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.

总结 基于人群的研究确定了影响复杂人类疾病的常见遗传变异， 在研究设计、质量控制和基因型等重要任务中， 归责随着绘图研究的重点现在已经转移到调查下一代中的罕见变异， 世代测序项目，新的机会存在，利用人口遗传学，以最大限度地提高 从这些调查中回来。因为迄今为止的研究往往集中在欧洲人口 因此，至关重要的是，新的方法提供了工具，以分析来自更大的人口多样性的数据。 该项目以第一个供资期的生产性努力为基础，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的罕见风险变异。(1)我们会设计 选择用于基因组和外显子组测序的个体子样品的方法， 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异的统计功效。(2)我们将提高变异识别的准确性， 特别是在低覆盖率数据中，以及对于具有挑战性的插入缺失和拷贝数变异， 从群体中密切相关的单倍型中积累的变异识别管道证据。这种方法 将特别有益于混合和遗传多样性的群体，其中单倍型变异是 特别重要的是，选择一个信息丰富的单倍型子集来帮助变异体识别是最重要的。 值(3)我们将使用群体遗传学原理来改善测序研究中的样本质量控制。 首先，我们解决了样品污染的共同挑战，这对变体识别产生不利影响， 下游分析。我们将产生一种方法来估计混合型的次要贡献者的基因型。 样品，从而能够识别污染信号的来源群体。该识别 进一步促进变体识别并允许混合样品的计算机解卷积。第二，加强 为了在大型项目中共享样本，我们将制定方法，从非 重叠标记集。我们的方法将减少花费精力获得序列的风险， 这将有助于充分利用未得到充分研究的人口的历史低密度数据。（四） 我们将结合人类人口增长和自然选择研究的新进展， 稀有变异测试和识别强大的测试策略。对现有工具的评估往往忽视了重要的 种群遗传因素，如选择或加速生长;我们的方法将增强模型， 分析罕见变异的检测方法，使其适合感兴趣的人群。在整个项目中，我们将 使用来自TopMed和InPSYght研究的多群体基因组序列数据来测试我们的方法。 为了便于使用我们的方法，我们将制作，测试和分发新的公开可用的软件程序。