Detecting the number of clusters of individuals using the software STRUCTURE: a simulation study
Authored by G Evanno, S Regnaut, J Goudet
Date Published: 2005
DOI: 10.1111/j.1365-294x.2005.02553.x
Sponsors:
Swiss National Science Foundation (SNSF)
Platforms:
EasyPop
Model Documentation:
Other Narrative
Flow charts
Mathematical description
Model Code URLs:
Model code not found
Abstract
The identification of genetically homogeneous groups of individuals is a
long standing issue in population genetics. A recent Bayesian algorithm
implemented in the software STRUCTURE allows the identification of such
groups. However, the ability of this algorithm to detect the true number
of clusters (K) in a sample of individuals when patterns of dispersal
among populations are not homogeneous has not been tested. The goal of
this study is to carry out such tests, using various dispersal scenarios
from data generated with an individual-based model. We found that in
most cases the estimated `log probability of data' does not provide a
correct estimation of the number of clusters, K. However, using an ad
hoc statistic Delta K based on the rate of change in the log probability
of data between successive K values, we found that STRUCTURE accurately
detects the uppermost hierarchical level of structure for the scenarios
we tested. As might be expected, the results are sensitive to the type
of genetic marker used (AFLP vs. microsatellite), the number of loci
scored, the number of populations sampled, and the number of individuals
typed in each sample.
Tags
differentiation
Genetic diversity
Dispersal
Model
Population-structure
Multilocus genotype data
Flow
Assignment tests
Microsatellite analysis
Computer-program