Abstract
Clustering individuals based on genetic data has become commonplace in many genetic and ecological studies. Most often, statistical inference of population structure is done by applying model-based approaches, such as Bayesian clustering, aided by visualization using distance-based approaches, such as PCA (Principle Component Analysis). While existing distance-based approaches suffer from lack of statistical rigour, model-based approaches entail assumption of prior conditions such as that the subpopulations are at Hardy-Wienberg equilibria. Here we present a distance-based approach for inference of population structure using genetic data based on the network theory concept of community, a dense subgraph within a network. A network is constructed using the pairwise genetic-distance matrix of all sampled individuals, and utilizes community detection algorithms to partition the network into communities, interpreted as a partition of the population to subpopulations. The statistical significance of the structure can be estimated by using permutation tests to evaluate the significance of the partition’s modularity, a network theory concept measuring the strength in which partitions divide the network. In order to further characterize population structure, a measure of the Strength of Association (SA) for an individual to its assigned community is calculated, and the Strength of Association Distribution (SAD) of the communities is analysed to provide additional population structure details. The approach presented here provides a novel, computationally efficient, method for inference of population structure which does not assume an underlying model nor prior conditions, making inference potentially more robust. The method is implemented in the software NetStruct, available at https://github.com/GiliG/NetStruct.
