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，但这些变异的效应很小，总体上只能解释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使得首次对复杂疾病中罕见变异的重要性进行客观评估成为可能。在过去的几年里，从大量的实证研究中可以清楚地看出，罕见的变异是疾病风险的重要因素。这对精神疾病尤其有说服力，如精神分裂症和自闭症，在这些疾病中，常见的疾病易感性变异更难以识别。传统的关联测试策略对常见变异很有效，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都有大量此类变异，而且它们在实际规模的数据集中的频率计数很低。因此，为了有效地从当前生成的众多测序数据集中提取信息，开发强大的罕见变异分析方法是非常必要的。在本应用中，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遗传变异，具有特定的应用

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.