Genome-wide local ancestry and the functional consequences of admixture in African and European cattle populations

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During the last two millennia, genetic exchange in Africa has resulted in a complex tapestry of taurine-indicine ancestry, with most cattle populations exhibiting varying levels of admixture. Similarly, there are several Southern European cattle populations that also show evidence for historical gene flow from indicine cattle, the highest levels of which are found in the Central Italian White breeds. Here we use two different software tools (MOSAIC and ELAI) for local ancestry inference (LAI) with genome-wide high- and low-density SNP array data sets in hybrid African and Italian cattle populations and obtained broadly similar results despite critical differences in the two LAI methodologies used. Our analyses identified genomic regions with elevated levels of retained or introgressed ancestry from the African taurine, European taurine, Asian indicine lineages. Functional enrichment of genes underlying these ancestry peaks highlighted biological processes relating to immunobiology and olfaction, some of which may relate to differing susceptibilities to infectious diseases, including bovine tuberculosis, East Coast fever, and tropical theileriosis. Notably, for retained African taurine ancestry in admixed trypanotolerant cattle we observed enrichment of genes associated with haemoglobin and oxygen transport. This may reflect positive selection of genomic variants that enhance control of severe anaemia, a debilitating feature of trypanosomiasis disease, which severely constrains cattle agriculture across much of sub-Saharan Africa. Biological sciences/Genetics/Agricultural genetics Biological sciences/Genetics/Population genetics/Genetic variation Biological sciences/Evolution/Evolutionary genetics Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Introduction Long acknowledged in plants (Anderson, 1949), gene flow and hybridisation between interfertile taxa are increasingly recognised as important evolutionary processes in animals (Hedrick, 2013; Payseur and Rieseberg, 2016; Taylor and Larson, 2019). Genetic exchange between populations can provide an abundant source of new functional genomic variation—both adaptive and maladaptive—that can generate novel combinations of alleles at individual genes, and interacting gene loci, thereby altering gene regulatory networks, biochemical pathways, physiological outputs, and ultimately phenotypic outcomes (Arnold and Kunte, 2017; Edelman and Mallet, 2021; Tigano and Friesen, 2016). In this respect, hybrid zones, where evolutionary distinct but interfertile animal taxa interact to produce admixed populations, represent natural laboratories for evolutionary studies (Hewitt, 1988). It has also been observed that gene flow, reticulate evolution, and admixture between distinct lineages and from wild congeners are common features of many domestic animal species, including pigs ( Sus scrofa ), dogs ( Canis familiaris ), sheep ( Ovis aries ), goats ( Capra hircus ), and chickens ( Gallus gallus ) (Frantz et al , 2015; Freedman and Wayne, 2017; Lv et al , 2022; Pogorevc et al , 2024; Wang et al , 2020b). Cattle were domesticated from the now extinct aurochs ( Bos primigenius ) (Bailey et al , 1996) and humpless Bos taurus (taurine) cattle were some of the first large ruminants to be domesticated 10−11,000 years ago in the Fertile Crescent region (Conolly et al , 2011; Larson et al , 2014; Zeder, 2017). Approximately 2,000 years later, humped Bos indicus (indicine or zebu) cattle were domesticated in present-day Pakistan (Utsunomiya et al , 2019) and analyses of genome-scale DNA sequence data show that the B. taurus and B. indicus lineages likely diverged 150–500 kya (Chen et al , 2018; Wang et al , 2018; Wu et al , 2018). Consequently substantial genomic differences have evolved between the two subspecies, making hybrid cattle an excellent resource for addressing fundamental scientific questions concerning the role of gene flow, admixture, and introgression in mammalian microevolution (Bahbahani et al , 2017; Chen et al , 2018; Chen et al , 2023; Flori et al , 2014; Friedrich et al , 2023; Kim et al , 2020; Kwon et al , 2022; Mbole-Kariuki et al , 2014; McTavish and Hillis, 2014; Ward et al , 2022; Wu et al , 2018). Cattle populations from several regions around the globe exhibit evidence of B. taurus / B. indicus admixture, although gene flow and genomic introgression between the two subspecies is most well understood and surveyed in Africa (Decker et al , 2014; Hanotte et al , 2002; Hanotte et al , 2000; Kim et al , 2020; MacHugh et al , 1997). Domestication and the subsequent spread and interactions of different taurine and indicine cattle populations has resulted in gradients of B. taurus and B. indicus ancestry across the African continent (Hanotte et al , 2002; Mwai et al , 2015). There are approximately 150 breeds of indigenous cattle in sub-Saharan Africa and African cattle represent a complex tapestry of African B. taurus and Asian B. indicus ancestry, with some populations also exhibiting significant non-African B. taurus genetic influence (Hanotte et al , 2002; Kim et al , 2020; MacHugh et al , 1997). The indigenous B. taurus cattle of Africa are generally adapted to humid and subhumid zones associated with sedentary subsistence farming and face particular disease challenges as a consequence (FAO, 2015). As a result of their longer history of exposure and adaptation to high pathogen and parasite burdens on the continent, African B. taurus cattle have several advantages over B. indicus cattle in terms of disease tolerance and resistance (de Clare Bronsvoort et al , 2013; Mwai et al , 2015). Cattle that are predominantly indicine in ancestry are normally transhumant livestock adapted to the arid and semi-arid regions of the continent and are favoured by many farmers due to their larger size and higher production yields, while hybrid populations tend to inhabit environments somewhere between these extremes (FAO, 2015; Mwai et al , 2015). One particularly important disease for African cattle is African animal trypanosomiasis (AAT) or nagana, a wasting disease caused by parasitic protozoa of the genus Trypanosoma transmitted by biting insect vectors such as tsetse flies ( Glossina spp.), which causes fever, severe weight loss and anaemia (MacGregor et al , 2021; Steverding, 2008). Cattle agriculture in sub-Saharan Africa is severely constrained by AAT because, even with the availability of trypanocidal drugs, the high susceptibility of many breeds to trypanosomiasis renders them unproductive in regions with significant tsetse burdens (Berthier et al , 2015; Yaro et al , 2016). However, some African B. taurus breeds have a tolerance of trypanosome infection termed “trypanotolerance”, which enables these cattle to control parasitaemia and anaemia, making them more productive than trypanosusceptible breeds in many areas of West and Central Africa (Berthier et al , 2015; Murray and Black, 1985). These trypanotolerant populations, which include the longhorn N’Dama and shorthorn Baoule, Lagune, and Somba breeds, are therefore an important genetic resource as they are uniquely suited to livestock production in these areas (Berthier et al , 2015; Yaro et al , 2016). Trypanotolerance has been shown to be a heritable multigenic trait, with variability in tolerance among individual animals within trypanotolerant populations (Kambal et al , 2023). Some African B. taurus / B. indicus hybrid cattle breeds are also known to exhibit trypanotolerance; however, trypanotolerant breeds with high levels of B. taurus ancestry have a greater capacity to control anaemia, while hybrid animals exhibit intermediate levels of control compared to trypanosusceptible B. indicus breeds (Bahbahani et al , 2018; Berthier et al , 2015). The genomic architecture of trypanotolerance in cattle remains poorly understood, although some candidate genes have been proposed, and identification of genes and genomic regulatory elements (GREs) underpinning the trait may facilitate introduction or enhancement of the trait via genome-enabled breeding or genome editing (Yaro et al , 2016). In contrast to the complex nature of African cattle ancestry, the majority of European cattle populations are comprised of pure European B. taurus ancestry; however, there are several breeds in Southern Europe that are known to exhibit modest levels of African B. taurus and/or B. indicus ancestry (Upadhyay et al , 2019). The most well-characterised of these, which also have the highest levels of indicine admixture, are the group of populations known as Central Italian White cattle (Barbato et al , 2020). Compared to temperate taurine cattle, B. indicus cattle have enhanced heat and drought tolerance and introgression of genomic variants from B. indicus into Central Italian White cattle may have made these breeds better adapted to extreme summer heat events on the Italian peninsula (Hooyberghs et al , 2019). Admixture and introgression among populations can be studied at a sub-chromosomal level using statistical methods for surveying locus-specific or local ancestry, which in contrast to global ancestry proportions, corresponds to the ancestry of specific genomic segments that consist of unbroken ancestry blocks from different donor populations (Gompert and Buerkle, 2013). A range of methods for local ancestry inference (LAI) using genome-scale data have been developed (Tan and Atkinson, 2023; Wu et al , 2021). Two widely used software tools are Efficient Local Ancestry Inference ( ELAI ) (Guan, 2014) and MOSAIC Organizes Segments of Ancestry In Chromosomes ( MOSAIC ) (Salter-Townshend and Myers, 2019). ELAI fits a two-layer hidden Markov model (HMM) that allows ancestry switching anywhere along the genome; however, it requires the donor reference populations and the approximate number of generations since the admixture occurred to be preassigned. Additionally, the donor reference populations should be as genetically similar to the original source populations as possible. MOSAIC also fits a two-layer HMM but employs a different strategy that determines how closely related each segment of chromosome in every admixed individual genome is to chromosomal segments in individual genomes from potential donor reference populations and infers a stochastic relationship between donor reference panels and mixing populations. Unlike other methods, MOSAIC does not require the donor reference populations to be direct surrogates for the original source populations and it can also infer the number of generations since the start of an admixture process. However, the MOSAIC algorithm requires phased haplotypes and a recombination rate map. For the present study we performed a range of population genomics analyses and comparative LAI using the ELAI and MOSAIC software tools with a panel of African and European cattle breeds that exhibit varying levels of African taurine, European taurine, and Asian indicine ancestries. Two different genome-wide SNP data sets were used: a high-density SNP data set consisting of more than 600,000 SNPs and a low-density data set encompassing approximately 30,000 SNPs. These analyses allowed us to assess the ELAI and MOSAIC algorithms as tools for LAI in admixed cattle. We were also able to systematically catalogue and functionally evaluate genomic regions exhibiting evidence for elevated levels of introgressed or retained ancestry from the three cattle lineages. Materials and Methods High-density genome-wide cattle SNP data sets For this study new Illumina ® BovineHD 777K BeadChip SNP data sets were generated for 39 African cattle (23 Somba, 8 N’Dama and 8 Boran). The Somba breed data were obtained using DNA samples previously published as part of a microsatellite-based survey of cattle genetic diversity (Freeman et al , 2004) and were generated by Weatherbys Scientific (Naas, Ireland) using standard procedures for Illumina ® SNP array genotyping. The N’Dama and Boran data were obtained using cattle DNA samples from a trypanosome challenge time-course experiment (O'Gorman et al , 2009) and were generated by Neogen Europe (Ayr, Scotland) also using standard procedures. Additional Illumina ® BovineHD 777K BeadChip data sets were obtained from published studies (Bahbahani et al , 2017; Barbato et al , 2020; Upadhyay et al , 2017; Verdugo et al , 2019; Ward et al , 2022; Wragg et al , 2022) and the Web-Interfaced next generation Database Exploration (WIDDE) repository Sempéré et al (2015). The total data set consisted of high-density 777K SNP data for 1,030 cattle before filtering and 24 different populations were represented, including three European B. taurus populations (Holstein Friesian, Angus, and Jersey); three African B. taurus populations (Muturu, Lagune, and Guinean N’Dama); three B. indicus populations (Tharparkar, Gir, and Nelore); five European hybrid populations (Romagnola, Chianina, Marchigiana, Maremmana, and Alentejana); five trypanotolerant African hybrid populations (hybrid N’Dama, Borgou, Somba, Keteku, and Sheko) and five trypanosusceptible African hybrid populations (Ankole, Nganda, East African Shorthorn Zebu, Karamojong, and Boran). The cattle BovineHD 777K SNP data were converted to binary PLINK files with Illumina ® allele coding for the FORWARD strand as required using PLINK (v. 1.90 beta 6.25) (Chang et al , 2015) and SNPchiMp (v. 3) (Nicolazzi et al , 2015). The sample data were then merged with PLINK (v. 1.90 beta 6.25). Figure 1 illustrates the overall study workflow including the genome assembly updating, data preparation, and filtering steps, which are described in the following subsections and that were implemented prior to the population genomics analyses. Table 1 shows the taxonomic, breed, geographical, sample number (pre- and post-SNP data filtering), and sources for the BovineHD 777K BeadChip SNP data sets. There was a total of 750 individual animal BovineHD 777K BeadChip SNP data sets retained after filtering. Updating the bovine genome assembly The BovineHD 777K BeadChip SNP locations were updated from the UMD3.1 bovine genome assembly to the current assembly ARS-UCD1.2 (Rosen et al , 2020) using coordinates from the National Animal Genome Research Program (NAGRP) Data Repository genotyping array SNP mapping to ARS-UCD1.2 resource (Schnabel, 2018) and PLINK (v. 1.90 beta 6.25). Data preparation and filtering Generation of a low-density SNP array data set To produce a comparative low-density SNP array data set, the high density BovineHD 777K SNP data set was downsampled to the subset of the 46,713 SNPs in common with the Illumina ® Bovine SNP50 BeadChip using PLINK (v. 1.90 beta 6.25). A list of the Bovine SNP50 BeadChip SNPs from the NAGRP Data Repository (Schnabel, 2018) was used for this purpose and modified with dplyr (v. 1.1.2) (Wickham et al , 2023a) and readr (v. 2.1.4 (Wickham et al , 2023b) with R (v. 4.3.0) (R Core Team, 2023). Missing SNP removal Individual animals that had missing SNP call rates exceeding 0.95 from the low-density data set were removed using a missing genotype filter with PLINK (v. 1.90 beta 6.25). The same set of animals were also removed from the high-density data set. Table 1. Group, code, population, origin, number of samples pre- and post-filtering and sources of SNP data used in this study. Group Code Population Country of origin No. pre-filtering No. post-filtering Source* European Bos taurus HOLS Holstein Friesian Netherlands 60 36 a European Bos taurus ANGU Angus United Kingdom 42 26 a European Bos taurus JERS Jersey United Kingdom 38 23 a European hybrid ROMA Romagnola Italy 51 51 b, a European hybrid CHIA Chianina Italy 19 19 b, c European hybrid MARC Marchigiana Italy 13 13 b European hybrid MARE Maremmana Italy 9 5 c European hybrid ALEN Alentejana Portugal 11 6 d, c African Bos taurus MUTU Muturu Nigeria 39 19 e African Bos taurus LAGU Lagune Benin 5 5 a African Bos taurus NDAG N’Dama Guinea Guinea 70 47 e, a, f Trypanotolerant African hybrid NDAM N’Dama hybrid Unspecified, Togo, Kenya 65 41 b, d, g Trypanotolerant African hybrid BORG Borgou Benin 50 50 e Trypanotolerant African hybrid SOMB Somba Benin 33 23 g Trypanotolerant African hybrid KETE Keteku Nigeria 22 22 e Trypanotolerant African hybrid SHEK Sheko Ethiopia 16 16 a Trypanosusceptible African hybrid ANKO Ankole Uganda 50 25 f Trypanosusceptible African hybrid NGAN Nganda Uganda 53 27 f, e Trypanosusceptible African hybrid EASZ East African Shorthorn Zebu Kenya 111 111 f, e Trypanosusceptible African hybrid KARA Karamojong Uganda 16 16 f Trypanosusceptible African hybrid BORA Boran Kenya 90 90 h, g Bos indicus THAR Tharparkar Pakistan 13 13 b Bos indicus GIR Gir India 85 28 a, f Bos indicus NELO Nelore Brazil 69 38 a, c, f Total 1030 750 * a Sempéré et al (2015), b Barbato et al (2020), c Upadhyay et al (2017), d Verdugo et al (2019), e Ward et al (2022), f Bahbahani et al (2017), g this study, h Wragg et al (2022). Removal of duplicate samples by identity-by-state filtering Duplicate samples present in two or more data sources were removed using PLINK (v. 1.90 beta 6.25) and a previously described identity-by-state methodology (Browett et al , 2018). The method was modified to select one from each pair of animals that had an identity-by-state value greater than or equal to 0.99 using dplyr (v. 1.1.2), readr (v. 2.1.4), and stringr (v. 1.5.0) (Wickham, 2023) with R (v. 4.3.0). The resulting list of sample duplicates were removed from the high- and low-density data sets with PLINK (v. 1.90 beta 6.25). The results were visualised using dplyr (v. 1.1.2), ggh4x (v. 0.2.4) (van den Brand, 2023), ggplot2 (v. 3.4.2) (Wickham, 2009), ggtext (v. 0.1.2) (Wilke and Wiernik, 2022), readr (v. 2.1.4), and stringr (v. 1.5.0) with R (v. 4.3.0). Colours were generated from viridis (v. 0.6.3) (Garnier et al , 2023) and khroma (v. 1.10.0). Removal of admixed animals from the reference populations An inbreeding analysis in PLINK (v. 1.90 beta 6.25) was used to remove animals that showed evidence for significant admixture in the reference populations (three European B. taurus populations: Holstein Friesian, Angus, and Jersey; three African B. taurus populations: Muturu, Lagune, and Guinean N’Dama; and three B. indicus populations: Tharparkar, Gir, and Nelore). To do this, outlier animals with statistically lower inbreeding values than the rest of the population were identified via boxplots using dplyr (v. 1.1.2), readr (v. 2.1.4), and tidyr (v. 1.3.0) (Wickham et al , 2023d) with R (v. 4.3.0). The resulting list of animals were removed from the high- and low-density data sets. A systematic inbreeding analysis was then performed with PLINK (v. 1.90 beta 6.25) and the output was modified using dplyr (v. 1.1.2) and readr (v. 2.1.4) with R (v. 4.3.0) to identify animals with the top 25 and bottom 25 inbreeding values across the three European B. taurus populations (Holstein Friesian, Angus, and Jersey). These samples were then removed from the high- and low-density data sets to balance the numbers of animals across the reference groups. A final inbreeding analysis of the low-density data set after the filters were applied was performed to compare the results with those of the high-density data set. Results from the inbreeding analyses were visualised using dplyr (v. 1.1.2), ggh4x (v. 0.2.4), ggplot2 (v. 3.4.2), ggtext (v. 0.1.2), and readr (v. 2.1.4), with R (v. 4.3.0). Colours were generated from viridis (v. 0.6.3) and khroma (v. 1.10.0). Filtering of SNPs by call rate and minor allele frequency The high- and low-density data sets were filtered to retain autosomal SNPs with a minimum call rate of 95% and minor allele frequency (MAF) of at least 5% with PLINK (v. 1.90 beta 6.25). The methodologies used for this process have been described in a previous study (McHugo et al , 2019). Population genomics analyses Principal component analysis Principal component analysis (PCA) was performed for the high- and low-density data sets using smartpca after file conversion with convertf, both part of EIGENSOFT package (v. 7.1.2) (Patterson et al , 2006). The results were visualised using dplyr (v. 1.1.2), ggplot2 (v. 3.4.2), patchwork (v. 1.1.2) (Pedersen, 2023), readr (v. 2.1.4), stringr (v. 1.5.0), and tidyr (v. 1.3.0) with R v. 4.3.0). Colours were generated from viridis (v. 0.6.3) and khroma (v. 1.10.0). Genetic structure analysis Genetic structure analysis was performed for the high- and low-density data sets using structure_threader (v. 1.3.4) (Pina-Martins et al , 2017) with fastStructure (v. 1.0) (Raj et al , 2014). The structure analysis was carried out with the model complexity or number of populations ( K ) set from 2 to 25. The chooseK function was used to test the outputs to find a range of values of K that best accounted for the structure