Institute of Molecular, Cell and Systems Biology, College of Medical, Veterinary and Life Sciences, University of Glasgow, Glasgow G12 8QQ, UK.
Hum Mol Genet. 2012 Jun 1;21(11):2450-63. doi: 10.1093/hmg/dds059. Epub 2012 Feb 24.
Several human genetic diseases are associated with inheriting an abnormally large unstable DNA simple sequence repeat. These sequences mutate, by changing the number of repeats, many times during the lifetime of those affected, with a bias towards expansion. These somatic changes lead not only to the presence of cells with different numbers of repeats in the same tissue, but also produce increasingly longer repeats, contributing towards the progressive nature of the symptoms. Modelling the progression of repeat length throughout the lifetime of individuals has potential for improving prognostic information as well as providing a deeper understanding of the underlying biological process. A large data set comprising blood DNA samples from individuals with one such disease, myotonic dystrophy type 1, provides an opportunity to parameterize a mathematical model for repeat length evolution that we can use to infer biological parameters of interest. We developed new mathematical models by modifying a proposed stochastic birth process to incorporate possible contraction. A hierarchical Bayesian approach was used as the basis for inference, and we estimated the distribution of mutation rates in the population. We used model comparison analysis to reveal, for the first time, that the expansion bias observed in the distributions of repeat lengths is likely to be the cumulative effect of many expansion and contraction events. We predict that mutation events can occur as frequently as every other day, which matches the timing of regular cell activities such as DNA repair and transcription but not DNA replication.
几种人类遗传疾病与遗传异常大的不稳定 DNA 简单序列重复有关。这些序列通过改变重复次数，在受影响个体的一生中多次发生突变，偏向于扩展。这些体细胞变化不仅导致同一组织中存在具有不同重复数目的细胞，而且还产生越来越长的重复序列，导致症状的进行性。在个体的一生中模拟重复长度的进展具有改善预后信息的潜力，同时提供对潜在生物学过程的更深入理解。一个包含患有这种疾病之一的肌强直性营养不良 1 型的个体的血液 DNA 样本的大型数据集提供了参数化重复长度进化数学模型的机会，我们可以使用该模型推断出感兴趣的生物学参数。我们通过修改提议的随机出生过程来开发新的数学模型，以纳入可能的收缩。分层贝叶斯方法被用作推断的基础，我们估计了群体中的突变率分布。我们使用模型比较分析首次揭示，在重复长度分布中观察到的扩展偏差可能是许多扩展和收缩事件的累积效应。我们预测突变事件可能每两天发生一次，这与 DNA 修复和转录等常规细胞活动的时间相匹配，但与 DNA 复制不匹配。
