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

将发病年龄与基因间相互作用联系起来的生物信息学策略

• 批准号: 9097781
• 负责人: Jiang Gui
• 金额: $ 35.24万
• 依托单位: DARTMOUTH COLLEGE
• 依托单位国家: 美国
• 财政年份: 2015
• 资助国家: 美国
• 起止时间: 2015-07-01 至 2018-06-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9097781
• 关键词: Affect; Age of Onset; Algorithms; BRCA1 gene; Bioinformatics; Biology; Cardiovascular Diseases; Chronic Disease; Communities; Computer software; Data; Data Analyses; Detection; Diabetes Mellitus; Dimensions; Disease; Europe; Gene Order; Genes; Genetic Polymorphism; Genetic study; Genome; Genotype; Investigation; Knowledge; Lead; Link; Machine Learning; Malignant Neoplasms; Malignant neoplasm of lung; Maps; Methods; Modeling; Mutation; North America; Patients; Phenotype; Probability; Research; Rheumatoid Arthritis; Risk; Role; Staging; Statistical Methods; Testing; Texas; Time; United Kingdom; Variant; base; cancer genome; case control; clinically relevant; gene environment interaction; gene interaction; genome wide association study; high risk; human disease; learning strategy; novel; open source; population based; rare variant; risk variant; screening; simulation; survival outcome; targeted treatment

 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.

 描述（由申请人提供）： 预计癌症、心血管疾病和糖尿病等慢性疾病的发病时间会受到多个基因-基因相互作用的影响，这增加了基因型-表型映射关系的复杂性。不幸的是，参数统计方法，如考克斯回归缺乏足够的权力，以检测高阶基因-基因相互作用，由于数据的稀疏性。机器学习方法提供了更强大的替代方案，但依赖于计算密集型搜索方法来识别顶级模型。我们在这里提出开发一个强大的和计算效率高的生物信息学策略，结合机器学习算法和考克斯回归识别基因和基因与环境的相互作用模型，与慢性疾病的发病时间。具体而言，我们首先提出开发一种新的稳健生存多因素重复性降低方法（RS-MDR），用于检测影响人类疾病发作时间的罕见变异（AIM 1）中的基因-基因相互作用。RS-MDR方法的功效将通过与模拟研究中的其他现有方法进行比较来评估。然后，我们建议使用RS-MDR的构造诱导方法来改变基因-基因相互作用模型的表示空间，并应用L1惩罚考克斯回归来确定一组可以预测患者生存概率的相互作用模型（AIM 2）。我们假设，RS-MDR可以有效地识别高阶相互作用模型和组合的方法提供了一个强大的和计算效率高的方式来选择一组相互作用模型。我们将使用来自GWAS研究的广泛模拟来彻底评估这一假设。接下来，我们将应用新的组合方法来检测和表征来自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）数据中的基因-基因和基因-环境相互作用。真实的数据分析结果将用于改进该方法。最后，我们将把提出的方法作为开源R软件包（AIM 4）的一部分发布.我们预计，所提出的方法将联合收割机的强度从参数和非参数的方法，使检测的相互作用模型，共同影响慢性疾病的发病时间。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，并且可能更具临床相关性。此外，使用GWAS研究预测发病时间的遗传因素的研究尚未积极进行，尽管这些信息与发现BRCA 1突变等高风险变异的相关性。