Selected article for: "enrichment score and gene gene set"
Author: Zhang, Kunlin; Cui, Sijia; Chang, Suhua; Zhang, Liuyan; Wang, Jing
Title: i-GSEA4GWAS: a web server for identification of pathways/gene sets associated with traits by applying an improved gene set enrichment analysis to genome-wide association study
  • Document date: 2010_7_1
    • ID: jjfcuybb_7
    Snippet: In i-GSEA, we implement SNP label permutation instead of phenotype label permutation to analyze SNP P-values and to correct gene and gene set variation and multiply k/K to the ES to get the significance proportion based enrichment score (SPES), where k is the proportion of significant genes of the gene set and K is the proportion of significant genes of the total genes in the GWAS. Here, significant genes are defined as the genes mapped with at l.....
    Document: In i-GSEA, we implement SNP label permutation instead of phenotype label permutation to analyze SNP P-values and to correct gene and gene set variation and multiply k/K to the ES to get the significance proportion based enrichment score (SPES), where k is the proportion of significant genes of the gene set and K is the proportion of significant genes of the total genes in the GWAS. Here, significant genes are defined as the genes mapped with at least one of the top 5% of all SNPs. Instead of ES which focuses on the total significance coming from either a few or many significant genes, SPES emphasizes on total significance coming from high proportion of significant genes. So, i-GSEA trends to pick up pathways/gene sets including a high proportion of significant genes and is more appropriate for study of the combined effects of possibly modest SNPs/genes in complex disease. This ensures i-GSEA the improved sensitivity.

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