In the context of genetic association data, GSEA has been suggested to be a promising approach to identify sets of functionally related genes, such as biological pathways, enriched for associations of modest effects (hard to detect with single-marker analysis) on a polygenic disease or trait [23]. Several groups have begun to apply different variations of GSEA to GWA studies to study disorders such as Parkinson's disease [23], dyslipidemia [25], T2D [26]–[28], Crohn's disease [25], [29], [30], and multiple sclerosis [31]. While the principal concept is similar in these studies, alternative implementations differ substantially, for example in how genes are scored or enrichment is evaluated. In addition, researchers have only begun to evaluate the ranges of parameters (e.g. effect size or fraction of causal genes) under which gene-set approaches have power to identify associations not found by single-variant analysis [32], [33].
