GWAS.BAYES

GWAS.BAYES performs genome-wide association studies (GWAS) using Bayesian techniques for Gaussian phenotypes.

Key Features:

• BICOSS implementation: Provides functionality for Bayesian iterative conditional stochastic search for GWAS (Williams, J., Ferreira, M. A., and Ji, T., 2022).
• BGWAS implementation: Supports Bayesian variable selection in linear mixed models with nonlocal priors for genome-wide association studies (Williams, J., Xu, S., Ferreira, M. A., 2023).
• GINA implementation: Includes functionality for the GINA method.
• Gaussian phenotype analysis: All methods currently support the analysis of Gaussian phenotypes.

Scientific Applications:

• Bayesian GWAS: Enables genome-wide association studies using Bayesian statistical approaches.
• Variable selection in linear mixed models: Supports Bayesian variable selection with nonlocal priors in linear mixed model frameworks for genetic association analysis.

Methodology:

Bayesian iterative conditional stochastic search; Bayesian variable selection in linear mixed models with nonlocal priors.