QUANTITATIVE METHODS TO MAP GENES FOR COMPLEX TRAITS

长期目标是开发新的分析方法，使研究人员能够找到隐藏在复杂特征背后的疾病基因集。这些方法利用标记和邻近疾病基因之间的遗传联系和联系不平衡，并提供例如对疾病基因的数量和位置的最大似然估计。预计结果将为识别易感基因开辟道路。一旦发现这样的基因，对其结构的了解将有希望导致对疾病病因的更深层次的理解，从而有可能改善或治愈这些特征。这三个具体目标是：1)将统计物理的伊辛模型应用于连锁和不平衡分析；2)研究系谱误差对不平衡分析的影响；3)在计算机程序中实现根据这一建议开发的方法学。

The long-term goal is to develop novel analytical methods that will enable researchers to find sets of disease genes underlying complex traits. These methods make use of genetic linkage and linkage disequilibrium between markers and nearby disease genes and provide, for example, maximum likelihood estimates for the numbers and positions of disease genes. Results are expected to open the way to identifying susceptibility genes. Once such genes are found, there is hope that knowledge of their structure will lead to a deeper understanding of disease etiology and, thus, to the possibility of ameliorating or curing these traits. The three specific aims are 1) to apply the Ising model of statistical physics to linkage and disequilibrium analysis, 2) to investigate effects of pedigree errors on disequilibrium analysis, and 3) to implement methodology developed under this proposal in computer programs.