Novel Statistical Methods for Human Gene Mapping

人类基因图谱的新统计方法

• 批准号: 7292731
• 负责人: MICHAEL PHILIP EPSTEIN
• 金额: $ 29.71万
• 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/7292731
• 关键词: Address; Biochemical Pathway; Biochemical Reaction; Case-Control Studies; Categories; Chromosome Mapping; Chromosomes; Complex; Computer software; Data; Development; Disease; Disease Outcome; Disease regression; 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; Numbers; Outcome; Paper; Pathway interactions; Performance; Procedures; Public Health; Rate; 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; genetic analysis; genetic variant; human disease; improved; interest; 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(申请人提供)：许多常见的人类疾病部分起源于基因组中发现的多种遗传变异的复杂影响。鉴于此类疾病对公众健康的巨大影响，有必要绘制相关的基因变异图，以加深我们对此类疾病的分子基础的了解，并改进疾病预防的筛查技术。为此，成功绘制复杂疾病的人类基因图谱需要开发和应用强大的统计方法，充分利用人类基因组计划的资源。这项拨款提出了一套这样的统计方法，要么解决了人类基因图谱研究中的新问题，要么改进了现有的问题解决方案。这些拟议的方法适用于各种遗传研究，因为它们涉及连锁、连锁不平衡以及复杂疾病和与疾病相关的数量性状的高维遗传分析。这些方法可以分为两大类：混合模型法和病例对照似然法。所考虑的混合建模程序包括用于连续和离散性状数据的通用方差分量(VC)映射框架和允许单倍型的改进的VC映射框架。还考虑了一种新的线性混合模型框架，该框架使用支持向量机回归来识别影响数量性状的大量遗传变量组合。所考虑的病例对照可能性程序是Epstein和Satten(2003)单倍型推断疾病方法的扩展。考虑的扩展包括协变量和单倍型-协变量的相互作用，以及明确的疾病结果。这项赠款中建议的统计方法有可能增加识别影响复杂疾病和与疾病相关的数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型糖尿病基因图谱研究数据的模拟来评估这些方法的性能。此外，这笔赠款将在方便用户的高效软件中实施拟议的统计方法，供公众分发。最后，这个项目将在确定和解决人类基因图谱研究中出现的未预见的统计问题方面发挥机会主义作用。