Efficient Analysis of SNPs & Haplotypes with Applications in Gene Mapping

SNP 的高效分析

• 批准号: 7020438
• 负责人: Jing Li
• 金额: $ 42.28万
• 依托单位: CASE WESTERN RESERVE UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2006
• 资助国家: 美国
• 起止时间: 2006-03-15 至 2009-02-28
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/7020438
• 关键词: clinical research; family genetics; genes; genetics; genotype; human; model

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.

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，用于分析人类群体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，人们做出了大量努力来识别所有常见的遗传变异，如单核苷酸多态(SNPs)。虽然已经发现了数百万个SNPs，但仍迫切需要模型和工具来描述人类的遗传变异，并促进复杂疾病/性状相关基因的定位。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来解决SNPs和单倍型分析中的一些基本问题，并将其应用于基因关联图谱。更具体地说，我们建议设计高效和健壮的算法来从家系的基因类型推断单倍型，归因于缺失的SNP，发现单倍型结构，并选择信息(标签)SNPs。我们还将开发可以利用单倍型识别疾病基因的计算模型。这一提议的重点是开发新的组合算法、数据挖掘方法、统计技术以及健壮和用户友好的工具。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。算法将在公共数据库(例如HapMap项目)上执行，以及在我们的合作者正在进行的过程中生成的其他人类数据 项目，包括关于各种妊娠并发症的数据集、囊性纤维性疾病的修饰基因和迟发性阿尔茨海默病的遗传影响。我们预计，该项目将产生一系列高效和有效的算法和软件工具，这些算法和软件工具将对广泛的生物医学研究社区有用，并将极大地促进人类遗传变异及其与复杂疾病/特征的关联的研究。拟议的项目与美国国立卫生研究院在其路线图倡议中确定的三个主题中的两个很好地契合：在新发现途径主题下的生物信息学和计算生物学研究，以及在未来研究团队主题下的跨学科研究。