatics tools, various methods have been developed to detect regions subject to selection in multiple species. These include within-population approaches based on linkage disequilibrium (LD; iHs, rEHH, and LDD), site frequency spectrum (Tajima’s D and Fay and Wu’s H), and reduced local variability (runs of homozygosity [ROH]), as well as between-population statistical methods such as single-site differentiation (F ST ) and differentiation based on haplotypes (XP-EHH) (Qanbari et al ., 2010; Gautier and Vitalis, 2012; Saravanan et al ., 2020). In recent years, there has been increased interest in identifying selection signatures for high-altitude and tropical adaptation in livestock species. The Qinghai-Tibet and Bolivian Altiplano Plateaus are among the most extreme high-altitude regions in the world, making them ideal models for studying high-altitude adaptation in a diverse range of native species, including humans (Yi et al ., 2010; Peng et al ., 2011; Xu et al ., 2011; Lorenzo et al ., 2014), domestic animals (Qiu et al. , 2012; Li et al ., 2014; Wang et al. , 2015; Ma et al. , 2019b), and wildlife (Cai et al. , 2013; Ge et al. , 2013). Conversely, selection signatures have also been extensively studied in cattle bred in tropical environments in Africa (Tijjani et al. , 2022; Kambal et al. , 2023), Asia (Nayak et al. , 2024) and South America (Maiorano et al. , 2018). Over the last five centuries, it is expected that Bolivian cattle residing at high and low altitude have developed physiological strategies and morphological features to adapt to the harsh conditions of the Altiplano Plateaus and tropical environment, respectively. Furthermore, highland Bolivian Creole cattle are traditionally utilised by local communities for subsistence farming, primarily in milk and cheese production. In recent decades, Holstein cattle from Argentina and Uruguay have been introduced into the Bolivian highlands to improve dairy production. However, this European breed exhibits low adaptability to altitude and a high mortality rate, often attributed to high altitude pulmonary hypertension (HAPH) resulting from chronic exposure to hypoxia or other stress factors prevalent at high altitude (Wang et al. , 2018). Consequently, local community oral communications suggest the crossbreeding between Creole and Holstein resulted in cattle adapted to high altitude with increased production. The objectives of the present study were: (1) to identify selection signatures in the genome of Bolivian Creole cattle considering their divergent adaptation to highland and tropical lowland environments. For this purpose, a microarray dataset was analysed using a population differentiation method (F ST ), two LD-based methods (Rsb and XP-EHH), and a local variability reduction method (ROH); and (2) to evaluate whether the percentage of Holstein ancestry in regions under selection, differs from the average genome-wide estimated proportion. MATERIALS AND METHODS Animal samples Hair and blood samples were collected from 130 Bolivian Creole cattle from Altiplano highlands and tropical lowlands regions. Highland Bolivian Creoles (HBC; n = 75) were sampled at three locations in the departments of La Paz, Oruro, and Cochabamba at an altitude of 3,700 - 4,000 metres above sea level (Table 1 and Fig. 1). Environmental conditions include average annual precipitation of 300 - 400 mm, concentrated in summer (between January and March), and a media temperature of 8°C, ranging from 0°C to 20°C (https://senamhi.gob.bo). Lowland Bolivian Creoles (LBC; n = 55) samples were collected from three different sites in the vast plain of the department of Santa Cruz (Table 1 and Fig. 1). These animals live in subtropical conditions at an altitude of 200 - 500 metres above sea level, with an average annual rainfall of 800 - 1000 mm and a mean temperature of 25.4°C, ranging from 16°C to 32°C (https://senamhi.gob.bo). In addition, DNA samples from Holstein (Ho; n = 88) and Zebu breeds (ZEB; Brahman, n = 45 and Nellore, n = 4) were included. The Institutional Committee on Care and Use of Experimental Animals (CICUAL) from the School of Veterinary Sciences of the National University of La Plata (Buenos Aires, Argentina) reviewed and approved all animal procedures (89-1-18T CICUAL). Table 1. Sampling sites of Highland Bolivian Creole (HBC) and Lowland Bolivian Creole (LBC). Population Sampling site n Altitude (MSL) HBC San Pedro de Totora, Oruro Department 52 4,000 Bolivar, Cochabamba Department 14 3,735 Omasuyos, La Paz Department 9 3,840 LBC Palmar Tapera, Santa Cruz Department 29 230 - 380 Chiquitos, Santa Cruz Department 16 416 Obispo Santistevan, Santa Cruz Department 10 450 DNA extraction and genotyping Genomic DNA was extracted from hair samples using the DNeasy Blood & Tissue Kit (Qiagen, Hilden, Germany) and from blood using the Wizard Genomic DNA Purification kit (Promega, MA, USA). Samples were genotyped in a GeneTitan TM platform (Applied Biosystems™, CA, USA) using the microarrays Axiom TM Bos1 Genotyping Array r3 (Applied Biosystems™) containing 648,855 SNPs and the custom array ArBos1 containing 58,088 SNPs. A single genotype matrix was constructed using the merge function in PLINK v1.9 software (Purcell et al ., 2007), resulting in a final database of 48,360 common SNPs. Raw data were processed using Axiom TM Analysis Suite software 4.0 (Applied Biosystems™), setting sample and SNPs call rates at ≥ 97%. Datasets were exported in .PED and .MAP file format for further analysis. The SNPs positions were assigned according to the bovine genome assembly UMD 3.1. Population structure and relationships Population structure and the degree of admixture were determined in HBC, LBC, Ho, and ZEB. SNPs were filtered using the --indep 50 5 2 command in PLINK v1.9 resulting in a set of 21,834 unlinked genetic markers. Considering the historical data of a possible introgression of foreign genetics in Bolivian Creole cattle, the admixture with Ho and ZEB breeds was tested by a Bayesian clustering-model implemented in fastSTRUCTURE (Raj et al ., 2014). The ChooseK algorithm indicated that the model complexity that maximises marginal likelihood was K2, and the model components used to explain structure in data was K3. The graphical representation of the results was performed using Distruct v.2.3 (Chhatre, 2018). Additionally, a principal component analysis (PCA) was performed to assess the divergence of individuals from each population using the function --pca in PLINK v1.9. The results were visualised using the R library ggplot2. Inference of local ancestry To infer Ho local ancestry within the HBC genome, haplotype phasing was first performed using SHAPEIT5 software (Hofmeister et al ., 2023). Then, Flare (Browning et al ., 2023) was used to determine ancestry origin (Creole cattle or Ho) for every SNP in each HBC individual using the default parameters with the exception of em=false. Local ancestry was estimated at chromosome and whole-genome levels. Selection footprint analysis To detect candidate regions for high altitude and tropical lowland adaptation, selection footprints were analysed using four methods: the fixation index statistic (F ST ; based on gene frequencies), Rsb and XP-EHH (based on haplotype extension), and ROH (a local variability reduction method; Sabeti et al. , 2002, 2007; Voight et al. , 2006; Saravanan et al ., 2020). The average F ST was calculated using the --fst command in PLINK v1.9, and the pairwise F ST was estimated using the OutFLANK R package for LBC, HBC, and Ho (Whitlock and Lotterhos, 2015). This script calculates the divergence in individual SNP allele frequencies between pairs of populations using the Lewontin-Krakauer F ST method with an accurate null model. Significance was determined by examining the right tail of the null model distribution, with a p-value of <0.01 considered significant. Genomic window sizes were determined based on regions containing significant SNPs separated by no more than 0.25 Mb. For extreme SNPs, an additional 0.25 Mb was included on the external side of the window. The F ST values were visualised in a Manhattan plot using the qqman package in RStudio v4.3.3. For LD-based analysis, the obtained haplotypes using SHAPEIT5 were used to compute Rsb (Voight et al. , 2006) and XP-EHH (Sabeti et al ., 2007) using the rehh R package (Gautier and Vitalis, 2012). Rsb and XP-EHH statistics were designed to detect regions with high levels of haplotype homozygosity over an unexpectedly long distance (relative to neutral expectations) and measure the amount of extended haplotype across populations. The 1% top value was set as the threshold to select the potential SNPs under selection. Genomic window sizes were determined based on regions containing significant SNPs separated by no more than 0.25 Mb. For extreme SNPs, an additional 0.25 Mb was added to the external side of the window. A R-script developed by Gorssen et al . (2021) was used to detect ROH in HBC, LBC, and Ho. ROH incidence was calculated as the percentage of animals with a SNP within a ROH segment for a given population. The minimal threshold for detection of ROH islands was set to 30%, meaning that a ROH had to be present in at least 30% of the animals of each population to be included in a ROH island (Supplementary Table S1). Results were visualised in Manhattan plots using the qqman R package. SNPs located within the ROH island were filtered using the --from-bp --to-bp command in PLINK 1.9 and the linkage disequilibrium (LD) between the SNPs included in detected windows was estimated with the r 2 parameter and visualised using the Haploview 4.2 software (Barrett et al. , 2005). Gene ontology The regions identified by each selection footprint methodology were compared to determine the overlapping windows, and then the positions were converted to the ARS-UCD 1.2 reference genome assembly using the Lift Genome Annotations from UCSC (https://genome.ucsc.edu/cgi-bin/hgLiftOver). Genes and QTLs included in the common windows were retrieved from the National Center for Biotechnology Information (NCBI, https://www.ncbi.nlm.nih.gov), FAANGMINE (https://faangmine.rnet.missouri.edu) and Cattle QTL Database https://www.animalgenome.org/cgi-bin/QTLdb/index). Functional analysis was performed using DAVID (https://david.ncifcrf.gov) to detect biological enrichment pathways and terms. In addition, the individual functional significance of some genes was reviewed based on literature. RESULTS Population structure and relationships To evaluate the genetic composition within the studied Creole populations and the gene introgression of foreign breeds (Ho and ZEB), two analyses were performed. The results from the cluster analysis using fastSTRUCTURE are presented in Fig. 2a. When considering K = 2, the Taurine/Zebuine genetic component was evidenced, with negligible levels of ZEB component in Bolivian Creole cattle, probably due to the origin of Nelore and Brahman from Creole dams and/or some Zebuine introgression into Creole. The ZEB estimated average percentage was 3.08% (±4.75%) for HBC and 8.76% (±3.82%) for LBC. In K = 3, this ZEB influence was indistinguishable. Instead, two Taurine components were observed, one common to HBC and LBC and the other corresponding to Ho. Noteworthy, the HBC showed an average Ho introgression of 31.26% (±12.15%), ranging from 6.33% to 50.31%. The PCA analysis showed that PC1 accounted for 48.39% of the variance and clearly differentiated the ZEB and the three Taurine populations. PC2 explained 10.97% of the genetic variance and separated the individuals from LBC, HBC, and Ho (Fig. 2b). In particular, HBC appeared between Ho and LBC, which could be related with a possible introgression of Ho genes into this population, in agreement with the cluster analysis results. PCA without ZEB animals endorsed this result (Supplementary Fig. S1). Most individuals from each population differentiated with both components suggesting the presence of enough population structure necessary to perform further selection footprint analysis. Inference of local ancestry analysis The inference of local ancestry for each SNP in HBC showed an average