LINKAGE DISEQUILIBRIUM MAPPING USING THE COALESCENT

使用 COALESCENT 进行连锁不平衡图谱

• 批准号: 6138900
• 负责人: Joseph Felsenstein
• 金额: $ 17.16万
• 依托单位: UNIVERSITY OF WASHINGTON
• 依托单位国家: 美国
• 财政年份: 1999
• 资助国家: 美国
• 起止时间: 1999-01-01 至 2001-12-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/6138900
• 关键词: alleles; computer program /software; gene expression; gene mutation; genetic markers; genetic recombination; linkage disequilibriums; linkage mapping; mathematical model; model design /development; population genetics; quantitative trait loci

Genetic linkage maps can be created using pedigree methods, in which individuals whose relationships are known can be studied. These allow us to infer the locations in the genome where genetic crossovers occur. They have the limitation that there are only a limited number of individuals in a pedigree. When we try to make a genetic map of markers that are close together in the genome, we may not see any crossovers between those markers, even on a rather large pedigree. Linkage disequilibrium mapping uses individuals sampled from a large population. They are connected by a pedigree which is much deeper in time, and thus has many more individuals in it and many more opportunities for crossover to occur. The difficulty is that we do not known the pedigree, and must use the genetic data to estimate it. The random trees of ancestry of gene copies in a large population are called coalescents. The widely-used statistical method known as maximum likelihood can be used to analyze linkage disequilibrium mapping, by summing up the likelihood over all the possible coalescent trees that could explain the data. The number of these trees is vast, but it has been possible to approximate likelihoods in coalescents successfully using random sampling methods. We have developed such a sampling method, a Metropolis-Hastings sampler, for the case of recombining loci. It is proposed to adapt this to linkage disequilibrium mapping. One of the problems that has to be solved to do this is to make use of data that consists of diploid genotypes. It is proposed to do this by an additional stage of random sampling, so as to sum over all the ways that the diploid genotypes could be resolved into haplotypes. We also need to be able to correct for the ascertainment bias that is introduced when a disease allele is preferentially sampled, with less attention paid to the normal allele. It is proposed to do this by treating the disease alleles as if they were a separate population, exchanging genetic material with the normal alleles by crossing-over and mutation. We have existing methods for coalescent likelihoods for geographically structured populations, and methods from these can be used to accomplish this. For some of the genetic markers, such as Single Nucleotide Polymorphisms, there are also ascertainment problems which arise because those sites that show no polymorphism are not scored. It is proposed to use a simple correction to the likelihood to cope with this. We will make available computer programs in C++ to compute the likelihoods, and distribute them, free, over the Internet as source code, documentation, and executables.

可以使用谱系方法创建遗传连锁图，其中可以研究关系已知的个体。这些使我们能够推断基因组中发生遗传交叉的位置。 它们的局限性在于，一个谱系中的个体数量有限。 当我们尝试绘制基因组中靠近的标记的遗传图谱时，即使在相当大的谱系上，我们也可能看不到这些标记之间的任何交叉。 连锁不平衡作图使用从大量人群中采样的个体。 它们通过时间上更深的谱系联系在一起，因此其中有更多的个体，也有更多发生交叉的机会。 困难在于我们不知道谱系，必须使用遗传数据来估计它。 大群体中基因拷贝的随机祖先树称为聚结树。 广泛使用的统计方法（称为最大似然）可用于分析连锁不平衡映射，通过总结所有可能解释数据的合并树的似然。 这些树的数量巨大，但可以使用随机抽样方法成功地估计合并的可能性。 我们针对重组基因座的情况开发了这样一种采样方法，即 Metropolis-Hastings 采样器。 建议将此适应连锁不平衡作图。 为此必须解决的问题之一是利用由二倍体基因型组成的数据。 建议通过额外的随机抽样阶段来做到这一点，以便总结可将二倍体基因型解析为单倍型的所有方式。 我们还需要能够纠正当优先采样疾病等位基因而较少关注正常等位基因时引入的确定偏差。 建议通过将疾病等位基因视为一个单独的群体，通过交叉和突变与正常等位基因交换遗传物质来实现这一点。 我们有针对地理结构人群的合并可能性的现有方法，并且可以使用这些方法来实现这一目标。 对于一些遗传标记，例如单核苷酸多态性，也存在确定问题，因为那些没有表现出多态性的位点没有被评分。 建议对可能性使用简单的修正来解决这个问题。我们将提供 C++ 计算机程序来计算可能性，并以源代码、文档和可执行文件的形式通过互联网免费分发它们。