Ngwa, Julius S.Manning, AlisaGrimsby, Jonna L.Lu, ChenZhuang, Wei V.DeStefano, Anita L.2012-04-092011Ngwa, Julius S., Alisa K. Manning, Jonna L. Grimsby, Chen Lu, Wei V. Zhuang, and Anita L. DeStefano. 2011. Pathway analysis following association study. BMC Proceedings 5(Suppl 9): S18.1753-6561http://nrs.harvard.edu/urn-3:HUL.InstRepos:8519648Genome-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.en-USJournal Article2012-04-0910.1186/1753-6561-5-S9-S18