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Scott R. Diehl, PhD, Awarded a Five-Year, $1,846,563 Grant From the NIH/NIDCR to Study "Gene Mapping of Susceptibility to Periodontitis."

Scott R. Diehl, PhD, Director for the Center for Pharmacogenomics and Complex Disease Research at UMDNJ-New Jersey Dental School, has been awarded a five-year, $1,846,563 grant from the NIH/National Institute of Dental and Craniofacial Research to study "Gene Mapping of Susceptibility to Periodontitis."

Genetic differences among individuals have a major effect on the risk of chronic periodontitis, the focus of this new study. This disease affects over 20% of adults age 40 and above. It is the major cause of tooth loss in older Americans. In addition, recent findings indicate that periodontitis may significantly increase risk of cardiovascular disease and osteoporosis. In women of childbearing age, it may be associated with preterm delivery and low birth weight.

Chronic periodontitis is a "complex disease." Both inherited genetic variation and environmental factors, such as smoking, hygiene and pathogenic bacteria, interact to determine individuals' risk. Diehl's previous studies of adult twins demonstrated that the disease has a heritability of 50 percent, indicating that genes and environment play an equal role in determining whether a person is affected.

Diehl previously studied an early onset form of periodontitis that affects teenagers and young adults. This is less common, occurring in about 1 in 1,000 Caucasians and 1 in 100 African Americans. Although rare, the early onset form can be very severe and lead to loss of numerous teeth.

"Our gene mapping analyses of early onset aggressive periodontitis families identified dozens of candidate genes with strong to moderate support for involvement in this disease subtype," says Diehl. "We have also shown that periodontitis-related phenotypes such as serum immunoglobulin levels have highly significant heritability."

Major advances in human molecular genomics techniques for analysis of single nucleotide polymorphisms (SNPs) and statistical genetic strategies such as haplotype mapping greatly increase power to identify genes underlying complex traits such as periodontitis. Diehl's Center at UMDNJ is building very high-throughput robotic systems for processing over 100,000 SNP assays/day and robust information management systems essential for tracking and analyzing these vast amounts of data. A critical component needed for success in this approach is availability of very large, high-quality clinical populations.

Diehl and his team will test 500 SNPs in 100 candidate genes for association with periodontitis in 7,300 subjects from the National Health and Examination Survey III, 200 cases and 100 healthy controls recruited in Minnesota; and 234 subjects from Diehl's previously reported twin study from Virginia. They will evaluate both quantitative and traditional disease classification measures, and analyze environmental risk and antibody responses to periodontal pathogens.

"It is important to recognize that the absence of a simple one-to-one mapping between disease gene and disease phenotype doesn't diminish the value of identifying genes that determine individual differences in susceptibility," says Diehl. "These genetic variants still have great potential to explain a substantial portion of disease risk in the population and to predict risk for individuals."

Such knowledge can optimize use of health care resources by allocating greater monitoring or preventive treatment to individuals at highest risk. Knowledge of disease etiology at the level of specific genes may lead to improved therapies designed to correct specific biochemical aberrations caused by gene defects. It may also be beneficial to classify subtypes of disease using a system based on gene defects rather than solely on differences in clinical presentation. Different therapeutic approaches might then be most effectively targeted to the different gene defect disease subtypes.

Finally, one of the greatest benefits that might accrue from identifying gene mutations in a complex disease such as periodontitis may be greatly enhanced power to understand environmental factors. "Once we begin to account for genetic sources of variation in disease risk by direct measurement of appropriate gene mutations, removal of this 'noise' from our studies should greatly enhance our ability to understand environmental effects," Diehl predicts.

Dr. Diehl's research will be highlighted in an upcoming issue of "Inside iScience"