Complementary approaches to analyse genetic data in late onset Alzheimer's disease (LOAD)

Shi, Hui (2012) Complementary approaches to analyse genetic data in late onset Alzheimer's disease (LOAD). PhD thesis, University of Nottingham.



Alzheimer's disease is the most common form (~60-80%) of dementia, currently affecting approximately half a million people in the UK and ~30 million people worldwide. The autosomal dominant form of AD represents a small proportion (~1-2%) of AD cases and is genetically well characterised. The vast majority of AD cases that show symptoms later in life (>65 years of age) are genetically complex. This type of AD, also known as late onset Alzheimer's disease (LOAD) disease, is still highly heritable with an estimated heritability of up to 76% (Gatz et al., 2006).

Unfortunately, there is no cure for this devastating disease. Investigating genetic factors influencing the risk of LOAD is imperative for development of effective therapeutic treatments and more accurate diagnosis.

A cross-platform comparison of four Genome-wide association studies (GWAS) was performed in an effort to identify novel genetic associations with LOAD (Chapter 3). A TRIM15 SNP rs929156 demonstrated significant evidence of association with LOAD with a p-value approaching genome-wide significance (p = 8.77 x 10-8) and an odds ratio that showed consistent effect on risk (OR = 1.1, p = 0.03). Within this chapter, a bio-informatic program to automate the process of GWAS meta-analysis taking into account linkage disequilibrium (LD) is also presented. Subsequently two fragments of the TRIM15 gene (including both 5’ and 3’ end flanking regions) were sequenced using the ABI SOLiDTM next generation sequencing technology. This was a pilot study using a DNA pooling strategy to determine whether this region harbours multiple rare variants which are associated with the disease (Chapter 4).

Lastly, a candidate gene study combined with whole genome analysis was performed in an effort to search for genetic variants influencing human ageing using LOAD GWAS data (Chapter 5).

Item Type:Thesis (PhD)
Supervisors:Morgan, K.
Kalsheker, N.
Uncontrolled Keywords:Late Onset Alzheimer's disease, GWAS, ageing, genes, next generation sequencing
Faculties/Schools:UK Campuses > Faculty of Medicine and Health Sciences > School of Molecular Medical Sciences
ID Code:2449
Deposited By:MR Hui Shi
Deposited On:23 Oct 2012 12:19
Last Modified:23 Oct 2012 12:19

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