in the data (Raj et al , 2014). The results were visualised using dplyr (v. 1.1.2), ggh4x (v. 0.2.4), ggplot2 (v. 3.4.2), ggtext (v. 0.1.2), magick (v. 2.8.1) (Ooms, 2023) with ImageMagick (v. 6.9.12.96) (ImageMagick Studio LLC, 2023), magrittr (v. 2.0.3) (Bache and Wickham, 2022), patchwork (v. 1.1.2), readr (v. 2.1.4), stringr (v. 1.5.0) and tidyr (v. 1.3.0) with R (v. 4.3.0). Colours were generated from viridis (v. 0.6.3) and khroma (v. 1.10.0). Phylogenetic analysis An additional sample of three gaur ( B. gaurus ) that were genotyped using the BovineHD 777K BeadChip (Sempéré et al , 2015; Verdugo et al , 2019) were available to use as an outgroup. After the pre-processing steps described above were performed to convert the data to binary PLINK files with Illumina ® allele coding for the FORWARD strand on the ARS-UCD1.2 bovine genome assembly, the B. gaurus data set was filtered with PLINK (v. 1.90 beta 6.25) to retain only SNPs present in the high-density data set. The gaur data were then merged with the high-density data set and an additional filter was applied with PLINK (v. 1.90 beta 6.25) to retain autosomal SNPs with a minimum call rate of 95% and minor allele frequency of at least 5%. A gzipped allele frequency cluster file was produced with PLINK (v. 1.90 beta 6.25) and the resulting file was converted to TreeMix format using the plink2treemix python script provided with the TreeMix software package (v. 1.13) (Pickrell and Pritchard, 2012). Phylogenetic analysis was performed for both the high- and low-density SNP data sets using TreeMix (v. 1.13) with the number of migration edges ( m ) set from 1 to 15 for ten iterations using windows of SNPs ( k ) increasing from 100 to 1000 by increments of 100. The OptM package (v. 0.1.6) (Fitak, 2021) was used with R (v. 4.3.0) to calculate the mean and standard deviation (SD) across the 10 iterations for the composite likelihood ( L ( m )), proportion of variance explained and the second-order rate of change ( Δm ) across migration edges ( m ). The results were visualised using dplyr (v. 1.1.2), ggplot2 (v. 3.4.2), ggtext (v. 0.1.2) and patchwork (v. 1.1.2) with R (v. 4.3.0). The BITE R package (v. 1.2.0008) (Milanesi et al , 2017) was also used to generate a Unix shell script customised to perform 100 TreeMix bootstrap replicates for the selected numbers of migration edges ( m ). The results were visualised using ape (v. 5.7.1) (Paradis and Schliep, 2019), dplyr (v. 1.1.2), ggh4x (v. 0.2.4), ggnewscale (v. 0.4.9) (Campitelli, 2023), ggplot2 (v. 3.4.2), ggtext (v. 0.1.2), ggtree (v. 3.9.0.1) (Yu, 2022), patchwork (v. 1.1.2), stringr (v. 1.5.0), tidytree (v. 0.4.4) (Yu, 2022), and treeio (v. 1.25.2) (Wang et al , 2020a) with R (v. 4.3.0) and a modified version of the script provided with TreeMix. Colours were generated from viridis (v. 0.6.3) and khroma (v. 1.10.0). Local ancestry estimation MOSAIC analysis The high- and low-density SNP data sets were separated by chromosome with PLINK (v. 1.90 beta 6.25) and each chromosome was phased with SHAPEIT (v. 2.r904) (O'Connell et al , 2014). The resulting segregated chromosome SNP data files were converted to MOSAIC format using the R script provided with MOSAIC (v. 1.5.0) (Salter-Townshend and Myers, 2019) and R (v. 4.3.0). Recombination rate files were prepared from a cattle recombination map (Ma et al , 2015) using an R script adapted from Ward et al (2022) with R (v. 4.3.0). For each hybrid population three-way local ancestry analysis was performed across all autosomes without F ST estimation and assuming an effective population size ( N e ) of 400 using MOSAIC (v. 1.5.0), dplyr (v. 1.1.2), and stringr (v. 1.5.0) with R (v. 4.3.0). The potential donor populations were the three European B. taurus populations (Holstein Friesian, Angus, and Jersey), the three African B. taurus populations (Muturu, Lagune, and Guinean N’Dama) and the three B. indicus populations (Tharparkar, Gir, and Nelore). ELAI analysis The high- and low-density SNP data sets were separated and converted into BIMBAM format for each population and chromosome with PLINK (v. 1.90 beta 6.25). Local ancestry analysis was carried out for each hybrid population and autosome with 30 expectation-maximization (EM) steps, 3 upper clusters, 15 lower clusters, and 200 mixing generations using ELAI (v. 1.0) (Guan, 2014). The donor populations for each hybrid population were selected based on the results of the MOSAIC analysis. Local ancestry analysis comparison The local ancestry results were extracted using dplyr (v. 1.1.2, MOSAIC (v. 1.5.0), parallel (v. 4.3.0) (R Core Team, 2023), readr (v. 2.1.4), scales (v. 1.2.1) (Wickham et al , 2023c), stringr (v. 1.5.0), and tibble (v. 3.2.1) (Müller and Wickham, 2023) with R (v. 4.3.0). Mean ancestry scores across the individual hybrid animals and a genome-wide z -score for each of the three ancestry components were calculated for each hybrid population. Weighted mean ancestry scores and z -scores were calculated across the hybrid populations within each group of European hybrids, trypanotolerant African hybrids, and trypanosusceptible African hybrids for a subset of the hybrid populations selected to only include populations with a minimum of 15 animals and a relatively stable level of admixture based on a visual examination of the structure results. The local ancestry results were visualised using dplyr (v. 1.1.2), ggplot2 (v. 3.4.2), magick (v. 2.8.1) with ImageMagick (v. 6.9.12.96), magrittr (v. 2.0.3), parallel (v. 4.3.0), patchwork (v. 1.1.2), readr (v. 2.1.4), scales (v. 1.2.1), stringr (v. 1.5.0), and tibble (v. 3.2.1) with R (v. 4.3.0). Colours were generated from khroma (v. 1.10.0). The correlation between the local ancestry results from MOSAIC and ELAI were visualised using dplyr (v. 1.1.2), ggplot2 (v. 3.4.2), ggtext (v. 0.1.2), parallel (v. 4.3.0), patchwork (v. 1.1.2), readr (v. 2.1.4), and stringr (v. 1.5.0) with R (v. 4.3.0). Colours were generated from viridis (v. 0.6.3). Functional enrichment of introgressed regions Functional enrichment was performed and visualised using dplyr (v. 1.1.2), ggplot2 (v. 3.4.2), ggrepel (v. 0.9.3) (Slowikowski, 2023), gprofiler2 (v. 0.2.2) (Kolberg et al , 2023), magick (v. 2.8.1) with ImageMagick (v. 6.9.12.96), magrittr (v. 2.0.3), parallel (v. 4.3.0), readr (v. 2.1.4), and stringr (v. 1.5.0) with R (v. 4.3.0). Colours were generated from khroma (v. 1.10.0). The background set was the set of genes within 1 Mb up- and downstream from a SNP in the high-density data set. The query sets were the genes within 1 Mb up and downstream from the SNPs with a z -score ≥ 2.0 for each of the ancestries. Results High-density genome-wide cattle SNP data sets After filtering for missing genotypes (30 samples removed), identity-by-state (Figure S1; 194 samples removed), and inbreeding (Figure S2, Figure S3; 56 samples removed), there were 750 animals in the high- and low-density SNP data sets (Table 1). Filtering for autosomal SNPs with a minimum call rate of 95% and MAF of at least 5% retained 614,026 SNPs in the high-density data set with a total genotyping rate of 98.20%, and 30,706 SNPs in the low-density data set with a total genotyping rate of 95.91%. Population genomic analyses Principal component analysis The first principal component (PC1) explained 53.59% of the of the total variation for PC1–10 in the high-density SNP data set and separated the B. taurus and B. indicus lineages (Figure 2). The second principal component (PC2) explained a further 19.71% of the total variation for PC1–10 in the high-density SNP data set and separated the European B. taurus and African B. taurus lineages (Figure 2). The hybrid animals were dispersed among the reference populations with the European hybrid animals clustering close to the European B. taurus group and the African hybrid animals mostly located between the African B. taurus and B. indicus groups (Figure 2). The trypanotolerant African hybrid individuals are closest to the African B. taurus group while the trypanosusceptible African hybrid animals are closest to the B. indicus group (Figure 2). The same pattern was observed for the low-density SNP data set (Figure S4). Genetic structure analysis The structure results for K = 2 separates the B. taurus and B. indicus ancestries in the high-density SNP data set (Figure 3). With K = 3, the structure results divide the European and African B. taurus and B. indicus ancestries (Figure 3). For the high-density SNP data set the model complexity that maximizes marginal likelihood was 16 and the model components used to explain the structure in the data was 17 (Figure S5, Figure S6). For the low-density SNP data set the model complexity that maximizes marginal likelihood and the model components used to explain the structure in the data was 16 (Figure S7, Figure S8). Phylogenetic analysis After the B. gaurus outgroup animals were added and filters for autosomal SNPs with a minimum call rate of 95% and MAF of at least 5% were applied there were 613,334 SNPs in the high-density SNP data set with a total genotyping rate of 99.65%, and 30,644 SNPs in the low-density SNP data set with a total genotyping rate of 99.50%. The optimum number of migration edges indicated by the first peak in Δm for both the high- and low-density SNP data sets is three while the number of migration edges required to explain 99.8% of the variation in the data is 12 and 11 for the high- and low-density SNP data sets, respectively (Figure S9, Figure S10). The phylogenetic analysis results clearly distinguish and group the European B. taurus , African B. taurus , and B. indicus populations into different clades with a high degree of confidence (Figure 4). The European hybrid populations are grouped with the European B. taurus populations with a similarly high degree of confidence, while the trypanotolerant and trypanosusceptible African hybrid populations are placed between the African B. taurus and B. indicus populations with varying degrees of confidence (Figure 4). This pattern holds regardless of the number of migration edges or SNP data set density (Figures S11–S15). The introduction of migration edges into the phylogenetic tree indicates admixture between the hybrid African and African B. taurus populations when m is set to 3 (Figure 4, Figure S14). For higher values of m , the admixture shown includes the European hybrid populations (Figure S12, Figure S15). Local ancestry estimation Weighted mean local ancestry results were calculated for each of the three ancestry components (European B. taurus , African B. taurus , and B. indicus ) for each of the three hybrid groups (European hybrid, trypanotolerant African hybrid, and trypanosusceptible African hybrid) using the mean results from populations with more than 15 samples (Table 1), and relatively stable hybridisation based on visual examination of the structure results at K = 3 (Figure 3). The European hybrid group included the Romagnola and Chianina populations, the trypanotolerant African hybrid group included the Borgou and Sheko populations, and the trypanosusceptible African hybrid group included the Ankole, Nganda, East African Shorthorn Zebu, Karamojong, and Boran populations. The results for the high-density SNP data set showed similar patterns using both MOSAIC and ELAI when examined visually, as did the ELAI results for the low-density SNP data set (Figure 5). The MOSAIC results for the low-density SNP data set exhibited a noticeable smoothening across the genome for all three ancestry components in all three hybrid groups (Figure 5). This was particularly evident for the ancestry components with lower proportions, such as the African B. taurus and B. indicus components in the European hybrid group, and the European B. taurus component in the trypanotolerant African hybrid group (Figure 5). When individual chromosome results were examined, the high-density SNP local ancestry results for MOSAIC and ELAI and the low-density SNP ELAI results showed peaks for the various ancestry components around the major histocompatibility complex (MHC) located on BTA23, although this was not evident for the low-density MOSAIC results (Figure S16–S27). Correlation plots between the MOSAIC and ELAI results for each ancestry component along each chromosome indicated positive correlations for the high-density SNP results for all three hybrid groups (Figure S28–S30) while the low-density SNP results indicated much weaker or no correlations (Figure S31–S33). To identify SNPs within the peaks of local ancestry for each ancestry component genome-wide z -scores of the weighted mean local ancestry results were used to select SNPs with z -scores ≥ 2.0 for each software and data set (Table 2). There were no SNPs that passed the z ≥ 2 threshold for the European B. taurus ancestry component in the European hybrid group for the high-density SNP MOSAIC and ELAI results and the low-density SNP MOSAIC results, while the trypanotolerant and trypanosusceptible African hybrid groups had the lowest number of SNPs passing the z ≥ 2 threshold for the African B. taurus and B. indicus ancestry components, respectively (Table 2). Similar proportions of SNPs were found for each software and SNP data set (Table 2). Table 2. Numbers of SNPs with a z -score ≥ 2.0 for weighted mean European B. taurus , African B. taurus and B. indicus ancestry components for the European hybrid, trypanotolerant African hybrid, and trypanosusceptible African hybrid groups across all autosomes for the MOSAIC and ELAI analyses of high- and low-density SNP data sets. The numbers in brackets indicate the percentage of the total number of SNPs in each data set. Group Ancestry HD MOSAIC LD MOSAIC HD ELAI LD ELAI European hybrid European B. taurus 0 (0%) 0 (0%) 0 (0%) 151 (0.49%) African B. taurus 16,511 (2.69%) 1,546 (5.03%) 26,561 (4.33%) 1,337 (4.35%) B. indicus 31,674 (5.16%) 1,865 (6.07%) 33,110 (5.39%) 1,415 (4.61%) Trypanotolerant African hybrid European B. taurus 23,816 (3.88%) 1,490 (4.85%) 25,743 (4.19%) 1,229 (4.00%) African B. taurus 7,728 (1.26%) 179 (0.58%) 9,056 (1.47%) 627 (2.04%) B. indicus 18,367 (2.99%) 723 (2.35%) 18,083 (2.94%) 801 (2.61%) Trypanosusceptible African hybrid European B. taurus 26,035 (4.24%) 1,423 (4.63%) 25,083 (4.09%) 1,173 (3.82%) African B. taurus 16,998 (2.77%) 494 (1.61%) 23,356 (3.80%) 1,093 (3.56%) B. indicus 10,434 (1.70%) 49 (1.60%) 7,823 (1.27%) 460 (1.50%) Total 614,026 30,706 614,026 30,706 Functional enrichment of introgressed regions The proportions of the numbers of genes found within 1 Mb up- and downstream from each SNP with a z -score ≥ 2.0 are similar to those of the numbers of SNPs found for each ancestry component in the hybrid groups for each software and SNP data set (Table 2, Table S1). There were no European B. taurus SNPs that passed the z ≥ 2 threshold; consequently, there were no European B. taurus genes for functional enrichment in the European hybrid group (Table 2, Table S1). The top driver GO terms for the African B. taurus genes in the European hybrid group included terms related to the MHC ( GO:0042613 MHC class II protein complex ) and other aspects of the immune system ( GO:0019882 antigen processing and presentation , GO:0001914 regulation of T cell mediated cytotoxicity , and GO:0004930 G protein-coupled receptor activity ); protein and DNA complexes and protein binding ( GO:0000786 nucleosome , GO:0030527 structural constituent of chromatin , GO:0046982 protein heterodimerization activity , GO:0065004 protein-DNA complex assembly ); and olfaction ( GO:0004984 olfactory receptor activity and GO:0050911 detection of chemical stimulus involved in sensory perception of smell , Figure 6A). The top B. indicus driver GO terms also included terms relating to the MHC ( GO:0042613 MHC class II protein complex ) and other immune terms ( GO:0019882 antigen processing and presentation and GO:0002684 positive regulation of immune system process ), as well as cell membrane and signalling activity ( GO:0001594 trace-amine receptor activity , GO:0009897 external side of plasma membrane , and GO:0004364 glutathione transferase activity , Figure 6A). The trypanotolerant African hybrid group also had driver GO terms relating to the MHC ( GO:0002486 antigen processing and presentation of endogenous peptide antigen via MHC class I via ER pathway, TAP-independent and GO:0002476 antigen processing and presentation of endogenous peptide antigen via MHC class Ib ); other components of the immune system ( GO:0007186 G protein-coupled receptor signalling pathway ); and olfaction ( GO:0004984 olfactory receptor activity and GO:0050911 detection of chemical stimulus involved in sensory perception of smell ) among the European B. taurus terms (Figure 6B). In addition there were also a number of terms relating to L-amino acid transmembrane transport ( GO:0097638 L-arginine import across plasma membrane , GO:0000064 L-ornithine transmembrane transporter activity , GO:1903352 L-ornithine transmembrane transport , GO:1903401 L-lysine transmembrane transport , and GO:0015189 L-lysine transmembrane transporter activity , Figure 6B). The top African B. taurus terms related to haemoglobin and oxygen binding and transport ( GO:0005833 haemoglobin complex , GO:0015671 oxygen transport , GO:0019825 oxygen binding ), while the top B. indicus terms related to metabolic processes ( GO:0047023 androsterone dehydrogenase activity , GO:0030647 aminoglycoside antibiotic metabolic process , GO:0047086 ketosteroid monooxygenase activity , GO:0042448 progesterone metabolic process , GO:0004032 alditol:NADP+ 1-oxidoreductase activity , GO:0032052 bile acid binding , and GO:0016614 oxidoreductase activity, acting on CH-OH group of donors , Figure 6B). For the trypanosusceptible African hybrid group the only driver GO term for the European B. taurus ancestry component related to intracellular organelles ( GO:0043229 intracellular organelle , Figure 6C). The top African B. taurus terms included those related to the MHC ( GO:0042613 MHC class II protein complex , and GO:0023026 MHC class II protein complex binding ); other components of the immune system ( GO:0019882 antigen processing and presentation , GO:0001914 regulation of T cell mediated cytotoxicity , and GO:0042605 peptide antigen binding ); olfaction ( GO:0004984 olfactory receptor activity , and GO:0050911 detection of chemical stimulus involved in sensory perception of smell ); and protein-DNA complex and protein binding ( GO:0030527 structural constituent of chromatin , GO:0000786 nucleosome , GO:0046982 protein heterodimerization activity , Figure 6C). The top B. indicus terms included cell adhesion ( GO:0007156 homophilic cell adhesion via plasma membrane adhesion molecules ) and metal ion binding ( GO:0005507 copper ion binding , Figure 6C). Similar GO term enrichment patterns were also observed using low-density SNP data with MOSAIC (Figure S34), and for ELAI with both high- and low-density SNP data (Figure S35, Figure S36). Discussion The results of the population genomic analyses in hybrid European and African cattle populations are consistent with previously published studies that have used modest numbers of genetic markers (e.g., microsatellites) and genome-wide SNP data (Barbato et al , 2020; Decker et al , 2014; Hanotte et al , 2002; Kim et al , 2020; MacHugh et al , 1997; Ward et al , 2022). Visualisation of PCA results by plotting PC1 and PC2 recovered the classic “ Bos triangle” with the first two PCs explaining a very high proportion of the total variation for PC1–10 within the data (73.30%). PC1 and PC2 separated the reference European B. taurus , African B. taurus , and B. indicus populations with the hybrid animal samples dispersed within the triangle with locations determined by three-way global admixture proportions (Figure 2). The locations of the various hybrid populations nearer to the reference populations they share the most ancestry with is in agreement with previous studies (Bahbahani et al , 2017; Barbato et al , 2020; Upadhyay et al , 2017; Verdugo et al , 2019; Ward et al , 2022; Wragg et al , 2022). In addition, the clustering of some of the animals in the African trypanotolerant hybrid populations with the African B. taurus reference populations indicate that some of these animals have very high levels of African taurine ancestry (Figure 2). In this regard, it is important to note that although a diverse panel of European B. taurus , African B. taurus , B. indicus , and hybrid cattle in the design and validation of the BovineHD 777K BeadChip (Illumina, 2015), ascertainment bias may affect the placement of hybrid cattle in a PCA plot (Dokan et al , 2021; McTavish and Hillis, 2015). However, genome-wide multi-locus dimension reduction tools are typically substantially less affected by ascertainment bias than analyses such as estimation of diversity statistics such as the fixation index ( F ST ) or selection signal