Integration of genetic and functional data to identify drug targets and enhance risk prediction
Integrating brain expression and protein-protein interaction data with genomic data identified a network of immune-related genes implicated in Alzheimer’s disease (AD) susceptibility.
We propose to use functional data from relevant tissues to refine this network and incorporate the results into measures of genetic disease risk prediction. In particular we will seek to incorporate epigenomic data into gene and pathway based analyses.
You will incorporate the results of these analyses into standard measures of genetic risk (polygenic risk scores) and investigate the extent to which this improves risk prediction.
The aims of the project are:
- develop methods for the integration of gene regulatory information (epigenomics) into pathway and gene based analysis of GWAS data
- use public and in-house tissue specific data to inform gene-wide analyses in AD
- prioritise genes and variants of interest in a tissue-specific way, for biological study of AD
- generate gene networks and polygenic scores based upon them, for AD risk prediction.