Alzheimer's disease (AD) is an incurable neurodegenerative disorder, and the most common form of dementia, characterised neuropathologically by Aβ plaques and tau tangles. Although the late-onset form of AD (LOAD) is highly heritable (-80%), genetic studies to date have only accounted for approximately half of this. Identification of the remaining risk may require new approaches. In this thesis three candidate genes/loci (CNTN2, FGA and SPARCL 1), encoding putative LOAD cerebro-spinal fluid biomarkers (Contactin-2, Fibrinogen alpha-chain and Sparc-like-1), have been subjected to complementary genetic approaches to unearth novel LOAD risk alleles. An 'LD-aware' meta-analysis of three LOAD genome-wide datasets was conducted to investigate disease association with common (MAF>5%) variation in each candidate gene. Secondly, SOliD next-generation resequencing of 150 samples (75 LOAD, 75 controls), PCR enriched for candidate loci, was conducted to identify novel rare variation. Selected variants were subsequently validated and replicated in larger series (n=1453) using TaqMan genotyping. Meta-analysis revealed linked SNPs (rs7523477, rs4951168) downstream of CNTN2 associated with LOAD (p=3x10-5, OR=1.23(1.01-1.49) n=4898). However, this has not been replicated by subsequent GWAS. Resequencing identified 28 novel rare variations over all candidate loci. A SPARCL 1 variant (located at 88451921 (hg19)) showed association with LOAD in a UK population (p=0.023, OR=1.95(1.1 0-3.46)). Replication of this association will be required in independent series. In the post-GWAS era, new approaches to identify the remaining heritability of LOAD must be embraced. Not only will the identification of new mutations conferring risk for Alzheimer's disease benefit diagnosis, it will also foster a greater understanding of disease pathways - to which treatments can be targeted.
| Identifer | oai:union.ndltd.org:bl.uk/oai:ethos.bl.uk:588064 |
| Date | January 2012 |
| Creators | Medway, Christopher William |
| Publisher | University of Nottingham |
| Source Sets | Ethos UK |
| Detected Language | English |
| Type | Electronic Thesis or Dissertation |
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