Population genetics for large-scale sequencing studies of diverse populations

摘要 基于人群的研究确定了影响复杂人类疾病的常见基因变异，这依赖于 在研究设计、质量控制和基因分型等重要任务中高度依赖群体遗传学原理 推卸责任。随着作图研究的重点现在转移到研究下一代的稀有变异- 世代测序项目，存在着利用群体遗传学最大化 从这些调查中归来。因为到目前为止，研究往往集中在欧洲的人口 对于人类的血统而言，至关重要的是，新的方法必须提供工具来分析来自更多不同人群的数据。 这个项目建立在第一个资助期富有成效的努力的基础上，提出了利用 研究人类群体遗传学以加强基因组测序的设计、分析和解释 研究，并侧重于分析不同人群中罕见的风险变异。(1)我们会设计 选择用于基因组和外显子组测序的个体亚样的方法，特别是在混合测序中 和结构化的人口。这样的子样本将使研究人员有可能最大限度地发挥其潜力 以获得检测罕见疾病变种的统计能力。(2)提高变量调用的准确性， 特别是在低覆盖率的数据中，并通过在 从群体中密切相关的单倍型中积累的变异呼唤管道证据。这种方法 将在混合和遗传多样性的群体中特别有益，在这些群体中，单倍型变异是 特别重要的是，选择一个信息丰富的单倍型子集来辅助变异呼叫是最有意义的 价值。(3)在测序研究中，我们将利用群体遗传学原理来改进样本质量控制。 首先，我们解决了样本污染的共同挑战，这对变量调用和 下游分析。我们将提出一种方法来估计混合遗传病的次要贡献者的基因类型 样品，从而能够识别污染信号的起源群体。这一身份证明 进一步促进了变体调用，并允许混合样品的电子解卷积。第二，加强 在大型项目中共享样本，我们将制定方法，以发现来自非 重叠的标记集。我们的方法将减少花费努力来获得不会的序列的风险 将得到充分利用，还将协助利用研究不足人口中的历史低密度数据。(4) 我们将纳入人口增长和自然选择研究的新进展，以进行评估 罕见的变种测试，并确定强大的测试策略。对当前工具的评估往往忽略了重要的 人口遗传因素，如选择或加速生长；我们的方法将增强 分析罕见的变异检测方法，使其适合感兴趣的人群。在整个项目中，我们将 使用TopMed和InPSYight研究中的多种群基因组序列数据来测试我们的方法。 为了方便使用我们的方法，我们将生产、测试和分发新的公开可用的软件程序。

Summary Population-based studies identifying common genetic variants that affect complex human diseases have relied heavily on population-genetic principles in important tasks such as study design, quality control, and genotype imputation. As the emphasis of mapping studies has now shifted to investigating rare variants in next- generation sequencing projects, new opportunities exist for leveraging population genetics to maximize the return from these investigations. Because studies thus far have often focused on populations of European descent, it is critical that new methods provide tools to analyze data from a greater diversity of populations. This project builds on productive efforts in the first funding period, proposing methods that capitalize on the study of human population genetics to enhance the design, analysis, and interpretation of genome sequencing studies, and focusing on analysis of rare risk variants in diverse human populations. (1) We will devise methods for selecting subsamples of individuals for genome and exome sequencing, particularly in admixed and structured populations. Such subsamples will make it possible for researchers to maximize their potential for achieving statistical power to detect rare disease variants. (2) We will enhance variant-calling accuracy, particularly in low-coverage data and for challenging indels and copy-number variants, by including in the variant-calling pipeline evidence accumulated from closely related haplotypes in the population. This approach will be particularly beneficial in admixed and genetically diverse populations, in which haplotype variation is especially significant and selecting an informative haplotype subset to assist in variant-calling is of greatest value. (3) We will use population-genetic principles to improve sample quality control in sequencing studies. First, we address the common challenge of sample contamination, which adversely affects variant-calling and downstream analyses. We will produce a method to estimate the genotypes of the minor contributor of a mixed sample, thus enabling the population of origin of a contaminating signal to be identified. This identification further facilitates variant-calling and permits in silico deconvolution of mixed samples. Second, to enhance the sharing of samples in large projects, we will devise methods to uncover duplicate or related samples from non- overlapping marker sets. Our approach will reduce the risk of expending effort to obtain sequence that will not be fully utilized, and will also assist in making use of historical low-density data in understudied populations. (4) We will incorporate new advances in the study of human population growth and natural selection for evaluating rare-variant tests and identifying powerful testing strategies. Evaluations of current tools often ignore important population-genetic factors such as selection or accelerating growth; our methods will enhance models for analyzing rare-variant testing methods, tailoring them to populations of interest. Throughout the project, we will use multi-population genome sequence data from the TopMed and InPSYght studies to test our approaches. To facilitate use of our methods, we will produce, test, and distribute new publicly available software programs.