detection, which use individual SNP locus frequency-based statistics (Albrechtsen et al , 2010; Malomane et al , 2018; Porto Neto and Barendse, 2010). The results of the genetic structure analysis for K = 2 and K = 3 mirror those of the PCA with the first major split evident for the B. taurus and B. indicus populations and the second split separating the African and European B. taurus populations (Figure 3). The locations of animals in the hybrid populations reflect global admixture proportions that are in agreement with both their positions on the PCA and previous studies (Bahbahani et al , 2017; Barbato et al , 2020; Upadhyay et al , 2017; Verdugo et al , 2019; Ward et al , 2022; Wragg et al , 2022) (Figure 2). The number of modelled K values that best explain the variation among the 24 populations examined in the study was 16–17, indicating that some of the populations are closely related to the point that they may not be genetically distinct discrete populations (Raj et al , 2014). The genetic structure results also show the variation within the hybrid populations in terms of global admixture (Figure 3). Some populations, such as the European and trypanosusceptible African hybrid groups, show a relatively consistent level of global admixture across each population while the trypanotolerant African hybrid group is more variable (Figure 3). This indicates that the European and trypanosusceptible African hybrid breeds are more long-established hybrid populations (“stable crossbreds”) and that the hybridisation within the African trypanotolerant hybrid populations is more recent and dynamic. Some of the more extreme examples, such as the N’Dama hybrid, Somba, and Keteku populations, indicate that some animals are not hybrids and are instead pure African B. taurus (Figure 3). This is also in agreement with the PCA results and is likely due to the origins of the samples from a range of studies that sampled animals from different populations that were classified as the same breed or breed subtype (Bahbahani et al , 2017; Barbato et al , 2020; Upadhyay et al , 2017; Verdugo et al , 2019; Ward et al , 2022; Wragg et al , 2022) (Figure 2). The results of the phylogenetic analysis are also in agreement with the PCA and genetic structure results (Figure 4). The reference populations are unambiguously separated into the expected groupings ( European B. taurus , African B. taurus , and B. indicus ) with bootstrap values of 99–100, as are the European hybrid populations (Figure 4). The trypanotolerant and trypanosusceptible African hybrid populations are spread between the African B. taurus and B. indicus reference populations, with some hybrid branch clusters exhibiting low bootstrap values, indicating instability in the clade structure because of taurine/indicine admixture (Figure 4). This is also where the strongest gene flow events are inferred as modelled migration edges, demonstrating the higher levels of indicine admixture in the trypanotolerant and trypanosusceptible African hybrid populations compared to the European hybrid populations (Figure 4). The local ancestry results show similar patterns of peaks and troughs dispersed across the genome for each LAI software tool used (MOSAIC and ELAI), the hybrid cattle group examined (European hybrid, trypanotolerant African hybrid, and trypanosusceptible African hybrid), and each genome-wide SNP data set analysed (low- or high-density); the exception being the low-density SNP data set results obtained using MOSAIC (Figure 5). This may be due to the rephasing algorithm implemented by default as part of the MOSAIC analysis, which may overcorrect for phasing errors in low-density SNP data set (Salter-Townshend and Myers, 2019). Alternatively, the smoothened results may be due to difficulty automatically estimating the age of admixture with low-density SNP data in MOSAIC (Salter-Townshend and Myers, 2019). Comparing the MOSAIC high-density SNP data set results with the ELAI results using both the high- and low-density SNP data sets (Figure 5), the similar genome-wide patterns of local ancestry obtained indicate that, despite the differences in the software used, there are robust signals of local ancestry discernible in these populations. This is evident in the marked three-way ancestry diversity around the MHC region on BTA23 seen in these results (Figures S16–S27), particularly for the European hybrid group using both MOSAIC and ELAI and the high-density SNP data set where there is a clear signature of elevated African taurine and indicine ancestry (Figure S16, Figure S22). This tendency for increased genomic introgression in the bovine MHC region is likely a consequence of the balancing selection that maintains high MHC gene polymorphism due to the key function of MHC class I and II proteins in presentation of antigenic peptides from rapidly evolving pathogens to CD4 + and CD8 + T cells and via interactions with receptors on natural killer (NK) cells (Codner et al , 2012; Ellis, 2004; Ellis and Hammond, 2014). Balancing selection acting on pre-existing trans-specific polymorphisms and introgressed variants would give rise to extensive polymorphism in the MHC region (Radwan et al , 2020) and this has been observed in several species (Hedrick, 2013), including Homo sapiens where there is evidence that Neanderthal ( Homo sapiens neanderthalensis ) and Denisovan ( Homo sapiens subsp. ‘Denisova’) MHC gene variants have readily introgressed into anatomically modern human populations (Liston et al , 2021; Racimo et al , 2015). However, it is also important to note that there are known difficulties in genotyping the MHC region (Dicks et al , 2021). The correlations observed between the MOSAIC and ELAI results for the high-density SNP data sets (Figures S28–S33) provides additional evidence that supports the visually apparent similarities in the local ancestry signals observed across the bovine genome using both software approaches. The proportions of the numbers of SNPs passing the genome-wide threshold ( z -score ≥ 2.0) were similar for the MOSAIC and ELAI analyses using both the high- and low-density SNP data sets, which despite the visual differences in the local ancestry results, indicates that similar numbers of ancestry peaks can be detected using the z -score approach (Table 2). The lack of SNPs passing the threshold for the European B. taurus ancestry component in the European hybrid group is likely due to the high and relatively uniform proportion of the European B. taurus ancestry component across the genome. This would give rise to a situation such that no SNPs could pass the threshold of two standard deviations from the mean (Figure 5). Similarly, the lower numbers of SNPs passing the threshold for the African B. taurus and B. indicus ancestry components for the trypanotolerant and trypanosusceptible African hybrid groups, respectively, is likely due to the higher proportions of the reference population ancestries to which each hybrid group is most closely related (Figure 5). The introgressed genomic regions for the three hybrid population samples show several distinct patterns in terms of functional enrichment. All three hybrid groups had significant driver GO terms relating to the MHC (Figure 6), which directly encompass MHC genes (e.g., MHC class I and II) and other genes encoding proteins that interact with MHC gene products. Visual examination of the local ancestry results supports this observation as do previous LAI studies in cattle (Figure 5, Figures S16–S27) (Buggiotti et al , 2021; Chen et al , 2020; Guan et al , 2022; Li et al , 2023). Other immune system related driver GO terms were also found to be significant for the three hybrid groups. Several of these terms contain genes that are either up- or downstream from MHC genes in biological pathways, underscoring the importance of MHC-related immunobiology in admixed cattle. More generally, it is notable that immune genes are well represented in the top functional enrichment categories for the introgressed genomic regions since there are well documented differences among European B. taurus , African B. taurus , and B. indicus cattle populations in terms of susceptibilities to various infectious diseases such as bovine tuberculosis caused by Mycobacterium bovis (Allen et al , 2010; Lee et al , 2024); East Coast fever and tropical theileriosis caused by Theileria parva and Theileria annulate , respectively (Bahbahani and Hanotte, 2015); and AAT caused by Trypanosoma spp. (Yaro et al , 2016). In this regard, many of the genes highlighted by LAI through retention of taurine ancestry in the trypanotolerant African hybrid population may represent putative candidate genes underlying the multigenic trypanotolerance trait. For example, in this group, genes associated with haemoglobin, and oxygen binding and transport cellular processes were highlighted by the GO term functional enrichment for retained African B. taurus genomic ancestry (Figure 6). This may reflect positive selection of genomic variants that enhance control of anaemia, which is understood to be a key feature of the trypanotolerance trait in cattle (Kambal et al , 2023). Driver GO terms relating to olfaction were also significantly enriched across the three hybrid cattle groups (Figure 6). Genes related to olfaction, such as olfactory receptor (OR) genes, have been identified in previous functional population genomics analyses of admixed cattle populations with taurine and indicine ancestry. These include, for example, genes containing breed-specific missense SNPs in admixed Ethiopian cattle (Zegeye et al , 2023), genes within genomic regions with evidence for selection signatures in admixed Turkish and Chinese cattle (Demir et al , 2023; Sun et al , 2023), and genes in population-differentiated copy-number variation regions (CNVRs) in African hybrid cattle (Jang et al , 2021). This may be due to the relatively large number of OR genes dispersed across the cattle genome, which, at more than 800 functional loci is comparable to the OR gene repertoire in the domestic dog ( Canis familiaris ) (Lee et al , 2013; Niimura and Nei, 2007). However, recent studies have suggested that more than 500 olfactory receptors may be expressed by macrophages, immune cells involved in detection and phagocytosis of pathogens (Orecchioni et al , 2022). Macrophages are the host’s first line of defence to mycobacterial infections with evasion and reprogramming of host macrophages being key components of host-pathogen interaction (Hall et al , 2024). In this regard, it is therefore noteworthy that sequence variation at olfactory receptor gene loci has been shown to be associated with susceptibility to M. bovis infection in cattle (Ring et al , 2019). An alternative hypothesis for enrichment of olfaction-related genes, however, could relate to detection of odorants associated with MHC diversity and selection of mates (Santos et al , 2010; Ziegler et al , 2010), although this is unlikely to be a major factor in managed male-biased cattle husbandry systems. Similarly, cattle populations under intensive human control and management are unlikely to require a keen sense of smell to find food and avoid danger; however, introgressive natural selection is likely to be acting on olfaction-related genes in free-ranging admixed African cattle populations exposed to a wide range of environmental and predation challenges (Mwai et al , 2015). The comparative LAI analyses we have performed using low- and high-density SNP array data sets in various groups of admixed cattle with taurine and indicine genomic ancestry provides a framework for applying LAI to much larger data sets that will encompass millions of SNPs. In addition, our results will provide a context for understanding the genomic basis of heterosis in admixed cattle, particularly as it dissipates beyond the F 1 generation (Syrstad, 1985). Also, identification of genomic regions that have been subject to introgressive selection will provide important information for genome-enabled breeding in admixed cattle populations, particularly in Africa (Marshall et al , 2019; Mrode et al , 2019). Finally, the methodologies that we describe here can be applied to other hybrid cattle populations, for example, admixed breeds in Anatolia and the Middle East that have had much longer histories of taurine/indicine genetic exchange (Verdugo et al , 2019). Declarations Acknowledgements We thank Morris Agaba, Olivier Hanotte, Stephen J. Kemp, John A. Browne, Daniel G. Bradley, and Stephen V. Gordon for assistance with sample resources and for useful scientific discussion. This research work was funded by Science Foundation Ireland (SFI) under Investigator Programme Awards (grant nos: SFI/01/F.1/B028 and SFI/15/IA/3154). JAW was supported by the Centre for Research Training in Genomics Data Science (grant no. SFI/18/CRT/6214). Author contribution statement GPM was responsible for analysis, data curation, lab work, interpretation of results, study design, visualisation, and writing - original draft. JAW was responsible for data provision, lab work, interpretation of results, and writing - review & editing. SIN was responsible for data provision, interpretation of results, and writing - review & editing. LAF was responsible for data provision, interpretation of results, and writing - review & editing. MST was responsible for interpretation of results, software provision, and writing - review & editing. EWH was responsible for lab work, sample collection and provision, and writing - review & editing. GMO was responsible for lab work, sample collection and provision, and writing - review & editing. KGM was responsible for lab work, sample collection and provision, and writing - review & editing. TJH was responsible for guidance and writing - review & editing. DEM was responsible for data provision, funding acquisition, lab work, interpretation of results, sample collection and provision, study design, supervision, and writing - original draft. Conflict of Interest The authors declare no competing interests. Data archiving New Illumina ® BovineHD 777K BeadChip SNP data sets generated for this study have been deposited in the Dryad data repository at doi.org/10.5061/dryad.w3r22810n. The computer code required to repeat and reproduce the analyses is available at doi.org/10.5281/zenodo.11491949. Research Ethics Statement For this study new Illumina ® BovineHD 777K BeadChip SNP data sets were generated for 39 individuals (23 Somba, 8 N’Dama and 8 Boran). 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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-4622059","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":323824020,"identity":"261d86c8-3400-4577-9687-9497ac60e32c","order_by":0,"name":"David MacHugh","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA6UlEQVRIiWNgGAWjYDCCA0DEA2JIMDAwM1SARBgbSNFyhkgtDHAtjG1QEXyA7/gZwwNvGLbJ88/uMX5dOO+wPN8B5sYH+LRInskxODiH4bbhjDtnzKxnbjtsOPMAY7MBPi0GB9ISDvMw3GbcIJFjZsy77XCCwQHGNgm8Ws4/A2uxh2iZA9bS/gOvlhvJB0BaEoFajB/zNkBswaeDQfLG4wMH5xjcTp5xI62MmedYuuHMw4zNeB3Gdz6x+cObitu2/TOSN3/mqbGW5zve/vADXmsgzgOTbBDDmQmrhwNmIgwfBaNgFIyCkQgAzGVT2QKQWCcAAAAASUVORK5CYII=","orcid":"https://orcid.org/0000-0002-8112-4704","institution":"University College Dublin","correspondingAuthor":true,"prefix":"","firstName":"David","middleName":"","lastName":"MacHugh","suffix":""},{"id":323824021,"identity":"0b08c399-cc3d-44f4-95df-22788c2e8439","order_by":1,"name":"Gillian McHugo","email":"","orcid":"https://orcid.org/0000-0001-6920-0041","institution":"University College Dublin","correspondingAuthor":false,"prefix":"","firstName":"Gillian","middleName":"","lastName":"McHugo","suffix":""},{"id":323824022,"identity":"575894f0-6aa7-41ff-90cc-08cda42bcaf6","order_by":2,"name":"James Ward","email":"","orcid":"https://orcid.org/0000-0002-2191-5301","institution":"University College Dublin","correspondingAuthor":false,"prefix":"","firstName":"James","middleName":"","lastName":"Ward","suffix":""},{"id":323824023,"identity":"6ba46de5-a36c-4e1a-9035-4c9bab2d7762","order_by":3,"name":"Said Ng’ang’a","email":"","orcid":"","institution":"Queen Mary University of London","correspondingAuthor":false,"prefix":"","firstName":"Said","middleName":"","lastName":"Ng’ang’a","suffix":""},{"id":323824024,"identity":"986081b6-bfe6-416a-a3b1-fc45f5d162f0","order_by":4,"name":"Laurent Frantz","email":"","orcid":"","institution":"Ludwig Maximilian University","correspondingAuthor":false,"prefix":"","firstName":"Laurent","middleName":"","lastName":"Frantz","suffix":""},{"id":323824025,"identity":"81703bc0-b047-4875-86bf-8ec3b2a22fe8","order_by":5,"name":"Michael Salter-Townshend","email":"","orcid":"","institution":"University College Dublin","correspondingAuthor":false,"prefix":"","firstName":"Michael","middleName":"","lastName":"Salter-Townshend","suffix":""},{"id":323824026,"identity":"1a80900d-c825-4cd4-8d5d-dd1800eb3447","order_by":6,"name":"Emmeline Hill","email":"","orcid":"https://orcid.org/0000-0002-1805-2250","institution":"University College Dublin","correspondingAuthor":false,"prefix":"","firstName":"Emmeline","middleName":"","lastName":"Hill","suffix":""},{"id":323824027,"identity":"39a3f82a-faeb-4bba-888c-5cfceee23e21","order_by":7,"name":"Grace O'Gorman","email":"","orcid":"","institution":"UK Agri-Tech Centre","correspondingAuthor":false,"prefix":"","firstName":"Grace","middleName":"","lastName":"O'Gorman","suffix":""},{"id":323824028,"identity":"5a4d3f1d-e67e-4024-9520-92b2729545b3","order_by":8,"name":"Kieran Meade","email":"","orcid":"","institution":"University College Dublin","correspondingAuthor":false,"prefix":"","firstName":"Kieran","middleName":"","lastName":"Meade","suffix":""},{"id":323824029,"identity":"b3abf44d-20e3-4419-ba94-021fae1de681","order_by":9,"name":"Thomas Hall","email":"","orcid":"","institution":"University College Dublin","correspondingAuthor":false,"prefix":"","firstName":"Thomas","middleName":"","lastName":"Hall","suffix":""}],"badges":[],"createdAt":"2024-06-22 13:20:20","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4622059/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4622059/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1038/s41437-024-00734-w","type":"published","date":"2024-11-08T05:00:00+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":61491553,"identity":"0355df8b-59a9-447a-8ab2-5bfe3438fdb9","added_by":"auto","created_at":"2024-07-31 10:53:56","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":1074673,"visible":true,"origin":"","legend":"\u003cp\u003eDiagram showing study workflow. Cattle images by Tracy A. Heath, T. Michael Keesey and Steven Traver via \u003ca href=\"https://www.phylopic.org/\"\u003ephylopic.org\u003c/a\u003e. Colours were generated from the khroma R package (v. 1.10.0) (Frerebeau, 2023).\u003c/p\u003e","description":"","filename":"Picture1.png","url":"https://assets-eu.researchsquare.com/files/rs-4622059/v1/65bdfbddcea49b7b18b4aad7.png"},{"id":61491560,"identity":"6dc94d93-93b1-42bb-a695-9cf613b2c0fe","added_by":"auto","created_at":"2024-07-31 10:53:56","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":502673,"visible":true,"origin":"","legend":"\u003cp\u003eA. Principal component analysis of the selected high-density SNP data set with cattle samples coloured according to population showing the first two principal components (PC1 and PC2), and B. bar chart of the proportion of variance for the top ten PCs.\u003c/p\u003e","description":"","filename":"Picture2.png","url":"https://assets-eu.researchsquare.com/files/rs-4622059/v1/5b808684b5ab58d9a747855c.png"},{"id":61491559,"identity":"e6489811-67f6-4f8b-aaaa-aafe181c6934","added_by":"auto","created_at":"2024-07-31 10:53:56","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":443167,"visible":true,"origin":"","legend":"\u003cp\u003eHierarchical clustering of the high-density SNP data. Results are shown for a range of assumed values for the number of ancestral populations (\u003cem\u003eK\u003c/em\u003e = 2–3).\u003c/p\u003e","description":"","filename":"Picture3.png","url":"https://assets-eu.researchsquare.com/files/rs-4622059/v1/23914b25ec4ba9075ae837cc.png"},{"id":61492311,"identity":"dddc81df-8377-49b9-a5e2-0971a74bfc5a","added_by":"auto","created_at":"2024-07-31 11:01:56","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":755216,"visible":true,"origin":"","legend":"\u003cp\u003eA. TreeMix phylogenetic tree for the high-density SNP data set with bootstrap values and three migration edges, and B. heatmap showing the standard error values.