Statistical Methods and Algorithms for Population Genomic Inference

项目摘要/摘要 系统发育学是我们理解生物学的基础，并在人类的许多领域具有翻译应用 健康包括流行病学、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史的信息，包括物种PHY-PHY。 亲缘关系和分化时间，种群人口统计，以及可能的杂交或混杂事件。怎么- 提取这些信息需要先进的概率模型以及有效的统计和计算 方法：研究方法。这是因为种群遗传过程是随机的，而来自密切相关物种的序列是随机的 高度相似，仅包含有关某些参数的弱历史信息。因此，至关重要的是， 开发参数统计方法，最大限度地利用从数据中提取的信息。在这个项目中，我们的目标是 为了开发有效的fi贝叶斯计算方法来分析多物种下的基因组规模数据集- 合并-导入(MSCI)模型。 拟议的研究将在BPP程序中开发和实施新的算法和统计方法 推断物种间渐渗事件的数量、方向、时间和强度(目标1)。这个 然后，程序将自然地适应模型中的深度融合和引入。这也将是 允许开发一种新的贝叶斯方法来推断特定基因座(基因组区域)的概率 是从二倍体个体的每个序列的特定物种混合事件中引入的(目标2)。这 这个问题具有广泛的关联性，并且一直是关于原始人混合体的一个非常感兴趣的主题。另一个 有用的扩展将是使用EffiEncient New在成对的种群之间增加正在进行的迁移 移民模型制定(目标3)。该方法将提供迁移率的参数估计， 尤其适用于在野生种群中设计安全的CRISPR基因驱动实验。物种的范围 BPP程序可以应用的范围将通过纳入参数更丰富的DNA模型来扩展 替换(GTR+G)，更好地适应每个站点的多个替换，是分析更多内容所必需的 远亲物种。此外，我们将允许化石校准和轻松的分子钟(包括 我们的另一个发散时间估计程序的特点(MCMCtree到BPP)(目标4)。化石定标 将允许以年为单位来估计分歧时间，而不是预期的DNA替换。要拓宽 程序的可访问性对于没有命令行编程经验的用户来说，我们将进一步开发一个交叉的 BPP平台图形用户界面(BPPg)，使用现代的Java脚本框架(Aim 5)。最后，统计性能 将通过模拟和分析来研究该方法并将其与其他方法(如果存在)进行比较 范例数据集(目标6)。

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).