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.
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.
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.
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