Genetics, Vol. 151, 895-913, February 1999, Copyright © 1999

The Effect of Overdominance on Characterizing Deleterious Mutations in Large Natural Populations

Jin-Long Lia, Jian Lia, and Hong-Wen Denga
a Osteoporosis Research Center and Department of Biomedical Sciences, Creighton University, Omaha, Nebraska 68131

Corresponding author: Hong-Wen Deng, Osteoporosis Research Center, Creighton University, 601 N. 30th St., Suite 6787, Omaha, NE 68131., deng{at}creighton.edu (E-mail)

Communicating editor: M. A. ASMUSSEN

Alternatives to the mutation-accumulation approach have been developed to characterize deleterious genomic mutations. However, they all depend on the assumption that the standing genetic variation in natural populations is solely due to mutation-selection (M-S) balance and therefore that overdominance does not contribute to heterosis. Despite tremendous efforts, the extent to which this assumption is valid is unknown. With different degrees of violation of the M-S balance assumption in large equilibrium populations, we investigated the statistical properties and the robustness of these alternative methods in the presence of overdominance. We found that for dominant mutations, estimates for U (genomic mutation rate) will be biased upward and those for (mean dominance coefficient) and (mean selection coefficient), biased downward when additional overdominant mutations are present. However, the degree of bias is generally moderate and depends largely on the magnitude of the contribution of overdominant mutations to heterosis or genetic variation. This renders the estimates of U and not always biased under variable mutation effects that, when working alone, cause U and to be underestimated. The contributions to heterosis and genetic variation from overdominant mutations are monotonic but not linearly proportional to each other. Our results not only provide a basis for the correct inference of deleterious mutation parameters from natural populations, but also alleviate the biggest concern in applying the new approaches, thus paving the way for reliably estimating properties of deleterious mutations.





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