Computational Haplotype Analysis for SNPs

SNP 的计算单倍型分析

• 批准号: 6463868
• 负责人: JUN S LIU
• 金额: $ 35.1万
• 依托单位: HARVARD UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2002
• 资助国家: 美国
• 起止时间: 2002-07-01 至 2006-06-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/6463868
• 关键词: biotechnology; fluorescence polarization; gene expression; genetic disorder diagnosis; genetic markers; genetic polymorphism; genotype; high throughput technology; human population genetics; linkage disequilibriums; mathematical model; model design /development; oligonucleotides; polymerase chain reaction; single nucleotide polymorphism

DESCRIPTION (provided by applicant): For a complex trait, the pedigree-based llinkage analysis is effective for localizing disease-related genes to within 1cM. Fine-mapping of the disease mutation within this region is often achieved by linkage disequilibrium analysis using haplotypes. Haplotypes can also be very useful for stratifying patients and directing drugs to appropriate subpopulation. The discovery of human single nucleotides polymorphism (SNP) as genetic marker's greatly increases the density of the genetic map and enhances our ability in developing novel disease diagnostic tools and individualized drug therapies. However, with 1.65 million human SNPs single nucleotides polymorphism (SNP) markers identified, the haplotype determination and analysis present a daunting challenge to scientists. The general goal of this research is to advance our computational capability for both the construction of population haplotypes and the utilization of the whole haplotype information in LD mapping. The specific aims of the proposed research are (a) to propose more efficient and scalable computational strategies for haplotype determination, (b) to demonstrate that in silico approaches to haplotype determinations are effective and economical alternatives to experimental ones, (c) to establish a computational platform that allows for a direct output of haplotypes from high-throughput genotyping readouts, and (d) to design a Bayesian framework for simultaneous haplotype determination, LD mapping, and patients clustering.

描述（由申请人提供）：对于复杂性状， 连锁分析是将疾病相关基因定位于 1厘米。在这个区域内的疾病突变的精细定位通常是可以实现的 连锁不平衡分析。单倍型也可以是 对患者进行分层和将药物导向适当的 亚群人类单核苷酸多态性（SNP）的发现， 遗传标记大大增加了遗传图谱的密度， 我们在开发新型疾病诊断工具和个性化治疗方面的能力 药物治疗然而，有165万个人类单核苷酸多态性， 多态性（SNP）标记鉴定，单倍型测定和分析 对科学家来说是一个艰巨的挑战。本研究的总体目标是 是为了提高我们的计算能力， 群体单倍型及全单倍型信息在 LD映射。拟议研究的具体目标是：（a）提出更多 用于单倍型确定的有效和可扩展的计算策略， (b)为了证明单倍型测定的计算机模拟方法， （c）建立一个有效和经济的替代方案， 计算平台，其允许直接输出来自 高通量基因分型读出，和（d）设计贝叶斯框架， 同时进行单倍型测定、LD作图和患者聚类。