Ho introgression value of 0.357, which was similar to fastSTRUCTURE results. This analysis also showed uneven percentages of Ho origin among chromosomes, with values ranging from 0.14 (BTA2) to 0.63 (BTA20). This disparate distribution was also observed within chromosomes, with enriched regions (>0.5) in BTA20, BTA15 and BTA13, among others (Fig. S2). Few of the regions enriched with Ho ancestry coincide with peaks found in the HBC-LBC comparison, particularly the one on BTA20. Notably, this region (34-44 Mb) contains milk-related QTLs, such as milk yield, milk fat and protein percentages, and milk acid content, all of which were found in Holstein cattle (https://www.animalgenome.org/cgi-bin/QTLdb/index). Footprint analysis based on loci genetic divergence The first approach to detect evidence of selection was performed via genetic differentiation (F ST ) between pairs of populations: HBC vs LBC, HBC vs Ho and LBC vs Ho. These analyses consider that a statistically significant F ST value at a locus, compared to the genomic average, could indicate evidence of selection. The F ST estimations for the HBC-LBC, HBC-Ho, and LBC-Ho comparisons resulted in average weighted values of 0.033, 0.049 and 0.085 respectively. These results reinforce those from PCA and fastSTRUCTURE and were expected considering the genetic distances between the breeds and historical data. The results for the pairwise F ST index are presented in Fig. 3a-c and Supplementary Table S2. Considering the p-value right tail threshold of 0.01, this analysis identified 1,081 SNPs for HBC-LBC, 915 SNPs for HBC-Ho and 695 SNPs for LBC-Ho. These corresponded to F ST values ≥ 0.161, 0.248, and 0.418, respectively. In all three comparisons, significant SNPs were found across all chromosomes, encompassing a large number of genes (9,373 for HBC-LBC, 7,993 for HBC-Ho and 6,713 for LBC-Ho). As shown in Fig. 3, the most significant peak was observed for HBC-LBC in BTA 20 which also corresponds to the largest SNP window. Remarkably, this region included the candidate gene proposed for slick phenotype (Littlejohn et al. , 2014). Footprint detection by haplotype-based analysis Rsb and XP-EHH LD-based methods were also used to detect selection footprints. Results for the six pairwise comparisons are presented in Fig. 4 and 5 and Supplementary Table S3. In the HBC vs LBC comparison, the Rsb method identified 444 SNPs distributed along 16 chromosomes, while the XP-EHH method detected 480 markers distributed across 18 chromosomes using a 1% threshold. The most significant peaks, observed in BTA20 and BTA23, were consistent across both Rsb and XP-EHH methods. The region on BTA20 included the gene previously reported for slick hair phenotype (PRLR, Littlejohn et al. , 2014), while BTA23 included the major histocompatibility complex genes harbouring BoLA-DQA, BoLA-DQB and BoLA-DRB. Three prominent peaks in BTA2, BTA5 and BTA10 were also observed in this comparison using XP-EHH. For HBC vs Ho, Rsb showed 446 SNPs distributed in 17 chromosomes and the most prominent peaks were located in BTA20 and BTA23, followed by several discrete peaks in BTA2, BTA5, BTA9, BTA10, BTA16 and BTA26. The XP-EHH method detected 446 SNPs in 19 chromosomes; the most prominent peaks were located in BTA20 and BTA23, as observed in Rsb, and also in BTA5 and BTA26. For LBC vs Ho comparison, Rsb showed 441 SNPs distributed in 28 chromosomes, and the significant peaks were spotted in BTA20 and BTA23, followed by several discrete peaks in multiple chromosomes. The XP-EHH method detected 441 SNPs in 23 chromosomes, being the most prominent peaks in BTA20 and BTA23 and other discrete peaks in several chromosomes. These results were expected given the larger pairwise genetic distance. Footprint detection by ROH analysis The analysis, conducted using an R script developed by Gorssen et al . (2021), revealed predominantly low levels of ROH incidence across populations (<30% of the analysed individuals of each population), with the exception of a notable ROH island observed in LBC on chromosome 20, around 36-40 Mb. This island encompasses 106 SNPs and was found in 19 out of 47 animals (40.43%). Within this window, 47 genes were annotated in the ARS-UCD 1.2 cattle genome reference, including the Slick candidate gene (Fig. 6b and Table S4). Noteworthy, this ROH corresponded to the selection footprints observed when LBC and HBC were compared using Rsb and XP-EHH interpopulation methods. Neither HBC nor Ho presented ROH islands above the defined threshold (Fig. 6a, c and Table S4). The LD analysis of this LBC window showed 27 blocks that included multiple haplotypes with high levels of LD among them. As expected, this window exhibited low LD values in HBC except for the region between 38.41 and 38.60 Mb. Interestingly, this short LD block was also observed in Ho (Fig. 7a-c). Common regions between methods When comparing Rsb-XP-EHH, 16, 29 and 27 windows were found in HBC vs LBC, HBC vs Ho, and LBC vs Ho, respectively (Supplementary Table S3). For HBC vs LBC, the genomic regions were distributed in 10 chromosomes, comprising 23.67 Mb of the total genome, and contained 292 genes and 2,814 reported QTLs (Supplementary Table S3). The main signals were observed in BTA20 and BTA23 while other chromosomes (BTA 2, 5, and 10) also contained significant peaks (Fig. 4a and 5a). Remarkably, this was in agreement with the higher F ST values for HBC vs LBC and the homozygosity island observed for LBC in BTA20 (Fig. 3b and 7b). For HBC vs Ho, the regions spanned a total of 23.05 Mb distributed in 13 chromosomes and included 559 genes and 3,904 reported QTLs (Supplementary Table S3). The main peaks were observed in BTA23, followed by BTA20, 2, 5, 11, 13, 16, and 26 (Fig. 4b and 5b). Finally, the analysis of LBC vs Ho evidenced significant SNP windows in 16 chromosomes and covered 21.99 Mb. These regions included 274 genes and 4,898 reported QTLs with the main peaks observed in BTA20, 23, 26, and 5 (Fig. 4c, 5c and Supplementary Table S3). In these last comparisons (HBC vs Ho and LBC vs Ho) the F ST results did not show a clear peak although there were significant values across all chromosomes (Fig. 3b, c). The Venn diagram detailed the regions shared between the three comparisons analyses (Supplementary Fig. S3). Although most peaks were observed in the same chromosomes, there was no common window including the three of them. Two, four and ten windows were shared by two comparisons (Supplementary Fig. S3), which could indicate footprints related to highland and subtropical adaptation, and the difference in historical origin and selection process between Holstein and Creole cattle. As expected, the region containing the Slick candidate gene (PRLR) was detected when LBC was compared to the other two populations, which could explain their adaptation to warm climate. Functional analysis The functional analysis was performed using the genes located in the common regions for HBC and LBC comparison. Most of the detected significant enriched KEGG pathways were related to immune response and to autoimmune and infectious diseases, including antigen processing and presentation, Th1 and Th2 cell differentiation, Th17 cell differentiation, among others. Also, pathways related to glutathione and retinol metabolisms were observed (Table S4). Furthermore, fourteen significant terms for biological processes related to immune system function and metabolism (glutathione, retinol, and steroid; Table S4) were observed. DISCUSSION Creole cattle were brought to the American continent by Spanish conquerors in 1493. They were initially introduced to tropical environments in the Caribbean islands and then spread across the continent (Primo, 1992). Towards the south, Lima (Peru) was the main focus of dispersal and they crossed to western Bolivia, a region characterised for having one of the three main plateaus across the world, the Andean Plateau, located at ~3,000 m above the sea level (Primo, 1992; Friedrich and Wiener, 2020). These high-altitude environments have lower barometric pressure and ~40% lower atmospheric oxygen pressure than the sea level, colder temperatures, and increased UV radiation. As a result of long-term evolution, Creole cattle from the highland developed several strategies and mechanisms to evolve variations in the cardio-respiratory system, metabolic pathways, and morphological traits in order to adapt to their local environment at high altitude (Li et al. , 2021). During the XVI century, the migration continued from the west highland to the tropical and subtropical plains in eastern Bolivia. This region required the adaptation to new environmental conditions and the development of resistance to specific diseases (e.g. tick, babesiosis, anaplasmosis) and heat stress, and changes in phenotypic features like coat, as shorter and thinner hair is beneficial in warm climates (da Silva et al ., 2003; Bayssa et al ., 2021). Adaptive evolutionary mechanisms may involve changes in one or few loci with major effects or in multiple loci with small effects (Storz et al ., 2010; Friedrich and Wiener, 2020). These genotypic, and consequently phenotypic, variations were essential to allow them to survive and develop in these harsh habitats (Storz et al. , 2010; Aguirre-Rofrio et al. , 2019; Friedrich and Wiener, 2020; Rojas-Espinosa et al. , 2023; Alvarez-Cecco et al. , 2024). To assess selection footprints, it is essential to propose a correct hypothesis and select the appropriate population to validate it. For this reason, firstly the population structure and purity of HBC and LBC were evaluated. The fastSTRUCTURE and PCA clearly differentiate both populations. The cluster analysis showed 3-8% of common ancestry between zebu breeds (Nelore and Bahman) and Bolivian Creole populations. This could be a consequence of a Zebu introgression on Creole cattle and/or the foundational origin of those Zebu breeds that include absorption of Creole dams. Lirón et al . (2006) reported Zebu introgression using autosomal (4-8%) and holandric (≈10%) microsatellites in Bolivian Creole cattle. Additionally, HBC presented high levels of Ho introgression which could be related to the introduction of Holstein animals into the Highland region according to oral history. This crossbreeding strategy was carried out in order to improve milk production since pure Holstein individuals exhibited high mortality rates in this highland environment. Considering this context, HBC-Ho and LBC-Ho comparisons were added in the current footprint analysis to identify regions under selection due to Ho introgression. The analyses used for selection footprints and gene ontology were interpreted together in order to find common regions and reveal the potential mechanisms involved in divergent adaptation to highland and subtropical environments. The most prominent common region was located in the centre of BTA20. This region contains the candidate gene responsible for the slick-hair phenotype often found in Creole cattle breeds (Olson et al. , 2003; Porto-Neto et al. , 2018; Sosa et al., 2021; Sosa et al. , 2022). Littlejohn et al . (2014) first reported a mutation in the prolactin receptor (PRLR) responsible for the slick-hair phenotype in purebred Senepol caused by a single base deletion [20:39136558 GC > G] in exon 10 that introduces a stop codon and results in a truncated protein. Additional studies in South American Creole cattle breeds from warm environments found that individuals with slick-hair phenotype were discordant with this reported frameshift mutation. Instead, three nonsense variants leading to stop codons and a SNP which produced a synonymous mutation were found, all in the same region of the PRLR sequence encoding the cytoplasmic portion of the protein receptor (Porto-Neto et al. , 2018). All these collectively termed slick mutations generate truncated proteins with nearly identical effects on the protein function. Although the specific mechanism by which these mutations alter prolactin signalling is not known, they appear to enhance the inhibition of hair growth caused by the prolactin (Sosa et al. , 2022). Therefore, the slick-haired animals