Novel computational approaches to characterize the effects of rare functional outlier variants on cis- and trans-regulatory disease processes

摘要 最近大规模全基因组数据集的出现揭示了稀有基因的惊人规模 在人类群体中存在变异。越来越多的证据表明，罕见的基因变异可能会导致 对多种复杂疾病表型的深刻影响；然而，这些表型的系统特征 变种受到当前队列大小和解释方法的限制。一种强大的新兴方法 对于稀有变异的解释是在整合功能数据，以实现体内稀有变异驱动的分析 分子失调，由基因组、功能和表型的大规模数据集成实现 资源。在这项提案中，我们概述了计算和统计方法，以系统地注释和 在全基因组范围内分离出对多种分子表型有极端影响的罕见变异 (稀有分子离群值变种)，并通过整合生物库规模的表型数据，其下游 对各种复杂疾病风险的影响。我们最近在GTEx和TOPMed中应用了这种方法，以表明 利用异常基因表达为鉴定大的表型效应提供了一个强大的框架 已知对复杂疾病有影响的基因变异。 具体地说，在这项建议中，我们将结合大规模基因组和多样化的多组学数据来 开发和扩展新的计算和统计方法，以提供第一个系统的表征 由罕见基因变异造成的个性化复杂疾病风险。我们的方法很容易应用。 在任何复杂疾病领域进行研究，包括人体测量学、神经学和癌症研究。我们的 这些努力将增加我们对罕见变异如何与多基因疾病风险预测相互作用的理解 源自多基因风险评分，目前仅限于影响相对较小的常见变异GWA次，以及 展示罕见的分子离群值变体如何提供系统地表征顺式和非顺式的框架 全基因模型中的跨调控疾病网络影响核心疾病基因。 此外，我们概述了初步结果，表明罕见的分子离群值变体实质上 与基因组注释方法(即蛋白质)相比，提高发现大效应稀有变异的能力 截断变体-仅限于编码区)，并概述了一种量化这些影响的方法 疾病预测和药物靶向应用。 总体而言，这些活动将增加我们对复杂疾病遗传学的理解。对.的使用 纯遗传方法，如GWAS，需要队列大小在数百万以内，这说明了重要性 将功能基因组数据应用到我们的方法中。我们在发布软件和流水线方面有着良好的记录 实现以前的方法，并将在公共存储库上迅速提供任何新工作。我们的努力 将为理解快速增长的稀有变异的发现提供重要贡献 基因组数据以及对解释这些变异的方法的迫切需要。

SUMMARY The recent availability of large-scale whole genome datasets has revealed the startling scale of rare genetic variation present in human populations. There is increasing evidence that rare genetic variants can have profound effects on multiple complex disease phenotypes; however, the systematic characterization of these variants is limited by current cohort sizes and approaches to interpretation. One powerful emerging approach for rare variant interpretation is in the integration of functional data to enable in vivo assay of rare variant-driven molecular dysregulation, enabled by large-scale data integration of genomic, functional, and phenotypic resources. In this proposal, we outline computational and statistical approaches to systematically annotate and isolate – on a genome-wide scale – rare variants linked with extreme effects on multiple molecular phenotypes (rare molecular outlier variants) and, through integrating biobank-scale phenotypic data, their downstream effects on diverse complex disease risk. We recently applied this approach in GTEx and TOPMed to show that utilizing outlier gene expression provides a powerful framework for identifying large phenotypic-effect rare variants in genes with known impact on complex diseases. Specifically, in this proposal we will combine large-scale genomic and diverse multi-omics data to develop and extend novel computational and statistical methods to provide the first systematic characterization of personalized complex disease risk contributed by rare genetic variants. Our methods are readily applicable to research in any complex disease area, including anthropometric, neurological and cancer research. Our efforts will increase our understanding of how rare variants interact with polygenic disease risk predictions derived from polygenic risk scores, currently limited to relatively small-effect common variant GWAS hits, and show how rare molecular outlier variants provide a framework for systematically characterizing both cis- and trans-regulatory disease networks impacting core disease genes as theorized in the omnigenic model. Furthermore, we outline preliminary results suggesting that rare molecular outlier variants substantially increase power for uncovering large-effect rare variants over genome annotation methods (namely, protein truncating variants – limited to coding regions only), and outline an approach for quantifying these effects in disease prediction and drug targeting applications. Overall, these activities will increase our understanding of complex disease genetics. The use of genetic-only methods such as GWAS would require cohort sizes within the millions, illustrating the importance of functional genomic data to our approach. We have a strong track record of releasing software and pipelines to implement prior methods, and will make any new work rapidly available on public repositories. Our efforts will provide important contributions to understanding the rapidly growing discovery of rare variants from whole genome data and the urgent need for methods to interpret these variants.