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

描述(申请人提供)：尽管全基因组关联研究(GWAS)已经确定了数千个疾病易感基因座，但这些基因的潜在遗传结构 没有对区域进行充分的研究，而且指数Gwas信号很可能起源于一个或多个尚未确定的因果变异。为了进一步定位潜在的因果变量(S) 后续实验，大量人口的精细测绘研究正在进行中。到目前为止，精细定位研究使用的标准方法不能解释目前可用的全部信息，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，有可能在精细图谱研究中增加一层功能信息，增强定位因果变异的能力。我们在这里建议开发一个统计框架，将功能和遗传信息结合在一起。我们将基于细胞特定的功能注释(例如，转录起始位置、蛋白质编码)、与组织特定的基因表达的关联以及相关的表型(例如，乳房X光摄影密度)来建立变体特定的先验。我们将利用公开可用的ENCODE数据来获取每个基因变体的功能注释。我们将根据每个遗传变异的派生先验和与感兴趣的结果相关的证据来估计每个遗传变异的后验概率。这样的后验概率可以用来确定遗传变异的优先顺序，以便在实验室进行进一步的后续研究 布景。我们建议的方法将是灵活的，因为它将联合建模内部(例如测序和基因表达数据)和外部(例如ENCODE)源。它还将允许每个区域的多个因果基因座，并同时联合评估所有基因座，使该方法能够在这些基因座之间“借用”信息。为了确保可推广性，我们将进行广泛的模拟研究，考虑到许多可能的情况。我们将把我们的方法应用于一个多种族乳腺癌定向测序数据集，该数据集包括2,288例乳腺癌病例和2,323名对照。对于所有女性，我们拥有GWAS和12个GWAS确定的乳腺癌区域的高深度测序数据，总长度为5,500 kb。对于这些女性中的一个子集，我们还拥有正常组织和肿瘤组织中的乳房X光摄影密度(n=1,000)和全基因组表达数据(n=250)，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，在乳腺癌流行病学、人口遗传学、精细图谱、统计方法和面向遗传学社区的公开可用的软件包方面制作了备受瞩目的出版物。我们的工作有可能弥合最初对基因组中与疾病相关的区域进行筛查和优先选择特定变异进行进一步功能分析之间的差距。这些方法将对理解疾病的潜在生物学具有重要意义，这是后GWAS时代的一项重大挑战。

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 index GWAS signal originates from one or many yet unidentified causal variants. In order to localize potential causal variant(s) for further follow-up 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 functional and genetic information. We will build variant-specific priors based on cell-specific functional annotation (e.g. transcript starting sites, protein coding), associations ith tissue-specific gene expression and correlated phenotypes (e.g. mammographic density). We will capitalize on the publically available ENCODE data to acquire functional annotation for each genetic variant. We will estimate posterior probabilities for each genetic variant based on their derived prior and 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. Our proposed method will be flexible in that it will jointly model internal (e.g. sequencig and gene expression data) and external (e.g. ENCODE) sources. It will also allow for multiple causal loci at each region and jointly assess all loci simultaneously, allowing the method to "borrow" information between the loci. 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 all women, we have GWAS and high-depth sequencing data for 12 GWAS-identified breast cancer regions, spanning a total of 5,500 kb. 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 breast cancer epidemiology, population genetics, fine- mapping, statistical methods 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.