NSF Postdoctoral Fellowship in Biology: Evaluate how the spatial distribution of discrete genetic patterns influences ancestry estimation in spatial and nonspatial methods

这项行动资助了2022财年的NSF生物学博士后研究奖学金，扩大了生物学中代表性不足的群体的参与。该研究金支持研究员的一项研究和培训计划，该计划将增加在生物学领域代表性不足的群体的参与。本研究将评估使用非传统动物系统束带蛇估计祖先的方法的性能。该研究将调查物种内的遗传变异如何在空间上发生变化，并使祖先估计复杂化。在过去的二十年里，人类基因组学取得了迅速的进步，商业家谱产业的兴起，以及对全球人类多样性和移民历史的重新评估。该项目将有助于对进化原理及其在生命树中的应用的一般理解。该项目的更广泛影响包括：建立一个基金会，根据分类的社会后果向更广泛的受众讲授人口识别的复杂性;为科学家举办讲习班，以增加计算和统计分析知识。理解种群结构需要从离散的种群结构中分离出空间上连续变化的模式，这可能是由人口统计学，渐渗和自然选择的任何组合引起的。直到最近，祖先估计方法才开始共同考虑这两种模式。然而，他们的行为在复杂的情况下，特别是在响应潜在的系统发育结构和/或遗传贡献的未采样的谱系，是不清楚的。使用模拟和高通量序列数据产生的三个同域杂交束带蛇（Thamnophis）与不同的分布，研究员将评估如何在空间和非空间方法的空间范围的离散遗传模式的影响祖先估计。该研究员还将扩展空间建模，以共同推断系统发育结构和连续变化中的祖先，从而推进该领域的方法工具。所产生的数据将适用于超出本项目和群体遗传学范围的问题，使这项工作的产品对生物学的多个学科有价值。研究员将利用教学和外联方面的背景，向广大受众传播研究成果。研究员还将探索公众对人口遗传学的理解的广泛影响，通过开发教学工具，既增加生物学中的种族代表性，又直接解决该学科的种族主义遗产。此外，该研究员还将通过为没有计算和统计分析背景的研究人员举办会议讲习班，弥合群体遗传学理论和应用之间的差距。该奖项反映了NSF的法定使命，并通过使用基金会的智力价值和更广泛的影响审查标准进行评估，被认为值得支持。

This action funds an NSF Postdoctoral Research Fellowship in Biology for FY 2022, Broadening Participation of Groups Underrepresented in Biology. The Fellowship supports a research and training plan for the Fellow that will increase the participation of groups underrepresented in biology. This research will evaluate the performance of methods for estimating ancestry using a nontraditional animal system, garter snakes. The research will investigate how genetic variation within species can change across space and complicate ancestry estimation. The last two decades have seen rapid advances in human genomics, the rise of the commercial genealogy industry, and a reassessment of global human diversity and migration history. This project will contribute to the general understanding of evolutionary principles and their applications across the tree of life. The broader impacts of the project include developing a foundation to teach broader audiences the complexity of population identification in light of the social consequences of categorization and workshops designed for scientists to increase knowledge of computational and statistical analysis. Understanding population structure requires disentangling patterns of continuous variation across space from discrete population structure, which can be caused by any combination of demography, introgression, and natural selection. Only recently have ancestry estimation methods begun to jointly consider these two patterns. However, their behavior in complex scenarios, in particular in response to underlying phylogenetic structure and/or genetic contribution from unsampled lineages, is unclear. Using simulated and high-throughput sequence data generated from three sympatric hybridizing garter snakes (Thamnophis) with different distributions, the Fellow will assess how the spatial extent of discrete genetic patterns across space influence ancestry estimation in spatial and nonspatial methods. The Fellow will also extend spatial modeling to jointly infer phylogenetic structure and ancestry amidst continuous variation, therefore advancing methodological tools for the field. The data generated will be amenable to questions beyond the scope of this project and population genetics, making the products of this work valuable to multiple disciplines in biology. The Fellow will use a background in teaching and outreach to disseminate the research to broad audiences. The Fellow will also explore the broad implications of public understanding of population genetics across axes of social importance by developing teaching tools that both increase racial representation in biology, and directly address the racist legacies of the discipline. Furthermore, the Fellow will bridge the gap between theory and application in population genetics by developing conference workshops that cater to researchers without robust backgrounds in computational and statistical analysis.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.