Adaptive Statistical Methods for Genetic Association Studies

遗传关联研究的自适应统计方法

• 批准号: 8258713
• 负责人: Charles L Kooperberg
• 金额: $ 35.2万
• 依托单位: FRED HUTCHINSON CANCER RESEARCH CENTER
• 依托单位国家: 美国
• 财政年份: 2011
• 资助国家: 美国
• 起止时间: 2011-04-19 至 2015-02-28
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8258713
• 关键词: Address; Algorithms; Complex; Computational Technique; Data; Development; Disease; Disease Outcome; Ensure; Environment; Environmental Risk Factor; Equilibrium; Estimation Techniques; Exons; Genes; Genetic; Genome; Genomics; Individual; Lasso; Logic; Methodology; Methods; Modeling; Modification; Pathway interactions; Principal Investigator; Psyche structure; Research Personnel; Resource Sharing; Risk; Single Nucleotide Polymorphism; Statistical Methods; Techniques; Technology; Testing; Variant; base; disorder risk; exome; flexibility; gene environment interaction; gene interaction; genetic association; genetic variant; genome wide association study; improved; independency; interest; next generation; predictive modeling; public health relevance; response; software development; trait

DESCRIPTION (provided by applicant): The major focus of this project is the development of methodologies for high-dimensional data that arise from new emerging high-throughput genomic technologies. The types of data that we focus on are single nucleotide polymorphism (SNP) data from genome-wide association studies (GWAS) and whole genome exome sequencing data, though many methods developed here can be readily applied to other types of high-dimensional data. One feature of these data is that the number of predictors (genes or SNPs) p is typically much larger than the number of observations n. The key to handle these high-dimensional data is to reduce the dimensionality effectively. There are several challenges in reducing the dimensionality. First, there are many variants which contribute to complex diseases. GWAS target common variants that typically only have modest effects, whereas variants in sequencing studies that have larger effects are more rare. The consequence is that the variants that are associated with the trait do not stand out, because of stochastic variation as well as the number of variants under study. Secondly, many of these variants act in combination with environmental factors and other variants. This poses even more challenges, as the number of potential gene-environment and gene-gene interactions is much greater than the number of marginal analyses. Thirdly, to elucidate complex disease risk, a comprehensive approach which considers many genetic variants, environmental factors, and their interactions is needed. Developing methods that deal with large numbers of variants and environmental factors is the focus of this project. Using adaptive function estimation techniques, which have been developed for many large nonparametric regression problems, we will develop a suite of statistical and computational techniques for the identification of environmental factors that modify genetic effects, for the predicting of disease risk from many thousands of SNPs, and for identifying significant predictors in exome sequencing studies. In adaptive function estimation, an unknown function is modeled as a combination of many basis functions. Model selection techniques, such as the lasso and boosting, have been developed for selecting which combination of basis functions is best at predicting a (disease) outcome. These approaches are very suited to the problems studied in this project. The investigators on this project are directly involved in a number of genetic association studies as (principal) investigator. The specific aims that we propose are in response to actual analysis problems facing these projects. This direct relation to projects ensures the relevance of the methods we intend to develop. PUBLIC HEALTH RELEVANCE: The major focus of this proposal is the development of analytical approaches for high-dimensional data that arise from genome-wide association studies and whole exome sequencing studies. In particular, we propose to develop adaptive methods to construct predictive models and to identify gene-environment interactions in GWAS, and to improve power for association studies in whole exome sequencing studies.

描述（由申请人提供）：该项目的主要重点是开发新出现的高通量基因组技术产生的高维数据的方法。我们关注的数据类型是来自全基因组关联研究（GWAS）的单核苷酸多态性（SNP）数据和全基因组外显子组测序数据，尽管这里开发的许多方法可以很容易地应用于其他类型的高维数据。这些数据的一个特征是预测因子（基因或SNP）的数量p通常远大于观测值的数量n。处理这些高维数据的关键是有效地降维。 在降低维度方面存在一些挑战。首先，有许多变异导致复杂的疾病。GWAS针对通常仅具有适度影响的常见变异，而测序研究中具有较大影响的变异则更为罕见。其结果是，与性状相关的变异并不突出，因为随机变异以及正在研究的变异的数量。第二，这些变量中的许多变量与环境因素和其他变量相结合。这带来了更多的挑战，因为潜在的基因-环境和基因-基因相互作用的数量远远大于边际分析的数量。第三，为了阐明复杂的疾病风险，需要考虑许多遗传变异、环境因素及其相互作用的综合方法。开发处理大量变量和环境因素的方法是该项目的重点。 使用自适应函数估计技术，这已经开发了许多大型非参数回归问题，我们将开发一套统计和计算技术，用于识别环境因素，修改遗传效应，从成千上万的SNP预测疾病风险，并确定显着的预测因子外显子组测序研究。在自适应函数估计中，未知函数被建模为许多基函数的组合。已经开发了模型选择技术，例如套索和提升，用于选择基函数的哪种组合最适合预测（疾病）结果。这些方法非常适合本项目所研究的问题。 该项目的研究者作为（主要）研究者直接参与了许多遗传关联研究。我们提出的具体目标是针对这些项目面临的实际分析问题。这种与项目的直接关系确保了我们打算开发的方法的相关性。 公共卫生关系：该提案的主要重点是开发全基因组关联研究和全外显子组测序研究中产生的高维数据的分析方法。特别是，我们建议开发自适应的方法来构建预测模型，并确定GWAS中的基因-环境相互作用，并提高全外显子组测序研究中关联研究的能力。