ena-STUDY-MAX PLANCK INSTITUTE FOR  EVOLUTIONARY BIOLOGY-14-09-2016-15:38:54:026-118
Our hypothesis is that the host genetics controls to some extent the composition of the skin microbiota. To test this hypothesis, we first generated a fifteen generation from an advanced mouse intercross line, defined the relative abundances of skin resident bacterial taxa, both using the 16S rRNA gene copy (DNA)- and transcript (RNA) level, and performed QTL mapping on the active (RNA)- and standing (DNA) communities.
Recent studies highlight the utility of quantitative trait locus (QTL) mapping for determining the contribution of host genetics to inter-individual variation in the microbiota. We previously demonstrated that similar to the gut microbiota, abundances of bacterial taxa in the skin are significantly influenced by host genetic variation. In this study, we analyzed the skin microbiota of mice from the 15th generation of an advanced intercross line using a novel approach of extending bacterial trait mapping to both the 16S rRNA gene copy (DNA)- and transcript (RNA) level, which reflect relative bacterial cell number and activity, respectively. Remarkably, the combination of highly recombined individuals and 53,203 informative SNPs allowed the identification of genomic intervals as small as <0.1 megabases containing single genes. Furthermore, the inclusion of 16S rRNA transcript-level mapping dramatically increased the number of significant associations detected, with five versus 21 significant SNP-bacterial trait associations based on DNA- compared to RNA-level profiling, respectively
Recent studies highlight the utility of quantitative trait locus (QTL) mapping for determining the contribution of host genetics to inter-individual variation in the microbiota. We previously demonstrated that similar to the gut microbiota, abundances of bacterial taxa in the skin are significantly influenced by host genetic variation. In this study, we analyzed the skin microbiota of mice from the 15th generation of an advanced intercross line using a novel approach of extending bacterial trait mapping to both the 16S rRNA gene copy (DNA)- and transcript (RNA) level, which reflect relative bacterial cell number and activity, respectively. Remarkably, the combination of highly recombined individuals and 53,203 informative SNPs allowed the identification of genomic intervals as small as <0.1 megabases containing single genes. Furthermore, the inclusion of 16S rRNA transcript-level mapping dramatically increased the number of significant associations detected, with five versus 21 significant SNP-bacterial trait associations based on DNA- compared to RNA-level profiling, respectively