Robust inference of population structure from next-generation sequencing data with systematic differences in sequencing.

ABSTRACT: Motivation:Inferring population structure is important for both population genetics and genetic epidemiology. Principal components analysis (PCA) has been effective in ascertaining population structure with array genotype data but can be difficult to use with sequencing data, especially when low depth leads to uncertainty in called genotypes. Because PCA is sensitive to differences in variability, PCA using sequencing data can result in components that correspond to differences in sequencing quality (read depth and error rate), rather than differences in population structure. We demonstrate that even existing methods for PCA specifically designed for sequencing data can still yield biased conclusions when used with data having sequencing properties that are systematically different across

SUBMITTER: Liao P 

PROVIDER: S-EPMC6031038 | biostudies-literature | 2018 Apr

REPOSITORIES: biostudies-literature