Literature DB >> 17094271

Risk factor interactions and genetic effects associated with post-operative atrial fibrillation.

Alison A Motsinger1, Brian S Donahue, Nancy J Brown, Dan M Roden, Marylyn D Ritchie.   

Abstract

Postoperative Atrial Fibrillation (PoAF) is the most common arrhythmia after heart surgery, and continues to be a major cause of morbidity. Due to the complexity of this condition, many genes and/or environmental factors may play a role in susceptibility. Previous findings have shown several clinical and genetic risk factors for the development of PoAF. The goal of this study was to determine whether interactions among candidate genes and a variety of clinical factors are associated with PoAF. We applied the Multifactor Dimensionality Reduction (MDR) method to detect interactions in a sample of 940 adult subjects undergoing elective procedures of the heart or great vessels, requiring general anesthesia and sternotomy or thoracotomy, where 255 developed PoAF. We took a random sample of controls matched to the 255 AF cases for a total sample size of 510 individuals. MDR is a powerful statistical approach used to detect gene-gene or gene-environment interactions in the presence or absence of statistically detectable main effects in pharmacogenomics studies. We chose polymorphisms in three (IL-6, ACE, and ApoE) candidate genes, all previously implicated in PoAF risk, and a variety of environmental factors for analysis. We detected a single locus effect of IL-6 which is able to correctly predict disease status with 58.8% (p<0.001) accuracy. We also detected an interaction between history of AF and length of hospital stay that predicted disease status with 68.34% (p<0.001) accuracy. These findings demonstrate the utility of novel computational approaches for the detection of disease susceptibility genes. While each of these results looks interesting, they only explain part of PoAF susceptibility. It will be important to collect a larger set of candidate genes and environmental factors to better characterize the development of PoAF. Applying this approach, we were able to elucidate potential associations with postoperative atrial fibrillation.

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Year:  2006        PMID: 17094271

Source DB:  PubMed          Journal:  Pac Symp Biocomput        ISSN: 2335-6928


  10 in total

1.  Machine learning for detecting gene-gene interactions: a review.

Authors:  Brett A McKinney; David M Reif; Marylyn D Ritchie; Jason H Moore
Journal:  Appl Bioinformatics       Date:  2006

2.  AMBIENCE: a novel approach and efficient algorithm for identifying informative genetic and environmental associations with complex phenotypes.

Authors:  Pritam Chanda; Lara Sucheston; Aidong Zhang; Daniel Brazeau; Jo L Freudenheim; Christine Ambrosone; Murali Ramanathan
Journal:  Genetics       Date:  2008-09-09       Impact factor: 4.562

Review 3.  Arrhythmia pharmacogenomics: methodological considerations.

Authors:  Dan M Roden; Prince J Kannankeri; Dawood Darbar
Journal:  Curr Pharm Des       Date:  2009       Impact factor: 3.116

4.  Information-theoretic metrics for visualizing gene-environment interactions.

Authors:  Pritam Chanda; Aidong Zhang; Daniel Brazeau; Lara Sucheston; Jo L Freudenheim; Christine Ambrosone; Murali Ramanathan
Journal:  Am J Hum Genet       Date:  2007-10-03       Impact factor: 11.025

5.  Genetic variation in the β1-adrenergic receptor is associated with the risk of atrial fibrillation after cardiac surgery.

Authors:  Janina M Jeff; Brian S Donahue; Kristin Brown-Gentry; Dan M Roden; Dana C Crawford; C Michael Stein; Daniel Kurnik
Journal:  Am Heart J       Date:  2013-10-17       Impact factor: 4.749

6.  Genomics: risk and outcomes in cardiac surgery.

Authors:  Tjorvi E Perry; Jochen D Muehlschlegel; Simon C Body
Journal:  Anesthesiol Clin       Date:  2008-09

7.  Characterization of genome-wide association-identified variants for atrial fibrillation in African Americans.

Authors:  Jessica T Delaney; Janina M Jeff; Nancy J Brown; Mias Pretorius; Henry E Okafor; Dawood Darbar; Dan M Roden; Dana C Crawford
Journal:  PLoS One       Date:  2012-02-23       Impact factor: 3.240

8.  The effect of alternative permutation testing strategies on the performance of multifactor dimensionality reduction.

Authors:  Alison A Motsinger-Reif
Journal:  BMC Res Notes       Date:  2008-12-30

Review 9.  A roadmap to multifactor dimensionality reduction methods.

Authors:  Damian Gola; Jestinah M Mahachie John; Kristel van Steen; Inke R König
Journal:  Brief Bioinform       Date:  2015-06-24       Impact factor: 11.622

10.  Electrophysiological changes preceding the onset of atrial fibrillation after coronary bypass grafting surgery.

Authors:  Feng Xiong; Yalin Yin; Bruno Dubé; Pierre Pagé; Alain Vinet
Journal:  PLoS One       Date:  2014-09-23       Impact factor: 3.240

  10 in total

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