Functional and population genetic architectures of complex disease

项目摘要 包括神经精神疾病在内的复杂疾病遗传学的最新进展表明， 疾病遗传性的大部分由许多常见的非编码变异的作用来解释。其中大多数都是 变异个体对疾病风险的影响微乎其微，但总的来说，它们贡献了大部分可测量的 大多数疾病的遗传风险，通常占遗传性双胞胎估计的三分之一到一半。 这些观察引发了关于表型变异背后的生物学的三个重要问题： 首先，与疾病相关的个体非编码变异如何影响生物功能？一个不断增长的 许多研究，包括本续签申请的主要人员发表的研究，提供了 统计分析，将遗传和表观遗传学数据联系起来，以确定活跃在细胞类型中的调控元件 是疾病生物学的关键。然而，监管行动的机制只是几个例子。 基因与疾病的联系。 第二，致病变种是如何共同干扰基因、生物通路和网络的？ 并引发一种疾病表型？尽管有关于基因和功能模块的大量知识 经过几十年的实验研究积累，我们对特定的基因程序和 数以千计的致病变异通过网络影响疾病表型。 第三，基因变异与常见的神经精神疾病如何稳定地保持在 考虑到与这些疾病相关的健康状况的丧失？已建立的群体遗传学模型 适用于罕见疾病的基因与最新的常见疾病遗传学数据不一致。 我们建议开发新的统计和计算方法，以从 基因组数据，并将这些方法应用于基因组规模的基因型-表型数据集。 我们将设计新的策略，将功能基因组和分子表型数据与疾病相结合 关联结果阐明了非编码变异体的近端调控功能。我们提出了新的 在生物组织范围的不同水平上解释遗传关联数据的统计方法 从个体调控相互作用到基因、途径和网络。我们将使用种群遗传学 解决常见疾病等位基因结构起源的概念性问题的模型，尤其是， 神经精神疾病。我们的合作有广泛的出版记录和制作记录 在上一个资金周期中广泛使用的开源软件。我们有大量的计算和 统计专业知识，但我们的方法始终植根于数据。所有建议的方法开发都将是 以现有的超过200万的大规模遗传学数据集和功能基因组数据集为指导 样本。

Project Summary Recent progress in the genetics of complex diseases, including neuropsychiatric diseases, has revealed that the bulk of disease heritability is explained by the action of many common non-coding variants. Most of these variants exert individually tiny effects on disease risk, but collectively they contribute most of the measurable genetic risk for most diseases and typically account for between a third and half of twin estimates of heritability. These observations motivate three important questions concerning the biology underlying phenotypic variation: First, how do individual non-coding variants associated to disease impact biological function? A growing number of studies, including those published by the Key Personnel of this renewal application, provide statistical analyses that link genetic and epigenetic data to identify regulatory elements active in cell types that are key to disease biology. However, the mechanism of regulatory action is understood just for a few examples of genetic associations to disease. Second, how do the causative variants conspire together to perturb genes, biological pathways and networks and induce a disease phenotype? Despite the body of knowledge on genes and functional modules accumulated by decades of experimental research, we lack understanding of specific gene programs and networks through which the thousands of causative variants act to impact disease phenotypes. Third, how is genetic variation associated to common neuropsychiatric diseases stably maintained in the population given the loss of fitness associated to these disorders? Established population genetics models applicable to rare diseases are inconsistent with recent data on common disease genetics. We propose to develop new statistical and computational methods to generate biological insights from genomic data and to apply these methods to genome-scale genotype-phenotype datasets. We will design new strategies to combine functional genomic and molecular phenotype data with disease association results to shed light on the proximal regulatory function of non-coding variants. We propose new statistical methods to interpret genetic association data at different levels of biological organization ranging from individual regulatory interactions to genes, pathways and networks. We will use population genetics models to address conceptual issues of the origin of allelic architecture of common disease and, in particular, neuropsychiatric diseases. Our collaboration has an extensive publication record and a record of producing widely-used open-source software in the previous funding cycle. We have extensive computational and statistical expertise, but our approach is always rooted in data. All proposed method development will be guided by available large-scale genetics datasets and functional genomics datasets spanning over 2 million samples.