Chunk #57 — Materials and Methods — Meta-Analysis Gene-set Enrichment of variaNT Associations (MAGENTA) — Step 4: Gene set enrichment analysis of genome-wide association data
To test for over-representation of genes with modest genetic effects on a complex disease or trait in predefined sets of genes, we developed a gene set enrichment analysis (GSEA) algorithm that is applied to gene association p-values adjusted for confounding effects. This algorithm does not require the genotypes of individuals in the association scans in order to estimate gene set enrichment significance. Our GSEA test was inspired by the original GSEA algorithm applied to expression data [9], [24], more recently modified for SNP association data [23], [25], [28], [32], but uses a different statistical test. The null hypothesis is that the gene association score ranks of all genes with index g that belong to a given gene set gs are randomly distributed. The alternative hypothesis is that there is an over-representation in gene set gs of gene score ranks above a given rank cutoff compared to multiple random gene sets of identical size that were randomly sampled from all genes in the genome.
