The relationship between genotype and phenotype in amyotrophic lateral sclerosis

Student thesis: Doctoral ThesisDoctor of Philosophy


Amyotrophic lateral sclerosis (ALS) is a devastating neurodegenerative disease characterised by progressive, widespread, degeneration of the upper and lower motor neurons until death from respiratory paralysis. Two major facets of ALS are the heterogenous patient population and complex biological architecture. This heterogeneity may obstruct the discovery of effective therapies, none of which presently exist. Refining knowledge about the genetic basis of the disease could improve understanding of patient phenotypes while improved classification of disease subgroups and biological mechanisms overlapping with other diseases may enable discovery of much-needed treatments.

Accordingly, the focus of this thesis is upon genotype-phenotype relationships in ALS. Included within are investigations of (1) disease risk for people found to harbour certain genetic variants, (2) disease subgroups identified via data-driven approaches or defined by specific genetic variation, and (3) genetic overlaps with Alzheimer’s disease, frontotemporal dementia, Parkinson’s disease, and schizophrenia. Several tools were developed across these studies, including (1) a novel method for calculating genetic penetrance, implemented within an R function and companion web-tool, (2) a web-server for comparing the ALS phenotype across different groups of people, and (3) a command-line workflow for statistical fine-mapping and colocalisation analysis. These utilities have been made freely available for use in future research.

The first chapter of the thesis overviews current understanding of ALS, focused on its clinical and genetic spectrum, and outlines the current therapeutic landscape and previous attempts to identify homogenous disease subtypes. Subsequent chapters summarise the objective of each study, general methodology, and the investigations performed. The thesis concludes with an overall summary of findings and outlines directions for future research building upon this work.
Date of Award1 Aug 2023
Original languageEnglish
Awarding Institution
  • King's College London
SupervisorAmmar Al-Chalabi (Supervisor) & Alfredo Iacoangeli (Supervisor)

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