are characterised by a short sleek hair coat and lower follicle density. This feature confers superior ability to thermoregulate under heat stress conditions, through heat loss from skin convection and conduction (Olson et al. , 2003; Porto-Neto et al., 2018; Florez-Murillo et al., 2021; Sosa et al. , 2021). This selection sweep was detected when LBC was compared to HBC and Ho, but was not observed in HBC-Ho. In LBC, this chromosomal region exhibited higher LD and lower haplotypic diversity than HBC, evidencing the positive selection of slick-hair phenotype in Creole cattle from eastern Bolivia. Moreover, considering the reported QTLs within this region, the matched peaks of selection footprints and Ho ancestry may indicate selection towards dairy production while maintaining the long hair necessary to adapt to western highland Bolivia. Other candidate genes found in this region were SLC45A2 , HSPB3, and DNAJC21 . SLC45A2 is involved in the melanin synthesis and is associated with skin and coat pigmentation variation in several species (Mariat et al. , 2003; Soejima et al. , 2007; Dooley et al. , 2013; Wang et al ., 2016; Bâlteanu et al. , 2021). Ding et al. (2022) found a relationship between the different alleles of SLC45A2 and heat tolerance in indigenous Chinese cattle. Meanwhile, HSPB3 and DNAJC21 are heat shock proteins (HSPs) and have been related to thermotolerance through association, selection footprint and transcriptomic studies (Otto et al. , 2019; Lemal et al. , 2023; Wang et al. , 2024; Alvarez-Cecco et al. , 2024). In agreement, QTLs for coat texture and hair length were reported in these regions (https://www.animalgenome.org/cgi-bin/QTLdb/index). These results support the divergent adaptation related with the local environment of Bolivian Creole cattle, while short slick-hair is beneficial for heat loss through skin convection and conduction in lowland subtropical environments, long hair is desirable for highland Creole cattle which are exposed to colder temperatures. Considering environmental microorganisms and adaptation, livestock in high or lowland areas are exposed to different pathogens. It has been reported that the diversity and composition of the skin microbiome, which is associated with animal’s health, is different when comparing high and lowland adapted individuals (Zeng et al. , 2017; Sun et al. , 2019; Ma et al ., 2019a). In this sense, it was expected that the second main peak was detected in the Bovine Lymphocyte Antigen (BoLA) system located in BTA23. This selection sweep included Class I (BoLA Class I , BoLA-NC1 , TRIM , JSP.1 ) and Class IIa (e.g., BoLA-DQA , BoLA-DQB , BoLA-DRB3 ) genes and QTLs related to immune response, infectious diseases, and parasite load. Previous works have demonstrated association between different resistance to specific diseases and the allelic variability of BoLA genes. Particularly, BoLA-DRB3 alleles were widely associated with infectious diseases, such as virus-induced lymphoma and proviral load in bovine leukemia virus (BLV) infection, somatic cell count in milk in mastitis, endo and ectoparasites, immune response traits, response to vaccination and production traits (e.g., milk yield). Furthermore, BoLA-DQA1 was associated with proviral load in BLV infection and mastitis (Reviewed in Takeshima and Aida, 2008; Aida et al. , 2015). As mentioned above, this observed peak on BTA23 could be due to the differential exposure to pathogens. Cattle from tropical regions exhibit high resistance to infestation by ectoparasites (e.g., ticks; Ortega et al. , 2023) and endoparasites (e.g., anaplasma, babesia; Casa et al ., 2023) while highland Creole cattle are more susceptible, particularly evidenced when they are moved to tropical plains. Additionally, one of the main features of the BoLA region is the gene copy number variation. Qiu et al . (2012), studying the high altitude adapted Yak species, proposed that the presence of multicopy genes plays a relevant role in the divergent genetic architecture of adaptation, particularly in the functional categories ‘olfactory sensation’ and ‘host defence immunity’. Remarkably, this region on BTA23 contained several genes that belong to the olfactory receptor family ( OR genes) and reported QTLs related to immune response, tick resistance and disease susceptibility (https://www.animalgenome.org/cgi-bin/QTLdb/index). Other peaks included genes involved in immune response, located on BTA20 ( IL7R , IL6ST , IL31RA , C6 , C7, OSMR, LIFR ), and BTA5 ( STAT6 , NKG2A , IRAK4, KLR and CLEC genes). GO analysis evidenced significant enriched KEGG pathways and biological processes related to adaptive immunity and immune response to multiple diseases. These findings support the hypothesis that Bolivian Creole cattle from highland and tropical environments present divergent adaptation in response to the differential exposure to pathogens. The adaptation to highland or tropical habitats also involves metabolic pathways and morphological traits. Reduced oxygen availability is the primary stressor of high altitude conditions and restricts the correct functioning of respiratory and cardiovascular systems (Ivy & Scott, 2015). Moreover, chronic hypoxia increases the production of reactive oxygen species (ROS; Wang et al. , 2024). The reduction of the overall metabolic rate and modifications of the oxygen cascade and haematological system, such as red blood cell count and amount of haemoglobin, are necessary to cope with low oxygen levels (Hochachka et al ., 1996; Weber, 2007; Stortz et al ., 2010). In addition, smaller body size decreases the energy demands (Friedrich and Wiener, 2020). Experimental works have demonstrated that hypoxia upregulates the expression of ANXA2 and NDUFA4L2 genes, found in BTA10 and BTA5 respectively, through the direct action of hypoxia-inducible factor-1 ( HIF-1; Huang et al. , 2011; Liu et al. , 2021). Remarkably, the HIF gene family was extensively associated with altitude adaptation in livestock species and other mammals (Reviewed in Friedrich and Wiener, 2020). It has also been shown that ANXA2 belongs to a common pathway relevant to fibrin homeostasis and angiogenesis, while NDUFA4L2 plays a key role in the development of pulmonary artery hypertension (PAH; Jacovina et al. , 2009; Huang et al. , 2011; Hajjar, 2015; Liu et al. , 2021). In addition, a candidate gene for haematological parameters, CPLANE1, and two vascular endothelial growth factor (VEGF) genes, NRP1 and NRP2 , were identified (Oh et al. , 2002; Alghamdi et al. , 2020; Yang et al. , 2024). While angiogenesis helps to increase blood flow and oxygen supply under low oxygen conditions in high altitude, this physiological process of growing new blood vessels in the skin improves the heat dissipation in tropical environments. The enrichment analysis also evidenced pathways and terms related to glutathione ( GSTA1-5 in BTA23) and retinol metabolism ( HSD17B6 , RDH16 and SDR9C7 in BTA5), which could be indicative of an oxidative and heat stress response. Previous works evidenced the selection of antioxidase-related genes in hypoxia-tolerant mammals (Wang et al. , 2024). While GST gene family plays an important role in cellular detoxification to reduce the damage caused by ROS, HSD17B6 catalyses the oxido-reduction of different molecules (Deng et al. , 2024). Regarding retinol metabolism, heat stress can decrease vitamin levels, including retinol, retinoyl β-glucuronide and biotin which have anti-oxidative properties removing ROS (Yang et al. , 2022). Finally, in agreement with previous reports about body size and adaptation to different environments, four candidate genes related to height and carcass conformation were spotted in the sweep selection regions including PLAG1, CHCHD7 , CAP2 and ARL15 (Purfield et al. , 2019; Ghoreishifar et al. , 2020; Zhang et al. , 2022; Zhao et al. , 2022). In conclusion, the findings in the present work suggest that the divergent adaptation of Bolivian Creole cattle populations involves multiple and complex mechanisms. This includes changes in coat features and other morphological traits, immune response, and metabolic processes such as hypoxia and stress response. The inference ancestry analysis evidenced an uneven distribution of Ho introgression in the HBC genome. Except for BTA20, the enrichment regions did not match the selection footprints. Therefore, these sweeps could be a consequence of divergent adaptation of Bolivian Creole cattle to highland and tropical lowland environments. Declarations ACKNOWLEDGEMENTS The authors thank the Centro de Investigación Agrícola Tropical (CIAT, Santa Cruz, Bolivia) and Centro de Ecología Aplicada Simón I. Patiño (Santa Cruz, Bolivia) for providing us with the bovine samples. This study was funded by the National Council for Scientific and Technical Investigations (CONICET, Argentina, Grant PUE-2016 N° 22920160100004CO), the National Fund for Scientific and Technological Investigation (FONCYT-ANPCyT, Argentina, Grant N° PICT-2016-3033), National University of La Plata, Argentina (Grant V247) and the Fondo Argentino de Cooperación Sur-Sur y Triangular (FO.AR; Grant 6560). AUTHOR CONTRIBUTION STATEMENT Conceived and designed the work: MEF, ARM, and GG. Sample collection and data acquisition: JAPR, ALV, and GG. Analysed the data: OM, PAC, LHO, and FC. Contributed to reagents/materials/analysis tools acquisition: GG, JAPR, and PPG. Drafted or revised the manuscript: OM, ARM, and GG. Approved the final version: OM, PAC, LHO, JAPR, FC, ALV, PPG, MEF, ARM, and GG. CONFLICT OF INTEREST The authors declare no competing interests. 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Anim Biotechnol 33(2):273–278 Zhao F, McParland S, Kearney F, Du L, Berry DP (2015) Detection of selection signatures in dairy and beef cattle using high-density genomic information. Genet Sel Evol 47(1):49 Zhao L, Li F, Yuan L, Zhang X, Zhang D, Li X, et al. (2022) Expression of ovine CTNNA3 and CAP2 genes and their association with growth traits. Gene 807:145949 Additional Declarations There is no duality of interest Supplementary Files FigS1.PCAwithoutzebu.pdf FigS2.Chromosomes.pdf FigS3.VennPlot.png TableS1.ROHparameters.docx TableS2.FST.xlsx TableS3.RsbXP.xlsx TableS4.PathwaysandGOterms.xlsx SupplementaryMaterial.docx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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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-4492487","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":315463709,"identity":"72ddd9dc-13c0-4860-9b0f-9925f6fbc179","order_by":0,"name":"Guillermo Giovambattista","email":"data:image/png;base64,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","orcid":"","institution":"National University of La Plata","correspondingAuthor":true,"prefix":"","firstName":"Guillermo","middleName":"","lastName":"Giovambattista","suffix":""},{"id":315463710,"identity":"ec4cfb5f-b92c-4892-91fb-905e1d6243a4","order_by":1,"name":"Olivia Marcuzzi","email":"","orcid":"","institution":"National University of La Plata","correspondingAuthor":false,"prefix":"","firstName":"Olivia","middleName":"","lastName":"Marcuzzi","suffix":""},{"id":315463711,"identity":"8b1c22b2-eba2-4f34-8713-608c2147cded","order_by":2,"name":"Paulo Alvarez Cecco","email":"","orcid":"","institution":"National University of La Plata","correspondingAuthor":false,"prefix":"","firstName":"Paulo","middleName":"Alvarez","lastName":"Cecco","suffix":""},{"id":315463712,"identity":"541b5413-bd84-4078-a757-9ca7dd14e574","order_by":3,"name":"Leonidas Olivera","email":"","orcid":"","institution":"National University of La Plata","correspondingAuthor":false,"prefix":"","firstName":"Leonidas","middleName":"","lastName":"Olivera","suffix":""},{"id":315463713,"identity":"9cbdedf6-e244-4169-9db6-eaed9bb65fe1","order_by":4,"name":"Juan Pereira Rico","email":"","orcid":"","institution":"Universidad Autónoma Gabriel René Moreno","correspondingAuthor":false,"prefix":"","firstName":"Juan","middleName":"Pereira","lastName":"Rico","suffix":""},{"id":315463714,"identity":"0333d9dd-661b-4e33-8852-a506bd99241f","order_by":5,"name":"Francisco Calcaterra","email":"","orcid":"","institution":"National