Integrative approaches for mapping the genetic risk of complex traits

项目总结/摘要 尽管全基因组关联研究（GWAS）在识别许多 复杂性状和疾病的风险基因座，在绝大多数这些基因座上， 遗传变异和疾病风险在很大程度上仍然未知。这就禁止了新药的开发 目标、个性化治疗或准确预测高危个体。为了解决这一差距， 后GWAS研究正在经历一场“大数据”革命，这场革命的推动力是成本呈指数级下降， 高通量基因组测定。多层数据（遗传变异、转录组水平、表观遗传 修饰、组织特异性调节位点的定位等）在越来越大的 一群人这就需要新的计算和统计方法， 整合各种类型的数据（遗传学、表观遗传学、转录组学）以了解 GWAS风险位点的疾病。在这里，我们建议开发新的方法和技术，并将其应用于 深入了解常见疾病和特征的遗传基础。重要的是，我们的目标是规避基因组 隐私问题（通常禁止访问大规模GWAS数据）， 直接在汇总统计水平（例如变量效应量）。我们将应用现有的和新开发的 GWAS汇总数据集的方法，涵盖30多个性状和疾病，涵盖1，000，000多个表型 测量，连同超过7,000个生物化学和进化遗传指标的目录， 功能以及超过10，000个个体的遗传变异，基因表达和疾病状态 已经被测量了。

PROJECT SUMMARY/ABSTRACT Although genome-wide association studies (GWAS) have been extremely successful in identifying numerous risk loci for complex traits and diseases, at the vast majority of these loci, the causal mechanism between genetic variation and disease risk remains largely unknown. This prohibits the development of novel drug targets, personalized treatments or accurate prediction of high-risk individuals. In the quest to address this gap, post-GWAS studies are experiencing a “big data” revolution driven by the exponentially decreasing costs of high-throughput genomic assays. Multiple layers of data (genetic variation, transcriptome levels, epigenetic modifications, localization of tissue-specific regulatory sites, etc.) are routinely collected in increasingly large cohorts of individuals. This raises the need for new computational and statistical methods that are able to integrate various types of data (genetic, epigenetic, transcriptomic) to understand the causal mechanism of disease at GWAS risk loci. Here we propose to develop new methods and techniques and to apply them to gain insights to the genetic basis of common disease and traits. Importantly, we aim to circumvent genomic privacy issues (that often prohibit access to large-scale GWAS data) by proposing techniques that operate directly at the summary statistic level (e.g. variant effect sizes). We will apply existing and newly developed methods on GWAS summary data sets over 30 traits and diseases spanning more than 1,000,000 phenotype measurements, joint with a catalogue of over 7,000 biochemical and evolutionary genetic metrics of functionality as well as over 10,000 individuals for which genetic variation, gene expression and disease status has been measured.