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)
Project #
Application #
Study Section
Special Emphasis Panel (ZAG1-ZIJ-7 (O4))
Program Officer
Miller, Marilyn
Project Start
Project End
Budget Start
Budget End
Support Year
Fiscal Year
Total Cost
Indirect Cost
University of Pennsylvania
Schools of Dentistry
United States
Zip Code
Hohman, Timothy J; Dumitrescu, Logan; Barnes, Lisa L et al. (2018) Sex-Specific Association of Apolipoprotein E With Cerebrospinal Fluid Levels of Tau. JAMA Neurol 75:989-998
Chung, Jaeyoon; Wang, Xulong; Maruyama, Toru et al. (2018) Genome-wide association study of Alzheimer's disease endophenotypes at prediagnosis stages. Alzheimers Dement 14:623-633
Jansen, Willemijn J; Wilson, Robert S; Visser, Pieter Jelle et al. (2018) Age and the association of dementia-related pathology with trajectories of cognitive decline. Neurobiol Aging 61:138-145
Blue, Elizabeth E; Bis, Joshua C; Dorschner, Michael O et al. (2018) Genetic Variation in Genes Underlying Diverse Dementias May Explain a Small Proportion of Cases in the Alzheimer's Disease Sequencing Project. Dement Geriatr Cogn Disord 45:1-17
Leung, Yuk Yee; Valladares, Otto; Chou, Yi-Fan et al. (2018) VCPA: genomic Variant Calling pipeline and data management tool for Alzheimer's Disease Sequencing Project. Bioinformatics :
Guerreiro, R; Orme, T; Naj, A C et al. (2018) Is APOE ?4 required for Alzheimer's disease to develop in TREM2 p.R47H variant carriers? Neuropathol Appl Neurobiol :
Karch, Celeste M; Wen, Natalie; Fan, Chun C et al. (2018) Selective Genetic Overlap Between Amyotrophic Lateral Sclerosis and Diseases of the Frontotemporal Dementia Spectrum. JAMA Neurol 75:860-875
Blue, E E; Yu, C-E; Thornton, T A et al. (2018) Variants regulating ZBTB4 are associated with age-at-onset of Alzheimer's disease. Genes Brain Behav 17:e12429
Li, Xinzhong; Wang, Haiyan; Long, Jintao et al. (2018) Systematic Analysis and Biomarker Study for Alzheimer's Disease. Sci Rep 8:17394
Lobach, Iryna (2018) Bias in parameter estimates due to omitting gene-environment interaction terms in case-control studies. Genet Epidemiol 42:838-845

Showing the most recent 10 out of 194 publications