Optimum mutation rate and degree of dominance as determined by the principle of minimum genetic load View Full Text


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Article Info

DATE

1960-06

AUTHORS

Motoo Kimura

ABSTRACT

It is demonstrated that by introducing what may be called the principle of minimum genetic load, the spontaneous mutation rate and the average degree of dominance of deleterious mutant genes may be derived theoretically from the total genetic damage and the rate of substitution of genes in horotelic evolution. The relations connecting these quantities may be expressed by a pair of equations:\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document} $$\left. \begin{gathered} \Sigma _\mu = \frac{{0 \cdot 3419E}}{{\bar h}}\left( {1 + 1 \cdot 720\bar h + ...} \right) \hfill \\ \bar h = 0 \cdot 6838\sqrt {\frac{E}{{2D}}} \left( {1 + 1 \cdot 018 \sqrt {\frac{E}{{2D}}} + ...} \right) \hfill \\ \end{gathered} \right\}$$ \end{document}, where Σμ is the spontaneous mutation rate per gamete per generation,h is the average degree of dominance in fitness of deleterious mutant genes,D is the total mutational damage or approximately the rate of inbreeding depression in fitness per unit increase in the inbreeding coefficient andE is the rate of substitution of genes in horotelic evolution. The above formulae are sufficiently simple to be checked by observational data now available. The present result offers, as a byproduct, a new theory of dominance which can account for the partial dominance in fitness of the normal alleles of “recessive” deleterious genes.The implication of the principle of minimum genetic load for a cyclical change in environmental condition is also discussed. More... »

PAGES

21-34

References to SciGraph publications

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/bf02985336

DOI

http://dx.doi.org/10.1007/bf02985336

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1014217276


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