DOI: 10.5061/dryad.8w9ghx3wq
title: Genetic Distances between badgers (based on microsatellite-derived estimates of relatedness) and M. bovis isolates (based on pairwise SNP distances) alongside categories describing social group membership, age and sex categories

description: Pathogens rarely mix freely throughout host populations, and the presence
 of barriers to transmission can be detected as patterns of increased
 genetic isolation among pathogen isolates. Despite the importance of
 transmission patterns in host societies and the risk of epizootics from
 wildlife disease systems, barriers to open pathogen transmission are
 poorly understood in wild hosts. We tested the influence of host kinship
 and social structure on genetic divergence among strains of Mycobacterium
 bovis, the causative agent of bovine tuberculosis, in a wild badger
 population. We measured genetic distances between M. bovis isolates from
 badger hosts that varied in their own genetic similarity (a proxy for
 kinship) and in their social group affiliations. Using jack-knifing
 analyses to control for pseudoreplication, we found that genetic distances
 between pathogen isolates decreased with increasing kinship of host dyads,
 but only when hosts shared the same social group.  Our findings
 suggest that the open transmission of bTB in wild hosts is constrained by
 a combination of social and kin structure, in particular the sharing of
 similar pathogen strains among kin within social groups. We discuss the
 implications of these transmission structures for the understanding and
 management of wildlife disease.
description: <strong><em>Host
 Genotyping</em></strong> On first
 capture of each individual badger, a hair sample was routinely taken
 (approx. 20-30 hairs plucked from the rump) and stored in  80% ethanol
 before being submitted for DNA extraction and genotyping (Carpenter, Pope
 et al. 2005).  Full details on genotyping are available elsewhere
 (Marjamäki, Dugdale et al. 2019); however, in brief, individuals trapped
 between 1993 and 2002 were genotyped based on the DNA extraction protocols
 set out in Carpenter, Pope et al. 2005, whilst hair samples from
 individuals trapped after this period were genotyped at the NERC
 Biomolecular Analysis Facility (University of Sheffield), as described in
 Marjamäki et al. 2019. As hair genotyping had taken place in batches,
 cross-validation protocols were used, including re-genotyping a subset of
 subsamples; this is described in detail elsewhere (Marjamäki, Dugdale et
 al. 2019). Genotype data from 108 individual badgers were used in the
 study (51 males, 57 females from 25 different social groups, trapped
 between 1993 and 2019 inclusive). Details of the capture histories of the
 badgers included in this study are shown in Supplementary Figure
 1. We used 20 microsatellite markers to derive
 genotypes, each with 4-7 alleles (Carpenter, Dawson et al. 2003).
 Additional information on marker performance and comparison of relatedness
 estimators is available in the Supplementary Material. Pairwise
 relatedness between badgers was therefore estimated between all pairs of
 individuals in the study (n = 108) using the Queller and Goodnight rxy
 estimator (Queller and Goodnight 1989). The expected rxy relatedness
 coefficients between individuals based on relationship are as follows:
 parent/offspring and full sibling 0.5, half sibling 0.25, and unrelated 0
 (Gautschi, Jacob et al. 2003). Negative values of rxy may occur if gene
 frequencies of the two compared individuals differ from the population
 mean in opposite directions (Queller and Goodnight 1989). The relatedness
 coefficients allowed the assignment of categorical relationships between
 pairs of badgers (identifying full and half siblings, etc); however,
 simulation testing suggested that the power of 20 microsatellite markers
 to accurately differentiate between relatedness classes was likely to be
 limited, hence relatedness coefficient was used as a continuous variable
 in the analyses.
 <strong><em>Pathogen
 Genotyping</em></strong> Clinical
 samples routinely collected from anaesthetised badgers during sampling
 included oesophageal and tracheal aspirates, urine, faeces, and swabs of
 bite-wounds or abscesses. Supplementary Figure 1 shows the culture
 sampling histories of the 108 badgers included in this study. These
 samples were processed and seeded onto media selective for <em>M.
