Detecting selection in low-coverage high-throughput sequencing data using principal component analysis.

ABSTRACT:

Background

Identification of selection signatures between populations is often an important part of a population genetic study. Leveraging high-throughput DNA sequencing larger sample sizes of populations with similar ancestries has become increasingly common. This has led to the need of methods capable of identifying signals of selection in populations with a continuous cline of genetic differentiation. Individuals from continuous populations are inherently challenging to group into meaningful units which is why existing methods rely on principal components analysis for inference of the selection signals. These existing methods require called genotypes as input which is problematic for studies based on low-coverage sequencing data.

Materials and methods

We have extended two

SUBMITTER: Meisner J 

PROVIDER: S-EPMC8480091 | biostudies-literature | 2021 Sep

REPOSITORIES: biostudies-literature