Cell type-specific genomic signatures, past and present: using mammalian data to gain insight into human brain disorders
File(s)
Author(s)
Murphy, Kitty
Type
Thesis
Abstract
Brain disorders encompass a spectrum of neurological, neuropsychiatric, and neurodegenerative conditions. As leading causes of ill health, disability, and premature death, it’s crucial that we improve our understanding of their aetiology. Rather than being driven by a single genetic cause, most brain traits and disorders are influenced by multiple genetic and environmental factors. These genetic risk factors often reside within DNA sequences involved in gene regulation, conserved genomic regions, and functionally annotated regions specific to various cell types. These discoveries underscore the need for in-depth study of these genomic regions, particularly at a cell type-specific level.
This thesis evaluates how diverse approaches across genomic subdisciplines contribute to understanding cell type-specific genomic variation in brain traits and disorders. Chapter 2 investigates the evolution of protein-coding sequences in primate genomes, identifying nervous system cell types and phenotypes acted on by evolutionary pressures. Chapter 3 assesses whether a machine learning model can accurately predict primate gene expression, with the aim of utilising predicted expression values as input into a model of gene expression evolution.
There is also increasing evidence for epigenetic dysregulation in brain disorders. In chapter 4, a novel cell type deconvolution tool is presented for inferring cell type-specific epigenetic signatures in brain disorders. Meanwhile, chapter 5 focuses on how genetic variation shapes the transcriptomic and chromatin landscape of microglia in Alzheimer’s disease.
The thesis concludes with a discussion of overarching themes, general limitations, and remaining challenges alongside future directions. Increasing the repertoire of cell type datasets across different disease states, the lifespan, and species is crucial, along with establishing a consensus on cell type definitions. Once this has been achieved, the approaches presented in this thesis will hopefully serve as a framework for further unravelling the (epi-)genomic variation and cell types critical to human evolution and disease.
This thesis evaluates how diverse approaches across genomic subdisciplines contribute to understanding cell type-specific genomic variation in brain traits and disorders. Chapter 2 investigates the evolution of protein-coding sequences in primate genomes, identifying nervous system cell types and phenotypes acted on by evolutionary pressures. Chapter 3 assesses whether a machine learning model can accurately predict primate gene expression, with the aim of utilising predicted expression values as input into a model of gene expression evolution.
There is also increasing evidence for epigenetic dysregulation in brain disorders. In chapter 4, a novel cell type deconvolution tool is presented for inferring cell type-specific epigenetic signatures in brain disorders. Meanwhile, chapter 5 focuses on how genetic variation shapes the transcriptomic and chromatin landscape of microglia in Alzheimer’s disease.
The thesis concludes with a discussion of overarching themes, general limitations, and remaining challenges alongside future directions. Increasing the repertoire of cell type datasets across different disease states, the lifespan, and species is crucial, along with establishing a consensus on cell type definitions. Once this has been achieved, the approaches presented in this thesis will hopefully serve as a framework for further unravelling the (epi-)genomic variation and cell types critical to human evolution and disease.
Version
Open Access
Date Issued
2024-05-15
Date Awarded
01/09/2024
License URL
Advisor
Skene, Nathan
Gentleman, Steve
Marzi, Sarah
Sponsor
Medical Research Council (Great Britain)
Publisher Department
Department of Brain Sciences
Publisher Institution
Imperial College London
Qualification Level
Doctoral
Qualification Name
Doctor of Philosophy (PhD)