Functional and genomic context in pathway analysis of GWAS data

Area of Science:

• Genetics and Bioinformatics
• Complex Disease Research

Background:

• Gene set analysis (GSA) is valuable for identifying polygenic effects in complex diseases.
• Current GSA techniques vary widely, leading to confusion and difficulty comparing results.

Purpose of the Study:

• To provide an overview of GSA methodologies.
• To offer guidelines for enhancing the interpretability and comparability of GSA results.

Main Methods:

• Review of data sources for gene set construction.
• Overview of statistical methods for gene set association testing.
• Analysis of choices in defining gene sets, assigning SNPs, and aggregating effects.

Main Results:

• Identification of diverse approaches in GSA.
• Highlighting the impact of methodological choices on results.
• Emphasis on the need for standardized practices.

Conclusions:

• A clear understanding of GSA choices is crucial for robust and comparable findings.
• Standardized guidelines can improve the reliability of GSA in complex disease research.