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P2‐030: INVESTIGATING THE ROLE OF CLU, PICALM, AND CR1 IN ALZHEIMER'S DISEASE
Author(s) -
Lord Jenny,
Turton James,
Braae Anne,
Barber Imelda,
Medway Christopher,
Brown Kristelle,
Morgan Kevin
Publication year - 2014
Publication title -
alzheimer's and dementia
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 6.713
H-Index - 118
eISSN - 1552-5279
pISSN - 1552-5260
DOI - 10.1016/j.jalz.2014.05.703
Subject(s) - single nucleotide polymorphism , genome wide association study , missense mutation , biology , genetic association , genetics , locus (genetics) , snp , disease , gene , mutation , medicine , genotype
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 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 in an attempt to uncover potentially causative and rare variants that may explain the observed association signals. A pipeline was developed for the handling of pooled NGS data following a comparison of several different combinations of programs. 33 exonic SNPs were found within the three genes, along with over 1000 non-coding variants. 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. 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, suggesting avenues for future research in AD genetics. 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.