Pbine

Pbine applies numerical integration to combine p-values across studies to increase statistical power and improve significance estimation in meta-analyses for GWASs and TWASs.

Key Features:

• Numerical Integration Method: Uses a numerical integration approach to combine p-values from multiple studies.
• Enhanced Statistical Power: Integrates information across studies to increase power for detecting subtle effects.
• Efficiency and Accuracy: Demonstrates computational efficiency across sample sizes and accurate estimation of statistical significance, suitable for large-scale GWASs and TWASs.
• Control of False Positives: Provides improved control of false-positive rates compared with Fisher's method under independence assumptions.
• Comparison to Traditional Methods: Addresses limitations of Fisher's method, permutation, and decorrelation approaches regarding independence assumptions, computational efficiency, and accuracy.

Scientific Applications:

• Genome-Wide Association Studies (GWASs): Combines p-value data from multiple GWASs to help identify genomic regions associated with complex diseases such as rheumatoid arthritis and asthma.
• Transcriptome-Wise Association Studies (TWASs): Increases statistical significance in individual TWASs to aid discovery of gene expression patterns linked to various conditions.

Methodology:

Pbine combines p-values via numerical integration and uses simulation studies to evaluate Type I error rates, statistical power, computational efficiency, and accuracy of significance estimation.