NSFDEB-NERC: Machine learning tools to discover balancing selection in genomes from spatial and temporal autocorrelations

NSFDEB-NERC：机器学习工具，用于从空间和时间自相关中发现基因组中的平衡选择

• 批准号: 2302258
• 负责人: Michael DeGiorgio
• 金额: $ 64.81万
• 依托单位: Florida Atlantic University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-07-01 至 2026-06-30
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2302258&HistoricalAwards=false
• 关键词: NSFDEB; NERC; Machine; learning; tools

Understanding why individuals within species are so genetically diverse is a fundamental problem in evolutionary biology and genetics. This individual genetic diversity, and its causes, has important consequences for biodiversity conservation, agricultural biology, and biomedicine. Balancing selection is a process that promotes and maintains genetic diversity over time. Despite a few well-known examples, however, little is known about recent or fleeting balancing selection, likely because its genetic clues are subtle and difficult to distinguish from those left by other adaptive and nonadaptive processes. Detecting balancing selection in genome data is further complicated by technical issues, such as missing or degraded DNA sequence data, which are not accounted for by current methods. The primary goal of this project is to tackle these challenges by designing state-of-the-art tools based on recent advances in artificial intelligence, which provide strategies for identifying signals of past evolutionary events in genetic data. These tools will be made freely available in a public repository, enabling widespread use. In addition, this project will actively engage local high school students in coding and machine learning through the iDeepLearn summer workshop, and other students from groups under-represented in STEM through outreach programs at the FAU campus high school. Together, these planned activities will facilitate future advancements in our understanding of balancing selection across diverse taxonomic groups, as well as foster participation of traditionally underrepresented high school students in STEM research. Detecting balancing selection is enhanced by using temporally sampled genetic data often accessed from ancient DNA, which presents numerous technical hurdles. This research seeks to develop novel machine- and deep-learning methods that can identify genomic signatures of recent and transient balancing selection from spatially and temporally sampled genetic data, while accounting for technical issues encountered by researchers working with ancient DNA and nonmodel organimsm. The project will specifically address detecting balancing selection from data that are incomplete, low-quality, unphased, or pooled under settings for which there is uncertainty in genetic and demographic parameters. Developed methods will be applied to human, mosquito, and fruit fly testbeds, as these study systems have evidence for diverse modes of balancing selection, and publicly available datasets with characteristics of the technical hurdles the projects seeks to overcome. These methods will also be implemented as open-source tools applicable to a wide range of data types common across model and nonmodel organisms, empowering future studies of adaptation by removing barriers imposed by limitations of data quality and demographic knowledge, and ultimately leading to novel insights in the understanding of adaptive history across the tree of life. Workshops on machine learning in population genomics will be developed and delivered for high school girls as part of the iDeepLearn summer program at Florida Atlantic University. In both the US and the UK, multiple STEM-related career events aimed at secondary school pupils will be developed and delivered.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.

理解为什么物种内的个体在基因上如此多样化是进化生物学和遗传学的一个基本问题。这种个体遗传多样性及其成因对生物多样性保护、农业生物学和生物医学具有重要影响。平衡选择是一个促进和维持遗传多样性的过程。然而，尽管有一些众所周知的例子，人们对最近或短暂的平衡选择知之甚少，可能是因为它的遗传线索很微妙，很难与其他适应和非适应过程留下的线索区分开来。检测基因组数据中的平衡选择由于技术问题而变得更加复杂，例如DNA序列数据的缺失或降解，这是目前方法无法解释的。该项目的主要目标是通过设计基于人工智能最新进展的最先进工具来解决这些挑战，这些工具提供了识别遗传数据中过去进化事件信号的策略。这些工具将在公共存储库中免费提供，从而实现广泛使用。此外，该项目将通过iDeepLearn暑期研讨会积极吸引当地高中生参与编码和机器学习，并通过FAU校园高中的外展项目吸引其他在STEM领域未被充分代表的群体的学生。总之，这些计划中的活动将促进我们对不同分类群体之间平衡选择的理解的未来进步，并促进传统上代表性不足的高中生参与STEM研究。通过使用通常从古代DNA中获取的临时采样遗传数据来增强检测平衡选择，这存在许多技术障碍。本研究旨在开发新的机器和深度学习方法，可以从空间和时间采样的遗传数据中识别近期和短暂平衡选择的基因组特征，同时考虑研究人员在研究古代DNA和非模式生物时遇到的技术问题。该项目将专门解决从不完整、低质量、未分阶段或在遗传和人口参数不确定的设置下汇集的数据中检测平衡选择的问题。开发的方法将应用于人类、蚊子和果蝇的试验台，因为这些研究系统有证据证明平衡选择的不同模式，以及具有项目寻求克服的技术障碍特征的公开可用数据集。这些方法也将作为开源工具实施，适用于模型和非模式生物中常见的广泛数据类型，通过消除数据质量和人口统计学知识限制所带来的障碍，增强未来的适应研究，并最终在理解生命之树的适应历史方面产生新的见解。作为佛罗里达大西洋大学iDeepLearn暑期项目的一部分，将为高中女生开发和提供关于人口基因组学中的机器学习的讲习班。在美国和英国，针对中学生的多个stem相关职业活动将被开发和实施。该奖项反映了美国国家科学基金会的法定使命，并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，被认为值得支持。