Piero Cossu1, Gian Luca Dedola1, Fabio Scarpa1, Daria Sanna1, Tiziana Lai1, Ferruccio Maltagliati2, Marco CuriniGalletti1, Marco Casu1
Patterns of spatial genetic variation in Patella ulyssiponensis: insights from the western Mediterranean marine
ecoregion
1
Dipartimento di Scienze della Natura e del Territorio - Sezione di Zoologia, Archeozoologia e Genetica - Università di
Sassari, Via Muroni 25, 07100 Sassari, Italy
2
Dipartimento di Biologia - Unità di Biologia Marina - Università di Pisa, Via Derna 1, 56126 Pisa, Italy
Corresponding author:
Marco Casu
Phone: +39 079 228924
Fax: +39 079 228665
E-mail address: marcasu@uniss.it
Supplementary Materials and methods
Scoring of fragments
ISSR fragments are interpreted as dominant di-allelic Mendelian markers (Wolfe & Liston, 1998). Band presence is
scored as (1), and represents the dominant phenotype (namely AA and Aa individuals); band absence corresponds to the
recessive phenotype (aa individuals) and is scored as (0). In this study, only fragments scored in two replicated
individuals were used. To this end, we used the following procedure to check for reproducibility and reliability of
bands. First, fragments were scored independently by two co-authors of the study, and only bands recorded by both of
them were retained to the next step. Then, following Caballero et al. (2008) we discarded the bands with an overall
frequency below 2% unless these bands were unique to a population. Such populations were re-amplified to check if
these bands were amplification artefacts. Finally, reproducibility was also checked by repeating amplification on 20%
of the specimens for each independent PCR. Individuals were chosen in order to maximise the number of bands, and
only fragments equally scored in two replicates obtained from the same individual were used.
Detection of outlier loci
Outlier loci for selection were detected using DFDIST (Beaumont & Nichols, 1996) and Bayescan (Foll & Gaggiotti,
2008). To minimize the detection of false positives: 1) probability values were corrected for multiple tests; and 2) only
loci detected as outliers by both methods were retained (Paris et al. 2010). Both methods rely upon the departure of F ST
values observed at each locus from their neutral expectations. DFDIST is modified to analyse dominant data (Beaumont
& Balding, 2004) by implementing the Bayesian method to estimate the allelic frequencies from the recessive
phenotypes (Zhivotovsky, 1999). The method uses coalescent simulations to generate a neutral distribution of F ST as a
function of heterozigosity, assuming an island model with symmetrical migration among demes of equal size. Detection
of candidate loci under selection (outliers) was carried out using global F ST values estimated according to Caballero et
al. (2008). We simulated 100 demes to generate a null distribution based on 50,000 simulated loci.
Bayescan is a modified version of the approach of Beaumont and Balding (2004), founded on the ground that F ST values
reflect locus-specific effects, such as selection, and population-specific as effective size and immigration rates
(Gaggiotti et al., 2009). A hierarchical Bayesian approach estimates these values using a logistic regression to model
locus and population effects, and the posterior probability of including a locus-specific effect is estimated by a
reversible jump Markov chain Monte-Carlo (MCMC) approach (Foll and Gaggiotti, 2008). We used a threshold = 10 to
set the prior odds for the neutral model with respect to a model with selection, which correspond to a strong evidence
for markers to be considered under selection (log10PO ≥ 1). We used 10 pilot runs of 5,000 iterations each to adjust the
proposal distributions, and then the simulation was run for 500,000 iterations after a burn-in phase of 50,000. Samples
were collected every 50 iterations (thinning interval) to obtain a final sample size of 10,000. False discovery rate at 5%
was used to correct for multiple testing. We modelled uncertainty about the inbreeding coefficient from a uniform
distribution; assumption of HWE or assuming a beta distribution with default parameters in BAYESCAN did not affect
the results (data not shown).
Bayesian clustering: the locprior model
The model proposed by Hubisz et al. (2009) uses information about sampling locations of individuals as weak priors to
improve the power of structure to assign individuals to clusters, in particular when there is weak genetic structure. The
method implemented in Structure is similar to that used in BAPS but allows for the possibility that sample group
information might be uninformative or partially informative about genetic population structure. Remarkably, the
method: 1) does not tend to find structure when there is none; 2) can ignore the sampling information when the ancestry
of individual is uncorrelated with sampling locations; and 3) gives the same answers of models without sampling
information when the signal of population structure is strong (Hubisz et al., 2009).
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