A Biologically Informed Method for Detecting Associations with Rare Variants

检测与稀有变异关联的生物学方法

• 批准号: 8723722
• 负责人: Carrie Colleen Buchanan Moore
• 金额: $ 3.21万
• 依托单位: VANDERBILT UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2011
• 资助国家: 美国
• 起止时间: 2011-09-16 至 2015-05-08
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8723722
• 关键词: Address; Age; Algorithms; Alleles; Biologic Characteristic; Biological; Biology; Complex; Data; Data Analyses; Data Set; Databases; Disease; Environment; Etiology; Foundations; Frequencies; Gene Expression Profile; Gene Frequency; Genes; Genetic; Genetic Epistasis; Genetic Research; Genome; Genotype; Goals; Grouping; Heritability; Heterogeneity; Individual; Knowledge; Lead; Learning; Linkage Disequilibrium; Linkage Disequilibrium Mapping; Macular degeneration; Methodology; Methods; Modeling; Multivariate Analysis; Paint; Pathway interactions; Performance; Proteome; Publishing; Research Personnel; Resources; Signal Transduction; Simulate; Single Nucleotide Polymorphism; Technology; Testing; Variant; Weight; analytical method; analytical tool; base; case control; design; flexibility; gene environment interaction; gene interaction; genome sequencing; genome wide association study; genome-wide; head-to-head comparison; improved; insight; interest; knowledge base; meetings; new technology; novel; rare variant; risk variant; simulation; sound; tool; trait

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.

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，任何给定的复杂性状中的大部分可遗传成分仍有待解释。允许表征罕见变异、结构变异和表达数据的新技术为性状关联提供了新的见解。不幸的是，数据的创建速度快于现场分析数据的速度。当前的共变分析方法无法管理序列数据；因此，需要设计用于管理高通量数据的新方法。这些新方法还应该能够分析相互作用（上位性和基因环境），并准备好纳入其他“组学”数据，因为它越来越可用。单一方法执行这些复杂任务的能力将使研究人员能够描绘出包含多种形式的遗传和环境信息的性状的完整图景。识别基因与环境的相互作用尤为重要，因为环境是少数可改变的变量之一。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功能作为指导，将罕见变异折叠到加权箱中。这对于减少序列数据的计算负载以及提高检测罕见变异之间关联的能力是必要的。然后可以立即测试分箱变体和常见变体的关联性（退出此管道）或包装用于生物过滤器。在第二步中，生物过滤器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显着关联性。在这两个步骤中，生物信息均源自基因组和疾病相关基因组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，以及对任何统计显着结果的相关生物学进行推断。该项目的第一个目标是开发 BioBn，一种使用领域知识来指导稀有变体的折叠和分箱的算法。第二个目标是使用模拟数据将该方法与其他已发布的折叠方法进行比较。第三个目标是创建一个由 Biofilter 折叠和评估数据的管道，特别是在年龄相关性黄斑变性研究中使用个体测试基因与环境的相互作用。

项目成果

Genomic analyses with biofilter 2.0: knowledge driven filtering, annotation, and model development.

使用生物过滤器 2.0 进行基因组分析：知识驱动的过滤、注释和模型开发。

• 发表时间: 2013-12-30
• 影响因子: 4.5
• 作者: Pendergrass, Sarah A;Frase, Ale;Wallace, John;Wolfe, Daniel;Katiyar, Neerja;Moore, Carrie;Ritchie, Marylyn D
• 通讯作者: Ritchie, Marylyn D