Haplotype Linkage and Association Mapping of Quantitative Trait Loci

数量性状基因座的单倍型连锁和关联作图

• 批准号: 0505025
• 负责人: Ruzong Fan
• 金额: --
• 依托单位: Texas A&M Research Foundation
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2005
• 资助国家: 美国
• 起止时间: 2005-06-01 至 2007-05-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0505025&HistoricalAwards=false
• 关键词: Haplotype; Linkage; Association; Mapping; Quantitative

The objectives of the project are to develop statistical methods and algorithms for linkage and association mapping of quantitative trait loci. The research includes: (1) to extend existing methodology to analyze non-temporal genetic data; (2) to create novel approaches and models to analyze temporally longitudinal human genetic data. The projects include the model building and testing, well-designed simulation studies, and applications to empirical datasets. Algorithms and software will be developed based on the techniques developed in the research. For longitudinal human genetic data, multiple measurements of quantitative or qualitative traits are taken for each individual over time, in addition to the genotype information and covariates such as gender, age, and familial income. The theory of stochastic processes will be applied to build models and methods in longitudinal genetic study.In human genetics, one important issue is to locate and to identify important genetic variants/determinants of complex traits. Complex diseases are familial, but the mode of inheritance is uncertain. Many common diseases are complex disorders, such as asthma, diabetes, Alzheimer's disease, psychiatric disorders, Parkinson's disease, cardiovascular disease, and arthritis. With the development of the Human Genome Project, high resolution micro-satellite and chromosome-wide haplotype maps of human genome, enormous amounts of genetic data on human chromosomes are becoming available. The opportunities for genome-wide scan to map complex disease genes are tremendous. However, it is not yet clear how to extract the most useful information for complex disease gene mapping. To fully utilize the massive genetic data for complex disease gene mappings, novel mathematical and statistical methods are crucial. The investigator develops appropriate models and handy algorithms in linkage and association mapping of complex diseases. This helps to identify important genetic variants/determinants of complex traits. The Broader Impacts are: (1) To advance discovery of common disease genes and to facilitate identification of drug targets for medical sciences and the pharmaceutical industry to benefit society and to enhance public health; (2) to make algorithms and software for disease gene mapping publicly available, and to upgrade and enhance general research and education infrastructure; (3) to help develop an interdisciplinary graduate program in training a new generation of researchers in bioinformatics.

该项目的目标是发展数量性状基因座的连锁和关联作图的统计方法和算法。研究内容包括：（1）扩展现有的方法来分析非时间遗传数据;（2）创建新的方法和模型来分析时间纵向人类遗传数据。这些项目包括模型的建立和测试，精心设计的模拟研究，以及经验数据集的应用。算法和软件将根据研究中开发的技术开发。对于纵向人类遗传数据，除了基因型信息和协变量（如性别、年龄和家庭收入）外，还对每个个体随时间推移的定量或定性性状进行多次测量。随机过程理论将被应用于纵向遗传学研究的模型和方法。在人类遗传学中，一个重要的问题是定位和识别复杂性状的重要遗传变异/决定因素。复杂疾病是家族性的，但遗传方式不确定。许多常见疾病是复杂的疾病，如哮喘、糖尿病、阿尔茨海默病、精神疾病、帕金森病、心血管疾病和关节炎。随着人类基因组计划、高分辨率微卫星和全染色体单倍型图谱的发展，人类染色体上的大量遗传数据正在变得可用。全基因组扫描绘制复杂疾病基因图谱的机会是巨大的。然而，目前还不清楚如何提取最有用的信息复杂的疾病基因定位。为了充分利用大量的遗传数据进行复杂的疾病基因定位，新的数学和统计方法至关重要。研究者开发适当的模型和方便的算法在复杂疾病的连锁和关联映射。这有助于识别复杂性状的重要遗传变异/决定因素。更广泛的影响是：（1）促进常见疾病基因的发现，并协助医学和制药业确定药物靶点，以造福社会和促进公众健康;（2）公开疾病基因图谱的算法和软件，并提升和加强一般研究和教育基础设施;（3）协助发展跨学科的研究生课程，培养新一代的生物信息学研究人员。