Haplotype Analysis in Linkage Disequilibrium Mapping

描述(申请人提供)：基于连锁不平衡(LD)的全基因组关联方法为检测导致复杂人类疾病(如高血压、糖尿病、肥胖、癌症等)的遗传变异提供了一种很有前途的方法。与基于单标记的方法相比，基于单倍型的方法可能为绘制疾病基因图谱提供额外的能力。更重要的是，单倍型可能导致对影响遗传标记之间相关性的因素的洞察，即连锁不平衡(LD)，这种洞察可能提供理解人类进化所必需的信息，并可能捕捉到两个或更多因果变异之间的顺式相互作用。然而，利用大量紧密连锁的SNPs进行单倍型分析才刚刚起步，给科学家带来了巨大的挑战。此外，大多数现有的方法没有考虑到HapMap项目即将提供的单倍型结构，也没有在这方面进行评估。这个项目的总体目标是开发统计和计算工具和方法，用于分析复杂疾病基因的连锁不平衡图谱中的单倍型。本项目的具体目标是：(1)开发高效的算法来估计单倍型频率和确定普通家系中的单倍型配置，以获得大量具有重组子的紧密连锁遗传标记。(2)定义基于单倍型共享的新测试统计量，用于定位与复杂人类疾病相关的基因。(3)评估标签SNPs在控制质量和数量性状的基因连锁不平衡定位中的作用。在此背景下，将比较不同的标签SNP选择方法，并将研究在设计高效和有效的标签SNP选择算法时几个关键问题的影响。(4)向科学界发布用户友好的软件。所提出的方法有望帮助发现导致复杂人类疾病的基因，并最终增强我们理解这些疾病的能力。

DESCRIPTION (provided by applicant): Genome-wide association methods based on linkage disequilibrium (LD) offer a promising approach to detect genetic variations that are responsible for complex human diseases, such as hypertension, diabetes, obesity, cancers, etc. Approaches based on haplotypes may provide additional power to map disease genes than those based on single markers. More importantly, haplotypes may lead to insights on the factors influencing the dependencies among genetic markers, i.e. linkage disequilibrium (LD), and such insights may provide information essential to understand human evolution and may capture cis-interactions between two or more causal variants. However, the haplotype analysis using a large number of tightly linked SNPs is just being developed and poses great challenges to scientists. Furthermore, most existing methods have not considered the haplotype structure that will soon be provided by the HapMap project and have not been evaluated in this context. The overall goal of this project is to develop statistical and computational tools and methods for the analysis of haplotypes in linkage disequilibrium mapping of complex disease genes. The specific objectives of this project are: (1) Develop efficient algorithms to estimate haplotype frequencies and determine haplotype configurations in general pedigrees for a large number of tightly linked genetic markers with recombinants. (2) Define new test statistics based on haplotype sharing for mapping genes responsible for complex human diseases. (3) Assess the power using tag SNPs in linkage disequilibrium mapping of genes that are responsible for qualitative and quantitative traits. In this context, different methods for tag SNP selection will be compared and the effect of several critical issues in designing efficient and effective algorithms for tag SNP selection will be investigated. (4) Release user-friendly software to the scientific community. The proposed methods are expected to aid the discovery of genes that are responsible for complex human diseases and finally enhance our ability to understand them.