ABI Innovation: Posterior Predictive Checks of Evolutionary Models.

在生物基础设施部生物信息学进展的支持下，俄亥俄州立大学的布莱恩·卡斯滕斯教授和他的研究小组将进一步开发P2M2软件包，以便更好地估计多个事件可能如何塑造种群遗传学。生物体之间的大多数差异最终可以追溯到包括人类在内的基因变异。由于种群大小的变化、种群间的迁徙或长期的隔离等事件，一个物种的遗传差异模式会出现在不同的群体中。例如，理解如何在有迁徙限制的一定规模的自然种群中建立有益的突变，对于理解人类健康、保护遗传学、农业育种和许多其他研究领域非常重要。最近的技术进步使从许多个体获得基因序列变得快速而廉价，前提是可以收集样本。许多软件包提供了一些模型，可以根据当前的遗传数据估计以前的事件是什么样子，包括种群规模或迁移率等生物参数。然而，每一种方法都假设了一个特定的人口统计学数学模型，并且仅限于从可能影响遗传变异的生物过程的子集中估计参数。分析模型的假设与真实种群历史之间的较差匹配将产生不准确的参数估计，这可能会误导生物学推断。该项目将开发一种软件，使生物学家能够评估特定的软件包对一组给定的基因数据的适用性。因此，它将通过提高从遗传数据中提取的生物学推断的质量来造福社会，从保护濒危物种的努力到对病毒病原体历史的调查。贝叶斯推理通常用于分析遗传数据，因为它提供了一种在计算上有效的方法来识别参数空间的高概率区域，但所有这种推理都取决于分析中选择的模型。虽然现有的分析模型可以估计与所有种群水平的生物过程相关的参数，如遗传漂移、系统发育差异、基因流动、种群大小变化等，但由于计算的局限性，任何给定的分析模型都无法纳入不止几个这样的过程。生物学家通常会直观地选择使用哪种分析方法，通常缺乏方法来评估给定遗传数据的模型的绝对统计拟合。因此，从遗传数据分析得出的推论实际上是以用于分析数据的模型的适当性为条件的，尽管它们很少以这样的术语呈现。拟议的工作将开发和实施对P2C2M R程序包的相当大的扩展，该程序包目前实施后验预测模拟，以评估单一模型-多物种合并模型-的统计适合性。这项工作将扩展P2C2M，以便可以评估其他合并方法的统计适合性。通过扩展P2C2M，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推断的整个过程中的重要一步。生物学家投入了大量精力来证明他们用来分析数据的模型的合理性，这些模型使用语言推理和定性论证，但通常缺乏以直接定量的方式这样做的工具和统计框架。P2C2M将在项目完成时提供这些工具。作为扩展的P2C2M R包的直接结果，进化遗传学家做出的推论将更有洞察力，因为研究人员和他们的受众将对这些推论所依据的分析模型的选择更有信心。这项工作将加强具有重要社会效益的生物推断，例如识别神秘物种，了解入侵物种和病媒的人口统计学，以及濒危物种中等位基因的移动。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

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/.