Genome Wide Haplotype Association Analysis

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）提供了有价值的 用于检测导致复杂人类疾病的遗传变异的信息， 高血压、糖尿病、肥胖和中风。单倍型，即同一染色体上等位基因的组合 作为一个单位遗传，可能会对人类基因组的LD结构提供有价值的见解， 为绘制疾病基因图谱提供了额外的力量。这种见解不仅对疾病基因的研究有用， 作图，但也在其他领域，如人口遗传学，其中单倍型信息已被用来 研究迁移和移民率、遗传人口学和人类进化史。国际 HapMap计划旨在开发人类基因组的单体型图，已经开始提供 这些宝贵的资源反过来又可以促进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性（SNP）的单倍型分析是非常需要的，它还 带来了巨大的挑战。该项目的总体目标是开发新的统计和计算 方法和软件工具，用于在复杂人类疾病基因的作图中分析单体型。的 本项目的具体目标是：（1）开发有效的算法来估计单体型频率， 在存在信息缺失的基因型的情况下确定个体单倍型构型， 无关个体样本中的基因分型错误;（2）开发统计方法来识别一组 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，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)发布强大的和用户友好的软件，实现所提出的方法，以科学 社区免费。将对公开可用的数据（例如， 来自HapMap项目），以及我们合作者正在进行的项目中产生的其他人类数据， 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。 该项目与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，将大大促进人类遗传变异及其 与复杂疾病有关。这将有助于对这些疾病的新知识的追求。所提出的方法有望帮助发现负责复杂人类基因的基因。 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。

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.