University of La Plata","correspondingAuthor":false,"prefix":"","firstName":"Francisco","middleName":"","lastName":"Calcaterra","suffix":""},{"id":315463715,"identity":"e5baa8c4-398c-4453-a1f6-1750d6b2594e","order_by":6,"name":"Ariel Loza Vega","email":"","orcid":"","institution":"Universidad Autónoma Gabriel René Moreno","correspondingAuthor":false,"prefix":"","firstName":"Ariel","middleName":"Loza","lastName":"Vega","suffix":""},{"id":315463716,"identity":"b83917d5-e590-4e54-97b8-8e19295d7013","order_by":7,"name":"Pilar Peral Garcia","email":"","orcid":"","institution":"National University of La Plata","correspondingAuthor":false,"prefix":"","firstName":"Pilar","middleName":"Peral","lastName":"Garcia","suffix":""},{"id":315463717,"identity":"d8829030-95f2-4232-9eeb-54736f674c12","order_by":8,"name":"María Fernandez","email":"","orcid":"","institution":"National University of La Plata","correspondingAuthor":false,"prefix":"","firstName":"María","middleName":"","lastName":"Fernandez","suffix":""},{"id":315463718,"identity":"21356aa3-328b-4ab3-9404-f7883d282674","order_by":9,"name":"Andres Rogberg Muñoz","email":"","orcid":"","institution":"University of Buenos Aires","correspondingAuthor":false,"prefix":"","firstName":"Andres","middleName":"Rogberg","lastName":"Muñoz","suffix":""}],"badges":[],"createdAt":"2024-05-28 17:30:15","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4492487/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4492487/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":59570300,"identity":"19c084fc-7bc8-434d-8299-fc414f55b947","added_by":"auto","created_at":"2024-07-03 09:56:50","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":86460,"visible":true,"origin":"","legend":"\u003cp\u003eSampling sites of Highland Bolivian Creole (HBC; red) and Lowland Bolivian Creole (LBC; yellow).\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-4492487/v1/c5512a7499784c5ef2ac6e32.png"},{"id":59570301,"identity":"e8883699-80ca-4926-b5ab-d66ba7ee5b30","added_by":"auto","created_at":"2024-07-03 09:56:50","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":122198,"visible":true,"origin":"","legend":"\u003cp\u003ea. Cluster analysis (K2-K3) and b. Principal Component Analysis (PCA) for Highland Bolivian Creole (HBC), Lowland Bolivian Creole (LBC), Holstein (Ho) and Zebu breeds (ZEB).\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-4492487/v1/e60da0c007c13dcafdec2a40.png"},{"id":59570305,"identity":"eff62e5b-9988-4c89-8820-ad3b9edd511d","added_by":"auto","created_at":"2024-07-03 09:56:51","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":747616,"visible":true,"origin":"","legend":"\u003cp\u003eManhattan plots for the pairwise F\u003csub\u003eST\u003c/sub\u003e between: a. Highland Bolivian Creole (HBC) and Lowland Bolivian Creole (LBC); b. Highland Bolivian Creole (HBC) and Holstein (Ho); c. Lowland Bolivian Creole (LBC) and Holstein (Ho). A threshold of 1% was set to determine significant F\u003csub\u003eST\u003c/sub\u003e values (red line).\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-4492487/v1/c6b3586af0ff061a69af29f5.png"},{"id":59570777,"identity":"790cb2b8-b845-47ce-9035-8c44f549e835","added_by":"auto","created_at":"2024-07-03 10:04:50","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":123926,"visible":true,"origin":"","legend":"\u003cp\u003eManhattan plots for Rsb between: a. Highland Bolivian Creole (HBC) and Lowland Bolivian Creole (LBC); b. Highland Bolivian Creole (HBC) and Holstein (Ho); c. Lowland Bolivian Creole (LBC) and Holstein (Ho; c). A threshold of 1% of the values was set to determine significant SNPs (black dotted line).\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-4492487/v1/c097bd7ac2499387287f9f86.png"},{"id":59571256,"identity":"b6119e3f-b507-4f97-8cf6-d55b11537b43","added_by":"auto","created_at":"2024-07-03 10:12:51","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":124374,"visible":true,"origin":"","legend":"\u003cp\u003eManhattan plots for XP-EHH between: a. Highland Bolivian Creole (HBC) and Lowland Bolivian Creole (LBC); b. Highland Bolivian Creole (HBC) and Holstein (Ho); c. Lowland Bolivian Creole (LBC) and Holstein (Ho). A threshold of 1% of the values was set to determine significant SNPs (black dotted line).\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-4492487/v1/a4cf7e7e3875f83b3d5cdb10.png"},{"id":59570310,"identity":"6702ae5e-f163-4265-800f-76ab46d0d74c","added_by":"auto","created_at":"2024-07-03 09:56:51","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":97598,"visible":true,"origin":"","legend":"\u003cp\u003eManhattan plots for ROH in Highland Bolivian Creole (HBC; a), Lowland Bolivian Creole (LBC; b) and Holstein (Ho; c).\u003c/p\u003e","description":"","filename":"6.png","url":"https://assets-eu.researchsquare.com/files/rs-4492487/v1/45a18df1219c3ab18784d643.png"},{"id":59570311,"identity":"8be22a88-63a5-42f3-b6d8-4016abcd8e09","added_by":"auto","created_at":"2024-07-03 09:56:51","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":257095,"visible":true,"origin":"","legend":"\u003cp\u003eHaploview plots for Highland Bolivian Creole (HBC; a), Lowland Bolivian Creole (LBC; b) and Holstein (Ho; c).\u003c/p\u003e","description":"","filename":"7.png","url":"https://assets-eu.researchsquare.com/files/rs-4492487/v1/853b0b7303000651a6b6251d.png"},{"id":69429495,"identity":"281d550a-baa9-4bc0-8314-59191340ba1f","added_by":"auto","created_at":"2024-11-20 09:18:27","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1998001,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4492487/v1/9da15b52-f349-43c7-829c-9f844e106758.pdf"},{"id":59570776,"identity":"86581caf-f5d6-4a56-ad58-36225204cb29","added_by":"auto","created_at":"2024-07-03 10:04:50","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":11640,"visible":true,"origin":"","legend":"","description":"","filename":"FigS1.PCAwithoutzebu.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4492487/v1/756ff29f658b85adececdee4.pdf"},{"id":59570307,"identity":"474a9fd4-600b-443b-bf6b-1b8084cc3541","added_by":"auto","created_at":"2024-07-03 09:56:51","extension":"pdf","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":578645,"visible":true,"origin":"","legend":"","description":"","filename":"FigS2.Chromosomes.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4492487/v1/b6b2b141bcf3f05826680209.pdf"},{"id":59570303,"identity":"616493c5-b555-4203-b822-ef6976fa2ca1","added_by":"auto","created_at":"2024-07-03 09:56:50","extension":"png","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":6480,"visible":true,"origin":"","legend":"","description":"","filename":"FigS3.VennPlot.png","url":"https://assets-eu.researchsquare.com/files/rs-4492487/v1/673d38bf224fa19fd4518882.png"},{"id":59570315,"identity":"b36bc264-1bfb-4e76-a938-40adfbbf6f62","added_by":"auto","created_at":"2024-07-03 09:56:51","extension":"docx","order_by":4,"title":"","display":"","copyAsset":false,"role":"supplement","size":7909,"visible":true,"origin":"","legend":"","description":"","filename":"TableS1.ROHparameters.docx","url":"https://assets-eu.researchsquare.com/files/rs-4492487/v1/6f8ced4a3cfb79d58eae41a1.docx"},{"id":59570309,"identity":"4aa4f808-aa8b-4216-bb69-6df8b36a9bd2","added_by":"auto","created_at":"2024-07-03 09:56:51","extension":"xlsx","order_by":5,"title":"","display":"","copyAsset":false,"role":"supplement","size":226721,"visible":true,"origin":"","legend":"","description":"","filename":"TableS2.FST.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-4492487/v1/8b65963742c3cb09bfd08346.xlsx"},{"id":59570780,"identity":"1d4c8af8-87f1-49d8-80c1-3527336e62b1","added_by":"auto","created_at":"2024-07-03 10:04:51","extension":"xlsx","order_by":6,"title":"","display":"","copyAsset":false,"role":"supplement","size":71011,"visible":true,"origin":"","legend":"","description":"","filename":"TableS3.RsbXP.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-4492487/v1/8e0a6f35429c3e1b10059032.xlsx"},{"id":59570314,"identity":"07661a98-74ef-4902-bf55-ac2a0ec8ff7d","added_by":"auto","created_at":"2024-07-03 09:56:51","extension":"xlsx","order_by":7,"title":"","display":"","copyAsset":false,"role":"supplement","size":35681,"visible":true,"origin":"","legend":"","description":"","filename":"TableS4.PathwaysandGOterms.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-4492487/v1/a86649fa765f198eb4e5b91f.xlsx"},{"id":59570778,"identity":"dfd5f8c1-cef7-47f5-b066-f945c1a5215f","added_by":"auto","created_at":"2024-07-03 10:04:51","extension":"docx","order_by":8,"title":"","display":"","copyAsset":false,"role":"supplement","size":14233,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryMaterial.docx","url":"https://assets-eu.researchsquare.com/files/rs-4492487/v1/70b40e217813323d24873b66.docx"}],"financialInterests":"There is no duality of interest","formattedTitle":"Divergent adaptation to highland and tropical environments in Bolivian Creole cattle","fulltext":[{"header":"INTRODUCTION","content":"\u003cp\u003eIn 1493, Spanish conquerors brought Creole cattle to the American continent (Primo, 1992). They were initially introduced to the Caribbean islands and then transported to South and North America during the first half of the 16th century. The routes to South America included journeys from the Caribbean to the northern coast (actual Colombia and Venezuela), as well as routes from Central America via the Pacific coast to the Alto Per\u0026uacute;, then extending through the todays Bolivia and Paraguay, and further south to the pampa and Patagonia (Wilkins, 1984; Primo, 1992; Felius, 1995). Concurrently, Portuguese cattle were directly shipped to the actual Brazilian territory (De Alba, 1978; Primo, 1992). These animals quickly adapted to the continent\u0026rsquo;s diverse environmental conditions, leading to an exponential increase in their population. Nowadays, local Creole cattle breeds persist in nearly all American countries (http://www.ansi.okstate.edu/breeds/cattle/). In Bolivia, several Creole cattle populations have adapted to multiple environments such as seasonal floodplains, dry forests, tropical plains, temperate valleys, and highland plains. Highland Bolivian Creoles are predominantly located in the western region of the country, spanning the Departments of Oruro, Potosi and part of La Paz, Cochabamba, and Chuquisaca. These animals are raised at altitudes ranging from 2,500 to 4,200 metres, characterised by a cool and dry environment. In contrast, lowland Creoles inhabit the eastern region, which includes tropical savannahs and subtropical dry forests within the Departments of El Beni, Santa Cruz, and parts of La Paz, Cochabamba, and Chuquisaca, situated at altitudes below 500 metres above sea level (https://senamhi.gob.bo).\u003c/p\u003e\n\u003cp\u003eThe adaptation process to a particular environment requires the development of specific physiological and morphological characteristics. For example, high altitude environmental conditions could include low oxygen levels, cold temperatures, high UV exposure and limited food availability (Friedrich and Wiener, 2020). In contrast, the adaptation to warm tropical and subtropical environments primarily requires heat tolerance, where changes in coat features and vascularization of the dermis are beneficial. Additionally, the development of resistance to parasites, especially ticks, and other infectious diseases is crucial (Barendse, 2017). Adaptation is the result of evolutionary and ontogenetic events that contribute to genetic changes over generations.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eSelection is one of the most important genetic processes that cause changes in specific genomic regions, consequently creates unique genetic patterns or footprints known as selection signatures (Mignon-Grasteau \u003cem\u003eet al.\u003c/em\u003e, 2005; Saravanan \u003cem\u003eet al.