Genetic Risk Factors for Periodontitis Bryan Michalowicz, D.D.S - - PowerPoint PPT Presentation

Genetic Risk Factors for Periodontitis

Bryan Michalowicz, D.D.S Division of Periodontology

Pathogenic Bacteria Susceptible Host Modifying Environmental Factors Periodontitis is a COMMOM, COMPLEX, MULTIFACTORIAL disease.

Periodontitis Diagnoses

• Aggressive Periodontitis (Grade II
• r III, Stage C under new scheme)

– Localized – Generalized

• Chronic (Adult) Periodontitis

Localized Aggressive Periodontitis

• Incisors and 6-year molars
• Saucer-like defects

Disorder Protein or Tissue Defect Leukocyte Adhesion Deficiency Type I CD18 (b-2 integrin chain of the LFA molecule) Leukocyte Adhesion Deficiency Type II CD15 (neutrophil ligand for E and P selectins); inborn error in fucose metabolism Chronic and Cyclic Neutropenias Various Chediak-Higashi Syndrome Abnormal transport of vesicles to and from neutrophil lysosomes caused by mutations in the lysosomal trafficking regulator gene Ehler-Danlos Syndrome [types IV & VIII] Type III collagen for EDS type IV, unknown for EDS type VIII Papillon-Lefevre Syndrome Cathepsin C (dipeptidyl aminopeptidase I) Hypophosphatasia Tissue non-specific alkaline phosphatase

Cyclic Neutropenia

Leukocyte adhesion deficiency

Papillon-Lefèvre Syndrome

Papillon-Lefèvre Syndrome

• several point mutations in the cathepsin C

gene are associated with the phenotype

• mutations may be missense, nonsense,

insertions or deletions

• almost total loss of enzyme activity in PLS

patients (i.e., homozygotes or compound heterozygotes)

• reduced enzyme activity in obligate carriers

Cathepsin C

• lysosomal protease present in neutrophils

and macrophages

• dipeptidyl aminopeptidase I (removes

dipeptides from amino terminus of protein)

• chromosomal location: 11q14.1-q14.3

Approaches to Evaluate Genetic Risk in Humans

• Segregation Analyses
• Twin and Family Studies
• Association (Case-Control) Studies

– Candidate gene approach – Genome-wide associations (GWAS)

Segregation Analyses of Aggressive Periodontitis

• AR, AD, and X-linked modes of inheritance

have all been proposed

• Largest collection of families to date favors

AD inheritance

• Frequency of disease allele greater in blacks

than whites

• Highlights genetic heterogeneity

TWIN STUDY DESIGN

• Differences between MZ (identical) twins of a pair are

due to differences in environment.

• Differences between DZ twins of a pair are due to

differences in environment plus unshared genes.

• Differences (in correlations) between MZ and DZ

twins is due to the effects of one-half the genetic variance (the difference in gene sharing between mz and dz twins)

• Twice this difference [2(rMZ-rDZ)] is heritability

Classic twin study assumptions

• Twins are representative of non-twins
• The environments are similar for MZ and

DZ twins (especially relevant for behavioral studies)

• Genes and environment don’t interact (for

estimating heritability as described)

Reared Together and Apart Twin Correlations for Clinical Measures

0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 AL PD Gingivitis

MZT MZA DZT

Michalowicz et al., 1991

Twin Correlations for Presence of Periodontal Pathogens

0.1 0.2 0.3 0.4 0.5

P.i. P.g. A.a. E.c. F.n.

MZ DZ Michalowicz et al, J Periodontol. 1999;70(3):263-73.

Conclusions

• Approximately 50% of the population variance for

attachment loss and probing depth is attributed to genetic variance

• Genetic factors do not significantly influence

levels of plaque or calculus, or the presence of specific bacteria in subgingival plaque

• The family environment does not significantly

influence measures of disease in adults

Nibali et al. J Dent Res. 2019 Jun;98(6):632-641

All twin studies combined Excluding those where disease was self-reported Other family studies

Twin study of dental caries

• 20,839 complete twin pairs with known zygosity and caries data

Results of variance decomposition for caries indices and caries trajectories. Each bar represents a caries trait, and the stacked components represent the relative contributions of components A (additive genetic factors), C (shared environmental factors), and E (nonshared environmental factors) to variation in that trait. DMFSa and DFSa refer to proximal surfaces.

J Dent Res. 2020 Mar;99(3):264-270.

• From cross sectional data, heritability ranged from 49 to 63%.

Association Studies using candidate gene markers

• Case-control study design
• Exploit phenomenon that alleles at nearby

loci co-segregate (are in “linkage disequilibrium”)

• Focus on common genetic variations, e.g.,

single nucleotide polymorphisms (SNPs) with frequencies > 5%

Candidate Genes for Periodontal Disease

• Cytokines, including interleukin-1
• Vitamin D receptor
• N-formyl peptide receptor
• Class II HLA antigens (DR, DQ, DP)
• Cathepsins
• Toll-like receptors
• MMPs

Genotype distributions and frequencies of the minor alleles by periodontal diagnosis and race

Polymorphism

Genotype Caucasians p Blacks p Patients Controls Patients Controls IL6 −174 GG 124 (39.0%) 42 (29.2%) 0.12 81 (90.0%) 38 (84.4%) 0.35 CG 142 (44.7%) 74 (51.4%) 9 (10.0%) 7 (15.6%) CC 52 (16.4%) 28 (19.4%) 0 (0%) 0 (0%) IL6 −1363 GG 263 (85.7%) 112 (77.8%) 0.02 88 (98.5%) 43 (95.6%) 0.23 TG 48 (14.0%) 28 (19.4%) 1 (1.1%) 2 (4.4%) TT 1 (0.3%) 4 (2.8%) 0 (0%) 0 (0%)

