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: NE/Y003519/1
• 负责人: Matteo Fumagalli
• 金额: $ 15.51万
• 依托单位: Queen Mary University of London
• 依托单位国家: 英国
• 项目类别: Research Grant
• 财政年份: 2023
• 资助国家: 英国
• 起止时间: 2023 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=NE%2FY003519%2F1
• 关键词: NSFDEB; NERC; Machine; learning; tools

OverviewBalancing selection is a key driver of adaptive evolution that maintains variation within and acrossspecies. Yet, despite a few notable examples, little is known about recent or fleeting balancing selection,likely because its genomic footprints are difficult to distinguish from those of neutral evolution. Whereasthe renaissance of artificial intelligence has transformed how we search for adaptive genomic regions, thecharacterization of recent balancing selection from contemporary genomic data remains difficult.Moreover, data obtained from historical samples and nonmodel organisms are often fraught with technicalhurdles that available methods are ill-equipped to navigate. Hence, the objective of this proposal is todevelop a suite of deep learning tools for studying recent and transient balancing selection fromtemporally and spatially sampled genomic data, while ensuring that these methods account for the manytechnical challenges encountered in nonmodel study systems. Specifically, we will design predictors fordetecting signals and learning parameters of balancing selection from incomplete, low-quality, andunphased ancient samples (Aim 1), using approaches that circumvent the uncertainty in genetic anddemographic parameters (Aim 2), and that extend to data generated by cost-efficient pooled sequencingstrategies (Aim 3). We will deploy these new tools to three empirical datasets, which respectivelyencompass a set of recent balancing selection case studies for which the technical issues tackled byAims 1, 2, and 3 are designed to overcome. Preliminary findings support the promise and feasibility of theproposed aims, and we expect these tools to provide the evolution community with a powerful frameworkto address currently unanswerable questions about adaptation in both model and nonmodel systems.Intellectual MeritElucidating the processes underlying adaptive maintenance of variation within species is a fundamentalproblem in evolutionary biology, and one for which available tools are ill-equipped to address from thevast, often non-ideal, data that exist for nonmodel study systems. The PIs have demonstrated success indesigning statistical and machine learning methods for uncovering footprints of adaptation andaddressing targeted hypotheses about balancing selection across several study systems. Thus, they arewell-poised to develop the proposed deep learning approaches for studying balancing selection, byleveraging genomic, spatial, and temporal autocorrelations across a variety of data types characteristic ofthose from nonmodel organisms. Availability of these methods will facilitate studies of balancing selectionwhen data are incomplete, low-quality, and unphased (Aim 1), when genetic and demographicparameters are uncertain (Aim 2), and when genotype information at the individual level is unavailable(Aim 3). Moreover, our proposed applications of these methods to a diversity of study systems willaddress questions regarding the roles and specific modes of balancing selection at different temporal andgeographic scales. Finally, the developed tools will be applicable to a wide range of data types commonacross model and nonmodel organisms, empowering future studies of adaptation by removing barriersimposed by limitations of data quality and current knowledge of demographic history.

平衡选择是维持物种内和物种间变异的适应性进化的关键驱动力。然而，尽管有几个值得注意的例子，人们对最近或短暂的平衡选择知之甚少，可能是因为它的基因组足迹很难与中性进化的基因组足迹区分开来。虽然人工智能的复兴改变了我们搜索适应性基因组区域的方式，但最近从当代基因组数据中进行平衡选择的特征仍然很困难。此外，从历史样本和非模式生物获得的数据往往充满了技术障碍，现有的方法装备不足。因此，这项建议的目标是开发一套深度学习工具，用于研究从时间和空间采样的基因组数据中最近和瞬时平衡选择，同时确保这些方法解决非模型研究系统中遇到的许多技术挑战。具体地说，我们将设计预报器，用于从不完整、低质量和非阶段性的古代样本中检测信号和学习选择的学习参数(目标1)，使用规避遗传和人口参数不确定性的方法(目标2)，并将这些方法延伸到具有成本效益的集合测序策略产生的数据(目标3)。我们将把这些新工具部署到三个经验数据集，这三个数据集分别包含一组最近的平衡选择案例研究，目标1、2和3解决的技术问题旨在克服这些研究。初步发现支持提出的目标的前景和可行性，我们期待这些工具为进化界提供一个强大的框架，以解决目前无法回答的关于模型和非模型系统中的适应的问题。智力价值评估潜在的物种内变异的适应性维持过程是进化生物学中的一个基本问题，对于这个问题，现有的工具装备不足，无法从存在于非模型研究系统的大量、通常不理想的数据中进行处理。PI已经成功地设计了统计和机器学习方法，以揭示适应的足迹，并解决了关于在几个研究系统中平衡选择的目标假设。因此，他们做好了充分的准备，通过利用基因组、空间和时间上的自相关性，研究非模式生物特有的各种数据类型的深度学习方法，来研究平衡选择。这些方法的可获得性将有助于在数据不完整、低质量和非阶段性(目标1)、当遗传和人口统计参数不确定(目标2)以及当无法获得个体一级的基因信息时(目标3)进行平衡选择的研究。此外，我们建议将这些方法应用于不同的研究系统，将解决有关在不同时间和地理尺度上平衡选择的角色和具体模式的问题。最后，所开发的工具将适用于模型生物体和非模型生物体中常见的多种数据类型，通过消除数据质量限制和现有人口历史知识所造成的障碍，增强未来适应研究的能力。