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Radomski N, Cadel-Six S, Cherchame E, Felten A, Barbet P, Palma F, Mallet L, Le Hello S, Weill FX, Guillier L, Mistou MY. A Simple and Robust Statistical Method to Define Genetic Relatedness of Samples Related to Outbreaks at the Genomic Scale - Application to Retrospective Salmonella Foodborne Outbreak Investigations. Front Microbiol 2019; 10:2413. [PMID: 31708892 PMCID: PMC6821717 DOI: 10.3389/fmicb.2019.02413] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/08/2019] [Accepted: 10/07/2019] [Indexed: 12/21/2022] Open
Abstract
The investigation of foodborne outbreaks (FBOs) from genomic data typically relies on inspecting the relatedness of samples through a phylogenomic tree computed on either SNPs, genes, kmers, or alleles (i.e., cgMLST and wgMLST). The phylogenomic reconstruction is often time-consuming, computation-intensive and depends on hidden assumptions, pipelines implementation and their parameterization. In the context of FBO investigations, robust links between isolates are required in a timely manner to trigger appropriate management actions. Here, we propose a non-parametric statistical method to assert the relatedness of samples (i.e., outbreak cases) or whether to reject them (i.e., non-outbreak cases). With typical computation running within minutes on a desktop computer, we benchmarked the ability of three non-parametric statistical tests (i.e., Wilcoxon rank-sum, Kolmogorov-Smirnov and Kruskal-Wallis) on six different genomic features (i.e., SNPs, SNPs excluding recombination events, genes, kmers, cgMLST alleles, and wgMLST alleles) to discriminate outbreak cases (i.e., positive control: C+) from non-outbreak cases (i.e., negative control: C-). We leveraged four well-characterized and retrospectively investigated FBOs of Salmonella Typhimurium and its monophasic variant S. 1,4,[5],12:i:- from France, setting positive and negative controls in all the assays. We show that the approaches relying on pairwise SNP differences distinguished all four considered outbreaks in contrast to the other tested genomic features (i.e., genes, kmers, cgMLST alleles, and wgMLST alleles). The freely available non-parametric method written in R has been designed to be independent of both the phylogenomic reconstruction and the detection methods of genomic features (i.e., SNPs, genes, kmers, or alleles), making it widely and easily usable to anybody working on genomic data from suspected samples.
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Affiliation(s)
- Nicolas Radomski
- ANSES, Laboratory for Food Safety, Université PARIS-EST, Maisons-Alfort, France
| | - Sabrina Cadel-Six
- ANSES, Laboratory for Food Safety, Université PARIS-EST, Maisons-Alfort, France
| | - Emeline Cherchame
- ANSES, Laboratory for Food Safety, Université PARIS-EST, Maisons-Alfort, France
| | - Arnaud Felten
- ANSES, Laboratory for Food Safety, Université PARIS-EST, Maisons-Alfort, France
| | - Pauline Barbet
- ANSES, Laboratory for Food Safety, Université PARIS-EST, Maisons-Alfort, France
| | - Federica Palma
- ANSES, Laboratory for Food Safety, Université PARIS-EST, Maisons-Alfort, France
| | - Ludovic Mallet
- ANSES, Laboratory for Food Safety, Université PARIS-EST, Maisons-Alfort, France
| | - Simon Le Hello
- Unité des Bactéries Pathogènes Entériques, Institut Pasteur, Centre National de Référence des Salmonella, Paris, France
| | - François-Xavier Weill
- Unité des Bactéries Pathogènes Entériques, Institut Pasteur, Centre National de Référence des Salmonella, Paris, France
| | - Laurent Guillier
- ANSES, Laboratory for Food Safety, Université PARIS-EST, Maisons-Alfort, France
| | - Michel-Yves Mistou
- ANSES, Laboratory for Food Safety, Université PARIS-EST, Maisons-Alfort, France
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