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Genetic evidence implicates the immune system and cholesterol metabolism in the aetiology of Alzheimer's disease.

ABSTRACT: BACKGROUND:Late 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. METHODOLOGY:We 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 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 LOAD. SIGNIFICANCE:Processes 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.

SUBMITTER: Jones L

PROVIDER: S-EPMC2981526 | biostudies-literature | 2010 Nov

REPOSITORIES: biostudies-literature

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Genetic evidence implicates the immune system and cholesterol metabolism in the aetiology of Alzheimer's disease.

Jones Lesley L Holmans Peter A PA Hamshere Marian L ML Harold Denise D Moskvina Valentina V Ivanov Dobril D Pocklington Andrew A Abraham Richard R Hollingworth Paul P Sims Rebecca R Gerrish Amy A Pahwa Jaspreet Singh JS Jones Nicola N Stretton Alexandra A Morgan Angharad R AR Lovestone Simon S Powell John J Proitsi Petroula P Lupton Michelle K MK Brayne Carol C Rubinsztein David C DC Gill Michael M Lawlor Brian B Lynch Aoibhinn A Morgan Kevin K Brown Kristelle S KS Passmore Peter A PA Craig David D McGuinness Bernadette B Todd Stephen S Holmes Clive C Mann David D Smith A David AD Love Seth S Kehoe Patrick G PG Mead Simon S Fox Nick N Rossor Martin M Collinge John J Maier Wolfgang W Jessen Frank F Schürmann Britta B Heun Reinhard R Kölsch Heike H van den Bussche Hendrik H Heuser Isabella I Peters Oliver O Kornhuber Johannes J Wiltfang Jens J Dichgans Martin M Frölich Lutz L Hampel Harald H Hüll Michael M Rujescu Dan D Goate Alison M AM Kauwe John S K JS Cruchaga Carlos C Nowotny Petra P Morris John C JC Mayo Kevin K Livingston Gill G Bass Nicholas J NJ Gurling Hugh H McQuillin Andrew A Gwilliam Rhian R Deloukas Panos P Al-Chalabi Ammar A Shaw Christopher E CE Singleton Andrew B AB Guerreiro Rita R Mühleisen Thomas W TW Nöthen Markus M MM Moebus Susanne S Jöckel Karl-Heinz KH Klopp Norman N Wichmann H-Erich HE Rüther Eckhard E Carrasquillo Minerva M MM Pankratz V Shane VS Younkin Steven G SG Hardy John J O'Donovan Michael C MC Owen Michael J MJ Williams Julie J

PloS one 20101115 11

<h4>Background</h4>Late 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.<h4>Methodology</h4>We applied a recently developed tool for mining GWAS data for biologically meaningful information to a LOAD GWAS ...[more]

PMID: 21085570

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Project description:Identifying genetic risk factors underpinning different aspects of Alzheimer's disease has the potential to provide important insights into pathogenesis. Moving away from simple case-control definitions, there is considerable interest in using quantitative endophenotypes, such as those derived from imaging as outcome measures. Previous genome-wide association studies of imaging-derived biomarkers in sporadic late-onset Alzheimer's disease focused only on phenotypes derived from single imaging modalities. In contrast, we computed a novel multi-modal neuroimaging phenotype comprising cortical amyloid burden and bilateral hippocampal volume. Both imaging biomarkers were used as input to a disease progression modelling algorithm, which estimates the biomarkers' long-term evolution curves from population-based longitudinal data. Among other parameters, the algorithm computes the shift in time required to optimally align a subjects' biomarker trajectories with these population curves. This time shift serves as a disease progression score and it was used as a quantitative trait in a discovery genome-wide association study with n = 944 subjects from the Alzheimer's Disease Neuroimaging Initiative database diagnosed as Alzheimer's disease, mild cognitive impairment or healthy at the time of imaging. We identified a genome-wide significant locus implicating LCORL (rs6850306, chromosome 4; P = 1.03 × 10-8). The top variant rs6850306 was found to act as an expression quantitative trait locus for LCORL in brain tissue. The clinical role of rs6850306 in conversion from healthy ageing to mild cognitive impairment or Alzheimer's disease was further validated in an independent cohort comprising healthy, older subjects from the National Alzheimer's Coordinating Center database. Specifically, possession of a minor allele at rs6850306 was protective against conversion from mild cognitive impairment to Alzheimer's disease in the National Alzheimer's Coordinating Center cohort (hazard ratio = 0.593, 95% confidence interval = 0.387-0.907, n = 911, PBonf = 0.032), in keeping with the negative direction of effect reported in the genome-wide association study (βdisease progression score = -0.07 ± 0.01). The implicated locus is linked to genes with known connections to Alzheimer's disease pathophysiology and other neurodegenerative diseases. Using multimodal imaging phenotypes in association studies may assist in unveiling the genetic drivers of the onset and progression of complex diseases.

Dataset Information

Genetic evidence implicates the immune system and cholesterol metabolism in the aetiology of Alzheimer's disease.

Publications

Genetic evidence implicates the immune system and cholesterol metabolism in the aetiology of Alzheimer's disease.

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