Genome-wide association study of osteonecrosis of the jaw in Danish patients receiving antiresorptive therapy for osteoporosis: A case-control study

Abstract

BackgroundPrior studies of the pharmacogenomics of osteonecrosis of the jaw (ONJ) have had various methodological limitations, including using candidate gene selection as their sole strategy, a small number of ONJ cases, or a study population based on an oncology setting. ObjectivesThe aim of our case-control study was to evaluate previously reported associations between genetic factors and ONJ, which were based on either genome-wide association studies (GWAS) or candidate gene approaches. Furthermore, we aimed to identify genetic risk factors for ONJ by using GWAS to determine single-nucleotide polymorphisms (SNPs) with statistically significant differences in frequency between ONJ patients and osteoporosis controls. MethodsPatients with medically confirmed ONJ and who were registered in the Scandinavian Cohort of ONJ patients were included. Controls from the general population were matched on age (±5 years), sex, and cumulative antiresorptive drug exposure. The ONJ diagnosis date for cases corresponded to the index date for matched controls. DNA isolation, genotyping, and data analyses were performed by Q2/EA Genomics using standard protocols and best practices. Blood or tissue samples for 55 ONJ cases and 125 controls were collected. Due to the low quality of the tissue samples, final analyses were based on blood samples of 40 ONJ cases and 124 controls. ResultsWe detected no significant genome-wide associations. Of the 43 SNPs with ONJ association in prior studies, none were replicated in our study. ConclusionsEven though our study sample is the largest to date, we had limited statistical power for GWAS but adequate power for replication analyses. Our study provides no evidence for any genetic predisposition to ONJ. Future studies could increase their statistical power by combining ONJ GWAS datasets and by performing a meta-analysis or pursuing a sequencing strategy in order to identify rare variants.