SMetABF

SMetABF performs Bayesian meta-analysis of Genome-Wide Association Studies (GWAS) by optimizing Approximate Bayes Factors (ABFs) with Shotgun Stochastic Search to rapidly integrate multiple studies and identify associated genetic loci.

Key Features:

• Shotgun Stochastic Search (SSS): Refines the MetABF framework to optimize Approximate Bayes Factors (ABFs) and reduce computational time and memory usage when exploring model space.
• Performance efficiency: Simulation studies demonstrate faster runtime and improved precision compared with exhaustive methods and traditional Markov Chain Monte Carlo (MCMC) approaches.
• Application to GWAS data: Applied to real GWAS datasets to identify genetic loci associated with Parkinson's disease (PD) and to investigate links with autoimmune disorders.

Scientific Applications:

• Variant discovery in complex traits: Enables identification of variants and novel loci associated with complex traits and diseases via GWAS meta-analysis.
• Parkinson's disease genetics: Facilitates discovery of loci associated with Parkinson's disease (PD) in meta-analytic GWAS datasets.
• Cross-trait genetic association: Supports investigation of genetic links between PD and autoimmune disorders.

Methodology:

Implements a Bayesian framework using Approximate Bayes Factors (ABFs) and incorporates Shotgun Stochastic Search (SSS) to refine the MetABF approach, with performance evaluated by simulation studies against exhaustive methods and MCMC.