Genome wide association studies (GWAS) have so far identified more than 30 common low penetrance variants for ovarian cancer; but it is estimated that thousands more risk variants await discovery. In the post-GWAS era a complex set of challenges for the identification, functional characterization and utility of susceptibility alleles have emerged including: (i) Identifying the causal genetic variants and regulatory targets driving cancer development at risk loci; (ii) Identifying the susceptibility genes associated with risk variants; (iii) Establishing if there are common biological networks that explain the functional mechanisms underlying multiple risk loci. Clinically, identifying the genetic risk component of ovarian cancer will likely lead to improved disease prevention through population screening and disease prevention strategies; and understanding the function of risk loci may lead to the discovery of clinical biomarkers and novel targeted therapies, analogous to the paradigm of PARP therapy for BRCA1 or BRCA2 mutation carriers. The current proposal is designed to address many of these challenges for ovarian cancer in the post- GWAS era with respect to genetic risk variants that target splicing mechanisms of target susceptibility genes. Specifically we aim to: (1) Integrate the largest GWAS datasets for ovarian cancer with RNA sequencing data for hundreds of ovarian normal and cancer tissues to identify genetic risk variants associated specifically with differential splicing; (2) Establish the functional mechanisms associated splice associated risk variants for ovarian cancer risk loci using functional assays to analyse the effects of splicing on their target genes on tumor development in novel experimental models of ovarian cancer precursor tissues. These studies will establish a statistical and functional pipeline to evaluate the contribution of risk associated splice variants in other complex traits and disorders
for Lay Audience Genetic studies have founds hundreds of common genetic variations in the population that affect a person's risk of a multitude of different diseases including cancer, for most of these variants, we neither know the functional reasons why they cause disease, nor the genes that are the biological targets. In this proposal, we plan to use a combination of functional assays, genetic information and computational methods to work out the function of known genetic variants that influence ovarian cancer risk. We anticipate that these approaches and the methods we use can work out the function of genetic variants and genes that cause disease, leading to clinical benefits to individuals in the population.
