DISEQUILIBRIUM MAPPING OF COMPLEX GENETIC DISEASES

复杂遗传疾病的不平衡图谱

• 批准号: 7074476
• 负责人: Bruce RANNALA
• 金额: $ 8.35万
• 依托单位: UNIVERSITY OF CALIFORNIA AT DAVIS
• 依托单位国家: 美国
• 财政年份: 1999
• 资助国家: 美国
• 起止时间: 1999-01-01 至 2006-06-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/7074476
• 关键词: alleles; computer data analysis; computer program /software; computer simulation; gene expression; gene frequency; gene mutation; genetic disorder; genetic markers; genetic polymorphism; genetic recombination; human data; human population genetics; linkage disequilibriums; mathematical model; method development; phenotype; quantitative trait loci; restriction fragment length polymorphism; statistics /biometry

DESCRIPTION (provided by applicant): This research will develop new statistical methods for fine-scale localization of the positions, in the human genome, of mutations that influence susceptibility to complex genetic diseases. Complex genetic diseases such as multiple sclerosis and type II diabetes are a result of the interaction of multiple genes, and the environment. Complex genetic diseases are relatively common in the population and therefore have a large socioeconomic impact and are of great importance for public health. Conventional methods for identifying mutations that cause simple genetic disorders (such as cystic fibrosis) use linkage analysis of markers transmitted to affected and unaffected relatives. These methods often have low power to detect complex disease mutations due to such factors as incomplete penetrance. A more powerful approach for identifying genes influencing complex genetic diseases uses population association studies of unrelated affected and normal individuals. Polymorphisms with detectable frequency differences between the two groups are candidate disease genes. One goal of the proposed research is to develop new statistical methods for fine-scale mapping of disease mutations using population linkage disequilibrium; these methods will be specifically designed for use in fine-mapping of genes influencing complex diseases. Another goal is to develop statistical tools to facilitate population association analyses of complex diseases. These tools include new statistical methods for inferring haplotype phase (which marker alleles are carried on which chromosomes) using new kinds of genotyping data, and new statistical methods for determining the rates of recombination among markers at a fine scale in the human genome. These new methods will together provide powerful bioinformatics tools, and computer programs, for use by researchers carrying out association studies aimed at identifying genes influencing complex diseases.

描述（由申请人提供）： 这项研究将开发新的统计方法，用于在人类基因组中对影响复杂遗传疾病敏感的突变进行细胞定位。复杂的遗传疾病，例如多发性硬化症和II型糖尿病是多个基因和环境相互作用的结果。复杂的遗传疾病在人群中相对普遍，因此具有很大的社会经济影响，并且对公共卫生非常重要。鉴定引起简单遗传疾病的突变（例如囊性纤维化）的常规方法使用传播给受影响和未影响亲属的标记的连锁分析。这些方法通常具有低功率来检测由于不完整的渗透率等因素而引起的复杂疾病突变。一种鉴定影响复杂遗传疾病的基因的更强大的方法使用人群关联研究无关受影响和正常人的研究。两组之间具有可检测到的频率差异的多态性是候选疾病基因。拟议的研究的目标之一是开发新的统计方法，以使用种群连锁不平衡来对疾病突变进行精细映射；这些方法将专门设计用于绘制影响复杂疾病的基因。另一个目标是开发统计工具，以促进人口协会的复杂疾病分析。这些工具包括使用新型的基因分型数据推断单倍型相的新统计方法（在哪种染色体上携带了标记等位基因），以及用于确定人类基因组中标记中标记物之间重组率的新统计方法。这些新方法将共同提供强大的生物信息学工具和计算机程序，用于研究旨在识别影响复杂疾病的基因的研究人员。