Organelle gene diversity under migration, mutation, and drift: equilibrium expectations, approach to equilibrium, effects of heteroplasmic cells, and comparison to nuclear genes.

Author:

Birky C W,Fuerst P,Maruyama T

Abstract

Abstract We developed stochastic population genetic theory for mitochondrial and chloroplast genes, using an infinite alleles model appropriate for molecular genetic data. We considered the effects of mutation, random drift, and migration in a finite island model on selectively neutral alleles. Recurrence equations were obtained for the expectation of gene diversities within zygotes, within colonies, and between colonies. The variables are number and sizes of colonies, migration rates, sex ratios, degree of paternal transmission, number of germ line cell divisions, effective number of segregating organelle genomes, and mutation rate. Computer solutions of the recurrence equations were used to study the approach to equilibrium. Gene diversities equilibrate slowly, while GST, used to measure population subdivision, equilibrates rapidly. Approximate equilibrium equations for gene diversities and GST can be obtained by substituting Neo and me, simple functions of the numbers of breeding or migrating males and females and of the degree of paternal transmission, for the effective numbers of genes and migration rates in the corresponding equations for nuclear genes. The approximate equations are not valid when the diversity within individuals is large compared to that between individuals, as is often true for the D-loop of animal mtDNA. We used the exact equations to verify that organelle genes often show more subdivision than nuclear genes; however, we also identified the range of breeding and migrating sex ratios for which population subdivision is greater for nuclear genes. Finally, we show that gene diversities are higher for nuclei than for organelles over a larger range of sex ratios in a subdivided population than in a panmictic population.

Publisher

Oxford University Press (OUP)

Subject

Genetics

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