\u003c/p\u003e","description":"","filename":"Picture4.png","url":"https://assets-eu.researchsquare.com/files/rs-4622059/v1/5090b36230bd6534f6b7c9fe.png"},{"id":61491555,"identity":"9df18072-fc77-4034-b0db-d0c90bbf4785","added_by":"auto","created_at":"2024-07-31 10:53:56","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":1761088,"visible":true,"origin":"","legend":"\u003cp\u003eLocal ancestry plots showing weighted mean European \u003cem\u003eB. taurus\u003c/em\u003e, African \u003cem\u003eB. taurus\u003c/em\u003e, and \u003cem\u003eB. indicus\u003c/em\u003e ancestry components for European hybrid, trypanotolerant African hybrid, and trypanosusceptible African hybrid groups across all autosomes for the MOSAIC and ELAI analyses of high- and low-density SNP data sets. Each vertical line on the circular genome plots represents a SNP and is coloured according to the ancestry results.\u003c/p\u003e","description":"","filename":"Picture5.png","url":"https://assets-eu.researchsquare.com/files/rs-4622059/v1/d3375a17fa18cd779d2730b9.png"},{"id":61491554,"identity":"cc37f259-ec0a-4c73-80ef-87a1b377df0a","added_by":"auto","created_at":"2024-07-31 10:53:56","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":976227,"visible":true,"origin":"","legend":"\u003cp\u003eg:Profiler functional enrichment of introgressed regions in European, and trypanotolerant and trypanosusceptible African hybrid populations detected with MOSAIC and high-density SNP data. Each circle represents a significantly enriched GO term with the size indicating the ratio of the intersection between the term and the introgressed genes. The y-axis shows the −log\u003csub\u003e10\u003c/sub\u003e(\u003cem\u003eP\u003c/em\u003e\u003csub\u003eadj.\u003c/sub\u003e) value and the horizontal panels and colours indicate the ancestry component. The vertical panels indicate the source of the term, and position within each panel groups terms from the same GO subtree. The top driver GO terms (up to a maximum of 10) are indicated with a black outline and label.\u003c/p\u003e","description":"","filename":"Picture6.png","url":"https://assets-eu.researchsquare.com/files/rs-4622059/v1/dc3dd6a7ffcaa95740cdfc35.png"},{"id":68606127,"identity":"de768f53-119c-4564-95e4-35335420206a","added_by":"auto","created_at":"2024-11-09 08:07:00","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":7527659,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4622059/v1/4d2da006-c437-404e-b8ee-bc31d8a343a5.pdf"},{"id":61491558,"identity":"4872573d-526b-4691-b6d0-da6f9d5ffd4c","added_by":"auto","created_at":"2024-07-31 10:53:56","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":11257801,"visible":true,"origin":"","legend":"Supplementary Material","description":"","filename":"McHugoetal.HereditySUPP.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4622059/v1/07bee82db5085cede7646831.pdf"}],"financialInterests":"There is no duality of interest","formattedTitle":"Genome-wide local ancestry and the functional consequences of admixture in African and European cattle populations","fulltext":[{"header":"Introduction","content":"\u003cp\u003eLong acknowledged in plants\u0026nbsp;(Anderson, 1949), gene flow and hybridisation between interfertile taxa are increasingly recognised as important evolutionary processes in animals\u0026nbsp;(Hedrick, 2013; Payseur and Rieseberg, 2016; Taylor and Larson, 2019). Genetic exchange between populations can provide an abundant source of new functional genomic variation\u0026mdash;both adaptive and maladaptive\u0026mdash;that can generate novel combinations of alleles at individual genes, and interacting gene loci, thereby altering gene regulatory networks, biochemical pathways, physiological outputs, and ultimately phenotypic outcomes\u0026nbsp;(Arnold and Kunte, 2017; Edelman and Mallet, 2021; Tigano and Friesen, 2016). In this respect, hybrid zones, where evolutionary distinct but interfertile animal taxa interact to produce admixed populations, represent natural laboratories for evolutionary studies\u0026nbsp;(Hewitt, 1988). It has also been observed that gene flow, reticulate evolution, and admixture between distinct lineages and from wild congeners are common features of many domestic animal species, including pigs (\u003cem\u003eSus scrofa\u003c/em\u003e), dogs (\u003cem\u003eCanis familiaris\u003c/em\u003e), sheep (\u003cem\u003eOvis aries\u003c/em\u003e), goats (\u003cem\u003eCapra hircus\u003c/em\u003e), and chickens (\u003cem\u003eGallus gallus\u003c/em\u003e)\u0026nbsp;(Frantz\u003cem\u003e\u0026nbsp;et al\u003c/em\u003e, 2015; Freedman and Wayne, 2017; Lv\u003cem\u003e\u0026nbsp;et al\u003c/em\u003e, 2022; Pogorevc\u003cem\u003e\u0026nbsp;et al\u003c/em\u003e, 2024; Wang\u003cem\u003e\u0026nbsp;et al\u003c/em\u003e, 2020b).\u003c/p\u003e\n\u003cp\u003eCattle were domesticated from the now extinct aurochs (\u003cem\u003eBos primigenius\u003c/em\u003e)\u0026nbsp;(Bailey\u003cem\u003e\u0026nbsp;et al\u003c/em\u003e, 1996)\u0026nbsp;and humpless \u003cem\u003eBos taurus\u003c/em\u003e (taurine) cattle were some of the first large ruminants to be domesticated 10\u0026minus;11,000 years ago in the Fertile Crescent region\u0026nbsp;(Conolly\u003cem\u003e\u0026nbsp;et al\u003c/em\u003e, 2011; Larson\u003cem\u003e\u0026nbsp;et al\u003c/em\u003e, 2014; Zeder, 2017). Approximately 2,000 years later, humped \u003cem\u003eBos indicus\u003c/em\u003e (indicine or zebu) cattle were domesticated in present-day Pakistan\u0026nbsp;(Utsunomiya\u003cem\u003e\u0026nbsp;et al\u003c/em\u003e, 2019)\u0026nbsp;and analyses of genome-scale DNA sequence data show that the \u003cem\u003eB. taurus\u003c/em\u003e and \u003cem\u003eB. indicus\u003c/em\u003e lineages likely diverged 150\u0026ndash;500 kya\u0026nbsp;(Chen\u003cem\u003e\u0026nbsp;et al\u003c/em\u003e, 2018; Wang\u003cem\u003e\u0026nbsp;et al\u003c/em\u003e, 2018; Wu\u003cem\u003e\u0026nbsp;et al\u003c/em\u003e, 2018). Consequently substantial genomic differences have evolved between the two subspecies, making hybrid cattle an excellent resource for addressing fundamental scientific questions concerning the role of gene flow, admixture, and introgression in mammalian microevolution\u0026nbsp;(Bahbahani\u003cem\u003e\u0026nbsp;et al\u003c/em\u003e, 2017; Chen\u003cem\u003e\u0026nbsp;et al\u003c/em\u003e, 2018; Chen\u003cem\u003e\u0026nbsp;et al\u003c/em\u003e, 2023; Flori\u003cem\u003e\u0026nbsp;et al\u003c/em\u003e, 2014; Friedrich\u003cem\u003e\u0026nbsp;et al\u003c/em\u003e, 2023; Kim\u003cem\u003e\u0026nbsp;et al\u003c/em\u003e, 2020; Kwon\u003cem\u003e\u0026nbsp;et al\u003c/em\u003e, 2022; Mbole-Kariuki\u003cem\u003e\u0026nbsp;et al\u003c/em\u003e, 2014; McTavish and Hillis, 2014; Ward\u003cem\u003e\u0026nbsp;et al\u003c/em\u003e, 2022; Wu\u003cem\u003e\u0026nbsp;et al\u003c/em\u003e, 2018).\u003c/p\u003e\n\u003cp\u003eCattle populations from several regions around the globe exhibit evidence of \u003cem\u003eB. taurus\u003c/em\u003e/\u003cem\u003eB. indicus\u003c/em\u003e admixture, although gene flow and genomic introgression between the two subspecies is most well understood and surveyed in Africa\u0026nbsp;(Decker\u003cem\u003e\u0026nbsp;et al\u003c/em\u003e, 2014; Hanotte\u003cem\u003e\u0026nbsp;et al\u003c/em\u003e, 2002; Hanotte\u003cem\u003e\u0026nbsp;et al\u003c/em\u003e, 2000; Kim\u003cem\u003e\u0026nbsp;et al\u003c/em\u003e, 2020; MacHugh\u003cem\u003e\u0026nbsp;et al\u003c/em\u003e, 1997). Domestication and the subsequent spread and interactions of different taurine and indicine cattle populations has resulted in gradients of \u003cem\u003eB. taurus\u003c/em\u003e and \u003cem\u003eB. indicus\u003c/em\u003e ancestry across the African continent\u0026nbsp;(Hanotte\u003cem\u003e\u0026nbsp;et al\u003c/em\u003e, 2002; Mwai\u003cem\u003e\u0026nbsp;et al\u003c/em\u003e, 2015). There are approximately 150 breeds of indigenous cattle in sub-Saharan Africa and African cattle represent a complex tapestry of African \u003cem\u003eB. taurus\u003c/em\u003e and Asian \u003cem\u003eB. indicus\u003c/em\u003e ancestry, with some populations also exhibiting significant non-African \u003cem\u003eB. taurus\u003c/em\u003e genetic influence\u003cem\u003e\u0026nbsp;\u003c/em\u003e(Hanotte\u003cem\u003e\u0026nbsp;et al\u003c/em\u003e, 2002; Kim\u003cem\u003e\u0026nbsp;et al\u003c/em\u003e, 2020; MacHugh\u003cem\u003e\u0026nbsp;et al\u003c/em\u003e, 1997). The indigenous \u003cem\u003eB. taurus\u003c/em\u003e cattle of Africa are generally adapted to humid and subhumid zones associated with sedentary subsistence farming and face particular disease challenges as a consequence\u0026nbsp;(FAO, 2015). As a result of their longer history of exposure and adaptation to high pathogen and parasite burdens on the continent, African \u003cem\u003eB. taurus\u003c/em\u003e cattle have several advantages over \u003cem\u003eB. indicus\u003c/em\u003e cattle in terms of disease tolerance and resistance\u0026nbsp;(de Clare Bronsvoort\u003cem\u003e\u0026nbsp;et al\u003c/em\u003e, 2013; Mwai\u003cem\u003e\u0026nbsp;et al\u003c/em\u003e, 2015). Cattle that are predominantly indicine in ancestry are normally transhumant livestock adapted to the arid and semi-arid regions of the continent and are favoured by many farmers due to their larger size and higher production yields, while hybrid populations tend to inhabit environments somewhere between these extremes\u0026nbsp;(FAO, 2015; Mwai\u003cem\u003e\u0026nbsp;et al\u003c/em\u003e, 2015).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eOne particularly important disease for African cattle is African animal trypanosomiasis (AAT) or nagana, a wasting disease caused by parasitic protozoa of the genus \u003cem\u003eTrypanosoma\u003c/em\u003e transmitted by biting insect vectors such as tsetse flies (\u003cem\u003eGlossina\u003c/em\u003e spp.), which causes fever, severe weight loss and anaemia\u0026nbsp;(MacGregor\u003cem\u003e\u0026nbsp;et al\u003c/em\u003e, 2021; Steverding, 2008). Cattle agriculture in sub-Saharan Africa is severely constrained by AAT because, even with the availability of trypanocidal drugs, the high susceptibility of many breeds to trypanosomiasis renders them unproductive in regions with significant tsetse burdens\u0026nbsp;(Berthier\u003cem\u003e\u0026nbsp;et al\u003c/em\u003e, 2015; Yaro\u003cem\u003e\u0026nbsp;et al\u003c/em\u003e, 2016). However, some African \u003cem\u003eB. taurus\u003c/em\u003e breeds have a tolerance of trypanosome infection termed \u0026ldquo;trypanotolerance\u0026rdquo;, which enables these cattle to control parasitaemia and anaemia, making them more productive than trypanosusceptible breeds in many areas of West and Central Africa\u0026nbsp;(Berthier\u003cem\u003e\u0026nbsp;et al\u003c/em\u003e, 2015; Murray and Black, 1985). These trypanotolerant populations, which include the longhorn N\u0026rsquo;Dama and shorthorn Baoule, Lagune, and Somba breeds, are therefore an important genetic resource as they are uniquely suited to livestock production in these areas\u0026nbsp;(Berthier\u003cem\u003e\u0026nbsp;et al\u003c/em\u003e, 2015; Yaro\u003cem\u003e\u0026nbsp;et al\u003c/em\u003e, 2016).\u003c/p\u003e\n\u003cp\u003e\u0026nbsp; Trypanotolerance has been shown to be a heritable multigenic trait, with variability in tolerance among individual animals within trypanotolerant populations\u0026nbsp;(Kambal\u003cem\u003e\u0026nbsp;et al\u003c/em\u003e, 2023). Some African \u003cem\u003eB. taurus\u003c/em\u003e/\u003cem\u003eB. indicus\u003c/em\u003e hybrid cattle breeds are also known to exhibit trypanotolerance; however, trypanotolerant breeds with high levels of \u003cem\u003eB. taurus\u003c/em\u003e ancestry have a greater capacity to control anaemia, while hybrid animals exhibit intermediate levels of control compared to trypanosusceptible \u003cem\u003eB. indicus\u003c/em\u003e breeds\u0026nbsp;(Bahbahani\u003cem\u003e\u0026nbsp;et al\u003c/em\u003e, 2018; Berthier\u003cem\u003e\u0026nbsp;et al\u003c/em\u003e, 2015). The genomic architecture of trypanotolerance in cattle remains poorly understood, although some candidate genes have been proposed, and identification of genes and genomic regulatory elements (GREs) underpinning the trait may facilitate introduction or enhancement of the trait via genome-enabled breeding or genome editing\u0026nbsp;(Yaro\u003cem\u003e\u0026nbsp;et al\u003c/em\u003e, 2016).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eIn contrast to the complex nature of African cattle ancestry, the majority of European cattle populations are comprised of pure European \u003cem\u003eB. taurus\u003c/em\u003e ancestry; however, there are several breeds in Southern Europe that are known to exhibit modest levels of African \u003cem\u003eB. taurus\u003c/em\u003e and/or \u003cem\u003eB. indicus\u003c/em\u003e ancestry\u0026nbsp;(Upadhyay\u003cem\u003e\u0026nbsp;et al\u003c/em\u003e, 2019). The most well-characterised of these, which also have the highest levels of indicine admixture, are the group of populations known as Central Italian White cattle\u0026nbsp;(Barbato\u003cem\u003e\u0026nbsp;et al\u003c/em\u003e, 2020). Compared to temperate taurine cattle, \u003cem\u003eB. indicus\u003c/em\u003e cattle have enhanced heat and drought tolerance and introgression of genomic variants from \u003cem\u003eB. indicus\u003c/em\u003e into Central Italian White cattle may have made these breeds better adapted to extreme summer heat events on the Italian peninsula\u0026nbsp;(Hooyberghs\u003cem\u003e\u0026nbsp;et al\u003c/em\u003e, 2019).\u003c/p\u003e\n\u003cp\u003eAdmixture and introgression among populations can be studied at a sub-chromosomal level using statistical methods for surveying locus-specific or local ancestry, which in contrast to global ancestry proportions, corresponds to the ancestry of specific genomic segments that consist of unbroken ancestry blocks from different donor populations\u0026nbsp;(Gompert and Buerkle, 2013). A range of methods for local ancestry inference (LAI) using genome-scale data have been developed\u0026nbsp;(Tan and Atkinson, 2023; Wu\u003cem\u003e\u0026nbsp;et al\u003c/em\u003e, 2021). Two widely used software tools are \u003cem\u003eEfficient Local Ancestry Inference\u003c/em\u003e (\u003cem\u003eELAI\u003c/em\u003e)\u0026nbsp;(Guan, 2014)\u0026nbsp;and \u003cem\u003eMOSAIC Organizes Segments of Ancestry In Chromosomes\u003c/em\u003e (\u003cem\u003eMOSAIC\u003c/em\u003e)\u0026nbsp;(Salter-Townshend and Myers, 2019). ELAI fits a two-layer hidden Markov model (HMM) that allows ancestry switching anywhere along the genome; however, it requires the donor reference populations and the approximate number of generations since the admixture occurred to be preassigned. Additionally, the donor reference populations should be as genetically similar to the original source populations as possible. MOSAIC also fits a two-layer HMM but employs a different strategy that determines how closely related each segment of chromosome in every admixed individual genome is to chromosomal segments in individual genomes from potential donor reference populations and infers a stochastic relationship between donor reference panels and mixing populations. Unlike other methods, MOSAIC does not require the donor reference populations to be direct surrogates for the original source populations and it can also infer the number of generations since the start of an admixture process. However, the MOSAIC algorithm requires phased haplotypes and a recombination rate map.\u003c/p\u003e\n\u003cp\u003eFor the present study we performed a range of population genomics analyses and comparative LAI using the ELAI and MOSAIC software tools with a panel of African and European cattle breeds that exhibit varying levels of African taurine, European taurine, and Asian indicine ancestries. Two different genome-wide SNP data sets were used: a high-density SNP data set consisting of more than 600,000 SNPs and a low-density data set encompassing approximately 30,000 SNPs. These analyses allowed us to assess the ELAI and MOSAIC algorithms as tools for LAI in admixed cattle. We were also able to systematically catalogue and functionally evaluate genomic regions exhibiting evidence for elevated levels of introgressed or retained ancestry from the three cattle lineages.\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cp\u003eHigh-density genome-wide cattle SNP data sets\u003c/p\u003e\n\u003cp\u003eFor this study new Illumina\u003csup\u003e\u0026reg;\u003c/sup\u003e BovineHD 777K BeadChip SNP data sets were generated for 39 African cattle (23 Somba, 8 N\u0026rsquo;Dama and 8 Boran). The Somba breed data were obtained using DNA samples previously published as part of a microsatellite-based survey of cattle genetic diversity (Freeman\u003cem\u003e\u0026nbsp;et al\u003c/em\u003e, 2004)\u0026nbsp;and were generated by Weatherbys Scientific (Naas, Ireland) using standard procedures for Illumina\u003csup\u003e\u0026reg;\u003c/sup\u003e SNP array genotyping. The N\u0026rsquo;Dama and Boran data were obtained using cattle DNA samples from a trypanosome challenge time-course experiment (O\u0026apos;Gorman\u003cem\u003e\u0026nbsp;et al\u003c/em\u003e, 2009)\u0026nbsp;and were generated by Neogen Europe (Ayr, Scotland) also using standard procedures.\u0026nbsp;Additional Illumina\u003csup\u003e\u0026reg;\u003c/sup\u003e BovineHD 777K BeadChip data sets were obtained from published studies (Bahbahani\u003cem\u003e\u0026nbsp;et al\u003c/em\u003e, 2017; Barbato\u003cem\u003e\u0026nbsp;et al\u003c/em\u003e, 2020; Upadhyay\u003cem\u003e\u0026nbsp;et al\u003c/em\u003e, 2017; Verdugo\u003cem\u003e\u0026nbsp;et al\u003c/em\u003e, 2019; Ward\u003cem\u003e\u0026nbsp;et al\u003c/em\u003e, 2022; Wragg\u003cem\u003e\u0026nbsp;et al\u003c/em\u003e, 2022)\u0026nbsp;and the Web-Interfaced next generation Database Exploration (WIDDE) repository\u0026nbsp;Semp\u0026eacute;r\u0026eacute;\u003cem\u003e\u0026nbsp;et al\u003c/em\u003e (2015).\u003c/p\u003e\n\u003cp\u003eThe total data set consisted of high-density 777K SNP data for 1,030 cattle before filtering and 24 different populations were represented, including three European \u003cem\u003eB. taurus\u003c/em\u003e populations (Holstein Friesian, Angus, and Jersey); three African \u003cem\u003eB. taurus\u003c/em\u003e populations (Muturu, Lagune, and Guinean N\u0026rsquo;Dama); three \u003cem\u003eB. indicus\u003c/em\u003e populations (Tharparkar, Gir, and Nelore); five European hybrid populations (Romagnola, Chianina, Marchigiana, Maremmana, and Alentejana); five trypanotolerant African hybrid populations (hybrid N\u0026rsquo;Dama, Borgou, Somba, Keteku, and Sheko) and five trypanosusceptible African hybrid populations (Ankole, Nganda, East African Shorthorn Zebu, Karamojong, and Boran). The cattle BovineHD 777K SNP data were converted to binary PLINK files with Illumina\u003csup\u003e\u0026reg;\u003c/sup\u003e allele coding for the FORWARD strand as required using PLINK (v. 1.90 beta 6.25) (Chang\u003cem\u003e\u0026nbsp;et al\u003c/em\u003e, 2015)\u0026nbsp;and SNPchiMp (v. 3)\u0026nbsp;(Nicolazzi\u003cem\u003e\u0026nbsp;et al\u003c/em\u003e, 2015). The sample data were then merged with PLINK (v. 1.90 beta 6.25).\u003c/p\u003e\n\u003cp\u003eFigure 1 illustrates the overall study workflow including the genome assembly updating, data preparation, and filtering steps, which are described in the following subsections and that were implemented prior to the population genomics analyses. Table 1 shows the taxonomic, breed, geographical, sample number (pre- and post-SNP data filtering), and sources for the BovineHD 777K BeadChip SNP data sets. There was a total of 750 individual animal BovineHD 777K BeadChip SNP data sets retained after filtering.\u003c/p\u003e\n\u003cp\u003eUpdating the bovine genome assembly\u003c/p\u003e\n\u003cp\u003eThe BovineHD 777K BeadChip SNP locations were updated from the UMD3.1 bovine genome assembly to the current assembly ARS-UCD1.2\u0026nbsp;(Rosen\u003cem\u003e\u0026nbsp;et al\u003c/em\u003e, 2020) using coordinates from the National Animal Genome Research Program (NAGRP) Data Repository \u003cem\u003egenotyping array SNP mapping to ARS-UCD1.2 resource\u003c/em\u003e (Schnabel, 2018) and PLINK (v. 1.90 beta 6.25).\u003c/p\u003e\n\u003cp\u003eData preparation and filtering\u003c/p\u003e\n\u003cp\u003eGeneration of a low-density SNP array data set\u003c/p\u003e\n\u003cp\u003eTo produce a comparative low-density SNP array data set, the high density BovineHD 777K SNP data set was downsampled to the subset of the 46,713 SNPs in common with the Illumina\u003csup\u003e\u0026reg;\u003c/sup\u003e Bovine SNP50 BeadChip using PLINK (v. 1.90 beta 6.25). A list of the Bovine SNP50 BeadChip SNPs from the NAGRP Data Repository (Schnabel, 2018) was used for this purpose and modified with dplyr (v. 1.1.2) (Wickham\u003cem\u003e\u0026nbsp;et al\u003c/em\u003e, 2023a)\u0026nbsp;and readr (v. 2.1.4\u0026nbsp;(Wickham\u003cem\u003e\u0026nbsp;et al\u003c/em\u003e, 2023b) with R (v. 4.3.0) (R Core Team, 2023).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eMissing SNP removal\u003c/p\u003e\n\u003cp\u003eIndividual animals that had missing SNP call rates exceeding 0.95 from the low-density data set were removed using a missing genotype filter with PLINK (v. 1.90 beta 6.25). The same set of animals were also removed from the high-density data set.