Inference of Selection from Genetic Time Series Using Various Parametric Approximations to the Wright-Fisher Model.

ABSTRACT: Detecting genomic regions under selection is an important objective of population genetics. Typical analyses for this goal are based on exploiting genetic diversity patterns in present time data but rapid advances in DNA sequencing have increased the availability of time series genomic data. A common approach to analyze such data is to model the temporal evolution of an allele frequency as a Markov chain. Based on this principle, several methods have been proposed to infer selection intensity. One of their differences lies in how they model the transition probabilities of the Markov chain. Using the Wright-Fisher model is a natural choice but its computational cost is prohibitive for large population sizes so approximations to this model based on parametric distributions have been proposed

SUBMITTER: Paris C 

PROVIDER: S-EPMC6893182 | biostudies-literature | 2019 Dec

REPOSITORIES: biostudies-literature