Reconstructing Past Population Dynamics and Movements from Genomic and Environmental Data
type: CreativeWork
description: Understanding how past changes shaped today’s global ecology is one of the central topics of evolutionary studies. Difficulty in obtaining direct evidence, such as fossils and remains, has greatly limited our ability to understand the process. The last decade has witnessed great advances that facilitate better reconstruction of the past; in terms of data, sequencing techniques, especially ancient DNA, provide direct evidence of the genetic history of the sample. Better modelling techniques also allow us to use larger scale data and integrate with other lines of evidence, like global climate reconstructions, to reconstruct the past using ecological models. This thesis aims to explore new computational methods to reconstruct the past of different species with various data availability, and to answer the ecological questions behind those. Ancient genomics has been a powerhouse for human history studies, but the expanding size and complexity of datasets have become major challenges for analysis and interpretation. In the second chapter, I studied how to reconstruct human genetic history with Biobank-level genomic data, a challenge of both scalability and reliability. I built a new framework, NORNE, for detecting individual genetic connectivity from unphased genotype data with high scalability. I show that NORNE can detect fine-scale genetic structure with a resolution similar to state-of-the-art methods whilst being much more effective, enabling it to analyse extremely large datasets. Using NORNE on a dataset of ancient DNA from over 6,000 humans across the globe, I show that NORNE can capture broad and fine-scale genetic structure of the past. Furthermore, with geographical and temporal information, NORNE captures connections indicating major migration events and mobility change in the past. I then show how NORNE can be used to reveal individual-level migration histories over the past 6,000 years with case studies for the UK, Hungary and the Roopkund lake. New methods …
abstract: Understanding how past changes shaped today’s global ecology is one of the central topics of evolutionary studies. Difficulty in obtaining direct evidence, such as fossils and remains, has greatly limited our ability to understand the process. The last decade has witnessed great advances that facilitate better reconstruction of the past; in terms of data, sequencing techniques, especially ancient DNA, provide direct evidence of the genetic history of the sample. Better modelling techniques also allow us to use larger scale data and integrate with other lines of evidence, like global climate reconstructions, to reconstruct the past using ecological models. This thesis aims to explore new computational methods to reconstruct the past of different species with various data availability, and to answer the ecological questions behind those. Ancient genomics has been a powerhouse for human history studies, but the expanding size and complexity of datasets have become major challenges for analysis and interpretation. In the second chapter, I studied how to reconstruct human genetic history with Biobank-level genomic data, a challenge of both scalability and reliability. I built a new framework, NORNE, for detecting individual genetic connectivity from unphased genotype data with high scalability. I show that NORNE can detect fine-scale genetic structure with a resolution similar to state-of-the-art methods whilst being much more effective, enabling it to analyse extremely large datasets. Using NORNE on a dataset of ancient DNA from over 6,000 humans across the globe, I show that NORNE can capture broad and fine-scale genetic structure of the past. Furthermore, with geographical and temporal information, NORNE captures connections indicating major migration events and mobility change in the past. I then show how NORNE can be used to reveal individual-level migration histories over the past 6,000 years with case studies for the UK, Hungary and the Roopkund lake. New methods …
author: Xue, Zhe
keywords: ecology, population genetics
license: https://apollo8-f-pro.lib.cam.ac.uk/bitstreams/584359e1-d199-495d-b5bd-86a3468506a3/download
license: http://purl.org/NET/rdflicense/allrightsreserved
date published: 2025-01-04
date modified: 2026-07-22T22:24:07Z
provider: Cambridge University
publisher: Networked Digital Library of Theses and Dissertations
type: WebPage
source organization: University of Cambridge

PropertyValue
property ID: DOI
value: 10.17863/CAM.119893