Pathway-based analysis of GWAS datasets: effective but caution required

Area of Science:

• Genetics
• Bioinformatics
• Psychiatry

Background:

• Pathway-based analysis is an emerging method for identifying disease-causal genes from genomic datasets.
• Its application in psychiatry is relatively new, despite success in other complex diseases.
• Genome-wide association studies (GWAS) provide valuable data for pathway analysis.

Purpose of the Study:

• To discuss critical issues in pathway-based analysis for neuropsychiatric disorders.
• To compare a previous pathway analysis of GWAS data with a new analysis.
• To evaluate the power of different statistical tests for pathway analysis in GWAS.

Main Methods:

• Comparative analysis of pathway analysis methodologies.
• Application of pathway analysis to genome-wide association study datasets in neuropsychiatric disorders.
• Computation of statistical power for gene set enrichment analysis, hypergeometric test, and SNP ratio test.

Main Results:

• Pathway-based analysis is a powerful approach for identifying disease-associated genes.
• Comparison with prior studies highlights critical considerations for interpretation.
• The power of various statistical tests was computed to guide future research.

Conclusions:

• Pathway-based analysis is effective for psychiatric genetics research.
• Caution is advised when interpreting results from pathway analysis of GWAS data.
• The study provides insights to enhance future pathway-based analyses in psychiatry.