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

总结 最近大规模的全基因组数据集的可用性揭示了罕见的遗传学的惊人规模。 在人群中存在变异。越来越多的证据表明，罕见的遗传变异可以 对多种复杂疾病表型的深刻影响;然而，这些疾病的系统特征 变异受当前队列规模和解释方法的限制。一种强大的新兴方法 对于罕见变异的解释是整合功能数据，以实现罕见变异驱动的体内测定。 分子失调，通过基因组，功能和表型的大规模数据整合实现 资源在这个建议中，我们概述了计算和统计方法，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体 (rare分子离群变异），并通过整合生物库规模的表型数据， 对多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， 利用离群基因表达提供了一个强有力的框架，以确定大的表型效应罕见 已知对复杂疾病有影响的基因变异。 具体来说，在这项提案中，我们将联合收割机结合大规模基因组学和多样化的多组学数据， 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 罕见遗传变异导致的个性化复杂疾病风险。我们的方法很容易应用 研究任何复杂的疾病领域，包括人体测量，神经和癌症研究。我们 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 来自多基因风险评分，目前仅限于相对较小的影响常见变异GWAS命中， 显示了罕见的分子异常变异体如何为系统地表征顺式和 跨调节疾病网络影响核心疾病基因，如在全基因模型中理论化的。 此外，我们概述了初步结果，表明罕见的分子离群变异， 相对于基因组注释方法（即，蛋白质），发现大效应罕见变体能力增加 截短变体-仅限于编码区），并概述了在 疾病预测和药物靶向应用。 总的来说，这些活动将增加我们对复杂疾病遗传学的理解。使用 仅遗传学方法（如GWAS）需要数百万人的队列规模，这说明了 功能性基因组数据的应用。我们在发布软件和管道方面有着良好的记录 以实现先前的方法，并将使任何新的工作迅速提供公共知识库。我们的努力 将提供重要的贡献，了解快速增长的发现罕见的变异，从整个 基因组数据和迫切需要的方法来解释这些变异。

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.