Pathway Analysis Following Association Study
Ngwa, Julius S.
Grimsby, Jonna L.
Zhuang, Wei V.
DeStefano, Anita L.
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CitationNgwa, 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.
AbstractGenome-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.
Citable link to this pagehttp://nrs.harvard.edu/urn-3:HUL.InstRepos:8519648
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