Functional Effects of Ovarian Cancer Risk Variants

摘要 到目前为止，全基因组关联研究已经确定了20多种常见的低外显性。 卵巢癌的变种；但据估计，还有数千个风险变种等待发现。在 后GWAS时代对识别、功能表征和用途的一系列复杂挑战 已经出现了易感等位基因，包括：(I)确定因果遗传变异和调节 (2)确定与风险相关的易感基因 变异体；(Iii)确定是否有共同的生物网络来解释作用机制 潜在的多个风险区域。在临床上，确定卵巢癌的遗传风险成分很可能 通过人口筛查和疾病预防战略改进疾病预防；以及 了解危险基因的功能可能会导致临床生物标志物的发现和新的靶向 治疗，类似于BRCA1或BRCA2突变携带者的PARP治疗范例。 目前的提案旨在解决卵巢癌在后一年面临的许多挑战- GWASERA包括：(1)识别不同基因的其他新的、常见的变异易感等位基因 卵巢癌的组织学亚型；(2)建立导致疾病的作用机制 基于可能的SNPs和SNPs的识别和特征的卵巢癌危险基因 易感基因是易感基因的靶点；(3)基于基因全基因组图谱的功能模型 卵巢癌易感基因的扰动，以确定共同的机制和生物学 驱动肿瘤发生的途径；(4)将功能数据集与遗传关联数据集集成到 提高这些研究的能力，以确定更多的卵巢癌易感基因。

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.