Dissecting genetic and non-genetic heterogeneity in predisposition to Alzheimer's disease and vascular traits in pleiotropic context

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NIH/NIA and Alzheimer’s association emphasize that capturing complexity in etiology of Alzheimer’s disease (AD) and AD-related dementias (AD/ADRD) may substantially advance the understanding of the AD/ADRD pathogenesis. Mechanisms of complexity may involve various endogenous (e.g., genetics, epigenetic, cellular, physiology) and exogenous (e.g., environmental exposures, social milieu) factors, including those of vascular origin, and their interactions. It is recognized that novel insights into the complex biology and heterogeneity of AD/ADRD are needed to develop efficient interventions that can be tailored to a person’s unique risk profile. The objective of this project is to identify personalized (i.e., more homogeneous, group-specific) genetic and non-genetic profiles of risk of, protection against, and resilience to AD/ADRD and vascular diseases in the disease-specific and pleiotropic contexts. Our approach leverages an array of comprehensive methods which ensure synergism in dissecting genetic and non-genetic heterogeneity in predisposition to AD/ADRD in pleiotropic context. This approach overcomes core weakness in the rigor of prior studies characterizing effects of different factors “one by one” using analysis-specific methods. High potential of our approach is supported by our recent publications and rich data from the existing studies. We will address the following specific aims: Aim 1. Identify specific and pleiotropic loci for AD/ADRD and vascular traits from the exome-wide association study. Aim 2. Dissect heterogeneity leveraging the analysis of molecular signatures defined as differences in linkage disequilibrium patterns in affected and unaffected subjects. Aim 3. Identify personalized genetic profiles of AD/ADRD-specific and pleiotropic risks, protection, and resilience using rigorous methods. Aim 4. Characterize the functional roles of SNPs from the identified mono/polygenic variants and biological roles of genes for these SNPs. Characterize transcription pathways for SNPs using individual-level gene expression and epigenetic data and summary statistics from the available expression and methylation quantitative trait loci studies.):