title: Pathway analysis following association study.
abstract: 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.
authors.affiliation: Department of Biostatistics, School of Public Health, Boston University, 715 Albany Street, Boston, MA 02118, USA. ngwaj@bu.edu.
authors.forename: Julius S
authors.initials: JS
authors.lastname: Ngwa
authors.forename: Alisa K
authors.initials: AK
authors.lastname: Manning
authors.forename: Jonna L
authors.initials: JL
authors.lastname: Grimsby
authors.forename: Chen
authors.initials: C
authors.lastname: Lu
authors.forename: Wei V
authors.initials: WV
authors.lastname: Zhuang
authors.forename: Anita L
authors.initials: AL
authors.lastname: Destefano