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，其中可以研究其关系的个体。这些使我们能够推断出发生遗传交叉的基因组中的位置。 他们的局限性是，血统中只有数量有限的人。 当我们尝试制作基因组中靠近的标记的遗传图时，即使在相当大的血统上，我们也可能看不到这些标记之间的任何交叉。 连锁不平衡映射使用从大量人群中取样的个体。 它们通过及时更深得多的血统相连，因此其中有更多的人，还有更多的交叉发生机会。 困难是我们不知道血统，必须使用遗传数据来估计它。 大量人群中基因副本的随机树被称为结合。 被称为最大似然的广泛统计方法可用于分析链接不平衡映射，通过在所有可能解释数据的融化树上总结可能性。 这些树的数量很大，但是可以使用随机抽样方法成功地在合并中近似可能的可能性。 我们已经开发了一种采样方法，即一种大都市杂物采样器，用于重组基因座。 提议将其适应为连锁不平衡映射。 必须解决的问题之一是使用由二倍体基因型组成的数据。 建议通过随机抽样的附加阶段来实现这一目标，以便总和可以将二倍体基因型可以分解为单倍型的所有方式。 我们还需要能够纠正疾病等位基因优先采样时引入的确定偏置，而对正常等位基因的关注较少。 有人建议通过对待疾病等位基因来做到这一点，就好像它们是一个单独的人群一样，通过跨越和突变将遗传物质与正常等位基因交换。 我们有现有的用于地理结构种群的合并可能性的方法，可以使用这些方法来实现这一目标。 对于某些遗传标记，例如单核苷酸多态性，也出现了确定性问题，因为那些没有表现出多态性的位点没有评分。 有人建议对可能性的可能性进行简单的校正来应对。我们将在C ++中提供计算机程序，以计算可能性，并通过Internet作为源代码，文档和可执行文件免费分发它们。