Efficient Analysis of SNPs & Haplotypes with Applications in Gene Mapping

描述（由申请人提供）： 总体目标是开发有效的算法和软件工具，用于分析人群中的遗传变异及其与表型变异的关系。将DNA序列的变异与表型差异相关联一直是生物医学研究中的重大挑战之一。随着人类基因组计划的完成，已经做出了大量努力来鉴定所有常见的遗传变异，例如单核苷酸多态性（SNP）。虽然已经鉴定了数百万个SNP，但非常需要模型和工具来表征人类的遗传变异，并促进复杂疾病/性状基因的定位。为了满足这一需求，我们建议开发新的算法方法和软件工具，以解决一些基本问题的SNP和单体型分析与基因关联映射的应用。更具体地说，我们建议设计高效和强大的算法来推断单倍型从基因型的系谱，估算缺失的SNP，发现单倍型结构，并选择信息（标签）SNP。我们还将开发计算模型，可以利用单倍型在疾病基因的识别。这项建议的重点是开发新的组合算法，数据挖掘方法，统计技术，以及强大的和用户友好的工具。重点是算法的效率，因为现有的方法不能处理整个基因组水平的数据集。这些算法将在公共数据库（例如HapMap项目）以及我们的合作者正在进行的研究中产生的其他人类数据上进行。 这些项目包括关于各种妊娠并发症、囊性纤维化疾病的修饰基因和迟发性阿尔茨海默病的遗传影响的数据集。我们预计，该项目将产生一个全方位的高效和有效的算法和软件工具，这将是有用的广泛的生物医学研究界，并将大大促进人类遗传变异及其与复杂疾病/性状的关联的研究。拟议的项目非常适合NIH在其路线图倡议中确定的三个主题中的两个主题：以发现新途径为主题的生物信息学和计算生物学研究以及以未来研究团队为主题的跨学科研究。

DESCRIPTION (provided by applicant): The overall objective is to develop efficient algorithms and software tools for the analysis of genetic variation in human populations and its association with phenotypic variation. Correlating variations in DMA sequences with phenotypic differences has been one of the grand challenges in biomedical research. With the completion of the human genome project, substantial effort has been made to identify all common genetic variations such as single nucleotide polymorphisms (SNPs). While millions of SNPs have been identified, there is a great need for models and tools to characterize genetic variation in humans, and to facilitate the localization of genes underling complex diseases/traits. To meet the need, we propose to develop novel algorithmic approaches and software tools to address some of the fundamental issues in the analysis of SNPs and haplotypes with applications in gene association mapping. More specifically, we propose to devise efficient and robust algorithms to infer haplotypes from genotypes on a pedigree, impute missing SNPs, discover the haplotype structure, and select informative (tag) SNPs. We will also develop computational models that could utilize haplotypes in the identification of disease genes. The focus of this proposal is the development of novel combinatorial algorithms, datamining approaches, statistical techniques, as well as robust and user friendly tools. The emphasis is on the efficiency of algorithms because existing methods could not handle data sets at the whole genome level. The algorithms will be performed on the public databases (e.g. the HapMap project), as well as other human data generated in our collaborators' ongoing projects, including data sets concerning various complications of pregnancy, modifier genes of the cystic fibrosis disease and the genetic effects of late-onset Alzheimer disease. We anticipate that this project will result in a full spectrum of efficient and effective algorithms and software tools that 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/traits. The proposed project fits well in two of the three themes that NIH has identified in its Roadmap initiatives: research in bioinformatics and computational biology under the theme of New Pathways to Discovery and interdisciplinary research under the theme of Research Teams of the Future.