Project information
Pre-clinical genotype-phenotype predictors of Alzheimer's disease and other dementia (APGeM)

Information

This project doesn't include Faculty of Medicine. It includes Central European Institute of Technology. Official project website can be found on muni.cz.
Project Identification
237250
Project Period
6/2014 - 5/2016
Investor / Pogramme / Project type
European Union
MU Faculty or unit
Central European Institute of Technology
Cooperating Organization
University of Oslo

The main aim is to establish genotype-phenotype matching in incipient Alzheimer's disease (AD) and Lewy-body
dementia (LBD) and Parkinson’s disease (PD). Several lines of evidence suggest common factors causing dementia
development in these diseases, and that common genetic variants each conferring a small risk, may be involved. We will
identify the polygenetic risk factors for neurodegenerative dementias and investigate how these new "pre-morbid (ie
genetic) markers" together with "pre-dementia phenotypic markers" predict disease development, and may form latent
etiopathogenic pre-dementia classes relevant for prevention strategies. Thus, APGeM has three sub-aims: Aim 1)
Identify pre-morbid candidate genetic markers. Novel statistical methods applied on Genome-wide association
studies (GWAS) and rare variants associated with neurodegenerative disease will be used to develop prediction
algorithms. Aim 2) Validate the candidate genetic markers in cohorts with established cases. Polygenic pre-morbid
markers will subsequently be used in combination with dementia phenotypes, based on imaging, neurochemistry /
proteomics and neuropsychology to improve diagnosis and validate identified markers (aim1) in a clinical setting with
established dementia diagnoses. Aim 3) Develop clinical and laboratory prediction tools and test their usefulness
in longitudinal studies. The generalization performance of the algorithm based on combined polygenic pre-morbid
markers and clinical pre-dementia markers will be validated in cohorts containing early clinical and pre-clinical cases
from Clinical Networks, and their ability to predict progression to dementia will be examined.

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