Modelling Human Genomic Variations Using Markov Random Field: A Feasibility Study

使用马尔可夫随机场模拟人类基因组变异：可行性研究

• 批准号: EP/W016109/1
• 负责人: David Talavera
• 金额: $ 10.25万
• 依托单位: University of Manchester
• 依托单位国家: 英国
• 项目类别: Research Grant
• 财政年份: 2022
• 资助国家: 英国
• 起止时间: 2022 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=EP%2FW016109%2F1
• 关键词: Modelling; Human; Genomic; Variations; Using

Humans are different, and those differences are encoded in our genome. However, not all the positions in the genome contain differences, and not all the differences occur independently of each other. Evolution, migrations and chance have resulted in certain combinations of variants being more frequent than others. We plan to demonstrate that a probabilistic approach called Markov Random Field can be useful for modelling human genomic variation. First, we will computationally simulate "synthetic" data that resembles true genomic data: we will simulate how human populations expanded, migrated and mixed, and how survival and the possibility of reproduction depends on health and fitness. Then, we will use the Markov Random Field approach for modelling the simulated variation. The main aim will be to identify co-dependant genomic variants (i.e. combinations of variants that occur more often than expected). Having identified the co-dependencies, we will aim to discriminate between types of combinations of variants. Some combinations of variants might be frequent because variants are close in the genome, and hence are usually passed from parents to children. Other combinations may just reflect the distribution of variants across different subpopulations. Finally, other variants may be co-dependant because there is a synergistic fitness effect between those genomic positions; some combinations of variants would be beneficial, whereas others might be detrimental. After having demonstrated the use of a Markov Random Field in modelling simulated human genomic variation, we will do a pilot project studying genomic variation observed in a cohort of half-million individuals collected in the United Kingdom. The long-term aim of this project is to use this modelling approach in the identification of genomic variants associated with genetic diseases.

人类是不同的，这些差异编码在我们的基因组中。然而，并非基因组中的所有位置都包含差异，并且并非所有差异都彼此独立地发生。进化、迁移和偶然性导致某些变异组合比其他变异组合更频繁。我们计划证明一种称为马尔可夫随机场的概率方法可以用于模拟人类基因组变异。首先，我们将在计算上模拟类似于真实基因组数据的“合成”数据：我们将模拟人类人口如何扩张、迁移和混合，以及生存和繁殖的可能性如何取决于健康和健身。然后，我们将使用马尔可夫随机场方法来模拟模拟变化。主要目的将是识别共同依赖的基因组变异（即比预期更频繁发生的变异组合）。在确定了相互依赖性之后，我们将旨在区分变体组合的类型。一些变异的组合可能是频繁的，因为变异在基因组中很接近，因此通常从父母传给孩子。其他组合可能只是反映了不同亚群中变异的分布。最后，其他变体可能是相互依赖的，因为这些基因组位置之间存在协同适应性效应;变体的一些组合将是有益的，而其他组合可能是有害的。在演示了使用马尔可夫随机场模拟人类基因组变异后，我们将进行一个试点项目，研究在英国收集的50万人队列中观察到的基因组变异。该项目的长期目标是使用这种建模方法来识别与遗传疾病相关的基因组变异。