Introduction

The house mouse (Mus musculus) is among the best-established model organisms for genetic studies in mammals, having been utilized in biomedical research for decades and leading to many fundamental discoveries (Vandenbergh 2000; Guénet and Bonhomme 2003). Furthermore, mice have been used successfully to explore broad evolutionary questions of speciation and hybrid zones, adaptation, and karyotype evolution amongst others (Sage et al. 1993; Phifer-Rixey and Nachman 2015). Their status as a laboratory model has helped develop them as a staple of evolutionary biology research, given the ease with which wild mice can be reared in captivity, as well as the now abundant genomic resources available for the study of mice.

The earliest recognized instances of Mus musculus have been traced back approximately 1 Mya to central Asia and Northern India (Boursot et al. 1993; Suzuki and Aplin 2012). Mus musculus then began to diverge 0.25–0.5 Mya (Bonhomme and Searle 2012; Phifer-Rixey et al. 2020) into three major subspecies, and with human-mediated movements have gained large ranges: M. m. domesticus (Western Europe, the Americas, Africa, and Australasia), M. m. musculus (Eastern Europe and Northern Asia), and M. m. castaneus (Southern Asia). These subspecies can be distinguished morphologically (Macholán 1996), with biochemical and cytogenetic markers (Bonhomme et al. 1984), with mitochondrial DNA (mtDNA) (Prager et al. 1998), and now on the basis of genome-wide SNP or sequence data (Yang et al. 2009). Several other subdivisions of Mus musculus have been proposed on the basis of morphology, karyotype, mitochondrial haplotypes, or microsatellite data (Prager et al. 1998; Sage 1981; Piálek et al. 2005; Suzuki et al. 2013; Hardouin et al. 2015). M. m. molossinus is a hybrid of M. m. musculus and M. m. castaneus endemic to Japan (Yonekawa et al. 1988). A mitochondrial lineage in the Arabian Peninsula and Madagascar has been labeled M. m. gentilulus (Prager et al. 1998; Duplantier et al. 2002). The taxonomic status of a group of populations in Iran, Pakistan, Afghanistan, and northern India—overlapping with the ancestral range of house mice—is somewhat uncertain. Some were historically classified as M. m. bactrianus (Schwarz and Schwarz 1943) but they are now thought to represent deep divisions within M. m. castaneus (Rajabi-Maham et al. 2012; Hamid et al. 2017). In this manuscript, we refer to this group as M. musculus undefined, and include possible M. m. gentilulus specimens in this category.

The commensal relationship of the house mouse with humans, a characteristic that is absent in their closest relatives M. spretus, M. spicilegus, and M. macedonicus, has facilitated their global dispersal (Chevret et al. 2005; Weissbrod et al. 2017). The earliest fossil evidence suggests the commensalism of mice with humans began around 15 kya in the Levant, followed by expansion into northern and western Europe as a result of increased human trade and settlement (Cucchi et al. 2020). Mus musculus experienced far-reaching range expansion during the age of European exploration and colonization, allowing them to establish populations on oceanic islands and in the New World alongside human colonists (Boursot et al. 1993; Bonhomme and Searle 2012; Gabriel et al. 2010; Hardouin et al. 2010).

One of the most interesting and unusual features of European M. m. domesticus is the number and frequency of chromosomal rearrangements that segregate in natural populations. Numerous Robertsonian metacentric chromosomes (centromere-to-centromere fusions) have been described (Piálek et al. 2005; Capanna et al. 1976). The processes that generate these rearrangements and allow them to persist in populations, despite adverse effects on reproduction in heterozygotes, have been the subject of intense study over the past 50 years (Garagna et al. 2014; Giménez et al. 2017). Populations with a characteristic complement of metacentric chromosomes are referred to as “chromosomal races.” In this paper, we include the data for the chromosomal races as part of our broader study of the house mouse. A companion paper (Hughes et al. in preparation) will focus specifically on the genomics of chromosomal variation in the house mouse.

Given the diversity of habitat, evolutionary history, and phenotype of wild house mouse populations, as well as their close relationships to humans, they are a wellspring for potential scientific inquiry. However, the majority of studies focus on laboratory mice that stem from deliberate inbreeding (Phifer-Rixey and Nachman 2015). It is difficult to capture the true scale of genetic and phenotypic diversity of wild populations with what constitutes a small global sampling (Keane et al. 2011). The genomes of so-called “classical laboratory strains” of mice are a mosaic of the three major subspecies, with on average 92% M. m. domesticus ancestry (Yang et al. 2007, 2011; Didion and de Villena 2013). A smaller collection of inbred strains are derived from wild-caught ancestors from each of the major subspecies. A more complete understanding of the house mouse system necessarily requires a greater wealth of genetic information from wild populations than is presently available.

Here we make use of genome-wide SNP genotypes obtained with two microarray platforms to characterize the ancestry and population structure of a large survey of wild-caught mice from around the globe. We characterize the utility of these arrays for population-genetic analysis by comparison to whole-genome sequence (WGS) data from a representative subsample of mice. We explore the relationship between population structure, inbreeding, and characteristics of where mice are living. Our study represents one of the largest and most comprehensive descriptions of the genetic structure of house mouse populations to date.

