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: 8215203
• 负责人: PAOLA SEBASTIANI
• 金额: $ 16.36万
• 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 2015-01-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8215203
• 关键词: Age; Age of Onset; 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; 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. PUBLIC HEALTH RELEVANCE: 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. We propose a novel approach to build genetic risk models that include the effect of environmental factors, and to evaluate the method in genome wide association studies of exceptional longevity and of phenotypic diversity of sickle cell anemia. Our proposal will deliver a general class of genetic risk models, an approach for dissecting the effects of genes and environment, implementation of these methods in open source software and a better understanding of the role of genes and the environment to long and healthy lives, and to different phenotypes of sickle cell anemia.

描述（由申请人提供）：许多常见疾病是复杂的遗传特征，由众多基因的相互作用及其与环境因素的相互作用决定。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，与环境暴露信息一起可用于计算个体患疾病的风险，并建议适当的预防性治疗或生活方式的改变。虽然遗传和暴露数据可以提供破译复杂疾病的必要信息，但事实证明，构建可以根据遗传和暴露数据了解特定患者疾病风险的综合模型非常具有挑战性。人们已经提出了许多识别基因与环境相互作用的统计方法，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，解释具有许多相互作用的模型并将其转化为对临床医生有用的工具是很困难的。在 NIH/NHLBIR01 HL87681-01“镰状细胞性贫血和百岁老人的全基因组关联研究”的支持下，我们引入了一种新颖的贝叶斯方法，利用从超长寿命全基因组关联研究中发现的 200 多个 SNP 来估计超长寿命的遗传倾向。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，以图形方式显示每个 SNP 对预测的相对贡献。我们证明，遗传风险概况的聚类分析可以帮助将异常长寿的复杂表型解析为具有特征遗传特征的亚表型。在这里，我们建议扩展这种方法以包括环境因素的影响，并在镰状细胞性贫血的特殊寿命和表型多样性的全基因组关联研究中评估该方法的三个具体目标。 具体目标 1：开发一类新型贝叶斯遗传风险模型，其中包括基因 x 环境相互作用。我们建议扩展贝叶斯定向图形模型的类别，以包括非遗传风险因素及其与遗传变异的多重相互作用，并开发一个搜索程序来根据数据构建这些模型。该方法将适用于海量数据集，例如通过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生成基因 x 环境风险概况，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对特定目标 1 确定的风险状况进行聚类，并发现复杂性状的遗传 x 环境特征。该方法的相关性在于，它将有助于总结人群水平上许多遗传变异与危险因素之间复杂的相互作用，并了解基因和环境对疾病流行的相对贡献。具体目标 3：实施和评估。我们将使用 R 软件（开源）在统计包中实现这些程序。我们将评估模拟数据中的程序，以及两项真实的全基因组关联研究：镰状细胞性贫血的特殊寿命和表型多样性，其中我们拥有丰富的表型、遗传和暴露数据以及独立复制研究结果的机会。 公共卫生相关性：已经提出了许多识别基因与环境相互作用的统计方法，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。我们提出了一种新方法来建立遗传风险模型，其中包括环境因素的影响，并评估镰状细胞性贫血的超长寿命和表型多样性的全基因组关联研究中的方法。我们的提案将提供一类通用的遗传风险模型，一种剖析基因和环境影响的方法，在开源软件中实施这些方法，并更好地理解基因和环境对长寿和健康的生活以及镰状细胞性贫血的不同表型的作用。