Simulation of DNA Sequence Evolution Under Models of Recent Directional Selection

Overview

Journal Brief Bioinform

Publisher Oxford University Press

Specialty Biology

Date 2008 Dec 26

PMID 19109303

Citations 16

Authors

Yuseob Kim

Thomas Wiehe

Affiliations

Soon will be listed here.

Abstract

Computer simulation is an essential tool in the analysis of DNA sequence variation for mapping events of recent adaptive evolution in the genome. Various simulation methods are employed to predict the signature of selection in sequence variation. The most informative and efficient method currently in use is coalescent simulation. However, this method is limited to simple models of directional selection. Whole-population forward-in-time simulations are the alternative to coalescent simulations for more complex models. The notorious problem of excessive computational cost in forward-in-time simulations can be overcome by various simplifying amendments. Overall, the success of simulations depends on the creative application of some population genetic theory to the simulation algorithm.

Citing Articles

Population size rescaling significantly biases outcomes of forward-in-time population genetic simulations.

Dabi A, Schrider D Genetics. 2024; 229(1):1-57.

PMID: 39503241 PMC: 11708920. DOI: 10.1093/genetics/iyae180.

Biases in ARG-Based Inference of Historical Population Size in Populations Experiencing Selection.

Marsh J, Johri P Mol Biol Evol. 2024; 41(7).

PMID: 38874402 PMC: 11245712. DOI: 10.1093/molbev/msae118.

Population size rescaling significantly biases outcomes of forward-in-time population genetic simulations.

Dabi A, Schrider D bioRxiv. 2024; .

PMID: 38645049 PMC: 11030438. DOI: 10.1101/2024.04.07.588318.

The Impact of Purifying and Background Selection on the Inference of Population History: Problems and Prospects.

Johri P, Riall K, Becher H, Excoffier L, Charlesworth B, Jensen J Mol Biol Evol. 2021; 38(7):2986-3003.

PMID: 33591322 PMC: 8233493. DOI: 10.1093/molbev/msab050.

The impact of purifying and background selection on the inference of population history: problems and prospects.

Johri P, Riall K, Becher H, Excoffier L, Charlesworth B, Jensen J bioRxiv. 2021; .

PMID: 33501439 PMC: 7836109. DOI: 10.1101/2020.04.28.066365.

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