 bovis</em>. When bacterial growth was observed (this may take 6-12
 weeks), a single colony was inoculated in HPLC water and spoligotyped; the
 remaining growth was harvested into two tubes, each containing 1 ml of 7H9
 Middlebrook broth medium with 20% glycerol, and stored at -20°C. A total
 of 241 isolates from 108 badgers collected between 2000 and 2019 inclusive
 were recovered from the archive. Slopes of modified 7H11 media (Cohn,
 Waggoner et al. 1968) were seeded with the defrosted samples in October
 2020 and incubated at 37 ºC for a six-week period for re-growth under
 containment level 3 conditions. Successfully regrown isolates were heat
 killed in hot blocks at 80°C for 30 minutes, and standard Illumina
 protocols (NexteraXT kits) for generating libraries from heat-killed cell
 suspensions were applied.  These were then run on the Illumina NextSeq
 instrument with 2 x 150bp paired-end reads. Further details on
 bioinformatics protocols and SNP calling are included as supplementary
 material. Pairwise genetic distances (defined as the
 raw number of sites that differed in their genetic profile at 1569
 quality-filtered SNPs) between<em> M. bovis</em> isolates were
 calculated in R (v 4.0.2) using the package ‘ape’ (v. 5.6-2) (Paradis and
 Schliep 2019). <strong><em>Data
 analysis</em></strong> Analyses
 were carried out using R software version 4.5.0 (R Core Development Team
 2025). The impact of social group membership and host genetic population
 structure on the genetic distance between <em>M. bovis</em>
 isolates was analysed by constructing mixed models using the R package
 ‘lme4’ (Bates 2010). This was coupled with jack-knifing procedures to
 remove biases and pseudoreplication associated with the multiple
 representation of pathogen isolates and host individuals in the
 measurement of pairwise distances (Clarke, Rothery et al. 2002). In
 reviews of statistical methods to deal with the inherent pseudoreplication
 found in dissimilarity matrices, jack-knifing by “population” (here, host
 and pathogen isolate identity) has been found to be superior to other
 methods, especially following critique of the classic Mantel-testing
 approach (Balkenhol, Waits et al. 2009, Legendre, Fortin et al. 2015,
 Zeller, Creech et al. 2016, Shirk, Landguth et al. 2018). Genetic
 distances amongst badger genotypes were based on differences in alleles
 found across a set of microsatellite markers, and estimated using the
 Queller and Goodnight rxy estimator of relatedness (Queller and Goodnight
 1989) in the R package ‘related’ (Pew, Muir et al. 2015).
 Each badger’s main social group was defined as the group that it
 had been resident in for the longest period of its capture history
 (according to trapping records) as this was found previously to be the
 best spatial predictor of genetic distance between isolates by Crispell et
 al. using a smaller dataset from the same population (Crispell, Benton et
 al. 2019). In the majority of cases, this was also the individual’s natal
 group (of the 97 badgers first caught as cubs, 92 (95%) were recaptured in
 the same social group as they were assigned to at their first capture,
 i.e., their natal group). The main social group is, therefore, in the vast
 majority of cases, but not exclusively, analogous to the natal social
 group. Genetic distances for pairwise combinations of
 <em>M. bovis </em>isolates were categorised according to
 whether the badgers from which they were collected shared the same or
 different social groups in the year when the isolate was collected.
 Pairwise distances were also categorised according to the age classes of
 the badgers as follows: comparisons between isolates from two adult
 badgers (‘Adult-Adult’), between an adult and a cub or yearling
 (‘Adult-Young’), and between pairs of cubs/yearlings (‘Young-Young’). Data
 were restricted to only include pairwise comparisons where isolates had
 been collected within 3 years of one another, in order to only include
 individuals likely to have been in the population at the same time. This
 is in line with recent estimates of the median survival time of badgers in
 this population as 3.6 years (Konzen, Delahay et al. 2024). We also
 categorised pairwise isolate distances according to the sex of the host
 badgers. To account for temporal divergence in pathogen genotypes
 (consistent with the molecular clock hypothesis (Bromham and Penny 2003)),
 the number of years between isolate collection dates was also included in
 the model as a fixed effect.   To test for the effect
 of social group membership and kin structure on <em>M. bovis
 </em>isolate distances, we constructed a mixed effects model using
 the R package ‘lme4’ (Bates 2010) with genetic distance between pathogen
 isolates as the response variable.  Genetic dissimilarity between hosts
 (modelled as a continuous variable using the rxy relatedness estimator
 (Queller and Goodnight 1989)), age-class comparison, sex based comparison
 (M:M, F:F, M:F), time between isolate collection dates and social group
 membership (whether the badgers were in the same social group at the time
 of sampling) were all included as fixed effects. A term was also included
 for the interaction between host genetic similarity and social group
 membership. As individual badgers contributed multiple dyadic data points
 to the response variable, the identities of the badgers in each pairwise
 comparison were included as random effects in the model. Counts of SNP
 differences in the response variable were treated as
 Poisson-distributed. A jack-knifing procedure was used
 to remove bias from the mixed model’s estimates of fixed effects caused by
 pseudoreplication due to multiple representation of individual hosts and
 pathogen isolates in pairwise comparisons (Clarke, Rothery et al. 2002).