Materials and methods

Sample collection

Mice were collected at 268 locations in 33 countries (recorded in Supplementary Table S1) between 1990 and 2015. All trapping and euthanasia was conducted according to the animal use guidelines of the institutions with which providers were affiliated at the time of collection. Trapping on Southeast Farallon Island was carried out during two seasons, in 2011 and 2012; and on Floreana during a single season in 2012. Particular sampling effort was dedicated to regions with high karyotypic diversity such as Greece, the Swiss-Italian Alps, and the island of Madeira, where hybridization between chromosomal races is known to occur (Britton-Davidian et al. 2000; Hauffe et al. 2012).

Samples of one or more tissues (including tail, liver, spleen, muscle, and brain) from each individual were shipped to the University of North Carolina during an 8-year period (2010–2017). We assigned each individual a unique identifier as follows: CC:LLL_SSSS:DD_UUUU (C = ISO 3166-1-alpha2 country code; L = locality designation; S = nominal subspecies; D = diploid chromosome number and U = sequential numeric ID).

Genotypes for some individuals in this study have been used in prior studies on a selfish genetic element in European M. m. domesticus (Didion et al. 2016) and/or on an introduced population of house mice in New Zealand (predominantly M. m. domesticus but with genomic contributions from M. m. musculus and M. m. castaneus) (Veale et al. 2018).

DNA preparation and genotyping

Whole-genomic DNA was isolated from tissue samples using Qiagen Gentra Puregene or DNeasy Blood & Tissue kits according to the manufacturer’s instructions. Genotyping was performed using either the Mega Mouse Universal Genotyping Array (MegaMUGA) or its successor, GigaMUGA (GeneSeek, Lincoln, NE). Genotypes were called using Illumina BeadStudio (Illumina Inc, Carlsbad, CA). Only samples with <10% missing calls were retained for analysis. Sample sexes were confirmed by comparing the number of non-missing calls on the Y chromosome to the number of heterozygous calls on the X chromosome (Supplementary Fig. S1).

Whole-genome sequencing

WGS data were obtained from the European Nucleotide Archive for 136 mice representing natural populations of M. m. domesticus (Europe: PRJEB9450 (Harr et al. 2016); North America: PRJNA397406 (Phifer-Rixey et al. 2018); Gough Island: PRJNA587779 (Wang et al. 2017), PRJNA352398 (Gray et al. 2015)), M. m. castaneus (PRJEB2176) (Halligan et al. 2013), M. m. musculus (PRJEB14167, PRJEB11742), M. spretus (PRJEB11742) (Harr et al. 2016) plus one M. spicilegus (PRJEB11513) (Neme and Tautz, 2016) individual.

New sequencing data was generated for five individuals (EC:FLO_STND:40_6255, EC:FLO_STND:40_3043, US:SEF_STND:40_3023, US:SEF_STND:40_3023, UK:INA_STND:40_278). Sequencing was performed at the University of North Carolina High Throughput Sequencing Facility. Whole-genomic DNAs were sheared by ultrasonication and the resulting fragments were size selected to target size 350 bp using a PippinPrep system (Sage Sciences, Beverly, MA). The UNC High Throughput Sequencing Facility generated sequencing libraries using Kapa DNA Library Preparation Kits (Kapa Biosystems, Wilmington, MA). Each library was run on its own lane of a HiSeq4000 (Illumina Inc, Carlsbad, CA), and generated 150 bp paired-end reads.

Genotype calling from whole-genome sequencing

Raw reads were aligned to the mm10 mouse reference genome with bwa mem v0.7.15; optical duplicates were marked with samblaster v0.1.22 and excluded from further analyses. Genotypes were called at 64,886 target sites corresponding to known SNPs with unique probe sequences on both MegaMUGA and GigaMUGA arrays (see below) using GATK v4.1.0.0 Handsaker et al. (2011). Joint genotype calling was performed across all samples using the HaplotypeCaller – CombineGVCFs – GenotypeGVCFs workflow. Full details can be found in the scripts on GitHub at https://github.com/andrewparkermorgan/wild_mouse_genetic_survey.

Genotype merging and filtering

Genotypes from the two array platforms and WGS were merged into a single matrix as follows. Karl Broman’s re-annotation of the MUGA family of arrays (https://github.com/kbroman/MUGAarrays) was used as a starting point. For the 64,886 sites targeted on both MegaMUGA and GigaMUGA, a single representative probe on each array was selected (some sites are targeted by multiple probes). Alleles were swapped such that all genotypes were reported on the positive strand and prepared for merging.

Genotypes were successfully called in WGS at 62,489 (96.3%) of the target sites. Of these, 57,945 (89.3%) were biallelic among WGS samples and had the same alternate allele as targeted on the arrays. These sites represent the intersection of the two array platforms and WGS. Array and WGS genotypes were then merged into a single VCF file with bcftools isec v1.9.

The merged genotype matrix has dimensions 57,945 sites × 814 individuals with a final genotyping rate 97.1%. It is available on Dryad at https://doi.org/10.5061/dryad.ncjsxkswt.

