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: 2014
• 资助国家: 加拿大
• 起止时间: 2014-01-01 至 2015-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=546673
• 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.

这个研究项目将使用从自然种群中取样的个体DNA来深入了解他们的DNA序列是如何在基因上相关的。个体在特定的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位点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有可能具有亲缘关系。就像我们在家谱中追踪姓氏一样，我们根据来自同一祖先副本的血统来追踪特定DNA位点上的DNA变体的关系。在任何给定的基因座上，样本变异之间的关系都可以用二叉祖先树来概括。从数学群体遗传学的角度来看，我们有一个随机的模型来描述这棵树，叫做聚结树，还有一个模型来描述基因型位点集合上的一组树，叫做祖先重组图。这些随机模型在没有任何基因型数据的情况下，为我们提供了关于哪种关系是合理的预期。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”。考虑这种分布的方式是，在给定的位点上，没有单一的、已知的祖先树与变异有关，而是有非常多的可能的树，它们具有不同的概率权重。基因型数据大大缩小了可能性，但即便如此，树木的数量仍然大得难以管理。本研究提出了一种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。这些方法将使从DNA数据中提取的有关祖先关系的统计推断更加可靠。来自DNA数据的可靠推断非常重要，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）许多关键研究问题的基础。