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. 2026 Jul 27;18(8):evag187. doi: 10.1093/gbe/evag187

New Country for Old Mice: The Recent Colonization History of Mus musculus domesticus in the Americas

Kennedy D Agwamba 1,2,✉,c,b, Sofia I Gabriel 3,b, Jeremy B Searle 4, Jeff Wall 5, Michael W Nachman 6,7,8,✉,c
Editor: Diego Ortega-Del Vecchyo
PMCID: PMC13469698  PMID: 42504080

Abstract

Humans have profoundly influenced the distribution of organisms globally, particularly those that live in close association with human societies. The western house mouse, Mus musculus domesticus, expanded from the Fertile Crescent into Europe several thousand years ago alongside early agricultural societies and was subsequently introduced worldwide through European maritime expansion. Given their close ecological association, human commensals, such as the house mouse, are expected to reflect historic patterns of human movement. Using a dataset of 276 exome sequences of mice sampled from Western Europe, North America, and South America, we reconstruct the demographic history of house mice in the Americas and draw genetic connections to contemporary European population from which they were historically sourced. Admixture graphs reveal that house mice from the Atlantic coast of the Iberian Peninsula are the sister group to all American populations, and a secondary contribution from southern UK populations is evident in North America. Analyses of population structure further support shared ancestry between the Atlantic coast of the Iberian Peninsula and both American continents, between the United Kingdom and North America, between Portugal and Brazil, and between Spain and Argentina. Demographic modeling reveals that American mouse populations diverged roughly within the last 500 years, consistent with the post-15th century arrival of Western European people to the Americas. Together, these results identify Western European mouse populations that share close ancestry with American mouse populations and illustrate how the demographic history of a widespread human commensal species can mirror fine-scale patterns of human migration.

Keywords: house mouse, demographic inference, population history, genetics, genomics, phylogenetics

Significance.

Long-standing commensal relationships provide a powerful lens through which to study human-mediated dispersal. The western house mouse, a foundational model organism in genetics and evolutionary biology, spread globally alongside human populations following the rise of agriculture. Yet, the genomic consequences of this recent expansion remain insufficiently characterized. Using exome-wide data from 276 mice sampled across Western Europe and the Americas, we reconstruct the colonization history of house mice across the Western Hemisphere. We identify the western Iberian Peninsula as the primary source of American populations and detect a secondary contribution from the southern United Kingdom to North America. Divergence dates fall within the past ∼500 years, consistent with post-15th century European maritime expansion and settlement in the Americas. These results demonstrate that population genomic structure in house mice mirrors migratory patterns and geopolitical imprints of colonial human populations, highlighting how genome-scale data can reveal the ecological consequences of anthropogenic dispersal.

Introduction

During the Anthropocene, human migration has facilitated the global dispersal of many species outside of their native range (Hulme 2009; Pimentel et al. 2000; Genovesi et al. 2012). House mice, as human commensals exploiting stored food supplies and adapting to live in anthropogenic habitats, first associated with humans roughly 12,000 to 15,000 years ago in the Fertile Crescent and subsequently migrated with humans to new geographic areas (Pocock et al. 2005; Cucchi et al. 2005, 2020; Weissbrod et al. 2017; Agwamba and Nachman 2023; Agwamba et al. 2025). In particular, the western house mouse, Mus musculus domesticus, migrated with humans to Western Europe around 2,000 to 3,000 years ago (Cucchi et al. 2020; Agwamba et al. 2025). M. m. domesticus further expanded its geographic range to the Americas, Africa, and Oceania, closely tracking the global maritime movements and settlement history of Western European people over the last millennium (Phifer-Rixey and Nachman 2015).

The transoceanic dispersal of house mice as stowaways on ships of seafaring Europeans has been supported by previous studies using mitochondrial genetic markers, which have allowed a broad-brush assessment of the colonization history of the western house mouse (Gabriel et al. 2010, 2015, 2024; Jones et al. 2012; Jones et al. 2013). These studies examined genetic affinities between European house mice and house mice from the Americas and the Atlantic islands, offering insight into the initial westward spread of house mice. However, because these studies were based on short mitochondrial sequences, they had limited precision and revealed only matrilineal patterns of inheritance. Moreover, these studies were unable to utilize the powerful inferential methods available for genomic data to reveal demographic history. By contrast, genome-wide data allow direct estimation of ancestry proportions, phylogenetic relationships, migration events, and divergence times. Such data can therefore move beyond identifying likely source regions and permit explicit testing of hypotheses regarding the timing of introduction, admixture among source populations, and alternative demographic scenarios involving multiple introductions associated with European colonization of the Americas.

