Statistical methods for studies of rare variants

研究罕见变异的统计方法

• 批准号: 9116300
• 负责人: SHAMIL SUNYAEV
• 金额: $ 45.2万
• 依托单位: BRIGHAM AND WOMEN'S HOSPITAL
• 依托单位国家: 美国
• 财政年份: 2013
• 资助国家: 美国
• 起止时间: 2013-08-15 至 2017-05-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9116300
• 关键词: Accounting; Address; Area; Biological; Collaborations; Communities; Complex; Computer software; DNA; DNA Sequence; Data; Data Set; Disease; Ensure; Evaluation; Funding; Future; Genes; Genetic; Genetic study; Genome; Grant; Health; Heritability; Human; Human Genetics; Human Genome; Individual; Left; Maps; Mental disorders; Methods; Mutation; Paper; Pattern; Phenotype; Population; Problem Solving; Publications; Publishing; Research; Resources; Risk; Role; Sample Size; Sampling; Signal Transduction; Statistical Methods; Stratification; Structure; Technology; Testing; Therapeutic Intervention; United States National Institutes of Health; Variant; Work; base; comparative genomics; design; disorder risk; exome; exome sequencing; functional genomics; genome wide association study; genomic data; interest; mutation screening; neuropsychiatry; programs; rare variant; risk variant; simulation; success; targeted treatment; trait

DESCRIPTION (provided by applicant): Genome-wide association studies focusing on common variants have explained a fraction of the heritable risk for many complex traits, but for many psychiatric diseases, the majority of heritable risk remains unknown. It is widely believed that rare variants also contribute to disease risk, and we and others have published examples of rare variants that contribute to psychiatric disease. Improvements in technology have now made it possible to generate large comprehensive data sets focusing on rare variants, using exome sequencing as well as the exome chip that we designed. We propose to assess the overall contribution of rare variants to disease heritability, develop statistical tests to localize these signals that are robust to population stratification, and build a map of mutation rates across the human genome for application to analysis of de novo mutations and case-only association tests. We will guide our research using >40,000 samples from psychiatric disease data sets. In Specific Aim 1 we will quantify components of heritability attributable to rare variants. Initial exome sequencing studies in complex traits have had limited success in identifying new disease genes. This leaves the field of genetics at a crossroads. Should even greater resources be invested in sequencing studies with very large sample sizes, or should the focus shift to other approaches? We will explore the idea that even if current sample sizes are not large enough to identify new genes, they are large enough to quantify the components of heritability explained by rare variants. We will develop new methods and apply them to several psychiatric disease data sets. This work will quantify the potential of future sequencing studies in larger sample sizes to identify new disease genes. In Specific Aim 2 we will extend rare variant tests to account for population stratification. We and others have developed statistical tests for multiple rare variants, including both burden and over-dispersion tests. These tests can succeed in detecting genes containing multiple associated rare variants, but only if sample sizes are very large. Unfortunately, large sample sizes increase the dangers of false-positive associations due to population stratification. Recent work showing differing patterns of population structure in common versus rare variants highlights the dangers of applying standard approaches using information from common variants. We will develop new methods to effectively correct for population stratification in rare variant tests and perform extensive simulations to demonstrate the efficacy of each approach. In Specific Aim 3 we will build a map of mutation rates across the human genome. We and others have recently shown that de novo mutation screens have a potential to identify genes of interest for neuropsychiatric phenotypes. We will construct a mutation rate map informed by comparative genomics and functional genomics data and will develop new statistical approaches for the analysis of human de novo mutations and their involvement in psychiatric diseases.

描述（由申请人提供）：针对常见变体的全基因组协会研究解释了许多复杂特征的遗传风险的一部分，但是对于许多精神病患者来说，大多数可遗传的风险仍然未知。人们普遍认为，罕见的变体也会导致疾病风险，我们和其他人也发表了有助于精神病的稀有变体的例子。技术的改进已经使使用我们设计的外显子组测序以及我们设计的外显子芯片生成大量综合数据集，重点关注稀有变体。我们建议评估稀有变体对疾病遗传力的总体贡献，开发统计测试以定位这些信号对人群分层的稳健信号，并在人类基因组中建立突变率图，以应用于从头突变的分析和仅病例的关联测试。我们将使用精神病数据集中的40,000个样本来指导我们的研究。在特定目标1中，我们将量化可归因于稀有变体的遗传力的组成部分。在复杂性状中的最初外显子组测序研究在鉴定新疾病基因方面取得了有限的成功。这使遗传学领域在十字路口。是否应该将更多的资源投资于样本量非常大的测序研究中，还是重点应该转向其他方法？我们将探讨以下想法：即使当前的样本量不足以识别新基因，它们也足够大，可以量化稀有变体所解释的遗传力的组成部分。我们将开发新方法，并将其应用于几个精神病疾病数据集。这项工作将量化较大样本量的未来测序研究以鉴定新疾病基因的潜力。在特定目标2中，我们将扩展稀有变体测试以说明人口分层。我们和其他人已经开发了多种稀有变体的统计测试，包括负担和过度分散测试。这些测试可以成功地检测包含多种相关稀有变体的基因，但前提是样本量非常大。不幸的是，大型样本量增加了由于人口分层而导致的假阳性关联的危险。最近的工作显示了共同的种群结构的不同模式与稀有变体的不同模式突出了使用常见变体信息应用标准方法的危险。我们将开发新的方法，以有效地纠正稀有变体测试中的种群分层，并进行广泛的模拟以证明每种方法的功效。在特定目标3中，我们将在人类基因组中建立突变率图。我们和其他人最近表明，从头突变筛选具有鉴定神经精神型表型感兴趣的基因的潜力。我们将构建一个通过比较基因组学和功能基因组学数据告知的突变率图，并将开发新的统计方法来分析人类从头突变及其参与精神病疾病。