Statistical Methods for the Design and Interpretation of Deep Resequencing Studie

描述（由申请人提供）：生成序列数据的能力正在迅速成为现实。测序工作已经在全基因组关联研究（GWAS）确定的关联峰周围的候选基因区域进行，为“全外显子组”和最终的全基因组测序研究铺平了道路。综合测序有可能揭示大量的低频变异，但用于GWAS的大多数统计关联方法可能是不够的，因为它们针对的是常见的变异，并且已经优化用于一次识别单个变异的关联，因此，不能解释作用于同一基因座的多个变异。为了使测序研究充分发挥其潜力，开发新的统计方法至关重要。我们建议为靶向和全基因组测序方法开发新方法。在具体目标1中，我们将开发用于识别靶区域内的因果变异的统计方法，例如GWAS峰或候选基因。DNA测序提供了遗传变异的全貌，使得能够定位关联信号，以便在由于连锁不平衡而导致的相关变体的背景下鉴定真正的致病等位基因。我们将设计统计策略来寻找关联峰背后的因果变异。我们将考虑在一个基因座上存在多个致病等位基因。在具体目标2中，我们将开发用于测序研究的统计方法，以最佳地捕获由同一疾病基因内作用的多个罕见变体产生的关联信号。最初的重点将是候选基因测序，着眼于全外显子组甚至全基因组测序。单个罕见等位基因与疾病的关联很难检测，因为低频等位基因在单变异关联测试中的能力有限。我们将开发结合来自同一基因（或途径）的多个罕见变异的方法，并将基因（途径）而不是单个等位基因作为关联检验的单位。最近的研究表明，某些数量表型的基因在一个表型极端的个体中显示出过量的罕见编码变异。除了在一次测试中结合多种罕见变异外，我们还将开发同时融合罕见和常见变异的方法，这在全基因组测序最终变得实用时将非常重要。在具体目标3中，我们将评估靶向和全基因组方法的功效，并使用基于经验测序数据集等位基因频率分布的群体遗传模型生成研究设计建议。我们将对测序策略、样本量和特定人群的纳入提出建议。所有功效计算和建议将严格依赖于关于等位基因频率分布的假设，我们将使用经验序列数据对其进行严格建模。我们的群体遗传模型将包括复杂的人口历史，重组和自然选择，除了突变和遗传漂变。 研究叙述：对人类遗传变异的研究已经开始带来巨大的回报，因为专注于常见遗传变异的全基因组关联研究（GWAS）已经确定了许多复杂疾病的风险变异。然而，对于大多数疾病来说，这些发现所解释的遗传可遗传性的比例非常小，这激发了深入的重测序研究，这将能够识别罕见的风险变体。这些重测序研究将需要新的统计方法，这将有很大的潜力，进一步了解疾病的病因，导致可能的药物靶点，也可能是有用的诊断测试在健康个体。

DESCRIPTION (provided by applicant): The ability to generate sequence data is rapidly becoming a reality. Sequencing efforts are already underway at candidate gene regions surrounding association peaks identified by genome-wide association studies (GWAS), paving the way for "whole-exome" and, ultimately, whole-genome sequencing studies. Comprehensive sequencing has the potential to reveal a vast trove of low frequency variants, but most statistical association methods used for GWAS are likely inadequate because they are targeted towards common variants and have been optimized for identifying associations at a single variant at a time, and therefore, do not account for multiple variants acting at the same locus. For sequencing studies to attain their full potential, the development of new statistical methods will be critical. We propose to develop new methods for both targeted and genome-wide sequencing approaches. In Specific Aim 1 we will develop statistical methods for identifying causal variants inside a targeted region, such as a GWAS peak or candidate gene. DNA sequencing provides a complete picture of genetic variation, enabling the localization of association signal(s) in order to identify true causal alleles against a background of correlated variants due to linkage disequilibrium. We will design statistical strategies for finding causal variants underlying association peaks. We will consider the presence of multiple causal alleles at a locus. In Specific Aim 2 we will develop statistical methods for sequencing studies to optimally capture the association signal arising from multiple rare variants acting within the same disease gene. The initial focus will be on candidate gene sequencing with an eye towards whole-exome and even whole-genome sequencing. Associations of individual rare alleles with disease are difficult to detect because low-frequency alleles have limited power in single-variant association tests. We will develop methods combining multiple rare variants from the same gene (or pathway) and treat genes (pathways) rather than individual alleles as the unit for the association test. Recent studies demonstrate that genes underlying certain quantitative phenotypes display an excess of rare coding variation in individuals at one phenotypic extreme. In addition to combining multiple rare variants in a single test, we will also develop methods incorporating both rare and common variants, which will be important when whole- genome sequencing eventually becomes practical. In Specific Aim 3 we will assess the power of both targeted and genome-wide approaches and generate study design recommendations, using a population genetic model based on allele frequency distributions from empirical sequencing data sets. We will make recommendations on sequencing strategies, sample sizes, and inclusion of specific populations. All power calculations and recommendations will critically depend on assumptions about allele frequency distributions, which we will rigorously model using empirical sequence data. Our population genetic model will incorporate complex demographic histories, recombination and natural selection in addition to mutation and genetic drift. RESEARCH NARRATIVE: The study of human genetic variation has already begun to pay big dividends, as genome- wide association studies (GWAS) focusing on common genetic variation has identified risk variants for numerous complex diseases. However, for most diseases the fraction of genetic heritability explained by these findings is extremely small, motivating deep resequencing studies, which will be able to identify rare risk variants. These resequencing studies will require new statistical methods that will have great potential for furthering our understanding of disease etiology, leading to possible drug targets, and may also be useful for diagnostic testing in healthy individuals.