Previous studies have shown that the geographic distributions and demographic histories of human commensal species, such as black rats (Aplin et al. 2011; Puckett et al. 2016; Yu et al. 2022), brown rats (Puckett and Munshi-South 2019), pigeons (Stringham et al. 2012), and house sparrows (Robbins 1973), have been shaped as a consequence of human migration, trade, and colonization associated with European imperialism. These studies form part of a broader literature on ecological imperialism and the biotic consequences of empire, which recognizes that European expansion redistributed entire communities of plants, animals, and microorganisms around the globe (Crosby 1972, 1986). However, few studies have attempted to reconstruct introductions associated with specific episodes of European colonization in the Americas using genome-scale data. House mice are particularly well suited for this purpose because their dependence on human settlements creates an unusually close correspondence between human and mouse demographic histories (Agwamba and Nachman 2023; Agwamba et al. 2025).

The recent human colonization history of the Americas provides a compelling historical context for the study of commensal species such as the house mouse. Notably, the Portuguese, Spanish, and English held substantial and enduring colonial presences in the Americas, thus creating complex historical pathways for the introduction of house mice. We hypothesize that genome-wide ancestry in American populations of M. m. domesticus will mirror the major axes of European colonial expansion in both source and timing. Specifically, we predict that (i) mice from western Iberia associated with early Spanish and Portuguese trans-Atlantic voyages will form the primary ancestral source for populations across the Americas; (ii) North American populations will show substantial ancestry from the United Kingdom, consistent with later English colonization; and (iii) regions shaped by overlapping colonial spheres of influence will exhibit detectable genomic admixture reflecting secondary introductions. Under this model, divergence times between Western European and American populations should coincide with historically documented waves of transoceanic migration beginning in the late 15th century, and patterns of genetic structure among American populations should recapitulate the geopolitical partitioning of colonial territories. Here, we investigate these hypotheses using exome sequences of 83 contemporary samples of house mice sampled from their presumed native ranges in Western Europe and 193 contemporary samples from North and South America. Analysis of variation across the genome reveals a close connection between the colonization history of humans and house mice and provides a comprehensive model for understanding the diversity and dispersal of this commensal species across the Western hemisphere.

Results

Globally Distributed Populations of Mice Form Distinct Genetic Clusters

We analyzed genome-wide single nucleotide polymorphisms (SNPs) from exome sequences of wild-caught house mice from Europe and the Americas. These included 193 North and South American house mice from latitudinal transects in North, Central, and South America (Phifer-Rixey et al. 2018; Ferris et al. 2021; Gutiérrez-Guerrero et al. 2024) and 83 Western European house mice (Harr et al. 2016; Agwamba et al. 2025) (Fig. 1a). Most of the mice were collected as population samples consisting of 8 to 10 individuals, with individuals separated by at least 500 m to minimize the likelihood of sampling close relatives. Additional individuals were included to improve geographic coverage in regions not represented by these population samples. For analyses requiring population assignments, individuals collected within the same geographic region were grouped into a single population when they formed a common genetic cluster in principal component analysis and showed no evidence of genetic subdivision (Fig. S1). A complete list of individuals, sampling localities, and population assignments is provided in Table S1. Sequences from North and South American mice were generated using exome-capture probes targeting coding regions and adjacent flanking sequences. European samples consisted of whole-genome sequences from which the corresponding exome-capture regions were extracted. Sequencing depth was broadly comparable across datasets (see Methods for details).

Fig. 1.

For image description, please refer to the figure legend and surrounding text.

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Geographic locations and population structure of house mouse samples from Western Europe, North America, and South America. a) Map displays approximate sampling locations of house mice from North America, Mexico, South America, Atlantic Iberia, Mediterranean, Northern Europe, and Iran. Each point corresponds to a population as defined in their original study (see Table S1). b) Principal component analysis of house mice from corresponding sampled geographic locations in (a).

Principal component analysis (PCA) shows that house mouse samples form clusters concordant with geographic location, and their relative locations in PC space approximately mirror their relative locations on a map (Fig. 1b; Fig. S1), as seen in human populations (eg Novembre et al. 2008). PC1, accounting for 19% of the variation, largely separates populations by latitude, while PC2, accounting for 11% of the variation, separates populations mainly by longitude.

