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

概述平衡选择是适应性进化的关键驱动力，保持物种内和物种间的差异。然而，尽管有一些值得注意的例子，但人们对最近或短暂的平衡选择知之甚少，这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。尽管人工智能的复兴改变了我们寻找适应性基因组区域的方式，但从当代基因组数据中描述最近的平衡选择仍然很困难。此外，从历史样本和非模式生物中获得的数据往往充满了技术障碍，现有方法无法驾驭。因此，该提案的目标是开发一套深度学习工具，用于从时间和空间采样的基因组数据中研究最近和短暂的平衡选择，同时确保这些方法能够解决非模型研究系统中遇到的许多技术挑战。具体来说，我们将设计预测器，用于检测信号和学习参数的平衡选择从不完整的，低质量的，和unphased古老的样本（目标1），使用的方法，规避遗传和人口统计参数的不确定性（目标2），并扩展到数据产生的成本效益汇集sequencingstrategies（目标3）。我们将部署这些新的工具，以三个经验数据集，其中分别包括一组最近的平衡选择的案例研究，解决的技术问题的目标1，2和3是为了克服。初步研究结果支持了所提出的目标的承诺和可行性，我们希望这些工具能为进化界提供一个强大的框架，以解决目前在模型和非模型系统中无法回答的关于适应的问题。智力功绩阐明物种内适应性维持变异的过程是进化生物学中的一个基本问题，而现有的工具并不足以从非模型研究系统中存在的大量的、通常是非理想的数据中解决这个问题。PI已经证明成功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，他们已经准备好开发所提出的深度学习方法来研究平衡选择，通过利用非模型生物体的各种数据类型的基因组，空间和时间自相关性。这些方法的可用性将促进平衡选择的研究，当数据不完整，低质量，和unphased（目标1），当遗传和人口统计参数是不确定的（目标2），当基因型信息在个人水平上是不可用的（目标3）。此外，我们提出的应用这些方法的多样性的研究系统将解决的问题，在不同的时间和地理尺度的平衡选择的作用和具体模式。最后，开发的工具将适用于广泛的数据类型的共同跨越模型和非模型生物，通过消除障碍所带来的数据质量和人口统计历史的现有知识的限制，使未来的适应研究。

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.