Wong Wesley, Griggs Allison D, Daniels Rachel F, Schaffner Stephen F, Ndiaye Daouda, Bei Amy K, Deme Awa B, MacInnis Bronwyn, Volkman Sarah K, Hartl Daniel L, Neafsey Daniel E, Wirth Dyann F
Department of Immunology and Infectious Diseases, Harvard T. H. Chan School of Public Health, Boston, MA, 02115, USA.
Broad Institute, Cambridge, MA, 02142, USA.
Genome Med. 2017 Jan 24;9(1):5. doi: 10.1186/s13073-017-0398-0.
As public health interventions drive parasite populations to elimination, genetic epidemiology models that incorporate population genomics can be powerful tools for evaluating the effectiveness of continued intervention. However, current genetic epidemiology models may not accurately simulate the population genetic profile of parasite populations, particularly with regard to polygenomic (multi-strain) infections. Current epidemiology models simulate polygenomic infections via superinfection (multiple mosquito bites), despite growing evidence that cotransmission (a single mosquito bite) may contribute to polygenomic infections.
Here, we quantified the relatedness of strains within 31 polygenomic infections collected from patients in Thiès, Senegal using a hidden Markov model to measure the proportion of the genome that is inferred to be identical by descent.
We found that polygenomic infections can be composed of highly related parasites and that superinfection models drastically underestimate the relatedness of strains within polygenomic infections.
Our findings suggest that cotransmission is a major contributor to polygenomic infections in Thiès, Senegal. The incorporation of cotransmission into existing genetic epidemiology models may enhance our ability to characterize and predict changes in population structure associated with reduced transmission intensities and the emergence of important phenotypes like drug resistance that threaten to undermine malaria elimination activities.
随着公共卫生干预措施促使寄生虫种群走向消除，纳入群体基因组学的遗传流行病学模型可成为评估持续干预效果的有力工具。然而，当前的遗传流行病学模型可能无法准确模拟寄生虫种群的群体遗传特征，尤其是在多基因组（多菌株）感染方面。尽管越来越多的证据表明共传播（单次蚊虫叮咬）可能导致多基因组感染，但目前的流行病学模型通过重复感染（多次蚊虫叮咬）来模拟多基因组感染。
在此，我们使用隐马尔可夫模型来测量推断为同源相同的基因组比例，从而量化了从塞内加尔捷斯的患者中收集的31例多基因组感染中菌株之间的亲缘关系。
我们发现多基因组感染可能由高度相关的寄生虫组成，并且重复感染模型极大地低估了多基因组感染中菌株之间的亲缘关系。
我们的研究结果表明，共传播是塞内加尔捷斯多基因组感染的主要促成因素。将共传播纳入现有的遗传流行病学模型可能会增强我们表征和预测与传播强度降低以及耐药性等重要表型出现相关的种群结构变化的能力，而这些表型可能会威胁到疟疾消除活动。
