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: 2017
• 资助国家: 加拿大
• 起止时间: 2017-01-01 至 2018-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=635749
• 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.

该研究计划将使用从自然种群中取样的个体的DNA来深入了解其DNA序列在遗传上的相关性。个体在某些DNA位置或位点携带的DNA变量对是数据。这些数据（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有可能是相关的。正如我们追踪族谱中的姓氏一样，我们从同一祖先副本的下降方面追踪了特定DNA基因座的DNA变体的关系。在任何给定的基因座上，都可以通过二进制祖先树来概括采样变体之间的关系。从数学种群遗传学中​​，我们为这棵树提供了一个随机或随机模型，称为“融合”，一个在基因分型基因座中的树木套件的模型，称为祖先重组图。这些随机模型使我们对没有任何基因型数据的哪种关系是合理的。但是，一旦我们收集了基因型数据，就需要修改这些先前的期望，因为数据提供了一些DNA变体比其他DNA变体更相关的线索。根据基因型数据，这些修饰给出了祖先树的“后验分布”。思考这种分布的方法是，没有一个已知的祖先树在给定的基因座上关联变体，而是具有不同概率权重的大量可能的树。基因型数据有助于缩小可能性的范围，但是即使如此，树木的数量仍然很大。这项研究提出了新的统计方法，以有效地探索大量关系可能性，并分配这些可能性准确的概率权重。这些方法将使与祖先关系有关的更可靠的统计推论从DNA数据中提取。 DNA数据的可靠推断很重要，因为祖先关系的概念是遗传学和相关领域的许多关键研究问题，例如病毒学，生态学，动物学和人类遗传学。