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）：关注常见变异的全基因组关联研究已经解释了许多复杂性状的一小部分遗传风险，但对于许多精神疾病，大部分遗传风险仍然未知。人们普遍认为，罕见变异也会增加疾病风险，我们和其他人已经发表了导致精神疾病的罕见变异的例子。现在，技术的进步使得使用外显子组测序以及我们设计的外显子组芯片生成专注于罕见变异的大型综合数据集成为可能。我们建议评估罕见变异对疾病遗传性的总体贡献，开发统计测试来定位这些对群体分层稳健的信号，并构建整个人类基因组的突变率图谱，用于分析从头突变和仅病例关联测试。我们将使用来自精神疾病数据集的超过 40,000 个样本来指导我们的研究。在具体目标 1 中，我们将量化归因于罕见变异的遗传力组成部分。最初针对复杂性状的外显子组测序研究在识别新的疾病基因方面取得的成功有限。这使得遗传学领域处于十字路口。是否应该在样本量非常大的测序研究上投入更多的资源，还是应该将重点转移到其他方法？我们将探讨这样的想法：即使当前的样本量不足以识别新基因，但它们也足以量化由罕见变异解释的遗传性组成部分。我们将开发新方法并将其应用于多个精神疾病数据集。这项工作将量化未来更大样本量测序研究的潜力，以识别新的疾病基因。在具体目标 2 中，我们将扩展稀有变异测试以解释群体分层。我们和其他人开发了针对多种罕见变体的统计测试，包括负担测试和过度分散测试。这些测试可以成功检测包含多个相关罕见变异的基因，但前提是样本量非常大。不幸的是，由于人群分层，大样本量增加了假阳性关联的危险。最近的研究显示了常见变异与罕见变异的种群结构不同模式，这凸显了使用常见变异信息应用标准方法的危险。我们将开发新方法来有效纠正罕见变异测试中的群体分层，并进行广泛的模拟以证明每种方法的有效性。在具体目标 3 中，我们将构建人类基因组突变率图。我们和其他人最近表明，从头突变筛选有可能识别神经精神表型感兴趣的基因。我们将根据比较基因组学和功能基因组学数据构建突变率图，并将开发新的统计方法来分析人类新生突变及其与精神疾病的关系。