Statistical methods for pathway analysis of genome-wide data for association with complex genetic traits

Area of Science:

• Genetics and Bioinformatics
• Statistical Genomics

Background:

• Pathway analysis methods were initially designed for gene expression data.
• Recent advancements enable pathway analysis on genome-wide association study (GWAS) data.

Purpose of the Study:

• To provide a comprehensive overview of statistical methods for pathway analysis in GWAS.
• To guide researchers in selecting appropriate pathway analysis techniques.
• To discuss factors influencing analysis power and unresolved statistical issues.

Main Methods:

• Review of various statistical approaches for pathway analysis.
• Detailed comparison of method strengths and weaknesses.
• Discussion of gene coverage and pathway selection strategies.

Main Results:

• Identification of different statistical methods applicable to GWAS pathway analysis.
• Elucidation of factors impacting the power of these analyses.
• Highlighting of current challenges and unresolved statistical questions.

Conclusions:

• Understanding diverse pathway analysis methods is crucial for genetic trait association studies.
• Proper selection of methods and consideration of influencing factors enhance study power.
• Availability of software tools facilitates pathway analysis in GWAS.