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

描述（由申请人提供）：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，但这些基因座中的潜在遗传结构仍不清楚。 然而，GWAS信号区域的研究还没有完全完成，GWAS信号很可能来源于一个或多个尚未鉴定的致病变异体。为了定位潜在的因果变异以用于进一步的后续实验，正在进行大群体中的精细定位研究。迄今为止，精细定位研究使用的标准方法无法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，可能会在精细定位研究中增加一层功能信息，从而增强定位因果变异的能力。在这里，我们建议开发一个统计框架，将功能和遗传信息。我们将基于细胞特异性功能注释（例如DNase I超敏位点，蛋白质编码），与组织特异性基因表达和相关表型的关联建立变体特异性先验。我们将利用实验室可用的ENCODE数据来获得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和与感兴趣结果相关的证据来估计其后验概率。这样的后验概率然后可以用于优先考虑遗传变异，以便在实验室环境中进一步随访。与现有方法相比，我们提出的方法是独特的，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，TCGA）来源。它还将允许每个区域的多个因果变异，并同时联合评估所有基因座，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靶向测序数据集，该数据集包括2,288例乳腺癌病例和2,323例对照，我们已经为这些病例和对照生成了12个GWAS确定的乳腺癌区域的高深度测序数据。对于这些女性的一个子集，我们还拥有正常和肿瘤组织中的乳腺摄影密度（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。我们已经组建了一个多学科的研究团队，在精细绘图，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制作了备受瞩目的出版物。我们的工作有可能弥合基因组中与疾病相关区域的初步筛选与进一步功能分析的特定变体之间的差距。这些方法将对理解疾病的潜在生物学产生重要影响，这是后GWAS时代的一个重大挑战。

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 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.