Relatedness

Cryptic relatives were identified on the basis of autosomal genotypes with akt kin v.3beb346 (Arthur et al. 2017). Hereafter we denote the n × n kinship matrix K and its entries kij where i, j are indices for individuals and i ≠ j. Kinship estimation was performed separately in each taxon (M. m. domesticus, M. m. musculus, M. m. castaneus, M. musculus undefined, M. spretus) because allele frequencies are highly stratified between these groups. For each pair with kinship coefficient >0.10, one member was removed at random to yield a set of 651 putatively unrelated individuals. We confirmed that qualitative results from analyses of population structure by both principal components analysis (PCA) and ADMIXTURE (see below) were insensitive to the exact composition of the putatively unrelated subset by re-running analyses on 10 random unrelated subsets. Results from this exercise are shown in Supplementary Figs. S2 and S3.

Analyses of population structure

PCA was performed with akt pca v.3beb346 (Arthur et al. 2017), using only autosomal sites with genotyping rate >90% and minor-allele frequency >1%. PCs were first estimated from a subset of individuals selected as follows to maximize geographic representation while balancing sample size across geographic locations. Starting from the list of 651 putatively unrelated individuals, we randomly selected up to 10 from each geographic location. This yielded 255 M. m. domesticus, 24 M. m. musculus, 39 M. m. castaneus, 25 M. musculus undefined, and 6 M. spretus individuals. Genotypes from the entire cohort were then projected onto the PCs estimated in the representative subset.

A Y chromosome phylogenetic tree was inferred from 19 Y-linked sites with <10% missing genotypes (among males only), using neighbor-joining as implemented in the bionjs() function in the ape package v5.1 (Paradis et al. 2004). The tree was re-rooted using M. spicilegus as outgroup.

Further ancestry analyses within M. m. domesticus were performed by running ADMIXTURE in unsupervised mode with K = 2, …, 5 components on a genotype matrix including only the 56,201 autosomal sites. As for PCA, we estimated component-specific allele frequencies (the P matrix) using a representative subset of individuals and estimated ancestry proportions (the Q matrix) with the values of P held fixed, to avoid distortion due to over-sampling of certain locations. ADMIXTURE is best understood as a “grade-of-membership” model (Erosheva 2006) that models allele frequencies in each individual as a mixture of allele frequencies in K putative source populations. For this interpretation to hold, the true source populations must be well differentiated and present in the dataset at hand. Furthermore, other demographic processes with strong effects on allele frequencies, such as population bottlenecks or rapid expansion, must not have occurred. These assumptions are often violated in practice, including in our study. Under these conditions, the notion of an “optimal” value of K loses its meaning (Lawson et al. 2018). We treat ADMIXTURE as a descriptive tool that reveals different levels of population structure at increasing values of K and do not attempt to find an optimal value for K.

A “population tree” was produced with TreeMix v1.12r231 (Pickrell and Pritchard 2012). M. spretus individuals from Spain were used as outgroup to root trees. Runs were performed with m = 0, …, 3 gene-flow edges. Outgroup f3-statistics were calculated with the threepop utility and four-population f4-statistics for admixture graphs with the fourpop utility, both included in the TreeMix suite; standard errors were calculated by block jackknife over blocks of 500 sites.

Inbreeding

Individual inbreeding coefficients (\({\hat{F}}_{{{{\rm{ROH}}}}}\)) were estimated as the fraction of the autosomes covered by runs of homozygosity (ROH). This has previously been shown to be a reasonably consistent estimator of recent inbreeding (McQuillan et al. 2008). ROH were identified separately in each individual using bcftools roh v1.9 (Narasimhan et al. 2016) with taxon-specific allele frequencies (estimated over unrelated individuals only in each of M. m. domesticus, M. m. musculus, M. m. castaneus), constant recombination rate 0.5 cM/Mb (Liu et al. 2014), and arbitrarily assigning array genotypes a Phred-scale GT = 30 (corresponding to error rate 0.001) per the authors’ recommendations. Other HMM parameters were left at their defaults after experimenting with a wide range of parameter settings, and finding the root mean squared error (relative to estimates of \({\hat{F}}_{{{{\rm{ROH}}}}}\) from WGS data) to be insensitive to these parameters. We then excluded segments <2 cM in length. Mean and median length of remaining putative ROH segments were 13.2 Mb (287 sites) and 6.8 Mb (151 sites), respectively.

Between-group differences in inbreeding coefficients were evaluated using linear models with \({{{\rm{logit}}}}({\hat{F}}_{{{{\rm{ROH}}}}})\) as the response variable. For these analyses, we excluded four mice (SH:GOU_STND:40_6256, SH:GOU_STND:40_6257, SH:GOU_STND:40_6258, SH:GOU_STND:40_6259) that were members of a laboratory colony established from wild-caught founders from Gough Island, but had been partially inbred in the laboratory.

Maps and plotting

Figures were created with the R packages ggplot2 v3.3.1 (Wickham 2016), ggbeeswarm v0.7.0 (https://github.com/eclarke/ggbeeswarm), viridis v0.5.1 (https://CRAN.R-project.org/package=viridis), maps v3.3.0 (https://CRAN.R-project.org/package=maps), cowplot v0.9.3 (https://CRAN.R-project.org/package=cowplot), popcorn v0.0.2 (https://github.com/andrewparkermorgan/popcorn), and mouser v0.0.1 (https://github.com/andrewparkermorgan/mouser).

