Regression-based ranking of pathogen strains with respect to their contribution to natural epidemics.
Genetic variation in pathogen populations may be an important factor driving heterogeneity in disease dynamics within their host populations. However, to date, we understand poorly how genetic diversity in diseases impact on epidemiological dynamics because data and tools required to answer this questions are lacking. Here, we combine pathogen genetic data with epidemiological monitoring of disease progression, and introduce a statistical exploratory method to investigate differences among pathogen strains in their performance in the field. The method exploits epidemiological data providing a measure of disease progress in time and space, and genetic data indicating the relative spatial patterns of the sampled pathogen strains. Applying this method allows to assign ranks to the pathogen strains with respect to their contributions to natural epidemics and to assess the significance of the ranking. This method was first tested on simulated data, including data obtained from an original, stochastic, multi-strain epidemic model. It was then applied to epidemiological and genetic data collected during one natural epidemic of powdery mildew occurring in its wild host population. Based on the simulation study, we conclude that the method can achieve its aim of ranking pathogen strains if the sampling effort is sufficient. For powdery mildew data, the method indicated that one of the sampled strains tends to have a higher fitness than the four other sampled strains, highlighting the importance of strain diversity for disease dynamics. Our approach allowing the comparison of pathogen strains in natural epidemic is complementary to the classical practice of using experimental infections in controlled conditions to estimate fitness of different pathogen strains. Our statistical tool, implemented in the R package StrainRanking, is mainly based on regression and does not rely on mechanistic assumptions on the pathogen dynamics. Thus, the method can be applied to a wide range of pathogens.
UR546 Biostatistics and Spatial Processes, INRA, Avignon, France es that the sum of the estimated propor.
Samuel
S
Soubeyrand
Metapopulation Research Group, Department of Biosciences, University of Helsinki, Helsinki, Finland.
Charlotte
C
Tollenaere
UR546 Biostatistics and Spatial Processes, INRA, Avignon, France es that the sum of the estimated propor.
Emilie
E
Haon-Lasportes
Metapopulation Research Group, Department of Biosciences, University of Helsinki, Helsinki, Finland.
Anna-Liisa
AL
Laine
D000465:Algorithms; D001203:Ascomycota / Q000145:classification / Q000235:genetics / Q000502:physiology*; D003198:Computer Simulation; D014644:Genetic Variation; D005838:Genotype; D054884:Host-Pathogen Interactions; D010935:Plant Diseases / Q000382:microbiology*; D018515:Plant Leaves / Q000382:microbiology; D010943:Plantago / Q000382:microbiology*; D020641:Polymorphism, Single Nucleotide; D012044:Regression Analysis; D013045:Species Specificity; D013997:Time Factors