Bioinformatics strategies to relate age of onset with gene-gene interaction

 描述(由申请人提供)： 癌症、心血管疾病和糖尿病等慢性疾病的发病时间预计会受到多个基因-基因相互作用的影响，这些相互作用增加了基因-表型映射关系的复杂性。不幸的是，由于数据的稀疏性，像Cox回归这样的参数统计方法缺乏足够的能力来检测高阶基因-基因相互作用。机器学习方法提供了一种更强大的选择，但依赖于计算密集型搜索方法来识别顶级模特。我们建议开发一种强大且计算高效的生物信息学策略，将机器学习算法和Cox回归相结合，用于识别与慢性病发病时间相关的基因-基因和基因-环境相互作用模型。具体地说，我们首次提出了一种新的稳健的生存多因素降维方法(RS-MDR)，用于检测影响人类疾病发病时间的罕见变异(AIM1)中的基因-基因交互作用。在仿真研究中，将RS-MDR方法与其他已有方法进行比较，以评估该方法的性能。然后，我们提出利用RS-MDR的构造归纳法改变基因-基因交互作用模型的表示空间，并应用L1惩罚COX回归来识别一组能够预测患者生存概率的交互作用模型(AIM2)。我们假设RS-MDR能够有效地识别高阶交互模型，该组合方法为选择一组交互模型提供了一种强大的计算效率的方法。我们将使用来自GWAS研究的大量模拟来彻底评估这一假说。接下来，我们将应用新的组合方法来检测和表征基因-基因和基因-环境的相互作用，该方法来自于肺癌和类风湿性关节炎(AIM 3)的大规模人群研究的全基因组关联研究(GWAS)数据。实际数据分析的结果将被用来改进方法。最后，我们将把提出的方法作为开源R软件包(AIM4)的一部分进行分发。我们预计，建议的方法将结合参数方法和非参数方法的优点，并能够检测共同影响慢性病发病时间的相互作用模型。这一点很重要，因为发病时间比病例对照状态有更多的变化，而且可能更具临床相关性。此外，尽管这一信息与发现BRCA1突变等高危变异有关，但尚未使用GWAS研究积极开展预测发病时间的遗传因素的研究。

 DESCRIPTION (provided by applicant): Time to onset of chronic diseases such as cancer, cardiovascular disease, and diabetes is expected to be influenced by multiple gene-gene interactions that add to the complexity of the genotype-phenotype mapping relationship. Unfortunately, parametric statistical methods such as Cox regression lack sufficient power to detect high-order gene-gene interactions due to the sparseness of the data. Machine learning methods offer a more powerful alternative but rely on computationally-intensive searching methods to identify the top models. We propose here to develop a powerful and computationally efficient bioinformatics strategy that combines machine learning algorithm and Cox regression for identifying gene-gene and gene-environment interaction models that are associated with time of onset of chronic disease. Specifically, we firs propose to develop a novel Robust Survival Multifactor Dimensionality Reduction method (RS-MDR) for the detection of gene-gene interactions in rare variants that influence time of onset of human disease (AIM 1). The power of RS-MDR method will be evaluated by comparing it to other existing methods in simulation studies. We then propose to change the representation space of the gene-gene interaction models using RS-MDR's construction induction method and apply L1 penalized Cox regression to identify a set of interaction models that can predict patients' survival probability (AIM 2). We hypothesize that RS-MDR can effectively identify high order interaction models and the combined approach provides a powerful and computational efficient way to select a set of interaction models. We will use extensive simulations that are derived from GWAS studies to thoroughly evaluate this hypothesis. Next, we will apply the new combined method for detecting and characterizing gene-gene and gene-environment interactions in genome-wide association study (GWAS) data from large population-based studies of lung cancer and rheumatoid arthritis (AIM 3). Results from the real data analysis will be used to refine the method. Finally, we will distribute the proposed method as part of an open- source R software package (AIM 4). We anticipate that the proposed method will combine the strength from both parametric and non-parametric methods and enable detection of interaction models that are jointly affecting time of onset of chronic diseases. This is important because time of onset has more variation than case-control status and it may be more clinically relevant. Furthermore, studies of genetic factors predicting time of onset have not been pursued aggressively using GWAS studies, despite the relevance of this information for the discovery of high risk variants like mutations in BRCA1.