NSF Postdoctoral Fellowship in Biology FY 2021: Comparative methods and model clades for evolutionary developmental biology

该行动资助了2021财年的NSF生物学博士后研究奖学金，即调查基因组，环境和表型之间相互作用的生命规则的综合研究。该研究金支持研究员的研究和培训，以创新的方式为生活规则领域做出贡献。该研究将使用大型数据集来研究进化如何导致生物多样性。用于收集具有数千个测量的生物性状的大量数据的方法变得越来越普遍并且生成成本低廉。此类数据的实例包括基因表达的发育测定。随着这些数据变得越来越普遍，这些数据在物种之间的比较也越来越普遍。然而，进化生物学家多年来一直警告说，物种之间的比较必须使用数学模型来解释物种之间的相关性，否则他们可能会错误识别物种之间的模式。该研究金将侧重于开发跨物种数据比较的新方法。这项研究很重要，因为在这个全球快速变化的时代，需要强大的进化模型来准确预测生物多样性在未来可能发生的变化。此外，这项研究将通过招募和指导计算生物学分析中代表性不足的群体的本科生来扩大科学参与。该研究项目将产生一个新的比较框架，用于分析跨物种基因表达的高维数据。基因表达数据在发育生物学的分析中是常见的，然而目前这些数据在分类群之间的比较往往没有考虑到观察结果的进化非独立性。存在着丰富的文献比较方法，旨在统计比较性状的类群，但他们的实施高维连续性状面临技术限制。为了克服这些局限性，首先，研究员将确定和扩展进化模型，以描述表达数据在分类群中的分布。这种分析将要求研究员实现特定的培训目标，包括寻求数学建模方面的高级知识，以克服分析具有数千个参数的数据集时遇到的常见挑战。其次，该研究员将把这些方法应用于跨组织和物种的基因表达的真实数据，测试关于遗传、基因通路和基因产物类型解释变异的程度的假设。这项研究的结果将是一个比较框架，可以立即应用于跨物种的基因表达数据集，这些数据集将作为免费的、公开访问的开源软件发布。该奖项反映了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。

This action funds an NSF Postdoctoral Research Fellowship in Biology for FY 2021, Integrative Research Investigating the Rules of Life Governing Interactions Between Genomes, Environment and Phenotypes. The fellowship supports research and training of the fellow that will contribute to the area of Rules of Life in innovative ways. The funded research will use large datasets to investigate how evolution leads to biodiversity. Methods for collecting large amounts of data with thousands of measured biological traits are becoming increasingly common and inexpensive to generate. Examples of such data include developmental assays of gene expression. As this data becomes more common, so do comparisons of these data across species. However, evolutionary biologists have warned for years that comparisons across species must be performed using mathematical models that account for how related species are, or they risk misidentifying patterns across species. This fellowship will focus on developing new methods for comparing data across species. This research is important given that, in this era of rapid global change, robust models of evolution are needed to accurately predict how biodiversity is likely to change in the future. Furthermore, this research will broaden participation in science by recruiting and mentoring undergraduate students from underrepresented groups in computational biological analysis. This research project will result in a new comparative framework for analyzing high-dimensional data on gene expression across species. Gene expression data are common in assays of developmental biology, yet current comparisons of these data across taxa often fail to consider the evolutionary non-independence of observations. There exists a rich literature of comparative methods designed to statistically compare traits across taxa, but their implementation for high-dimensional continuous traits faces technical limitations. To overcome these limitations, first the fellow will identify and extend evolutionary models to describe the distribution of expression data across taxa. This analysis will require the fellow to achieve specific training objectives, including seeking out advanced knowledge in mathematical modeling, to overcome common challenges in analyzing datasets that have many thousands of parameters. Second, the fellow will apply these methods to real-world data on gene expression across tissues and species, testing hypotheses about the degree to which phylogeny, gene pathway, and gene product type explain variation. The results of this study will be a comparative framework that can be immediately applied to datasets of gene expression across species, which will be released as free, publicly accessible, open-source software.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.