\u003c/em\u003e, 2020; Falchi \u003cem\u003eet al\u003c/em\u003e., 2023). Identifying these signals can be useful for determining genes and beneficial mutations that occur in a given population, including those adapted to different environments (Zhao \u003cem\u003eet al\u003c/em\u003e., 2015). With the emerging era of genomics and the advent of high-density SNP arrays, next-generation sequencing (NGS) technologies, and bioinformatics tools, various methods have been developed to detect regions subject to selection in multiple species. These include within-population approaches based on linkage disequilibrium (LD; iHs, rEHH, and LDD), site frequency spectrum (Tajima\u0026rsquo;s D and Fay and Wu\u0026rsquo;s H), and reduced local variability (runs of homozygosity [ROH]), as well as between-population statistical methods such as single-site differentiation (F\u003csub\u003eST\u003c/sub\u003e) and differentiation based on haplotypes (XP-EHH) (Qanbari \u003cem\u003eet al\u003c/em\u003e., 2010; Gautier and Vitalis, 2012; Saravanan \u003cem\u003eet al\u003c/em\u003e., 2020).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eIn recent years, there has been increased interest in identifying selection signatures for high-altitude and tropical adaptation in livestock species. The Qinghai-Tibet and Bolivian Altiplano Plateaus\u0026nbsp;are among the most extreme high-altitude regions in the world, making them ideal\u0026nbsp;models for studying high-altitude adaptation in a diverse range of native species, including humans (Yi \u003cem\u003eet al\u003c/em\u003e., 2010; Peng \u003cem\u003eet al\u003c/em\u003e., 2011; Xu \u003cem\u003eet al\u003c/em\u003e., 2011; Lorenzo \u003cem\u003eet al\u003c/em\u003e., 2014), domestic animals (Qiu \u003cem\u003eet al.\u003c/em\u003e, 2012; Li \u003cem\u003eet al\u003c/em\u003e., 2014; Wang \u003cem\u003eet al.\u003c/em\u003e, 2015; Ma \u003cem\u003eet al.\u003c/em\u003e, 2019b), and wildlife (Cai \u003cem\u003eet al.\u003c/em\u003e, 2013; Ge \u003cem\u003eet al.\u003c/em\u003e, 2013). Conversely, selection signatures have also been extensively studied in cattle bred in tropical environments in Africa (Tijjani \u003cem\u003eet al.\u003c/em\u003e, 2022; Kambal \u003cem\u003eet al.\u003c/em\u003e, 2023), Asia (Nayak \u003cem\u003eet al.\u003c/em\u003e, 2024) and South America (Maiorano \u003cem\u003eet al.\u003c/em\u003e, 2018). Over the last five centuries, it is expected that Bolivian cattle residing at high and low altitude have developed physiological strategies and morphological features to adapt to the harsh conditions of the Altiplano Plateaus and tropical environment, respectively.\u003c/p\u003e\n\u003cp\u003eFurthermore, highland Bolivian Creole cattle are traditionally utilised by local communities for subsistence farming, primarily in milk and cheese production. In recent decades, Holstein cattle from Argentina and Uruguay have been introduced into the Bolivian highlands to improve dairy production. However, this European breed exhibits low adaptability to altitude and a high mortality rate, often attributed to high altitude pulmonary hypertension (HAPH) resulting from chronic exposure to hypoxia or other stress factors prevalent at high altitude (Wang \u003cem\u003eet al.\u003c/em\u003e, 2018). Consequently, local community oral communications suggest the crossbreeding between Creole and Holstein resulted in cattle adapted to high altitude with increased production.\u003c/p\u003e\n\u003cp\u003eThe objectives of the present study were: (1) to identify selection signatures in the genome of Bolivian Creole cattle considering their divergent adaptation to highland and tropical lowland environments. For this purpose, a microarray dataset was analysed using a population differentiation method (F\u003csub\u003eST\u003c/sub\u003e), two LD-based methods (Rsb and XP-EHH), and a local variability reduction method (ROH); and (2) to evaluate whether the percentage of Holstein ancestry in regions under selection, differs from the average genome-wide estimated proportion.\u003c/p\u003e"},{"header":"MATERIALS AND METHODS","content":"\u003cp\u003e\u003cstrong\u003eAnimal samples\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eHair and blood samples were collected from 130 Bolivian Creole cattle from Altiplano highlands and tropical lowlands regions. Highland Bolivian Creoles (HBC; n = 75) were sampled at three locations in the departments of La Paz, Oruro, and Cochabamba at an altitude of 3,700 - 4,000 metres above sea level (Table 1 and Fig. 1). Environmental conditions include average annual precipitation of 300 - 400 mm, concentrated in summer (between January and March), and a media temperature of 8\u0026deg;C, ranging from 0\u0026deg;C to 20\u0026deg;C (https://senamhi.gob.bo). Lowland Bolivian Creoles (LBC; n = 55) samples were collected from three different sites in the vast plain of the department of Santa Cruz (Table 1 and Fig. 1). These animals live in subtropical conditions at an altitude of 200 - 500 metres above sea level, with an average annual rainfall of 800 - 1000 mm and a mean temperature of 25.4\u0026deg;C, ranging from 16\u0026deg;C to 32\u0026deg;C (https://senamhi.gob.bo). In addition, DNA samples from Holstein (Ho; n = 88) and Zebu breeds (ZEB; Brahman, n = 45 and Nellore, n = 4) were included. The Institutional Committee on Care and Use of Experimental Animals (CICUAL) from the School of Veterinary Sciences of the National University of La Plata (Buenos Aires, Argentina) reviewed and approved all animal procedures (89-1-18T CICUAL).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 1.\u003c/strong\u003e Sampling sites of Highland Bolivian Creole (HBC) and Lowland Bolivian Creole (LBC).\u0026nbsp;\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"583\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"18.524871355060036%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003ePopulation\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"53.00171526586621%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eSampling site\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.0325900514579756%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003en\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.44082332761578%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eAltitude (MSL)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"18.524871355060036%\" valign=\"top\"\u003e\n \u003cp\u003eHBC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"53.00171526586621%\" valign=\"top\"\u003e\n \u003cp\u003eSan Pedro de Totora, Oruro Department\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.0325900514579756%\" valign=\"top\"\u003e\n \u003cp\u003e52\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.44082332761578%\" valign=\"top\"\u003e\n \u003cp\u003e4,000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"18.524871355060036%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"53.00171526586621%\" valign=\"top\"\u003e\n \u003cp\u003eBolivar, Cochabamba Department\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.0325900514579756%\" valign=\"top\"\u003e\n \u003cp\u003e14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.44082332761578%\" valign=\"top\"\u003e\n \u003cp\u003e3,735\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"18.524871355060036%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"53.00171526586621%\" valign=\"top\"\u003e\n \u003cp\u003eOmasuyos, La Paz Department\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.0325900514579756%\" valign=\"top\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.44082332761578%\" valign=\"top\"\u003e\n \u003cp\u003e3,840\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"18.524871355060036%\" valign=\"top\"\u003e\n \u003cp\u003eLBC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"53.00171526586621%\" valign=\"top\"\u003e\n \u003cp\u003ePalmar Tapera, Santa Cruz Department\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.0325900514579756%\" valign=\"top\"\u003e\n \u003cp\u003e29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.44082332761578%\" valign=\"top\"\u003e\n \u003cp\u003e230 - 380\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"18.524871355060036%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"53.00171526586621%\" valign=\"top\"\u003e\n \u003cp\u003eChiquitos, Santa Cruz Department\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.0325900514579756%\" valign=\"top\"\u003e\n \u003cp\u003e16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.44082332761578%\" valign=\"top\"\u003e\n \u003cp\u003e416\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"18.524871355060036%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"53.00171526586621%\" valign=\"top\"\u003e\n \u003cp\u003eObispo Santistevan, Santa Cruz Department\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.0325900514579756%\" valign=\"top\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.44082332761578%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;450\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003eDNA extraction and genotyping\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eGenomic DNA was extracted from hair samples using the DNeasy Blood \u0026amp; Tissue Kit (Qiagen, Hilden, Germany) and from blood using the\u0026nbsp;Wizard Genomic DNA Purification kit (Promega, MA, USA). Samples were genotyped in a GeneTitan\u003csup\u003eTM\u003c/sup\u003e platform (Applied Biosystems\u0026trade;, CA, USA) using the microarrays Axiom\u003csup\u003eTM\u003c/sup\u003e Bos1 Genotyping Array r3 (Applied Biosystems\u0026trade;) containing 648,855 SNPs and the custom array ArBos1 containing 58,088 SNPs. A single genotype matrix was constructed using the merge function in PLINK v1.9 software (Purcell \u003cem\u003eet al\u003c/em\u003e., 2007), resulting in a final database of 48,360 common SNPs. Raw data were processed using Axiom\u003csup\u003eTM\u003c/sup\u003e Analysis Suite software 4.0 (Applied Biosystems\u0026trade;), setting sample and SNPs call rates at \u0026ge; 97%. Datasets were exported in .PED and .MAP file format for further analysis. The SNPs positions were assigned according to the bovine genome assembly UMD 3.1.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePopulation structure and relationships\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ePopulation structure and the degree of admixture were determined in HBC, LBC, Ho, and ZEB. SNPs were filtered using the --indep 50 5 2 command in PLINK v1.9 resulting in a set of 21,834 unlinked genetic markers. Considering the historical data of a possible introgression of foreign genetics in Bolivian Creole cattle, the admixture with Ho and ZEB breeds was tested by a Bayesian clustering-model implemented in fastSTRUCTURE (Raj \u003cem\u003eet al\u003c/em\u003e., 2014). The ChooseK algorithm indicated that the model complexity that maximises marginal likelihood was K2, and the model components used to explain structure in data was K3. The graphical representation of the results was performed using Distruct v.2.3 (Chhatre, 2018). Additionally, a principal component analysis (PCA) was performed to assess the divergence of individuals from each population using the function --pca in PLINK v1.9. The results were visualised using the R library ggplot2.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eInference of local ancestry\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo infer Ho local ancestry within the HBC genome, haplotype phasing was first performed using SHAPEIT5 software (Hofmeister \u003cem\u003eet al\u003c/em\u003e., 2023). Then, Flare (Browning\u003cem\u003e\u0026nbsp;et al\u003c/em\u003e., 2023) was used to determine ancestry origin (Creole cattle or Ho) for every SNP in each HBC individual using the default parameters with the exception of em=false. Local ancestry was estimated at chromosome and whole-genome levels. \u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSelection footprint analysis\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo detect candidate regions for high altitude and tropical lowland adaptation, selection footprints were analysed using four methods: the fixation index statistic (F\u003csub\u003eST\u003c/sub\u003e; based on gene frequencies), Rsb and XP-EHH (based on haplotype extension), and ROH (a local variability reduction method; Sabeti \u003cem\u003eet al.