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTable 1. Group, code, population, origin, number of samples pre- and post-filtering and sources of SNP data used in this study.\u0026nbsp;\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"642\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.37325038880249%\"\u003e\n \u003cp\u003e\u003cstrong\u003eGroup\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.020217729393469%\"\u003e\n \u003cp\u003e\u003cstrong\u003eCode\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.573872472783826%\"\u003e\n \u003cp\u003e\u003cstrong\u003ePopulation\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.12908242612753%\"\u003e\n \u003cp\u003e\u003cstrong\u003eCountry of origin\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.81959564541213%\"\u003e\n \u003cp\u003e\u003cstrong\u003eNo. pre-filtering\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.81959564541213%\"\u003e\n \u003cp\u003e\u003cstrong\u003eNo. post-filtering\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.26438569206843%\"\u003e\n \u003cp\u003e\u003cstrong\u003eSource*\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.37325038880249%\"\u003e\n \u003cp\u003eEuropean\u003c/p\u003e\n \u003cp\u003e\u003cem\u003eBos taurus\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.020217729393469%\"\u003e\n \u003cp\u003eHOLS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.573872472783826%\"\u003e\n \u003cp\u003eHolstein Friesian\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.12908242612753%\"\u003e\n \u003cp\u003eNetherlands\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.81959564541213%\"\u003e\n \u003cp\u003e60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.81959564541213%\"\u003e\n \u003cp\u003e36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.26438569206843%\"\u003e\n \u003cp\u003ea\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.37325038880249%\"\u003e\n \u003cp\u003eEuropean\u003c/p\u003e\n \u003cp\u003e\u003cem\u003eBos taurus\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.020217729393469%\"\u003e\n \u003cp\u003eANGU\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.573872472783826%\"\u003e\n \u003cp\u003eAngus\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.12908242612753%\"\u003e\n \u003cp\u003eUnited Kingdom\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.81959564541213%\"\u003e\n \u003cp\u003e42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.81959564541213%\"\u003e\n \u003cp\u003e26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.26438569206843%\"\u003e\n \u003cp\u003ea\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.37325038880249%\"\u003e\n \u003cp\u003eEuropean\u003c/p\u003e\n \u003cp\u003e\u003cem\u003eBos taurus\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.020217729393469%\"\u003e\n \u003cp\u003eJERS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.573872472783826%\"\u003e\n \u003cp\u003eJersey\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.12908242612753%\"\u003e\n \u003cp\u003eUnited Kingdom\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.81959564541213%\"\u003e\n \u003cp\u003e38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.81959564541213%\"\u003e\n \u003cp\u003e23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.26438569206843%\"\u003e\n \u003cp\u003ea\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.37325038880249%\"\u003e\n \u003cp\u003eEuropean hybrid\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.020217729393469%\"\u003e\n \u003cp\u003eROMA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.573872472783826%\"\u003e\n \u003cp\u003eRomagnola\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.12908242612753%\"\u003e\n \u003cp\u003eItaly\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.81959564541213%\"\u003e\n \u003cp\u003e51\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.81959564541213%\"\u003e\n \u003cp\u003e51\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.26438569206843%\"\u003e\n \u003cp\u003eb, a\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.37325038880249%\"\u003e\n \u003cp\u003eEuropean hybrid\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.020217729393469%\"\u003e\n \u003cp\u003eCHIA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.573872472783826%\"\u003e\n \u003cp\u003eChianina\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.12908242612753%\"\u003e\n \u003cp\u003eItaly\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.81959564541213%\"\u003e\n \u003cp\u003e19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.81959564541213%\"\u003e\n \u003cp\u003e19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.26438569206843%\"\u003e\n \u003cp\u003eb, c\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.37325038880249%\"\u003e\n \u003cp\u003eEuropean hybrid\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.020217729393469%\"\u003e\n \u003cp\u003eMARC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.573872472783826%\"\u003e\n \u003cp\u003eMarchigiana\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.12908242612753%\"\u003e\n \u003cp\u003eItaly\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.81959564541213%\"\u003e\n \u003cp\u003e13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.81959564541213%\"\u003e\n \u003cp\u003e13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.26438569206843%\"\u003e\n \u003cp\u003eb\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.37325038880249%\"\u003e\n \u003cp\u003eEuropean hybrid\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.020217729393469%\"\u003e\n \u003cp\u003eMARE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.573872472783826%\"\u003e\n \u003cp\u003eMaremmana\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.12908242612753%\"\u003e\n \u003cp\u003eItaly\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.81959564541213%\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.81959564541213%\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.26438569206843%\"\u003e\n \u003cp\u003ec\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.37325038880249%\"\u003e\n \u003cp\u003eEuropean hybrid\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.020217729393469%\"\u003e\n \u003cp\u003eALEN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.573872472783826%\"\u003e\n \u003cp\u003eAlentejana\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.12908242612753%\"\u003e\n \u003cp\u003ePortugal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.81959564541213%\"\u003e\n \u003cp\u003e11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.81959564541213%\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.26438569206843%\"\u003e\n \u003cp\u003ed, c\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.37325038880249%\"\u003e\n \u003cp\u003eAfrican\u003c/p\u003e\n \u003cp\u003e\u003cem\u003eBos taurus\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.020217729393469%\"\u003e\n \u003cp\u003eMUTU\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.573872472783826%\"\u003e\n \u003cp\u003eMuturu\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.12908242612753%\"\u003e\n \u003cp\u003eNigeria\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.81959564541213%\"\u003e\n \u003cp\u003e39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.81959564541213%\"\u003e\n \u003cp\u003e19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.26438569206843%\"\u003e\n \u003cp\u003ee\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.37325038880249%\"\u003e\n \u003cp\u003eAfrican\u003c/p\u003e\n \u003cp\u003e\u003cem\u003eBos taurus\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.020217729393469%\"\u003e\n \u003cp\u003eLAGU\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.573872472783826%\"\u003e\n \u003cp\u003eLagune\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.12908242612753%\"\u003e\n \u003cp\u003eBenin\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.81959564541213%\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.81959564541213%\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.26438569206843%\"\u003e\n \u003cp\u003ea\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.37325038880249%\"\u003e\n \u003cp\u003eAfrican\u003c/p\u003e\n \u003cp\u003e\u003cem\u003eBos taurus\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.020217729393469%\"\u003e\n \u003cp\u003eNDAG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.573872472783826%\"\u003e\n \u003cp\u003eN\u0026rsquo;Dama Guinea\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.12908242612753%\"\u003e\n \u003cp\u003eGuinea\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.81959564541213%\"\u003e\n \u003cp\u003e70\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.81959564541213%\"\u003e\n \u003cp\u003e47\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.26438569206843%\"\u003e\n \u003cp\u003ee, a, f\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.37325038880249%\"\u003e\n \u003cp\u003eTrypanotolerant African hybrid\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.020217729393469%\"\u003e\n \u003cp\u003eNDAM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.573872472783826%\"\u003e\n \u003cp\u003eN\u0026rsquo;Dama hybrid\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.12908242612753%\"\u003e\n \u003cp\u003eUnspecified, Togo, Kenya\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.81959564541213%\"\u003e\n \u003cp\u003e65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.81959564541213%\"\u003e\n \u003cp\u003e41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.26438569206843%\"\u003e\n \u003cp\u003eb, d, g\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.37325038880249%\"\u003e\n \u003cp\u003eTrypanotolerant African hybrid\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.020217729393469%\"\u003e\n \u003cp\u003eBORG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.573872472783826%\"\u003e\n \u003cp\u003eBorgou\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.12908242612753%\"\u003e\n \u003cp\u003eBenin\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.81959564541213%\"\u003e\n \u003cp\u003e50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.81959564541213%\"\u003e\n \u003cp\u003e50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.26438569206843%\"\u003e\n \u003cp\u003ee\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.37325038880249%\"\u003e\n \u003cp\u003eTrypanotolerant African hybrid\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.020217729393469%\"\u003e\n \u003cp\u003eSOMB\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.573872472783826%\"\u003e\n \u003cp\u003eSomba\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.12908242612753%\"\u003e\n \u003cp\u003eBenin\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.81959564541213%\"\u003e\n \u003cp\u003e33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.81959564541213%\"\u003e\n \u003cp\u003e23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.26438569206843%\"\u003e\n \u003cp\u003eg\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.37325038880249%\"\u003e\n \u003cp\u003eTrypanotolerant African hybrid\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.020217729393469%\"\u003e\n \u003cp\u003eKETE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.573872472783826%\"\u003e\n \u003cp\u003eKeteku\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.12908242612753%\"\u003e\n \u003cp\u003eNigeria\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.81959564541213%\"\u003e\n \u003cp\u003e22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.81959564541213%\"\u003e\n \u003cp\u003e22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.26438569206843%\"\u003e\n \u003cp\u003ee\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.37325038880249%\"\u003e\n \u003cp\u003eTrypanotolerant African hybrid\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.020217729393469%\"\u003e\n \u003cp\u003eSHEK\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.573872472783826%\"\u003e\n \u003cp\u003eSheko\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.12908242612753%\"\u003e\n \u003cp\u003eEthiopia\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.81959564541213%\"\u003e\n \u003cp\u003e16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.81959564541213%\"\u003e\n \u003cp\u003e16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.26438569206843%\"\u003e\n \u003cp\u003ea\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.37325038880249%\"\u003e\n \u003cp\u003eTrypanosusceptible African hybrid\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.020217729393469%\"\u003e\n \u003cp\u003eANKO\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.573872472783826%\"\u003e\n \u003cp\u003eAnkole\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.12908242612753%\"\u003e\n \u003cp\u003eUganda\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.81959564541213%\"\u003e\n \u003cp\u003e50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.81959564541213%\"\u003e\n \u003cp\u003e25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.26438569206843%\"\u003e\n \u003cp\u003ef\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.37325038880249%\"\u003e\n \u003cp\u003eTrypanosusceptible African hybrid\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.020217729393469%\"\u003e\n \u003cp\u003eNGAN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.573872472783826%\"\u003e\n \u003cp\u003eNganda\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.12908242612753%\"\u003e\n \u003cp\u003eUganda\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.81959564541213%\"\u003e\n \u003cp\u003e53\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.81959564541213%\"\u003e\n \u003cp\u003e27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.26438569206843%\"\u003e\n \u003cp\u003ef, e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.37325038880249%\"\u003e\n \u003cp\u003eTrypanosusceptible African hybrid\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.020217729393469%\"\u003e\n \u003cp\u003eEASZ\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.573872472783826%\"\u003e\n \u003cp\u003eEast African Shorthorn Zebu\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.12908242612753%\"\u003e\n \u003cp\u003eKenya\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.81959564541213%\"\u003e\n \u003cp\u003e111\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.81959564541213%\"\u003e\n \u003cp\u003e111\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.26438569206843%\"\u003e\n \u003cp\u003ef, e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.37325038880249%\"\u003e\n \u003cp\u003eTrypanosusceptible African hybrid\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.020217729393469%\"\u003e\n \u003cp\u003eKARA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.573872472783826%\"\u003e\n \u003cp\u003eKaramojong\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.12908242612753%\"\u003e\n \u003cp\u003eUganda\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.81959564541213%\"\u003e\n \u003cp\u003e16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.81959564541213%\"\u003e\n \u003cp\u003e16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.26438569206843%\"\u003e\n \u003cp\u003ef\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.37325038880249%\"\u003e\n \u003cp\u003eTrypanosusceptible African hybrid\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.020217729393469%\"\u003e\n \u003cp\u003eBORA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.573872472783826%\"\u003e\n \u003cp\u003eBoran\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.12908242612753%\"\u003e\n \u003cp\u003eKenya\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.81959564541213%\"\u003e\n \u003cp\u003e90\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.81959564541213%\"\u003e\n \u003cp\u003e90\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.26438569206843%\"\u003e\n \u003cp\u003eh, g\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.37325038880249%\"\u003e\n \u003cp\u003e\u003cem\u003eBos indicus\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.020217729393469%\"\u003e\n \u003cp\u003eTHAR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.573872472783826%\"\u003e\n \u003cp\u003eTharparkar\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.12908242612753%\"\u003e\n \u003cp\u003ePakistan\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.81959564541213%\"\u003e\n \u003cp\u003e13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.81959564541213%\"\u003e\n \u003cp\u003e13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.26438569206843%\"\u003e\n \u003cp\u003eb\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.37325038880249%\"\u003e\n \u003cp\u003e\u003cem\u003eBos indicus\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.020217729393469%\"\u003e\n \u003cp\u003eGIR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.573872472783826%\"\u003e\n \u003cp\u003eGir\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.12908242612753%\"\u003e\n \u003cp\u003eIndia\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.81959564541213%\"\u003e\n \u003cp\u003e85\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.81959564541213%\"\u003e\n \u003cp\u003e28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.26438569206843%\"\u003e\n \u003cp\u003ea, f\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.37325038880249%\"\u003e\n \u003cp\u003e\u003cem\u003eBos indicus\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.020217729393469%\"\u003e\n \u003cp\u003eNELO\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.573872472783826%\"\u003e\n \u003cp\u003eNelore\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.12908242612753%\"\u003e\n \u003cp\u003eBrazil\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.81959564541213%\"\u003e\n \u003cp\u003e69\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.81959564541213%\"\u003e\n \u003cp\u003e38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.26438569206843%\"\u003e\n \u003cp\u003ea, c, f\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.37325038880249%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"9.020217729393469%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"17.573872472783826%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"19.12908242612753%\"\u003e\n \u003cp\u003e\u003cstrong\u003eTotal\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.81959564541213%\"\u003e\n \u003cp\u003e1030\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.81959564541213%\"\u003e\n \u003cp\u003e750\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.26438569206843%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e* a\u0026nbsp;Semp\u0026eacute;r\u0026eacute;\u003cem\u003e\u0026nbsp;et al\u003c/em\u003e (2015), b Barbato\u003cem\u003e\u0026nbsp;et al\u003c/em\u003e (2020), c Upadhyay\u003cem\u003e\u0026nbsp;et al\u003c/em\u003e (2017), d Verdugo\u003cem\u003e\u0026nbsp;et al\u003c/em\u003e (2019), e Ward\u003cem\u003e\u0026nbsp;et al\u003c/em\u003e (2022), f Bahbahani\u003cem\u003e\u0026nbsp;et al\u003c/em\u003e (2017), g this study, h Wragg\u003cem\u003e\u0026nbsp;et al\u003c/em\u003e (2022).\u003c/p\u003e\n\u003cp\u003eRemoval of duplicate samples by identity-by-state filtering\u003c/p\u003e\n\u003cp\u003eDuplicate samples present in two or more data sources were removed using PLINK (v. 1.90 beta 6.25) and a previously described identity-by-state methodology\u0026nbsp;(Browett\u003cem\u003e\u0026nbsp;et al\u003c/em\u003e, 2018). The method was modified to select one from each pair of animals that had an identity-by-state value greater than or equal to 0.99 using dplyr (v. 1.1.2), readr (v. 2.1.4), and stringr (v. 1.5.0)\u0026nbsp;(Wickham, 2023)\u0026nbsp;with R (v. 4.3.0). The resulting list of sample duplicates were removed from the high- and low-density data sets with PLINK (v. 1.90 beta 6.25). The results were visualised using dplyr (v. 1.1.2), ggh4x (v. 0.2.4)\u0026nbsp;(van den Brand, 2023), ggplot2 (v. 3.4.2)\u0026nbsp;(Wickham, 2009), ggtext (v. 0.1.2)\u0026nbsp;(Wilke and Wiernik, 2022),\u0026nbsp;readr (v. 2.1.4), and stringr (v. 1.5.0) with R (v. 4.3.0). Colours were generated from viridis (v. 0.6.3)\u0026nbsp;(Garnier\u003cem\u003e\u0026nbsp;et al\u003c/em\u003e, 2023)\u0026nbsp;and khroma (v. 1.10.0).