Results

Our dataset consists of 814 mice (Fig. 1 and Supplementary Table S1) spanning the three principal subspecies of the house mouse found across its global range (M. m. domesticus, M. m. musculus, M. m. castaneus plus two early-generation hybrids), the outgroup species M. spretus and M. spicilegus, and a group with ill-defined ancestry provisionally labeled “M. musculus undefined.” Our collection includes 218 mice carrying Robertsonian translocations, representing 37 chromosomal races with diploid number (2N) between 22 and 39. The majority of specimens were genotyped with Illumina Infinium SNP arrays (Morgan et al. 2016) and the remainder were obtained from published WGS data (Harr et al. 2016; Neme and Tautz 2016; Pezer et al. 2015). Sample sizes by taxon and genotyping platform are summarized in Table 1. Briefly, the two array datasets were merged on overlapping markers, and WGS samples were added by calling genotypes at array sites. WGS data were also used to identify and remove problematic sites. Full details of data processing and quality control are provided in the Materials and methods. The final genotype matrix comprises 57,945 sites spanning 2390 Mb (99.4%) and 164 Mb (97.9%) of autosomal and X chromosome sequence, respectively; corresponding to 1296 cM (89.9%) and 70.6 cM (88.9%) on the genetic map, respectively. Median spacing between adjacent sites is 28.9 kb (median absolute deviation [MAD] 25.9 kb) on the autosomes and 51.1 kb (MAD 51.8 kb) on the X chromosome (Supplementary Fig. S4). The mutation spectrum is biased toward transitions (Ti:Tv = 4.71) as a result of technical constraints imposed by the array platform, as described elsewhere (Morgan et al. 2016).

Fig. 1: Geographic distribution of wild house mice used in this study.
figure 1

One point per individual, colored by species or subspecies of origin.

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Table 1 Count of samples used in this study by taxon and genotyping platform.
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The genotyping arrays used in this study have a number of design properties that constrain their use for population genetics studies (Morgan et al. 2016). The number of markers that are polymorphic within M. m. domesticus (54,929, 94.8%) is much greater than within M. m. musculus (24,330, 42.0%) or M. m. castaneus (32,633, 56.3%) (Table 2). The site frequency spectrum, expected to have an approximately exponential shape under a wide range of demographic scenarios (Fu, 1995), is strongly biased toward intermediate frequencies (Supplementary Fig. S5), especially within M. m. domesticus. As a consequence, many summary statistics that can be derived from the site frequency spectrum (pairwise diversity, Watterson’s θ, Tajima’s D) do not have interpretable values. The effects of SNP ascertainment bias on population-genetic studies have been explored in detail elsewhere (for example, Lachance and Tishkoff 2013). It suffices to say here that array genotypes are still useful for analyzing population structure and ancestry if used with appropriate caution.

Table 2 Number and proportion of markers polymorphic in each taxon, by chromosome type (A = autosomal, X = X-linked, Y = Y-linked, M = mitochondrial).
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Microarray genotypes recover subspecies relationships

To confirm nominal subspecies assignments for our samples, we performed PCA on autosomal genotypes for a representative subset of 374 individuals. Three major clusters are clearly observed, corresponding to the three principal subspecies (Fig. 2A). Both M. m. domesticus and M. m. musculus form relatively tight clusters in the top PCs, while M. m. castaneus forms two sub-clusters corresponding to mice from Taiwan and India, respectively. This is consistent with existing evidence for greater variation within M. m. castaneus compared to the other two major subspecies (e.g., Geraldes et al. 2008). M. musculus undefined specimens project near the M. m. castaneus cluster, consistent with prior reports. To directly examine phylogenetic relationships, we chose the only non-recombining locus for which our genotype matrix contains sufficient data—the Y chromosome. A neighbor-joining tree from Y-linked sites recovers the split between M. m. domesticus and all other groups, as well as at least one instance of intersubspecific Y chromosome introgression from a non-M. m. domesticus into a M. m. domesticus individual from Porto Santo (Fig. 2B). M. m. castaneus and M. m. musculus cannot be distinguished in this analysis due to a lack of subspecies-diagnostic markers on the Y chromosome in the merged genotype matrix.

Fig. 2: Array genotypes discriminate between Mus musculus subspecies.
figure 2

A PCA on autosomal genotypes. B Neighbor-joining tree of Y chromosome haplotypes from representative individuals. Two distinct haplogroups within M. m. domesticus are marked with dark and light gray bars, respectively.

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Population structure in European M. m. domesticus follows a latitudinal gradient

The primary focus of our work is population structure within M. m. domesticus. We again performed PCA anchored by a representative subset of individuals. Several patterns are evident. First, the major axis of variation is a south-to-north gradient across Europe and western Eurasia. We emphasize this by assigning individuals to three major geographic groups in Fig. 3A. Plotting the first PC against location makes the spatial gradient clear (Fig. 3B). Second, individuals cluster by location within broader geographic groups (Fig. 3C–E). Third, M. m. domesticus that have dispersed outside Eurasia tend to cluster with northern European populations.

