STATISTICAL METHODS FOR STUDYING DISEASE GENE HISTORY

描述：(申请人摘要)人类基因组计划已经 产生了无数长的人类DNA序列。如此长的序列可能包括 一个完整的基因及其侧翼区域。这一进步和到来 快速DNA测序技术的发展将使大型DNA测序成为可能 人类种群样本的区域，尤其是包含 致病突变。或者，人们可以研究微卫星。 与突变密切相关的标记。这个项目的目标是 发展群体遗传学理论、统计方法和计算机 一种研究历史和维护机制的算法 在人群中导致疾病的突变。具体目标是(1) 为易受点突变影响的DNA区域发展了一种合并理论， 自然选择和重组，(2)发展一个联合理论 微卫星基因座易受逐步突变、自然选择和 重组。当前的合并方法假定种群平衡， 并不适合研究受自然因素影响的最近的突变 选择AF508等突变的囊性纤维化。我们建议 开发一种合并方法，其中包含一种模拟 携带突变体的亚群的历史。(3)制定方法，以 根据DNA序列或从DNA序列中估计引起突变的疾病的年龄 微卫星标记。(4)制定评估选择的方法 系数和检验关于选择系数的假设 一种致病突变。我们将开发的方法之一是 逐个站点的多态数据，并且不需要确定 DNA单倍型序列。因此，这种方法将特别有用 当通过DNA进行样本中的大规模多态筛选时 “奇普斯”。(5)开发适用于上述方法的计算机程序包 在万维网上，以及(6)应用理论和方法进行评估 囊性纤维化AF508突变体的年龄和选择系数。

DESCRIPTION: (Applicant's abstract) The Human Genome Project has already produced numerous long human DNA sequences. Such a long sequence may cover an entire gene and also its flanking regions. This progress and the advent of rapid DNA sequencing techniques will allow the sequencing of large DNA regions of samples from human populations, especially regions containing a disease-causing mutation. Alternatively, one may study microsatellite markers closely linked to the mutation. The goal of this project is to develop population genetics theory, statistical methods and computer algorithms for studying the history and the mechanism of maintenance of a disease-causing mutation in a population. The specific aims are (1) to develop a coalescent theory for a DNA region subject to point mutation, natural selection and recombination, (2) to develop a coalescent theory for microsatellite loci subject to stepwise mutation, natural selection and recombination. Current coalescent methods assume an equilibrium population, and are not appropriate for studying a recent mutation subject to natural selection, such as the AF508 mutation of cystic fibrosis. We propose to develop a coalescent approach incorporating a method for simulating the history of the subpopulation bearing the mutant. (3) to develop methods for estimating the age of a disease causing mutation from DNA sequences or from microsatellite markers. (4) to develop methods for estimating the selection coefficients and for testing hypotheses about the selection coefficients of a disease-causing mutation. One of the methods we will develop is for site-by-site polymorphism data, and does not require the determination of DNA haplotype sequences. Therefore, the method will be particularly useful when large scale screening of polymorphisms in a sample is done by DNA "chips". (5) to develop a computer package for the above methods accessible on the World Wide Web, and (6) to apply the theory and methods to estimate the age and selection coefficients of the AF508 mutant of cystic fibrosis.