Mining genomic data to inform cancer prevention &  treatment strategies


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Richard Martin1
1University of Bristol

Abstract

Genome-wide association studies (GWAS), based on large research consortia and population biobanks, test associations of human traits with genetic variation across millions of loci in tens of thousands of individuals. Since 2005, >3540 GWAS publications have reported nearly 70,000 SNP-phenotype associations. In the basic sciences, high-throughput assays (measuring the genome, methylome, metabolome, transcriptome, and proteome) have enriched understanding of genome function.

These diverse studies provide an un-paralleled resource for understanding the causal underpinnings of human traits. Mendelian randomization (MR) uses genetic instrumental variables to test the causal effects of potentially modifiable exposures or novel drug targets on outcomes. MR has become a major tool in: understanding cancer and CVD aetiology and mechanisms; predicting the benefits of novel therapies and their combinations; and the identification of adverse drug effects and drug repurposing opportunities.  We can readily integrate epigenomics, transcriptomics, metabolomics and proteomics with data on hundreds of diseases in our openly accessible MR-Base database (www.mrbase.org), mapping the influence of these molecular traits on complex human diseases.

I will discuss the use of MR and MR-Base as an open resource to allow the international scientific community to more effectively and rapidly capitalize on the opportunities for developing new public health and clinical interventions provided by novel high-throughput assays and causal analysis methods to enhance disease prevention and therapeutic research.