\u003c/p\u003e\n\u003cp\u003eRemoval of admixed animals from the reference populations\u003c/p\u003e\n\u003cp\u003eAn inbreeding analysis in PLINK (v. 1.90 beta 6.25) was used to remove animals that showed evidence for significant admixture in the reference populations (three European \u003cem\u003eB. taurus\u003c/em\u003e populations: Holstein Friesian, Angus, and Jersey; three African \u003cem\u003eB. taurus\u003c/em\u003e populations: Muturu, Lagune, and Guinean N\u0026rsquo;Dama; and three \u003cem\u003eB. indicus\u003c/em\u003e populations: Tharparkar, Gir, and Nelore). To do this, outlier animals with statistically lower inbreeding values than the rest of the population were identified via boxplots using dplyr (v. 1.1.2), readr (v. 2.1.4), and tidyr (v. 1.3.0)\u0026nbsp;(Wickham\u003cem\u003e\u0026nbsp;et al\u003c/em\u003e, 2023d)\u0026nbsp;with R (v. 4.3.0). The resulting list of animals were removed from the high- and low-density data sets. A systematic inbreeding analysis was then performed with PLINK (v. 1.90 beta 6.25) and the output was modified using dplyr (v. 1.1.2) and readr (v. 2.1.4) with R (v. 4.3.0) to identify animals with the top 25 and bottom 25 inbreeding values across the three European \u003cem\u003eB. taurus\u003c/em\u003e populations (Holstein Friesian, Angus, and Jersey). These samples were then removed from the high- and low-density data sets to balance the numbers of animals across the reference groups. A final inbreeding analysis of the low-density data set after the filters were applied was performed to compare the results with those of the high-density data set. Results from the inbreeding analyses were visualised using dplyr (v. 1.1.2), ggh4x (v. 0.2.4), ggplot2 (v. 3.4.2), ggtext (v. 0.1.2), and readr (v. 2.1.4), with R (v. 4.3.0). Colours were generated from viridis (v. 0.6.3) and khroma (v. 1.10.0).\u003c/p\u003e\n\u003cp\u003eFiltering of SNPs by call rate and minor allele frequency\u003c/p\u003e\n\u003cp\u003eThe high- and low-density data sets were filtered to retain autosomal SNPs with a minimum call rate of 95% and minor allele frequency (MAF) of at least 5% with PLINK (v. 1.90 beta 6.25). The methodologies used for this process have been described in a previous study\u0026nbsp;(McHugo\u003cem\u003e\u0026nbsp;et al\u003c/em\u003e, 2019).\u003c/p\u003e\n\u003cp\u003ePopulation genomics analyses\u003c/p\u003e\n\u003cp\u003ePrincipal component\u0026nbsp;analysis\u003c/p\u003e\n\u003cp\u003ePrincipal component analysis (PCA) was performed for the high- and low-density data sets using smartpca after file conversion with convertf, both part of EIGENSOFT package (v. 7.1.2)\u0026nbsp;(Patterson\u003cem\u003e\u0026nbsp;et al\u003c/em\u003e, 2006). The results were visualised using dplyr (v. 1.1.2), ggplot2 (v. 3.4.2), patchwork (v. 1.1.2)\u0026nbsp;(Pedersen, 2023), readr (v. 2.1.4), stringr (v. 1.5.0), and tidyr (v. 1.3.0) with R v. 4.3.0). Colours were generated from viridis (v. 0.6.3) and khroma (v. 1.10.0).\u003c/p\u003e\n\u003cp\u003eGenetic structure analysis\u003c/p\u003e\n\u003cp\u003eGenetic structure analysis was performed for the high- and low-density data sets using structure_threader (v. 1.3.4)\u0026nbsp;(Pina-Martins\u003cem\u003e\u0026nbsp;et al\u003c/em\u003e, 2017)\u0026nbsp;with \u0026nbsp;fastStructure (v. 1.0)\u0026nbsp;(Raj\u003cem\u003e\u0026nbsp;et al\u003c/em\u003e, 2014). The structure analysis was carried out with the model complexity or number of populations (\u003cem\u003eK\u003c/em\u003e) set from 2 to 25. The chooseK function was used to test the outputs to find a range of values of \u003cem\u003eK\u003c/em\u003e that best accounted for the structure in the data\u0026nbsp;(Raj\u003cem\u003e\u0026nbsp;et al\u003c/em\u003e, 2014). The results were visualised using dplyr (v. 1.1.2), ggh4x (v. 0.2.4), ggplot2 (v. 3.4.2), ggtext (v. 0.1.2), magick (v. 2.8.1)\u0026nbsp;(Ooms, 2023)\u0026nbsp;with ImageMagick (v. 6.9.12.96)\u0026nbsp;(ImageMagick Studio LLC, 2023), magrittr (v. 2.0.3)\u0026nbsp;(Bache and Wickham, 2022), \u0026nbsp;patchwork (v. 1.1.2), readr (v. 2.1.4), stringr (v. 1.5.0) and tidyr (v. 1.3.0) with R (v. 4.3.0). Colours were generated from viridis (v. 0.6.3) and khroma (v. 1.10.0).\u003c/p\u003e\n\u003cp\u003ePhylogenetic analysis\u003c/p\u003e\n\u003cp\u003eAn additional sample of three gaur (\u003cem\u003eB. gaurus\u003c/em\u003e) that were genotyped using the BovineHD 777K BeadChip\u0026nbsp;(Semp\u0026eacute;r\u0026eacute;\u003cem\u003e\u0026nbsp;et al\u003c/em\u003e, 2015; Verdugo\u003cem\u003e\u0026nbsp;et al\u003c/em\u003e, 2019)\u0026nbsp;were available to use as an outgroup. After the pre-processing steps described above were performed to convert the data to binary PLINK files with Illumina\u003csup\u003e\u0026reg;\u003c/sup\u003e allele coding for the FORWARD strand on the ARS-UCD1.2 bovine genome assembly, the \u003cem\u003eB. gaurus\u003c/em\u003e data set was filtered\u0026nbsp;with PLINK (v. 1.90 beta 6.25) to retain only SNPs present in the high-density data set.\u0026nbsp;The gaur data were then merged with the high-density data set and an additional filter was applied with PLINK (v. 1.90 beta 6.25) to retain autosomal SNPs with a minimum call rate of 95% and minor allele frequency of at least 5%. A gzipped allele frequency cluster file was produced with PLINK (v. 1.90 beta 6.25) and the resulting file was converted to TreeMix format using the plink2treemix python script provided with the TreeMix software package (v. 1.13)\u0026nbsp;(Pickrell and Pritchard, 2012).\u003c/p\u003e\n\u003cp\u003ePhylogenetic analysis was performed for both the high- and low-density SNP data sets using TreeMix (v. 1.13) with the number of migration edges (\u003cem\u003em\u003c/em\u003e) set from 1 to 15 for ten iterations using windows of SNPs (\u003cem\u003ek\u003c/em\u003e) increasing from 100 to 1000 by increments of 100. The OptM package (v. 0.1.6)\u0026nbsp;(Fitak, 2021)\u0026nbsp;was used with R (v. 4.3.0) to calculate the mean and standard deviation (SD) across the 10 iterations for the composite likelihood (\u003cem\u003eL\u003c/em\u003e(\u003cem\u003em\u003c/em\u003e)), proportion of variance explained and the second-order rate of change (\u003cem\u003e\u0026Delta;m\u003c/em\u003e) across migration edges (\u003cem\u003em\u003c/em\u003e). The results were visualised using dplyr (v. 1.1.2), ggplot2 (v. 3.4.2), ggtext (v. 0.1.2) and patchwork (v. 1.1.2) with R (v. 4.3.0). The BITE R package (v. 1.2.0008)\u0026nbsp;(Milanesi\u003cem\u003e\u0026nbsp;et al\u003c/em\u003e, 2017)\u0026nbsp;was also used to generate a Unix shell script customised to perform 100 TreeMix bootstrap replicates for the selected numbers of migration edges (\u003cem\u003em\u003c/em\u003e). The results were visualised using ape (v. 5.7.1)\u0026nbsp;(Paradis and Schliep, 2019), dplyr (v. 1.1.2), ggh4x (v. 0.2.4), ggnewscale (v. 0.4.9)\u0026nbsp;(Campitelli, 2023), ggplot2 (v. 3.4.2), ggtext (v. 0.1.2), ggtree (v. 3.9.0.1)\u0026nbsp;(Yu, 2022), patchwork (v. 1.1.2), stringr (v. 1.5.0), tidytree (v. 0.4.4)\u0026nbsp;(Yu, 2022), and treeio (v. 1.25.2)\u0026nbsp;(Wang\u003cem\u003e\u0026nbsp;et al\u003c/em\u003e, 2020a)\u0026nbsp;with R (v. 4.3.0) and a modified version of the script provided with TreeMix. Colours were generated from viridis (v. 0.6.3) and khroma (v. 1.10.0).\u003c/p\u003e\n\u003cp\u003eLocal ancestry estimation\u003c/p\u003e\n\u003cp\u003eMOSAIC analysis\u003c/p\u003e\n\u003cp\u003eThe high- and low-density SNP data sets were separated by chromosome with PLINK (v. 1.90 beta 6.25) and each chromosome was phased with SHAPEIT (v. 2.r904)\u0026nbsp;(O\u0026apos;Connell\u003cem\u003e\u0026nbsp;et al\u003c/em\u003e, 2014). The resulting segregated chromosome SNP data files were converted to MOSAIC format using the R script provided with MOSAIC (v. 1.5.0)\u0026nbsp;(Salter-Townshend and Myers, 2019)\u0026nbsp;and R (v. 4.3.0). Recombination rate files were prepared from a cattle recombination map\u0026nbsp;(Ma\u003cem\u003e\u0026nbsp;et al\u003c/em\u003e, 2015)\u0026nbsp;using an R script adapted from\u0026nbsp;Ward\u003cem\u003e\u0026nbsp;et al\u003c/em\u003e (2022)\u0026nbsp;with R (v. 4.3.0). For each hybrid population three-way local ancestry analysis was performed across all autosomes without \u003cem\u003eF\u003c/em\u003e\u003csub\u003eST\u003c/sub\u003e estimation and assuming an effective population size (\u003cem\u003eN\u003csub\u003ee\u003c/sub\u003e\u003c/em\u003e) of 400 using MOSAIC (v. 1.5.0), dplyr (v. 1.1.2), and stringr (v. 1.5.0) with R (v. 4.3.0). The potential donor populations were the three European \u003cem\u003eB. taurus\u003c/em\u003e populations (Holstein Friesian, Angus, and Jersey), the three African \u003cem\u003eB. taurus\u003c/em\u003e populations (Muturu, Lagune, and Guinean N\u0026rsquo;Dama) and the three \u003cem\u003eB. indicus\u003c/em\u003e populations (Tharparkar, Gir, and Nelore).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eELAI analysis\u003c/p\u003e\n\u003cp\u003eThe high- and low-density SNP data sets were separated and converted into BIMBAM format for each population and chromosome with PLINK (v.\u0026nbsp;1.90 beta 6.25). Local ancestry analysis was carried out for each hybrid population and autosome with 30 expectation-maximization (EM) steps, 3 upper clusters, 15 lower clusters, and 200 mixing generations using ELAI (v. 1.0)\u0026nbsp;(Guan, 2014). The donor populations for each hybrid population were selected based on the results of the MOSAIC analysis.\u003c/p\u003e\n\u003cp\u003eLocal ancestry analysis comparison\u003c/p\u003e\n\u003cp\u003eThe local ancestry results were extracted using dplyr (v. 1.1.2, MOSAIC (v. 1.5.0), parallel (v. 4.3.0)\u0026nbsp;(R Core Team, 2023), \u0026nbsp;readr (v. 2.1.4), scales (v. 1.2.1)\u0026nbsp;(Wickham\u003cem\u003e\u0026nbsp;et al\u003c/em\u003e, 2023c), stringr (v. 1.5.0), and tibble (v. 3.2.1)\u0026nbsp;(M\u0026uuml;ller and Wickham, 2023)\u0026nbsp;with R (v. 4.3.0). Mean ancestry scores across the individual hybrid animals and a genome-wide \u003cem\u003ez\u003c/em\u003e-score for each of the three ancestry components were calculated for each hybrid population. Weighted mean ancestry scores and \u003cem\u003ez\u003c/em\u003e-scores were calculated across the hybrid populations within each group of European hybrids, trypanotolerant African hybrids, and trypanosusceptible African hybrids for a subset of the hybrid populations selected to only include populations with a minimum of 15 animals and a relatively stable level of admixture based on a visual examination of the structure results. The local ancestry results were visualised using dplyr (v. 1.1.2), ggplot2 (v. 3.4.2), magick (v. 2.8.1) with ImageMagick (v. 6.9.12.96), magrittr (v. 2.0.3), parallel (v. 4.3.0), patchwork (v. 1.1.2), readr (v. 2.1.4), scales (v. 1.2.1), stringr (v. 1.5.0), and tibble (v. 3.2.1) with R (v. 4.3.0). Colours were generated from khroma (v. 1.10.0). The correlation between the local ancestry results from MOSAIC and ELAI were visualised using dplyr (v. 1.1.2), ggplot2 (v. 3.4.2), ggtext (v. 0.1.2), parallel (v. 4.3.0), patchwork (v. 1.1.2), readr (v. 2.1.4), and stringr (v. 1.5.0) with R (v. 4.3.0). Colours were generated from viridis (v. 0.6.3).\u003c/p\u003e\n\u003cp\u003eFunctional enrichment of introgressed regions\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eFunctional enrichment was performed and visualised using dplyr (v. 1.1.2), ggplot2 (v. 3.4.2), ggrepel (v. 0.9.3)\u0026nbsp;(Slowikowski, 2023), gprofiler2 (v. 0.2.2)\u0026nbsp;(Kolberg\u003cem\u003e\u0026nbsp;et al\u003c/em\u003e, 2023), magick (v. 2.8.1) with ImageMagick (v. 6.9.12.96), magrittr (v. 2.0.3), parallel (v. 4.3.0), readr (v. 2.1.4), and stringr (v. 1.5.0) with R (v. 4.3.0). Colours were generated from khroma (v. 1.10.0). The background set was the set of genes within 1 Mb up- and downstream from a SNP in the high-density data set. The query sets were the genes within 1 Mb up and downstream from the SNPs with a \u003cem\u003ez\u003c/em\u003e-score \u0026ge; 2.0 for each of the ancestries.\u0026nbsp;\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003eHigh-density genome-wide cattle SNP data sets\u003c/p\u003e\n\u003cp\u003eAfter filtering for missing genotypes (30 samples removed), identity-by-state (Figure S1; 194 samples removed), and inbreeding (Figure S2, Figure S3; 56 samples removed), there were 750 animals in the high- and low-density SNP data sets (Table 1). Filtering for autosomal SNPs with a minimum call rate of 95% and MAF of at least 5% retained 614,026 SNPs in the high-density data set with a total genotyping rate of 98.20%, and 30,706 SNPs in the low-density data set with a total genotyping rate of 95.91%.\u003c/p\u003e\n\u003cp\u003ePopulation genomic analyses\u003c/p\u003e\n\u003cp\u003ePrincipal component analysis\u003c/p\u003e\n\u003cp\u003eThe first principal component (PC1) explained 53.59% of the of the total variation for PC1\u0026ndash;10 in the high-density SNP data set and separated the \u003cem\u003eB. taurus\u003c/em\u003e and \u003cem\u003eB. indicus\u003c/em\u003e lineages (Figure 2). The second principal component (PC2) explained a further 19.71% of the total variation for PC1\u0026ndash;10 in the high-density SNP data set and separated the European \u003cem\u003eB. taurus\u003c/em\u003e and African \u003cem\u003eB. taurus\u003c/em\u003e lineages (Figure 2). The hybrid animals were dispersed among the reference populations with the European hybrid animals clustering close to the European \u003cem\u003eB. taurus\u003c/em\u003e group and the African hybrid animals mostly located between the African \u003cem\u003eB. taurus\u003c/em\u003e and \u003cem\u003eB. indicus\u003c/em\u003e groups (Figure 2). The trypanotolerant African hybrid individuals are closest to the African \u003cem\u003eB. taurus\u003c/em\u003e group while the trypanosusceptible African hybrid animals are closest to the \u003cem\u003eB. indicus\u003c/em\u003e group (Figure 2). The same pattern was observed for the low-density SNP data set (Figure S4).\u003c/p\u003e\n\u003cp\u003eGenetic structure analysis\u003c/p\u003e\n\u003cp\u003eThe structure results for \u003cem\u003eK\u0026nbsp;\u003c/em\u003e= 2 separates the \u003cem\u003eB. taurus\u003c/em\u003e and \u003cem\u003eB. indicus\u003c/em\u003e ancestries in the high-density SNP data set (Figure 3). With \u003cem\u003eK\u0026nbsp;\u003c/em\u003e= 3, the structure results divide the European and African \u003cem\u003eB. taurus\u003c/em\u003e and \u003cem\u003eB. indicus\u003c/em\u003e ancestries (Figure 3). For the high-density SNP data set the model complexity that maximizes marginal likelihood was 16 and the model components used to explain the structure in the data was 17 (Figure S5, Figure S6). For the low-density SNP data set the model complexity that maximizes marginal likelihood and the model components used to explain the structure in the data was 16 (Figure S7, Figure S8).\u003c/p\u003e\n\u003cp\u003ePhylogenetic analysis\u003c/p\u003e\n\u003cp\u003eAfter the \u003cem\u003eB. gaurus\u003c/em\u003e outgroup animals were added and filters for autosomal SNPs with a minimum call rate of 95% and MAF of at least 5% were applied there were 613,334 SNPs in the high-density SNP data set with a total genotyping rate of 99.65%, and 30,644 SNPs in the low-density SNP data set with a total genotyping rate of 99.50%. The optimum number of migration edges indicated by the first peak in \u003cem\u003e\u0026Delta;m\u003c/em\u003e for both the high- and low-density SNP data sets is three while the number of migration edges required to explain 99.8% of the variation in the data is 12 and 11 for the high- and low-density SNP data sets, respectively (Figure S9, Figure S10). The phylogenetic analysis results clearly distinguish and group the European \u003cem\u003eB. taurus\u003c/em\u003e, African \u003cem\u003eB. taurus\u003c/em\u003e, and \u003cem\u003eB. indicus\u003c/em\u003e populations into different clades with a high degree of confidence (Figure 4). The European hybrid populations are grouped with the European \u003cem\u003eB. taurus\u003c/em\u003e populations with a similarly high degree of confidence, while the trypanotolerant and trypanosusceptible African hybrid populations are placed between the African \u003cem\u003eB. taurus\u003c/em\u003e and \u003cem\u003eB. indicus\u003c/em\u003e populations with varying degrees of confidence (Figure 4). This pattern holds regardless of the number of migration edges or SNP data set density (Figures S11\u0026ndash;S15). The introduction of migration edges into the phylogenetic tree indicates admixture between the hybrid African and African \u003cem\u003eB. taurus\u003c/em\u003e populations when \u003cem\u003em\u003c/em\u003e is set to 3 (Figure 4, Figure S14). For higher values of \u003cem\u003em\u003c/em\u003e, the admixture shown includes the European hybrid populations (Figure S12, Figure S15).\u003c/p\u003e\n\u003cp\u003eLocal ancestry estimation\u003c/p\u003e\n\u003cp\u003eWeighted mean local ancestry results were calculated for each of the three ancestry components (European \u003cem\u003eB. taurus\u003c/em\u003e, African \u003cem\u003eB. taurus\u003c/em\u003e, and \u003cem\u003eB. indicus\u003c/em\u003e) for each of the three hybrid groups (European hybrid, trypanotolerant African hybrid, and trypanosusceptible African hybrid) using the mean results from populations with more than 15 samples (Table 1), and relatively stable hybridisation based on visual examination of the structure results at \u003cem\u003eK\u0026nbsp;\u003c/em\u003e= 3 (Figure 3). The European hybrid group included the Romagnola and Chianina populations, the trypanotolerant African hybrid group included the Borgou and Sheko populations, and the trypanosusceptible African hybrid group included the Ankole, Nganda, East African Shorthorn Zebu, Karamojong, and Boran populations. The results for the high-density SNP data set showed similar patterns using both MOSAIC and ELAI when examined visually, as did the ELAI results for the low-density SNP data set (Figure 5). The MOSAIC results for the low-density SNP data set exhibited a noticeable smoothening across the genome for all three ancestry components in all three hybrid groups (Figure 5). This was particularly evident for the ancestry components with lower proportions, such as the African \u003cem\u003eB. taurus\u003c/em\u003e and \u003cem\u003eB. indicus\u003c/em\u003e components in the European hybrid group, and the European \u003cem\u003eB. taurus\u003c/em\u003e component in the trypanotolerant African hybrid group (Figure 5). When individual chromosome results were examined, the high-density SNP local ancestry results for MOSAIC and ELAI and the low-density SNP ELAI results showed peaks for the various ancestry components around the major histocompatibility complex (MHC) located on BTA23, although this was not evident for the low-density MOSAIC results (Figure S16\u0026ndash;S27). Correlation plots between the MOSAIC and ELAI results for each ancestry component along each chromosome indicated positive correlations for the high-density SNP results for all three hybrid groups (Figure S28\u0026ndash;S30) while the low-density SNP results indicated much weaker or no correlations (Figure S31\u0026ndash;S33). To identify SNPs within the peaks of local ancestry for each ancestry component genome-wide \u003cem\u003ez\u003c/em\u003e-scores of the weighted mean local ancestry results were used to select SNPs with \u003cem\u003ez\u003c/em\u003e-scores \u0026ge; 2.0 for each software and data set (Table 2). There were no SNPs that passed the \u003cem\u003ez\u003c/em\u003e \u0026ge; 2 threshold for the European \u003cem\u003eB. taurus\u003c/em\u003e ancestry component in the European hybrid group for the high-density SNP MOSAIC and ELAI results and the low-density SNP MOSAIC results, while the trypanotolerant and trypanosusceptible African hybrid groups had the lowest number of SNPs passing the \u003cem\u003ez\u003c/em\u003e \u0026ge; 2 threshold for the African \u003cem\u003eB. taurus\u003c/em\u003e and \u003cem\u003eB. indicus\u003c/em\u003e ancestry components, respectively (Table 2). Similar proportions of SNPs were found for each software and SNP data set (Table 2).\u003c/p\u003e\n\u003cp\u003eTable 2.\u0026nbsp;Numbers of SNPs with a\u0026nbsp;\u003cem\u003ez\u003c/em\u003e-score \u0026ge; 2.0 for\u0026nbsp;weighted mean European\u0026nbsp;\u003cem\u003eB. taurus\u003c/em\u003e, African\u0026nbsp;\u003cem\u003eB. taurus\u003c/em\u003e and\u0026nbsp;\u003cem\u003eB. indicus\u003c/em\u003e ancestry components for the European hybrid, trypanotolerant African hybrid, and trypanosusceptible African hybrid groups across all autosomes for the MOSAIC and ELAI analyses of high- and low-density SNP data sets. The numbers in brackets indicate the percentage of the total number of SNPs in each data set.