Once gene scores have been computed, pathway analysis tools use various strategies to aggregate them across sets of related genes. The most common approach used for analysing GWAS meta-analysis results, as exemplified by the popular GWAS pathway analysis tool MAGENTA, is based on binary enrichment tests, which rely on a threshold parameter to define which genes are significantly associated with the trait[3,18]. However, with this strategy potential contributions of weakly associated genes that just missed the threshold are lost and there is no clear guidance on how the threshold parameter should be set. Indeed, it seems common practice to keep the default parameter without knowing whether other choices would produce better results[5].
