eprintid: 1551628
rev_number: 16
eprint_status: archive
userid: 608
dir: disk0/01/55/16/28
datestamp: 2017-05-19 08:10:37
lastmod: 2019-10-17 07:34:51
status_changed: 2017-05-19 08:10:37
type: thesis
metadata_visibility: show
creators_name: Morgan, SL
title: The genetic landscape of amyotrophic lateral sclerosis
ispublished: unpub
divisions: A01
divisions: B02
divisions: C07
divisions: D07
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.
date: 2017-04-28
date_type: submitted
oa_status: green
full_text_type: other
thesis_class: doctoral_open
language: eng
thesis_view: UCL_Thesis
primo: open
primo_central: open_green
verified: verified_manual
elements_id: 1287848
lyricists_name: Morgan, Sarah
lyricists_id: SMORG67
actors_name: Morgan, Sarah
actors_name: Allington-Smith, Dominic
actors_id: SMORG67
actors_id: DAALL44
actors_role: owner
actors_role: impersonator
full_text_status: public
pages: 199
event_title: UCL (University College London)
institution: UCL (University College London)
department: Molecular Neuroscience
thesis_type: Doctoral
citation:        Morgan, SL;      (2017)    The genetic landscape of amyotrophic lateral sclerosis.                   Doctoral thesis , UCL (University College London).     Green open access   
 
document_url: https://discovery.ucl.ac.uk/id/eprint/1551628/1/Morgan__ThesisSarahMorgan_V40.pdf