Exploring potential functional variants in the Alzheimer’s disease associated genes, CD2AP, EPHA1 and CD33 - Braae, Anne (2016)
Little is known about the molecular biology of late onset Alzheimer’s disease (LOAD), the most common dementia in the elderly. Genetic loci associated with LOAD have been identified through genome-wide association studies (GWAS). However, the functional variants responsible for the observed GWAS association at each of the loci remain unknown. The aim of this project was to identify and assess potential functional rare variants at three associated loci, CD2AP, EPHA1 and CD33. Target enriched, pooled sequencing of 96 post-mortem confirmed LOAD patient samples was used to identify 1273 variants within the three GWAS loci. Variants were prioritised using a combination of in silico functional annotation and putative disease association. Disease association was assessed through comparison to an independent, imputed LOAD GWAS dataset (2067 cases, 7376 controls). 18 coding and untranslated region variants and 9 noncoding variants were prioritised for further investigation. Potential splicing variants in CD2AP (6:47544253A>G) and EPHA1 (rs6967117) were assessed using minigene assays, although neither were found to influence splicing products in vivo. Five untranslated variants from the three genes and a frameshift variant in CD33 (rs201074739) were assessed for potential cis-regulatory consequences using allelic expression imbalance in brain tissues and B-lymphoblast cell lines. Only the frameshift variant displayed significant allelic expression imbalance and was found to be targeted for nonsense-mediated decay. None of the prioritised variants investigated were both functional and significantly associated with LOAD. However, pooled next generation sequencing using target enrichment successfully identified potential functional rare variants in CD2AP, EPHA1 and CD33. Rare variants do have a role to play in late onset Alzheimer’s disease. With the development of additional functional databases and improvements imputing rare variants from GWAS datasets, the combined prioritisation strategy used in this thesis will be useful for similar studies investigating causal GWAS variants.