rdmc

rdmc implements statistical models to infer modes of convergent adaptation from population genomic data, enabling analysis of selection at local genomic scales across multiple related populations.

Key Features:

• Implementation: Implemented in R to apply analytical functions to population genomic datasets.
• Lee and Coop (2017) models: Builds upon the models of Lee and Coop (2017) for inferring modes of convergent adaptation.
• Input data: Accepts population genomic inputs, including whole genome sequencing data from related populations.
• Statistical inference: Implements statistical models to distinguish between different modes of convergent adaptation and assess selection at local genomic scales.

Scientific Applications:

• Population genetics: Infer convergent adaptation and test hypotheses about shared selection across related populations using genomic data.
• Local selection analysis: Identify and characterize selection at specific genomic loci across multiple populations to infer underlying genetic mechanisms.

Methodology:

Implements the statistical models of Lee and Coop (2017) to analyze population genomic and whole genome sequencing data and distinguish among modes of convergent adaptation at local genomic scales, grounded in population genetic theory.