For −174, GG vs. CC + CG: p = 0.044 (OR = 1.6, 95% CI = 1.0–2.4) For −1363, GG vs. TT + GT: p = 0.017 (OR = 1.8, 95% CI = 1.1–2.8)

Cytokine, Vol 45 (1), 2009, p50-54

Genome-wide associations studies (GWAS)

• Uses a case-control study design
• No pre-selection of “candidate gene” regions
• More informative with larger patient sample sizes and more

polymorphism genetic markers

• Typically use SNPs (single nucleotide polymorphisms)
• Include SNPs that are both assayed in patient samples and

imputed

• Test for SNPs whose alleles are distributed differently in cases

and controls

• Must control for false positive findings

(www.broad.mit.edu/diabetes/scandinavs/type2.html)

Type 2 Diabetes GWAS (>380K SNPs)

Cases Controls GWAS 1 141 500 GWAS2 142 479 Validation sample 155 341 Fine mapping 461 1383

Schaefer AS, et al. A genome-wide association study identifies GLT6D1 as a susceptibility locus for [aggressive]

• periodontitis. Hum Mol Genet. 2010;19(3):553

SNP rs1537415

GWAS1 GWAS2 Validation P-value 1.8 x 10–4 3.1 x 10–4 5.7 x 10–3 OR (95% CI) 1.67 (1.27–2.18) 1.65 (1.26–2.17) 1.47 (1.12–1.93) Controls 11 (%) 187 (38.3) 185 (38.6) 128 (37.5) 12 (%) 236 (48.4) 233 (48.6) 156 (45.7) 22 (%) 65 (13.3) 61 (12.7) 57 (16.7) Cases 11 (%) 36 (26.3) 33 (24.6) 38 (25.0) 12 (%) 65 (47.4) 70 (52.2) 79 (52.0) 22 (%) 36 (26.3) 31 (23.1) 35 (23.0)

Schaefer, A. S. et al. Hum. Mol. Genet. 2010 19:553-562

GLT6D1

• Glucosyltransferase
• Impairment of a potential GATA-3

transcription factor binding site at rs1537415

• Minor (disease-associated)

allele associated with decreased GATA-3 binding

Genome-wide association study of chronic periodontitis in a general German population (SHIP)

J Clin Periodontol 2013, Vol 40 (11): 977-985

• No genome-wide significant

SNP associations

• The proportion of the sex

age, and smoking status adjusted variance explained by additive effects of all common SNPs (Heritability) was 23% and 14% for mean PAL (top) and CDC/AAP disease definition (bottom), respectively.

Divaris K et al. Hum. Mol. Genet. 2013;22:2312-2324

761 had severe disease, 1920 moderate disease and 1823 healthy Genome-wide association results for severe (A) and moderate (B) chronic periodontitis. 2,135,236 SNPs tested, none reached genome-wide statistical significance For severe disease, the minor allele [C] of NIN showed a 3.5% enrichment among severe CP patients; OR = 1.89 (P = 3.5 × 10−7). Heritability for severe disease explained by all SNPs increased from 18 to 52% with the inclusion of a genome-wide interaction term with smoking.

Genome-wide association study of chronic periodontitis in a general US population (D-ARIC)

Nat Commun. 2019 Jun 24;10(1):2773.

Genome-wide analysis of dental caries and periodontitis combining clinical and self-reported data

Trait name DMFS DFSS Nteeth Periodontitis

Trait Decayed, missing and filled tooth surfaces # of natural teeth Presence or absence of periodontitis Phenotypic assessment Derived from clinical dental records Derived from clinical dental records or self-reports CDC/AAP definitions (4 studies) Two or more tooth surfaces with PD ≥ 5 mm, or at least four tooth surfaces with PD ≥4 mm (1 study) PD ≥5.5 mm in 2 or more sextants (1 study). Self report (1 study) # of studies in primary analysis 9 9 7 # of participants in primary analysis 26,792 27,949 17,353 cases, 28,210 controls

PD = probing depth

Caries/Dentures Periodontitis/ Loose teeth

Summary

• For dental caries/dentures, identified 47

significant associations, 45 of which were new

• For periodontitis/loose teeth, only one SNP

reached genome-wide significance (an intronic variant within SIGLEC5 (OR for periodontitis = 1.05)

• This same marker was identified in a previous

study of aggressive periodontitis

Conclusions – Aggressive periodontitis

• Familial
• Segregation analyses suggest transmission

as an AD disorder, although other modes have been proposed (Genetic heterogeneity)

• Strong environmental component (multi-

factorial)

• No major gene identified to date

Conclusions: Chronic Periodontitis

• Genetic factors play a role in defining risk

(Heritability estimated at about 30%)

• Using the candidate gene approach, only a

handful of polymorphisms have been validated in other studies (e.g., IL-1, IL-6, FcγR)

• GWAS have not validated previous SNP

associations (above)

– Risk associated with any one variant likely small, ORs < 2 – One SNP in the SIGLEC5 region appears to confer a statistically significant but very small increase in risk for disease

Clinical Utility of Genetic Tests

Talmud, P. J et al. BMJ 2010;340:b4838

Positive predictive value increases with prevalence OR = 2, sensitivity = 0.6

0.00 0.10 0.20 0.30 0.40 0.50 0.60 0.70 0.80 0.00 0.10 0.20 0.30 0.40 0.50 0.60 0.70 Prevalence PPV

Clinical implications of genetic epidemiological research

• Focus prevention/early treatment on those

most at risk

• Better determine prognosis
• Identify new disease pathways/drug targets
• Identify individuals who may benefit most

from host-modulating agents (pharmacogenetics)