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

In Genetic Analysis Workshop 18 data, we used a 3-stage approach to explore the benefits of pathway analysis in improving a model to predict 2 diastolic blood pressure phenotypes as a function of genetic variation. At stage 1, gene-based tests of association in family data of approximately 800 individuals found over 600 genes associated at p<0.05 for each phenotype. At stage 2, networks and enriched pathways were estimated with Cytoscape for genes from stage 1, separately for the 2 phenotypes, then examining network overlap. This overlap identified 4 enriched pathways, and 3 of these pathways appear to interact, and are likely candidates for playing a role in hypertension. At stage 3, using 157 maximally unrelated individuals, partial least squares regression was used to find associations between diastolic blood pressure and single-nucleotide polymorphisms in genes highlighted by the pathway analyses. However, we saw no improvement in the adjusted cross-validated R (2). Although our pathway-motivated regressions did not improve prediction of diastolic blood pressure, merging gene networks did identify several plausible pathways for hypertension.