ena-STUDY-ANSES-29-01-2018-14:36:33:908-190
Data analysis approaches for genotype-phenotype association studies based on next-generation sequencing: Application on Listeria monocytogenes growth in cold conditions.
Nowadays whole genome sequencing (WGS) offers new opportunities to explain intraspecific variability of food pathogens. Various bioinformatic tools already exist for the treatment of WGS-data of bacteria. Some of these analyses have the aim to compare DNA sequences from strains with the phenotypes of interest (stress survival, growth and or/ virulence) in order to find markers (e.g. absence/presence of plasmids, genes or variants) which are significantly associated with bacterial behaviors (genome wide association studies - GWAS).The object of this study is to present and explore some existing bioinformatic methods to correlate phenotype(s) and genotype(s).For this purpose, a data-set of 51 L.monocytogenes strains was used. For each strain, a qualitative phenotype at low temperature (2°C) was established and whole genomes of all strains were sequenced. First a GWAS based on the accessory-genome (gene level) was performed. In a second approach an analysis on single nucleotide polymorphisms (SNP) level was made. The outcome of the GWAS at gene level was a list of 114 genes with a statistical association with the tested phenotype. These genes included already involved genes in the cold adaption mechanism of L. monocytogenes, and genes associated to mobile genetic elements. Concerning SNP-GWAS, a group of 184 highly associated SNPs were highlighted; including SNPs corresponding to positions in genes which were already likely involved in cold adaption, hypothetical proteins and also SNPs in intergenic regions where for example promotors and regulators can be located.Such methodologies will probably be systematically applied to tempt identifying genetic markers associated to phenotype of interest for predicting the behavior of foodborne pathogens along the food chain.
Nowadays whole genome sequencing (WGS) offers new opportunities to explain intraspecific variability of food pathogens. Various bioinformatic tools already exist for the treatment of WGS-data of bacteria. Some of these analyses have the aim to compare DNA sequences from strains with the phenotypes of interest (stress survival, growth and or/ virulence) in order to find markers (e.g. absence/presence of plasmids, genes or variants) which are significantly associated with bacterial behaviors (genome wide association studies - GWAS).The object of this study is to present and explore some existing bioinformatic methods to correlate phenotype(s) and genotype(s).For this purpose, a data-set of 51 L.monocytogenes strains was used. For each strain, a qualitative phenotype at low temperature (2°C) was established and whole genomes of all strains were sequenced. First a GWAS based on the accessory-genome (gene level) was performed. In a second approach an analysis on single nucleotide polymorphisms (SNP) level was made. The outcome of the GWAS at gene level was a list of 114 genes with a statistical association with the tested phenotype. These genes included already involved genes in the cold adaption mechanism of L. monocytogenes, and genes associated to mobile genetic elements. Concerning SNP-GWAS, a group of 184 highly associated SNPs were highlighted; including SNPs corresponding to positions in genes which were already likely involved in cold adaption, hypothetical proteins and also SNPs in intergenic regions where for example promotors and regulators can be located.Such methodologies will probably be systematically applied to tempt identifying genetic markers associated to phenotype of interest for predicting the behavior of foodborne pathogens along the food chain.