Characterizing Deleterious Genomic Mutations in The Presence of Overdominance (a Computer Simulation Approach)
Most mutations with observable phenotypic effects are harmful. Characterizing Deleterious Genomic Mutations (DGMs) is essential in testing the numerous theories that have been developed to explain some fundamental biological phenomena. The mutation-accumulation (M-A) approach has been the predominant method of characterizing DGMs during the past few decades. Two alternatives to the labor- and time-consuming M-A approach, the inbreeding depression approach and the fitness moments approach, have been developed recently. Applying these two approaches is hampered because both depend on the assumption that the standing genetic variation in natural populations is solely due to mutation-selection (M-S) balance. According to this assumption, overdominance does not contribute to heterosis and standing genetic variation. The extent to which this assumption is valid is unknown. In this dissertation, I investigated the statistical properties and the robustness of these two alternative methods in the presence of overdominant mutations using computer simulations. To ensure that the computer simulations could be conducted at relatively controlled levels, two important indices (a and P) and associated analytical derivations were developed to characterize the relative contribution of overdominant mutations to heterosis and standing genetic variation in relation to the genomic parameters of these mutations. These two indices provide a standard for selecting proper input parameters of DGMs and overdominant mutations for computer simulations in this study. They also provide a basis for investigating a number of issues related to the contribution of dominant and overdominant mutations to inbreeding depression and standing genetic variation in natural populations. It is revealed that the contributions of overdominant mutations to heterosis and standing genetic variation are monotonic, but not linearly proportional to each other. Computer simulations and data analyses were performed to study the effects of overdominant mutations on characterizing DGMs with different degrees of violation of the M-S balance assumption in large equilibrium populations in the presence of overdominant mutations. For dominant mutations, estimates for U (DGM rate) are upwardly biased and those for h (mean dominance coefficient) and s (mean selection coefficient) are downwardly biased when additional overdominant mutations are present. However, the degrees of biases generally are moderate and largely depend on the magnitude of the contributions of overdominant mutations to heterosis and standing genetic variation. In addition, under variable mutation effects that usually cause U and s to be underestimated, the estimates of U and 5 are not. always biased in the presence of overdominant mutations. These results provide a basis for correcting inferences of DGM parameters in natural populations. They also alleviate the biggest concern of applying the two newly developed approaches and pave the way for reliably estimating properties of DGMs.
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