A Biologically Informed Method for Detecting Associations with Rare Variants

描述（由申请人提供）：全基因组关联研究（GWAS）在识别与表型差异相关的常见变异方面取得了一定的成功。然而，在任何给定的复杂性状中，大部分的可遗传成分还有待解释。新的技术，允许罕见的变异，结构变异和表达数据的特性提供了新的见解性状关联。不幸的是，数据的创建速度比现场能够分析它的速度要快。目前的常见变异分析方法不足以管理序列数据;因此，需要设计新的方法来管理高通量数据。这些新方法还应该能够分析相互作用（上位性和基因环境），并准备纳入其他“组学”数据，因为它越来越可用。一种单一的方法能够完成这些复杂的任务，这将使研究人员能够描绘出一幅包含多种形式的遗传和环境信息的性状的完整图景。确定基因与环境的相互作用是特别重要的，因为环境是少数几个可修改的变量之一。开发这种分析工具的一种方法是在两步分析中使用已知的生物信息。第一步使用基于知识的生物学和预测函数作为指导，将罕见变体折叠到加权箱中。这对于减少序列数据的计算负荷以及增加检测罕见变异之间的关联的能力是必要的。然后，可以立即测试分组的变体沿着常见变体的关联（退出该管道）或包装用于生物过滤器。第二步，Biofilter创建并评估潜在的相互作用（基因-基因或基因-环境）。然后在全基因组数据中测试这些相互作用模型的统计学显著关联。在这两个步骤中，生物信息来源于基因分组和疾病相关基因集的多个公共数据库的系统整合，以产生具有已建立的生物学基础的多SNP模型。结合先验知识的优点是：减少搜索空间，增加识别关联的能力，以及对任何统计学显著结果的相关生物学推断。该项目的第一个目标是开发BioBn，这是一种使用领域知识来指导罕见变体的折叠和分箱的算法。第二个目标是比较这种方法与其他已发表的塌陷方法，使用模拟数据。第三个目标是为Biofilter收集和评估的数据创建一个管道，特别是在视网膜相关黄斑变性研究中使用个体测试基因-环境相互作用。

DESCRIPTION (provided by applicant): Genome-wide association studies (GWAS) have been moderately successful in identifying common variants that are associated with phenotypic differences. However, the greater part of the heritable component in any given complex trait has yet to be explained. New technologies which allow for the characterization of rare variants, structural variants, and expression data are providing new insights into trait association. Unfortunately, the data is being created faster than the field is able to analyze it. Current common-variant analytical methods are not powered to manage sequence data; therefore, new methods designed to manage high-throughput data are necessary. These new methods should also be capable of analyzing interactions (epistasis and gene-environment) and prepared to incorporate other "-omic" data as it increasingly becomes available. A single method's ability to perform these complex tasks will enable the researcher to paint a complete picture of a trait that incorporates many forms of genetic and environmental information. Identifying gene-environment interactions are of particular importance since environment is one of few modifiable variables. One approach for developing this analytical tool is to use known biological information in a two step-analysis. The first step uses knowledge-based biology and predicted function as guides to collapse rare variants into weighted bins. This is necessary to decrease the computational load of sequence data, as well as increase the power of detecting an association among rare variants. The binned variants along with common variants can then be tested for association immediately (exit this pipeline) or be packaged for Biofilter. In the second step, Biofilter creates and assesses potential interactions (gene-gene or gene-environment). These interaction models are then tested in genome-wide data for statistically significant association. In both steps, the biological information is derived from a systematic integration multiple public databases of gene groupings and sets of disease-related genes to produce multi-SNP models that have an established biological foundation. The advantages of incorporating prior knowledge are: reduced search space, increased power to identify associations, and inference of relevant biology for any statistically significant result. The first goal of this project is to develop BioBn, an algorithm that will use domain-knowledge to guide the collapsing and binning of rare variants. The second goal is to compare this method to other published collapsing methods using simulated data. The third goal is to create a pipeline for data to be collapsed and evaluated by Biofilter, specifically to test for gene-environment interactions using individuals in an Age-relatd Macular Degeneration study.