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Genome Wide Haplotype Association Analysis

全基因组单倍型关联分析

基本信息

批准号：
8248753
负责人：
Nianjun Liu
金额：
$ 22.74万
依托单位：
UNIVERSITY OF ALABAMA AT BIRMINGHAM
依托单位国家：
美国
项目类别：
财政年份：
2008
资助国家：
美国
起止时间：
2008-04-01 至 2015-03-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/8248753
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 base gene discovery genetic variant genome-wide human data human disease insight interest migration novel prevent programs user friendly software

项目摘要

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 hapltoypes 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. 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.

连锁不平衡(LD，两个或更多基因座上等位基因的非随机关联)提供了有价值的用于检测导致复杂人类疾病的基因变异的信息，例如高血压、糖尿病、肥胖症和中风。单倍型，同一染色体上的等位基因组合可能为人类基因组的LD结构提供有价值的见解，并可能为绘制疾病基因图谱提供额外的动力。这样的见解可能不仅对疾病基因有用测绘，但也在其他领域，如群体遗传学，单倍型信息已被用于研究移民和移民率、基因人口学和人类进化史。国际足联旨在开发人类基因组单倍型图谱的HapMap项目已经开始提供宝贵的资源，可以反过来推动新的单倍型方法的开发和测试。虽然使用大量单核苷酸多态(SNPs)进行单倍型分析是非常需要的，它还带来了巨大的挑战。这个项目的总体目标是开发新的统计和计算用于分析复杂人类疾病基因图谱中单倍型的方法和软件工具。这个这个项目的具体目标是：(1)开发有效的算法来估计单倍型频率和在存在信息性缺失的基因型时确定个体单倍型配置 (2)开发统计方法，以确定一套用于疾病关联图谱的候选基因组区域；(3)开发基于单倍型的新的疾病基因图谱方法，可以处理信息缺失的基因类型和基因分型错误，可以结合来自多个感兴趣区域的信息，并对种群异质性具有强大的抵抗力；以及 (4)向科学界发布健壮和用户友好的软件，以实现所提出的方法免费提供社区服务。拟议的方法将对公开可用的数据(例如数据)执行来自HapMap项目)，以及我们的合作者正在进行的项目中生成的其他人类数据，包括关于左心室肥厚、类风湿性关节炎和肥胖的遗传效应的数据集。拟议的项目与NIH的使命密切相关，因为所完成的方法将对广泛的生物医学研究界，将极大地促进人类遗传变异及其与复杂疾病有关。这将有助于寻求关于这些疾病的新知识。所提出的方法有望帮助发现导致复杂人类的基因。疾病，帮助我们更好地了解它们，最终提高我们预防、诊断和治疗的能力这些疾病。