Current approaches for gene set analysis are still in an early stage of development. When different analysis methods are used, the resulting significant gene sets often vary substantially, even when the same dataset is used [2,3]. One possible reason might be the lack of statistical power in the tests, which are often borrowed from gene set analysis for microarray gene expression data. For many diseases, compared to the amount of differentiation in gene expression levels, effect sizes for SNPs that contribute to disease risk or are in linkage disequilibrium (LD) with the causal variants are typically much smaller. In a recent simulation study [4], we found for gene sets consisting of markers weakly associated with disease (nominal P-value < 0.05), all three gene set analysis methods examined – Gene Set Enrichment Analysis (GSEA) [5], Fisher’s exact test, and SNP Ratio Test [6] – lacked statistical power for detecting disease associated gene sets. Several recent studies also indicated that gene set analysis results are often prone to sources of bias including gene set size, LD patterns and overlapping genes [3,5,7,8]. Before gene set based approaches are used to draw significant conclusions, the limitations in these methods must be addressed first.
