Population structure detection software tools | Population genetics data analysis
Tools for estimating population structure from genetic data are now used in a wide variety of applications in population genetics. However, inferring population structure in large modern data sets imposes severe computational challenges.
Collates results generated by the program STRUCTURE. Structure Harvester provides a fast way to assess and visualize likelihood values across multiple values of K and hundreds of iterations for easier detection of the number of genetic groups that best fit the data. In addition, Structure Harvester will reformat data for use in downstream programs, such as CLUMPP, and when possible, executes the ‘‘Evanno’’ method. This program is available online or as a stand-alone version for local use.
Allows users to find pattern and structure in multivariate data. NTSYSpc assists users to calculate a phylogenetic tree that uses the neighbor-joining or unweighted pair-group method with averaging (UPGMA) methods for constructing dendrograms.
Uses principal components analysis to explicitly model ancestry differences between cases and controls along continuous axes of variation; the resulting correction is specific to a candidate marker’s variation in frequency across ancestral populations, minimizing spurious associations while maximizing power to detect true associations. The EIGENSOFT package has a built-in plotting script and supports multiple file formats and quantitative phenotypes.
Computes various genetic assignment criteria to assign or exclude reference populations as the origin of diploid or haploid individuals, as well as of groups of individuals, on the basis of multilocus genotype data. GeneClass allows the specific task of first-generation migrant detection. It includes several Monte Carlo resampling algorithms that compute for each individual its probability of belonging to each reference population or to be a resident (i.e., not a first-generation migrant) in the population where it was sampled. A user-friendly interface facilitates the treatment of large datasets.
Provides a way to use genetic data to identify species hybrids. NewHybrids is applicable not only to loci with fixed differences between species, but also to loci without fixed differences. Though prior knowledge may be incorporated into model, the method is able to cluster individuals in a mixed population without any a priori genetic knowledge of the species. The model-based approach is extendable to special sampling scenarios and different types of genetic markers.
Maximizes the proportion of total genetic variance due to differences between groups of populations. SAMOVA is a method based on a simulated annealing procedure. It permits to define groups of populations that are geographically homogeneous and maximally differentiated from each other. This algorithm also leads to the identification of genetic barriers between these groups.
Serves for population genetic investigation. POPGENE employs co-dominant and dominant markers to make analysis of genetic variation. It generates scientifically sound statistics and can assist users in the analyze of population genetic structure using the target markers.