GControl

GControl performs Bayesian analysis of case-control genetic data to identify genes associated with complex disorders while accounting for population stratification and cryptic relatedness.

Key Features:

• Bayesian Framework: Applies Bayesian statistical methods to incorporate prior information and provide probabilistic inference on genetic associations.
• Control for Population Stratification and Relatedness: Adjusts for population heterogeneity and cryptic relatedness to reduce spurious associations in case-control studies.
• Markov Chain Monte Carlo Algorithms: Uses Markov chain Monte Carlo (MCMC) algorithms to explore high-dimensional genetic parameter spaces.
• Insensitivity to Model Assumptions Violations: Maintains robustness to violations of typical model assumptions, including non-independence among cases.
• Bayesian Outlier Methods: Incorporates Bayesian outlier detection methods to identify significant associations and avoid reliance on Bonferroni multiple-testing corrections.
• Optimal Properties for Genetic Analysis: Combines advantages of case-control and family-based designs by controlling heterogeneity while retaining efficiency for detecting liability genes.

Scientific Applications:

• Genetic Studies of Complex Disorders: Identify genetic factors and liability genes contributing to complex diseases and disorders.
• Single Nucleotide Polymorphism (SNP) Analysis: Analyze dense SNP datasets from genomic studies.

Methodology:

Implements Bayesian inference using Markov chain Monte Carlo (MCMC) algorithms and Bayesian outlier detection, with explicit adjustments for population stratification and cryptic relatedness.