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

Lucas, Gavin, Lluis-Ganella, Carla, Subirana, Isaac, Musameh, Muntaser D., Ramon Gonzalez, Juan, Nelson, Christopher P., Senti, Mariano, the Myocardial Infarction Genetics Consortium, the Wellcome Trust Case Control Consortium, Schwartz, Stephen M., Siscovick, David, O'Donnell, Christopher J., Melander, Olle, Salomaa, Veikko, Purcell, Shaun, Altshuler, David, Samani, Nilesh J., Kathiresan, Sekar, Elosua, Roberto and Brown, Matthew (2012) Hypothesis-based analysis of gene-gene interactions and risk of myocardial infarction. Plos One, 7 8: e41730.1-e41730.8. doi:10.1371/journal.pone.0041730

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Author Lucas, Gavin
Lluis-Ganella, Carla
Subirana, Isaac
Musameh, Muntaser D.
Ramon Gonzalez, Juan
Nelson, Christopher P.
Senti, Mariano
the Myocardial Infarction Genetics Consortium
the Wellcome Trust Case Control Consortium
Schwartz, Stephen M.
Siscovick, David
O'Donnell, Christopher J.
Melander, Olle
Salomaa, Veikko
Purcell, Shaun
Altshuler, David
Samani, Nilesh J.
Kathiresan, Sekar
Elosua, Roberto
Brown, Matthew
Total Author Count Override 214
Title Hypothesis-based analysis of gene-gene interactions and risk of myocardial infarction
Journal name Plos One   Check publisher's open access policy
ISSN 1932-6203
Publication date 2012-08-02
Year available 2012
Sub-type Article (original research)
DOI 10.1371/journal.pone.0041730
Open Access Status DOI
Volume 7
Issue 8
Start page e41730.1
End page e41730.8
Total pages 8
Place of publication San Francisco, CA, United States
Publisher Public Library of Science
Collection year 2013
Language eng
Abstract 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).
Keyword Genomewide association
Complex diseases
Q-Index Code C1
Q-Index Status Confirmed Code
Institutional Status UQ

Document type: Journal Article
Sub-type: Article (original research)
Collections: Official 2013 Collection
UQ Diamantina Institute - Open Access Collection
UQ Diamantina Institute Publications
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Citation counts: TR Web of Science Citation Count  Cited 8 times in Thomson Reuters Web of Science Article | Citations
Scopus Citation Count Cited 7 times in Scopus Article | Citations
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Created: Mon, 15 Apr 2013, 11:57:06 EST by Kylie Hengst on behalf of UQ Diamantina Institute