Statistical methods for studies of rare variants

描述（由申请人提供）：专注于常见变异的全基因组关联研究已经解释了许多复杂特征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，技术的进步使得利用外显子组测序和我们设计的外显子组芯片，可以生成专注于罕见变异的大型综合数据集。我们建议评估罕见变异对疾病遗传力的总体贡献，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，用于分析新生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40000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的组成部分。复杂性状的初步外显子组测序研究在鉴定新的疾病基因方面取得了有限的成功。这使得遗传学领域处于一个十字路口。应该将更多的资源投入到样本量非常大的测序研究中，还是应该将重点转移到其他方法上？我们将探讨这样一种观点，即即使目前的样本量不足以识别新基因，但它们足以量化由罕见变异解释的遗传性的组成部分。我们将开发新的方法，并将其应用于几个精神疾病数据集。这项工作将量化未来更大样本量测序研究的潜力，以确定新的疾病基因。在具体目标2中，我们将扩展罕见变异测试以解释人口分层。我们和其他人已经开发了多种罕见变异的统计测试，包括负担测试和过度分散测试。这些测试可以成功地检测出含有多种相关罕见变异的基因，但前提是样本量非常大。不幸的是，由于人口分层，大样本量增加了假阳性关联的危险。最近的研究表明，在常见变异和罕见变异中，不同的种群结构模式突出了使用来自常见变异的信息的标准方法的危险。我们将开发新的方法来有效地纠正罕见变异测试中的人口分层，并进行广泛的模拟来证明每种方法的有效性。在Specific Aim 3中，我们将构建整个人类基因组的突变率图。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。我们将构建一个由比较基因组学和功能基因组学数据提供信息的突变率图，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。

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.