I'm doing a project for my Phylogenetic Biology and Analysis course and--given my interest in sexual and structural systems--I've elected to make an attempt to examine whether there are differences in rates of genetic change by sex among the apes and some human populations based on mating practices and other potentially relevant factors.
My hypothesis is that variations in mating practices--such as monogamy vs. polyandry, etc.--and locality of mates--such as patrilocality vs. neolocality, etc.--will affect the effective population size (Ne) by sex and therefore the rates of evolution within sex-specific parts of the genome. By comparing the rates of change in non-coding regions where selective pressure shouldn't have a strong effect and by using the rates of change in autosomal sequences to standardize the changes in mitochondrial DNA (mtDNA) and Y-chromosome sequences to correct for overall populations size (N), I can try to tease apart the average population ratios for the species within the apes.
If the phylogenies (mt-DNA, Y, autosomal) match in topography and I can get the ratios in the ape species, I want to run some human populations for the same data. Finally, I want to run a multimodel inference to examine what factors--calculated ratios, social factors, sexual dimorphism ratios, typical living arrangements--are most likely to be driving the effective population size.
It should be fun. I know I can get the mtDNA sequences, the others should be more fun. I also plan on using as Gibbon sequences (if available) as a standard for the rest because of their unique characteristics. Considering I'm relying on open access data...
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