\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"642\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.48367029548989%\"\u003e\n \u003cp\u003e\u003cstrong\u003eGroup\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.174183514774494%\"\u003e\n \u003cp\u003e\u003cstrong\u003eAncestry\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.085536547433904%\"\u003e\n \u003cp\u003e\u003cstrong\u003eHD MOSAIC\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.085536547433904%\"\u003e\n \u003cp\u003e\u003cstrong\u003eLD MOSAIC\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.085536547433904%\"\u003e\n \u003cp\u003e\u003cstrong\u003eHD ELAI\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.085536547433904%\"\u003e\n \u003cp\u003e\u003cstrong\u003eLD ELAI\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.48367029548989%\" rowspan=\"3\"\u003e\n \u003cp\u003eEuropean hybrid\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.174183514774494%\"\u003e\n \u003cp\u003eEuropean\u003cbr\u003e\u003cem\u003eB. taurus\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.085536547433904%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003cp\u003e(0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.085536547433904%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003cp\u003e(0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.085536547433904%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003cp\u003e(0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.085536547433904%\"\u003e\n \u003cp\u003e151\u003c/p\u003e\n \u003cp\u003e(0.49%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.13821138211382%\"\u003e\n \u003cp\u003eAfrican\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u003cem\u003eB. taurus\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.715447154471544%\"\u003e\n \u003cp\u003e16,511\u003c/p\u003e\n \u003cp\u003e(2.69%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.715447154471544%\"\u003e\n \u003cp\u003e1,546\u003c/p\u003e\n \u003cp\u003e(5.03%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.715447154471544%\"\u003e\n \u003cp\u003e26,561\u003c/p\u003e\n \u003cp\u003e(4.33%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.715447154471544%\"\u003e\n \u003cp\u003e1,337\u003c/p\u003e\n \u003cp\u003e(4.35%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.13821138211382%\"\u003e\n \u003cp\u003e\u003cem\u003eB. indicus\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.715447154471544%\"\u003e\n \u003cp\u003e31,674\u003c/p\u003e\n \u003cp\u003e(5.16%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.715447154471544%\"\u003e\n \u003cp\u003e1,865\u003c/p\u003e\n \u003cp\u003e(6.07%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.715447154471544%\"\u003e\n \u003cp\u003e33,110\u003c/p\u003e\n \u003cp\u003e(5.39%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.715447154471544%\"\u003e\n \u003cp\u003e1,415\u003c/p\u003e\n \u003cp\u003e(4.61%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.48367029548989%\" rowspan=\"3\"\u003e\n \u003cp\u003eTrypanotolerant African hybrid\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.174183514774494%\"\u003e\n \u003cp\u003eEuropean\u003cbr\u003e\u003cem\u003eB. taurus\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.085536547433904%\"\u003e\n \u003cp\u003e23,816\u003c/p\u003e\n \u003cp\u003e(3.88%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.085536547433904%\"\u003e\n \u003cp\u003e1,490\u003c/p\u003e\n \u003cp\u003e(4.85%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.085536547433904%\"\u003e\n \u003cp\u003e25,743\u003c/p\u003e\n \u003cp\u003e(4.19%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.085536547433904%\"\u003e\n \u003cp\u003e1,229\u003c/p\u003e\n \u003cp\u003e(4.00%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.13821138211382%\"\u003e\n \u003cp\u003eAfrican\u003c/p\u003e\n \u003cp\u003e\u003cem\u003eB. taurus\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.715447154471544%\"\u003e\n \u003cp\u003e7,728\u003c/p\u003e\n \u003cp\u003e(1.26%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.715447154471544%\"\u003e\n \u003cp\u003e179\u003c/p\u003e\n \u003cp\u003e(0.58%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.715447154471544%\"\u003e\n \u003cp\u003e9,056\u003c/p\u003e\n \u003cp\u003e(1.47%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.715447154471544%\"\u003e\n \u003cp\u003e627\u003c/p\u003e\n \u003cp\u003e(2.04%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.13821138211382%\"\u003e\n \u003cp\u003e\u003cem\u003eB. indicus\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.715447154471544%\"\u003e\n \u003cp\u003e18,367\u003c/p\u003e\n \u003cp\u003e(2.99%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.715447154471544%\"\u003e\n \u003cp\u003e723\u003c/p\u003e\n \u003cp\u003e(2.35%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.715447154471544%\"\u003e\n \u003cp\u003e18,083\u003c/p\u003e\n \u003cp\u003e(2.94%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.715447154471544%\"\u003e\n \u003cp\u003e801\u003c/p\u003e\n \u003cp\u003e(2.61%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.48367029548989%\" rowspan=\"3\"\u003e\n \u003cp\u003eTrypanosusceptible African hybrid\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.174183514774494%\"\u003e\n \u003cp\u003eEuropean\u003cbr\u003e\u003cem\u003eB. taurus\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.085536547433904%\"\u003e\n \u003cp\u003e26,035\u003c/p\u003e\n \u003cp\u003e(4.24%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.085536547433904%\"\u003e\n \u003cp\u003e1,423\u003c/p\u003e\n \u003cp\u003e(4.63%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.085536547433904%\"\u003e\n \u003cp\u003e25,083\u003c/p\u003e\n \u003cp\u003e(4.09%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.085536547433904%\"\u003e\n \u003cp\u003e1,173\u003c/p\u003e\n \u003cp\u003e(3.82%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.13821138211382%\"\u003e\n \u003cp\u003eAfrican\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u003cem\u003eB. taurus\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.715447154471544%\"\u003e\n \u003cp\u003e16,998\u003c/p\u003e\n \u003cp\u003e(2.77%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.715447154471544%\"\u003e\n \u003cp\u003e494\u003c/p\u003e\n \u003cp\u003e(1.61%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.715447154471544%\"\u003e\n \u003cp\u003e23,356\u003c/p\u003e\n \u003cp\u003e(3.80%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.715447154471544%\"\u003e\n \u003cp\u003e1,093\u003c/p\u003e\n \u003cp\u003e(3.56%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.13821138211382%\"\u003e\n \u003cp\u003e\u003cem\u003eB. indicus\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.715447154471544%\"\u003e\n \u003cp\u003e10,434\u003c/p\u003e\n \u003cp\u003e(1.70%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.715447154471544%\"\u003e\n \u003cp\u003e49\u003c/p\u003e\n \u003cp\u003e(1.60%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.715447154471544%\"\u003e\n \u003cp\u003e7,823\u003c/p\u003e\n \u003cp\u003e(1.27%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.715447154471544%\"\u003e\n \u003cp\u003e460\u003c/p\u003e\n \u003cp\u003e(1.50%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.48367029548989%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"16.174183514774494%\"\u003e\n \u003cp\u003e\u003cstrong\u003eTotal\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.085536547433904%\"\u003e\n \u003cp\u003e614,026\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.085536547433904%\"\u003e\n \u003cp\u003e30,706\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.085536547433904%\"\u003e\n \u003cp\u003e614,026\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.085536547433904%\"\u003e\n \u003cp\u003e30,706\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eFunctional enrichment of introgressed regions\u003c/p\u003e\n\u003cp\u003eThe proportions of the numbers of genes found within 1 Mb up- and downstream from each SNP with a \u003cem\u003ez\u003c/em\u003e-score \u0026ge; 2.0 are similar to those of the numbers of SNPs found for each ancestry component in the hybrid groups for each software and SNP data set (Table 2, Table S1). There were no European \u003cem\u003eB. taurus\u003c/em\u003e SNPs that passed the z \u0026ge; 2 threshold; consequently, there were no European \u003cem\u003eB. taurus\u003c/em\u003e genes for functional enrichment in the European hybrid group (Table 2, Table S1). The top driver GO terms for the African \u003cem\u003eB. taurus\u003c/em\u003e genes in the European hybrid group included terms related to the MHC (\u003cem\u003eGO:0042613 MHC class II protein complex\u003c/em\u003e) and other aspects of the immune system (\u003cem\u003eGO:0019882 antigen processing and presentation\u003c/em\u003e, \u003cem\u003eGO:0001914 regulation of T cell mediated cytotoxicity\u003c/em\u003e, and \u003cem\u003eGO:0004930 G protein-coupled receptor activity\u003c/em\u003e); protein and DNA complexes and protein binding (\u003cem\u003eGO:0000786 nucleosome\u003c/em\u003e, \u003cem\u003eGO:0030527 structural constituent of chromatin\u003c/em\u003e, \u003cem\u003eGO:0046982 protein heterodimerization activity\u003c/em\u003e, \u003cem\u003eGO:0065004 protein-DNA complex assembly\u003c/em\u003e); and olfaction (\u003cem\u003eGO:0004984 olfactory receptor activity\u003c/em\u003e and \u003cem\u003eGO:0050911 detection of chemical stimulus involved in sensory perception of smell\u003c/em\u003e, Figure 6A). The top \u003cem\u003eB. indicus\u003c/em\u003e driver GO terms also included terms relating to the MHC (\u003cem\u003eGO:0042613 MHC class II protein complex\u003c/em\u003e) and other immune terms (\u003cem\u003eGO:0019882 antigen processing and presentation\u003c/em\u003e and \u003cem\u003eGO:0002684 positive regulation of immune system process\u003c/em\u003e), as well as cell membrane and signalling activity (\u003cem\u003eGO:0001594 trace-amine receptor activity\u003c/em\u003e, \u003cem\u003eGO:0009897 external side of plasma membrane\u003c/em\u003e, and \u003cem\u003eGO:0004364 glutathione transferase activity\u003c/em\u003e, Figure 6A).\u003c/p\u003e\n\u003cp\u003eThe trypanotolerant African hybrid group also had driver GO terms relating to the MHC (\u003cem\u003eGO:0002486 antigen processing and presentation of endogenous peptide antigen via MHC class I via ER pathway, TAP-independent\u003c/em\u003e and \u003cem\u003eGO:0002476 antigen processing and presentation of endogenous peptide antigen via MHC class Ib\u003c/em\u003e); other components of the immune system (\u003cem\u003eGO:0007186 G protein-coupled receptor signalling pathway\u003c/em\u003e); and olfaction (\u003cem\u003eGO:0004984 olfactory receptor activity and GO:0050911 detection of chemical stimulus involved in sensory perception of smell\u003c/em\u003e) among the European \u003cem\u003eB. taurus\u003c/em\u003e terms (Figure 6B). In addition there were also a number of terms relating to L-amino acid transmembrane transport (\u003cem\u003eGO:0097638 L-arginine import across plasma membrane\u003c/em\u003e, \u003cem\u003eGO:0000064 L-ornithine transmembrane transporter activity\u003c/em\u003e, \u003cem\u003eGO:1903352 L-ornithine transmembrane transport\u003c/em\u003e, \u003cem\u003eGO:1903401 L-lysine transmembrane transport\u003c/em\u003e, and \u003cem\u003eGO:0015189 L-lysine transmembrane transporter activity\u003c/em\u003e, Figure 6B). The top African \u003cem\u003eB. taurus\u003c/em\u003e terms related to haemoglobin and oxygen binding and transport (\u003cem\u003eGO:0005833 haemoglobin complex\u003c/em\u003e, \u003cem\u003eGO:0015671 oxygen transport\u003c/em\u003e, \u003cem\u003eGO:0019825 oxygen binding\u003c/em\u003e), while the top \u003cem\u003eB. indicus\u003c/em\u003e terms related to metabolic processes (\u003cem\u003eGO:0047023 androsterone dehydrogenase activity\u003c/em\u003e, \u003cem\u003eGO:0030647 aminoglycoside antibiotic metabolic process\u003c/em\u003e, \u003cem\u003eGO:0047086 ketosteroid monooxygenase activity\u003c/em\u003e, \u003cem\u003eGO:0042448 progesterone metabolic process\u003c/em\u003e, \u003cem\u003eGO:0004032 alditol:NADP+ 1-oxidoreductase activity\u003c/em\u003e, \u003cem\u003eGO:0032052 bile acid binding\u003c/em\u003e, and \u003cem\u003eGO:0016614 oxidoreductase activity, acting on CH-OH group of donors\u003c/em\u003e, Figure 6B).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eFor the trypanosusceptible African hybrid group the only driver GO term for the European \u003cem\u003eB. taurus\u003c/em\u003e ancestry component related to intracellular organelles (\u003cem\u003eGO:0043229 intracellular organelle\u003c/em\u003e, Figure 6C). The top African \u003cem\u003eB. taurus\u003c/em\u003e terms included those related to the MHC (\u003cem\u003eGO:0042613 MHC class II protein complex\u003c/em\u003e, and \u003cem\u003eGO:0023026 MHC class II protein complex binding\u003c/em\u003e); other components of the immune system (\u003cem\u003eGO:0019882 antigen processing and presentation\u003c/em\u003e, \u003cem\u003eGO:0001914 regulation of T cell mediated cytotoxicity\u003c/em\u003e, and \u003cem\u003eGO:0042605 peptide antigen binding\u003c/em\u003e); \u0026nbsp;olfaction (\u003cem\u003eGO:0004984 olfactory receptor activity\u003c/em\u003e, and \u003cem\u003eGO:0050911 detection of chemical stimulus involved in sensory perception of smell\u003c/em\u003e); and protein-DNA complex and protein binding (\u003cem\u003eGO:0030527 structural constituent of chromatin\u003c/em\u003e, \u003cem\u003eGO:0000786 nucleosome\u003c/em\u003e, \u003cem\u003eGO:0046982 protein heterodimerization activity\u003c/em\u003e, Figure 6C). The top \u003cem\u003eB. indicus\u003c/em\u003e terms included cell adhesion (\u003cem\u003eGO:0007156 homophilic cell adhesion via plasma membrane adhesion molecules\u003c/em\u003e) and metal ion binding (\u003cem\u003eGO:0005507 copper ion binding\u003c/em\u003e, \u0026nbsp;Figure 6C).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eSimilar GO term enrichment patterns were also observed using low-density SNP data with MOSAIC (Figure S34), and for ELAI with both high- and low-density SNP data (Figure S35, Figure S36).\u0026nbsp;\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThe results of the population genomic analyses in hybrid European and African cattle populations are consistent with previously published studies that have used modest numbers of genetic markers (e.g., microsatellites) and genome-wide SNP data\u0026nbsp;(Barbato\u003cem\u003e\u0026nbsp;et al\u003c/em\u003e, 2020; Decker\u003cem\u003e\u0026nbsp;et al\u003c/em\u003e, 2014; Hanotte\u003cem\u003e\u0026nbsp;et al\u003c/em\u003e, 2002; Kim\u003cem\u003e\u0026nbsp;et al\u003c/em\u003e, 2020; MacHugh\u003cem\u003e\u0026nbsp;et al\u003c/em\u003e, 1997; Ward\u003cem\u003e\u0026nbsp;et al\u003c/em\u003e, 2022). Visualisation of PCA results by plotting PC1 and PC2 recovered the classic \u0026ldquo;\u003cem\u003eBos\u003c/em\u003e triangle\u0026rdquo; with the first two PCs explaining a very high proportion of the total variation for PC1\u0026ndash;10 within the data (73.30%). PC1 and PC2 separated the reference European \u003cem\u003eB. taurus\u003c/em\u003e, African \u003cem\u003eB. taurus\u003c/em\u003e, and \u003cem\u003eB. indicus\u003c/em\u003e populations with the hybrid animal samples dispersed within the triangle with locations determined by three-way global admixture proportions (Figure 2). The locations of the various hybrid populations nearer to the reference populations they share the most ancestry with is in agreement with previous studies\u0026nbsp;(Bahbahani\u003cem\u003e\u0026nbsp;et al\u003c/em\u003e, 2017; Barbato\u003cem\u003e\u0026nbsp;et al\u003c/em\u003e, 2020; Upadhyay\u003cem\u003e\u0026nbsp;et al\u003c/em\u003e, 2017; Verdugo\u003cem\u003e\u0026nbsp;et al\u003c/em\u003e, 2019; Ward\u003cem\u003e\u0026nbsp;et al\u003c/em\u003e, 2022; Wragg\u003cem\u003e\u0026nbsp;et al\u003c/em\u003e, 2022). In addition, the clustering of some of the animals in the African trypanotolerant hybrid populations with the African \u003cem\u003eB. taurus\u003c/em\u003e reference populations indicate that some of these animals have very high levels of African taurine ancestry (Figure 2). In this regard, it is important to note that although a diverse panel of European \u003cem\u003eB. taurus\u003c/em\u003e, African \u003cem\u003eB. taurus\u003c/em\u003e, \u003cem\u003eB. indicus\u003c/em\u003e, and hybrid cattle in the design and validation of the BovineHD 777K BeadChip\u0026nbsp;(Illumina, 2015), ascertainment bias may affect the placement of hybrid cattle in a PCA plot\u0026nbsp;(Dokan\u003cem\u003e\u0026nbsp;et al\u003c/em\u003e, 2021; McTavish and Hillis, 2015). However, genome-wide multi-locus dimension reduction tools are typically substantially less affected by ascertainment bias than analyses such as estimation of diversity statistics such as the fixation index (\u003cem\u003eF\u003c/em\u003e\u003csub\u003eST\u003c/sub\u003e) or selection signal detection, which use individual SNP locus frequency-based statistics\u0026nbsp;(Albrechtsen\u003cem\u003e\u0026nbsp;et al\u003c/em\u003e, 2010; Malomane\u003cem\u003e\u0026nbsp;et al\u003c/em\u003e, 2018; Porto Neto and Barendse, 2010).\u003c/p\u003e\n\u003cp\u003eThe results of the genetic structure analysis for \u003cem\u003eK\u003c/em\u003e = 2 and \u003cem\u003eK\u003c/em\u003e = 3 mirror those of the PCA with the first major split evident for the \u003cem\u003eB. taurus\u003c/em\u003e and \u003cem\u003eB. indicus\u003c/em\u003e populations and the second split separating the African and European \u003cem\u003eB. taurus\u003c/em\u003e populations (Figure 3). The locations of animals in the hybrid populations reflect global admixture proportions that are in agreement with both their positions on the PCA and previous studies\u0026nbsp;(Bahbahani\u003cem\u003e\u0026nbsp;et al\u003c/em\u003e, 2017; Barbato\u003cem\u003e\u0026nbsp;et al\u003c/em\u003e, 2020; Upadhyay\u003cem\u003e\u0026nbsp;et al\u003c/em\u003e, 2017; Verdugo\u003cem\u003e\u0026nbsp;et al\u003c/em\u003e, 2019; Ward\u003cem\u003e\u0026nbsp;et al\u003c/em\u003e, 2022; Wragg\u003cem\u003e\u0026nbsp;et al\u003c/em\u003e, 2022)\u0026nbsp;(Figure 2). The number of modelled \u003cem\u003eK\u003c/em\u003e values that best explain the variation among the 24 populations examined in the study was 16\u0026ndash;17, indicating that some of the populations are closely related to the point that they may not be genetically distinct discrete populations\u0026nbsp;(Raj\u003cem\u003e\u0026nbsp;et al\u003c/em\u003e, 2014). The genetic structure results also show the variation within the hybrid populations in terms of global admixture (Figure 3). Some populations, such as the European and trypanosusceptible African hybrid groups, show a relatively consistent level of global admixture across each population while the trypanotolerant African hybrid group is more variable (Figure 3). This indicates that the European and trypanosusceptible African hybrid breeds are more long-established hybrid populations (\u0026ldquo;stable crossbreds\u0026rdquo;) and that the hybridisation within the African trypanotolerant hybrid populations is more recent and dynamic. Some of the more extreme examples, such as the N\u0026rsquo;Dama hybrid, Somba, and Keteku populations, indicate that some animals are not hybrids and are instead pure African \u003cem\u003eB. taurus\u003c/em\u003e (Figure 3). This is also in agreement with the PCA results and is likely due to the origins of the samples from a range of studies that sampled animals from different populations that were classified as the same breed or breed subtype\u0026nbsp;(Bahbahani\u003cem\u003e\u0026nbsp;et al\u003c/em\u003e, 2017; Barbato\u003cem\u003e\u0026nbsp;et al\u003c/em\u003e, 2020; Upadhyay\u003cem\u003e\u0026nbsp;et al\u003c/em\u003e, 2017; Verdugo\u003cem\u003e\u0026nbsp;et al\u003c/em\u003e, 2019; Ward\u003cem\u003e\u0026nbsp;et al\u003c/em\u003e, 2022; Wragg\u003cem\u003e\u0026nbsp;et al\u003c/em\u003e, 2022)\u0026nbsp;(Figure 2).\u003c/p\u003e\n\u003cp\u003eThe results of the phylogenetic analysis are also in agreement with the PCA and genetic structure results (Figure 4). The reference populations are unambiguously separated into the expected groupings (\u003cem\u003eEuropean\u003c/em\u003e \u003cem\u003eB. taurus\u003c/em\u003e, African \u003cem\u003eB. taurus\u003c/em\u003e, and \u003cem\u003eB. indicus\u003c/em\u003e) with bootstrap values of 99\u0026ndash;100, as are the European hybrid populations (Figure 4). The trypanotolerant and trypanosusceptible African hybrid populations are spread between the African\u003cem\u003e\u0026nbsp;B. taurus\u0026nbsp;\u003c/em\u003eand \u003cem\u003eB. indicus\u003c/em\u003e reference populations, with some hybrid branch clusters exhibiting low bootstrap values, indicating instability in the clade structure because of taurine/indicine admixture (Figure 4). This is also where the strongest gene flow events are inferred as modelled migration edges, demonstrating the higher levels of indicine admixture in the trypanotolerant and trypanosusceptible African hybrid populations compared to the European hybrid populations (Figure 4).