GppFst is an open source R package that generates posterior predictive distributions of Fst and day under a neutral coalescent model to identify putative targets of selection from genomic data. Long Description (required) GppFst is a posterior predictive simulation (PPS) framework to generate theoretical distributions of FST and dXY under the neutral coalescent model for two populations that accounts for demographic parameters in a probabilistic framework. Importantly, our method allows users to explicitly test the null hypothesis of genetic drift when conducting genomic scans. PPS is a popular method for evaluating model fit within a Bayesian framework that has been used to test a variety of evolutionary models (Gelman et al., 2004; Reid et al., 2014). GppFst explicitly accounts for the demographic history of two genetically-isolated species, including multiple demographic and experimental parameters (and uncertainty in those parameters), such as sample sizes, demographic parameters, unequal rates of genetic drift within populations (unequal s), and divergence time. Our method allows users to simulate theoretical distributions that are conditioned on sampling multiple linked SNPs per locus – allowing users to take full advantage of large genomic datasets. We provide our PPS model in the package GppFst (Genomic Posterior Predictive distributions of FST), which offers a user-friendly, open-source framework to generate theoretical distributions of FST and dXY under the neutral coalescent model. https://github.com/radamsRHA/GppFst email@example.com Step 1: Use the attribute tree to add new attributes or remove pre-selected attributes to describe the simulator. Every sub-attribute is selected Not all sub-attributes are selected Fill Clear Expand Collapse Reset Summary of Proposed Changes Step 2: Review list of proposed attribute addition(s) and subtraction(s). Can't Find the Attribute You Are Looking For?
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GppFst Genomic posterior predictive simulations of FST and dXY for identifying outlier loci from population genomic data Bioinformatics; In Review,