背景:脊髓小脑共济失调2型(SCA2)和3型(SCA3/MJD)是由于CAG重复序列(CAGexp)的显性不稳定扩展引起的疾病。症状发作年龄(AO)与CAGexp长度相关。重复不稳定导致扩展重复的增加,重要的AO预期和最终的血统灭绝。正因为如此,补偿性力量有望作用于扩展等位基因的维持,但对他们了解甚少。目标:我们描述了CAGexp动力学,适应经典方程,并旨在估计从头扩展的后代将持续多少代。方法:采用数学模型来涵盖预期,健身,和等位基因分离;以及模型的经验数据。模型中包含的仲裁祖先突变具有文献中描述的最低CAGexp和最高AO。模拟一千代,直到等位基因被消除,固定,或者650代已经过去了。结果:所有SCA2谱系在10代的中位数中被消除。在SCA3/MJD谱系中,在29代的中位数中,有593人被淘汰。由于在第650代之后的预期,其他的被消除,或者随着CAG重复在扩展和未扩展范围之间过渡而无限期地保持。讨论:该模型预测的结果与经验数据兼容-非常古老的祖先SCA3/MJD单倍型,和从头SCA2扩展-,这在以前似乎是矛盾的。该模型将这些数据纳入可理解的动态中,可能对其他CAGexp障碍有用。
Background: Spinocerebellar ataxia types 2 (SCA2) and 3 (SCA3/MJD) are diseases due to dominant unstable expansions of CAG repeats (CAGexp). Age of onset of symptoms (AO) correlates with the CAGexp length. Repeat instability leads to increases in the expanded repeats, to important AO anticipations and to the eventual extinction of lineages. Because of that, compensatory forces are expected to act on the maintenance of expanded alleles, but they are poorly understood. Objectives: we described the CAGexp dynamics, adapting a classical equation and aiming to estimate for how many generations will the descendants of a de novo expansion last. Methods: A mathematical model was adapted to encompass anticipation, fitness, and allelic segregation; and empirical data fed the model. The arbitrated ancestral mutations included in the model had the lowest CAGexp and the highest AO described in the literature. One thousand generations were simulated until the alleles were eliminated, fixed, or 650 generations had passed. Results: All SCA2 lineages were eliminated in a median of 10 generations. In SCA3/MJD lineages, 593 were eliminated in a median of 29 generations. The other ones were eliminated due to anticipation after the 650th generation or remained indefinitely with CAG repeats transitioning between expanded and unexpanded ranges. Discussion: the model predicted outcomes compatible with empirical data - the very old ancestral SCA3/MJD haplotype, and the de novo SCA2 expansions -, which previously seemed to be contradictory. This model accommodates these data into understandable dynamics and might be useful for other CAGexp disorders.