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: 10087945
• 负责人: Bruce RANNALA
• 金额: $ 50.31万
• 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/10087945
• 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).

项目总结/摘要 系统发育是我们理解生物学的基础，并在人类的许多领域都有翻译应用。 包括流行病学、癌症生物学和免疫学。近缘物种的基因组序列 例如类人猿，包含了大量关于它们进化历史的信息，包括物种phy- logeny和分歧的时间，人口统计学，和可能发生的杂交或混合。怎么-- 然而，提取这些信息需要先进的概率模型和有效的统计和计算能力。 方法.这是因为种群遗传过程是随机的，来自密切相关物种的序列是随机的。 高度相似，仅包含关于某些参数的弱历史信息。因此，至关重要的是， 开发参数统计方法，使从数据中提取的信息最大化。在这个项目中，我们的目标 开发有效的贝叶斯计算方法，用于分析多物种下的基因组规模数据集， 聚结渐渗（MSci）模型。 拟议的研究将开发和实施新的算法和统计方法的程序bpp 推断物种间渐渗事件的数量、方向、时间和强度（目的1）。的 然后程序将自然地适应模型中的深度聚结和渐渗。这也将 允许开发一种新的贝叶斯方法，用于推断特定基因座（基因组区域） 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 这个问题具有广泛的相关性，并且一直是关于原始人混合物的强烈兴趣的主题。另一 有用的扩展将是使用一个有效的新方法， 迁移模型制定（目标3）。该方法将提供迁移率的参数估计， 这对于在野生种群中设计安全的CRISPR基因驱动实验尤其相关。种群范围 bpp程序可以应用的领域将通过引入更多参数丰富的DNA模型来扩展 取代（GTR+G），更好地适应每个位点的多个取代，是分析更多 远亲物种。此外，我们将允许化石校准和宽松的分子钟（包括 我们的另一个程序的特点，用于发散时间估计MCMCMCtree到bpp）（目标4）。化石校准 将允许以年为单位估计分歧时间，而不是预期的DNA替换。拓宽 无障碍程序的用户没有命令行程序的经验，我们将进一步开发一个跨- 使用现代JavaScript框架的bpp（BPPg）平台GUI（Aim 5）。最后，统计性能 的方法将进行研究，并与其他方法（当它们存在）的模拟和分析， 范例数据集（目标6）。