JASS

JASS performs joint analysis of genome-wide association study (GWAS) summary statistics to detect multivariate genetic associations across multiple phenotypes.

Key Features:

• Joint Statistical Analysis: Computes joint statistics over sets of selected GWAS summary statistics to enable multivariate association testing.
• Advanced Joint Tests: Integrates omnibus tests and weighted sum of Z-score tests for detecting associations across phenotypes.
• Data Cleaning and Harmonization: Provides routines for cleaning and harmonizing GWAS summary statistics prior to analysis.
• Efficient Algorithm Implementation: Implements an efficient algorithm to derive joint statistics rapidly for large-scale analyses.
• Optimized Data Management: Includes processes for optimized handling and management of large GWAS datasets.
• Implementation: Implemented in Python.

Scientific Applications:

• Secondary GWAS Analyses: Facilitates secondary analyses that combine multiple GWAS summary statistics to examine multiple phenotypes simultaneously.
• Multivariate Association Discovery: Identifies genetic variants associated with human traits and diseases through joint testing across phenotypes.
• Benchmarking and Real-World Evaluation: Supports benchmark analyses and application to real datasets to assess robustness of joint tests.

Methodology:

Computes joint statistics over selected GWAS results using omnibus and weighted sum of Z-score tests; includes data cleaning and harmonization routines; employs efficient algorithms for rapid joint-statistic computation and optimized data management; implemented in Python.