\u003c/p\u003e\n\u003cp\u003eThe local ancestry results show similar patterns of peaks and troughs dispersed across the genome for each LAI software tool used (MOSAIC and ELAI), the hybrid cattle group examined (European hybrid, trypanotolerant African hybrid, and trypanosusceptible African hybrid), and each genome-wide SNP data set analysed (low- or high-density); the exception being the low-density SNP data set results obtained using MOSAIC (Figure 5). This may be due to the rephasing algorithm implemented by default as part of the MOSAIC analysis, which may overcorrect for phasing errors in low-density SNP data set\u0026nbsp;(Salter-Townshend and Myers, 2019). Alternatively, the smoothened results may be due to difficulty automatically estimating the age of admixture with low-density SNP data in MOSAIC\u0026nbsp;(Salter-Townshend and Myers, 2019). Comparing the MOSAIC high-density SNP data set results with the ELAI results using both the high- and low-density SNP data sets (Figure 5), the similar genome-wide patterns of local ancestry obtained indicate that, despite the differences in the software used, there are robust signals of local ancestry discernible in these populations. This is evident in the marked three-way ancestry diversity around the MHC region on BTA23 seen in these results (Figures S16\u0026ndash;S27), particularly for the European hybrid group using both MOSAIC and ELAI and the high-density SNP data set where there is a clear signature of elevated African taurine and indicine ancestry (Figure S16, Figure S22). This tendency for increased genomic introgression in the bovine MHC region is likely a consequence of the balancing selection that maintains high MHC gene polymorphism due to the key function of MHC class I and II proteins in presentation of antigenic peptides from rapidly evolving pathogens to CD4\u003csup\u003e+\u003c/sup\u003e and CD8\u003csup\u003e+\u003c/sup\u003e T cells and via interactions with receptors on natural killer (NK) cells\u0026nbsp;(Codner\u003cem\u003e\u0026nbsp;et al\u003c/em\u003e, 2012; Ellis, 2004; Ellis and Hammond, 2014). Balancing selection acting on pre-existing trans-specific polymorphisms and introgressed variants would give rise to extensive polymorphism in the MHC region\u0026nbsp;(Radwan\u003cem\u003e\u0026nbsp;et al\u003c/em\u003e, 2020)\u0026nbsp;and this has been observed in several species\u0026nbsp;(Hedrick, 2013), including \u003cem\u003eHomo sapiens\u003c/em\u003e where there is evidence that Neanderthal (\u003cem\u003eHomo sapiens neanderthalensis\u003c/em\u003e)\u003cem\u003e\u0026nbsp;\u003c/em\u003eand Denisovan (\u003cem\u003eHomo sapiens\u0026nbsp;\u003c/em\u003esubsp. \u0026lsquo;Denisova\u0026rsquo;) MHC gene variants have readily introgressed into anatomically modern human populations\u0026nbsp;(Liston\u003cem\u003e\u0026nbsp;et al\u003c/em\u003e, 2021; Racimo\u003cem\u003e\u0026nbsp;et al\u003c/em\u003e, 2015). However, it is also important to note that there are known difficulties in genotyping the MHC region\u0026nbsp;(Dicks\u003cem\u003e\u0026nbsp;et al\u003c/em\u003e, 2021).\u003c/p\u003e\n\u003cp\u003eThe correlations observed between the MOSAIC and ELAI results for the high-density SNP data sets (Figures S28\u0026ndash;S33) provides additional evidence that supports the visually apparent similarities in the local ancestry signals observed across the bovine genome using both software approaches. The proportions of the numbers of SNPs passing the genome-wide threshold (\u003cem\u003ez\u003c/em\u003e-score \u0026ge; 2.0) were similar for the MOSAIC and ELAI analyses using both the high- and low-density SNP data sets, which despite the visual differences in the local ancestry results, indicates that similar numbers of ancestry peaks can be detected using the \u003cem\u003ez\u003c/em\u003e-score approach (Table 2). The lack of SNPs passing the threshold for the European \u003cem\u003eB. taurus\u003c/em\u003e ancestry component in the European hybrid group is likely due to the high and relatively uniform proportion of the European \u003cem\u003eB. taurus\u003c/em\u003e ancestry component across the genome. This would give rise to a situation such that no SNPs could pass the threshold of two standard deviations from the mean (Figure 5).\u0026nbsp;Similarly, the lower numbers of SNPs passing the threshold for the African \u003cem\u003eB. taurus\u003c/em\u003e and \u003cem\u003eB. indicus\u003c/em\u003e ancestry components for the trypanotolerant and trypanosusceptible African hybrid groups, respectively, is likely due to the higher proportions of the reference population ancestries to which each hybrid group is most closely related (Figure 5).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe introgressed genomic regions for the three hybrid population samples show several distinct patterns in terms of functional enrichment. All three hybrid groups had significant driver GO terms relating to the MHC (Figure 6), which directly encompass MHC genes (e.g., MHC class I and II) and other genes encoding proteins that interact with MHC gene products. Visual examination of the local ancestry results supports this observation as do previous LAI studies in cattle (Figure 5,\u0026nbsp;Figures S16\u0026ndash;S27)\u0026nbsp;(Buggiotti\u003cem\u003e\u0026nbsp;et al\u003c/em\u003e, 2021; Chen\u003cem\u003e\u0026nbsp;et al\u003c/em\u003e, 2020; Guan\u003cem\u003e\u0026nbsp;et al\u003c/em\u003e, 2022; Li\u003cem\u003e\u0026nbsp;et al\u003c/em\u003e, 2023). Other immune system related driver GO terms were also found to be significant for the three hybrid groups. Several of these terms contain genes that are either up- or downstream from MHC genes in biological pathways, underscoring the importance of MHC-related immunobiology in admixed cattle. More generally, it is notable that immune genes are well represented in the top functional enrichment categories for the introgressed genomic regions since there are well documented differences among European \u003cem\u003eB. taurus\u003c/em\u003e, African \u003cem\u003eB. taurus\u003c/em\u003e, and \u003cem\u003eB. indicus\u0026nbsp;\u003c/em\u003ecattle populations in terms of susceptibilities to various infectious diseases such as bovine tuberculosis caused by \u003cem\u003eMycobacterium bovis\u003c/em\u003e (Allen\u003cem\u003e\u0026nbsp;et al\u003c/em\u003e, 2010; Lee\u003cem\u003e\u0026nbsp;et al\u003c/em\u003e, 2024); East Coast fever and tropical theileriosis caused by \u003cem\u003eTheileria parva\u003c/em\u003e and \u003cem\u003eTheileria annulate\u003c/em\u003e, respectively\u0026nbsp;(Bahbahani and Hanotte, 2015); and AAT caused by \u003cem\u003eTrypanosoma\u003c/em\u003e spp.\u0026nbsp;(Yaro\u003cem\u003e\u0026nbsp;et al\u003c/em\u003e, 2016). In this regard, many of the genes highlighted by LAI through retention of taurine ancestry in the trypanotolerant African hybrid population may represent putative candidate genes underlying the multigenic trypanotolerance trait. For example, in this group, genes associated with haemoglobin, and oxygen binding and transport cellular processes were highlighted by the GO term functional enrichment for retained African \u003cem\u003eB. taurus\u003c/em\u003e genomic ancestry (Figure 6). This may reflect positive selection of genomic variants that enhance control of anaemia, which is understood to be a key feature of the trypanotolerance trait in cattle\u0026nbsp;(Kambal\u003cem\u003e\u0026nbsp;et al\u003c/em\u003e, 2023).\u003c/p\u003e\n\u003cp\u003eDriver GO terms relating to olfaction were also significantly enriched across the three hybrid cattle groups (Figure 6). Genes related to olfaction, such as olfactory receptor (OR) genes, have been identified in previous functional population genomics analyses of admixed cattle populations with taurine and indicine ancestry. These include, for example, genes containing breed-specific missense SNPs in admixed Ethiopian cattle\u0026nbsp;(Zegeye\u003cem\u003e\u0026nbsp;et al\u003c/em\u003e, 2023), genes within genomic regions with evidence for selection signatures in admixed Turkish and Chinese cattle\u0026nbsp;(Demir\u003cem\u003e\u0026nbsp;et al\u003c/em\u003e, 2023; Sun\u003cem\u003e\u0026nbsp;et al\u003c/em\u003e, 2023), and genes in population-differentiated copy-number variation regions (CNVRs) in African hybrid cattle\u0026nbsp;(Jang\u003cem\u003e\u0026nbsp;et al\u003c/em\u003e, 2021). This may be due to the relatively large number of OR genes dispersed across the cattle genome, which, at more than 800 functional loci is comparable to the OR gene repertoire in the domestic dog (\u003cem\u003eCanis familiaris\u003c/em\u003e)\u0026nbsp;(Lee\u003cem\u003e\u0026nbsp;et al\u003c/em\u003e, 2013; Niimura and Nei, 2007). However, recent studies have suggested that more than 500 olfactory receptors may be expressed by macrophages, immune cells involved in detection and phagocytosis of pathogens\u0026nbsp;(Orecchioni\u003cem\u003e\u0026nbsp;et al\u003c/em\u003e, 2022). Macrophages are the host\u0026rsquo;s first line of defence to mycobacterial infections with evasion and reprogramming of host macrophages being key components of host-pathogen interaction\u0026nbsp;(Hall\u003cem\u003e\u0026nbsp;et al\u003c/em\u003e, 2024). In this regard, it is therefore noteworthy that sequence variation at olfactory receptor gene loci has been shown to be associated with susceptibility to \u003cem\u003eM. bovis\u003c/em\u003e infection in cattle\u0026nbsp;(Ring\u003cem\u003e\u0026nbsp;et al\u003c/em\u003e, 2019).\u003c/p\u003e\n\u003cp\u003eAn alternative hypothesis for enrichment of olfaction-related genes, however, could relate to detection of odorants associated with MHC diversity and selection of mates\u0026nbsp;(Santos\u003cem\u003e\u0026nbsp;et al\u003c/em\u003e, 2010; Ziegler\u003cem\u003e\u0026nbsp;et al\u003c/em\u003e, 2010), although this is unlikely to be a major factor in managed male-biased cattle husbandry systems. Similarly, cattle populations under intensive human control and management are unlikely to require a keen sense of smell to find food and avoid danger; however, introgressive natural selection is likely to be acting on olfaction-related genes in free-ranging admixed African cattle populations exposed to a wide range of environmental and predation challenges\u0026nbsp;(Mwai\u003cem\u003e\u0026nbsp;et al\u003c/em\u003e, 2015).\u003c/p\u003e\n\u003cp\u003eThe comparative LAI analyses we have performed using low- and high-density SNP array data sets in various groups of admixed cattle with taurine and indicine genomic ancestry provides a framework for applying LAI to much larger data sets that will encompass millions of SNPs. In addition, our results will provide a context for understanding the genomic basis of heterosis in admixed cattle, particularly as it dissipates beyond the F\u003csub\u003e1\u003c/sub\u003e generation\u0026nbsp;(Syrstad, 1985). Also, identification of genomic regions that have been subject to introgressive selection will provide important information for genome-enabled breeding in admixed cattle populations, particularly in Africa\u0026nbsp;(Marshall\u003cem\u003e\u0026nbsp;et al\u003c/em\u003e, 2019; Mrode\u003cem\u003e\u0026nbsp;et al\u003c/em\u003e, 2019). Finally, the methodologies that we describe here can be applied to other hybrid cattle populations, for example, admixed breeds in Anatolia and the Middle East that have had much longer histories of taurine/indicine genetic exchange\u0026nbsp;(Verdugo\u003cem\u003e\u0026nbsp;et al\u003c/em\u003e, 2019).\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe thank Morris Agaba, Olivier Hanotte, Stephen J. Kemp, John A. Browne, Daniel G. Bradley, and Stephen V. Gordon for assistance with sample resources and for useful scientific discussion. This research work was funded by Science Foundation Ireland (SFI) under Investigator Programme Awards (grant nos: SFI/01/F.1/B028 and SFI/15/IA/3154). JAW was supported by the Centre for Research Training in Genomics Data Science (grant no. SFI/18/CRT/6214).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contribution statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eGPM was responsible for analysis, data curation, lab work, interpretation of results, study design, visualisation, and writing - original draft. JAW was responsible for data provision, lab work, interpretation of results, and writing - review \u0026amp; editing. SIN was responsible for data provision, interpretation of results, and writing - review \u0026amp; editing. LAF was responsible for data provision, interpretation of results, and writing - review \u0026amp; editing. MST was responsible for interpretation of results, software provision, and writing - review \u0026amp; editing. EWH was responsible for lab work, sample collection and provision, and writing - review \u0026amp; editing. GMO was responsible for lab work, sample collection and provision, and writing - review \u0026amp; editing. KGM was responsible for lab work, sample collection and provision, and writing - review \u0026amp; editing. TJH was responsible for guidance and writing - review \u0026amp; editing. DEM was responsible for data provision, funding acquisition, lab work, interpretation of results, sample collection and provision, study design, supervision, and writing - original draft.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflict of Interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData archiving\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNew Illumina\u003csup\u003e\u0026reg;\u003c/sup\u003e BovineHD 777K BeadChip SNP data sets generated for this study have been deposited in the Dryad data repository at doi.org/10.5061/dryad.w3r22810n. The computer code required to repeat and reproduce the analyses is available at doi.org/10.5281/zenodo.11491949.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResearch Ethics Statement \u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFor this study new Illumina\u003csup\u003e\u0026reg;\u003c/sup\u003e BovineHD 777K BeadChip SNP data sets were generated for 39 individuals (23 Somba, 8 N\u0026rsquo;Dama and 8 Boran). The Somba individuals were obtained from DNA samples that were previously published as part of microsatellite-based surveys of cattle genetic diversity in the early 1990s and the N\u0026rsquo;Dama and Boran individuals were obtained from unpublished DNA samples collected during a time-course infection experiment carried out in 2003. This livestock DNA sampling work was completed prior to the requirement for Institutional Permission in Ireland, which is based on European Union Directive 2010/63/EU; however, all efforts were made to ensure ethical handling of all animal subjects.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAlbrechtsen A, Nielsen FC, Nielsen R (2010). Ascertainment biases in SNP chips affect measures of population divergence. \u003cem\u003eMol Biol Evol\u003c/em\u003e \u003cstrong\u003e27\u003c/strong\u003e(11)\u003cstrong\u003e:\u003c/strong\u003e 2534-2547.\u003c/li\u003e\n\u003cli\u003eAllen AR, Minozzi G, Glass EJ, Skuce RA, McDowell SW, Woolliams JA\u003cem\u003e et al\u003c/em\u003e (2010). 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Self/nonself perception, reproduction and the extended MHC. \u003cem\u003eSelf Nonself\u003c/em\u003e\u003cstrong\u003e1\u003c/strong\u003e(3)\u003cstrong\u003e:\u003c/strong\u003e 176-191.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"heredity","isNatureJournal":false,"hasQc":false,"allowDirectSubmit":false,"externalIdentity":"hdy","sideBox":"Learn more about [Heredity](http://www.nature.com/hdy/)","snPcode":"41437","submissionUrl":"https://mts-hdy.nature.com/cgi-bin/main.plex","title":"Heredity","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"ejp","reportingPortfolio":"Nature AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-4622059/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4622059/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cem\u003eBos taurus\u003c/em\u003e (taurine) and \u003cem\u003eBos indicus\u003c/em\u003e (indicine) cattle diverged at least 150,000 years ago and, since that time, substantial genomic differences have evolved between the two lineages. During the last two millennia, genetic exchange in Africa has resulted in a complex tapestry of taurine-indicine ancestry, with most cattle populations exhibiting varying levels of admixture. Similarly, there are several Southern European cattle populations that also show evidence for historical gene flow from indicine cattle, the highest levels of which are found in the Central Italian White breeds. Here we use two different software tools (MOSAIC and ELAI) for local ancestry inference (LAI) with genome-wide high- and low-density SNP array data sets in hybrid African and Italian cattle populations and obtained broadly similar results despite critical differences in the two LAI methodologies used. Our analyses identified genomic regions with elevated levels of retained or introgressed ancestry from the African taurine, European taurine, Asian indicine lineages. Functional enrichment of genes underlying these ancestry peaks highlighted biological processes relating to immunobiology and olfaction, some of which may relate to differing susceptibilities to infectious diseases, including bovine tuberculosis, East Coast fever, and tropical theileriosis. Notably, for retained African taurine ancestry in admixed trypanotolerant cattle we observed enrichment of genes associated with haemoglobin and oxygen transport. This may reflect positive selection of genomic variants that enhance control of severe anaemia, a debilitating feature of trypanosomiasis disease, which severely constrains cattle agriculture across much of sub-Saharan Africa.\u003c/p\u003e","manuscriptTitle":"Genome-wide local ancestry and the functional consequences of admixture in African and European cattle populations","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-07-31 10:53:51","doi":"10.21203/rs.3.rs-4622059/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"revise","date":"2024-09-30T15:23:52+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"This content is not available.","date":"2024-09-28T02:52:53+00:00","index":3,"fulltext":"This content is not available."},{"type":"editorInvitedReview","content":"This content is not available.","date":"2024-09-05T18:43:27+00:00","index":2,"fulltext":"This content is not available."},{"type":"editorInvitedReview","content":"This content is not available.","date":"2024-08-04T14:28:22+00:00","index":1,"fulltext":"This content is not available."},{"type":"reviewerAgreed","content":"This content is not available.","date":"2024-07-25T07:25:26+00:00","index":3,"fulltext":"This content is not available."},{"type":"reviewerAgreed","content":"This content is not available.","date":"2024-07-24T15:13:25+00:00","index":2,"fulltext":"This content is not available."},{"type":"reviewerAgreed","content":"This content is not available.","date":"2024-07-08T02:16:34+00:00","index":1,"fulltext":"This content is not available."},{"type":"reviewersInvited","content":"","date":"2024-07-07T22:12:35+00:00","index":"","fulltext":""},{"type":"submitted","content":"Heredity","date":"2024-06-22T13:16:00+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-06-22T13:16:00+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"heredity","isNatureJournal":false,"hasQc":false,"allowDirectSubmit":false,"externalIdentity":"hdy","sideBox":"Learn more about [Heredity](http://www.nature.com/hdy/)","snPcode":"41437","submissionUrl":"https://mts-hdy.nature.com/cgi-bin/main.plex","title":"Heredity","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"ejp","reportingPortfolio":"Nature AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"f6b760cf-391c-4e0e-b103-dacec676aed9","owner":[],"postedDate":"July 31st, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[{"id":34243412,"name":"Biological sciences/Genetics/Agricultural genetics"},{"id":34243413,"name":"Biological sciences/Genetics/Population genetics/Genetic variation"},{"id":34243414,"name":"Biological sciences/Evolution/Evolutionary genetics"}],"tags":[],"updatedAt":"2024-11-09T08:06:51+00:00","versionOfRecord":{"articleIdentity":"rs-4622059","link":"https://doi.org/10.1038/s41437-024-00734-w","journal":{"identity":"heredity","isVorOnly":false,"title":"Heredity"},"publishedOn":"2024-11-08 05:00:00","publishedOnDateReadable":"November 8th, 2024"},"versionCreatedAt":"2024-07-31 10:53:51","video":"","vorDoi":"10.1038/s41437-024-00734-w","vorDoiUrl":"https://doi.org/10.1038/s41437-024-00734-w","workflowStages":[]},"version":"v1","identity":"rs-4622059","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4622059","identity":"rs-4622059","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}