Analyzing Genotypic Diversity Using Poppr

Once data is imported into R, the user can dynamically access and manipulate the population hierarchy with the function splitcombine(), subset the data set by population with popsub(), and check for cloned multilocus genotypes using mlg(). For data sets that include clones, the poppr function clonecorrect() will censor clones with respect to any level of a population hierarchy. In the case of missing data we use the commonly implemented, most parsimonious approach of treating missing states as novel alleles. This inherently makes analysis sensitive to missing data and genotyping error, but the user has tools available such as missingno() to filter out missing data at a per-individual or per-locus level. The user can also decide how uninformative loci (e.g., alleles occurring at minor frequencies; monomorphic loci; fixed heterozygous loci) are treated using the function informloci(). Thus, the user can specify a frequency for removal of uninformative loci. The user is encouraged to conduct analysis with and without missing data/uninformative loci to assess sensitivity to these issues when making inferences. A full list of functions available in poppr is provided in Table 1.
Typical analyses in poppr start with summary statistics for diversity, rarefaction, evenness, MLG counts, and calculation of distance measures such as Bruvo’s distance, providing a suitable stepwise mutation model appropriate for microsatellite markers (Bruvo et al., 2004 (link)). Poppr will define MLGs in your data set, show where they cross populations, and can produce graphs and tables of MLGs by population that can be used for further analysis with the R package vegan (Oksanen et al., 2013 ). Many of the diversity indices calculated by the vegan function diversity() are useful in analyzing the diversity of partially clonal populations. For this reason, poppr features a quick summary table (Table 2) that incorporates these indices along with the index of association, I_A (Brown, Feldman & Nevo, 1980 (link); Smith et al., 1993 (link)), and its standardized form, ${\overline{r}}_d$ , which accounts for the number of loci sampled (Agapow & Burt, 2001 (link)). Both measures of association can detect signatures of multilocus linkage and values significantly departing from the null model of no linkage among markers are detected via permutation analysis utilizing one of four algorithms described in Table 3 (Agapow & Burt, 2001 (link)). The user can specify the number of samples taken from the observed data set to obtain the null distribution expected for a randomly mating population. Detailed examples of these analyses can be found in the poppr manual.