Fig. 3: Population structure in M. m. domesticus.
figure 3

A PCA on autosomal genotypes (PC1 vs PC2 shown), with individuals colored according to membership in broad geographic groups named after predominant European areas represented in each. B PC1 coordinate versus sampling location. C–E Same PCA plot as in panel A, but emphasizing substructure within large-scale geographic groups. Points colored according to population of origin.

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We also used ADMIXTURE (Alexander et al. 2009) to estimate ancestry proportions with increasing numbers of ancestry components (Supplementary Fig. S6). This analysis shows that ancestry profiles within geographically defined populations are generally cohesive. At the most coarse scale (K = 2), we again see a broad division between populations from the Mediterranean and the Iberian Peninsula (light blue component) and those from northern Europe and the New World (dark blue component) (Fig. 4). There is a similar result to the TreeMix analysis (Fig. 5A) with the two groupings again evident. The southern European grouping represents well the known initial colonization of Europe by M. m. domesticus based on archeological data (Cucchi et al. 2005) (Fig. 5B). The colonization route and derivation of the northern and central European groupings is less clear.

Fig. 4: Ancestry decomposition with ADMIXTURE.
figure 4

A Individuals plotted by location, colored according to ancestry proportion. B Ancestry proportions estimated with ADMIXTURE for K = 2. Population labels shortened for legibility: US:GA = Georgia, US:FL = Florida, US:VA = Virginia, US:MD = Maryland, US:PA = Pennsylvania, US:NH = New Hampshire, NZ = New Zealand.

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Fig. 5: Origins of European M. m. domesticus populations.
figure 5

A Population tree inferred by TreeMix with populations colored by broad geographic group as in Fig. 3. B Model for dispersal of M. m. domesticus across Europe, based on current distributions of the different groups and archeological interpretations (Cucchi et al. 2005). The derivation and route of colonization of the northern and central European groupings (shown in green and purple) is unclear (as indicated by dashed lines).

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Inbreeding is pervasive but highly variable

The deme structure of natural mouse populations predisposes to inbreeding, and several mechanisms of kin recognition and inbreeding avoidance based on scent have also been proposed (Yamazaki et al. 1976; Hurst et al. 2001; Sherborne et al. 2007). Previous estimates of inbreeding in wild mice vary widely within and between populations, with an average between 0.2 and 0.5 based on microsatellites (Hardouin et al. 2015; Ihle et al. 2006) or SNPs (Laurie et al. 2007). We used the proportion of the autosomes contained in ROH as an estimator of the inbreeding coefficient (\({\hat{F}}_{{{{\rm{ROH}}}}}\)), as it has been shown to be relatively powerful and less prone to bias due to stratification of allele frequencies than other estimators (Keller et al. 2011). \({\hat{F}}_{{{{\rm{ROH}}}}}\) values vary widely within and between subspecies, shown in Fig. 6A (ANOVA: F2,760 = 4.88, p = 7.8 × 10−3). The highest values are observed in M. m. domesticus and the lowest in M. m. castaneus (t = 2.93, padj = 9.8 × 10−3 by Tukey’s post-hoc method). Among females, who carry two copies of the X chromosome, homozygosity on the X chromosome is positively correlated with \({\hat{F}}_{{{{\rm{ROH}}}}}\) estimated from autosomal sites (Fig. 6B) as expected. Inbreeding estimates tend to be consistent within geographically defined groups, and there is significant heterogeneity across these groups (ANOVA: F24,676 = 14.3, p < 10−6) (Fig. 6C). Some of the highest \({\hat{F}}_{{{{\rm{ROH}}}}}\) values are on some of the smallest islands (Heligoland, Orkney, Farallon, Floreana), but there are also small islands with much lower values (Madeira, Porto Santo) (Fig. 6C). Although there is a nominal difference in mean \({\hat{F}}_{{{{\rm{ROH}}}}}\) between mice with standard versus Robertsonian karyotypes (t = 14.1, p = 1.8 × 10−4), the distribution of \({\hat{F}}_{{{{\rm{ROH}}}}}\) among mice with standard karyotypes clearly spans that of Robertsonian mice (Fig. 6D). The relationship between karyotype variation and inbreeding is explored further in the companion manuscript (Hughes et al. in preparation).

Fig. 6: Inbreeding across mouse subspecies and populations.
figure 6

A Inbreeding coefficients estimated from autosomal genotypes, by nominal subspecies of origin. Open dot, median; thick bar, interquartile range; thin bar, 5th–95th percentiles. B Autosomal vs X chromosome inbreeding coefficients for 374 female mice. Black line, linear regression through all points; gray band, 95% confidence region. Spearman's (rank) correlation and corresponding p value shown in the upper right. C Inbreeding coefficients by population within M. m. domesticus. Populations are sorted by mean \({\hat{F}}_{{{{\rm{ROH}}}}}\). D Comparison of inbreeding coefficients in M. m. domesticus with standard vs Robertsonian (Rb) karyotypes.

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ROH reflect sharing of chromosomal segments identical by descent between an individual’s parents (ignoring rare instances of uniparental disomy). When the common ancestor from which these segments were inherited is deep in the past, the expected length of the shared segment is shorter; when the common ancestor is more recent, the shared segment is on average longer. Deeper pedigree connections may arise from small historical population sizes or founder effects. More recent pedigree connections reflect mating between close relatives; that is, consanguinity. A genome-wide estimator of homozygosity such as \({\hat{F}}_{{{{\rm{ROH}}}}}\) thus comprises a mixture of shared segments from pedigree connections of varying depth. The distribution of ROH segment lengths is informative for both recent and historical demographic processes.