Phylogenetic Analyses Reveal a Western Iberian Origin for All Mice in the Americas and a Secondary Migration Wave Into North America From the Southern United Kingdom

We investigated the genetic relationship between European and American house mice using TreeMix, a model that builds maximum likelihood population trees while accounting for gene flow between diverged populations using migration edges (Pickrell and Pritchard 2012). The TreeMix hill-climbing method starts from an optimal mixture-free tree and sequentially adds migration edges to account for deviations between the model predictions and the observed data. This ensures that the fit of the model improves with the addition of each edge (see Fig. S2), but it also leaves the TreeMix algorithm susceptible to falling into local optima (Molloy et al. 2021; Nielsen et al. 2023). To ensure that we selected an optimal number of migration edges in the TreeMix analysis, we used OptM, which exploits the second-order rate of change in likelihood to select an ideal number of edges across incremental values on the number of edges (Fitak 2021). The number of edges with the greatest second-order rate of change in likelihood was m = 1 (Fig. 2a and b). In this admixture graph, all populations of house mice from the Americas are inferred to share their most recent common ancestor with contemporary house mouse populations from the Atlantic coast of the Iberian Peninsula (ie Portugal and southwest Spain). The sole migration edge goes from the southern United Kingdom to the base of the North American taxon with a migration weight (m.w.) of 0.290147.

Fig. 2.

For image description, please refer to the figure legend and surrounding text.

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Phylogenetic modeling of genetic relationship between Western European and American house mice. a) Admixture graph of M. m. domesticus constructed using TreeMix with one migration edge. The migration edge depicts gene flow from the southern UK (S. UK) to the base of the North American taxon. b) Results from OptM (Fitak 2021) depicting second-order rate of change in likelihood (Δm) as a function of the number of migration edges, m, for TreeMix results considering up to 18 migration edges. c) D-suite results displaying signals of gene flow between Western European and American house mice. The f-branch statistic, fb, measures excess allele sharing between individual populations labeled on the x axis against the same populations and internal branch nodes of expanded phylogeny depicted on the y axis. The f-branch statistic ranges from 0, colored in white, to the highest relative fb value, colored in dark red in the scaled colored matrix. To avoid false positives from short internal branches, D-suite sets fb to 0 for any nonsignificant results. Gray cells represent comparisons that cannot be computed due to limitations in the tree structure—such as those involving the most basal internal branches, where no appropriate four-taxon test exists. Location abbreviations (in alphabetical order): Arizona (AZ), Brasilia (BRAS), Edmonton (EDM), Florida (FL), Gaiman (GAIM), Georgia (GRG), Germany (GER), Guernsey (GUER), Iran (IRA), Italy (ITA), Manaus (MANA), Maringa (MRNGA), Mexico (MEX), Missoula, Montana (MSSL), North France (N. FRA), Northeast Spain (NE SPA), New Hampshire/Vermont (NHVT), Northern UK (N. UK), Pennsylvania (PENN), Porto Velho (POVE), Portugal (PTGL), Provo, Utah (PROVO), South France (S. FRA), Southern UK (S. UK), St. George, Utah (USTG), Southwest Spain (SW SPA), Tandil (TAND), Uruguaiana (URGA), Ushuaia (USHU), Virginia (VIRG). The strongest signal of shared alleles unites mice from Mexico (MEX) and mice from southwest Spain (SW SPA) and Portugal (PTGL), as indicated by the first two dark red squares in the row corresponding to Mexico (16 rows from the bottom). The next strongest signal unites the ancestral lineage leading to the southern UK (S. UK) with Florida and other North American samples, as indicated by the horizontal group of dark red squares nine rows from the bottom.

The next optimal admixture graph supported under OptM contained four edges (ie m = 4; Fig. 2b; Fig. S2). At m = 4, in addition to the migration edge from the southern United Kingdom to North America (m.w. = 0.30822), we observed a migration edge from SW Spain to Mexico (m.w. = 0.293483), a migration edge from the United Kingdom to the base of eastern North America (m.w. = 0.102384), and a migration edge from Mexico to Arizona (m.w. = 0.16857). Notably, the migration weights (which indicate the relative strength of gene flow) suggest that the greatest level of gene flow among these populations was from the southern United Kingdom to North America.

To provide further support for the migration events inferred using TreeMix, we used D-suite to compute the f-branch statistic to highlight populations connected by excess allele sharing (Fig. 2c) (Malinsky et al. 2021). The strongest signal indicates that mice from Mexico share excess derived alleles with mice from southwest Spain and, to a lesser degree, Portugal, as indicated by the dark red colors in the first two squares adjacent to Mexico in Fig. 2c. This draws a direct connection between the Mexican population and the Spanish population, or more broadly, the Atlantic Iberian house mice, that reflects human colonization history and recapitulates the results found in the phylogenetic analysis. The next strongest signal of gene flow connects the ancestral lineage leading to the southern United Kingdom with Florida. The non-independence of the many possible four-taxon D tests can result in correlated D-statistics in related lineages. Indeed, the signal of gene flow from the southern United Kingdom to Florida extends to all the North American samples to a lesser extent (row of red squares nine rows from the bottom in Fig. 2c), suggesting gene flow from the southern United Kingdom to North America, as depicted in the TreeMix analysis (Fig. 2a). The strength of the signal to Florida may be due to more recent gene flow into Florida or a higher frequency of introgressed alleles. We also observed signals of admixture in Florida in a previous study (Agwamba and Nachman 2023), and here, we use the f3 statistics to demonstrate that mice from Florida can be explicitly modeled as a mixture of mice from the United Kingdom or North America and mice from the Atlantic Iberian Peninsula or South America (Table S2). The third largest f-branch signal indicates gene flow between southwestern Spain and the internal branch leading to all South American house mice.

