Preparing Association Analysis Software Tools for Next Generation Sequencing Data

 描述（由申请人提供）：大规模关联研究中下一代测序数据的可用性提供了一个独特的研究机会。这些数据包含确定许多精神健康表型和精神疾病的因果疾病易感基因座（DSL）所需的信息。为了将丰富的信息转化为DSL发现，需要强大的统计方法。到目前为止，已经提出了大量的罕见变异关联测试。然而，它们并没有包含关于变体的所有重要信息。到目前为止，现有的方法都没有考虑到变异的物理位置。在假设有害的DSL和保护性的DSL集群在不同的基因组区域，我们将开发一个通用的关联分析框架，是建立在空间聚类方法。该框架将能够处理复杂的表型，例如二元、定量等，并适用于不同的研究设计，即以家庭为基础的研究和无关受试者的设计。如果领域特定语言确实集群化，那么该方法统计能力的提高将具有实际意义，从而能够发现领域特定语言。在没有DSL集群的情况下，我们的方法将实现与现有方法相似的功率水平。此外，为了测试更大的基因组区域的关联，我们将开发基于网络的关联方法。与现有方法相比，基于网络的方法将具有足够的能力用于更大的基因组区域，并且同时提供对驱动关联的变体之间的复杂关系的直观理解，从而促进新的生物学见解。该方法可以结合复杂的表型和不同的设计类型。我们还将使用关于罕见变异的物理位置的信息来检测群体亚结构/混合。由于罕见变体在遗传上比常见变体年轻得多，因此考虑变体的物理位置及其聚类的方法将提供比现有方法更精细的序列数据中群体子结构的分辨率图片，例如，EIGENSTRAT。我们将使用社区检测算法对遗传同质亚组中的研究对象进行分类。所有拟议的方法将在用户友好的软件包与现有的用户社区，即PBAT，NPBAT和R。

 DESCRIPTION (provided by applicant): The availability of next-generation sequencing data in large-scale association studies provides a unique research opportunity. The data contains the information that is required to identify causal disease susceptibility loci (DSL) for many mental health phenotypes and psychiatric diseases. In order to translate the wealth of information into DSL discovery, powerful statistical methodology is required. So far, a large number of rare variant association tests have been proposed. However, they do not incorporate all the important information about the variants. So far, none of the existing approaches takes the physical location of the variant into account. Under the assumption that deleterious DSLs and protective DSLs cluster in different genomic regions, we will develop a general association analysis framework that is built on spatial clustering approaches. The framework will be able to handle complex phenotypes, e.g. binary, quantitative, etc., and be applicable to different study designs, i.e. family-based studies and designs of unrelated subjects. If the DSLs cluster indeed, the increase of statistical power of the approach will be of practical relevance, enabling the discovery of DSLs. In the absence of DSL clustering, our approach will achieve similar power levels as existing methodology. Furthermore, in order to test larger genomic regions for association, we will develop network-based association methodology. The network-based approach will have sufficient power for larger genomic region than existing approaches, and, at the same time, provide an intuitive understanding of the complex relationships between the variants that drive the association, fostering new biological insights. The approach can incorporate complex phenotypes and different design types. We will also use the information about the physical locations of the rare variants to detect population substructure/admixture. Since rare variants are genetically much younger than common variants, approaches that take the physical locations of the variants and their clustering into account will provide a much finer resolution picture of population substructure in sequence data than existing approaches, e.g., EIGENSTRAT. We will use community-detection algorithm for the classification of study subjects in genetic homogenously subgroups. All the proposed methodology will be implemented in user- friendly software packages with existing user-communities, i.e. PBAT, NPBAT and R.