New and efficient approaches to Markov chain Monte Carlo sampling of gene genealogies conditional on observed genetic data

以观察到的遗传数据为条件的基因谱系马尔可夫链蒙特卡罗抽样的新且有效的方法

• 批准号: 222886-2013
• 负责人: Graham, Jinko
• 金额: $ 1.09万
• 依托单位: Simon Fraser University
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2013
• 资助国家: 加拿大
• 起止时间: 2013-01-01 至 2014-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=523871
• 关键词: New; efficient; approaches; Markov; chain

This research program will use the DNA of individuals sampled from natural populations to gain insight into how their DNA sequences are related, genetically. The pairs of DNA variants that individuals carry at certain DNA locations, or loci, are the data. These data, referred to as genotypes, can help narrow down the possible relationships among sequences. For example, two individuals with identical genotypes at a large number of DNA loci are more likely to be related than two with different genotypes. Just as we trace surnames in genealogy, we trace relationships for the DNA variants at a particular DNA locus in terms of descent from the same ancestral copy. At any given locus, the relationships among the sampled variants can be summarized by a binary ancestral tree. From mathematical population genetics, we have a stochastic or random model for this tree called the coalescent, and a model for the suite of trees at the set of genotyped loci called the ancestral recombination graph. These stochastic models provide us with expectations about what sorts of relationships are reasonable, without any genotype data. However, once we collect the genotype data, these prior expectations need to be modified because the data give clues that some DNA variants are more related than others. These modifications in light of the genotype data give the "posterior distribution" of the ancestral trees. The way to think about this distribution is that there is no single, known ancestral tree relating the variants at a given locus, but rather an extremely large number of possible trees that have different probability weightings. The genotype data help narrow down the possibilities a great deal but, even so, the number of trees is still unmanageably large. This research proposes new statistical methods to explore the vast number of relationship possibilities efficiently, and to assign these possibilities accurate probability weights. The methods will enable more reliable statistical inference pertaining to ancestral relationships to be extracted from DNA data. Reliable inference from DNA data is important because the concept of ancestral relationships underlies many key research problems in genetics and allied fields such as virology, ecology, zoology and human genetics.

这项研究计划将使用从自然种群中取样的个体的DNA，以深入了解他们的DNA序列在遗传上是如何相关的。 个体在某些DNA位置或基因座上携带的DNA变异对就是数据。这些数据被称为基因型，可以帮助缩小序列之间可能的关系。例如，在大量DNA基因座上具有相同基因型的两个个体比具有不同基因型的两个个体更可能相关。就像我们在家谱中追踪姓氏一样，我们追踪特定DNA基因座上DNA变异的关系，从同一祖先拷贝的血统来看。在任何给定的基因座，采样的变体之间的关系可以通过二元祖先树来总结。从数学群体遗传学，我们有一个随机或随机模型，这棵树称为联合，和一个模型的套件树在一组基因型位点称为祖先重组图。这些随机模型为我们提供了关于什么样的关系是合理的预期，而没有任何基因型数据。 然而，一旦我们收集了基因型数据，这些先前的预期需要修改，因为数据提供了一些DNA变异比其他变异更相关的线索。根据基因型数据的这些修改给出了祖先树的“后验分布”。考虑这种分布的方法是，在给定的基因座上，没有一个单一的已知的祖先树与变异相关，而是有非常大量的可能的树，它们具有不同的概率权重。基因型数据有助于缩小可能性很大，但即使如此，树木的数量仍然是无法管理的大。本研究提出了新的统计方法来有效地探索大量的关系可能性，并为这些可能性分配准确的概率权重。这些方法将使更可靠的统计推断有关的祖先关系，从DNA数据中提取。从DNA数据可靠的推断是重要的，因为祖先关系的概念是遗传学和相关领域，如病毒学，生态学，动物学和人类遗传学的许多关键研究问题的基础。