Email updates

Keep up to date with the latest news and content from BMC Proceedings and BioMed Central.

This article is part of the supplement: Genetic Analysis Workshop 17: Unraveling Human Exome Data

Open Access Proceedings

Pathway analysis following association study

Julius S Ngwa1*, Alisa K Manning1, Jonna L Grimsby2, Chen Lu1, Wei V Zhuang1 and Anita L DeStefano13

Author Affiliations

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

For all author emails, please log on.

BMC Proceedings 2011, 5(Suppl 9):S18  doi:10.1186/1753-6561-5-S9-S18

Published: 29 November 2011

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.