Haplotype Analysis in Linkage Disequilibrium Mapping

描述（由申请人提供）：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。基于单倍型的方法可能比基于单标记的方法提供更多的能力来绘制疾病基因。更重要的是，单倍型可能会导致对影响遗传标记之间依赖关系的因素的见解，即连锁不平衡（LD），这种见解可能为理解人类进化提供必要的信息，并可能捕获两个或更多因果变异之间的顺式相互作用。然而，利用大量紧密连接的snp进行单倍型分析的研究刚刚起步，给科学家们带来了巨大的挑战。此外，大多数现有的方法都没有考虑到HapMap项目即将提供的单倍型结构，也没有在这种情况下进行评估。该项目的总体目标是开发用于分析复杂疾病基因连锁不平衡定位中的单倍型的统计和计算工具和方法。该项目的具体目标是：(1)开发有效的算法来估计单倍型频率，并确定与重组体紧密相连的大量遗传标记的一般谱系中的单倍型配置。(2)定义新的基于单倍型共享的检测统计量，用于定位导致复杂人类疾病的基因。(3)利用标签单核苷酸多态性评估基因连锁不平衡定位的能力，这些基因负责质量和数量性状。在此背景下，将比较不同的标签SNP选择方法，并研究设计高效和有效的标签SNP选择算法的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，并最终提高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 2 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.