Integrative genomics reveals novel molecular pathways and gene networks for coronary artery disease.

Ville-Petteri Mäkinen,Reijo Laaksonen,Winfried März,Stephen E Epstein,Nilesh J Samani,Thomas Quertermous,Ayellet V Segrè,Muredach P Reilly,Sekar Kathiresan,Svati H Shah,Tianxiao Huan,Ruth Mcpherson,Xia Yang,Aldons J Lusis,Jeanette Erdmann,Jun Zhu,Heribert Schunkert,Stanley L Hazen,Stefan Blakenberg,Qingying Meng,Christopher P Nelson,Sujoy Ghosh,Candace Levian,Themistocles L Assimes,Majid Nikpay,Mete Civelek,Bin Zhang,Christina Willenborg,Christopher J O’donnell ,Alexandre F.r Stewart ,Juan C Vivar

doi:10.1371/journal.pgen.1004502

Abstract

The majority of the heritability of coronary artery disease (CAD) remains unexplained, despite recent successes of genome-wide association studies (GWAS) in identifying novel susceptibility loci. Integrating functional genomic data from a variety of sources with a large-scale meta-analysis of CAD GWAS may facilitate the identification of novel biological processes and genes involved in CAD, as well as clarify the causal relationships of established processes. Towards this end, we integrated 14 GWAS from the CARDIoGRAM Consortium and two additional GWAS from the Ottawa Heart Institute (25,491 cases and 66,819 controls) with 1) genetics of gene expression studies of CAD-relevant tissues in humans, 2) metabolic and signaling pathways from public databases, and 3) data-driven, tissue-specific gene networks from a multitude of human and mouse experiments. We not only detected CAD-associated gene networks of lipid metabolism, coagulation, immunity, and additional networks with no clear functional annotation, but also revealed key driver genes for each CAD network based on the topology of the gene regulatory networks. In particular, we found a gene network involved in antigen processing to be strongly associated with CAD. The key driver genes of this network included glyoxalase I (GLO1) and peptidylprolyl isomerase I (PPIL1), which we verified as regulatory by siRNA experiments in human aortic endothelial cells. Our results suggest genetic influences on a diverse set of both known and novel biological processes that contribute to CAD risk. The key driver genes for these networks highlight potential novel targets for further mechanistic studies and therapeutic interventions.

Highlights

Coronary artery disease (CAD) remains a leading cause of death worldwide despite a variety of available interventions to reduce cardiovascular events
Our first aim was to test if any of the known biological pathways curated in Reactome, Biocarta and Kyoto Encyclopedia of Genes and Genomes (KEGG) databases [18,19] was more likely to harbor tissue-specific Expression SNPs (eSNPs) that were associated with CAD in genome-wide association studies (GWAS) (Figure 1A)
Tissuespecific eSNPs from CAD related human cells or tissues including adipose tissue, liver, human aortic endothelial cells (HAECs), blood, as well as a pooled eSNP set from multiple tissues and cell types, were used for eSNP-to-gene mapping, yielding five sets of eSNPs mapped to each pathway (Materials and Methods)

Summary

Introduction

Coronary artery disease (CAD) remains a leading cause of death worldwide despite a variety of available interventions to reduce cardiovascular events. The SNP associations themselves rarely provide evidence on their downstream functional consequences, which has prompted the need to integrate DNA variants with functional data to better understand the pathogenic processes. Genes and their downstream products comprise a complex regulatory machinery that sustains the delicate homeostasis of an organism in a changing environment [5]. The eSNP sets can be directly compared with SNP-to-disease associations from a GWAS to connect gene networks to disease

Methods

Results

Conclusion