Models and tools for studying genetic and cultural variation [electronic resource]

A fundamental goal of population genetics is to understand how historical events and processes, such as speciation, migration, natural selection, and drift, have contributed to genetic variation among modern individuals. In humans, factors that contribute to genetic variation also include cultural phenomena and practices such as marriage customs and membership in cultural or linguistic groups that act as either barriers to, or catalysts for, contact and mating. Mathematical models of genetic evolution can be applied to make inferences about factors that have influenced genetic variation among populations over time. Analyses of cultural data can enhance these analyses by identifying cultural phenomena that have contributed to contact and isolation among populations and by providing an additional source of information that can be used to infer demographic histories. In this thesis, I first describe work on mathematical modeling approaches that can be used to infer the historical relationships among populations, and to model the effects of these relationships on present-day genetic diversity. Next, I describe empirical analyses of cultural variation that shed light on recent cultural, geographic, and demographic factors that have influenced both cultural and genetic diffusion among populations. The first three chapters focus on mathematical models of genetic variation. In Chapter 1, I apply a coalescent model to reduce the expected error in an existing algorithm (the GLASS method) for inferring the historical relationships among populations or species. The new method I develop provides fast and accurate estimates of the topological and temporal relationships among a set of extant populations. These estimates can be used to obtain accurate null models for downstream analyses, such as comparative genetic studies to identify signals of adaptation. In Chapter 2, I extend the model of Chapter 1 to derive expressions for the theoretical accuracy of algorithms that perform genotype imputation, a key component of many genome-wide association studies of the genetic bases of phenotypic traits. The expressions I derive can be used to guide sampling designs for collecting panels of imputation reference haplotypes, thus improving the power of genome-wide association studies to detect the genetic variants that underlie phenotypic variation. Coalescent models like those presented in chapters 1 and 2 can be computationally difficult to implement on modern genomic data sets with many sampled individuals. The complexity of these computations can be reduced by making use of an approximation to the coalescent model in which the number of ancestral alleles in a population is assumed to change deterministically as time moves backwards. In Chapter 3, I describe general procedures for applying this deterministic approximation to obtain functionally simple and computationally fast approximations to coalescent formulas that are otherwise challenging to compute on data sets with many sampled individuals. In chapters 4 and 5, I present empirical analyses designed to identify cultural factors that can affect genetic variation, as well as factors that can affect both genetic and cultural differences among populations. In Chapter 4, I present an analysis of linguistic and cultural diversity in the United States, with the goal of understanding factors that have contributed to cultural isolation and diffusion among demographic groups over time. In Chapter 5, I present a joint analysis of genetic and linguistic data in Cape Verde, an archipelago near the coast of western Africa with a long history of genetic and linguistic admixture between European and African populations. This analysis sheds light on demographic and geographical factors that affect both genetic and linguistic variation, and on the degree to which linguistic inheritance parallels genetic inheritance. The modeling approaches, theory, and analyses presented in this thesis provide a set of tools that facilitate studies of the factors that affect genetic and cultural variation within and among populations.

Keywords: Biology.
Advisor: Rosenberg Noah
Feldman Marcus W. advisor. Thesis advisor
Tuljapurkar Shripad