Genomics & Informatics

  • 제10권2호
  • /
  • Pages.123-127
  • /
  • 2012
  • /
  • 1598-866X(pISSN)
  • /
  • 2234-0742(eISSN)
한국유전체학회 (Korea Genome Organization)
권호 표지
DOI QR코드

DOI QR Code

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)
  • 투고 : 2012.05.04
  • 심사 : 2012.05.22
  • 발행 : 2012.06.30
PDF 다운로드
⟨ 이전 논문 다음 논문 ⟩

초록

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.

키워드

참고문헌

  1. Lambert JC, Grenier-Boley B, Chouraki V, Heath S, Zelenika D, Fievet N, et al. Implication of the immune system in Alzheimer's disease: evidence from genome-wide pathway analysis. J Alzheimers Dis 2010;20:1107-1118. https://doi.org/10.3233/JAD-2010-100018
  2. Baranzini SE, Galwey NW, Wang J, Khankhanian P, Lindberg R, Pelletier D, et al. Pathway and network-based analysis of genome-wide association studies in multiple sclerosis. Hum Mol Genet 2009;18:2078-2090. https://doi.org/10.1093/hmg/ddp120
  3. Torkamani A, Topol EJ, Schork NJ. Pathway analysis of seven common diseases assessed by genome-wide association. Genomics 2008;92:265-272. https://doi.org/10.1016/j.ygeno.2008.07.011
  4. Nam D, Kim J, Kim SY, Kim S. GSA-SNP: a general approach for gene set analysis of polymorphisms. Nucleic Acids Res 2010;38:W749-W754. https://doi.org/10.1093/nar/gkq428
  5. Zhang K, Cui S, Chang S, Zhang L, Wang J. 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. Nucleic Acids Res 2010;38:W90-W95. https://doi.org/10.1093/nar/gkq324
  6. Holden M, Deng S, Wojnowski L, Kulle B. GSEA-SNP: applying gene set enrichment analysis to SNP data from genome-wide association studies. Bioinformatics 2008;24:2784-2785. https://doi.org/10.1093/bioinformatics/btn516
  7. Medina I, Montaner D, Bonifaci N, Pujana MA, Carbonell J, Tarraga J, et al. Gene set-based analysis of polymorphisms: finding pathways or biological processes associated to traits in genome-wide association studies. Nucleic Acids Res 2009;37:W340-W344. https://doi.org/10.1093/nar/gkp481
  8. Guo YF, Li J, Chen Y, Zhang LS, Deng HW. A new permutation strategy of pathway-based approach for genome-wide association study. BMC Bioinformatics 2009;10:429. https://doi.org/10.1186/1471-2105-10-429
  9. Cho YS, Chen CH, Hu C, Long J, Ong RT, Sim X, et al. Meta-analysis of genome-wide association studies identifies eight new loci for type 2 diabetes in east Asians. Nat Genet 2012;44:67-72.
  10. Cho YS, Go MJ, Kim YJ, Heo JY, Oh JH, Ban HJ, et al. A large-scale genome-wide association study of Asian populations uncovers genetic factors influencing eight quantitative traits. Nat Genet 2009;41:527-534. https://doi.org/10.1038/ng.357
  11. Ashburner M, Ball CA, Blake JA, Botstein D, Butler H, Cherry JM, et al. Gene ontology: tool for the unification of biology. Nat Genet 2000;25:25-29. https://doi.org/10.1038/75556
  12. Kanehisa M, Goto S. KEGG: kyoto encyclopedia of genes and genomes. Nucleic Acids Res 2000;28:27-30. https://doi.org/10.1093/nar/28.1.27
  13. Benjamini Y, Hochberg Y. Controlling the false discovery rate: a practical and powerful approach to multiple testing. J R Stat Soc Series B Methodol 1995;57:289-300.
  14. Subramanian A, Tamayo P, Mootha VK, Mukherjee S, Ebert BL, Gillette MA, et al. Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles. Proc Natl Acad Sci U S A 2005;102:15545-15550. https://doi.org/10.1073/pnas.0506580102

피인용 문헌

  1. Gene Set Analyses of Genome-Wide Association Studies on 49 Quantitative Traits Measured in a Single Genetic Epidemiology Dataset vol.11, pp.3, 2013, https://doi.org/10.5808/GI.2013.11.3.135
  2. Human genome-guided identification of memory-modulating drugs vol.110, pp.46, 2013, https://doi.org/10.1073/pnas.1314478110