Ancestral Contributions of House Mice From Atlantic Iberia and Southern United Kingdom to the Americas

The phylogenetic tree in Fig. 2a shows the relationship between mice from the Atlantic coast of the Iberian Peninsula and from the southern United Kingdom to mice in the Americas. To directly infer the relative genetic contributions of mice in Atlantic Iberia and the southern United Kingdom to the mice in the Americas, we performed supervised inference of shared ancestral fractions using ADMIXTURE (Alexander et al. 2009).

In a supervised estimation of proportions of shared ancestry from Atlantic Iberia and the United Kingdom in the American populations, much of South America's ancestral proportions mapped to Atlantic Iberian ancestry (between 92% and 99%) (Fig. 3b). In the Mexican population, between 5% and 24% mapped to UK ancestry. In contrast, in the North American population, UK ancestry makes up most of the inferred ancestral fractions. However, both Florida (between 14% and 28%) and western North America (between 0.7% and 25%) possess notable levels of ancestry with Atlantic Iberia.

Fig. 3.

For image description, please refer to the figure legend and surrounding text.

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Inference of Western European ancestry in North and South America. a) Schematic summarizing the inferred historical relationship between UK and Iberian house mice with recently introduced populations in North and South America. b) ADMIXTURE plot using supervised analysis mapping proportions of shared ancestry found in North America and South America shared with Atlantic Iberian (red) and UK populations (blue) of house mice. Locations in the Americas in the ADMIXTURE plot, from left to right, are as follows: New Hampshire/Vermont (NHVT), Pennsylvania (PENN), Virginia (VIRG), Georgia (GRG), Florida (FL), Edmonton (EDM), Missoula, Montana (MSSL), Provo, Utah (PROVO), St. George, Utah (USTG), Arizona (AZ), Mexico (MEX), Manaus (MANA), Porto Velho (POVE), Brasilia (BRAS), Maringa (MRNGA), Uruguaiana (URGA), Tandil (TAND), Gaiman (GAIM), Ushuaia (USHU). c) Provided phylogenetic inference finds mice from the Americas share a more recent ancestral lineage with mice from the Atlantic coast of the Iberian Peninsula than with mice from the Mediterranean or Northern Europe (Fig. 2), GADMA was used to infer two-epoch, single split demographic models by log-likelihood provided a single population size change before and after divergence between Atlantic Iberia house mice and house mouse populations in the Americas (Virginia, Manaus, Provo, and Gaiman). The resulting schematic summarizes the results from GADMA in Fig. S3. The striped region covers the range of point estimates for divergence times (253 to 551 years ago) inferred by the demographic models of the highest composite likelihood.

Private alleles can identify ancestry by highlighting genetic variants that are unique to a group (Neel and Thompson 1978; Calafell et al. 1998; Szpiech et al. 2008; Dures et al. 2019; Bergström et al. 2020; Hollfelder et al. 2020; vonHoldt et al. 2024). Analyzing the distribution of private alleles allowed us to identify shared variants, which likely resulted from disproportionate genetic contributions from different Atlantic Iberian populations to South America (Fig. 4). To test the hypothesis that the colonization history of South America by the Portuguese and the Spanish is reflected in patterns of genetic variation in mice, we employed rarefaction analysis to compute the mean number of private alleles per locus between pairings of the South American populations with Portugal or SW Spain (Szpiech et al. 2008). Populations from northern South American, all sampled in Brazil, share a greater mean number of private alleles with Portugal compared to SW Spain (Fig. 4a). Conversely, populations from southern South America, consisting of three Argentinian populations and one Brazilian population directly bordering Argentina, share a greater mean number of private alleles with SW Spain compared to Portugal (Fig. 4b). This pattern is also reflected in a supervised admixture analysis, which largely infers shared ancestry between Portugal and northern South America, and majority shared ancestry between SW Spain and southern South America (Fig. 4c).

Fig. 4.

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Distribution of Atlantic Iberian private alleles in South America. Rarefaction analysis provides a) mean number of private alleles per locus as a function of sample size for Portuguese and Spanish (SW Spain) private alleles found in N. (northern) South America and b) mean number of private alleles per locus as a function of sample size for Portuguese and Spanish private alleles found in S. (southern) South America. Rarefaction curves in orange align with pairings to Portugal, while green aligns with pairings with SW Spain. These color codings are consistent with those found in c) a supervised inference of Spanish and Portuguese ancestry in South America.