To examine these patterns in our data we turned our attention to locations for which we have relatively dense sampling: Southeast Farallon Island, off the coast of California; Floreana Island, in the Galapagos; Gough Island, a remote island in the south Atlantic; the grounds of a schoolhouse in Centreville, Maryland; and horse stables in the towns of Laurel, North Potomac and Chevy Chase, Maryland. Overall \({\hat{F}}_{{{{\rm{ROH}}}}}\) varies between populations (Fig. 7A) (ANOVA: F6,150 = 45.5, p < 10−6). All individuals on Floreana and Farallon have high \({\hat{F}}_{{{{\rm{ROH}}}}}\) values. In Maryland, individuals range from effectively outbred (Centreville) to those that are as inbred as the mice on the small Pacific islands (Chevy Chase). Inbreeding on Gough Island, despite its remoteness, is relatively low. The empirical cumulative distributions of ROH segment lengths by individual are shown in (Fig. 7B); there is a clear difference in distribution between locations (p < 10−6, Anderson–Darling test), although this comparison does not take into account differences in total span of ROH. For purposes of qualitative comparisons, we summarized ROH into bins of increasing size, from 2 cM (the detection threshold applied in this study) to >20 cM (Fig. 7C). To test for difference between geographic locations in aggregate ROH across length bins we used PERMANOVA, a non-parametric analog to ANOVA for multivariate data. Binned ROH distributions do differ by population (pseudo-F6,149 = 36.8, permutation p < 10−3). Borrowing from analytical results and simulations in Ringbauer et al. (2021), it can be shown that segments 4–8 cM in length reflect background relatedness due to small population sizes while segments >20 cM in length are only observed in matings between close kin or in very small populations (N < 500). Here a striking pattern emerges. Although inbreeding is high on the islands of Floreana (median \({\hat{F}}_{{{{\rm{ROH}}}}}=0.30\)) and Farallon (0.36), nearly all ROH lies in segments <8 cM. The same is true on Gough Island, with the exception of a single individual. By contrast, in commensal populations in Maryland stables with similar overall inbreeding (Laurel, median \({\hat{F}}_{{{{\rm{ROH}}}}}=0.18\); North Potomac, 0.26; Chevy Chase, 0.35) the majority of ROH lies in segments >12 or >20 cM. Inbreeding in the Centreville population is low (median \({\hat{F}}_{{{{\rm{ROH}}}}}=0.02\)).

Fig. 7: Inbreeding in selected deeply sampled groups of M. m. domesticus.
figure 7

A Inbreeding coefficients (\({\hat{F}}_{{{{\rm{ROH}}}}}\)) grouped by population for selected populations. Populations are sorted by mean \({\hat{F}}_{{{{\rm{ROH}}}}}\). “Ctrvle" = Centreville, MD. B Cumulative distribution of ROH segment lengths (in cM) for each individual, grouped by population. Blue line shows pooled cumulative distribution for population. C Distribution of ROH by individual, binned by ROH length.

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These results suggest that inbreeding in the Maryland populations is driven primarily by kin mating. We next examined patterns of relatedness within and between populations inferred from autosomal markers. The estimated kinship matrix is shown in Fig. 8A, with rows and columns hierarchically clustered to emphasize close relationships. Relatedness is clearly higher within than between geographically separate groups, leading to block structure in the kinship matrix that corresponds to our geographically defined population labels (shown by colored bars along the edges of the matrix). We detect substructure even within populations that occupy the same horse barn, consistent with older literature (Selander 1970; Pocock et al. 2005). The distribution of pairwise kinship coefficients (Kij) is shown in Fig. 8B. Within each of the three populations from horse stables (North Potomac, Laurel, Chevy Chase) inferred kinship for most pairs is at the level expected for first cousins (0.0625) or closer, with many pairs as similar as full siblings (0.25). (It is important to note that these expected values assume unrelated parents.) The range of kinship coefficients as well as the proportion of pairs with Kij ≈ 0 is largest in Laurel. This is also the population with the broadest range of \({\hat{F}}_{{{{\rm{ROH}}}}}\), including 45% of individuals without ROH > 20 cM (see Fig. 7C). The distribution of kinship coefficients in the Chevy Chase population is unimodal, centered at Kij = 0.18, inbreeding is high (median \({\hat{F}}_{{{{\rm{ROH}}}}}=0.34\)) and all but one individual have two or more ROH > 20 cM in length. Mice in each barn are thus effectively a clan comprising one or a few extended families. The characteristics of North Potomac are intermediate between Laurel and Chevy Chase. Within the Centreville population, only a few pairs are closely related.

Fig. 8: Deme structure in selected populations.
figure 8

A Kinship matrix (estimated from autosomal genotypes) for Maryland mice, hierarchically clustered. Row and column colors indicate population membership. B Distribution of pairwise kinship coefficients (Kij) within populations. Large dot, median; thick bar, interquartile range; thin bar, 5th–95th percentiles. Reference lines show expected values for half-siblings (0.125), full siblings (0.250), and monozygotic twins (0.500) in the absence of inbreeding. Filled dots above each column indicate mean \({\hat{F}}_{{{{\rm{ROH}}}}}\) in each population.

