As described above, Pascal computes aggregate statistics without the need for defining a set of significant genes. We thus sought to compare this strategy with methods based on the hypergeometric or rank-sum tests. To this end, we tested performance on association results for four blood lipid traits obtained from of the CoLaus cohort[27]. We used a large meta-analysis of 188,577 individuals to define a reference set of associated pathways for each of the four lipid traits[23]. We then applied both pathway analysis methods to three non-overlapping, small subsets (1500 individuals) of the CoLaus study and compared how well the resulting pathways matched the reference set from the large study. We used the area under the precision-recall curve (AUC-PR) to quantify the performance of each method. Note that our choice was driven by the fact that precision-recall curves are preferred over receiver-operator-characteristic (ROC) curves when only a small fraction of tested pathways are in the reference set[30]. Our results show that Pascal outperforms both the hypergeometric and rank-sum based approaches (Fig 4A). Importantly, the better performance of Pascal is observed across
