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

Abstract

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 R2. Although our pathway-motivated regressions did not improve prediction of diastolic blood pressure, merging gene networks did identify several plausible pathways for hypertension.

Pathway Analysis Approaches for Rare and Common Variants: Insights From Genetic Analysis Workshop 18

“…The three contributions that used simulated data [Almeida et al., ; Greco et al., ; Hu and Paterson, ] had the explicit knowledge of which genes were causal in the pathogenesis of hypertension; the other analyses relied on various proxy measures of causality. For example, both Dufresne et al [] and Edwards et al [] used only exonic variants, and all other analyses were restricted to variants that could be mapped to known genes. Aslibekyan et al [] explicitly compared variance contributions of SNVs within known blood pressure pathway genes with those of SNVs located within 50 kb upstream of the transcription start site and within 50 kb downstream of the stop codon; they found that genic regions played a more prominent role in the genetic architecture of hypertension than the flanking regions.…”

“…Furthermore, despite a large number of options for pathway definitions, genome coverage was still low. For example, using Cytoscape [Shannon et al., ], Dufresne et al [] identified pathways for only 35% of the candidate genes (120 annotated genes of 600 in the selected list).…”

“…An important factor motivating all seven approaches was lowering the astronomical multiple‐testing burden of whole‐genome sequence analyses by incorporating a priori biological knowledge. However, reducing the number of tested hypotheses to the number of predefined pathways rarely yielded statistically significant results, even with relatively liberal correction methods such as the false discovery rate [Alsulami et al., ; Dufresne et al., ; Hu and Paterson, ]. Moreover, Greco et al [] reported poor performance of various collapsing techniques in the context of large sets of SNVs, suggesting that the reduction in analytic dimensions either was insufficient or masked causal signal.…”

“…The four remaining research groups attempted to find pathways that were significantly associated with the real or simulated phenotypes. Dufresne et al [] started by calculating gene‐based phenotype associations with DBP, adjusting for the complex pedigree structure in a mixed‐effects model [Oualkacha et al., ]. They then used Cytoscape [Shannon et al., ] to determine whether any pathways showed an overabundance of genes with at least modest association with DBP ( P < 0.05).…”

“…For example, both Dufresne et al [2014] and Edwards et al [2014] used only exonic variants, and all other analyses were restricted to variants that could be mapped to known genes. Aslibekyan et al [2014] explicitly compared variance contributions of SNVs within known blood pressure pathway genes with those of SNVs located within 50 kb upstream of the transcription start site and within 50 kb downstream of the stop codon; they found that genic regions played a more prominent role in the genetic architecture of hypertension than the flanking regions.…”