Abstract
BackgroundLate Onset Alzheimer's disease (LOAD) is the leading cause of dementia. Recent large genome-wide association studies (GWAS) identified the first strongly supported LOAD susceptibility genes since the discovery of the involvement of APOE in the early 1990s. We have now exploited these GWAS datasets to uncover key LOAD pathophysiological processes.MethodologyWe applied a recently developed tool for mining GWAS data for biologically meaningful information to a LOAD GWAS dataset. The principal findings were then tested in an independent GWAS dataset.Principal FindingsWe found a significant overrepresentation of association signals in pathways related to cholesterol metabolism and the immune response in both of the two largest genome-wide association studies for LOAD.SignificanceProcesses related to cholesterol metabolism and the innate immune response have previously been implicated by pathological and epidemiological studies of Alzheimer's disease, but it has been unclear whether those findings reflected primary aetiological events or consequences of the disease process. Our independent evidence from two large studies now demonstrates that these processes are aetiologically relevant, and suggests that they may be suitable targets for novel and existing therapeutic approaches.
Highlights
Alzheimer’s disease (AD) is the leading cause of dementia [1,2] with a heritability of 56–79% [3]
Principal Findings: We found a significant overrepresentation of association signals in pathways related to cholesterol metabolism and the immune response in both of the two largest genome-wide association studies for Late Onset Alzheimer’s disease (LOAD)
In the genome-wide association studies (GWAS) study of Harold et al [7] involving approximately 12,000 AD cases and controls, we observed a considerable excess of SNPs surpassing different thresholds of significance when compared with those expected by chance (Table 1), suggesting the existence of many LOAD susceptibility loci that were not detected at genome-wide significance
Summary
Alzheimer’s disease (AD) is the leading cause of dementia [1,2] with a heritability of 56–79% [3]. The implicated genes show biologically relevant relationships between each other [12,13,14,15] This is true for SNPs in genes for which there is weaker individual evidence for association that falls short of stringent levels of genome-wide significance and statistical approaches have recently been developed to identify sets of functionally related genes containing genetic variants that collectively show evidence for association [14,16]. We confirmed the results using gene set enrichment [15] and set-based analyses [17] to uncover sets of functionally related genes showing evidence for association with disease. The identification of such patterns in association datasets is likely to be crucial in moving beyond the genetic data to an understanding of function
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