GWAMA

GWAMA performs meta-analysis of genome-wide association study (GWA) summary statistics to detect genetic loci associated with binary and quantitative phenotypes.

Key Features:

• Meta-Analysis Models: Supports fixed-effect and random-effect meta-analyses for directly genotyped and imputed single nucleotide polymorphisms (SNPs), reporting allelic odds ratios with 95% confidence intervals for binary traits and allelic effect sizes with standard errors for quantitative phenotypes.
• Summary-Statistics Integration: Integrates summary statistics from multiple GWA studies for combined analysis.
• Heterogeneity Handling: Accounts for between-study variability and heterogeneity through random-effect modeling.
• Error Trapping: Incorporates robust error trapping facilities to detect and handle input and computational issues.
• Data Formatting and Visualization: Provides scripts to format input files from individual association studies and tools to generate graphical summaries of genome-wide meta-analysis results.

Scientific Applications:

• Statistical Power Enhancement: Combines data from multiple GWA studies within the same population to increase power to detect genetic associations.
• Genetic Locus Discovery: Identifies loci associated with complex traits such as type 2 diabetes and obesity.
• Genetics and Epidemiology Research: Supports investigations into the genetic basis of diseases to inform diagnostic and therapeutic research.

Methodology:

Integrates summary statistics from multiple GWA studies and applies fixed-effect and random-effect meta-analysis models to estimate allelic odds ratios with 95% confidence intervals for binary traits and allelic effect sizes with standard errors for quantitative traits, while accounting for between-study heterogeneity and supporting directly genotyped and imputed SNPs.