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Discussion

The house mouse Mus musculus has been an important model organism in ecology, evolutionary biology, and medical science for more than a century. Here we present a survey of ancestry, population structure, and inbreeding in a large and geographically diverse sample of wild mice (Fig. 1) using genotypes at 57,945 genome-wide SNP markers.

On a global scale, house mice comprise three major subspecies that are morphologically and genetically distinct and radiated from an ancestral population in central Asia (Phifer-Rixey and Nachman 2015; Boursot et al. 1993). M. m. domesticus, M. m. musculus, and M. m. castaneus are well differentiated in our data (Fig. 2). We can detect substructure within M. m. musculus and M. m. castaneus but do not explore this in detail because of the small sample size in these subspecies and ascertainment of SNPs primarily in M. m. domesticus. An additional group of mice collected in India, Pakistan, the Middle East, Madagascar, and East Africa and collectively labeled “M. musculus undefined” in our study have a genetic affinity with M. m. castaneus. Work by Hardouin et al. (2015), using nuclear microsatellites and with much more extensive sampling, showed clear evidence of M. m. castaneus-like ancestry in these regions as well as ancestry components distinct from any of the three major subspecies. This accords with previous evidence for deep branches within M. m. castaneus (Prager et al. 1998; Suzuki et al. 2013; Duplantier et al. 2002; Rajabi-Maham et al. 2012) based on mitochondrial sequences. Our study supports these findings but adds little additional detail. The taxonomic status of these populations remains uncertain.

We focus on population structure in M. m. domesticus, the dominant subspecies in western Europe and the Mediterranean. Through its commensal relationship with human colonists, this is also the subspecies that has expanded its range to the Americas, Australia, New Zealand, and numerous islands around the globe within the past several centuries (Bonhomme and Searle 2012; Gabriel et al. 2010, 2011; Tichy et al. 1994; Searle et al. 2009). Within the range of M. m. domesticus in Europe, we find clear evidence for a south-to-north ancestry gradient in multiple complementary analyses (Figs. 3–5). Archeological evidence and prior genetic surveys based primarily on mitochondrial sequences indicate that M. m. domesticus colonized southern and western Europe from the Levant via seafaring routes in the Mediterranean (Bonhomme and Searle 2012; Cucchi et al. 2020, 2005; Bonhomme et al. 2011; García-Rodríguez et al. 2018). This explains the southern grouping that we identify with our SNP data; the derivation and colonization routes of the central and northern European groupings that we have identified are less clear (Fig. 5B). The colonization of central and northern Europe by M. m. domesticus from the Mediterranean could have occurred via overland or coastal routes (Cucchi et al. 2005; Jones et al. 2011). Previous studies suggest differences between the various defined mtDNA lineages in northern Europe in the manner of their arrivals (Jones et al. 2013). In the same way as we have here found a distinction between southern and northern Europe using genome-wide SNP data, the same can be seen with mtDNA: with the inflated representation of particular lineages in the north which are not common in the south (Jones et al. 2011, 2013). Considering the next step of colonization, from Europe to elsewhere: mice in the eastern United States, from as far south as Florida to as far north as New Hampshire, have predominantly northern European-like ancestry and males carry a northern European Y haplogroup (Figs. 2B and 4A). More specifically, mice from the eastern United States have ancestry profiles most similar to mice from New Zealand and Australia; and among mainland European populations, most similar to mice from mainland Scotland, Denmark, and Germany (Fig. 4 and Supplementary Fig. S6).

It is important to note that we have almost certainly not sampled the proximal source populations in Europe including, in particular, England and Scandinavia. Nonetheless, we can use populations we have sampled as imperfect proxies for the unsampled groups. Our data suggest that North American house mouse populations are likely descended primarily from populations in northern Europe. This is despite extensive Spanish activity in southern parts of North America from the sixteenth century CE onwards, which might have been expected to leave a mark on the mouse populations. (Our results are consistent with a prior RFLP analysis that identified polymorphisms shared between mice from Brittany and Florida (Tichy et al. 1994). Given the size of the North American continent and the distance between early European settlements, multiple introductions from different European sources seem overwhelmingly more likely than a single introduction event. We also note that M. m. castaneus ancestry is also present in the western United States (Orth et al. 1998). Considering Australia and New Zealand, where house mice also appear to have northern European ancestry from our analysis, mtDNA data indicate mouse colonization from the British Isles, consistent with known human visitations during the eighteenth century CE and subsequent colonization of those areas (Searle et al. 2009; Gabriel et al. 2011). It seems unlikely that indigenous people brought house mice to the Americas, Australia or New Zealand; if they did, those populations have been replaced by subsequent waves of colonization.

