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提供了一种有前途的方法来检测导致复杂人类疾病（如高血压、糖尿病、肥胖症、癌症等）的遗传变异。与基于单一标记的方法相比，基于单倍型的方法可以提供额外的能力来定位疾病基因。更重要的是，单倍型可能导致对影响遗传标记之间的依赖性的因素的见解，即连锁不平衡（LD），并且这种见解可能提供理解人类进化所必需的信息，并且可能捕获两个或更多个因果变体之间的顺式相互作用。然而，利用大量紧密连锁的SNP进行单倍型分析才刚刚发展，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单体型结构，这将很快提供的HapMap项目，并没有在这方面进行评估。该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连锁不平衡作图中的单倍型。本项目的具体目标是：（1）针对大量紧密连锁的遗传标记与重组体，开发有效的算法来估计单倍型频率并确定一般家系中的单倍型构型。(2)定义基于单倍型共享的新检验统计量，用于绘制负责复杂人类疾病的基因。(3)评估使用标签SNP在负责质量和数量性状的基因的连锁不平衡作图中的功效。在这种情况下，不同的标签SNP选择方法进行比较，并在设计高效和有效的算法标签SNP选择的几个关键问题的影响将进行调查。(4)向科学界发布用户友好型软件。这些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它们的能力。

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.