Novel Statistical Methods for Human Gene Mapping

人类基因图谱的新统计方法

• 批准号: 7682282
• 负责人: MICHAEL PHILIP EPSTEIN
• 金额: $ 29.15万
• 依托单位: EMORY UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2006
• 资助国家: 美国
• 起止时间: 2006-09-27 至 2011-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/7682282
• 关键词: Address; Biochemical Pathway; Biochemical Reaction; Case-Control Studies; Categories; Chromosome Mapping; Chromosomes; Complex; Computer software; Data; Development; Disease; Disease Outcome; Environment; Environmental Risk Factor; Finland; Genetic; Genome; Grant; Haplotypes; Human Gene Mapping; Human Genome Project; International; Investigation; Lead; Linkage Disequilibrium; Machine Learning; Maps; Methodology; Methods; Modeling; Molecular; Nature; Non-Insulin-Dependent Diabetes Mellitus; Normal Statistical Distribution; Other Genetics; Outcome; Paper; Pathway interactions; Performance; Procedures; Public Health; Relative (related person); Research Design; Research Personnel; Resources; Screening procedure; Series; Severity of illness; Solutions; Statistical Methods; Techniques; Testing; United States; Variant; base; case control; disorder prevention; flexibility; genetic analysis; genetic variant; human disease; improved; interest; non-genetic; novel; programs; simulation; trait; user-friendly

DESCRIPTION (provided by applicant): Many common human diseases originate in part from the complicated effects of multiple genetic variants found throughout the genome. Given the enormous impact of such diseases on public health, it is imperative to map relevant genetic variants to improve our understanding of the molecular basis of such diseases, as well as improve screening techniques for disease prevention. To this end, successful human gene mapping of complex diseases requires the development and application of powerful statistical methods that fully utilize the resources of the Human Genome Project. This grant proposes a set of such statistical methods that either address novel problems or improve existing solutions to problems in human gene mapping studies. These proposed methods are applicable to a variety of genetic studies as they address topics in linkage, linkage disequilibrium, and high-dimensional genetic analyses of complex diseases and disease-related quantitative traits. The methods can be partitioned into the two general groups: mixed-modeling procedures and case-control likelihood procedures. The mixed-modeling procedures considered include a general variance-component (VC) mapping framework for continuous and discrete trait data and a modified VC mapping framework that allows for haplotypes. Also considered is a novel linear-mixed-model framework that identifies a large combination of genetic variants that influence a quantitative trait using support-vector- machine regression. The case-control likelihood procedures considered are extensions of the approach of Epstein and Satten (2003) for haplotype inference on disease. Extensions considered include allowing for covariates and haplotype-covariate interactions, and also categorical disease outcomes. The proposed statistical methods in this grant have the potential to increase the power to identify genetic variants that influence complex diseases and disease-related quantitative traits. This project will evaluate the performance of these methods using simulations based on the study design and data from an existing gene mapping study of type 2 diabetes. Also, this grant will implement the proposed statistical methods in user- friendly, efficient software for public distribution. Finally, this project will be opportunistic in identifying and addressing unforseen statistical problems arising in human gene mapping studies.

描述（由申请人提供）：许多常见的人类疾病部分源自整个基因组中多种遗传变异的复杂作用。鉴于此类疾病对公共卫生的巨大影响，必须绘制相关的遗传变异，以提高我们对疾病分子基础的理解，并改善预防疾病的筛查技术。为此，复杂疾病的成功人类基因映射需要充分利用人类基因组项目资源的强大统计方法的开发和应用。该赠款提出了一组此类统计方法，这些方法要么解决新的问题或改善人类基因映射研究中问题的现有解决方案。这些提出的方法适用于各种遗传研究，因为它们涉及链接，连锁不平衡以及复杂疾病和与疾病相关的定量性状的高维遗传分析的主题。这些方法可以分为两个一般组：混合模型程序和病例对照可能性程序。考虑的混合模型过程包括连续和离散性状数据的一般方差 - 组件（VC）映射框架以及允许单倍型的修改的VC映射框架。还考虑了一种新型的线性混合模式框架，该框架识别出大量遗传变异的组合，这些遗传变异使用支持矢量 - 机器回归影响定量性状。考虑的病例对照可能性程序是Epstein and Satten（2003）对疾病单倍型推断的方法的扩展。所考虑的扩展包括允许协变量和单倍型 - 融合型相互作用以及分类疾病结果。该赠款中提出的统计方法具有增加识别影响复杂疾病和与疾病相关的定量性状的遗传变异的能力。该项目将使用基于研究设计和现有2型糖尿病基因映射研究的数据来评估这些方法的性能。此外，该赠款将在用户友好，高效的软件中实施拟议的统计方法，以进行公共发行。最后，该项目将在识别和解决人类基因映射研究中产生的不可预测的统计问题方面具有机会主义。