RDS003-Diversity

The goal of this project is to identify and prioritize genetic variations that contribute to late-onset Alzheimer’s disease (AD). The planned multi-modal meta-analysis study will integrate genetic information from multiple AD datasets (ADSP, EADB GWAS, ADGC, FinGen, UK Biobank, CHARGE consortia) with other publicly available multi-omics data including gene expression (GTEx), epigenetics (GEO) and neuro-imaging. The objective, here, is to extend and refine polygenic risk scores (PRS) to work across ethnicities and prioritize genes and pathways that are causal in nature to development of late-onset AD in different ancestry cohorts. Retrospective, case-control analysis setup against the backdrop of other neurodegenerative diseases will also help identify lead variants that are specific to AD enabling the elucidation of mechanistic pathways that lead to the disease etiology.
A well-known limitation of PRS scores derived from single ethnicity datasets is that its predictive power declines in cross-ethnicity cohorts. To address this, we want to investigate the possibility of creating orthogonal effect scores (OES) from multimodal analysis for gene prioritization and causality determination. The OES will be used to further refine PRS in different ethnicities and possibly identify common emerging pathways. Linkage disequilibrium (LD), quantitative trait loci (QTL) analysis, tissue colocalization, pathway networks and siRNA screening data along with functional implications of mutations, evolutionarily conservation of genomic regions and overlap with non-coding regulatory sites (eg. DNAse I hypersensitivity sites) will be used in the generation of OES.
This orthogonal scoring system will be tested for its ability to refine PRS for achieving higher AD risk prediction in known AD cases. The contrived OES will enable creation of in-silico admixed populations and association of AD risk in these populations. The ability to predict AD risk correctly in admixed populations will be immensely useful in associating PRS signatures to AD risk in real patients.

The genetic landscape of late onset AD is complex and its pathophysiology has been elusive in spite of advances in high-throughput genomic techniques. The main limitations in characterizing the disease are, presence of multiple pathways resulting in the disease pathophysiology, lack of large data cohorts that encompass multiple ethnicities leading to low predictive power for AD risk across studies, and difficulty in identifying the causal genomic changes from the many AD-associated loci.

This study aims at addressing the latter two limitations by integrating AD data containing mutations, expression, modifications and changes in AD-associated gene loci to guide their prioritization in prediction of AD risk. This prioritization method has the potential to lead us to causative genomic changes that can be traced to key AD related biological pathways and provide the elusive pathophysiological signature for AD across ethnicities.