Hypothesis-based analysis of gene-gene interactions and risk of myocardial infarction.

• Main Authors: Gavin Lucas, Carla Lluís-Ganella, Isaac Subirana, Muntaser D Musameh, Juan Ramon Gonzalez, Christopher P Nelson, Mariano Sentí, Myocardial Infarction Genetics Consortium, Wellcome Trust Case Control Consortium, Stephen M Schwartz, David Siscovick, Christopher J O'Donnell, Olle Melander, Veikko Salomaa, Shaun Purcell, David Altshuler, Nilesh J Samani, Sekar Kathiresan, Roberto Elosua
• Format: Article
• Language: English
• Published: Public Library of Science (PLoS) 2012-01-01
• Series: PLoS ONE
• Online Access: http://europepmc.org/articles/PMC3410908?pdf=render

The genetic loci that have been found by genome-wide association studies to modulate risk of coronary heart disease explain only a fraction of its total variance, and gene-gene interactions have been proposed as a potential source of the remaining heritability. Given the potentially large testing burden, we sought to enrich our search space with real interactions by analyzing variants that may be more likely to interact on the basis of two distinct hypotheses: a biological hypothesis, under which MI risk is modulated by interactions between variants that are known to be relevant for its risk factors; and a statistical hypothesis, under which interacting variants individually show weak marginal association with MI. In a discovery sample of 2,967 cases of early-onset myocardial infarction (MI) and 3,075 controls from the MIGen study, we performed pair-wise SNP interaction testing using a logistic regression framework. Despite having reasonable power to detect interaction effects of plausible magnitudes, we observed no statistically significant evidence of interaction under these hypotheses, and no clear consistency between the top results in our discovery sample and those in a large validation sample of 1,766 cases of coronary heart disease and 2,938 controls from the Wellcome Trust Case-Control Consortium. Our results do not support the existence of strong interaction effects as a common risk factor for MI. Within the scope of the hypotheses we have explored, this study places a modest upper limit on the magnitude that epistatic risk effects are likely to have at the population level (odds ratio for MI risk 1.3-2.0, depending on allele frequency and interaction model).