Alzheimer's disease genetics consortium: new Alzheimer's disease genes and gene interactions

Abstract

Late-onset Alzheimer Disease (LOAD) is a highly heritable neurological disorder, with susceptibility influenced by known genetic risk factors at CLU, PICALM, and BIN1. The Alzheimer's Disease Genetics Consortium (ADGC) examined gnome-wide genotyping data from 11,840 LOAD cases and 10,931 cognitive controls to identify novel genetic variants and gene-gene interactions associated with LOAD. The ADGC dataset consists of fifteen datasets genotyped using genome-wide genotyping platforms (Illumina or Affymetrix). All cases met standard clinical LOAD definitions (or were confirmed through autopsy); controls were age-matched to cases and were cognitively intact at exam. To create a common set of SNPs across all datasets, genotype imputation was performed using IMPUTE software and the October 2010 release of the 1,000 Genomes Project data as a reference. Association analysis was performed within each dataset using logistic regression for case-control datasets and generalized estimating equations (GEE) for datasets that include family members. For both family and case-control datasets genotype was modeled using an additive model on the dosage of the minor allele. Age at onset/exam, gender, and variables for population substructure were included as covariates. Data were then analyzed across the datasets using a meta-analysis approach. For the gene-gene interaction analysis, SNPs will be pruned based on LD to reduce redundancy and then filtered using Biofilter to identify potential interactions/effects using known biological connections. Association analysis of the interactions will be performed using logistic regression and an additive model. Ten distinct genomic regions showed SNP associations in the combined meta-analysis, including previously associated regions CLU, CR1, PICALM, BIN1, and APOE10−8. Additional associated regions include MS4A, EPHA1, CD33, and ABCA7. Analysis of the gene-gene interaction is ongoing and will be presented. We have confirmed the association of several putative LOAD risk loci (APOE, CR1, CLU, PICALM and BIN1) and identified new risk loci as well (MS4A, EPHA1, CD33 and ABCA7). These associations confirm that common variations influence the etiology of LOAD. More importantly, they provide novel targets for functional assessment and further association studies.