P2‐030: Investigating the Role of Clu, Picalm, and Cr1 in Alzheimer's Disease

In 2009, two large genome wide association studies (GWAS) found associations between common single nucleotide polymorphisms (SNPs) at three loci (CLU, PICALM and CR1) and late onset Alzheimer's disease (AD) risk. The causal variants underlying these associations and how these impact on AD susceptibility remain unclear. Target enrichment and next generation sequencing (NGS) were used to completely resequence the three associated loci in 96 AD patients (8 pools of 12 samples) in an attempt to uncover potentially causative and rare variants that may explain the observed association signals. Data analysis was conducted using a pipeline developed specifically for the handling of pooled NGS data following a comparison of several different combinations of programs. To identify the variants most likely to be affecting AD risk, a two pronged approach was adopted. The variants were imputed in a large case-control cohort (2067 cases, 7376 controls) to test for association with AD, and the likely functional consequences of the variants were assessed using in silico resources 33 exonic SNPs were found within the three genes, along with over 1000 non-coding variants. Several of the analysed variants showed suggestive or significant association with AD in the imputed data, and/or were predicted to have consequences on the function or regulation of the genes. This included three intronic, significantly associated, potentially regulatory SNPs at the CLU locus; two potential splicing variants, plus a novel missense mutation in PICALM; and two rare missense mutations in CR1 which showed suggestive association with the disorder. Potentially functional and associated SNPs are being followed up with direct genotyping and experimental functional characterisation, which could help to elucidate the source of the association signals seen by GWAS. The whole method of pooled, targeted NGS and prioritisation using imputed data for association testing and in silico resources for functional analysis represents a new strategy for tracking down the illusive causation of GWAS signals.