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: 2866661
• 负责人: HONGYU ZHAO
• 金额: $ 14.77万
• 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 2002-01-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/2866661
• 关键词: Internet; biomedical resource; computer program /software; computer simulation; 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

Despite the tremendous advances that have occured in mapping genes for mendelian diseases, the discovery of genes that influence susceptibility to more common human diseases, particularly the neuropsychiatric disorders, has proceeded slowly The disease susceptibility of these disorders is very complex, and is likely influenced by mulitple genes having mall effects as well as many interacting environmental factors. Furthermore, population stratification, a major confounding factor in detecting disease- marker associations, presents a particular challenge to dissecting complex traits. Novel and powerful statistical methods are needed to take full advangtage of the enormous amount of information invaluable for human linkage studies that has accumulated over the past few years, including both highly informative markers and dense maps of those markers. The major objective of this proposal is to extend and develop powerful statistical methods to detect linkage to genes underlying complex traits that are robust to population stratification. Specifically, we propose to develop: (1) transmission disequilibrium tests that can jointl analyze multiple markers within a short physical distance on a chromosome, yielding far greater power than single-marker methods; (2) a unified framework for the transmission/disequilibrium test that is applicable to more general family structures than those that can be analyzed by existing methods; and (3) statistical methods for modeling and distinguishing different gene-gene interactions and gene-environment interactions. Both theoretical studies and extensive computer simulations will be carried out to evaluate and compare these methods with other approaches. The developed methodology will be applied to available data sets to detect linkage and to understand the complex etiologies of the traits of interest. Well-documented and efficient computer programs that implement the new and powerful methods will be extensively developed and tested under different computer operating systems. These programs will be made widely available to the scientific community through the World Wide Web. The new statistical methods and the powerful computer programs will provide biomedical researchers with important tools to extreact the maiximal information from the enormous amount of genomic information generated to map genes for complex traits and to understand the complex etiologies underlying these traits.

尽管基因定位已经取得了巨大的进步 对于孟德尔疾病，发现基因影响 更常见的人类疾病的易感性，特别是 神经精神疾病，进展缓慢， 这些疾病的易感性非常复杂， 受多基因影响，具有小的影响，以及许多 相互作用的环境因素。 此外，人口 分层是检测疾病的一个主要混杂因素， 标记关联，提出了一个特殊的挑战，解剖 复杂的特征 需要新的和强大的统计方法， 充分利用大量宝贵的信息 在过去几年中积累的人类联系研究， 年，包括高度信息化的标记和密集的地图， 这些标记。 这项建议的主要目的是扩大和发展 强大的统计方法来检测与潜在基因的联系 对种群分层具有鲁棒性的复杂性状。 具体而言，我们建议发展：（1）传输不平衡 可以在短时间内联合分析多个标记的测试 染色体上的物理距离，产生的能量远大于 单标记方法;（2）一个统一的框架 传递/不平衡检验，适用于更一般的 家庭结构比那些可以分析现有的 方法;（3）建模和识别的统计方法 不同的基因-基因相互作用和基因-环境相互作用。 理论研究和广泛的计算机模拟将是 进行评估和比较这些方法与其他 接近。 开发的方法将适用于现有的 数据集，以检测联系，并了解复杂的病因 of the traits特征of interest兴趣. 有据可查且高效的计算机程序， 新的和强大的方法将被广泛开发和测试 在不同的计算机操作系统下。 这些方案将 通过世界各地向科学界广泛提供 万维网。 新的统计方法和强大的计算机 项目将为生物医学研究人员提供重要工具， 从大量的基因组中提取最大信息， 为复杂性状绘制基因图谱， 了解这些特征背后的复杂病因。