Theoretical and Statistical Approaches to Understand Human Mitochondrial DNA Heteroplasmy Inheritance
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By working closely with our experimental collaborators, the computational simulation of mouse embryogenesis has been developed in our lab using their measurements of mouse mtDNA copy number. This experimental-computational interplay shows that the variation of offspring heteroplasmy level has been largely generated by random partition of mtDNA molecules during pre- and early postimplantation development.
By adapting a set of probability functions developed to describe the segregation of allele frequencies under a pure random drift process, we now can model mtDNA heteroplasmy distribution using parameters estimated from experimental data. The absence of an estimate of sampling error of mtDNA heteroplasmy variance may largely affect the biological interpretation drawn from this high-order statistic, thereby we have developed three different methods to estimate sampling error values for mtDNA heteroplasmy variance. Applying this error estimation to the comparison of mouse to human mtDNA heteroplasmy variance reveals the difference of the mitochondrial genetic bottleneck between these organisms.
In humans, the mothers who carry a high proportion of m.3243A>G mutation tend to have fewer daughters than sons. This offspring gender bias has been revealed by applying basic statistical tests on the human clinical pedigrees carrying this mtDNA mutation. This gender bias may partially determine the mtDNA mutation level among female family members.
In conclusion, the application of population genetic theory, statistical analysis, and computational simulation help us gain understanding of human mtDNA heteroplasmy inheritance. The results of these studies would be of benefit to both scientific research and clinical application.
- Doctoral Dissertations