Novel Statistical methods for DNA Sequencing Data, and applications to Autism.

DNA 测序数据的新统计方法及其在自闭症中的应用。

• 批准号: 8303934
• 负责人: Iuliana Ionita
• 金额: $ 33.97万
• 依托单位: COLUMBIA UNIVERSITY HEALTH SCIENCES
• 依托单位国家: 美国
• 财政年份: 2012
• 资助国家: 美国
• 起止时间: 2012-04-01 至 2016-03-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8303934
• 关键词: Area; Asthma; Astronomy; Autistic Disorder; Bipolar Disorder; Chromosomes; Collaborations; Communities; Complex; Computer software; DNA Sequence; Data; Data Set; Development; Disease; Disease Association; Disease susceptibility; Environmental Risk Factor; Epidemiology; Family; Frequencies; Genes; Genetic; Genomics; Heritability; Individual; Link; Mental disorders; Methodology; Methods; Play; Population; Public Health; Rare Diseases; Relative (related person); Research; Research Design; Research Personnel; Risk; Role; Scanning; Schizophrenia; Software Tools; Statistical Methods; Technology; Testing; Time; Variant; Work; base; case control; design; direct application; disorder risk; exome; genetic variant; genome wide association study; medical schools; method development; next generation; novel; population based; software development; statistics; trait; user friendly software

DESCRIPTION (provided by applicant): We propose to develop novel statistical methods and software tools for disease association testing with rare variants, with particular application to autism. Although genome-wide association studies have led to the discovery of many common variants reproducibly associated with various complex traits, these variants have small effect sizes and overall explain only a small fraction of the total estimated trait heritability. Recent advances in next-generation sequencing technologies allow for the first time an objective assessment of the importance of rare variants in complex diseases. Over the past few years it has become clear from numerous empirical studies that rare variants are an important contributor to disease risk. This is especially compelling for psychiatric diseases, such as schizophrenia and autism, where common disease susceptibility variants have been more difficult to identify. Traditional association testing strategies that have worked well for common variants have low power for the analysis of rare variants, mostly due to the large number of such variants in any genetic region and their low frequency counts in datasets of realistic sizes. Therefore development of powerful methods for rare variant analysis is greatly needed in order to efficiently extract information from the many sequencing datasets currently being generated. In this application we propose novel methods for both population- and family-based designs to identify rare genetic variants that influence risk to complex diseases, with particular application to autism. In particular, we focus on methods development in the following areas: family-based testing strategies for rare variants, unified testing strategies to efficiently combine family-base and population-based studies, and refinement strategies to identify causal rare variants once an overall association at a gene- or region-level has been established. We will implement the new methods in a comprehensive software package to be made available to the scientific community. Furthermore we will apply these methods to whole-exome data from 1000 autism cases, 1000 matched controls, and 500 autism trios. We believe the proposed research is very timely and has the potential to be of great public health importance through direct application to autism, and more broadly to other complex diseases. PUBLIC HEALTH RELEVANCE: Autism and other psychiatric diseases are major public health problems. The proposed statistical methodology with direct application to autism will help in the identification of genetic variants influencing autism risk, with important implications for public health.

描述（由申请人提供）：我们建议开发新的统计方法和软件工具，用于罕见变异的疾病关联测试，特别适用于自闭症。虽然全基因组关联研究已经发现了许多与各种复杂性状可重复相关的常见变异，但这些变异的效应量很小，总体上只能解释总估计性状遗传力的一小部分。新一代测序技术的最新进展首次允许客观评估复杂疾病中罕见变异的重要性。 在过去的几年里，许多实证研究已经清楚地表明，罕见变异是疾病风险的重要贡献者。这对于精神疾病，如精神分裂症和自闭症，特别引人注目，其中常见的疾病易感性变体更难以识别。传统的关联测试策略对常见变异的分析效果很好，但对罕见变异的分析能力很低，这主要是由于任何遗传区域中的大量此类变异以及它们在现实大小的数据集中的低频率计数。因此，非常需要开发用于罕见变异分析的强大方法，以便从当前生成的许多测序数据集中有效地提取信息。 在本申请中，我们提出了新的方法，用于基于群体和家族的设计，以识别影响复杂疾病风险的罕见遗传变异， 到自闭症。特别是，我们专注于以下领域的方法开发：以家族为基础的罕见变异的测试策略，统一的测试策略，以有效地结合联合收割机家族为基础和人口为基础的研究，和细化策略，以确定因果罕见变异一旦在基因或区域水平的整体关联已经建立。我们将在一个全面的软件包中实施这些新方法，供科学界使用。此外，我们将这些方法应用于1000例自闭症病例，1000例匹配对照和500例自闭症三人组的全外显子组数据。我们相信这项研究非常及时，并且有可能通过直接应用于自闭症以及更广泛的其他复杂疾病而具有重大的公共卫生重要性。 公共卫生相关性：自闭症和其他精神疾病是主要的公共卫生问题。拟议的统计方法与自闭症的直接应用将有助于识别影响自闭症风险的遗传变异，对公共卫生具有重要意义。