Abstract
BackgroundAlterations in gene expression are key events in disease etiology and risk. Poor reproducibility in detecting differentially expressed genes across studies suggests individual genes may not be sufficiently informative for complex diseases, such as myocardial infarction (MI). Rather, dysregulation of the ‘molecular network’ may be critical for pathogenic processes. Such a dynamic network can be built from pairwise non-linear interactions.ResultsWe investigate non-linear interactions represented in mRNA expression profiles that integrate genetic background and environmental factors. Using logistic regression, we test the association of individual GWAS-based candidate genes and non-linear interaction terms (between these mRNA expression levels) with MI. Based on microarray data in CATHGEN (CATHeterization in GENetics) and FHS (Framingham Heart Study), we find individual genes and pairs of mRNAs, encoded by 41 MI candidate genes, with significant interaction terms in the logistic regression model. Two pairs replicate between CATHGEN and FHS (CNNM2|GUCY1A3 and CNNM2|ZEB2).Analysis of RNAseq data from GTEx (Genotype-Tissue Expression) shows that 20 % of these disease-associated RNA pairs are co-expressed, further prioritizing significant interactions. Because edges in sparse co-expression networks formed solely by the 41 candidate genes are unlikely to represent direct physical interactions, we identify additional RNAs as links between network pairs of candidate genes. This approach reveals additional mRNAs and interaction terms significant in the context of MI, for example, the path CNNM2|ACSL5|SCARF1|GUCY1A3, characterized by the common themes of magnesium and lipid processing.ConclusionsThe results of this study support a role for non-linear interactions between genes in MI and provide a basis for further study of MI systems biology. mRNA expression profiles encoded by a limited number of candidate genes yield sparse networks of MI-relevant interactions that can be expanded to include additional candidates by co-expression analysis. The non-linear interactions observed here inform our understanding of the clinical relevance of gene-gene interactions in the pathophysiology of MI, while providing a new strategy in developing clinical biomarker panels.Electronic supplementary materialThe online version of this article (doi:10.1186/s12864-016-3075-6) contains supplementary material, which is available to authorized users.
Highlights
Alterations in gene expression are key events in disease etiology and risk
Focusing on established candidate genes, we searched for: (1) individual of mRNA transcripts and (2) interactions between mRNA transcripts, significantly associated with myocardial infarction (MI) status, noting those that replicate between the two cohorts
Genome-Wide Association Studies (GWAS) based candidate genes in MI The coronary artery disease (CAD) Genome Wide Association Study performed by the CARDIOGRAMplusC4D consortium published in 2014, including >60,000 cases and 130,000 controls, identified 45 loci associated with myocardial infarction and CAD at genome-wide significance (Additional file 1) [1, 2]
Summary
Poor reproducibility in detecting differentially expressed genes across studies suggests individual genes may not be sufficiently informative for complex diseases, such as myocardial infarction (MI). Dysregulation of the ‘molecular network’ may be critical for pathogenic processes Such a dynamic network can be built from pairwise non-linear interactions. Genetic risk of disease at the population level cannot be accounted for by individual genetic variants in single genes or even by summing the individual effects of dozens of genes – a gap often referred to as the ‘missing heritability’ [3, 4]. Other factors may involve additional loci not detectable by GWAS, non-additive interactions between candidate genes, and external factors modulating gene expression and interactions [7, 8]. Instead of restricting the analysis to genetic variants, we focus on RNA expression levels that integrate multiple factors including genetic differences influencing expression, genegene interactions, feedback mechanisms, and environmental influence
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