Bayesian reconstruction of transmission within outbreaks using genomic variants

Pathogen genome sequencing can reveal details of transmission histories and is a powerful tool in the fight against infectious disease. In particular, within-host pathogen genomic variants identified through heterozygous nucleotide base calls are a potential source of information to identify linked cases and infer direction and time of transmission. However, using such data effectively to model disease transmission presents a number of challenges, including differentiating genuine variants from those observed due to sequencing error, as well as the specification of a realistic model for within-host pathogen population dynamics. Here we propose a new Bayesian approach to transmission inference, BadTrIP (BAyesian epiDemiological TRansmission Inference from Polymorphisms), that explicitly models evolution of pathogen populations in an outbreak, transmission (including transmission bottlenecks), and sequencing error. BadTrIP enables the inference of host-to-host transmission from pathogen sequencing data and epidemiological data. By assuming that genomic variants are unlinked, our method does not require the computationally intensive and unreliable reconstruction of individual haplotypes. Using simulations we show that BadTrIP is robust in most scenarios and can accurately infer transmission events by efficiently combining information from genetic and epidemiological sources; thanks to its realistic model of pathogen evolution and the inclusion of epidemiological data, BadTrIP is also more accurate than existing approaches. BadTrIP is distributed as an open source package (https://bitbucket.org/nicofmay/badtrip) for the phylogenetic software BEAST2. We apply our method to reconstruct transmission history at the early stages of the 2014 Ebola outbreak, showcasing the power of within-host genomic variants to reconstruct transmission events.病原体基因组测序可揭示传播历史的细节，是对抗传染病的有力工具。具体而言，通过杂合核苷酸碱基检测得到的宿主内病原体基因组变异，是识别关联病例、推断传播方向与时间的潜在信息来源。然而，有效利用此类数据构建传染病传播模型面临诸多挑战：不仅需要区分真实变异与测序误差引发的观测变异，还需为宿主内病原体种群动态构建贴合实际的模型。为此，我们提出一种用于传播推断的新型贝叶斯方法BadTrIP（BAyesian epiDemiological TRansmission Inference from Polymorphisms，即基于多态性的贝叶斯流行病学传播推断），该方法显式建模暴发过程中病原体种群的演化、传播环节（含传播瓶颈）以及测序误差。BadTrIP可依托病原体测序数据与流行病学数据，实现宿主间传播事件的推断。由于假设基因组变异互不连锁，本方法无需开展计算量庞大且可靠性欠佳的单倍型个体重建工作。通过模拟实验验证，BadTrIP在多数场景下均具备稳健性，可通过高效整合遗传与流行病学信息准确推断传播事件；得益于其贴合实际的病原体演化模型与流行病学数据的融入，BadTrIP的准确性也优于现有同类方法。BadTrIP作为面向系统发育软件BEAST2的开源包（https://bitbucket.org/nicofmay/badtrip）进行发布。我们将该方法应用于2014年埃博拉疫情暴发早期的传播历史重构，以此展示宿主内基因组变异在传播事件重建中的应用效能。