Non-linear interactions between candidate genes of myocardial infarction revealed in mRNA expression profiles.

ABSTRACT:

Background

Alterations 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.

Results

We 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 mi

SUBMITTER: Hartmann K 

PROVIDER: S-EPMC5027110 | biostudies-literature | 2016 Sep

REPOSITORIES: biostudies-literature