Statistical Methods and Algorithms for Population Genomic Inference

群体基因组推断的统计方法和算法

• 批准号: 9886109
• 负责人: Bruce RANNALA
• 金额: $ 29.66万
• 依托单位: UNIVERSITY OF CALIFORNIA AT DAVIS
• 依托单位国家: 美国
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-02-01 至 2024-01-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9886109
• 关键词: Address; Admixture; Algorithms; Animals; Area; Bayesian Analysis; Bayesian Method; Biology; CRISPR gene drive; Calibration; Cancer Biology; Computational algorithm; Computing Methodologies; Conflict (Psychology); DNA; Data; Data Set; Demography; Development; Diploidy; Distant; Electrons; Epidemiology; Event; Formulation; Fossils; Genealogical Tree; Genetic; Genome; Genomic Segment; Genomics; Graph; Health; Hominidae; Human; Individual; Light; Methods; Modeling; Modernization; Performance; Phylogeny; Plants; Play; Pongidae; Population; Population Genetics; Probability; Process; Program Accessibility; Recording of previous events; Research; Risk; Role; Sampling; Site; Sorting - Cell Movement; Statistical Algorithm; Statistical Methods; Statistical Models; Statistical Study; Stochastic Processes; Time; Trees; algorithmic methodologies; design; experience; experimental study; gene drive system; genome analysis; genome-wide; genomic data; human disease; interest; migration; molecular clock; novel; programs; simulation; tumor immunology

Project Summary/Abstract Phylogeny is fundamental to our understanding of biology and has translational applications to many areas of human health including epidemiology, cancer biology and immunology. Genome sequences from closely related species such as the great apes contain a wealth of information about their evolutionary history, including the species phy- logeny and divergence times, population demography, and possible episodes of hybridization or admixture. How- ever, extracting this information requires advanced probability models and efficient statistical and computational methods. This is because population genetic processes are stochastic and sequences from closely related species are highly similar containing only weak historical information about some parameters. For this reason, it is critical to develop parametric statistical methods that maximize the information extracted from the data. In this project we aim to develop efficient Bayesian computational methods for analysis of genome-scale datasets under the multispecies- coalescent-with-introgression (MSci) model. The proposed research will develop and implement novel algorithms and statistical methods in the program bpp to infer the number, the directions, timings, and intensity of introgression events between species (Aim 1). The program will then accommodate naturally both deep coalescence and introgression in the model. This will also allow a novel Bayesian method to be developed for inferring the probability that particular loci (genomic regions) are introgressed from a particular species admixture event for each sequence of a diploid individual (Aim 2). This question is of broad relevance and has been a subject of intense interest with respect to hominid admixtures. Another useful extension will be the addition of ongoing migration between pairs of populations using an efficient new migration model formulation (Aim 3). The method will provide parameter estimates of migration rates that are particularly relevant for designing safe CRISPR gene drive experiments in wild populations. The range of species that the bpp program can be applied to will be expanded by incorporating a more parameter rich model of DNA substitution (GTR+G) that better accommodates multiple substitutions per site and is necessary for analyzing more distantly related species. Moreover, we will allow fossil calibrations and a relaxed molecular clock (incorporating the features of our other program for divergence time estimation MCMCtree into bpp)(Aim 4). Fossil calibrations will allow estimates of divergence times in units of years rather than expected DNA substitutions. To broaden the accessibility of the program to users without command line program experience we will further develop a cross- platform GUI for bpp (BPPg) using a modern Javascript framework (Aim 5). Finally, the statistical performance of the method will be studied and compared to other methods (when they exist) by simulations and by analysis of paradigmatic datasets (Aim 6).

项目摘要/摘要 系统发育是我们对生物学的理解至关重要的，并且对人类的许多领域都有翻译应用 健康，包括流行病学，癌症生物学和免疫学。来自密切相关物种的基因组序列 例如，大猿类包含有关其进化史的大量信息，包括物种Phy- 如何- 曾经，提取此信息需要高级概率模型以及有效的统计和计算 方法。这是因为种群遗传过程是随机的，并且来自密切相关的物种的序列是 高度相似，仅包含有关某些参数的弱历史信息。因此，至关重要 开发从数据中提取的信息最大化的参数统计方法。在这个项目中，我们的目标 为了开发有效的贝叶斯计算方法，用于分析在多种情况下基因组规模数据集 结合内部结合（MSCI）模型。 拟议的研究将在程序BPP中开发和实施新的算法和统计方法 推断物种之间渗入事件的数量，方向，时机和强度（AIM 1）。 然后，程序将在模型中自然地适应深层合并和渗入。这也会 允许开发一种新型的贝叶斯方法，以推断特定局部（基因组区域）的概率 从二倍体个体的每个序列的特定物种混合事件中渗入（AIM 2）。这 问题具有广泛的相关性，并且对人类混合物具有强烈的兴趣。其他 有用的扩展将是使用有效的新的新人群之间持续的迁移 迁移模型公式（AIM 3）。该方法将提供迁移率的参数估计值 与在野生人群中设计安全的CRISPR基因驱动实验特别相关。物种范围 可以通过合并更多参数的DNA模型来扩展BPP程序将扩展到 替换（gtr+g）每个站点的多次替代品，对于分析更多的替换是必要的 遥远的物种。此外，我们将允许化石校准和放松的分子时钟（合并） 我们的另一个针对BPP的分歧时间估计的程序的功能（AIM 4）。化石校准 将允许在几年内而不是预期的DNA取代的单位中估计分歧时间。扩大 该计划向没有命令行计划经验的用户访问性，我们将进一步开发一个交叉 使用现代JavaScript框架（AIM 5）的BPP平台GUI（BPPG）。最后，统计表现 该方法的研究将进行研究，并通过模拟和通过分析 范式数据集（AIM 6）。