Genome-wide association studies (GWAS) have been unequivocally successful. For epithelial ovarian cancer (EOC), our international Ovarian Cancer Association Consortium (OCAC) has contributed to the discovery of all known risk loci. Although more loci certainly exist, the anticipated modest effect sizes require large sample sizes to permit discrimination at accepted levels of genome-wide significance. Since virtually every case-control study in the world is already collaborating on these efforts as part of NCI's GAME-On OncoArray genotyping initiative, increasing the sample size is not an option in the near term. Rather, further progress requires innovative use of largely existing epidemiologic and molecular datasets. In the proposed study, we seek to build upon the observation that most GWAS ?hits? reside in non- coding regions of the genome and our preliminary data that support the hypothesis that a significant portion of inter-individual variability in EOC susceptibility reflects single nucleotide polymorphisms (SNPs) in long non-coding RNAs (lncRNAs). Our project will include three phases. First, we will catalogue all known germline genetic variants associated with lncRNAs and EOC susceptibility using existing genotype data on more than 28,000 cases and 36,000 controls from OCAC imputed to 1000 Genomes Project density, annotation from the most comprehensive lncRNA database (GENCODE), and data from the largest RNA-sequencing project performed to identify EOC-specific lncRNAs. We will then detect lncRNAs associated with EOC using existing RNA-sequencing (RNA-seq) and microarray data plus data to be generated from a whole transcriptome array. LncRNA expression quantitative trait loci (lnc- eQTL) analysis will be performed to identify the most promising lncRNA SNPs and lncRNAs, and network analyses will be conducted to highlight regulatory networks that may underlie EOC development. In the final phase, we will follow-up the most promising findings in the laboratory to explore the functional and biological basis. This integrative molecular epidemiologic approach which capitalizes on resources and expertise that are unique to OCAC may lead to the identification and characterization of novel SNPs and lncRNAs that may be of clinical utility in reducing the burden of EOC.
Public Health Relevance: Unlike many common cancers, validated approaches to reduce epithelial ovarian cancer (EOC) risk are at our disposal, such as use of oral contraceptives, tubal ligation, or bilateral oophorectomy. In this project, we aim to discover and characterize functional variants in long non-coding RNAs that will further inform our ability to identify women at increased risk for EOC so that personalized intervention strategies can be applied to prevent them from developing this fatal disease.
|Reid, Brett M; Permuth, Jennifer B; Chen, Y Ann et al. (2017) Integration of Population-Level Genotype Data with Functional Annotation Reveals Over-Representation of Long Noncoding RNAs at Ovarian Cancer Susceptibility Loci. Cancer Epidemiol Biomarkers Prev 26:116-125|