Alzheimer's disease (AD) is the most common form of dementia, now the leading cause of death in the UK, which affects more than 35 million people worldwide. Genome-wide association studies identified 20 genetic loci associated with disease susceptibility, however these only exerted small effects on risk. Next-generation sequencing is now being employed to identify more of the missing heritability. This project utilised whole-exome sequencing to explore genetic variation using the Brains for Dementia Research (BDR) resource, a well-characterised cohort of neuropathologically confirmed samples. Exome-wide and candidate gene approaches were employed to assess coding variants for association with AD, using single-variant and burden tests. Coding variants in other neurodegenerative disease genes were also analysed as potential susceptibility factors for AD. Furthermore, polygenic risk scores (PRS) were generated to explore the ability to classify case and control individuals based on their genetic profiles. A synonymous variant in PILRA (rs2405442) was nominally associated with 3-fold increased risk of AD, also contributing strongly to PILRA burden. It was previously linked to AD through risk gene ZCWPW1; however, it has not been directly associated until now. Additional variants in GWAS gene ABCA7 (rs3764645, rs3752234, rs3752237, rs4147915) and rare variants in CLU were also implicated, further supporting their roles in AD susceptibility. A variant in PD gene LRRK2 (rs35303786) inferred protection against AD, implicating potential pleiotropy across the two diseases. PRS could distinguish AD cases from controls with 85.3% accuracy and also identified controls with high PRS but no cognitive impairments. This could be useful for identifying individuals at risk of developing AD in the future. We have uncovered tentative associations both in established and newly identified loci; highlighting several interesting candidates for further investigation. Although there remains a large amount of missing heritability, we hope that as the BDR resource grows, we will achieve increased power to detect significant associations with AD.
| Identifer | oai:union.ndltd.org:bl.uk/oai:ethos.bl.uk:765405 |
| Date | January 2018 |
| Creators | Patel, Tulsi |
| Publisher | University of Nottingham |
| Source Sets | Ethos UK |
| Detected Language | English |
| Type | Electronic Thesis or Dissertation |
| Source | http://eprints.nottingham.ac.uk/52447/ |
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