The software works well with both small and large SNP datasets, depending on the degree of differences between the subpopulations. For closely related populations (those with a relatively small degree of differences between them), AWclust is capable of quickly processing the large datasets required to separate them.
Background:
Population structure analysis is important to genetic association
studies and evolutionary investigations. Parametric approaches, e.g.
STRUCTURE and L-POP, usually assume Hardy-Weinberg equilibrium (HWE)
and linkage equilibrium (LE) among loci in sample population
individuals. However, the assumptions may not hold and allele frequency
estimation may not be accurate in some data sets.
The improved version of STRUCTURE (version 2.1) can incorporate linkage
information among loci but is still sensitive to
high background linkage disequilibrium.
Nowadays, large-scale
single nucleotide polymorphisms (SNPs) are becoming popular in genetic
studies. Therefore, it is imperative to have software that makes full
use of these genetic data to generate inference
even when model assumptions do not hold or allele frequency estimation suffers
from high variation.
Results:
We have developed point-and-click software for non-parametric population
structure analysis distributed as an R package.
The software takes advantage of the large number of SNPs
available to categorize individuals into ethnically similar clusters
and it does not require any assumptions about population models.
Nor does it estimate allele frequencies.
Moreover, this software can also infer the
optimal number of populations.
Conclusions:
Our software tool employs non-parametric approaches to assign individuals
to clusters using SNPs. It provides efficient computation and an
intuitive way for researchers to explore ethnic relationships among
individuals. It can be complementary to parametric approaches in
population structure analysis.
Thank you for trying our software!
Feedback and Suggestions:
We're still trying to make AWclust a better program and welcome your
feedback and suggestions. Just email us at:
Xiaoyi Gao, ray.x.gao_at_gmail*dot*com
Joshua Starmer, josh*dot*starmer_at_gmail*dot*com