Patterns and Timing of Divergence and Admixture Link House Mouse Introduction to Human Colonization History

To determine the approximate time of divergence between Iberian and American populations, we estimated split times using dadi via GADMA for representative populations from different geographic areas (Fig. 3c; Fig. S3) (Gutenkunst et al. 2009; Noskova et al. 2020). Given a generation time of 1 year (Geraldes et al. 2008; Gray et al. 2014), the demographic models with the highest composite likelihood estimated divergence times of approximately 250 to 550 years ago. Each of the four models inferred a constant population size for the Iberian population following divergence and exponential growth for the American populations postdivergence. The point estimate for the divergence time for eastern North America (551 ± 220 years ago) predates the divergence time for western North America (253 ± 313 years ago), though the two confidence intervals do significantly overlap. This is also the case for northern South America (439 ± 570 years ago) relative to southern South America (315 ± 287 years ago). Inferred divergence times are dependent on the accuracy of fixed model parameters, such as mutation rate and generation time (Fig. S3). Given these constraints, the relative timing of the arrival of house mice to each region of the Americas aligns well with the history of Western European maritime expansion and colonization of the Americas.

Discussion

Humans historically have had a tremendous influence on their environment, impacting ecosystems and affecting the ecology of natural populations. House mice, for example, have lived in association with humans since at least 8,000 BCE (Cucchi et al. 2005). Since the advent of agriculture, house mice have exploited stored grain and other accessible food storage and have lived in human dwellings. As different human populations have migrated and colonized different regions of the world, mice have presumably traveled with them. This study explores the unique connection between house mice and humans within recent history in the Western Hemisphere, making comparisons of exome sequences of mice from Western Europe and the Americas, two areas tied by colonial history.

Clustering of genetic variation by the first two principal components broadly mirrors the geographic locations from which house mice were sampled. Human genetic variation also closely mirrors geography in densely sampled human populations such as in Western Europe (Novembre et al. 2008; Lazaridis et al. 2014; Gilbert et al. 2022). The population structure of house mice in Western Europe includes three genetic clusters corresponding to Northern Europe, the Mediterranean, and the Atlantic coast of the Iberian Peninsula (Fig. 1), consistent with previous surveys of genetic variation in Western Europe (Suzuki et al. 2013; Morgan et al. 2022; Agwamba et al 2025). These genetic clusters generally match well with the genetic structure found among human populations in Western Europe (Agwamba et al. 2025).

Analysis of population structure, phylogenetic relationships, and gene flow supports a connection between house mice in the Americas and those from the Atlantic coast of the Iberian Peninsula, as well as a connection between North American mice and those from the southern UK populations of the Northern European cluster. The results obtained using TreeMix indicate a shared common ancestry between Western European and American house mice that is consistent with the history of human colonization. Following the voyages of Columbus, numerous ships potentially harboring rodents as stowaways crossed the Atlantic Ocean to transport human migrants and cargo to North and South America (Crosby 1972; Gabriel et al. 2010; Jones et al. 2013). Having commissioned the exploration by Columbus, the Spanish were among the first Western European people to have significant exchanges with the Americas, followed by the Portuguese (Knight and Liss 1991). The phylogenetic relationship of house mice from the Atlantic coast of the Iberian Peninsula as the sister group to all house mice in the Americas reflects this historical relationship (Fig. 2). The United Kingdom commissioned voyages to the Americas in the late 16th century and concentrated their colonization efforts on North America beginning in the 17th century (Knight and Liss 1991; McCusker 2006). The migration edge from the southern United Kingdom to North America reflects this secondary wave of migration following the initial introduction of house mice from the Iberian Peninsula. These associations agree with previous reports of the shared ancestry between American and Western European mice based on smaller samples or fewer loci (Tichy et al. 1994; Morgan et al. 2022; Agwamba and Nachman 2023; Gabriel et al. 2024). Moreover, the timing of the introduction of house mice to the Americas can be inferred from the divergence dates estimated in our demographic models (Fig. 3; Fig. S3), which all lie roughly within the past several hundred years.

Recent work based on mtDNA suggested that mice from Latin America had a predominantly Southern European (Iberian) origin but also suggested additional contributions from Northern Europe and Macaronesia that may reflect complex introduction pathways aligned with colonial shipping networks (Gabriel et al. 2024). Our genome-wide analyses broadly support these conclusions but substantially refine them. In particular, our supervised estimation of ancestry proportions also corroborates the historical associations between Atlantic Iberia and South America and the United Kingdom and North America (Fig. 3b). The larger relative ancestral imprint of the Atlantic Iberian population in North American mice compared to the UK ancestral proportions in South American mice is consistent with findings from TreeMix (Fig. 2), which suggests that mice from Atlantic Iberia arrived first, followed by the introduction in North America of mice from the United Kingdom. Furthermore, analysis of gene flow in the mice specifically highlights a connection between Spain and Mexico (Fig. 2). The Spanish colonized the territory of present-day Mexico in 1521 and maintained a significant presence in this region until the state of Mexico gained independence in 1821 (Altman et al. 2003).

