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SUPPLEMENTARY METHODS
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Within group relatedness estimation
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Presence of related individuals in the population can affect estimates of
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population differentiation. Pairwise relatedness between the samples was
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estimated with the Triadic likelihood estimator (Wang, 2007) implemented in
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the program COANCESTRY. The TrioML method uses a third individual as
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reference to estimate the relatedness between two other individuals, thus
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reducing the chance of alleles identical in state being mistaken for alleles
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identical by descent. This method also allows for inbreeding and genotyping
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errors, and thus is suitable for most natural population studies. We estimated
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the average within group relatedness for each population of both the species.
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Test for neutrality of loci
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We tested if our microsatellite loci are selectively neutral using the
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method described in Beaumont & Balding (2004) as implemented in the
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program Bayescan v 2.1 (Foll & Gaggiotti, 2008) which uses outlier analysis to
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identify candidate loci under selection. As it uses the Bayesian approach, it
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incorporates uncertainty in allele frequencies due to small sample sizes. We ran
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the dataset for each species separately for 10,000 simulations with a thinning
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interval of 50 and a burn-in of 5000. Bayescan reports posterior probability for
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a model incorporating natural selection and introduces a criterion for model
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choice similar to Bayes factor called Posterior Odds (PO) which is a ratio of the
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posterior probabilities. Low values of log10(PO) suggest no evidence for
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selection.
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Calculation of genotyping errors
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We repeated PCR amplification and genotyping for 94 individuals of R.
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satarae and 27 individuals of R. rattus at least twice to quantify genotyping
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errors using the program GIMLET. We quantified four types of errors including
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allelic dropout and false alleles. The error rates for R. satarae were slightly
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higher than that for R. rattus (1% and 0.5% respectively).
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Detection of null alleles and estimation of null allele frequencies
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Since the same genetic markers were used on two different species, presence of
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null alleles is possible and could possibly explain observed heterozygote deficit.
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We evaluated this with the program MICROCHECKER (van Oosterhout et al.
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2004). Further, we estimated the frequency of null alleles for each locus and
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population using the Expectation Maximisation algorithm (Dempster et al.
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1977) implemented in the software FreeNA (Chapuis & Estoup 2007).
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REFERENCES
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Beaumont, M. A., & Balding, D. J. (2004). Identifying adaptive genetic
divergence among populations from genome scans. Molecular Ecology,
13(4), 969–980. Retrieved from
http://www.ncbi.nlm.nih.gov/pubmed/15012769
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Chapuis, M.-P., & Estoup, A. (2007). Microsatellite null alleles and estimation
of population differentiation. Molecular Biology and Evolution, 24(3),
621–31. doi:10.1093/molbev/msl191
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Foll, M., & Gaggiotti, O. (2008). A genome-scan method to identify selected
loci appropriate for both dominant and codominant markers: a Bayesian
perspective. Genetics, 180(2), 977–993. doi:10.1534/genetics.108.092221
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Wang, J. (2007). Triadic IBD coefficients and applications to estimating
pairwise relatedness. Genetical Research, 89(3), 135–53.
doi:10.1017/S0016672307008798
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