GWA methods have now been successfully used to detect disease-related susceptibility genes for a growing 1st of genetically complex disorders. In these studies, a critical element for success is that the sample size be large enough so that there is adequately power to detect genes with modest effect sizes at genome-wide significance. The sample needed to detect a given effect size depends on genetic heterogeneity, which is difficult to predict for AD. However, for other diseases such as type 2 diabetes, susceptibility genes with odds ratios of ~1.3 have been detected with initial discovery cohorts of 4,549 cases and 5,579 controls (phase 1, 3 studies combined) followed by a replication dataset of 10,053 cases and 12,289 controls (phase 2). Current genotyping platforms permit coverage of ~92% of the linkage disequilibrium landscape of the human genome using ~550,000 SNP's. Typically, ~1% of the top SNP's nominally detected in the discovery phase are then tested in the replication dataset. It is not unusual that validated loci not in the top tier of SNP's from the initial discovery experiment. Thus a large replication samples is critical to the success of these studies. The quality of the replication samples in terms of accurate diagnosis is critical to the success of GWA studies because incorrect diagnoses can result in reduced power to confirm true loci. The ADGC is being formed to collaboratively use the collective resources of AD research community to identify AD genes. The clinical, neuropathologic, molecular and statistical expertise exists within the AD research community. Also, much of the needed phenotype data and DNA samples also exist, gathered by the ADCs. The primary goal of the ADGC will be to identify variability in genes that influences susceptibility to AD. Susceptibility genes potentially influence onset-age, rate of progression through the prodromal and mild cognitive impairment (MCI) phase of the disease. Secondary goals are to identify genes that influence specific AD- related endophenotypes such as neuropathology features (e.g. amyloid load, tangle load, etc), biomarker measures [e.g. cerebral spinal fluid (CSF) A? and tau levels, MRI measures], rate-of-disease progression, responses to environmental factors (e.g. drugs, non-pharmaceutical environmental factors).

National Institute of Health (NIH)
National Institute on Aging (NIA)
Research Project--Cooperative Agreements (U01)
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Special Emphasis Panel (ZAG1-ZIJ-7 (O4))
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Snyder, Stephen D
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University of Pennsylvania
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