Statistical Methods for the Design and Interpretation of Deep Resequencing Studie

描述(申请人提供)：生成序列数据的能力正在迅速成为现实。全基因组关联研究(GWAS)确定的关联峰周围的候选基因区域的测序工作已经在进行中，为“全外显子组”以及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，但大多数用于GWAs的统计关联方法可能不够充分，因为它们针对共同的变异，并且已经被优化，以便一次识别单个变异的关联，因此，没有考虑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，开发新的统计方法将是至关重要的。我们建议为定向测序和全基因组测序方法开发新的方法。在具体目标1中，我们将开发统计方法来识别目标区域内的因果变异，例如GWA峰或候选基因。DNA测序提供了遗传变异的完整图景，使得能够定位关联信号(S)，以便根据连锁不平衡引起的相关变异的背景来识别真正的因果等位基因。我们将设计统计策略，寻找关联峰值背后的因果变量。我们将考虑在一个基因座上存在多个因果等位基因。在特定的目标2中，我们将开发用于测序研究的统计方法，以最佳地捕获来自同一疾病基因内作用的多个稀有变种的关联信号。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。个别稀有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量关联测试中的作用有限。我们将开发将同一基因(或途径)的多种罕见变异组合在一起的方法，并将基因(途径)而不是单个等位基因作为关联测试的单位。最近的研究表明，某些数量表型背后的基因在一个表型极端的个体中表现出过多的罕见编码变异。除了在一次测试中结合多种稀有变异外，我们还将开发同时融合稀有和常见变异的方法，这在全基因组测序最终变得实用时将是重要的。在具体目标3中，我们将评估靶向和全基因组方法的力量，并使用基于经验测序数据集的等位基因频率分布的群体遗传模型来生成研究设计建议。我们将就测序策略、样本大小和纳入特定人群提出建议。所有功率计算和建议都将严重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。我们的种群遗传模型将包括复杂的人口历史、重组和自然选择，以及突变和遗传漂移。 研究说明：人类遗传变异的研究已经开始带来巨大的红利，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，这些发现解释的遗传遗传性的比例非常小，这促使了深入的重新测序研究，这将能够识别罕见的风险变异。这些重新测序研究将需要新的统计方法，这些方法将具有巨大的潜力来进一步了解疾病病因，导致可能的药物靶点，并可能对健康个体的诊断测试有用。

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.