ABI Innovation: Posterior Predictive Checks of Evolutionary Models.

Ohio State University, The, Columbus OH

Investigators

Abstract

With support from the Advances in Biological Informatics in the Division of Biological Infrastructure, Professor Bryan Carstens and his research group at the Ohio State University will further develop the P2M2 software package, so that it can better estimate how multiple events may shape population genetics. Most of the differences among living organisms ultimately can be traced to genetic variation, including humans. Patterns of genetic differences in a species appear across different groups, due to events such as changes in population size, migration between populations, or long periods of isolation. Understanding, for example, how beneficial mutations can be established in a natural population of a certain size that has migration limits is important for understanding aspects of human health, conservation genetics, breeding in agriculture and many other research areas. Recent technological advances have made it fast and inexpensive to obtain genetic sequences from many individuals, assuming samples can be collected. A number of software packages provide models that estimate what prior events looked like from current genetic data, including biological parameters such as population size or migration rate. However, each assumes a particular mathematic model of population demography and are limited to estimating parameters from a subset of the biological processes that may influence genetic variation. A poor match between the assumptions of the analytical model and the true population history will produce inaccurate parameter estimates that are likely to mislead the biological inference. This project will develop software that enables biologists to assess how appropriate a particular software package is to a given set of genetic data. Therefore, it will benefit society by improving the quality of biological inferences drawn from genetic data, ranging from efforts to protect endangered species to investigations into the history of viral pathogens. Bayesian inference is commonly used to analyze genetic data because it provides a computationally efficient approach to identifying highly-probable regions of parameter space, but all such inference is conditional on the models chosen to use in the analysis. While analytical models exist that can estimate parameters associated with all population-level biological processes, such as genetic drift, phylogenetic divergence, gene flow, population size change, etc., computational limitations prevent any given analytical model of incorporating more than a handful of these processes. Biologists typically choose which analytical method to use intuitively, and generally lack approaches for assessing the absolute statistical fit of a model given the genetic data. Consequently, the inferences that result from the analysis of genetic data are effectively conditional on the appropriateness of the model used to analyze the data, although they are rarely presented in such terms. The proposed work will develop and implement a considerable expansion of the P2C2M R package, which currently implements posterior predictive simulation to assess the statistical fit of a single model - the multispecies coalescent model. The work will expand P2C2M such that the statistical fit of additional coalescent methods can be evaluated. By expanding P2C2M, the work promotes the consideration of model fit as an important step within the overall process of making biological inferences from genetic data. Biologists have devoted a great deal of energy to justify the models that they use to analyze their data using verbal reasoning and qualitative arguments, but have generally lacked the tools and statistical framework to do so in a direct quantitative manner. P2C2M will provide these tools by the time of project completion. As a direct consequence of the expanded P2C2M R package, the inferences made by evolutionary geneticists will be more insightful and because researchers and their audiences will have enhanced confidence in the choice of analytical models from which these inferences are derived. The work will enhance biological inferences with important societal benefit, such as the identification of cryptic species, understanding the demography of invasive species and disease vectors, and the movement of alleles across the landscape in endangered species. Updated project code will be available at https://cran.r-project.org/web/packages/P2C2M/index.html and other supplemental information distributed at https://carstenslab.osu.edu/.