Department of Statistics, 1 South Parks Road, University of Oxford, Oxford OX1 3TG, UK.
Proc Biol Sci. 2013 May 15;280(1762):20130696. doi: 10.1098/rspb.2013.0696. Print 2013 Jul 7.
The rates of escape and reversion in response to selection pressure arising from the host immune system, notably the cytotoxic T-lymphocyte (CTL) response, are key factors determining the evolution of HIV. Existing methods for estimating these parameters from cross-sectional population data using ordinary differential equations (ODEs) ignore information about the genealogy of sampled HIV sequences, which has the potential to cause systematic bias and overestimate certainty. Here, we describe an integrated approach, validated through extensive simulations, which combines genealogical inference and epidemiological modelling, to estimate rates of CTL escape and reversion in HIV epitopes. We show that there is substantial uncertainty about rates of viral escape and reversion from cross-sectional data, which arises from the inherent stochasticity in the evolutionary process. By application to empirical data, we find that point estimates of rates from a previously published ODE model and the integrated approach presented here are often similar, but can also differ several-fold depending on the structure of the genealogy. The model-based approach we apply provides a framework for the statistical analysis and hypothesis testing of escape and reversion in population data and highlights the need for longitudinal and denser cross-sectional sampling to enable accurate estimate of these key parameters.
针对宿主免疫系统（特别是细胞毒性 T 淋巴细胞 [CTL] 反应）产生的选择压力，病毒逃逸和回复的速度是决定 HIV 进化的关键因素。目前使用常微分方程（ODE）从横断面人群数据中估计这些参数的方法忽略了采样 HIV 序列的系统发生信息，这有可能导致系统偏差和高估确定性。在这里，我们描述了一种综合方法，该方法通过广泛的模拟进行了验证，它结合了系统发生推断和流行病学建模，以估计 HIV 表位中 CTL 逃逸和回复的速度。我们表明，从横断面数据推断病毒逃逸和回复的速度存在很大的不确定性，这是由于进化过程中的固有随机性造成的。通过对实际数据的应用，我们发现，先前发表的 ODE 模型和此处提出的综合方法的点估计速率通常相似，但也可能因系统发生的结构而相差数倍。我们应用的基于模型的方法为对人群数据中的逃逸和回复进行统计分析和假设检验提供了一个框架，并强调需要进行纵向和更密集的横断面采样，以能够准确估计这些关键参数。
