Uncovering the genetic basis of Alzheimer's Diseases by integrating GWAS with multiomics approaches across different ethnicities

Objectives

In this study we aim to understand the system level understanding of Alzheimer's disease (AD) by integrating GWAS with robust multiomics datasets across diverse ethnic groups and harmonization of the results to include associated genes and pathways to understand underlying disease mechanisms and to inform our understanding of biological continuum of the diseases. Investigating AD associated variants as quantitative trait loci for epigenetic, transcriptomic, and proteomic layers to explore how variants in genes perturb pathways leading to AD.

Study design

We will comprehensively examine the genetic architecture of Alzheimer's diseases based on different races (Caucasians, Latinos, Asians and Africans) GWAS data from publicly available datasets. We request access to as many datasets available in NIAGADS and other repositories like EADB-consortium, IGAP and we also have requested access to datasets through our literature search from corresponding authors for Asian cohorts. We will perform meta-analysis at two levels for GWAS datasets and find genome wide significant loci (GWS). Mendelian randomization analyses will be adapted to multi-omics setting through analyzing QTLs and GWS. We will perform correlation and enrichment analysis for significant findings from different omics layers.

Analysis Plan

Genome wide meta-analysis across different races to identify new loci and functional pathways influencing AD. Find genes most likely to be responsible for association signal with AD at each loci by applying mendelian randomization (MR) method. We plan to use MR method to combine multiomics (GWAS, eqtl, mqtl, aqtl and pqtl).

To our knowledge our findings reveal crosstalk between epigenetic, genomic, and transcriptomic determinants of AD pathogenesis and define catalogues of candidate genes. In addition, rare or population specific common variants can be identified thus genes with underlying genetic support for an association with AD are likely to encode successful drug targets in clinical development. This should further lead to patient stratification.