Prioritizing follow-up of GWAS loci using genetic and functional annotation data

使用遗传和功能注释数据优先跟进 GWAS 位点

• 批准号: 8753749
• 负责人: Sara Lindstroem
• 金额: $ 22.3万
• 依托单位: HARVARD SCHOOL OF PUBLIC HEALTH
• 依托单位国家: 美国
• 财政年份: 2014
• 资助国家: 美国
• 起止时间: 2014-08-15 至 2016-07-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8753749
• 关键词: Accounting; Binding Sites; Biology; Breast Cancer Epidemiology; Cells; Code; Communities; Computer software; Data; Data Set; Deoxyribonuclease I; Disease; Disease susceptibility; Encyclopedia of DNA Elements; Ensure; Ethnic Origin; Gene Expression; Genetic; Genetic Structures; Genetic Variation; Genome; Genome Mappings; Genomics; Genotype; Laboratories; Mammary Gland Parenchyma; Mammary Neoplasms; Mammographic Density; Maps; Methodology; Methods; Modeling; Normal tissue morphology; Outcome; Phenotype; Population; Population Genetics; Probability; Proteins; Publications; Research; Signal Transduction; Site; Source; Statistical Methods; The Cancer Genome Atlas; Tissue-Specific Gene Expression; Tissues; Tumor Tissue; Variant; Woman; Work; base; conditioning; deep sequencing; follow-up; functional genomics; genetic variant; genome wide association study; genome-wide; improved; interest; malignant breast neoplasm; non-genetic; novel; public health relevance; research study; risk variant; screening; simulation; success; transcription factor; tumor

DESCRIPTION (provided by applicant): Although genome-wide association studies (GWAS) have identified thousands of disease susceptibility loci, the underlying genetic structure in these regions is not fully studied and it is likely that the GWAS signal originates from one or many yet unidentified causal variants. In order to localize potential causal variant(s) for further follow-u experiments, fine-mapping studies in large populations are underway. To date, fine-mapping studies have used standard approaches that fail to account for the full array of information currently available such as associations with gene expression (eQTLs) and genomic functional annotation. With the advent of large-scale initiatives such as The Encyclopedia of DNA Elements (ENCODE) and The Cancer Genome Atlas (TCGA), it may be possible to include an additional layer of functional information to fine-mapping studies, enhancing the ability to localize causal variants. We here propose to develop a statistical framework that will incorporate both functional and genetic information. We will build variant-specific priors based on cell-specific functional annotation (e.g. DNase I hypersensitive sites, protein coding), associations with tissue-specific gene expression and correlated phenotypes. We will capitalize on the publically available ENCODE data to acquire functional annotation for each genetic variant. We will then estimate posterior probabilities for each genetic variant based on their derived prior an the evidence for association with the outcome of interest. Such posterior probabilities can then be used to prioritize genetic variants for further follow-up in a laboratory setting. Compared to existing approaches, our proposed method is unique in that it will jointly model internal (e.g. sequencing and gene expression data) and external (e.g. ENCODE, TCGA) sources. It will also allow for multiple causal variants at each region and jointly assess all loci simultaneously, allowing the method to "borrow" information between the regions. To ensure generalizability, we will conduct extensive simulation studies taking numerous possible scenarios into account. We will apply our method on a multi-ethnic breast cancer targeted sequencing dataset of 2,288 breast cancer cases and 2,323 controls for whom we have generated high-depth sequencing data for 12 GWAS-identified breast cancer regions. For a subset of these women, we also have mammographic density (n=1,000) and whole-genome expression data (n=250) in both normal and tumor tissue, allowing us to apply our method and jointly model empirical sequencing, gene expression and phenotype data. We have assembled a multi-disciplinary research team with a track record of producing high-profile publications in fine-mapping, statistical methods, breast cancer epidemiology, population genetics and publicly available software packages for the genetics community. Our work has the potential of bridging the gap between initial screening for regions in the genome that are associated with disease and prioritizing specific variants for further functional analysis. Such methods will have important implications for understanding the underlying biology of disease, a major challenge in the post-GWAS era.

描述（由申请人提供）：虽然全基因组关联研究（GWAS）已经确定了数千个疾病易感位点，但这些位点的潜在遗传结构 区域尚未得到充分研究，GWAS 信号很可能源自一个或多个尚未识别的因果变异。为了定位潜在的因果变异以进行进一步的后续实验，大规模人群的精细绘图研究正在进行中。迄今为止，精细绘图研究使用的标准方法无法解释当前可用的全部信息，例如与基因表达（eQTL）和基因组功能注释的关联。随着 DNA 元素百科全书 (ENCODE) 和癌症基因组图谱 (TCGA) 等大规模计划的出现，有可能在精细绘图研究中包含额外的功能信息层，从而增强本地化的能力因果变异。我们在此建议开发一个包含功能和遗传信息的统计框架。我们将根据细胞特异性功能注释（例如 DNase I 超敏感位点、蛋白质编码）、与组织特异性基因表达和相关表型的关联来构建特定变体的先验。我们将利用公开的 ENCODE 数据来获取每个遗传变异的功能注释。然后，我们将根据每个遗传变异的推导先验以及与感兴趣结果相关的证据来估计每个遗传变异的后验概率。然后可以使用这种后验概率来确定遗传变异的优先级，以便在实验室环境中进行进一步的随访。与现有方法相比，我们提出的方法的独特之处在于它将联合建模内部（例如测序和基因表达数据）和外部（例如 ENCODE、TCGA）来源。它还将允许每个区域存在多个因果变异，并同时联合评估所有基因座，从而允许该方法在区域之间“借用”信息。为了确保普遍性，我们将进行广泛的模拟研究，考虑多种可能的情况。我们将把我们的方法应用于包含 2,288 例乳腺癌病例和 2,323 例对照的多种族乳腺癌靶向测序数据集，我们为这些数据集生成了 12 个 GWAS 识别的乳腺癌区域的深度测序数据。对于这些女性的子集​​，我们还拥有正常组织和肿瘤组织中的乳房​​ X 光密度 (n=1,000) 和全基因组表达数据 (n=250)，这使我们能够应用我们的方法并联合建模经验测序、基因表达和表型数据。我们组建了一支多学科研究团队，在精细制图、统计方法、乳腺癌流行病学、群体遗传学和遗传学界公开可用的软件包方面拥有发表高调出版物的记录。我们的工作有可能弥合对基因组中与疾病相关的区域进行初步筛选和优先考虑特定变异以进行进一步功能分析之间的差距。这些方法对于理解疾病的潜在生物学具有重要意义，这是后 GWAS 时代的主要挑战。