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 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.

Genome-wide association studies (GWAS) of Alzheimer’s disease (AD) have identified multiple loci containing common variant risk alleles [1]. These findings offer new routes to understanding disease biology that could be used to design novel therapies. However, the causal genes, pathways and process are yet to be fully identified. Nevertheless, analytical approaches such as pathway analysis [2] and integration with chromatin annotations [3,4] have begun to identify the cell types and processes that are likely to be disrupted by AD risk alleles. Strikingly, these complimentary approaches have identified immune cells and pathways as the likely effectors of AD genetic risk. We have published influential pathway analyses implicating immunity, endocytosis, lipid transport [2] using in silico pathway analysis method [2]. However, the power of pathway analysis depends on the quality of the pathway annotation, which are sometimes questionable (e.g. electronic annotation of GO terms) and some genes not well annotated. We propose to increase power by incorporating “omics” data. For example, brain gene expression applied to Alzheimer’s genome-wide association study, generates modules of genes whose expression is correlated [5]. Using approach [6] we have integrated brain expression and protein-protein interaction data with GWAS data to identify a network of 56 immune-related genes implicated in AD susceptibility.

We have demonstrated enrichment of GWAS association in gene co-expression modules which proofs the importance of using multiple expression datasets when creating modules and refining the GWAS association signal. We propose to use public and in-house functional data from relevant tissues to further refine this network, identifying targets for future study. In particular we will seek to incorporate epigenomic data into gene and pathway based analyses. We will incorporate the results of these analyses into standard measures of genetic risk (polygenic risk scores [7]) and investigate the extent to which this improves risk prediction. The aims of the project are 1. Develop methods for the integration of gene regulatory information (epigenomics) into pathway and gene based analysis of GWAS data; 2. Use public and in-house tissue specific data to inform gene-wide analyses in AD; 3. Prioritise genes and variants of interest in a tissue-specific way, for biological study of AD; 4. Generate gene networks and polygenic scores bases upon them for AD risk prediction.

Funding: This studentship is funded through GW4BioMed MRC Doctoral Training Partnership. It consists of full UK/EU tuition fees, as well as a Doctoral Stipend matching UK Research Council National Minimum (£14,553 p.a. for 2017/18, updated each year).

Additional research and training funding is available over the course of the programme. This will cover costs such as research consumables, courses, conferences and travel. Additional competitive funds are available for high-cost training/research.

The research project listed is in competition with 40 other studentship projects available across the GW4 BioMed MRC Doctoral Training Partnership. Up to 8 studentships will be awarded to the best applicants.

You will need to complete both an application to the GW4 BioMed MRC DTP for an ‘offer of funding’ and to Cardiff University for an ‘offer to study’.

Offer of Funding
Applicants will apply for funding via the centralised online application form, between 11th May and 9.30am 8th June 2017 (click link below).

Offer of Study
Applicants should submit an application for postgraduate study via the Cardiff University Online Application Service https://tinyurl.com/klqxt3s

Applicants should select Doctor of Philosophy (Medicine), with a start date of October 2017.

In the research proposal section of your application, please specify the project title and supervisors of this project and copy the project description in the text box provided. In the funding section, please select “I will be applying for a scholarship / grant” and specify that you are applying for advertised funding from GW4 BioMed MRC DTP.

If you are applying for more than one Cardiff University project, please note this in the research proposal section.