ADNI data is made available to researchers around the world. As such, there are many active research projects accessing and applying the shared ADNI data. To further encourage Alzheimer’s disease research collaboration, and to help prevent duplicate efforts, the list below shows the specific research focus of the active ADNI investigations. This information is requested annually as a requirement for data access.
| Principal Investigator | |
| Principal Investigator's Name: | Cinnamon Bloss |
| Institution: | Scripps Translational Science Institute |
| Department: | Scripps Genomic Medicine |
| Country: | |
| Proposed Analysis: | TITLE: EARLY LIFE BRAIN DEVELOPMENT AND POLYGENIC RISK FOR ALZHEIMER’S DISEASE INVESTIGATORS: Cinnamon S. Bloss, Ph.D. Ashley Van Zeeland, Ph.D. Burcu F. Darst, B.S. Nicholas J. Schork, Ph.D. Terry Jernigan, Ph.D. Anders Dale, Ph.D. PING Consortium of Investigators ABSTRACT: There is evidence that the development of Alzheimer’s disease (AD), is associated with early-life risk factors, including poor perinatal conditions, sub-optimal early-life brain development, and decreased cognitive reserve (1). Along these lines, a recent study found that children with two prominent genetic risk factors for AD (i.e., a copy of the apolipoprotein ε4 allele (APOE-ε4) and a family history of AD), showed lower cognitive test performance relative to children without these risk factors (2). At least one neuroimaging study of children and adolescents has also suggested differences in brain development as a function of AD risk (3). Data from recent genome-wide association (GWA) studies of AD now offer the opportunity to perform similar studies, but to examine the brain developmental trajectory in terms of AD genetic risk across multiple loci, and even “genome-wide” genetic risk for AD. Methods for doing this are being developed and are such that individuals can be assigned a “score” on the basis of their polygenic risk (4), and that score can then be correlated with phenotypes of interest, in this case indices of brain development. In an example of this general approach, Reiman and colleagues (2008) calculated genetic risk scores for a group of late-middle-aged, cognitively normal individuals across a cluster of nine single nucleotide polymorphisms (SNPs) from seven cholesterol-related genes implicated in AD risk. They then correlated region-specific hypometabolism with this genetic risk score and found significant correlations in AD-affected brain regions (5). The current proposal is to leverage such an approach to assess early life brain developmental trajectories that may be associated with polygenic risk of AD. We propose to leverage publically-available AD GWA study data to develop a scoring procedure that will reflect polygenic AD risk. We will also account for self-reported family history of AD as ascertained via the PING medical history questionnaire. We will then assign AD risk scores to children enrolled in PING and correlate those scores with patterns of brain development. We hypothesize that children at high genetic risk for AD will show differences in their patterns of development in AD-affected brain regions and areas of cognition relative to children at low genetic risk for AD. Elucidation of the brain changes that may be occurring early in the lifespan may be helpful to understanding of the etiology of AD and could prove useful for the development of better treatments, as well as successful prevention strategies. REFERENCES: 1. A. R. Borenstein, C. I. Copenhaver, J. A. Mortimer, Alzheimer Dis Assoc Disord 20, 63 (Jan-Mar, 2006). 2. C. S. Bloss, D. C. Delis, D. P. Salmon, M. W. Bondi, Biol Psychiatry 64, 904 (Nov 15, 2008). 3. P. Shaw et al., Lancet Neurol 6, 494 (Jun, 2007). 4. S. M. Purcell et al., Nature 460, 748 (Aug 6, 2009). 5. E. M. Reiman et al., Neuroimage 40, 1214 (Apr 15, 2008). |
| Additional Investigators |