 Jackknifing removed each pathogen isolate in turn from the model and
 refitted to calculate pseudo-values and then jack-knifed estimates of the
 variance components and fixed effects of the model. These estimates
 provided jack-knifed 95% confidence intervals for the parameters of
 interest. Two isolates collected from badgers in the study area exhibited
 unusually large genetic distances as compared to the remainder of the
 isolates (Supplementary Figure 2). These isolates are known to be derived
 from cattle herds (as evidenced by their closest ancestral isolate on the
 wider phylogenetic tree being from a cow in the study area rather than a
 badger; also by their very high genetic distance from other isolates as
 determined by SNP differences): these were removed from the dataset prior
 to analysis.
description: # Genetic Distances between badgers (based on microsatellite-derived
 estimates of relatedness) and *M. bovis* isolates (based on pairwise SNP
 distances) alongside categories describing social group membership, age,
 and sex categories
 [https://doi.org/10.5061/dryad.8w9ghx3wq](https://doi.org/10.5061/dryad.8w9ghx3wq) ## Description of the data and file structure We tested the influence of host kinship and social structure on the population genetics of *Mycobacterium bovis*, the causative agent of bovine tuberculosis, in a wild badger population. Previous evidence suggested that social group boundaries constrain pathogen mixing and that cub infection risk is heightened by increasing prevalence of infected kin within social groups. We measured genetic distances between *M. bovis* isolates from badger hosts that varied in their own genetic similarity (a proxy for kinship) and in their social group affiliations. ### Files and variables #### File: Bentonetal\_HostPathogenGeneticDistances\_2025.csv **Description:**  ##### Variables * Focal.Animal.ID: Unique ID of each badger * Focal.Animal.Sex: Badger Sex * Focal.Animal.Age.FC: Badger age at first capture event (adult or cub) * Focal.Animal.Year.FC: Calendar year when the badger was first caught * Focal.Animal.Age.at.Sample.Year: Badger age when the corresponding *M. bovis* sample was collected from it * Focal.Animal.Current.AgeClass: Current estimated age class of the badger * Near.Animal.ID: Unique ID of the first badger in the pairwise comparison * Near.Animal.Sex: Sex of the first badger in the pairwise comparison * Near.Animal.Age.FC: Age at first capture of the first badger in the pairwise comparison * Near.Animal.Year.FC: Calendar year at first capture of the first badger in the pairwise comparison * Near.Animal.Age.at.Sample.Year: Estimated badger age at the year when the sample was collected (first badger in the pairwise comparison) * Near.Animal.Current.Age.Class: Estimated badger age class at the year when the sample was collected (first badger in the pairwise comparison) * Sex_Grouping: which sex grouping the pairwise badgers were: male/male, female/male, female/female * SexCategory: Grouping together between sex comparison; male/female or female/male grouped as 'between sexes' * Age_Grouping: which age grouping the pairwise badgers were: adult/cub, etc * AdultCategory: Grouping together inverse age comparisons: adult/cub and cub/adult grouped as the same category * AdultCategory_GROUPED: Grouping together all comparisons between young (yearling or cub) adults due to small sample sizes * Related_Category: Splitting badgers into likely relatives and non-relatives based on Q&G relatedness estimate * Focal.Sample.ID: ID of the first *M. bovis* sample in the pairwise comparison * Near.Sample.ID: ID of the second *M. bovis* sample in the pairwise comparison * Isolate.Genetic.Distance: Number of SNPs (single nucleotide polymorphisms) between the compared *M. bovis* isolates * Focal.Isolate.Year: Year when the first *M. bovis* isolate was collected from a clinical badger sample * Near.Isolate.Year: Year when the second *M. bovis* isolate was collected from a clinical badger sample * Time.Diff: Number of years between samples (directional) * Time.Diff.Without.Direction: Number of years between samples (non-directional) * GroupMatch: Whether the badgers in the pair were resident in the same social groups * SumCategory: Grouping of relatedness and same or different group categories * AllCategory_Sum: Grouping of all demographic categories * nonkin: categorical measure of badger genetic similarity (nonkin = below average similarity) * kinship: Estimated relatedness between badgers based on the Queller and Goodnight estimator (derived from microsatellite marker information) ## Code/software Code generated using R version 4.5.0 Packages used: lme4 ## Access information NA