Identification of genetic risk factors of periodontitis by combined QTL mapping in mice and subsequent genome-wide association analysis, sequencing, and high-throughput genotyping of patients of aggressive periodontitis

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Periodontitis is a chronic inflammatory disease of the oral cavity, resulting in destruction of the periodontal supporting tissues and likely, also in further systemic implications. A major proportion of the population variance with periodontitis can be attributed to genetic factors but only a proportion of the heritability has been explained. For identification of a comprehensive spectrum of genetic risk loci, we propose an unbiased strategy for the de novo generation of hypotheses of candidate gene regions in humans that takes into account the limitations of both genome-wide and candidate-gene association studies. In a systems genetics approach, we will use recombinant inbred mouse lines (RIL), namely the Collaborative Cross (CC), for mapping risk genes of periodontitis as quantitative trait loci (QTLs). The human orthologous chromosomal regions that correlate to the mouse-QTLs will be used as candidate loci for detailed analysis in large human case-control samples. These regions will be validated in silico for potential association with human periodontitis, using available genome-wide data (Illumina HumanOmniExpress-24 v1.0) from 624 German aggressive periodontitis patients and > 2,600 German population representative controls. The strongest associated variants will be replicated in an independent case-control analysis population of 600 AgP cases of North-West European and Turkish origin, 2,200 German chronic periodontitis cases and 3,000 ethnically matched controls. The causative variants will be identified by subsequent targeted resequencing. The findings will help to understand the molecular mechanisms, which influence disease susceptibility or resistance to oral bacterial infection and help to understand why this response often greatly differs between individuals. Through this, the project has the potential to guide early diagnosis and personalized medicine for chronic oral inflammation.