DISEQUILIBRIUM MAPPING OF COMPLEX GENETIC DISEASES

拟议研究的目标是开发新的理论和 复合体下的突变的高分辨率作图方法 人类的遗传病。这些方法将使用来自 一个或多个基因之间的群体水平连锁不平衡 标记物和疾病表型。LD映射的基础是 在疾病突变的染色体上发现标记单倍型 First Rose会倾向于在染色体上有更高的频率 这种突变。遗传标记和遗传标记之间的这种非随机关联 随着时间的推移，由于重组，疾病表型会以一定的速度减少。 这取决于标记与突变之间的物理距离。 这使得连锁的遗传标记和疾病之间的距离 使用数学模型估计突变的过程 遗传重组和疾病等位基因动态 人口。LD映射方法在应用于 经历过最近的开国事件和/或高 人口增长水平。用于LD映射的新的统计方法 将开发利用多个遗传标记和帐户 对于潜在的并发症，如复发的突变(相同的疾病 表型由单个疾病基因的不同突变引起)和 基因座异质性(突变导致相同的疾病表型 在几个不同的疾病基因中)。的统计性能 将通过计算机模拟来研究生成方法 疾病等位基因的人工种群和分析现有的 疾病突变所在位置的遗传标记数据 现在已经知道了。这些方法将适用于几种不同的 目前广泛使用的遗传标记类型包括 限制性片段长度多态(RFLP)、微卫星和 单核苷酸多态(SNP)。

The objective of the proposed research is to develop new theory and methods for high-resolution mapping of the mutations underlying complex genetic diseases of humans. The methods will use information from population level linkage disequilibrium (LD) between one or more genetic markers and a disease phenotype. The basis for LD mapping is that the marker haplotype found on the chromosome on which a disease mutation first arose will tend to have a higher frequency on chromosomes carrying that mutation. This non-random association between genetic markers and a disease phenotype decreases over time, due to recombination, at a rate that depends on the physical distance of the marker from the mutation. This allows the distances between linked genetic markers and a disease mutation to be estimated using a mathematical model of the process of genetic recombination and of the disease allele dynamics in the population. LD mapping methods are most effective when applied to populations that have experienced recent founding events and/or high levels of population growth. New statistical methods for LD mapping will be developed that make use of multiple genetic markers and account for potential complications such as recurrent mutation (the same disease phenotype results from different mutations in a single disease gene) and locus heterogeneity (the same disease phenotype results from mutations in several different disease genes). The statistical performance of the methods will be studied by using computer simulation to generate artificial populations of disease alleles and by analyzing existing genetic marker data for which the location of the disease mutation is now known. The methods will be suitable for use with several different types of genetic markers that are currently widely used including restriction fragment length polymorphisms (RFLPs), microsatellites, and single nucleotide polymorphisms (SNPs).