Collaborative Research: Reproductive heterogeneity in the structured coalescent framework

遏制病原体的影响和确保濒危或商业开发物种的生存是国家利益的问题。虽然收集这些病原体和濒危物种的遗传数据在过去十年中已成为标准，但这些数据需要翻译和分析才能对政策决策有用。这些数据通常用于描述该物种的近代和古代历史的模型。这些模型是建立在理论群体遗传学的基础上的，通常不是很灵活。这项研究解决了这样一个假设，即被研究的种群每一代的后代数量相对恒定。科学观察表明，这种假设是不正确的。例如，一些SARS-CoV-2毒株比其他毒株更容易感染人类，这表明具有新突变的祖先比其他祖先拥有更多的“后代”。本研究概括了常见的假设，并构建了一个框架，允许通过提供准确性的提高和偏差的减少来改进这些人口模型。 这项研究将导致创建一个软件工具，这将有利于研究界和培训下一代研究人员。对种群规模和遗传多样性的准确估计将有助于更好地控制病原体的爆发，规范商业捕捞的捕捞配额，以及保护濒危物种。本研究探讨了后代生产的异质性对群体系谱的影响，使用（1）一个理论框架，可以处理异质性和软件的开发，从基因组数据推断这种异质性。这个框架是基于分数合并扩展到多个，结构化的人口。这项研究扩展了一个单一的人口衍生的分数结合，将后代的变异性作为一个随机变量。这些新方法将被纳入广泛使用的开源计算机软件MIGRATE。然后将新方法与使用人工数据的多合并聚结方法进行比较。这些数据是使用（2）一个模拟器生成的，该模拟器考虑了影响个体后代数量的种群内和种群间的环境质量变化。(3)分析具有不同生活史的广泛物种的许多生物数据集的异质性影响：从病毒到座头鲸，从小地理尺度到大尺度。这些生物数据集将与实际科学家合作进行分析。软件和教程将在www.example.com上报道，https://peterbeerli.com.This奖反映了NSF的法定使命，并被认为值得通过使用基金会的知识价值和更广泛的影响审查标准进行评估来支持。http://popgen.sc.fsu.edu

Curbing the effects of pathogens and securing the survival of endangered or commercially exploited species is a matter of national interest. While collecting genetic data on these pathogens and endangered species has become standard over the last decade, the data requires translation and analysis to be useful for policy decisions. These data are commonly used in models that describe both the recent and ancient history of the species. These models are founded on theoretical population genetics and are often not very flexible. This research addresses the assumption that the populations that are being studied have a relatively constant number of offspring per generation. Scientific observation has shown that this assumption is incorrect. For example, some SARS-CoV-2 strains are more successful in infecting people than others, suggesting that the ancestor with a new mutation has many more 'offspring' than others. This research generalizes the common assumption and constructs a framework that allows for the improvement of these population models by offering an increase in accuracy and a decrease in bias. This research will result in the creation of a software tool that will benefit the research community and train the next generation researchers. Accurate estimates of population size and genetic diversity will lead to better control of pathogen outbreaks, regulation of catch quota for commercial fishing, and maintenance of endangered species. This research explores the effect of heterogeneity of offspring production on the genealogy of a population using (1) a theoretical framework that can handle heterogeneity and the development of software to infer this heterogeneity from genomic data. This framework is based on the fractional coalescent expanded to multiple, structured populations. The research extends a single-population derivation of the fractional coalescent that incorporates offspring variability as a random variable. These new methods will be incorporated into the widely-used open-source computer software MIGRATE. The new approach will then be compared with multi-merger coalescent methods using artificial data. These data are generated using (2) a simulator taking into account environmental quality changes within and among populations affecting the number of offspring an individual can have. (3) Analyses of the effect of heterogeneity for many biological datasets over a broad range of species with different life histories: from viruses to humpback whales and from small geographic scale to large scales. These biological datasets will be analyzed in collaboration with practical scientists. Software and tutorials will be reported on http://popgen.sc.fsu.edu and https://peterbeerli.com.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.