Identifying genetically driven gene dysregulation in Alzheimer's disease and related dementias using statistical data integration

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PROJECT SUMMARY Alzheimer’s disease (AD) and related dementias (ADRD) are highly heritable, severe, and complex brain disorders. Molecular profiling efforts of the brain have enabled us to understand more about how common variants may be associated with disease risk via regulating gene expression. However, the status quo as it pertains to the use of multiomics data for gene-trait association, is limited to independent transcriptome- and proteome-wide association studies. Here, we propose to: (1) additionally leverage epigenetic datasets and (2) apply a data-driven correlation-aware meta-analytic approach to integrate a wide range of brain cell-type and area specific imputed transcriptomes, proteomes and epigenomes. Our preliminary data suggest that this will greatly increase the power to identify genetically driven gene dysregulation associated with these disorders while controlling for the correlation between different genomic features. First, we will impute brain cell-type (based on FANS/FACS-sorted and single-nucleus data) and brain area (PFC, ACC, EC, STG, IFG) specific transcriptomes, proteomes and epigenomes for AD, frontotemporal lobar degeneration, and dementia with Lewy bodies. Secondly, for each disease, we will perform a data-driven correlation-aware meta-analysis of these imputed genomic features to identify key genes. Lastly, we will develop an open resource that provides for the rapid dissemination and open access to analyses and outcomes. To our knowledge, the proposed approach will result in the largest multiscale modeling of genetically driven gene dysregulation in the brain, will identify novel genes associated with dementias and will enable similar studies in other brain disorders as well.