hgv_beam

hgv_beam applies Bayesian Epistasis Association Mapping (BEAM) to detect significant single- and multi-locus SNP associations in case-control genome-wide association studies, identifying complex genetic interactions that contribute to disease susceptibility.

Key Features:

• BEAM (Bayesian Epistasis Association Mapping): Implements the BEAM framework to model epistatic interactions in genome-wide association studies.
• Bayesian partitioning model: Uses a Bayesian partitioning approach to integrate prior information and model joint effects of multiple genetic markers.
• Markov Chain Monte Carlo (MCMC): Employs MCMC sampling to compute posterior probabilities that specific sets of markers are associated with disease.
• Single- and multi-locus SNP association detection: Detects both single-locus and multi-locus SNP associations in case-control datasets.
• Scalability: Demonstrated to handle large-scale datasets involving thousands of markers for genome-wide studies.
• Empirical validation: Applied to and validated on an age-related macular degeneration (AMD) genome-wide association dataset.

Scientific Applications:

• Epistasis mapping in GWAS: Identification of statistical interactions among SNPs in genome-wide case-control studies.
• Analysis of sequencing-derived datasets: Applicable to complex datasets produced by next-generation DNA sequencing technologies for genetic association analysis.
• Investigation of complex disease genetics: Used to uncover candidate genetic interactions underlying diseases such as age-related macular degeneration.
• Target discovery and follow-up studies: Facilitates identification of candidate variant combinations for downstream functional validation and therapeutic investigation.

Methodology:

Implements the BEAM Bayesian partitioning model and uses Markov Chain Monte Carlo (MCMC) sampling to estimate posterior probabilities for single- and multi-locus SNP marker sets in case-control genome-wide association studies.