Collaborative Research: Reproductive heterogeneity in the structured coalescent framework

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Curbing the effects of pathogens and securing the survival of endangered or commercially exploited species is a matter of national interest. While collecting genetic data on these pathogens and endangered species has become standard over the last decade, the data requires translation and analysis to be useful for policy decisions. These data are commonly used in models that describe both the recent and ancient history of the species. These models are founded on theoretical population genetics and are often not very flexible. This research addresses the assumption that the populations that are being studied have a relatively constant number of offspring per generation. Scientific observation has shown that this assumption is incorrect. For example, some SARS-CoV-2 strains are more successful in infecting people than others, suggesting that the ancestor with a new mutation has many more 'offspring' than others. This research generalizes the common assumption and constructs a framework that allows for the improvement of these population models by offering an increase in accuracy and a decrease in bias. This research will result in the creation of a software tool that will benefit the research community and train the next generation researchers. Accurate estimates of population size and genetic diversity will lead to better control of pathogen outbreaks, regulation of catch quota for commercial fishing, and maintenance of endangered species. This research explores the effect of heterogeneity of offspring production on the genealogy of a population using (1) a theoretical framework that can handle heterogeneity and the development of software to infer this heterogeneity from genomic data. This framework is based on the fractional coalescent expanded to multiple, structured populations. The research extends a single-population derivation of the fractional coalescent that incorporates offspring variability as a random variable. These new methods will be incorporated into the widely-used open-source computer software MIGRATE. The new approach will then be compared with multi-merger coalescent methods using artificial data. These data are generated using (2) a simulator taking into account environmental quality changes within and among populations affecting the number of offspring an individual can have. (3) Analyses of the effect of heterogeneity for many biological datasets over a broad range of species with different life histories: from viruses to humpback whales and from small geographic scale to large scales. These biological datasets will be analyzed in collaboration with practical scientists. Software and tutorials will be reported on http://popgen.sc.fsu.edu and https://peterbeerli.com.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.