Use this URL to cite or link to this record in EThOS: https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.790810
Title: The genetic landscape of amyotrophic lateral sclerosis
Author: Morgan, S. L.
ISNI:       0000 0004 8499 5155
Awarding Body: UCL (University College London)
Current Institution: University College London (University of London)
Date of Award: 2017
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Abstract:
Next-generation sequencing (NGS) technologies have a vast number of advantages that have caused a growth in their application for uncovering the genetics of complex diseases. Amyotrophic lateral sclerosis (ALS) is one such disease that could benefit from this technique. As a rapid-onset disease, the time to diagnosis must match this speed if we want to increase our chances of finding a treatment drug that works. In a number of ALS cases, the diagnosis can be aided by genetics. However, we currently do not understand the full genetic background of ALS and so to address this issue, I have designed a screening panel to sequence 25 ALS-associated genes in 1,235 patients. This data was compared against 613 controls to perform a case-control analysis. Alongside mutation burden tests and tests for an oligogenic basis, I have additionally created a novel method, a pipeline assisted by machine learning, for uncovering high-dimensional genetic patterns that predispose an individual to ALS. The results indicate that there is an increase burden of rare variants in the UTRs of the genes SOD1, TARDBP, FUS, VCP, OPTN and UBQLN2 collectively. Additionally, we discovered an increased number of patients with two mutations in different ALS genes than would be expected by chance alone. Encompassed in these results is the finding of a novel ALS gene, MATR3, which we aided the first publication of. We have also screened CHCHD10 in ALS and frontotemporal dementia (FTD) finding confirmations of previously published mutations plus additional novel variants. A selection of 26 Argentinian ALS samples were included in the study which reveal 27 known and novel mutations across 17 patients. Lastly, machine learning methods are able to perform better than chance at predicting patients on the basis of their genetics. In conclusion, many cases of ALS, sporadic included, show a complex genetic interplay which, combined with the overall mutation burden, determine the risk and course of ALS.
Supervisor: Not available Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID: uk.bl.ethos.790810  DOI: Not available
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