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P1–330: Large scale candidate gene association studies in Alzheimer's disease
Author(s) -
Slifer Michael A.,
Gilbert John R.,
Lin Ping-I,
Liang Xueying,
Haines Jonathan L.,
Pericak-Vance Margaret A.
Publication year - 2006
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.2006.05.708
Subject(s) - dementia , disease , apolipoprotein e , family history , snp , candidate gene , genotyping , genetic association , alzheimer's disease , psychology , medicine , single nucleotide polymorphism , genetics , gene , biology , genotype
Background: Variants in over a hundred different candidate genes are reported to be associated with susceptibility to late-onset Alzheimer disease (LOAD). Although findings in several areas have been replicated in expanded samples from their original data set, only the APOE4 variant of the apolipoprotein E gene has been consistently replicated in multiple data sets. Furthermore, because of the large number of reports, many associations have never been systematically followed-up in an independent data set. Clinical diagnostic specificity, genetic heterogeneity, reporting/publication bias and complexity of defining adequate controls in a complex late-onset disorder may each account for some of difficulty replicating results. Objective(s): The purpose of our study is to use a large independent dataset (n 1000) of clinically well defined cases and controls to systematically examine variants in 50 previously reported candidate genes for association with LOAD. Methods: Genes will be prioritized based on the number of independent experimental methods supporting their role in AD. Both cases and controls undergo a structured clinical interview with comprehensive neuropsychometric evaluation including a CDR, 3MS, GDS, NCRAD-LOAD battery, NPI, and CSDD. Additionally, a neurological physical exam and thorough individual medical and family history are obtained. All cases meet NINCDS/ADRDA criteria for Alzheimer’s disease as agreed upon by unanimous decision of clinical experts experienced in dementia evaluation. Controls have no history of cognitive impairment, family history of LOAD in a first degree relative or any evidence of dementia on psychometric testing. Using dense SNP genotyping (approximately 1 SNP per 5 kb) we will examine the candidate genes for association with LOAD. From the multiple candidates, we will also appraise possible interactive genetic effects. Additionally, the detailed clinical information (e.g. age-at-onset, neuropsychiatric features, co-morbidities, etc.) will be used to reduce clinical and presumably genetic heterogeneity to refine association analyses. To correct for multiple testing, we will use false discovery rate methods as well as more conservative Bonferroni correction. Results and Conclusions: Utilizing the comprehensive data, we expect to discover additional as well as confirm (or refute) previously reported variants in candidate genes that are associated with LOAD in this well defined independent sample.