Grant Number:	1R01CA211707-01 Interpret this number
Primary Investigator:	Gayther, Simon
Organization:	Cedars-Sinai Medical Center
Project Title:	Functional Effects of Ovarian Cancer Risk Variants
Fiscal Year:	2017

Abstract Genome wide association studies (GWAS) have so far identified more than 20 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 including: (1) Identifying additional novel, common variant susceptibility alleles for the different histological subtypes of ovarian cancer; (2) Establishing the functional mechanisms driving disease at ovarian cancer risk loci based on the identification and characterization of the likely casual SNPs and targets susceptibility genes are risk loci; (3) Using genome wide profiling of functional models based on perturbation of ovarian cancer susceptibility genes, to identify common mechanisms and biological pathways driving tumorigenesis; (4) To integrate functional datasets with genetic association datasets to improve the power of these studies to identify additional ovarian cancer susceptibility loci.




Integrative multi-omics analyses to identify the genetic and functional mechanisms underlying ovarian cancer risk regions.
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