Human population history includes close connections between Brazil and Portugal and between Spain and much of the rest of South America. To investigate specific connections between Iberian and South American house mice, we compared private allele sharing between mouse populations in Portugal or Spain with those in South America (Fig. 4). Northern South American populations, all sampled from Brazil, share more private alleles with Portugal, while southern South American populations, consisting of three Argentinian populations and one Brazilian population directly bordering Argentina, share more private alleles with Spain, mirroring patterns in human history. Spain and Portugal famously divided their claims to the land and resources of the Americas with the 1494 Treaty of Tordesillas, which established a meridian granting Spain the lands to the West and granting Portugal the lands to the East of that meridian line. Under this treaty, the eastern portion of South America, encompassing largely modern-day Brazil, was claimed and subsequently colonized by the Portuguese (Lockhart and Schwartz 1983; Knight and Liss 1991; Burkholder and Johnson 2018). Human genetic studies have indicated strong patrilineal genetic signals from Portugal to Brazil and matrilineal signals from Spain to Argentina (Carvalho-Silva et al. 2001; Catelli et al. 2011). The sharing of private alleles between mice from Portugal and Brazil, and between those from Spain and Argentina, suggests genetic signatures consistent with historical patterns of human colonization.

Altogether, we discovered striking connections between the house mice found throughout North and South America and populations found in Western Europe. The substantial genomic dataset and associated analyses allowed us to establish ties between the history of colonization and settlement of house mice and humans in the Americas. Inferred dates of colonization suggest that mice arrived with early European colonists. Moreover, genetic affinities in mice match political and cultural connections in humans associated with colonizations by different countries. As close commensals, house mice remarkably mirror aspects of human migration history, and this concordance reflects the impact of human migration on the distribution of a commensal species.

Materials and Methods

Population Sampling and Sequence Extraction

We assembled a genomic dataset comprised of 100 North American house mice derived from two latitudinal transects sampled in eastern and western North America (Phifer-Rixey et al. 2018; Ferris et al. 2021), 93 Central and South American house mice (Gutiérrez-Guerrero et al. 2024), and 83 Western European house mice (Harr et al. 2016; Agwamba et al. 2025) (Fig. 1a). All sequences are publicly available from the NCBI Short Read Archive (SRA PRJNA1050608, PRJNA397150, PRJNA718321, PRJNA776897) or European Nucleotide Archive (ENA PRJEB9450).

In general, wild mice from Europe and the Americas were caught more than 500 m from one another to avoid sampling of closely related individuals, as this distance greatly exceeds the average dispersal distance of house mice (Pocock et al. 2005). Mice from North and South America were collected as population samples (typically N = 10 individuals per population) along latitudinal transects with populations separated by several hundred kilometers. Some of the mice collected in Europe consisted of population samples of ∼8 individuals, while additional samples were obtained in smaller groups with the aim of broadly covering presumptive geographic locations from which house mice were introduced to the Americas. All mice were collected between 1987 and 2013. The locations, identifiers, population of origin, and accession numbers for all samples are given in Table S1.

In all cases, DNA was extracted from the liver, spleen, ear, or kidney using salt extractions or Qiagen DNAeasy or Gentra Puregene kits. All reads were mapped to the mouse GRCm38/mm10 reference genome. The sequences from samples in the Americas consist of complete exomes (Phifer-Rixey et al. 2018; Ferris et al. 2021; Gutiérrez-Guerrero et al. 2024), while the sequences from samples in Europe were of whole genomes (Harr et al. 2016; Agwamba et al. 2025) from which exome sequences were extracted (see below). The average sequence depth was 15× for the eastern North American samples (Phifer-Rixey et al. 2018); 31× for the western North American samples (Ferris et al. 2021); 21× for the South American samples (Gutiérrez-Guerrero et al. 2024); 20× for the samples from Germany, France, and Iran (Harr et al. 2016); and 13× for all other European samples (Agwamba et al. 2025).

