A non-parametric approach to population structure inference using multilocus genotypes

Inference of population structure from genetic markers is helpful in diverse situations, such as association and evolutionary studies. In this paper, we describe a two-stage strategy in inferring population structure using multilocus genotype data. In the first stage, we use dimension reduction methods such as singular value decomposition to reduce the dimension of the data, and in the second stage, we use clustering methods on the reduced data to identify population structure. The strategy has the ability to identify population structure and assign each individual to its corresponding subpopulation. The strategy does not depend on any population genetics assumptions (such as Hardy-Weinberg equilibrium and linkage equilibrium between loci within populations) and can be used with any genotype data. When applied to real and simulated data, the strategy is found to have similar or better performance compared with STRUCTURE, the most popular method in current use. Therefore, the proposed strategy provides a useful alternative to analyse population data.