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Ancestral Paths: Redefining local genetic ancestry and its inference with application to Europeans


Type

Thesis

Change log

Authors

Pearson, Alice 

Abstract

Recently, two new approaches have transformed our understanding of human population history. Firstly, the sequencing of ancient genomes which gives us a snapshot of past genetic variation. We can therefore make inferences from observed genetic signatures present before historical events such as population bottlenecks and natural selection have obscured them from the modern gene pool. Ancient DNA has thus revealed what cannot be determined from modern genomes alone. Secondly, the development of methods that aim to reconstruct population genealogies from genetic variation data. Together with an understanding of how evolutionary processes alter genealogies, this has allowed inference of historical and ongoing processes in real world populations. The latest updates in these approaches now allow us to combine the two and infer genealogies involving both present-day and ancient individuals. In this thesis I present a new method to infer local ancestry along sample chromosomes. The method applies machine learning to tree sequences built from ancient and present-day genomes and is based on a deterministic model of population structure, within which I introduce the concept of ‘path ancestry’. I show with extensive simulation that the method is robust to a variety of demographic scenarios and generalises over model misspecification. Subsequent downstream analyses include estimating past effective population size, timing of population specific selection and the time since admixture for individuals. I apply the method to a large ancient DNA dataset covering Europe and West Eurasia to paint all sample chromosomes. I show that the inferred admixture ages are a better metric than sample ages alone for understanding movements of people across Europe in the past.

Description

Date

2022-09-21

Advisors

Durbin, Richard
Willerslev, Eske

Keywords

Population genetics, Ancient DNA, Mesolithic, Neolithic, Bronze age, Local ancestry

Qualification

Doctor of Philosophy (PhD)

Awarding Institution

University of Cambridge
Sponsorship
Wellcome Trust (214300/Z/18/Z)