Future work could attempt to enhance several other aspects of our pathway enrichment analysis. For example, here we mapped SNPs to genes only based on physical distance, while potential improvements could be attained by incorporating additional information, such as eQTL data[40] and functional annotations, to assign weights to different association signals within a locus. While our approach is amenable to such a weighting scheme, this would potentially require the introduction of tuneable parameters, which we avoided so far. Furthermore, one may attempt to redefine gene sets based on external unbiased large-scale molecular data, such as expression data, while so far we only used the established (but likely biased) pathway collections[4]. To this end, we already integrated Pascal into a pipeline to analyze the connectivity between trait-associated genes across over 400 tissue-specific regulatory, co-expression and protein-protein interaction networks, further demonstrating its value for network-based analysis of GWAS results (Marbach et al., submitted).
