Predicting SNP priors for Cancer GWASs

预测癌症 GWAS 的 SNP 先验

• 批准号: 9309874
• 负责人: IVAN P GORLOV
• 金额: $ 32万
• 依托单位: DARTMOUTH COLLEGE
• 依托单位国家: 美国
• 财政年份: 2017
• 资助国家: 美国
• 起止时间: 2017-08-01 至 2019-07-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9309874
• 关键词: Architecture; Area; Cancer Biology; Cancer Control; Cancer Model; Characteristics; Colon; Computer Simulation; Data; Data Discovery; Data Sources; Detection; Gene Frequency; Genes; Genetic; Genotype; Goals; Gray unit of radiation dose; Human; Human Genome; Individual; Knowledge; Learning; Link; Lung; Malignant Neoplasms; Methods; Minor; Modeling; Nature; Noise; Odds Ratio; Ovarian; Pattern; Phenotype; Predictive Cancer Model; Predisposition; Probability; Process; Prostate; Publishing; Research; Risk; Sample Size; Sampling; Single Nucleotide Polymorphism; Site; Stratification; Study Subject; Testing; Time; Validation; Variant; Vertebral column; base; cancer genome; cancer risk; cancer type; cost; cost effective; database of Genotypes and Phenotypes; design; genetic variant; genome wide association study; genome-wide; improved; innovation; inter-individual variation; malignant breast neoplasm; model building; novel; risk variant

ABSTRACT Current genotyping platforms for GWASs include millions SNPs. Genotyping of a large number of SNPs is beneficial because of higher chances of direct genotyping of causal variant. Hovewer, genotyping multiple SNPs elevates chances of false discoveries because of multiple testing. Identification of genetic control of cancer risk susceptibility was a hot research area for more than decade. Several hundred GWASs have been conducted and many risk associated SNPs were identified. Now it is time to learn from already published cancer GWASs to build a more efficient targeted strategy to identify cancer risk associated SNPs. Based on ours and others data we propose three-fold hypothesis: (i) Cancer risk associated SNPs share sets of common characteristics; (ii) Those characteristics can be used to identify SNPs with high propensity to be associated with cancer risk; (iii) Combining several GWASs enriched by existing knowledge on the cancer-risk gene predictors may substantially improve statistical power to identify novel cancer-associated SNPs. We will conduct targeted analyses of existing genotyping data for the five most common cancers: breast, colon, lung, ovarian, and prostate. Our preliminary results indicate that we will be able to identify SNPs with smaller effects on cancer risk. We will use dbGAP data for discovery and OncoArray data for validation. We propose 3 specific aims: Aim 1. To develop cancer type specific as well as pan-cancer models for predicting SNPs' probabilities to be associated with cancer risk. Aim 2. To conduct targeted analysis of gray zone SNPs with highest predicted probability to be cancer risk associated. Aim 3. To perform validation of the top most significant SNPs from Aim 2 using independent samples. Primary goal of this project is to identify SNPs with highest prior probabilities to be cancer risk associated and use them to perform targeted in silico GWASs for the five most common cancers. We will identify novel risk associated SNPs that in combination with already detected risk associated SNPs will better explain genetic control of cancer susceptibility. The proposed approach represents a cost- effective alternative of traditional GWASs.

摘要 目前用于GWAS的基因分型平台包括数百万个SNP。大量的基因分型 SNP是有益的，因为对因果变异进行直接基因分型的机会更高。霍弗， 对多个SNP进行基因分型会增加由于多次测试而导致的错误发现的可能性。 肿瘤易感性的遗传控制研究是近年来的一个热点研究领域， 十年已经进行了数百次GWAS，并检测了许多风险相关的SNP。 鉴定现在是时候从已经发布的癌症GWAS中学习以建立更高效的 有针对性的策略，以确定癌症风险相关的SNP。 基于我们和其他人的数据，我们提出了三重假设：（i）癌症风险相关的SNP （ii）这些特征可用于鉴定具有高多态性的SNP， （iii）结合现有的全球水资源评估系统， 关于癌症风险基因预测因子的知识可以大大提高识别癌症风险基因的统计能力。 新的癌症相关SNP。我们将对现有的基因分型数据进行有针对性的分析， 五种最常见的癌症：乳腺癌、结肠癌、肺癌、卵巢癌和前列腺癌。我们的初步结果表明 我们将能够识别出对癌症风险影响较小的SNP。我们将使用dbGAP数据 发现和OncoArray数据进行验证。我们提出三个具体目标： 目标1。开发癌症类型特异性以及泛癌症模型用于预测SNP的概率 与癌症风险有关。 目标2.对具有最高癌症预测概率的灰色区域SNP进行靶向分析 相关风险。 目标3.使用独立样本对Aim 2中最重要的SNP进行验证。 这个项目的主要目标是识别具有最高先验概率的SNPs是癌症风险 相关，并使用它们来执行针对五种最常见癌症的靶向计算机GWAS。我们 将鉴定与已经检测到的风险相关SNP组合的新的风险相关SNP 能更好地解释癌症易感性的遗传控制所提议的方法代表了一种成本- 传统GWAS的有效替代品。