Polygenic Risk Modelling in Periodontitis: Insights From a Feasibility Study of 4243 European Cases and Current Limitations

22/08/2026

AI Summary

Current polygenic scores show limited discriminative ability for periodontitis; AUCs near 0.50 and substantial overlap of case-control distributions.
PGS showed only a trend in the Spanish cohort and lacked significance in Dutch and SHIP datasets, reflecting small sample effects.
No strong genetic correlations with other traits were found; larger harmonised GWAS are necessary to improve risk prediction and assess pleiotropy.

AI Summary

This study investigates whether current GWAS data can be used to construct a polygenic score (PGS) capturing genetic susceptibility to severe PD.

MATERIALS AND METHODS: A PGS was developed in a three-step design, using a German EO-III/IV-C-PD GWAS (n = 692 cases, ≤ 35 years at diagnosis) as the base dataset, a Spanish EO-III/IV-C-PD GWAS (n = 441 cases) to optimise the score, and as validation a Dutch EO-III/IV-C-PD GWAS (n = 171 cases) and a German population-based GWAS with later-onset III/IV-PD (Studies of Health in Pomerania [SHIP], n = 2941 cases).

RESULTS: The PGS showed a trend towards association with disease status in the Spanish sample (Nagelkerke R2 = 0.4%, p = 0.06; AUC = 0.52; 95% confidence interval [CI]: 0.49-0.56), but not in the smaller Dutch dataset (R2 = 0.2%, p = 0.18; AUC = 0.52; 95% CI: 0.48-0.57) or in the SHIP dataset (AUC = 0.50, 95% CI: 0.48-0.52).

Basic summary

J Clin Periodontol. 2026 Aug 21. doi: 10.1111/jcpe.70195. Online ahead of print.

ABSTRACT

BACKGROUND AND AIM: Genetic susceptibility plays a particularly important role in early-onset (EO) and severe periodontitis (PD). The genetic risk remains largely unexplained because of limited sample sizes and heterogeneous phenotypes in genome-wide association studies (GWAS). This study investigates whether current GWAS data can be used to construct a polygenic score (PGS) capturing genetic susceptibility to severe PD.

RESULTS: The PGS showed a trend towards association with disease status in the Spanish sample (Nagelkerke R² = 0.4%, p = 0.06; AUC = 0.52; 95% confidence interval [CI]: 0.49-0.56), but not in the smaller Dutch dataset (R² = 0.2%, p = 0.18; AUC = 0.52; 95% CI: 0.48-0.57) or in the SHIP dataset (AUC = 0.50, 95% CI: 0.48-0.52). Case-control distributions overlapped substantially. Genetic correlation analyses revealed no strong overlap with other associated traits.

CONCLUSIONS: Current PGS models have limited case-control discriminative ability for PD. Larger harmonised studies are needed to enhance genetic risk prediction and clarify pleiotropic relationships.

PMID:42629962 | DOI:10.1111/jcpe.70195

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