Inferring Population Structure and Admixture Proportions in Low-Depth NGS Data.

ABSTRACT: We here present two methods for inferring population structure and admixture proportions in low-depth next-generation sequencing (NGS) data. Inference of population structure is essential in both population genetics and association studies, and is often performed using principal component analysis (PCA) or clustering-based approaches. NGS methods provide large amounts of genetic data but are associated with statistical uncertainty, especially for low-depth sequencing data. Models can account for this uncertainty by working directly on genotype likelihoods of the unobserved genotypes. We propose a method for inferring population structure through PCA in an iterative heuristic approach of estimating individual allele frequencies, where we demonstrate improved accuracy in samples with low and

SUBMITTER: Meisner J 

PROVIDER: S-EPMC6216594 | biostudies-literature | 2018 Oct

REPOSITORIES: biostudies-literature