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Title: Investigation of new strategies for identifying causal mechanisms in Alzheimer's disease taking bioinformatics approaches beyond GWAS
Author: Baker, Emily Ann
ISNI:       0000 0004 7651 8957
Awarding Body: Cardiff University
Current Institution: Cardiff University
Date of Award: 2018
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The ideal outcome for medical research is the ability to provide personalised treatment for the population. Genetics studies are necessary to achieve this, since a person's genetic code does not change over time. Alzheimer's disease (AD) is a neurodegenerative disorder with a significant genetic component. Clinical trials in AD are diffcult to design due to advanced neuropathological changes before development of symptoms, consequently, it would be beneficial to determine an individual's risk of AD. Genome-wide association studies (GWAS) have unearthed over 20 variants associated with AD. It is expected there are more variants associated with disease which may explain disease aetiology, however, GWAS is not able to detect additional variants without increasing sample size. Set-based analysis is an attractive alternative to determining the association of one single nucleotide polymorphism (SNP), since the combined effect of all SNPs in the set may be captured. A gene-based analysis using the MAGMA approach in the AD data shows this by finding additional independent gene associations using identical data. Polygenic Risk Scores (PRSs) are used for a variety of purposes in assessing the genetic liability to disorders. To further improve the power of set-based analyses, PRS as a setbased analysis is considered; this approach incorporates external data to improve power. This power increase is shown compared with other set-based methods using simulation studies and identifies two novel genes in imputed AD data; CSMD1 and MACROD2. The downside to the PRS approach is that it assumes independence between SNPs and thus data must be pruned for Linkage Disequilibrium (LD). Therefore, a novel approach which extends PRS and adjusts for LD is presented. This method is termed POLARIS and is shown to have better power compared to MAGMA in simulation studies and has determined three novel genes; PPARGC1A, RORA and ZNF423, in imputed AD data.
Supervisor: Not available Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID:  DOI: Not available