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

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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.