\u003c/em\u003e, 2002, 2007; Voight \u003cem\u003eet al.\u003c/em\u003e, 2006; Saravanan \u003cem\u003eet al\u003c/em\u003e., 2020).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe average F\u003csub\u003eST\u003c/sub\u003e was calculated using the --fst command in PLINK v1.9, and the pairwise F\u003csub\u003eST\u003c/sub\u003e was estimated using the OutFLANK R package for LBC, HBC, and Ho (Whitlock and Lotterhos, 2015). This script calculates the divergence in individual SNP allele frequencies between pairs of populations using the Lewontin-Krakauer F\u003csub\u003eST\u003c/sub\u003e method with an accurate null model. Significance was determined by examining the right tail of the null model distribution, with a p-value of \u0026lt;0.01 considered significant. Genomic window sizes were determined based on regions containing significant SNPs separated by no more than 0.25 Mb. For extreme SNPs, an additional 0.25 Mb was included on the external side of the window. The F\u003csub\u003eST\u003c/sub\u003e values were visualised in a Manhattan plot using the \u003cem\u003eqqman\u0026nbsp;\u003c/em\u003epackage in RStudio v4.3.3.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eFor LD-based analysis, the obtained haplotypes using SHAPEIT5 were used to compute Rsb (Voight \u003cem\u003eet al.\u003c/em\u003e, 2006) and XP-EHH (Sabeti \u003cem\u003eet al\u003c/em\u003e., 2007) using the \u003cem\u003erehh\u0026nbsp;\u003c/em\u003eR package (Gautier and Vitalis, 2012). Rsb and XP-EHH statistics were designed to detect regions with high levels of haplotype homozygosity over an unexpectedly long distance (relative to neutral expectations) and measure the amount of extended haplotype across populations. The 1% top value was set as the threshold to select the potential SNPs under selection. Genomic window sizes were determined based on regions containing significant SNPs separated by no more than 0.25 Mb. For extreme SNPs, an additional 0.25 Mb was added to the external side of the window.\u003c/p\u003e\n\u003cp\u003eA R-script developed by Gorssen \u003cem\u003eet al\u003c/em\u003e. (2021) was used to detect ROH in HBC, LBC, and Ho. ROH incidence was calculated as the percentage of animals with a SNP within a ROH segment for a given population. The minimal threshold for detection of ROH islands was set to 30%, meaning that a ROH had to be present in at least 30% of the animals of each population to be included in a ROH island (Supplementary Table S1). Results were visualised in Manhattan plots using the \u003cem\u003eqqman\u003c/em\u003e R package. SNPs located within the ROH island were filtered using the --from-bp --to-bp command in PLINK 1.9 and the linkage disequilibrium (LD) between the SNPs included in detected windows was estimated with the r\u003csup\u003e2\u003c/sup\u003e parameter and visualised using the Haploview 4.2 software (Barrett \u003cem\u003eet al.\u003c/em\u003e, 2005).\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eGene ontology\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe regions identified by each selection footprint methodology were compared to determine the overlapping windows, and then the positions were converted to the ARS-UCD 1.2 reference genome assembly using the Lift Genome Annotations from UCSC (https://genome.ucsc.edu/cgi-bin/hgLiftOver). Genes and QTLs included in the common windows were retrieved from the National Center for Biotechnology Information (NCBI, https://www.ncbi.nlm.nih.gov), FAANGMINE (https://faangmine.rnet.missouri.edu) and Cattle QTL Database https://www.animalgenome.org/cgi-bin/QTLdb/index). Functional analysis was performed using DAVID (https://david.ncifcrf.gov) to detect biological enrichment pathways and terms. In addition, the individual functional significance of some genes was reviewed based on literature.\u003c/p\u003e"},{"header":"RESULTS","content":"\u003cp\u003e\u003cstrong\u003ePopulation structure and relationships\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo evaluate the genetic composition within the studied Creole populations and the gene introgression of foreign breeds (Ho and ZEB), two analyses were performed. The results from the cluster analysis using fastSTRUCTURE are presented in Fig. 2a. When considering K = 2, the Taurine/Zebuine genetic component was evidenced, with negligible levels of ZEB component in Bolivian Creole cattle, probably due to the origin of Nelore and Brahman from Creole dams and/or some Zebuine introgression into Creole. The ZEB estimated average percentage was 3.08% (\u0026plusmn;4.75%) for HBC and 8.76% (\u0026plusmn;3.82%) for LBC. In K = 3, this ZEB influence was indistinguishable. Instead, two Taurine components were observed, one common to HBC and LBC and the other corresponding to Ho. Noteworthy, the HBC showed an average Ho introgression of 31.26% (\u0026plusmn;12.15%), ranging from 6.33% to 50.31%.\u003c/p\u003e\n\u003cp\u003eThe PCA analysis showed that PC1 accounted for\u0026nbsp;48.39% of the variance and clearly differentiated the ZEB and the three Taurine populations. PC2 explained 10.97% of the genetic variance and separated the individuals from LBC, HBC, and Ho (Fig. 2b). In particular, HBC appeared between Ho and LBC, which could be related with a possible introgression of Ho genes into this population, in agreement with the cluster analysis results. PCA without ZEB animals endorsed this result (Supplementary Fig. S1). Most individuals from each population differentiated with both components suggesting the presence of enough population structure necessary to perform further selection footprint analysis.\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eInference of local ancestry analysis\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe inference of local ancestry for each SNP in HBC showed an average Ho introgression value of 0.357, which was similar to fastSTRUCTURE results. This analysis also showed uneven percentages of Ho origin among chromosomes, with values ranging from 0.14 (BTA2) to 0.63 (BTA20). This disparate distribution was also observed within chromosomes, with enriched regions (\u0026gt;0.5) in BTA20, BTA15 and BTA13, among others (Fig. S2). Few of the regions enriched with Ho ancestry coincide with peaks found in the HBC-LBC comparison, particularly the one on BTA20. Notably, this region (34-44 Mb) contains milk-related QTLs, such as milk yield, milk fat and protein percentages, and milk acid content, all of which were found in Holstein cattle (https://www.animalgenome.org/cgi-bin/QTLdb/index).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFootprint analysis based on loci genetic divergence\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe first approach to detect evidence of selection was performed via genetic differentiation (F\u003csub\u003eST\u003c/sub\u003e) between pairs of populations: HBC vs LBC, HBC vs Ho and LBC vs Ho. These analyses consider that a statistically significant F\u003csub\u003eST\u003c/sub\u003e value at a locus, compared to the genomic average, could indicate evidence of selection. The F\u003csub\u003eST\u003c/sub\u003e estimations for the HBC-LBC, HBC-Ho, and LBC-Ho comparisons resulted in average weighted values of 0.033, 0.049 and 0.085 respectively. These results reinforce those from PCA and fastSTRUCTURE and were expected considering the genetic distances between the breeds and historical data.\u003c/p\u003e\n\u003cp\u003eThe results for the pairwise F\u003csub\u003eST\u003c/sub\u003e index are presented in Fig. 3a-c and Supplementary Table S2. Considering the p-value right tail threshold of 0.01, this analysis identified 1,081 SNPs for HBC-LBC, 915 SNPs for HBC-Ho and 695 SNPs for LBC-Ho. These corresponded to F\u003csub\u003eST\u003c/sub\u003e values \u0026ge; 0.161, 0.248, and 0.418, respectively. In all three comparisons, significant SNPs were found across all chromosomes, encompassing a large number of genes (9,373 for HBC-LBC, 7,993 for HBC-Ho and 6,713 for LBC-Ho). As shown in Fig. 3, the most significant peak was observed for HBC-LBC in BTA 20 which also corresponds to the largest SNP window. Remarkably, this region included the candidate gene proposed for slick phenotype (Littlejohn \u003cem\u003eet al.\u003c/em\u003e, 2014).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFootprint detection by haplotype-based analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eRsb and XP-EHH LD-based methods were also used to detect selection footprints. Results for the six pairwise comparisons are presented in Fig. 4 and 5 and Supplementary Table S3. In the HBC vs LBC comparison, the Rsb method identified 444 SNPs distributed along 16 chromosomes, while the XP-EHH method detected 480 markers distributed across 18 chromosomes using a 1% threshold. The most significant peaks, observed in BTA20 and BTA23, were consistent across both Rsb and XP-EHH methods. The region on BTA20 included the gene previously reported for slick hair phenotype (PRLR, Littlejohn \u003cem\u003eet al.\u003c/em\u003e, 2014), while BTA23 included the major histocompatibility complex genes harbouring BoLA-DQA, BoLA-DQB and BoLA-DRB. Three prominent peaks in BTA2, BTA5 and BTA10 were also observed in this comparison using XP-EHH. For HBC vs Ho, Rsb showed 446 SNPs distributed in 17 chromosomes and the most prominent peaks were located in BTA20 and BTA23, followed by several discrete peaks in BTA2, BTA5, BTA9, BTA10, BTA16 and BTA26. The XP-EHH method detected 446 SNPs in 19 chromosomes; the most prominent peaks were located in BTA20 and BTA23, as observed in Rsb, and also in BTA5 and BTA26. For LBC vs Ho comparison, Rsb showed 441 SNPs distributed in 28 chromosomes, and the significant peaks were spotted in BTA20 and BTA23, followed by several discrete peaks in multiple chromosomes. The XP-EHH method detected 441 SNPs in 23 chromosomes, being the most prominent peaks in BTA20 and BTA23 and other discrete peaks in several chromosomes. These results were expected given the larger pairwise genetic distance.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFootprint detection by ROH analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe analysis, conducted using an R script developed by Gorssen \u003cem\u003eet al\u003c/em\u003e. (2021), revealed predominantly low levels of ROH incidence across populations (\u0026lt;30% of the analysed individuals of each population), with the exception of a notable ROH island observed in LBC on chromosome 20, around 36-40 Mb. This island encompasses 106 SNPs and was found in 19 out of 47 animals (40.43%). Within this window, 47 genes were annotated in the ARS-UCD 1.2 cattle genome reference, including the Slick candidate gene (Fig. 6b and Table S4). Noteworthy, this ROH corresponded to the selection footprints observed when LBC and HBC were compared using Rsb and XP-EHH interpopulation methods. Neither HBC nor Ho presented ROH islands above the defined threshold (Fig. 6a, c and Table S4). The LD analysis of this LBC window showed 27 blocks that included multiple haplotypes with high levels of LD among them. As expected, this window exhibited low LD values in HBC except for the region between 38.41 and 38.60 Mb. Interestingly, this short LD block was also observed in Ho (Fig. 7a-c).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCommon regions between methods\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWhen comparing Rsb-XP-EHH, 16, 29 and 27 windows were found in HBC vs LBC, HBC vs Ho, and LBC vs Ho, respectively (Supplementary Table S3). For HBC vs LBC, the genomic regions were distributed in 10 chromosomes, comprising 23.67 Mb of the total genome, and contained 292 genes and 2,814 reported QTLs (Supplementary Table S3). The main signals were observed in BTA20 and BTA23 while other chromosomes (BTA 2, 5, and 10) also contained significant peaks (Fig. 4a and 5a). Remarkably, this was in agreement with the higher F\u003csub\u003eST\u003c/sub\u003e values for HBC vs LBC and the homozygosity island observed for LBC in BTA20 (Fig. 3b and 7b). For HBC vs Ho, the regions spanned a total of 23.05 Mb distributed in 13 chromosomes and included 559 genes and 3,904 reported QTLs (Supplementary Table S3). The main peaks were observed in BTA23, followed by BTA20, 2, 5, 11, 13, 16, and 26 (Fig. 4b and 5b). Finally, the analysis of LBC vs Ho evidenced significant SNP windows in 16 chromosomes and covered 21.99 Mb. These regions included 274 genes and 4,898 reported QTLs with the main peaks observed in BTA20, 23, 26, and 5 (Fig. 4c, 5c and Supplementary Table S3). In these last comparisons (HBC vs Ho and LBC vs Ho) the F\u003csub\u003eST\u003c/sub\u003e results did not show a clear peak although there were significant values across all chromosomes (Fig. 3b, c).