Population genetic inference from the adaptive immune system

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DESCRIPTION (provided by applicant): My graduate training in theoretical population genetics taught me how to use the current patterns of genetic diversity in a population to reconstruct the evolutionary path taken by that population. New technology has opened new frontiers for this type of modeling and analysis. In particular, the human adaptive immune system generates a tremendous amount of genetic diversity within a single individual via V(D)J recombination. Similar to evolution at the level of a population of individuals (i.e. a species), the population of immune cells within a single individual evolves over time in response to mutation, selection, migration, and genetic drift. Advances in sequencing technology allow fine-scale interrogation of this diversity within single individuals by deep sequencing of multiple loci at multiple timepoints. However, these new data require new methods for interpretation before biological questions can be answered. The specific aims of this project will be to: (1) extend existing population genetic models to infer the strength of selection from time-series data that tracks the diversity of a single population of immune cells (e.g., CD8 T cells) while accounting for sequencing error, (2) validate these models by applying them to extensive T cell data from mice, where invasive experimental techniques can be used, and more restricted human T cell data where a-b associations must be inferred, (3) develop a novel population genetic model linking the effects of selection in multiple cell populations simultaneously, such as when CD4 T cells synergistically "help" CD8 T cells of similar specificity. The data used in Aim 2 will be provided by my experimental collaborators, Dr. Rafi Ahmed and Dr. Joseph Blattman. My mentor Dr. Rustom Antia will guide my uniting of evolutionary theory with his specialty in theoretical immunology to further my goal of starting a new line of research in intra-individual population genetics.