Implications of haplotype structure in the human genome

描述（由申请人提供）：最近的研究表明，人类遗传变异具有“单倍型块结构”，使得每条染色体可以分解为具有强连锁不平衡（LD）和相对较少单倍型的大块，由广泛重组的短区域分隔。 本申请的主要目的是研究所观察到的单倍型结构的生物学意义，以及这种单倍型结构对人类疾病基因定位的实际意义。 为了实现这一目标，我们采取了跨学科的方法，涉及分子生物学家，群体遗传学家，遗传流行病学家，统计学家，计算机科学家和数学家。 我们通过五个相互关联的具体目标来实现这一目标：（1）开发有效的算法来提高DNA测序和杂交芯片检测多态性的准确性;（2）开发有效的计算算法来进行单倍型块划分和标签SNP选择;（3）研究观察到的块和实验确定的块之间的对应关系。（主要通过单个精子的基因分型）重组率;（4）探索单倍型进化的群体遗传学模型，包括单倍型结构存在的替代原因;（5）研究单倍型块结构对数量性状和质量性状关联研究的意义，并发展基于单倍型块结构的关联研究新方法。 我们将验证和应用新开发的方法，从各种来源的数据，公共和私人。 除了科学目标，我们将培养科学家在跨学科，定量方法来分析基因组多态性数据，并在生物信息学更普遍。这种培训的必要性是显而易见的。 我们在计算生物学培训方面有着良好的记录，并相信我们可以提供一个独特的学习环境。

DESCRIPTION (provided by applicant): Recent studies have indicated that human genetic variation has a "haplotype block structure" such that each chromosome can be decomposed into large blocks with strong linkage disequilibrium (LD) and relatively few haplotypes, separated by short regions of extensive recombination. The primary objective of this application is to study the biological significance of the observed haplotype structure and the practical implications of such haplotype structure for the mapping of genes responsible for human disease. To achieve this objective, we take an inter-disciplinary approach involving molecular biologists, population geneticists, genetic epidemiologists, statisticians, computer scientists, and mathematicians. We achieve the objective through five inter-related specific aims: (1) Develop efficient algorithms to improve the accuracy of polymorphism detection by both DNA sequencing and hybridization chips; (2) Develop efficient computational algorithms for haplotype block partitions and tag SNP selection; (3) Investigate the correspondence between the observed blocks and experimentally determined (primarily through genotyping of single sperm) rates of recombination; (4) Explore population genetics models of haplotype evolution that include alternative reasons for the presence of haplotype structure; and (5) Study the implications of haplotype block structure for association studies of both quantitative and qualitative traits, and develop novel statistical methods for association studies based on haplotype structure. We will validate and apply the newly developed methods on data from a variety of sources, both public and private. In addition to the scientific aims, we will train scientists in interdisciplinary, quantitative approaches to analyzing genomic polymorphism data, and in bioinformatics more generally. The need for such training is clear. We have a strong record of training in computational biology, and believe that we can provide a unique learning environment.