There are other instances where house mouse provenance, based on our SNP data, match well with known human history. Mice from Floreana Island in the Galápagos have Iberian-like ancestry (Fig. 3B). The first recorded European landing on the Galápagos was by a Spanish expedition in 1535, and the islands were visited by whaling ships and explorers from England and the United States over the next three centuries. A fire set by a crewmember of the whaling ship Essex burned over all of Floreana in 1820 and may have reduced, if not eliminated, any mouse population present (Jackson 1993). Our results suggest that the present-day mouse population is either a remnant of a population introduced either by initial Spanish contact, or introduced from another source with Iberian-like ancestry (perhaps another island in the archipelago or the Ecuadorian mainland). Mice on Madeira and Porto Santo have a clear affinity to coastal populations in Portugal. This diverges from previous work showing greater similarity of Madeiran mitochondrial haplotypes to those from northern Europe than from Portugal (Förster et al. 2009); but is consistent with the only other study of nuclear markers (Britton-Davidian et al. 2007). The small islands off the coast of New Zealand seem to have been colonized from a common source with New Zealand, as has been reported previously (Veale et al. 2018). One interesting result is that the mice from the Orkney Islands off the northern tip of Scotland show more affinity to mice from central Europe (Belgium, Northern France, Germany, Switzerland) than to other mice from northern Europe. Detailed studies of mouse mitochondrial sequences show a northern European affinity (Searle et al. 2009). However, it is notable that another species of small mammal, the common vole Microtus arvalis, was also introduced to Orkney by people, and the source area was in the vicinity of the Low Countries (Martínková et al. 2013).

An important finding in our study is the degree of inbreeding in many house mouse populations surveyed. We exploit the length distribution of ROH, which represent segments shared IBD between parents (Broman and Weber 1999), to make inferences both about individual inbreeding coefficients (\({\hat{F}}_{{{{\rm{ROH}}}}}\)) and population history (Kirin et al. 2010; Ceballos et al. 2018). ROH have several advantages for learning about recent population dynamics. First, ROH can be detected even with modest marker density and in the presence of marker ascertainment bias, and as such are a useful summary of genotyping array data from non-human and non-model species. Second, they contain information about population dynamics in the recent past (tens to a few hundred generations (Browning and Browning 2015), <<Ne), whereas the site frequency spectrum is dominated by events deeper in time. These advantages extend beyond our study system. As might be expected, mice from many of the small islands surveyed (Floreana, Farallon, Heligoland, Gough, Orkney; Fig. 7) are at the upper end of the distribution of \({\hat{F}}_{{{{\rm{ROH}}}}}\) values. Historical surveys of other island populations based on allozyme data reached similar conclusions (reviewed in Berry 1986). However physical isolation, at least at a geographic scale, is neither necessary nor sufficient to account for observed levels of inbreeding. We took advantage of populations surveyed during a single season (autumn 2014) at several locations in Maryland to explore this in detail. Mice caught on the grounds of an elementary school in rural Centreville, MD are effectively outbred (Fig. 7). By contrast, mice trapped in horse stables less than 100 km away, in an area with an identical climate and very similar landscape, tend to be much more inbred. Homozygosity in these populations lies mostly in long segments (>12 cM) that reflect recent consanguinity (Fig. 7). Within stables, individuals are mutually related and can be lumped into large family groups within which there is a gradient of (realized) genetic relatedness (Fig. 8).

These results support the idea that inbreeding in house mice is driven by fine-scale features of the habitat rather than broad geographic constraints. In a resource-rich environment like a horse barn—which may provide shelter, food, and relative protection from predators—mice tend to live in small demes founded by up to a few dozen individuals with a limited exchange between them, even over very short distances (Reimer and Petras 1967; Berry 1970; Lidicker 1976; Bronson 1979; Pocock et al. 2004). This of course leads to mating between close relatives, the signature of which is long segments of homozygosity. Mice in more austere habitats disperse over much larger distances and are less likely to encounter relatives among potential mates (Pocock et al. 2005; Berry 1970; Bronson 1979). In the case of isolated populations that have passed through a colonization bottleneck, such as on Farallon or Floreana, homozygosity may still be relatively high, but is distributed across shorter segments inherited identical by descent from the small pool of founder individuals (Fig. 7C) (Ceballos et al. 2018).

Pervasive inbreeding seems to be at odds with estimates of nucleotide diversity in M. m. domesticus, which are about three-fold higher than in humans (for example Harr et al. 2016; Geraldes et al. 2008; Salcedo et al. 2007) despite similar mutation rates per generation. However, provided demes are sufficiently fluid over time, populations behave as if they are approximately panmictic at the regional scale, despite being finely subdivided on the local scale (Berry 1986; Nagylaki 1977). Furthermore, our data show that the census population size needs not to be small—on Southeast Farallon Island, for example, may be very large (San Francisco Bay National Wildlife Refuge Complex, 2013)—for levels of homozygosity to be high. This implies limitations on conclusions that can be drawn about population demographic history from genetic data alone. Users of genetics in conservation should be aware of these limitations.

Many open questions remain. How does inbreeding affect the genetic load and rate of adaptation in natural mouse populations? The fact that inbred strains can be generated readily in the laboratory may suggest that much deleterious recessive variation has been purged. While inbreeding is common in M. m. domesticus, is it equally common in M. m. musculus, M. m. castaneus, and sister species M. spretus? And how does inbreeding affect the evolution of hybrid incompatibilities? High-quality WGS from a larger and more diverse panel of mice would be helpful to address these and other questions.