Genomics & Informatics

Korea Genome Organization (한국유전체학회)
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Performance Comparison of Two Gene Set Analysis Methods for Genome-wide Association Study Results: GSA-SNP vs i-GSEA4GWAS

  • Kwon, Ji-Sun (Department of Bioinformatics and Life Science, Soongsil University) ;
  • Kim, Ji-Hye (Department of Bioinformatics and Life Science, Soongsil University) ;
  • Nam, Doug-U (School of Nano-Bioscience and Chemical Engineering, Ulsan National Institute of Science and Technology) ;
  • Kim, Sang-Soo (Department of Bioinformatics and Life Science, Soongsil University)
  • Received : 2012.05.04
  • Accepted : 2012.05.22
  • Published : 2012.06.30
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Abstract

Gene set analysis (GSA) is useful in interpreting a genome-wide association study (GWAS) result in terms of biological mechanism. We compared the performance of two different GSA implementations that accept GWAS p-values of single nucleotide polymorphisms (SNPs) or gene-by-gene summaries thereof, GSA-SNP and i-GSEA4GWAS, under the same settings of inputs and parameters. GSA runs were made with two sets of p-values from a Korean type 2 diabetes mellitus GWAS study: 259,188 and 1,152,947 SNPs of the original and imputed genotype datasets, respectively. When Gene Ontology terms were used as gene sets, i-GSEA4GWAS produced 283 and 1,070 hits for the unimputed and imputed datasets, respectively. On the other hand, GSA-SNP reported 94 and 38 hits, respectively, for both datasets. Similar, but to a lesser degree, trends were observed with Kyoto Encyclopedia of Genes and Genomes (KEGG) gene sets as well. The huge number of hits by i-GSEA4GWAS for the imputed dataset was probably an artifact due to the scaling step in the algorithm. The decrease in hits by GSA-SNP for the imputed dataset may be due to the fact that it relies on Z-statistics, which is sensitive to variations in the background level of associations. Judicious evaluation of the GSA outcomes, perhaps based on multiple programs, is recommended.

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References

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