Variable set enrichment analysis in genome-wide association studies

Abstract

Complex diseases such as hypertension are inherently multifactorial and involve many factors of mild-to-minute effect sizes. A genome-wide association study (GWAS) typically tests hundreds of thousands of single-nucleotide polymorphisms (SNPs), and offers opportunity to evaluate aggregated effects of many genetic variants with effects that are too small to detect individually. The gene-set-enrichment analysis (GSEA) is a pathway-based approach that tests for such aggregated effects of genes that are linked by biological functions. A key step in GSEA is the summary statistic (gene score) used to measure the overall relevance of a gene based on all SNPs tested in the gene. Existing GSEA methods use maximum statistics sensitive to gene size and linkage equilibrium. We propose the approach of variable set enrichment analysis (VSEA) and study new gene score methods that are less dependent on gene size. The new method treats groups of variables (SNPs or other variants) as base units for summarizing gene scores and relies less on gene definition itself. The power of VSEA is analyzed by simulation studies modeling various scenarios of complex multiloci interactions. Results show that the new gene scores generally performed better, some substantially so, than existing GSEA extension to GWAS. The new methods are implemented in an R package and when applied to a real GWAS data set demonstrated its practical utility in a GWAS setting.

Pathway Analysis Using Information from Allele-Specific Gene Methylation in Genome-Wide Association Studies for Bipolar Disorder

“…Similarly, for genes in the non-ASM set, the smallest p-value ( min-p ) of all SNPs in a gene region was used to represent the gene-wise statistic values. To account for potential bias caused by using minimum p-values to represent gene-wise statistic for genes with various sizes, we adopted the method by Yang and colleagues [23] to calculate normalized gene scores via 10,000 permutations for all the genes we analyzed. For each gene, a gene-size adjusted gene score was calculated and used in the following pathway analyses.…”

Pathway-based genome-wide association analysis of coronary heart disease identifies biologically important gene sets

“…8 It is based on the method called GSEA, originally developed for differential gene expression analysis. GSEA derives an enrichment score to detect gene sets significantly enriched with differentially expressed genes.…”

“…The P-values were then calculated from the frequency of seeing a larger enrichment score in the observed than in the shuffled dataset. 8 For the VSEA analyses described in this paper, we used a library of 1395 gene sets compiled from the collections of the genetic pathways, molecular functions, and/or biological processes in the Kyoto Encyclopedia of Genes and Genomes (www.genome.ad.jp/kegg/pathway.html), BioCarta (http://www.biocarta. com/Default.aspx), and Gene Ontology (http://geneontology.org/) databases.…”

“…com/Default.aspx), and Gene Ontology (http://geneontology.org/) databases. 8 Based on the manufacturer's annotation of the Affymetrix 500K GeneChip array, we refined the gene-set library by removing genes that had no SNP included on the genotyping array. To reduce the impact of multiple testing and to avoid testing overly narrow or broad functional categories, our analysis only considered gene sets and the pathways that contained at least 3 and at most 200 genes.…”

“…8 In this report, we hypothesize that by applying VSEA to GWA data from the Framingham Heart Study, we will identify gene sets associated with coronary heart disease (CHD) that would be otherwise missed by conventional single-SNP analyses.…”