Motsinger Alison A, Donahue Brian S, Brown Nancy J, Roden Dan M, Ritchie Marylyn D
Department of Molecular Physiology & Biophysics, Center for Human Genetics Research, Vanderbilt University, Nashville, TN 37232, USA.
Pac Symp Biocomput. 2006:584-95.
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.
术后房颤（PoAF）是心脏手术后最常见的心律失常，并且仍然是发病的主要原因。由于这种疾病的复杂性，许多基因和/或环境因素可能在易感性方面发挥作用。先前的研究结果已经显示了PoAF发生的几种临床和遗传风险因素。本研究的目的是确定候选基因与多种临床因素之间的相互作用是否与PoAF相关。我们应用多因素降维（MDR）方法，在940名接受心脏或大血管择期手术、需要全身麻醉和胸骨切开术或胸廓切开术的成年受试者样本中检测相互作用，其中255人发生了PoAF。我们随机抽取了与255例房颤病例匹配的对照组，样本总量为510人。MDR是一种强大的统计方法，用于在药物基因组学研究中检测基因-基因或基因-环境相互作用，无论是否存在统计学上可检测的主效应。我们选择了三个（IL-6、ACE和ApoE）候选基因中的多态性，所有这些基因先前都与PoAF风险有关，以及多种环境因素进行分析。我们检测到IL-6的单个位点效应，其能够以58.8%（p<0.001）的准确率正确预测疾病状态。我们还检测到房颤病史与住院时间之间的相互作用，其预测疾病状态的准确率为68.34%（p<0.001）。这些发现证明了新型计算方法在检测疾病易感基因方面的实用性。虽然这些结果看起来都很有趣，但它们只解释了部分PoAF易感性。收集更多的候选基因和环境因素以更好地表征PoAF的发生将很重要。应用这种方法，我们能够阐明与术后房颤的潜在关联。
