Effect of non genetic factors on genetic signatures of complex traits

非遗传因素对复杂性状遗传特征的影响

• 批准号: 8604414
• 负责人: PAOLA SEBASTIANI
• 金额: $ 16.04万
• 依托单位: BOSTON UNIVERSITY MEDICAL CAMPUS
• 依托单位国家: 美国
• 财政年份: 2012
• 资助国家: 美国
• 起止时间: 2012-02-16 至 2016-01-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8604414
• 关键词: Age; Age of Onset; Bayesian Method; Bayesian Modeling; Cardiovascular Diseases; Centenarian; Characteristics; Cluster Analysis; Complex; Complex Genetic Trait; Computer software; Data; Data Set; Development; Disease; Environment; Environmental Exposure; Environmental Risk Factor; Evaluation; Genes; Genetic; Genetic Predisposition to Disease; Genetic Risk; Hereditary Disease; Hypertension; Individual; Joints; Life Style; Longevity; Methods; Modeling; National Heart, Lung, and Blood Institute; Patients; Phenotype; Population; Predictive Value; Prevalence; Procedures; Prophylactic treatment; Relative (related person); Risk; Role; SNP genotyping; Sample Size; Sickle Cell Anemia; Simulate; Statistical Methods; Techniques; Technology; Translations; United States National Institutes of Health; base; data modeling; disorder risk; expectation; experience; gene environment interaction; genetic risk factor; genetic variant; genome wide association study; healthy aging; next generation sequencing; non-genetic; novel; novel strategies; open source; tool; trait

DESCRIPTION (provided by applicant): Many common diseases are complex genetic traits determined by the interplay of numerous genes and their interactions with environmental factors. One of the expectations from genome wide association studies was that they would provide the decoder for these complex genetic diseases that together with information about environmental exposure could be used to compute individual risk for a disease and to suggest appropriate prophylactic treatments or lifestyle changes. While genetic and exposure data may provide the necessary information to decipher complex diseases, the construction of comprehensive models that can inform about disease risk of a specific patient based on genetic and exposure data has proved to be very challenging. Many statistical methods to identify gene x environment interactions have been proposed but sample size issues, efficient analytic approaches and feasible computations continue to be a challenge. Furthermore, interpretation of models with many interactions and translation into useful tools for clinicians is difficult. Supported by the NIH/NHLBIR01 HL87681-01, "Genome-Wide Association Studies in Sickle Cell Anemia and in Centenarians", we introduced a novel Bayesian approach to estimate the genetic predisposition to exceptional longevity using more than 200 SNPs discovered from a genome wide association study of exceptional longevity. The approach used an ensemble of models for prediction and to compute an individual's genetic risk profile that provides a graphical display of the relative contribution of each SNP for prediction. We demonstrated that cluster analysis of the genetic risk profiles can help dissect the complex phenotype of exceptional longevity into sub-phenotypes with characteristic genetic signatures. Here we propose to expand this approach to include the effect of environmental factors, and to evaluate the method in genome wide association studies of exceptional longevity and phenotypic diversity of sickle cell anemia in 3 specific aims. Specific Aim 1: Development of a novel class of Bayesian genetic risk models that include gene x environment interactions. We propose to expand the class of Bayesian directed graphical models to include non genetic risk factors and their multiple interactions with genetic variants, and to develop a search procedure to build these models from data. The approach will be applicable to massive data sets such as those produced, for example, from next generation sequencing technology. The Bayesian models generated with this approach will produce gene x environment risk profiles that can be used to graphically display and interpret the joint effect of genes and environment on the risk for disease of an individual. Specific Aim 2: Discovery of genetic x environment signatures of complex traits. We will develop a method to cluster risk profiles as determined with Specific Aim 1 and discover genetic x environment signatures of complex traits. The relevance of this method is that it will help summarize the complex interactions between many genetic variants and risk factors at a population level and understand the relative contribution of genes and environment to disease prevalence. Specific Aim 3: Implementation and Evaluation. We will implement the procedures in a statistical package using R software (open source). We will evaluate the procedures in simulated data, and two real genome wide association studies: exceptional longevity and phenotypic diversity in sickle cell anemia in which we have rich phenotypic, genetic and exposure data and the opportunity for independent replication of the findings.

描述（由申请人提供）：许多常见疾病是由众多基因的相互作用及其与环境因素的相互作用决定的复杂遗传性状。全基因组关联研究的期望之一是，它们将为这些复杂的遗传疾病提供解码器，这些解码器与环境暴露信息一起可用于计算疾病的个体风险，并建议适当的预防治疗或生活方式改变。虽然遗传和暴露数据可以为解读复杂疾病提供必要的信息，但事实证明，构建综合模型，根据遗传和暴露数据了解特定患者的疾病风险是非常具有挑战性的。已经提出了许多统计方法来识别基因x环境相互作用，但样本量问题，有效的分析方法和可行的计算仍然是一个挑战。此外，解释模型与许多相互作用和翻译成有用的工具，为临床医生是困难的。在NIH/NHLBIR 01 HL 87681 -01“镰状细胞贫血和百岁老人全基因组关联研究”的支持下，我们引入了一种新的贝叶斯方法，使用从异常长寿的全基因组关联研究中发现的200多个SNP来估计异常长寿的遗传倾向。该方法使用一组模型进行预测，并计算个体的遗传风险概况，该风险概况提供了每个SNP的相对贡献的图形显示以进行预测。我们证明了遗传风险谱的聚类分析可以帮助将异常长寿的复杂表型分解为具有特征遗传标记的亚表型。在这里，我们建议扩大这种方法，包括环境因素的影响，并评估该方法在全基因组关联研究的特殊寿命和表型多样性的镰状细胞性贫血在3个特定的目标。 具体目标1：开发一类新的贝叶斯遗传风险模型，包括基因x环境的相互作用。我们建议扩展贝叶斯定向图形模型的类，包括非遗传风险因素及其与遗传变异的多种相互作用，并开发一个搜索程序，从数据中构建这些模型。该方法将适用于大规模数据集，例如从下一代测序技术产生的数据集。用这种方法生成的贝叶斯模型将产生基因x环境风险曲线，这些曲线可用于以图形方式显示和解释基因和环境对个体疾病风险的联合影响。具体目标2：发现复杂性状的遗传x环境特征。我们将开发一种方法来聚类特定目标1确定的风险概况，并发现复杂性状的遗传x环境特征。这种方法的相关性在于，它将有助于总结许多遗传变异与人群水平上的风险因素之间的复杂相互作用，并了解基因和环境对疾病流行的相对贡献。具体目标3：执行和评价。我们将使用R软件（开源）在统计包中实现这些程序。我们将在模拟数据和两个真实的全基因组关联研究中评估程序：镰状细胞性贫血中的异常长寿和表型多样性，其中我们有丰富的表型，遗传和暴露数据以及独立复制发现的机会。