bgc

bgc estimates locus-specific introgression across multiple loci in admixed populations using a Bayesian genomic cline model to quantify variable introgression and detect genomic regions associated with adaptation or reproductive isolation.

Key Features:

• Bayesian framework: Estimates the joint posterior distribution of genomic cline model parameters using a Bayesian approach.
• Markov Chain Monte Carlo (MCMC): Samples complex posterior distributions with MCMC to estimate cline parameters and identify outlier loci with extreme introgression.
• Handling genotype uncertainty: Models uncertainty in genotypic states relevant to next-generation sequencing data.
• Incorporation of linked loci information: Integrates linked loci on a genetic map and accounts for linkage disequilibrium to improve introgression estimates.
• Sequence error accommodation: Accounts for sequence errors inherent to next-generation sequencing data.
• Efficient data management: Stores analysis results in HDF5 files for handling large datasets.
• Implementation and license: Implemented in C++ and distributed under the GNU Public License.

Scientific Applications:

• Evolutionary biology and population genetics: Quantifies locus-specific introgression to study evolutionary processes and population structure in admixed populations.
• Speciation and reproductive isolation: Identifies genomic regions with extreme introgression that may contribute to reproductive barriers between taxa.
• Adaptive trait mapping and hybrid-zone analysis: Pinpoints candidate loci associated with adaptive differences and characterizes gene flow dynamics across hybrid zones.

Methodology:

Implements the Bayesian genomic cline model with joint posterior estimation via MCMC, explicitly models genotype uncertainty and sequence error, incorporates linked loci using a genetic map and linkage disequilibrium information, and writes results to HDF5 files.