Functional Effects of Ovarian Cancer Risk Variants

摘要 全基因组关联研究（GWAS）迄今已确定了20多种常见的低多态性。 卵巢癌的风险变异;但据估计，还有数千种风险变异有待发现。在 后GWAS时代的一系列复杂的挑战，识别，功能表征和效用， 已经出现了易感等位基因，包括：（i）鉴定致病性遗传变异和调节性基因变异。 在风险位点驱动癌症发展的靶点;（ii）识别与风险相关的易感基因 （iii）确定是否存在解释功能机制的共同生物网络 潜在的多重风险位点。临床上，识别卵巢癌的遗传风险成分可能会 通过人口普查和疾病预防战略，改进疾病预防工作; 了解风险基因座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BRCA 1或BRCA 2突变携带者的PARP治疗范例。 目前的建议旨在解决卵巢癌在后 GWAS时代包括：（1）鉴定不同的新的、常见的变异易感性等位基因， 卵巢癌的组织学亚型;（2）建立疾病驱动的功能机制， 基于可能的偶然SNP的鉴定和表征的卵巢癌风险基因座， 目标易感基因是风险位点;（3）使用基于以下的功能模型的全基因组分析： 卵巢癌易感基因的干扰，以确定共同的机制和生物学 （4）整合功能数据集与遗传关联数据集， 提高这些研究的能力，以确定额外的卵巢癌易感基因座。

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.