<tt> <b>BalLeRMix</b> </tt>+: mixture model approaches for robust joint identification of both positive selection and long-term balancing selection
Xiaoheng Cheng, Michael DeGiorgio
Abstract
SUMMARY: 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. AVAILABILITY AND IMPLEMENTATION: BalLeRMix+ is freely available at https://github.com/bioXiaoheng/BallerMixPlus. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.