Fundamentally, these approaches to bias are best complemented by replication of pathway analysis findings in independent data sets. Strategies for pathway analyses can flexibly adapt to differences across data sets, and while these differences might impact SNP- or gene-level statistics [66], legitimately-associated pathways would be expected to exhibit significance or a strongly-trending signal across multiple studies. In this effort, a systematic framework illustrating key choices in pathway analyses (Figure 2) will limit major contributors of variance across studies and will guide investigators in selecting approaches that fit their study goals.
