Inferring Demographic History of Admixed Human Populations with SNP Array Data

Abstract

The demographic history of human populations, both archaic and modern, have been the focus of extensive research. Earlier studies were based on a small number of genetic markers but technological advances have made possible the examination of data at the genome scale to answer important questions regarding the history of our species. A widely used application of single nucleotide polymorphisms (SNPs) are genotyping arrays that allow the study of several hundred thousand of these sites at the same time. However, most of the SNPs present in commercial genotyping arrays have often been discovered by sampling a small number of chromosomes from a group of selected populations. This form of non-random discovery skews patterns of nucleotide diversity and can affect population genetic inferences. Although different methods have been proposed to take into account this ascertainment bias, the challenge remains because the exact discovery protocols are not known for most of the commercial arrays. In this dissertation, I propose a demographic inference pipeline that explicitly models the underlying SNP discovery and I implement this methodology in specific examples of admixture in human populations when only SNP array data are available. In the first chapter, I describe the developed pipeline and applied it to a known example of recent population admixture in Mexico. The inferred time of admixture between Iberian and Native American populations that gave rise to admixed Mexicans was in line with historical records, as opposed to previous published underestimates. Next, I examined different demographic models on the first human settlement in Easter Island and determined that the island of Mangareva is the most likely point of origin for this migration. Finally, I investigated the dynamics of the admixture process between the ancestral Jomon and Yayoi populations in different locations across Japan. The estimates of the time of this encounter were closer to dates inferred from anthropological data, in contrast with past works. The results show that the proposed framework corrects ascertainment bias to improve inference in cases when only SNP chip data are available, and for genotype data originated from different platforms.