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe Venn diagram detailed the regions shared between the three comparisons analyses (Supplementary Fig. S3). Although most peaks were observed in the same chromosomes, there was no common window including the three of them. Two, four and ten windows were shared by two comparisons (Supplementary Fig. S3), which could indicate footprints related to highland and subtropical adaptation, and the difference in historical origin and selection process between Holstein and Creole cattle. As expected, the region containing the Slick candidate gene (PRLR) was detected when LBC was compared to the other two populations, which could explain their adaptation to warm climate.\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunctional analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe functional analysis was performed using the genes located in the common regions for HBC and LBC comparison. Most of the detected significant enriched KEGG pathways were related to immune response and to autoimmune and infectious diseases, including antigen processing and presentation, Th1 and Th2 cell differentiation, Th17 cell differentiation, among others. Also, pathways related to glutathione and retinol metabolisms were observed (Table S4). Furthermore, fourteen significant terms for biological processes related to immune system function and metabolism (glutathione, retinol, and steroid; Table S4) were observed.\u003c/p\u003e"},{"header":"DISCUSSION","content":"\u003cp\u003eCreole cattle were brought to the American continent by Spanish conquerors in 1493. They were initially introduced to tropical environments in the Caribbean islands and then spread across the continent (Primo, 1992). Towards the south, Lima (Peru) was the main focus of dispersal and they crossed to western Bolivia, a region characterised for having one of the three main plateaus across the world, the Andean Plateau, located at ~3,000 m above the sea level (Primo, 1992; Friedrich and Wiener, 2020). These high-altitude environments have lower barometric pressure and ~40% lower atmospheric oxygen pressure than the sea level, colder temperatures, and increased UV radiation. As a result of long-term evolution, Creole cattle from the highland developed several strategies and mechanisms to evolve variations in the cardio-respiratory system, metabolic pathways, and morphological traits in order to adapt to their local environment at high altitude (Li \u003cem\u003eet al.\u003c/em\u003e, 2021). During the XVI century, the migration continued from the west highland to the tropical and subtropical plains in eastern Bolivia. This region required the adaptation to new environmental conditions and the development of resistance to specific diseases (e.g. tick, babesiosis, anaplasmosis) and heat stress, and changes in phenotypic features like coat, as shorter and thinner hair is beneficial in warm climates (da Silva \u003cem\u003eet al\u003c/em\u003e., 2003; Bayssa \u003cem\u003eet al\u003c/em\u003e., 2021). Adaptive evolutionary mechanisms may involve changes in one or few loci with major effects or in multiple loci with small effects (Storz \u003cem\u003eet al\u003c/em\u003e., 2010; Friedrich and Wiener, 2020). These genotypic, and consequently phenotypic, variations were essential to allow them to survive and develop in these harsh habitats (Storz \u003cem\u003eet al.\u003c/em\u003e, 2010; Aguirre-Rofrio \u003cem\u003eet al.\u003c/em\u003e, 2019; Friedrich and Wiener, 2020; Rojas-Espinosa \u003cem\u003eet al.\u003c/em\u003e, 2023; Alvarez-Cecco \u003cem\u003eet al.\u003c/em\u003e, 2024).\u003c/p\u003e\n\u003cp\u003eTo assess selection footprints, it is essential to propose a correct hypothesis and select the appropriate population to validate it. For this reason, firstly the population structure and purity of HBC and LBC were evaluated. The fastSTRUCTURE and PCA clearly differentiate both populations. The cluster analysis showed 3-8% of common ancestry between zebu breeds (Nelore and Bahman) and Bolivian Creole populations. This could be a consequence of a Zebu introgression on Creole cattle and/or the foundational origin of those Zebu breeds that include absorption of Creole dams. Lir\u0026oacute;n \u003cem\u003eet al\u003c/em\u003e. (2006) reported Zebu introgression using autosomal (4-8%) and holandric (\u0026asymp;10%) microsatellites in Bolivian Creole cattle. Additionally, HBC presented high levels of Ho introgression which could be related to the introduction of Holstein animals into the Highland region according to oral history. This crossbreeding strategy was carried out in order to improve milk production since pure Holstein individuals exhibited high mortality rates in this highland environment. Considering this context, HBC-Ho and LBC-Ho comparisons were added in the current footprint analysis to identify regions under selection due to Ho introgression.\u003c/p\u003e\n\u003cp\u003eThe analyses used for selection footprints and gene ontology were interpreted together in order to find common regions and reveal the potential mechanisms involved in divergent adaptation to highland and subtropical environments. The most prominent common region was located in the centre of BTA20. This region contains the candidate gene responsible for the slick-hair phenotype often found in Creole cattle breeds (Olson \u003cem\u003eet al.\u003c/em\u003e, 2003; Porto-Neto \u003cem\u003eet al.\u003c/em\u003e, 2018; Sosa \u003cem\u003eet al.,\u003c/em\u003e 2021; Sosa \u003cem\u003eet al.\u003c/em\u003e, 2022). Littlejohn \u003cem\u003eet al\u003c/em\u003e. (2014) first reported a mutation in the prolactin receptor (PRLR) responsible for the slick-hair phenotype in purebred Senepol caused by a single base deletion [20:39136558 GC \u0026gt; G] in exon 10 that introduces a stop codon and results in a truncated protein. Additional studies in South American Creole cattle breeds from warm environments found that individuals with slick-hair phenotype were discordant with this reported frameshift mutation. Instead, three nonsense variants leading to stop codons and a SNP which produced a synonymous mutation were found, all in the same region of the PRLR sequence encoding the cytoplasmic portion of the protein receptor (Porto-Neto \u003cem\u003eet al.\u003c/em\u003e, 2018). All these collectively termed slick mutations generate truncated proteins with nearly identical effects on the protein function. Although the specific mechanism by which these mutations alter prolactin signalling is not known, they appear to enhance the inhibition of hair growth caused by the prolactin (Sosa\u003cem\u003e\u0026nbsp;et al.\u003c/em\u003e, 2022). Therefore, the slick-haired animals are characterised by a short sleek hair coat and lower follicle density. This feature confers superior ability to thermoregulate under heat stress conditions, through heat loss from skin convection and conduction (Olson\u003cem\u003e\u0026nbsp;et al.\u003c/em\u003e, 2003; Porto-Neto\u003cem\u003e\u0026nbsp;et al.,\u003c/em\u003e 2018; Florez-Murillo \u003cem\u003eet al.,\u0026nbsp;\u003c/em\u003e2021; Sosa \u003cem\u003eet al.\u003c/em\u003e, 2021). This selection sweep was detected when LBC was compared to HBC and Ho, but was not observed in HBC-Ho. In LBC, this chromosomal region exhibited higher LD and lower haplotypic diversity than HBC, evidencing the positive selection of slick-hair phenotype in Creole cattle from eastern Bolivia. Moreover, considering the reported QTLs within this region, the matched peaks of selection footprints and Ho ancestry may indicate selection towards dairy production while maintaining the long hair necessary to adapt to western highland Bolivia. Other candidate genes found in this region were \u003cem\u003eSLC45A2\u003c/em\u003e, \u003cem\u003eHSPB3,\u0026nbsp;\u003c/em\u003eand\u003cem\u003e\u0026nbsp;DNAJC21\u003c/em\u003e. \u003cem\u003eSLC45A2\u003c/em\u003e is involved in the melanin synthesis and is associated with skin and coat pigmentation variation in several species (Mariat \u003cem\u003eet al.\u003c/em\u003e, 2003; Soejima \u003cem\u003eet al.\u003c/em\u003e, 2007; Dooley \u003cem\u003eet al.\u003c/em\u003e, 2013; Wang \u003cem\u003eet al\u003c/em\u003e., 2016; B\u0026acirc;lteanu \u003cem\u003eet al.\u003c/em\u003e, 2021). Ding \u003cem\u003eet al.\u003c/em\u003e (2022) found a relationship between the different alleles of \u003cem\u003eSLC45A2\u003c/em\u003e and heat tolerance in indigenous Chinese cattle. Meanwhile, \u003cem\u003eHSPB3\u0026nbsp;\u003c/em\u003eand\u003cem\u003e\u0026nbsp;DNAJC21\u003c/em\u003e are heat shock proteins (HSPs) and have been related to thermotolerance through association, selection footprint and transcriptomic studies (Otto \u003cem\u003eet al.\u003c/em\u003e, 2019; Lemal \u003cem\u003eet al.\u003c/em\u003e, 2023; Wang \u003cem\u003eet al.\u003c/em\u003e, 2024; Alvarez-Cecco \u003cem\u003eet al.\u003c/em\u003e, 2024). In agreement, QTLs for coat texture and hair length were reported in these regions (https://www.animalgenome.org/cgi-bin/QTLdb/index). These results support the divergent adaptation related with the local environment of Bolivian Creole cattle, while short slick-hair is beneficial for heat loss through skin convection and conduction in lowland subtropical environments, long hair is desirable for highland Creole cattle which are exposed to colder temperatures.\u003c/p\u003e\n\u003cp\u003eConsidering environmental microorganisms and adaptation, livestock in high or lowland areas are exposed to different pathogens. It has been reported that the diversity and composition of the skin microbiome, which is associated with animal\u0026rsquo;s health, is different when comparing high and lowland adapted individuals (Zeng \u003cem\u003eet al.\u003c/em\u003e, 2017; Sun \u003cem\u003eet al.\u003c/em\u003e, 2019; Ma \u003cem\u003eet al\u003c/em\u003e., 2019a). In this sense, it was expected that the second main peak was detected in the Bovine Lymphocyte Antigen (BoLA) system located in BTA23. This selection sweep included Class I (BoLA \u003cem\u003eClass I\u003c/em\u003e, \u003cem\u003eBoLA-NC1\u003c/em\u003e, \u003cem\u003eTRIM\u003c/em\u003e, \u003cem\u003eJSP.1\u003c/em\u003e) and Class IIa (e.g., \u003cem\u003eBoLA-DQA\u003c/em\u003e, \u003cem\u003eBoLA-DQB\u003c/em\u003e, \u003cem\u003eBoLA-DRB3\u003c/em\u003e) genes and QTLs related to immune response, infectious diseases, and parasite load. Previous works have demonstrated association between different resistance to specific diseases and the allelic variability of BoLA genes. Particularly, \u003cem\u003eBoLA-DRB3\u003c/em\u003e alleles were widely associated with infectious diseases, such as virus-induced lymphoma and proviral load in bovine leukemia virus (BLV) infection, somatic cell count in milk in mastitis, endo and ectoparasites, immune response traits, response to vaccination and production traits (e.g., milk yield). Furthermore, \u003cem\u003eBoLA-DQA1\u003c/em\u003e was associated with proviral load in BLV infection and mastitis (Reviewed in Takeshima and Aida, 2008; Aida \u003cem\u003eet al.\u003c/em\u003e, 2015). As mentioned above, this observed peak on BTA23 could be due to the differential exposure to pathogens. Cattle from tropical regions exhibit high resistance to infestation by ectoparasites (e.g., ticks; Ortega \u003cem\u003eet al.