Phevor combines multiple biomedical ontologies for accurate identification of disease-causing alleles in single individuals and small nuclear families.

Phevor integrates phenotype, gene function, and disease information with personal genomic data for improved power to identify disease-causing alleles. Phevor works by combining knowledge resident in multiple biomedical ontologies with the outputs of variant-prioritization tools. It does so by using an algorithm that propagates information across and between ontologies. This process enables Phevor to accurately reprioritize potentially damaging alleles identified by variant-prioritization tools in light of gene function, disease, and phenotype knowledge. Phevor is especially useful for single-exome and family-trio-based diagnostic analyses, the most commonly occurring clinical scenarios and ones for which existing personal genome diagnostic tools are most inaccurate and underpowered. Here, we present a series of benchmark analyses illustrating Phevor's performance characteristics. Also presented are three recent Utah Genome Project case studies in which Phevor was used to identify disease-causing alleles. Collectively, these results show that Phevor improves diagnostic accuracy not only for individuals presenting with established disease phenotypes but also for those with previously undescribed and atypical disease presentations. Importantly, Phevor is not limited to known diseases or known disease-causing alleles. As we demonstrate, Phevor can also use latent information in ontologies to discover genes and disease-causing alleles not previously associated with disease.

Validation of DNA-based prognostic testing to predict spinal curve progression in adolescent idiopathic scoliosis.

STUDY DESIGN: Validation of a prognostic DNA marker panel. OBJECTIVE: The goals of this study were to develop and test the negative predictive value of a prognostic DNA test for adolescent idiopathic scoliosis (AIS) and to establish clinically meaningful endpoints for the test. SUMMARY OF BACKGROUND DATA: Clinical features do not adequately predict which children diagnosed with minimal or mild AIS will have the progressive form of the disease; genetic markers associated with curve progression might offer clinically useful prognostic insights. METHODS: Logistic regression was used to develop an algorithm to predict spinal curve progression incorporating genotypes for 53 single nucleotide polymorphisms and the patient's presenting spinal curve (Cobb angle). Three cohorts with known AIS outcomes were selected to reflect intended-use populations with various rates of AIS progression: 277 low-risk females representing a screening cohort, 257 females representing higher risk patients followed at referral centers, and 163 high risk males. DNA was extracted from saliva, and genotypes were determined using TaqMan assays. AIS Prognostic Test scores ranging from 1 to 200 were calculated. RESULTS: Low-risk scores (<41) had negative predictive values of 100%, 99%, and 97%, respectively, in the tested populations. In the risk model, we used cutoff scores of 50 and 180 to identify 75% of patients as low-risk (<1% risk of progressing to a surgical curve), 24% as intermediate-risk, and 1% as high-risk. CONCLUSION: Prognostic testing for AIS has the potential to reduce psychological trauma, serial exposure to diagnostic radiation, unnecessary treatments, and direct and indirect costs-of-care related to scoliosis monitoring in low-risk patients. Further improvements in test performance are expected as the optimal markers for each locus are identified and the underlying biologic pathways are better understood. The validity of the test applies only to white AIS patients; versions of the test optimized for AIS patients of other races have yet to be developed.