MCMC_IBDfinder

MCMC_IBDfinder estimates posterior probabilities of identity by descent (IBD) sharing among multiple individuals from unphased single nucleotide polymorphism (SNP) data to detect shared genomic regions in the absence of pedigree information.

Key Features:

• Implementation: C++ program that performs the stated analyses.
• Markov Chain Monte Carlo (MCMC) methodology: Employs an MCMC approach tailored for unphased SNP data to probabilistically infer IBD sharing.
• Simultaneous multi-individual analysis: Infers IBD sharing among multiple individuals concurrently rather than only pairwise comparisons.
• Modeled genetic factors: Incorporates inbreeding, allele frequencies, genotyping error rates, and genomic distances into the IBD probability estimates.
• Disease-mapping application: Has been used to map a disease-causing mutation to a 2.2-Mb region using SNP data from five seemingly unrelated affected individuals with breast and/or ovarian cancer.

Scientific Applications:

• Human evolution studies: Identification of shared genomic regions across individuals supports analyses of population history and migration.
• Disease research and genetic epidemiology: Detection of IBD regions facilitates mapping of disease-associated variants among unrelated affected individuals, as in the breast and/or ovarian cancer example.
• Genetic diversity and population structure: Estimation of IBD sharing informs studies of genetic diversity, inbreeding, and population structure relevant to conservation and biodiversity research.

Methodology:

Implements a Markov Chain Monte Carlo (MCMC) algorithm in C++ to estimate posterior IBD probabilities from unphased SNP data while modeling simultaneous multi-individual IBD states and accounting for inbreeding, allele frequencies, genotyping error rates, and genomic distances.