The North and South American sequences in Phifer-Rixey et al. (2018), Ferris et al. (2021), and Gutiérrez-Guerrero et al. (2024) were derived using exome-capture probes, which capture both exons and adjacent noncoding regions. We extracted the intersection of these exome-capture regions from the European whole genomes using bedtools and generated a merged VCF using vcftools (Quinlan and Hall 2010; Danecek et al. 2011). To facilitate analyses based on discrete genotype calls (PCA, ADMIXTURE, TreeMix, f-statistics, and private allele analyses), we generated a filtered autosomal SNP dataset from the merged VCF. Sites were required to have a quality score > 30, genotype calls present in at least 95% of individuals, sequencing depth ≥ 3×, Hardy–Weinberg equilibrium P > 0.001, and minor allele frequency ≥ 5%. Following filtering, 82,704 autosomal SNPs remained. For PCA, ADMIXTURE, and TreeMix, sites were pruned using a window size of 50 kb and an r2 threshold of 0.5 (see Supplementary material). Demographic analyses were conducted separately and did not use this filtered SNP dataset. Instead, site frequency spectra were estimated directly from BAM files using ANGSD and realSFS as described below.

Population Structure and Analysis of Gene Flow

Principal components were computed using PLINK to generate a broad-scale genetic comparison of these samples based on genetic variation (Purcell et al. 2007). To investigate phylogenetic relationships and obtain a better understanding of global gene flow, we used TreeMix to infer a maximum likelihood population tree with migration events (Pickrell and Pritchard 2012). We used OptM to determine the tree with the ideal number of migration edges using the second-order rate of change in likelihood (Fitak 2021). To further support evidence of gene flow, f3 statistics were computed using qpfstat in ADMIXTOOLS, and f-branch statistics were computed using D-suite (Patterson et al. 2012; Malinsky et al. 2021).

We used ADMIXTURE to perform a supervised estimation of southern UK and Atlantic Iberian ancestry proportions in the American populations by first subsetting the original variant call file to include only UK, Atlantic Iberian, and American populations. Next, we created a .pop file to specify the individual samples of known ancestry from the UK and Atlantic Iberian populations. We then ran ADMIXTURE using the –supervised flag for K = 2.

We used ADZE (Allelic Diversity Analyzer) to compute the sample size-corrected mean number of private alleles per SNP between pairings of grouped populations found in either northern or southern regions of South America with Portugal or SW Spain (Szpiech et al. 2008). ADZE carries out a rarefaction procedure that draws repeated, equal-sized subsamples to estimate the distributions of shared alleles present privately to pairs of populations while not being found in any of the remaining populations, accounting for unequal sample sizes between compared populations. By computing the distribution of these pairwise private allele counts over all possible subsamples, we derived robust estimates of allelic uniqueness that are not biased by differences in sample sizes, while being able to assess the sensitivity of this uniqueness to the number of individuals sampled using the rarefaction plots.

Demographic Inference

Two-dimensional folded site frequency spectra were obtained for each Iberian-American population pairing using the realSFS program in ANGSD (Korneliussen et al. 2014). We restricted demographic analyses to synonymous SNPs, predicted using a variant effect predictor, to mitigate the effects of direct selection (McLaren et al. 2016). However, we acknowledge that synonymous sites may be subject to weak selection and may also be affected by linkage to nearby selected loci. Because our dataset was derived from exome-capture data, demographic inferences should therefore be interpreted with the caveat that some effects of linked selection may remain. We maintained the filters used in Agwamba and Nachman (2023), requiring filtered sites to have a mapping quality of 30 and a base quality score of 20. We removed poorly mapped, duplicate, nonunique, or unpaired reads.

We inferred divergence times between Iberian and American house mice using dadi (Diffusion Approximation for Demographic Inference) through GADMA (Genetic Algorithm for Demographic Model Analysis) (Gutenkunst et al. 2009; Noskova et al. 2020). GADMA conducts an automated search for the best-fit demographic model and parameters based on the number of populations and the number of epochs that are specified. Given that we were primarily interested in establishing the time when American and European populations diverged, we focused on two-epoch models, allowing for population size changes after divergence. We explored models with migration after divergence, but these models provided a poor fit to the data and yielded improbably old divergence times (possibly reflecting a signal of divergence between ancestral European populations). We also confined the parameter space for divergence time while allowing migration, but this led to parameter runaway, ie the behavior at unstable optima where parameters collapse toward zero or approach their upper bounds (Rosen et al. 2018; Wittwer et al. 2023). Parameter runaway can also result from poor identifiability, since the inclusion of populations that violate model assumptions (eg unsampled structure or ghost admixture) or provide weak demographic signal may leave parameters unconstrained (Rosen et al. 2018). To further eschew these confounding factors, we focused our inference of estimated divergence times to single populations representative of the different geographic regions sampled in the Americas.

