Pathway analysis following association study

详细信息    查看全文

• 作者：Julius S Ngwa (1)
  Alisa K Manning (1)
  Jonna L Grimsby (2)
  Chen Lu (1)
  Wei V Zhuang (1)
  Anita L DeStefano (1) (3)
  
• 刊名：BMC Proceedings
• 出版年：2011
• 出版时间：December 2011
• 年：2011
• 卷：5
• 期：9-supp
• 全文大小：183KB
• 参考文献：1. Zhong H, Yang X, Kaplan LM, Molony C, Schadt EE: Integrating pathway analysis and genetics of gene expression for genome-wide association studies. / Am J Hum Genet 2010, 86:581鈥?91. CrossRef
  2. Subramanian A, Tamayo P, Mootha VK, Mukherjee S, Ebert BL, Gillette MA, Paulovich A, Pomeroy SL, Golub TR, Lander ES, Mesirov JP: Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles. / Proc Natl Acad Sci U S A 2005, 102:15545鈥?5550. CrossRef
  3. Mootha VK, Lindgren CM, Eriksson KF, Subramanian A, Sihag S, Lehar J, Puigserver P, Carlsson E, Ridderstr氓le M, Laurila E, / et al.: PGC-1alpha-responsive genes involved in oxidative phosphorylation are coordinately downregulated in human diabetes. / Nat Genet 2003, 34:267鈥?73. CrossRef
  4. Wang K, Li M, Bucan M: Pathway-based approaches for analysis of genomewide association studies. / Am J Hum Genet 2007, 81:1278鈥?283. CrossRef
  5. Ingenuity Pathway Analysis (IPA) (Ingenuity System) [http://www.ingenuity.com]
  6. Almasy LA, Dyer TD, Peralta JM, Kent JW Jr, Charlesworth JC, Curran JE, Blangero J: Genetic Analysis Workshop 17 mini-exome simulation. / BMC Proceedings 2011,5(Suppl 9):S2. CrossRef
  7. Han F, Pan W: A data-adaptive sum test for disease association with multiple common or rare variants. / Hum Hered 2010, 70:42鈥?4. CrossRef
  8. Li B, Leal SM: Methods for detecting associations with rare variants for common diseases: application to analysis of sequence data. / Am J Hum Genet 2008, 83:311鈥?21. CrossRef
  9. Purcell S, Neale B, Todd-Brown K, Thomas L, Ferreira MA, Bender D, Maller J, Sklar P, de Bakker PI, Daly MJ, / et al.: PLINK: a tool set for whole-genome association and population-based linkage analyses. / Am J Hum Genet 2007, 81:559鈥?75. CrossRef
  10. R Development Core Team: A language and environment for statistical computing. [http://www.R-project.org] R Foundation for Statistical Computing, Vienna, Austria; 2010.
  11. Chasman DI: On the utility of gene set methods in genomewide association studies of quantitative traits. / Genet Epidemiol 2008, 32:658鈥?68. CrossRef
  12. GSEA: Molecular Signature Database MSigDB [http://www.broadinstitute.org/gsea/msigdb]
  
• 作者单位：Julius S Ngwa (1)
  Alisa K Manning (1)
  Jonna L Grimsby (2)
  Chen Lu (1)
  Wei V Zhuang (1)
  Anita L DeStefano (1) (3)
  
  1. Department of Biostatistics, School of Public Health, Boston University, 715 Albany Street, Boston, MA, 02118, USA
  2. General Medicine Division, Massachusetts General Hospital; and Harvard Medical School, 250 Longwood Avenue, Boston, MA, 02115, USA
  3. Department of Neurology, Boston University School of Medicine, 72 East Concord Street, Boston, MA, 02118, USA
  

文摘

Genome-wide association studies often emphasize single-nucleotide polymorphisms with the smallest p-values with less attention given to single-nucleotide polymorphisms not ranked near the top. We suggest that gene pathways contain valuable information that can enable identification of additional associations. We used gene set information to identify disease-related pathways using three methods: gene set enrichment analysis (GSEA), empirical enrichment p-values, and Ingenuity pathway analysis (IPA). Association tests were performed for common single-nucleotide polymorphisms and aggregated rare variants with traits Q1 and Q4. These pathway methods were evaluated by type I error, power, and the ranking of the VEGF pathway, the gene set used in the simulation model. GSEA and IPA had high power for detecting the VEGF pathway for trait Q1 (91.2% and 93%, respectively). These two methods were conservative with deflated type I errors (0.0083 and 0.0072, respectively). The VEGF pathway ranked 1 or 2 in 123 of 200 replicates using IPA and ranked among the top 5 in 114 of 200 replicates for GSEA. The empirical enrichment method had lower power and higher type I error. Thus pathway analysis approaches may be useful in identifying biological pathways that influence disease outcomes.