Genome Wide Haplotype Association Analysis

全基因组单倍型关联分析

• 批准号: 7589791
• 负责人: Nianjun Liu
• 金额: $ 23.2万
• 依托单位: UNIVERSITY OF ALABAMA AT BIRMINGHAM
• 依托单位国家: 美国
• 财政年份: 2008
• 资助国家: 美国
• 起止时间: 2008-04-01 至 2013-03-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/7589791
• 关键词: Accounting; Algorithms; Alleles; Attention; Biomedical Research; Charge; Chromosome Mapping; Chromosomes; Communities; Complex; Computing Methodologies; Data; Data Set; Demography; Development; Diabetes Mellitus; Diagnosis; Disease; Disease Association; Frequencies; Genes; Genetic; Genetic Variation; Genomics; Genotype; Goals; Haplotypes; Human; Human Genetics; Human Genome; Hypertension; Immigration; Individual; Inherited; International; Knowledge; Lead; Left Ventricular Hypertrophy; Link; Linkage Disequilibrium; Maps; Methods; Mission; Modeling; Molecular; Obesity; Operating System; Population; Population Genetics; Population Heterogeneity; Recording of previous events; Research; Resources; Rheumatoid Arthritis; Sampling; Single Nucleotide Polymorphism; Software Tools; Statistical Methods; Stroke; Structure; Technology; Testing; Variant; base; gene discovery; genetic variant; genome-wide; human data; human disease; insight; interest; migration; novel; prevent; programs; user friendly software

DESCRIPTION (provided by applicant): Linkage disequilibrium (LD, the non-random association of alleles at two or more loci) provides valuable information for detecting genetic variations that are responsible for complex human diseases such as hypertension, diabetes, obesity, and stroke. Haplotypes, the combinations of alleles on the same chromosome that were inherited as a unit, may offer valuable insights on the LD structure of the human genome and may provide additional power for mapping disease genes. Such insights may be useful not only in disease gene mapping, but also in other fields such as population genetics, where haplotype information has been used to study migration and immigration rates, genetic demography, and human evolutionary history. The international HapMap project, which aims to develop a haplotype map of the human genome, has already begun to provide valuable resources that can in turn motivate the development and testing of new haplotype methods. Although haplotype analysis using a large quantity of single nucleotide polymorphisms (SNPs) is in great need, it also poses great challenges. The overall goal of this project is to develop novel statistical and computational methods and software tools for the analysis of haplotypes in mapping of complex human disease genes. The specific objectives of this project are: (1) to develop efficient algorithms to estimate haplotype frequencies and determine individual haplotype configurations in the presence of informatively missing genotypes and genotyping errors in samples of unrelated individuals; (2) to develop statistical methods to identify a set of candidate genomic regions for use in disease association mapping; (3) to develop new haplotype-based disease gene mapping methods that can handle informatively missing genotypes and genotyping errors, that can combine information from multiple regions of interest, and that are robust to population heterogeneity; and (4) to release robust and user-friendly software, which implements the proposed methods, to the scientific community at no charge. The proposed methods will be performed on the publicly available data (e.g. data from the HapMap project), as well as other human data generated in our collaborators' ongoing projects, including data sets concerning genetic effects on left ventricular hypertrophy, rheumatoid arthritis, and obesity. The proposed project is closely related to NIH's mission in that the accomplished methods will be useful to the broad biomedical research community and will greatly facilitate the study of human genetic variation and its association with complex diseases. This will help in pursuit of new knowledge about these diseases. Relevance: The proposed methods are expected to aid the discovery of the genes that are responsible for complex human diseases, help us to better understand them, and finally enhance our ability to prevent, diagnose, and treat these diseases.

描述（由申请人提供）：连锁不平衡（LD，两个或多个基因座上等位基因的非随机关联）为检测导致复杂人类疾病（如高血压、糖尿病、肥胖和中风）的遗传变异提供了有价值的信息。单倍型是作为一个单位遗传的同一染色体上的等位基因的组合，可以为人类基因组的LD结构提供有价值的见解，并可以为绘制疾病基因提供额外的力量。这种见解不仅在疾病基因定位中有用，而且在其他领域也很有用，如群体遗传学，其中单倍型信息已被用于研究迁移和移民率，遗传人口学和人类进化史。旨在绘制人类基因组单体型图的国际单体型图项目已经开始提供宝贵的资源，这些资源反过来可以促进新的单体型方法的开发和测试。尽管使用大量单核苷酸多态性（SNP）的单倍型分析是非常需要的，但它也提出了巨大的挑战。该项目的总体目标是开发新的统计和计算方法以及软件工具，用于分析复杂人类疾病基因的单倍型。本项目的具体目标是：（1）开发有效的算法来估计单倍型频率，并在无关个体样本中存在信息缺失的基因型和基因分型错误的情况下确定个体单倍型构型;（2）开发统计方法来确定一组用于疾病关联作图的候选基因组区域;（3）开发新的基于单体型的疾病基因定位方法，该方法可以处理信息缺失的基因型和基因分型错误，可以联合收割机结合来自多个感兴趣区域的信息，并且对群体异质性具有鲁棒性;以及（4）免费向科学界发布实施所提议方法的强大且用户友好的软件。所提出的方法将在公开可用的数据（例如来自HapMap项目的数据）以及我们合作者正在进行的项目中生成的其他人类数据上进行，包括关于遗传对左心室肥大，类风湿性关节炎和肥胖的影响的数据集。拟议的项目是密切相关的NIH的使命，在完成的方法将是有用的，以广泛的生物医学研究界，并将大大促进人类遗传变异及其与复杂疾病的关联的研究。这将有助于对这些疾病的新知识的追求。 相关性：预计这些方法将有助于发现导致复杂人类疾病的基因，帮助我们更好地了解它们，并最终提高我们预防、诊断和治疗这些疾病的能力。