The goal of the Alzheimer's Disease Genetics Consortium (ADGC) is to completely resolve the genetics of Alzheimer's disease (AD). We will identify both common and rare variants to cause AD, alter AD risk, and protect against AD. To this goal, the ADGC will continue to aggregate large numbers of multiethnic cohorts, harmonized genetic and phenotype data, and perform coordinated analyses of both early and late-onset AD genetics, and mild cognitive impairment. The rationale for genetic studies is to predict who will develop AD, understand disease mechanism, and identify new therapeutic targets. This last reason is the most critical. Most ongoing drug trials target the A? peptide, the main component of amyloid plaques, and more recently tau, the protein that aggregates in neurofibrillary tangles (NFTs). However, though some A?-targeting therapies are promising, no disease modifying therapies exist. Even if these approaches are successful, multiple therapies may be needed to reach the ultimate goal of AD prevention. Thus, new targets are needed. Genetic studies are one method of identifying new drug targets. Targets supported by genetic evidence are 2-3 more likely to succeed than those without. For this reason, until therapies that effectively prevent AD at a cost that permits widespread use in all countries, we need additional genetic studies. To completely resolve AD genetics and develop new therapeutic targets, we will execute the following aims.
Aim 1 is to expand exiting cohorts and add new cohorts for genetic analyses. We will focus on assembling a multiethnic data set. We will expand Caucasian samples, Caribbean Hispanics, non-Caribbean Hispanics, African Americans, Asians and subjects from India.
Aims 2 is generate array genotyping and whole genome sequence (WGS) data for all cohorts, and generate imputation panels from WGS AD data These reference panels with publically available panels will be used to impute all samples.
Aim 3 is gene discovery using all available data including WES and WGA data in genome-wide analyses. We will analyze multiple ethnic groups separately and in trans-ethnic analyses.
Aim 4 is to perform genome-wide analysis of AD-related phenotypes including cardiovascular traits, cognitive phenotypes, MRI imaging, amyloid and tau PET imaging, and neuropathology phenotypes.
Aim 5 is post-association analyses. We will use bioinformatics approaches and partner with other investigators using biochemical approaches to link association loci to specific genes. Methods include genome-wide Capture-C and ATACSeq and massively parallel reporter assays. We will use pathway and gene cluster approaches and co-expression networks to interpret the biological significance of genes identified by this and other studies. In addition, we will continue to promote young investigators, and work with international collaborators such as IGAP to increase the sample size and approaches used.

Public Health Relevance

In the US, 5.7 million people have AD. By 2050, this number will be 13.8 million. In 2017, AD costs the US $277 billion in medical and out of pocket expenses and $232 billion for the economic value of unpaid caregiving1. Presently, no treatments are available and additional drug targets are needed. Genetic studies described here can provide those new targets.

National Institute of Health (NIH)
National Institute on Aging (NIA)
Research Project--Cooperative Agreements (U01)
Project #
Application #
Study Section
Neuroscience of Aging Review Committee (NIA)
Program Officer
Yao, Alison Q
Project Start
Project End
Budget Start
Budget End
Support Year
Fiscal Year
Total Cost
Indirect Cost
University of Pennsylvania
Schools of Medicine
United States
Zip Code
Cox, Jiayi Wu; Patel, Devanshi; Chung, Jaeyoon et al. (2018) An efficient analytic approach in genome-wide identification of methylation quantitative trait loci response to fenofibrate treatment. BMC Proc 12:44
Deming, Yuetiva; Dumitrescu, Logan; Barnes, Lisa L et al. (2018) Sex-specific genetic predictors of Alzheimer's disease biomarkers. Acta Neuropathol 136:857-872
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

Showing the most recent 10 out of 194 publications