Ancestry-based statistical methods for genetic studies

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The variation observed today in the human genome is a result of genetic processes, such as mutation, acting over time on the DNA of our ancestors. The 'gene genealogy' of a sample of genes from unrelated individuals is an ancestral tree that captures the history of these genetic events. If the gene genealogy was known, it could provide information about the location of mutations that contribute to observed characteristics. However, the true gene genealogy cannot be known since the time scale is on the order of tens of thousands of years. I am interested in how genetic data can be used to reconstruct this ancestral history and how the genealogy can be incorporated in statistical methods to relate observed characteristics with genetic variants. The over-arching vision of my research program - which draws on both statistical theory and population genetics - is to develop, test and implement innovative statistical methods for gene mapping that use the gene genealogy to model the genetic similarity between individuals. This is accomplished with two long-term objectives. The first objective of my research program is to propose and test a flexible `gene mapping' statistic that can handle different types of response variables, variable frequencies of the causal genetic variant in the population, and that includes non-genetic variables in the modelling. The second objective is to design a model and algorithm to probabilistically sample gene genealogies that are compatible with genetic data observed on families. This ambitious objective represents a merging of two types of trees in genetic data: family trees and gene genealogies. The central goal of this research program is to increase the utility and availability of tree-based gene mapping for researchers in human and population genetics. All techniques will be implemented in freely-available open source software for researchers in Canada and abroad. The statistical tools that I develop can be used by researchers in other disciplines, such as in human health and medical genetics. This research program will provide high-quality training opportunities for undergraduate, graduate and postdoctoral HQP. All HQP will acquire advanced statistical and computational skills, which will prepare them for their future careers as professional statisticians in government, industry or in an academic setting.