Statistical Methods to Map Genes for Complex Traits

绘制复杂性状基因图谱的统计方法

• 批准号: 7032625
• 负责人: HONGYU ZHAO
• 金额: $ 30.68万
• 依托单位: YALE UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 1999
• 资助国家: 美国
• 起止时间: 1999-02-01 至 2009-11-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/7032625
• 关键词: Internet; biomedical resource; biotechnology; computer program /software; computer simulation; computer system design /evaluation; family genetics; gene environment interaction; gene expression; gene interaction; genetic markers; genetic models; genetic susceptibility; genotype; human data; human population genetics; linkage mapping; mathematical model; model design /development; quantitative trait loci; statistics /biometry

DESCRIPTION (provided by applicant): Recent years have seen great progress in the identification of millions of single nucleotide polymorphisms (SNPs). The abundance of SNPs coupled with the developments of various platforms for high-throughput, high-quality genotyping have made genome-wide association studies within reach for human geneticists. This is further strengthened by the on-going HapMap project that is collecting valuable population genetics information on the allele frequencies and linkage disequilibrium (LD) patterns of millions of SNPs in four different populations, yielding valuable information that can be used to select SNPs in genetic association studies. However, statistical methods lag behind the rapid accumulation of biological information and technology advancement in that existing methods are either not appropriate or not optimized to address scientific questions using these data. Moreover, the new technologies have also led to new questions that are unique and need to be appropriately addressed in a timely fashion to fully realize the potential in these technologies. In this application, we focus on the design and analysis of high-density SNPs in genetic association and linkage studies to identify DMA variants associated with disease risk. The primary objectives are to investigate study design issues in genome-wide association studies and to develop statistically sound and computationally feasible methods for genetic linkage and association studies. More specifically, four specific aims will be achieved: (1) to investigate strategies in the design of genome-wide association studies; (2) to develop statistical methods for association studies in samples involving individuals having uncertainties on inbreeding patterns and relationships; (3) to develop statistical methods for linkage studies in the presence of LO among markers; and (4) to implement the developed statistical methods in user-friendly computer programs and make them available to the scientific community.

描述（由申请人提供）：近年来，在鉴定数百万个单核苷酸多态性（SNP）方面取得了很大进展。SNPs的丰富，加上高通量，高质量的基因分型的各种平台的发展，使人类遗传学家的全基因组关联研究触手可及。正在进行的HapMap项目进一步加强了这一点，该项目正在收集关于四个不同人群中数百万个SNP的等位基因频率和连锁不平衡（LD）模式的有价值的群体遗传学信息，产生可用于在遗传关联研究中选择SNP的有价值的信息。然而，统计方法落后于生物信息的快速积累和技术进步，现有的方法要么不适合，要么没有优化，以解决使用这些数据的科学问题。此外，新技术也带来了一些独特的新问题，需要及时妥善解决，以充分发挥这些技术的潜力。在本申请中，我们专注于设计和分析高密度SNP的遗传关联和连锁研究，以确定与疾病风险相关的DMA变异。主要目的是调查研究设计问题，在全基因组关联研究，并制定统计上合理的和计算上可行的方法，遗传连锁和关联研究。具体而言，本研究将实现以下四个具体目标：（1）研究全基因组关联研究的设计策略;（2）开发在近亲繁殖模式和关系不确定的个体中进行关联研究的统计方法;（3）开发在标记间存在LO的情况下进行连锁研究的统计方法;及（4）将已发展的统计方法应用于易于使用的电脑程式，并提供予科学界使用。