\u003c/em\u003e, 2023) and endoparasites (e.g., anaplasma, babesia; Casa \u003cem\u003eet al\u003c/em\u003e., 2023) while highland Creole cattle are more susceptible, particularly evidenced when they are moved to tropical plains. Additionally, one of the main features of the BoLA region is the gene copy number variation. Qiu \u003cem\u003eet al\u003c/em\u003e. (2012), studying the high altitude adapted Yak species, proposed that the presence of multicopy genes plays a relevant role in the divergent genetic architecture of adaptation, particularly in the functional categories \u0026lsquo;olfactory sensation\u0026rsquo; and \u0026lsquo;host defence immunity\u0026rsquo;. Remarkably, this region on BTA23 contained several genes that belong to the olfactory receptor family (\u003cem\u003eOR\u003c/em\u003e genes) and reported QTLs related to immune response, tick resistance and disease susceptibility (https://www.animalgenome.org/cgi-bin/QTLdb/index). Other peaks included genes involved in immune response, located on BTA20 (\u003cem\u003eIL7R\u003c/em\u003e, \u003cem\u003eIL6ST\u003c/em\u003e, \u003cem\u003eIL31RA\u003c/em\u003e, \u003cem\u003eC6\u003c/em\u003e, \u003cem\u003eC7, OSMR, LIFR\u003c/em\u003e), and BTA5 (\u003cem\u003eSTAT6\u003c/em\u003e, \u003cem\u003eNKG2A\u003c/em\u003e, IRAK4, \u003cem\u003eKLR\u0026nbsp;\u003c/em\u003eand \u003cem\u003eCLEC\u0026nbsp;\u003c/em\u003egenes). GO analysis evidenced significant enriched KEGG pathways and biological processes related to adaptive immunity and immune response to multiple diseases.\u0026nbsp;These findings support the hypothesis that Bolivian Creole cattle from highland and tropical environments present divergent adaptation in response to the differential exposure to pathogens.\u003c/p\u003e\n\u003cp\u003eThe adaptation to highland or tropical habitats also involves metabolic pathways and morphological traits. Reduced oxygen availability is the primary stressor of high altitude conditions and restricts the correct functioning of respiratory and cardiovascular systems (Ivy \u0026amp; Scott, 2015). Moreover, chronic hypoxia increases the production of reactive oxygen species (ROS; Wang \u003cem\u003eet al.\u003c/em\u003e, 2024). The reduction of the overall metabolic rate and modifications of the oxygen cascade and haematological system, such as red blood cell count and amount of haemoglobin, are necessary to cope with low oxygen levels (Hochachka \u003cem\u003eet al\u003c/em\u003e., 1996; Weber, 2007; \u003cem\u003eStortz et al\u003c/em\u003e., 2010). In addition, smaller body size decreases the energy demands (Friedrich and Wiener, 2020). Experimental works have demonstrated that hypoxia upregulates the expression of \u003cem\u003eANXA2\u0026nbsp;\u003c/em\u003eand \u003cem\u003eNDUFA4L2\u0026nbsp;\u003c/em\u003egenes, found in BTA10 and BTA5 respectively, through the direct action of hypoxia-inducible factor-1 (\u003cem\u003eHIF-1;\u0026nbsp;\u003c/em\u003eHuang \u003cem\u003eet al.\u003c/em\u003e, 2011; Liu \u003cem\u003eet al.\u003c/em\u003e, 2021). Remarkably, the HIF gene family was extensively associated with altitude adaptation in livestock species and other mammals (Reviewed in Friedrich and Wiener, 2020). It has also been shown that \u003cem\u003eANXA2\u003c/em\u003e belongs to a common pathway relevant to fibrin homeostasis and angiogenesis, while \u003cem\u003eNDUFA4L2\u0026nbsp;\u003c/em\u003eplays a key role in the development of pulmonary artery hypertension (PAH; Jacovina \u003cem\u003eet al.\u003c/em\u003e, 2009; Huang \u003cem\u003eet al.\u003c/em\u003e, 2011; Hajjar, 2015; Liu \u003cem\u003eet al.\u003c/em\u003e, 2021). In addition, a candidate gene for haematological parameters, \u003cem\u003eCPLANE1,\u0026nbsp;\u003c/em\u003eand two vascular endothelial growth factor (VEGF) genes, \u003cem\u003eNRP1\u0026nbsp;\u003c/em\u003eand \u003cem\u003eNRP2\u003c/em\u003e, were identified (Oh \u003cem\u003eet al.\u003c/em\u003e, 2002; Alghamdi \u003cem\u003eet al.\u003c/em\u003e, 2020; Yang \u003cem\u003eet al.\u003c/em\u003e, 2024). While angiogenesis helps to increase blood flow and oxygen supply under low oxygen conditions in high altitude, this physiological process of growing new blood vessels in the skin improves the heat dissipation in tropical environments. The enrichment analysis also evidenced pathways and terms related to glutathione (\u003cem\u003eGSTA1-5\u003c/em\u003e in BTA23) and retinol metabolism (\u003cem\u003eHSD17B6\u003c/em\u003e, \u003cem\u003eRDH16\u0026nbsp;\u003c/em\u003eand \u003cem\u003eSDR9C7\u0026nbsp;\u003c/em\u003ein BTA5), which could be indicative of an oxidative and heat stress response. Previous works evidenced the selection of antioxidase-related genes in hypoxia-tolerant mammals (Wang \u003cem\u003eet al.\u003c/em\u003e, 2024). While \u003cem\u003eGST\u003c/em\u003e gene family plays an important role in cellular detoxification to reduce the damage caused by ROS, HSD17B6 catalyses the oxido-reduction of different molecules (Deng\u003cem\u003e\u0026nbsp;et al.\u003c/em\u003e, 2024). Regarding retinol metabolism, heat stress can decrease vitamin levels, including retinol, retinoyl \u0026beta;-glucuronide and biotin which have anti-oxidative properties removing ROS (Yang \u003cem\u003eet al.\u003c/em\u003e, 2022). Finally, in agreement with previous reports about body size and adaptation to different environments, four candidate genes related to height and carcass conformation were spotted in the sweep selection regions including \u003cem\u003ePLAG1, CHCHD7\u003c/em\u003e, \u003cem\u003eCAP2\u0026nbsp;\u003c/em\u003eand \u003cem\u003eARL15\u003c/em\u003e (Purfield \u003cem\u003eet al.\u003c/em\u003e, 2019; Ghoreishifar \u003cem\u003eet al.\u003c/em\u003e, 2020; Zhang \u003cem\u003eet al.\u003c/em\u003e, 2022; Zhao \u003cem\u003eet al.\u003c/em\u003e, 2022).\u003c/p\u003e\n\u003cp\u003eIn conclusion, the findings in the present work suggest that the divergent adaptation of Bolivian Creole cattle populations involves multiple and complex mechanisms. This includes changes in coat features and other morphological traits, immune response, and metabolic processes such as hypoxia and stress response. The inference ancestry analysis evidenced an uneven distribution of Ho introgression in the HBC genome. Except for BTA20, the enrichment regions did not match the selection footprints. Therefore, these sweeps could be a consequence of divergent adaptation of Bolivian Creole cattle to highland and tropical lowland environments.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eACKNOWLEDGEMENTS\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors thank the Centro de Investigaci\u0026oacute;n Agr\u0026iacute;cola Tropical (CIAT, Santa Cruz, Bolivia) and Centro de Ecolog\u0026iacute;a Aplicada Sim\u0026oacute;n I. Pati\u0026ntilde;o (Santa Cruz, Bolivia) for providing us with the bovine samples. This study was funded by the National Council for Scientific and Technical Investigations (CONICET, Argentina, Grant PUE-2016 N\u0026deg; 22920160100004CO), the National Fund for Scientific and Technological Investigation (FONCYT-ANPCyT, Argentina, Grant N\u0026deg; PICT-2016-3033), National University of La Plata, Argentina (Grant V247) and the Fondo Argentino de Cooperaci\u0026oacute;n Sur-Sur y Triangular (FO.AR; Grant 6560).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAUTHOR CONTRIBUTION STATEMENT\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eConceived and designed the work: MEF, ARM, and GG. Sample collection and data acquisition: JAPR, ALV, and GG. Analysed the data: OM, PAC, LHO, and FC. Contributed to reagents/materials/analysis tools acquisition: GG, JAPR, and PPG. Drafted or revised the manuscript: OM, ARM, and GG. Approved the final version: OM, PAC, LHO, JAPR, FC, ALV, PPG, MEF, ARM, and GG. \u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCONFLICT OF INTEREST\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests.\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDATA ARCHIVING\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe genomic data used in the present study are available at the Open Science Framework platform (https://osf.io/cs726; DOI 10.17605/OSF.IO/CS726).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eRESEARCH ETHICS STATEMENT\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll animal procedures were reviewed and approved by the Institutional Committee on Care and Use of Experimental Animals (CICUAL) from the School of Veterinary Sciences of the National University of La Plata (Buenos Aires, Argentina; protocols 89-1-18T, 41.2.14T).\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eAguirre-Riofrio EL, Abad-Guam\u0026aacute;n RM, Uchuari-Pauta ML (2019) Morphometric evaluation of phenotypic groups of creole cattle of southern Ecuador. 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Gene 807:145949\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"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":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"selection footprints, heat stress, hypoxia, slick, BoLA, SNP","lastPublishedDoi":"10.21203/rs.3.rs-4492487/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4492487/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eBolivian Creole cattle populations evolved under low levels of breeding management and, during more than 500 years of natural selection, became adapted to various environments such as the contrasting highland and subtropical environments. Recently, highland Creole cattle were crossbred with Holstein to improve dairy production. The aim of this research was to evaluate the divergent adaptation through selection footprints of Bolivian Creole cattle from Andean highland and tropical lowlands, and to evaluate the effect of Holstein introgression in highland Creole. For this purpose, 130 Creole cattle (75 highland, 55 lowland) and 88 Holstein were genotyped using a microarray. The database was used to determine population structure and admixture and detect selection sweeps using F\u003csub\u003eST\u003c/sub\u003e, Rsb, XP-EHH and ROH. Ancestry inference suggested that selection peaks were not due to Holstein introgression. The NCBI database was used to retrieve genes from the common regions and then perform gene ontology analysis. The most prominent selection peaks were on BTA20 and BTA23 and included the \u003cem\u003ePRLR \u003c/em\u003e(slick phenotype) and\u003cem\u003e Class I\u003c/em\u003e and \u003cem\u003eIIa BoLA\u003c/em\u003e genes. Other windows contained candidate genes for hypoxia (\u003cem\u003eANXA2\u003c/em\u003e, \u003cem\u003eNDUFA4L2\u003c/em\u003e), angiogenesis, immune response (\u003cem\u003eIL7R\u003c/em\u003e, \u003cem\u003eIL6ST\u003c/em\u003e, \u003cem\u003eIL31RA\u003c/em\u003e, \u003cem\u003eC6\u003c/em\u003e, \u003cem\u003eC7,\u003c/em\u003e \u003cem\u003eSTAT6\u003c/em\u003e, \u003cem\u003eNKG2A\u003c/em\u003e, \u003cem\u003eIRAK4\u003c/em\u003e, \u003cem\u003eKLR, CLEC\u003c/em\u003e), oxidative stress (\u003cem\u003eGSTA, HSD17B6\u003c/em\u003e) and morphological traits (\u003cem\u003ePLAG1, CHCHD7\u003c/em\u003e, \u003cem\u003eCAP2,\u003c/em\u003e \u003cem\u003eARL15)\u003c/em\u003e. GO analysis revealed enrichment terms and pathways related to immune response, glutathione and retinol metabolism and reported QTLs for coat characteristics, immune response, and tick resistance. The results suggest the complex mechanism in the adaptation of Bolivian Creole cattle to the contrasting highland and subtropical environments.\u003c/p\u003e","manuscriptTitle":"Divergent adaptation to highland and tropical environments in Bolivian Creole cattle","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-07-03 09:56:46","doi":"10.21203/rs.3.rs-4492487/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"1f564217-39e8-4fde-b420-95f498a25461","owner":[],"postedDate":"July 3rd, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":33358326,"name":"Biological sciences/Genetics"},{"id":33358327,"name":"Biological sciences/Genetics/Agricultural genetics"}],"tags":[],"updatedAt":"2024-11-20T09:10:17+00:00","versionOfRecord":[],"versionCreatedAt":"2024-07-03 09:56:46","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4492487","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4492487","identity":"rs-4492487","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}