A Novel Bayesian Model Averaging Approach for Genome Wide Association Studies

用于全基因组关联研究的新型贝叶斯模型平均方法

基本信息

批准号：
7751499
负责人：
MICHAEL D SWARTZ
金额：
$ 7.7万
依托单位：
UNIVERSITY OF TX MD ANDERSON CAN CTR
依托单位国家：
美国
项目类别：
财政年份：
2009
资助国家：
美国
起止时间：
2009-08-01 至 2011-07-30
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/7751499
关键词：
Address Age Algorithms Bayesian Method Bioinformatics Biological Biological Models Cancer Center Candidate Disease Gene Cell physiology Clinical Code Communities Complex Data Data Set Disease Epidemiology Ethnic Origin Family Cancer History First Degree Relative Gender Genes Genetic Genetic Counseling Genetic Markers Genetic Predisposition to Disease Genetic Structures Genome Genome Scan Grant Individual Knowledge Lead Left Linkage Disequilibrium Logistic Regressions Malignant Neoplasms Malignant neoplasm of lung Markov Chains Methodological Studies Methods Modeling Pathway interactions Pattern Prevention Research Prevention strategy Process Proteins Recording of previous events Relative (related person)Reporting Research Research Personnel Risk Sample Size Sampling Simulate Single Nucleotide Polymorphism Smoker Smoking Staging Structure Techniques Testing Texas Tobacco Trust Uncertainty Universities alcohol exposure cancer prevention case control design disorder risk drinking falls gene discovery genetic analysis genetic association genetic risk factor genome wide association study genome-wide indexing insight lung cancer prevention mathematical model novel prevent response simulation success tool trait

项目摘要

DESCRIPTION (provided by applicant): A key component to preventing cancer is uncovering the genetics behind various cancers and the complex traits and diseases that lead to cancer. To uncover the genetic etiology for cancers and other complex diseases or traits, it is necessary to use methods that jointly consider multiple genetic components underlying the disease. Genome wide association (GWA) studies use methods to scan the genome looking for possible genetic associations with disease risk. However, many GWA studies perform the analysis using a univariate approach - treating each genetic marker as independent. Recently, methods for simultaneous significance testing and multivariate hierarchical models have started to consider multiple genes simultaneously, rather than univariately. While considering markers simultaneously, these methods restrict themselves to the assumption that when scanning the genome, the number of genes detected will be very small compared to the number of genes investigated. In response, we propose to develop novel, more powerful tools that use Bayesian model averaging methods to include genetic structure in the models, while simultaneously searching for genes in a complex disease, such as lung cancer, on a genome wide scale. Such models that include biological information can increase the power to detect small contributors to risk for complex diseases, and can still include sparsity information that controls for false positives. Recently, we completed a methodological study showing that Bayesian model averaging performs better than standard selection techniques using multivariate logistic regression in a hypothesis driven or candidate gene type approach. The central theme of this proposal is to develop Bayesian model averaging methods that incorporate genetic structure inherent to markers used in GWA studies that can also search through the immense number of markers available for GWA studies. We propose to develop fast Markov chain Monte Carlo algorithms for Bayesian model averaging techniques. We will calibrate the newly developed statistical techniques using simulation studies, and apply the new and calibrated methods to perform a GWA study of lung cancer using data already available at M. D. Anderson Cancer Center. The significance of this proposal is to develop new methods of performing GWA studies that will incorporate available biological information that can increase power and control false positives to detect genetic factors contributing to cancer.

描述（由申请人提供）：预防癌症的关键成分是发现各种癌症背后的遗传学以及导致癌症的复杂性状和疾病。为了揭示癌症和其他复杂疾病或特征的遗传病因，有必要使用共同考虑该疾病潜在的多种遗传成分的方法。基因组广泛的关联（GWA）研究使用方法来扫描基因组，以寻找可能与疾病风险的遗传关联。但是，许多GWA研究使用单变量方法进行分析 - 将每个遗传标记视为独立。最近，同时显着性测试和多元分层模型的方法已经开始同时考虑多个基因，而不是单变量。在同时考虑标记物的同时，这些方法限制了以下假设：扫描基因组时，与研究的基因数量相比，检测到的基因数量将非常小。作为回应，我们建议开发新颖，更强大的工具，这些工具使用贝叶斯模型平均方法在模型中包括遗传结构，同时在基因组大规模上同时搜索复杂疾病（例如肺癌）中的基因。包括生物学信息的模型可以增加检测出对复杂疾病风险的小贡献者的能力，并且仍然可以包括控制误报的稀疏信息。最近，我们完成了一项方法论研究，表明在假设驱动或候选基因类型方法中，使用多元逻辑回归使用多元逻辑回归的贝叶斯模型平均表现更好。该提案的中心主题是开发贝叶斯模型平均方法，该方法结合了GWA研究中使用的标记固有的遗传结构，这些遗传结构也可以搜索可用于GWA研究的大量标记。我们建议为贝叶斯模型平均技术开发快速马尔可夫链蒙特卡洛算法。我们将使用仿真研究校准新开发的统计技术，并使用新的和校准的方法使用M. D. Anderson Cancer Center已获得的数据对肺癌进行GWA研究。该提案的意义是开发进行GWA研究的新方法，这些方法将结合可用的生物学信息，以增加功率并控制假阳性以检测造成癌症的遗传因素。