Genetic Architecture of Complex Traits through the lens of Haplotypes and Local Ancestry Inference

Abstract

The question of how genetically different complex traits are in different populations has been longstanding and impacts the scientific approach to genetics. Since almost all living humans are mixtures of historical populations, we can only study the genetics of ancient populations by learning the ancestry of specific genetic factors.

This thesis investigates the genetic architecture of complex traits by associating haplotypes with populations and advancing local ancestry inference methodologies, and applying them to large-scale genetic studies. Complex traits, such as autoimmune diseases like multiple sclerosis, are influenced not only by interactions between genetic and environmental factors but also by population demographic histories. Traditional genome-wide association studies have provided insights into understanding the association between genetic variants and complex traits, but the fine-scale population structure within admixed populations cannot be properly investigated, which reduces the generalisability.

To address these challenges, this research develops innovative tools, such as SparsePainter, which provides efficient and scalable methods for analysing local and genome-wide ancestries, especially at fine-scale. To understand the contributions of ancient ancestries to complex diseases, methods utilizing local ancestry probabilities are developed and applied in the UK Biobank. The introduction of haplotype components computed by PBWTpaint, a PBWT-based tool that rapidly identifies long matches within a single dataset, offers an alternative to principal components, improving the resolution of ancestry representation, particularly in complex admixed populations. These methods enable more inclusive and accurate genetic analyses across diverse populations, providing the possibility to address historical and geographical biases in genetic research.

This thesis presents a practical implementation of these methods -- the analysis of MS genetic risk, which is based on the local ancestry inference using ancient DNAs as reference panel. This study shows that the MS risk emerged among ancient steppe pastoralist populations and propagated through European migration during the Bronze Age. LAI-derived methods, such as linkage disequilibrium of ancestry, reveal that positive selection drove the immune-related genetic variants to high frequency, in response to new pathogen exposures brought by lifestyle changes, which explains the diverse risk profile across the current Europe.

This thesis provides an efficient framework for using local ancestry inference to understand the genetic architecture of complex traits, providing reasonable potential for conducting genetic research in diverse populations, enabling the development of population-specific treatment strategies, and extending the benefits of genetic studies to all ethnic groups.

Date of Award	17 Jun 2025
Original language	English
Awarding Institution	University of Bristol
Supervisor	Daniel John Lawson (Supervisor)