Population genetics for large-scale sequencing studies of diverse populations

用于不同人群大规模测序研究的群体遗传学

• 批准号: 10063406
• 负责人: Noah Rosenberg
• 金额: $ 13.69万
• 依托单位: STANFORD UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2010
• 资助国家: 美国
• 起止时间: 2010-09-13 至 2022-06-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10063406
• 关键词: Achievement; Address; Affect; Algorithms; Alleles; Biological Assay; Collaborations; Complex; Computer software; Copy Number Polymorphism; Data; Data Set; Disease; Europe; European; Evaluation; Explosion; Frequencies; Funding; Gene Frequency; Genealogy; Genetic; Genetic Markers; Genetic Models; Genetic Phenomena; Genetic Polymorphism; Genetic Processes; Genetic Variation; Genome; Genotype; Growth; Haplotypes; Human; Individual; Investigation; Knowledge; Label; Large-Scale Sequencing; Linkage Disequilibrium; Methods; Minor; Modeling; Natural Selections; North America; Pattern; Phase; Phenotype; Play; Population; Population Genetics; Population Growth; Population Heterogeneity; Population Study; Quality Control; Rare Diseases; Recording of previous events; Research Design; Research Personnel; Risk; Role; Sample Size; Sampling; Sampling Studies; Signal Transduction; Site; Structural Models; Structure; Technology; Testing; Trans-Omics for Precision Medicine; Underrepresented Populations; Variant; case control; data management; data quality; density; design; disease phenotype; disorder risk; exome; exome sequencing; genetic variant; genome sequencing; genome wide association study; genome-wide; genome-wide analysis; genomic data; human disease; human population genetics; human population study; improved; in silico; information model; insertion/deletion mutation; interest; method development; next generation sequencing; novel strategies; patient population; programs; rapid detection; rare variant; risk variant; tool; whole genome

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.

概括 基于人群的研究确定影响复杂人类疾病的常见遗传变异已依赖 在研究设计，质量控制和基因型等重要任务中的种群基因原理严重掌握了 插补。随着映射研究的重点已经转移到研究了接下来的稀有变体 生成测序项目，利用种群遗传学来最大化的新机会 从这些调查中返回。因为到目前为止的研究经常集中在欧洲的人群上 下降，至关重要的是，新方法提供分析来自更多人群多样性的数据的工具。 该项目以第一个资金期间的生产努力为基础，提出了利用的方法 研究人群遗传学以增强基因组测序的设计，分析和解释 研究，并专注于分析各种人群中稀有风险变异的。 （1）我们将设计 选择个体的基因组和外显子组测序子类样本的方法，尤其是在混合中 和结构化人群。这样的子样本将使研究人员有可能最大化其潜力 为了实现统计能力检测罕见疾病变体。 （2）我们将提高变种准确性， 特别是在低覆盖数据以及挑战性的indels和copy-number变体中 从人群中密切相关的单倍型积累的变异呼叫管道证据。这种方法 将在混合和遗传多样化的种群中特别有益，其中单倍型变异是 特别重要的 价值。 （3）我们将使用种群遗传原理来改善测序研究中的样本质量控制。 首先，我们解决样本污染的共同挑战，这对变异呼叫和 下游分析。我们将产生一种估计混合的次要贡献者的基因型的方法 样本，从而使污染信号的起源人群得以识别。此标识 进一步促进了混合样品的变异呼叫和许可。第二，以增强 在大型项目中共享样品，我们将设计方法，以发现非 - 重叠标记集。我们的方法将降低花费努力以获取序列的风险 被充分利用，还将有助于利用研究研究的历史低密度数据。 （4） 我们将在研究人口增长和自然选择的研究中纳入新的进步以评估 稀有变化的测试并确定强大的测试策略。对当前工具的评估通常忽略重要 种群遗传因素，例如选择或加速增长；我们的方法将增强模型 分析稀有变化的测试方法，将其定制为感兴趣的种群。在整个项目中，我们将 使用来自顶级和INPSYGHT研究的多人群基因组序列数据来测试我们的方法。 为了促进我们的方法的使用，我们将生产，测试和分发新的公开软件程序。