DISEQUILIBRIUM MAPPING OF COMPLEX GENETIC DISEASES

描述（由申请人提供）： 这项研究将开发新的统计方法，用于对人类基因组中影响复杂遗传疾病易感性的突变的位置进行精细定位。复杂的遗传疾病，如多发性硬化症和II型糖尿病，是多基因和环境相互作用的结果。复杂遗传病在人群中相对常见，因此具有很大的社会经济影响，对公共卫生非常重要。用于鉴定引起简单遗传性疾病（如囊性纤维化）的突变的常规方法使用传递给受影响和未受影响亲属的标记的连锁分析。由于不完全突变等因素，这些方法检测复杂疾病突变的能力通常较低。一种更有效的方法是对无关的受影响个体和正常个体进行群体关联研究，以确定影响复杂遗传疾病的基因。两组之间具有可检测频率差异的多态性是候选疾病基因。拟议研究的一个目标是开发新的统计方法，用于使用群体连锁不平衡进行疾病突变的精细定位;这些方法将专门设计用于影响复杂疾病的基因的精细定位。另一个目标是开发统计工具，以促进复杂疾病的人口关联分析。这些工具包括使用新的基因分型数据推断单倍型阶段（哪些标记等位基因携带在哪些染色体上）的新统计方法，以及确定人类基因组中标记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，供研究人员进行旨在识别影响复杂疾病的基因的关联研究。

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.