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Statistical Modeling of Ancestral Processes

Explore ancestral processes through statistical analysis of polymorphism data, using phylogenetics and genetic methods to estimate genealogical parameters. Simulate data and compare statistical properties to established models to gain insights into population history.

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Statistical Modeling of Ancestral Processes

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  1. Statistical Modeling of Ancestral Processes Based on a Review by: N. Rosenberg and M. Nordborg

  2. Goal: to understand demographic history of humans based on polymorphism data • Since Polymorphisms are Random Processes, then they can be studied by their statistical properties • Method: Phylogenetics or Genetic Analysis Phylogenetics/Species Tree Genetic Anaylsis/Gene Tree

  3. What do Genetic Methods do? • Genealogical methods estimate parameters of random genealogical processes that give rise to each tree • Different sites can have different geneologies

  4. Coalescence is used to establish a Model For a Model you need: Coalescence, Mutation & Recombination You tabulate the statistical properties of your data and compare that against known data Simulate Data See which pattern your observed data corresponds to (see which prediction it looks like)

  5. Statistical Properties can vary - this is an example of an Average polymorphism rate Polymorphisms are proportionate to population size You may not want to look at the polymorphisms that are average. So, you would expand the parameters of your model to see if you data is expanded, collapsed, or bottlenecked The Model is relative to what you are trying to model

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