TS

TS applies a truncated statistic gene-based association test to GWAS summary data to detect novel disease-associated genes in complex traits.

Key Features:

• Truncated Statistic Methodology: Employs a truncated statistic approach to prioritize genes contributing to genetic associations using GWAS summary data.
• Gene-Based Analysis: Performs gene-level association tests rather than single-variant tests to facilitate integration with functional and pathogenic investigations.
• Robustness to Variant Effects: Maintains power when causal variants have heterogeneous effect directions and addresses limitations of burden and quadratic tests.
• Performance Compared to Existing Tests: Demonstrates improved identification of disease-associated genes relative to comparable gene-based methods according to reported evaluations.

Scientific Applications:

• Complex Disease Research: Applied to GWAS summary data for complex diseases including schizophrenia and type 2 diabetes (T2D) to identify associated genes.
• Identification of Novel Genes: Facilitates discovery of novel disease-associated genes and provides insights into potential mechanisms underlying associated traits.

Methodology:

Implements a truncated statistic for gene-based association testing; extensive simulation studies demonstrate comparative performance; implemented as a C program named TS.