NSF Award
2302258
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. <br/><br/>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.<br/><br/>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.
