ArticleBMC proceedings2009
Comparing gene set analysis methods on single-nucleotide polymorphism data from Genetic Analysis Workshop 16.
Article in BMC proceedings, 2009. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 34 papers, 1 of them a synthesis that pooled it.
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Who cites it
34 citing papers in PubMed, 1 synthesis or guideline pooled it.
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- Changes in selective pressures associated with human population expansion may explain metabolic and immune related pathways enriched for signatures of positive selection.BMC genomics · 2016Article
- The statistical properties of gene-set analysis.Nature reviews. Genetics · 2016Review
- Detection of convergent genome-wide signals of adaptation to tropical forests in humans.PloS one · 2015Article
- META-GSA: Combining Findings from Gene-Set Analyses across Several Genome-Wide Association Studies.PloS one · 2015Article
- Widespread signals of convergent adaptation to high altitude in Asia and america.American journal of human genetics · 2014Article
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4 authors.
Funding
Abstract
Recently, gene set analysis (GSA) has been extended from use on gene expression data to use on single-nucleotide polymorphism (SNP) data in genome-wide association studies. When GSA has been demonstrated on SNP data, two popular statistics from gene expression data analysis (gene set enrichment analysis [GSEA] and Fisher's exact test [FET]) have been used. However, GSEA and FET have shown a lack of power and robustness in the analysis of gene expression data. The purpose of this work is to investigate whether the same issues are also true for the analysis of SNP data. Ultimately, we conclude that GSEA and FET are not optimal for the analysis of SNP data when compared with the SUMSTAT method. In analysis of real SNP data from the Framingham Heart Study, we find that SUMSTAT finds many more gene sets to be significant when compared with other methods. In an analysis of simulated data, SUMSTAT demonstrates high power and better control of the type I error rate. GSA is a promising approach to the analysis of SNP data in GWAS and use of the SUMSTAT statistic instead of GSEA or FET may increase power and robustness.
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