*library_books*

## Similar protocols

## Protocol publication

[…] All pairs of loci were tested for linkage disequilibrium using a probability test in **Genepop** 4.0 . Critical significance levels for multiple testing were corrected applying a sequential Bonferroni correction. FreeNA was used to estimate null allele frequencies, for each locus in each population, according to the expectation maximization (EM) algorithm of Dempster et al. . The mean observed number of alleles per locus (A
L) and the number of private alleles (A
P) per population were computed using **GenAlEx** 6.5 , . Allelic richness (A
R, El Mousadik and Petit ), as implemented in the software FSTAT 2.9.3 , was used to make direct comparisons of the mean number of alleles among populations regardless of sample size. Expected heterozygosity (H
E) over all loci, observed heterozygosity over all loci (H
O), multilocus F
IS estimated through the fixation index of Weir and Cockerham were calculated using Genepop 4.0 . The exact tests of Guo and Thompson based on Markov chain iteration were used to test for departures from Hardy-Weinberg Equilibrium (HWE). To test for differences in amount of genetic variability (A
R and H
E) between habitats, a test of comparison among groups of populations using FSTAT 2.9.3 was performed with 9999 permutations. [...] Assignment of multi-locus genotypes to different clusters was examined using two methods. Following a Bayesian clustering method, we ran InStruct for K = 1 to K = 12 genetic clusters in mode 4 in order to infer the genetic structure and inbreeding coefficients. Whereas Structure minimizes deviations from HWE within an inferred population, InStruct considers inbreeding or selfing rate in the model. For each value of K, InStruct was run with ten independent chains, each chain being run along one million iterations with a burn-in of half a million and a thinning interval of ten steps. To determine the optimal K, mean log-likelihood of the data and ΔK were plotted for each K. An alternative method, implemented in the **adegenet** package 1.3–4 for R 2.15.1 , using K-means clustering of principal components for K = 1 to K = 39 and Bayesian Information Criterions was performed to assess the best number of genetic cluster. The two dissimilar approaches were used in this study, because different clustering approaches may lead to different conclusions , . In order to test whether the genetic differentiation is structured by habitats, population structure was also explored by performing Discriminant Analysis of Principal Components (DAPC; ) with habitats as grouping factor. DAPC analysis is a recent multivariate approach that does not make any assumption about HWE or linkage equilibrium. DAPC transforms genotypes using PCA as a prior step to a discriminant analysis. The latter is performed to a number of principal components retained by the user (60 representing 84% of total genetic variation in this study) in order to maximize the among-population variation and minimize the variation within predefined groups ; that is, habitat types in the present case. DAPC was applied using the adegenet package 1.3–4 for R 2.15.1 . […]

## Pipeline specifications

Software tools | Genepop, GenAlEx, adegenet |
---|---|

Application | Population genetic analysis |