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

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。尽管全基因组关联研究导致了许多与各种复杂性状可重复相关的常见变异的发现，但这些变异的效应大小很小，总体上只解释了总估计的性状遗传力的一小部分。新一代测序技术的最新进展首次使人们能够客观评估罕见变异在复杂疾病中的重要性。在过去的几年里，从大量的实证研究中可以清楚地看到，罕见的变异是疾病风险的一个重要因素。对于精神分裂症和自闭症等精神疾病来说，这一点尤其令人信服，因为这些疾病的常见易感变异更难识别。对常见变异有效的传统关联测试策略对稀有变异的分析能力较低，主要是因为在任何遗传区域中都有大量这样的变异，而且它们在实际大小的数据集中的频率很低。因此，为了有效地从目前产生的许多测序数据集中提取信息，迫切需要开发出用于稀有变异分析的强大方法。在这项应用中，我们提出了基于群体和基于家庭的设计的新方法，以识别影响复杂疾病风险的罕见基因变异，并具有特定的应用 自闭症。特别是，我们专注于以下领域的方法开发：基于家族的稀有变异检测策略，有效结合基于家族和基于人群的研究的统一检测策略，以及一旦在基因或区域水平上建立总体关联后识别因果稀有变异的改进策略。我们将在一个向科学界提供的综合软件包中实施这些新方法。此外，我们将把这些方法应用于来自1000个自闭症病例、1000个匹配对照和500个自闭症三组的完整外显子组数据。我们相信，这项拟议的研究非常及时，通过直接应用于自闭症以及更广泛的其他复杂疾病，有可能对公共卫生产生重大影响。 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。拟议的统计方法直接应用于自闭症，将有助于识别影响自闭症风险的基因变异，对公共卫生具有重要影响。

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.