1 Computational and RNA Biology, Department of Biology, Faculty of Science, Københavns Universitet2 Natural History Museum of Denmark, Faculty of Science, Københavns Universitet3 Natural History Museum of Denmark, Natural History Museum of Denmark, Faculty of Science, Københavns Universitet4 Computational and RNA Biology, Department of Biology, Faculty of Science, Københavns Universitet5 Natural History Museum of Denmark, Natural History Museum of Denmark, Faculty of Science, Københavns Universitet
Inference of population structure and individual ancestry is important both for population genetics and for association studies. With next generation sequencing technologies it is possible to obtain genetic data for all accessible genetic variations in the genome. Existing methods for admixture analysis rely on known genotypes. However, individual genotypes cannot be inferred from low-depth sequencing data without introducing errors. This article presents a new method for inferring an individual's ancestry that takes the uncertainty introduced in next generation sequencing data into account. This is achieved by working directly with genotype likelihoods that contain all relevant information of the unobserved genotypes. Using simulations as well as publicly available sequencing data, we demonstrate that the presented method has great accuracy even for very low-depth data. At the same time, we demonstrate that applying existing methods to genotypes called from the same data can introduce severe biases. The presented method is implemented in the NGSadmix software available at http://www.popgen.dk/software.