Original Research ARTICLE
Pathway-wide association study implicates multiple sterol transport and metabolism genes in HDL cholesterol regulation
- 1 Center for Applied Genomics, Children’s Hospital of Philadelphia, Philadelphia, PA, USA
- 2 Zilkha Neurogenetic Institute, University of Southern California, Los Angeles, CA, USA
- 3 Cardiovascular Institute, University of Pennsylvania,Philadelphia, PA, USA
- 4 Institute for Translational Medicine and Therapeutics, University of Pennsylvania, Philadelphia, PA, USA
- 5 Department of Biostatistics and Epidemiology, University of Pennsylvania, Philadelphia, PA, USA
- 6 Cardiovascular Research Institute, MedStar Health Research Institute, Washington Hospital Center, Washington, DC, USA
- 7 Genetics Division and Drug Discovery, GlaxoSmithKline, King of Prussia, PA, USA
- 8 Department of Pediatrics, University of Pennsylvania, Philadelphia, PA, USA
Pathway-based association methods have been proposed to be an effective approach in identifying disease genes, when single-marker association tests do not have sufficient power. The analysis of quantitative traits may be benefited from these approaches, by sampling from two extreme tails of the distribution. Here we tested a pathway association approach on a small genome-wide association study (GWAS) on 653 subjects with extremely high high-density lipoprotein cholesterol (HDL-C) levels and 784 subjects with low HDL-C levels. We identified 102 genes in the sterol transport and metabolism pathways that collectively associate with HDL-C levels, and replicated these association signals in an independent GWAS. Interestingly, the pathways include 18 genes implicated in previous GWAS on lipid traits, suggesting that genuine HDL-C genes are highly enriched in these pathways. Additionally, multiple biologically relevant loci in the pathways were not detected by previous GWAS, including genes implicated in previous candidate gene association studies (such as LEPR, APOA2, HDLBP, SOAT2), genes that cause Mendelian forms of lipid disorders (such as DHCR24), and genes expressing dyslipidemia phenotypes in knockout mice (such as SOAT1, PON1). Our study suggests that sampling from two extreme tails of a quantitative trait and examining genetic pathways may yield biological insights from smaller samples than are generally required using single-marker analysis in large-scale GWAS. Our results also implicate that functionally related genes work together to regulate complex quantitative traits, and that future large-scale studies may benefit from pathway-association approaches to identify novel pathways regulating HDL-C levels.
Keywords: GWAS, lipid, HDL-C, pathway analysis, cholesterol, sterol transport, sterol metabolism, genetic association
Citation: Wang K, Edmondson AC, Li M, Gao F, Qasim AN, Devaney JM, Burnett MS, Waterworth DM, Mooser V, Grant SFA, Epstein SE, Reilly MP, Hakonarson H and Rader DJ (2011) Pathway-wide association study implicates multiple sterol transport and metabolism genes in HDL cholesterol regulation. Front. Gene. 2:41. doi: 10.3389/fgene.2011.00041
Received: 05 May 2011; Paper pending published: 26 May 2011;
Accepted: 21 June 2011; Published online: 05 July 2011.
Edited by:Robert Klein, Memorial Sloan-Kettering Cancer Center, USA
Reviewed by:John Charles Huber, Texas A&M Health Science Center School of Rural Public Health, USA
Yiran Guo, Children’s Hospital of Philadelphia, USA
Sarah Buxbaum, Jackson State University, USA
Peter Holmans, Cardiff University, UK
Copyright: © 2011 Wang, Edmondson, Li, Gao, Qasim, Devaney, Burnett, Waterworth, Mooser, Grant, Epstein, Reilly, Hakonarson and Rader. This is an open-access article subject to a non-exclusive license between the authors and Frontiers Media SA, which permits use, distribution and reproduction in other forums, provided the original authors and source are credited and other Frontiers conditions are complied with.
*Correspondence: Kai Wang, University of Southern California Los Angeles, 1501 San Pablo Street, ZNI 221, Los Angeles, CA 90089, USA. e-mail: firstname.lastname@example.org