Pathway analysis for genetic association studies: to do, or not to do? That is the question

Area of Science:

• Genetics
• Bioinformatics
• Cardiovascular Disease Research

Background:

• Diastolic blood pressure (DBP) is a key cardiovascular risk factor.
• Genetic variation influences DBP, but identifying specific genetic contributors remains challenging.
• Pathway analysis offers a systems biology approach to understand complex genetic influences.

Purpose of the Study:

• To investigate the utility of pathway analysis in enhancing models for predicting DBP phenotypes.
• To identify genetic pathways associated with DBP using a multi-stage approach.
• To assess if pathway-informed genetic analysis improves DBP prediction accuracy.

Main Methods:

• Gene-based association tests were performed on family data (approx. 800 individuals).
• Network and pathway enrichment analyses were conducted using Cytoscape.
• Partial least squares regression was applied to predict DBP using selected single-nucleotide polymorphisms (SNPs).

Main Results:

• Over 600 genes showed association with DBP phenotypes (p<0.05) in stage 1.
• Pathway analysis identified 4 enriched pathways, with 3 showing interaction, suggesting a role in hypertension.
• Despite pathway focus, regression models did not improve cross-validated R-squared for DBP prediction.

Conclusions:

• Merging gene networks successfully identified plausible biological pathways implicated in hypertension.
• Pathway analysis, while not improving predictive accuracy in this study, provides valuable insights into hypertension's genetic architecture.
• Further research is warranted to refine pathway-based approaches for complex trait prediction.