The mid-latitude population of Virginia was used to represent eastern North America (Phifer-Rixey et al. 2018), and the mid-latitude population of Provo, Utah, was used to represent western North America (Ferris et al. 2021). Similarly, Manaus was chosen to represent northern South America since it was the most central population longitudinally. Gaiman was chosen as the most central population latitudinally of the southern South America populations. We allowed for 50 independent runs of GADMA for each pair of populations analyzed, restricting population size changes to a single event before and after divergence (eg one event of growth, contraction, or no size change, which could be sudden, linear, or exponential). While house mice may have up to four generations per year in the lab, wild mice breed seasonally and typically have only one or possibly two generations per year. Therefore, for all demographic analyses, we used a generation time of 1 year and a mutation rate of 4 × 10−9 (Geraldes et al. 2008). To generate confidence intervals for demographic parameters, we partitioned each dataset into nonoverlapping 100 kb intervals and generated bootstrap replicate site frequency spectra by resampling blocks with replacement. Parameter uncertainties were then calculated using the Godambe information matrix, which accounts for both the curvature of the composite likelihood surface and variance across bootstrap replicates.

Supplementary Material

evag187_Supplementary_Data
evag187_supplementary_data.pdf (874KB, pdf)

Acknowledgments

We are very grateful to all those involved in the collection, preservation, and sequencing of the original samples used for this work: Andreas Chavez, Kathleen Ferris, Daniel Förster, İslam Gündüz, Bettina Harr, Jeremy Herman, Jane Hurst, Eleanor Jones, Dana Lin, Felipe Martins, Pedro Martins da Silva, Marine Pascal, Megan Phifer-Rixey, Victor Rambau, Lydia Smith, Taichi Suzuki, and Joaquim Tapisso.

Contributor Information

Kennedy D Agwamba, Center for Computational Biology, University of California, Berkeley, CA, USA; Museum of Vertebrate Zoology, University of California, Berkeley, CA, USA.

Sofia I Gabriel, CE3C—Centro de Ecologia, Evolução e Alterações Ambientais, CHANGE—Instituto para as Alterações Globais e Sustentabilidade, Departamento de Biologia, Faculdade de Ciências da Universidade de Lisboa, Lisboa 1749-016, Portugal.

Jeremy B Searle, Department of Ecology and Evolutionary Biology, Cornell University, Ithaca, NY, USA.

Jeff Wall, Oregon National Primate Research Center, Oregon Health and Sciences University, Beaverton, OR, USA.

Michael W Nachman, Center for Computational Biology, University of California, Berkeley, CA, USA; Museum of Vertebrate Zoology, University of California, Berkeley, CA, USA; Department of Integrative Biology, University of California, Berkeley, CA, USA.

Supplementary Material

Supplementary material is available at Genome Biology and Evolution online.

Author Contributions

Conceptualization: K.D.A. and M.W.N.; methodology: K.D.A., J.W., and M.W.N.; data curation: K.D.A., S.I.G., J.B.S., M.W.N.; data processing: K.D.A.; formal analysis: K.D.A.; investigation: K.D.A. and M.W.N.; writing—original draft: K.D.A. and M.W.N.; writing—review and editing: K.D.A., J.B.S., and M.W.N.; funding acquisition: K.D.A. and M.W.N.

Funding

Funding supporting this research was provided by the Center of Computational Biology at the University of California Berkeley, the National Institute of Health’s National Institute on Aging Training Grant provided through the Department of Demography at the University of California Berkeley, and National Institute of General Medical Sciences grants to MWN (R01 GM127468 and R35 GM149304).

Data Availability

The data supporting the findings of this study were generated in previous studies and are publicly available. Genomic sequences and associated metadata from Europe are available under ENA study accession “PRJEB9450” and NCBI Sequence Read Archive accession “PRJNA1050608” (Harr et al. 2016; Agwamba et al. 2025). North and South America sequences are also available via NCBI SRA (PRJNA397150, PRJNA718321, PRJNA776897) (Phifer-Rixey et al. 2018; Ferris et al. 2021; Gutiérrez-Guerrero et al. 2024). All scripts and codes performed in this study are available on GitHub: https://github.com/kendama/Population-genetics-analysis-of-European-and-American-house-mice.

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Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

evag187_Supplementary_Data
evag187_supplementary_data.pdf (874KB, pdf)

Data Availability Statement

The data supporting the findings of this study were generated in previous studies and are publicly available. Genomic sequences and associated metadata from Europe are available under ENA study accession “PRJEB9450” and NCBI Sequence Read Archive accession “PRJNA1050608” (Harr et al. 2016; Agwamba et al. 2025). North and South America sequences are also available via NCBI SRA (PRJNA397150, PRJNA718321, PRJNA776897) (Phifer-Rixey et al. 2018; Ferris et al. 2021; Gutiérrez-Guerrero et al. 2024). All scripts and codes performed in this study are available on GitHub: https://github.com/kendama/Population-genetics-analysis-of-European-and-American-house-mice.

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