BalLeRMix1: mixture model approaches for robust joint identification of both positive selection and long-term balancing selection

Abstract

The growing availability of genomewide polymorphism data has fueled interest in detecting diverse selective processes affecting population diversity. However, no model-based approaches exist to jointly detect and distinguish the two complementary processes of balancing and positive selection. We extend the BalLeRMix B-statistic framework described in Cheng and DeGiorgio (2020) for detecting balancing selection and present BalLeRMix , which implements five B statistic extensions based on mixture models to robustly identify both types of selection. BalLeRMix is implemented in Python and computes the composite likelihood ratios and associated model parameters for each genomic test position.