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Statistical Methods for Genomic Analysis of Species Divergences

物种差异基因组分析的统计方法

基本信息

批准号：
BB/K000896/1
负责人：
Ziheng Yang
金额：
$ 42.57万
依托单位：
University College London
依托单位国家：
英国
项目类别：
Research Grant
财政年份：
2013
资助国家：
英国
起止时间：
2013 至无数据
项目状态：
已结题

来源：
https://gtr.ukri.org/projects?ref=BB%2FK000896%2F1
关键词：
Statistical Methods Genomic Analysis Species

项目摘要

Our evolutionary history is written in our genomes. By comparing DNA sequences from different species we can work out how the species are related. By comparing the DNA sequences of multiple individuals from the same species, we can estimate the population size and infer demographic changes (such as population bottleneck) of the species. Such studies fall into the domains of phylogenetics and population genetics. Genomic sequence data from multiple individuals of several closely related species allow powerful inference at the interface of phylogenetics and population genetics. One can use such data to estimate species divergence times and ancestral population sizes, accounting for lineage sorting, and to detect gene flow at the time speciation or to test different models of speciation Such data also allow delimitation of species (for example, to decide whether the sampled individuals belong to one or two species).To achieve those goals, powerful statistical methods and computational algorithms are necessary. In this project we will implement such methods within two well-established statistical frameworks: maximum likelihood and Bayesian inference. We will develop maximum likelihood methods for estimating migration rates between populations, and design likelihood ratio tests to test whether there is gene flow at the time of speciation (that is, whether speciation is clean). We will implement models that allow the migration rate to decrease over time since species divergence. Those methods will be useful for testing different speciation models such as allopatric and parapatric speciation. Computational difficulties will limit our likelihood methods to 2 or 3 sequences at each sampled locus. However the methods can accommodate a huge number of loci (indeed the whole genome), and with population data at some loci and species data at other loci, powerful inference is feasible. We will use computer simulations to examine the statistical properties of the new methods, and apply the methods to genomic datasets from the hominoids.We will introduce significant improvements and extensions to a Bayesian model-comparison approach to delimiting species using genomic sequence data. Published a year ago (Yang and Rannala 2010 Proc Natl Acad Sci USA 107:9264-9269), this method has attracted much attention among evolutionary biologists. This uses an algorithm called reversible-jump Markov chain Monto Carlo (rjMCMC) to sample different species-delimitation models, such as the one-species model (which assumes that all sampled individuals are from one single species) and the two species model (which assumes that the sampled individuals are from two distinct species). However, our current implementation in the computer program BPP has serious limitations and is inefficient in intermediate or large datasets. A major objective of this project is to improve the rjMCMC algorithm so that the program becomes feasible for analysis of large genomic-scale datasets. We will also parallelize the programs to improve the computational efficiency.

我们的进化史写在我们的基因组中。通过比较不同物种的DNA序列，我们可以弄清楚这些物种是如何相关的。通过比较来自同一物种的多个个体的DNA序列，我们可以估计该物种的种群规模，并推断该物种的人口统计学变化(如种群瓶颈)。这类研究属于系统发育学和种群遗传学领域。来自几个密切相关物种的多个个体的基因组序列数据允许在系统发育和种群遗传学的界面上进行强大的推断。人们可以使用这些数据来估计物种分化时间和祖先种群的大小，考虑到谱系排序，并在物种形成时检测基因流或测试不同的物种形成模型。这些数据还可以对物种进行划分(例如，确定采样的个体属于一个还是两个物种)。为了实现这些目标，需要强大的统计方法和计算算法。在这个项目中，我们将在两个成熟的统计框架内实现这些方法：最大似然法和贝叶斯推理。我们将开发估计种群间迁移率的最大似然方法，并设计似然比检验，以测试物种形成时是否存在基因流动(即物种形成是否干净)。我们将实施允许迁移率随着时间的推移而下降的模型，因为物种分化。这些方法将有助于测试不同的物种形成模式，如异地物种形成和近地物种形成。计算上的困难将限制我们的似然方法在每个采样轨迹上只有2到3个序列。然而，这些方法可以容纳大量的基因座(实际上是整个基因组)，并且利用一些基因座的种群数据和其他基因座的物种数据，强大的推断是可行的。我们将使用计算机模拟来检验新方法的统计特性，并将这些方法应用于来自人类的基因组数据集。我们将对使用基因组序列数据来界定物种的贝叶斯模型比较法进行重大改进和扩展。一年前发表的(Yang和Rannala 2010 Proc Natl Acad Sci USA 107：9264-9269)，这种方法引起了进化生物学家的极大关注。它使用一种称为可逆跳跃马尔可夫链蒙特卡罗(RjMCMC)的算法来采样不同的物种划分模型，例如单物种模型(假设所有采样的个体来自一个单一物种)和两个物种模型(假设采样的个体来自两个不同的物种)。然而，我们目前在计算机程序BPP中的实现具有严重的局限性，在中型或大型数据集上效率低下。这个项目的一个主要目标是改进rjMCMC算法，使该程序变得适用于大规模基因组数据集的分析。我们还将对程序进行并行化，以提高计算效率。