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ABI Innovation: Posterior Predictive Checks of Evolutionary Models.

ABI 创新：进化模型的后验预测检查。

基本信息

批准号：
1661029
负责人：
Bryan Carstens
金额：
$ 40.36万
依托单位：
Ohio State University
依托单位国家：
美国
项目类别：
Standard Grant
财政年份：
2017
资助国家：
美国
起止时间：
2017-05-15 至 2021-09-30
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=1661029&HistoricalAwards=false
关键词：
ABI Innovation Posterior Predictive Checks

项目摘要

With support from the Advances in Biological Informatics in the Division of Biological Infrastructure, Professor Bryan Carstens and his research group at the Ohio State University will further develop the P2M2 software package, so that it can better estimate how multiple events may shape population genetics. Most of the differences among living organisms ultimately can be traced to genetic variation, including humans. Patterns of genetic differences in a species appear across different groups, due to events such as changes in population size, migration between populations, or long periods of isolation. Understanding, for example, how beneficial mutations can be established in a natural population of a certain size that has migration limits is important for understanding aspects of human health, conservation genetics, breeding in agriculture and many other research areas. Recent technological advances have made it fast and inexpensive to obtain genetic sequences from many individuals, assuming samples can be collected. A number of software packages provide models that estimate what prior events looked like from current genetic data, including biological parameters such as population size or migration rate. However, each assumes a particular mathematic model of population demography and are limited to estimating parameters from a subset of the biological processes that may influence genetic variation. A poor match between the assumptions of the analytical model and the true population history will produce inaccurate parameter estimates that are likely to mislead the biological inference. This project will develop software that enables biologists to assess how appropriate a particular software package is to a given set of genetic data. Therefore, it will benefit society by improving the quality of biological inferences drawn from genetic data, ranging from efforts to protect endangered species to investigations into the history of viral pathogens.Bayesian inference is commonly used to analyze genetic data because it provides a computationally efficient approach to identifying highly-probable regions of parameter space, but all such inference is conditional on the models chosen to use in the analysis. While analytical models exist that can estimate parameters associated with all population-level biological processes, such as genetic drift, phylogenetic divergence, gene flow, population size change, etc., computational limitations prevent any given analytical model of incorporating more than a handful of these processes. Biologists typically choose which analytical method to use intuitively, and generally lack approaches for assessing the absolute statistical fit of a model given the genetic data. Consequently, the inferences that result from the analysis of genetic data are effectively conditional on the appropriateness of the model used to analyze the data, although they are rarely presented in such terms. The proposed work will develop and implement a considerable expansion of the P2C2M R package, which currently implements posterior predictive simulation to assess the statistical fit of a single model - the multispecies coalescent model. The work will expand P2C2M such that the statistical fit of additional coalescent methods can be evaluated. By expanding P2C2M, the work promotes the consideration of model fit as an important step within the overall process of making biological inferences from genetic data. Biologists have devoted a great deal of energy to justify the models that they use to analyze their data using verbal reasoning and qualitative arguments, but have generally lacked the tools and statistical framework to do so in a direct quantitative manner. P2C2M will provide these tools by the time of project completion. As a direct consequence of the expanded P2C2M R package, the inferences made by evolutionary geneticists will be more insightful and because researchers and their audiences will have enhanced confidence in the choice of analytical models from which these inferences are derived. The work will enhance biological inferences with important societal benefit, such as the identification of cryptic species, understanding the demography of invasive species and disease vectors, and the movement of alleles across the landscape in endangered species. Updated project code will be available at https://cran.r-project.org/web/packages/P2C2M/index.html and other supplemental information distributed at https://carstenslab.osu.edu/.

在生物基础设施部生物信息学进展的支持下，俄亥俄州州立大学的Bryan Carstens教授和他的研究小组将进一步开发P2 M2软件包，以便更好地估计多个事件如何塑造群体遗传学。生物体之间的大多数差异最终可以追溯到遗传变异，包括人类。一个物种的遗传差异模式出现在不同的群体中，这是由于种群规模的变化、种群之间的迁移或长期隔离等事件造成的。例如，了解如何在具有迁移限制的一定规模的自然种群中建立有益的突变，对于了解人类健康，保护遗传学，农业育种和许多其他研究领域的各个方面非常重要。最近的技术进步使得从许多个体获得基因序列变得快速和廉价，假设可以收集样本。许多软件包提供了模型，可以从当前的遗传数据中估计出先前事件的样子，包括种群规模或迁移率等生物参数。然而，每一个假设一个特定的数学模型的人口统计学，并限于估计参数的一个子集的生物过程，可能会影响遗传变异。分析模型的假设与真实种群历史之间的不匹配将产生不准确的参数估计，这可能会误导生物学推断。该项目将开发软件，使生物学家能够评估特定软件包对给定的一组遗传数据的适用程度。因此，从保护濒危物种到调查病毒病原体的历史，通过提高从遗传数据得出的生物学推断的质量，它将造福于社会。贝叶斯推断通常用于分析遗传数据，因为它提供了一种计算效率高的方法来识别参数空间的高概率区域，但所有这些推断都取决于分析中选择使用的模型。虽然存在可以估计与所有种群水平生物过程相关的参数的分析模型，例如遗传漂变、系统发育趋异、基因流动、种群规模变化等，计算的局限性阻止了任何给定的分析模型结合这些过程中的一小部分。生物学家通常选择使用哪种分析方法来直观地使用，并且通常缺乏评估给定遗传数据的模型的绝对统计拟合的方法。因此，从遗传数据分析中得出的推论实际上取决于用于分析数据的模型的适当性，尽管它们很少以这种方式提出。拟议的工作将开发和实施P2 C2M R包的相当大的扩展，该包目前实现了后验预测模拟，以评估单个模型的统计拟合-多物种聚结模型。这项工作将扩展P2 C2M，以便可以评估其他聚结方法的统计拟合。通过扩展P2 C2M，这项工作促进了对模型拟合的考虑，将其作为从遗传数据进行生物学推断的整个过程中的重要步骤。生物学家投入了大量的精力来证明他们使用语言推理和定性论证来分析数据的模型，但通常缺乏直接定量的工具和统计框架。P2 C2M将在项目完成时提供这些工具。作为扩展的P2 C2M R包的直接结果，进化遗传学家所做的推断将更有见地，因为研究人员和他们的观众将增强对这些推断所来源的分析模型的选择的信心。这项工作将增强具有重要社会效益的生物学推断，例如识别神秘物种，了解入侵物种和疾病媒介的人口统计学，以及濒危物种中等位基因在景观中的运动。更新的项目代码将在https://cran.r-project.org/web/packages/P2C2M/index.html上提供，其他补充信息将在https://carstenslab.osu.edu/上分发。