» Articles » PMID: 18791255

A General Extreme Value Theory Model for the Adaptation of DNA Sequences Under Strong Selection and Weak Mutation

Overview
Journal Genetics
Specialty Genetics
Date 2008 Sep 16
PMID 18791255
Citations 43
Authors
Affiliations
Soon will be listed here.
Abstract

Recent theoretical studies of the adaptation of DNA sequences assume that the distribution of fitness effects among new beneficial mutations is exponential. This has been justified by using extreme value theory and, in particular, by assuming that the distribution of fitnesses belongs to the Gumbel domain of attraction. However, extreme value theory shows that two other domains of attraction are also possible: the Fréchet and Weibull domains. Distributions in the Fréchet domain have right tails that are heavier than exponential, while distributions in the Weibull domain have right tails that are truncated. To explore the consequences of relaxing the Gumbel assumption, we generalize previous adaptation theory to allow all three domains. We find that many of the previously derived Gumbel-based predictions about the first step of adaptation are fairly robust for some moderate forms of right tails in the Weibull and Fréchet domains, but significant departures are possible, especially for predictions concerning multiple steps in adaptation.

Citing Articles

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.


The distribution of fitness effects during adaptive walks using a simple genetic network.

OBrien N, Holland B, Engelstadter J, Ortiz-Barrientos D PLoS Genet. 2024; 20(5):e1011289.

PMID: 38787919 PMC: 11156440. DOI: 10.1371/journal.pgen.1011289.


Towards evolutionary predictions: Current promises and challenges.

Wortel M, Agashe D, Bailey S, Bank C, Bisschop K, Blankers T Evol Appl. 2023; 16(1):3-21.

PMID: 36699126 PMC: 9850016. DOI: 10.1111/eva.13513.


Unpredictable repeatability in molecular evolution.

Das S, Krug J Proc Natl Acad Sci U S A. 2022; 119(39):e2209373119.

PMID: 36122210 PMC: 9522380. DOI: 10.1073/pnas.2209373119.


Extreme value theory as a framework for understanding mutation frequency distribution in cancer genomes.

Tokutomi N, Nakai K, Sugano S PLoS One. 2021; 16(8):e0243595.

PMID: 34424899 PMC: 8382180. DOI: 10.1371/journal.pone.0243595.


References
1.
Orr H . The distribution of fitness effects among beneficial mutations. Genetics. 2003; 163(4):1519-26. PMC: 1462510. DOI: 10.1093/genetics/163.4.1519. View

2.
Martin G, Lenormand T . The distribution of beneficial and fixed mutation fitness effects close to an optimum. Genetics. 2008; 179(2):907-16. PMC: 2429884. DOI: 10.1534/genetics.108.087122. View

3.
Gillespie J . A simple stochastic gene substitution model. Theor Popul Biol. 1983; 23(2):202-15. DOI: 10.1016/0040-5809(83)90014-x. View

4.
Rokyta D, Beisel C, Joyce P, Ferris M, Burch C, Wichman H . Beneficial fitness effects are not exponential for two viruses. J Mol Evol. 2008; 67(4):368-76. PMC: 2600421. DOI: 10.1007/s00239-008-9153-x. View

5.
Gillespie J . MOLECULAR EVOLUTION OVER THE MUTATIONAL LANDSCAPE. Evolution. 2017; 38(5):1116-1129. DOI: 10.1111/j.1558-5646.1984.tb00380.x. View