Joubert syndrome (JBTS) is a Mendelian disorder of the primary cilium defined by the clinical triad of hypotonia, developmental delay, and a distinct cerebellar malformation called the molar tooth sign. JBTS is inherited in an autosomal recessive, autosomal dominant, or X-linked recessive manner. Though over 40 genes have been identified as causal for JBTS, molecular diagnosis is not made in 30%-40% of individuals who meet clinical criteria. TOPORS encodes topoisomerase I-binding arginine/ serine-rich protein, and homozygosity for a TOPORS missense variant (c.29C > A; p. (Pro10Gln)) was identified in individuals with the ciliopathy oral-facial-digital syndrome in two families of Dominican descent. Here, we report an additional proband of Dominican ancestry with JBTS found by exome sequencing to be homozygous for the identical p.(Pro10Gln) TOPORS missense variant. Query of the Mount Sinai BioMe biobank, which includes 1880 individuals of Dominican ancestry, supports a high carrier frequency of the TOPORS p.(Pro10Gln) variant in individuals of Dominican descent. Our data nominates TOPORS as a novel causal gene for JBTS and suggests that TOPORS variants should be considered in the differential of ciliopathy-spectrum disease in individuals of Dominican ancestry.

Telemedicine has long been considered as an attractive alternative methodology in clinical genetics to improve patient access and convenience. Given the importance of the dysmorphology physical examination and anthropometric measurement in clinical genetics, many have wondered if lost information would hamper diagnosis. We previously addressed this question by analyzing thousands of diagnostic encounters in a single practice involving multiple practitioners and found no evidence for a difference in new molecular diagnosis rates. However, our previous study design resulted in variability in providers between in-person and telemedicine evaluation groups. To address this in our present study, we expanded our analysis to 1104 new patient evaluations seen by one highly experienced clinical geneticist across two 10-month periods before and after the start of the COVID-19 pandemic. Comparing patients seen in-person to those seen by telemedicine, we found significant differences in race and ethnicity, preferred language, and home zip code median income. The clinical geneticist intended to send more genetic testing for those patients seen by telemedicine, but due to issues with test authorization and sample collection, there was no difference in ultimate completion rate between groups. We found no significant difference in new molecular diagnosis rate. Overall, we find telemedicine to be an acceptable alternative to in-person evaluation for routine pediatric clinical genetics care.

Fibular aplasia, tibial campomelia, and oligosyndactyly (FATCO) syndrome (MIM 246570) is a rare disorder characterized by specific skeletal findings (fibular aplasia, shortened or bowed tibia, and oligosyndactyly of the foot and/or hand). Typically, no other anomalies, craniofacial dysmorphism, or developmental delays are associated.Here we report three unrelated individuals with limb anomalies consistent with FATCO syndrome who have been followed clinically for 5 years. Genetic testing of previously reported individuals with FATCO syndrome has not revealed a genetic diagnosis. However, no broader sequencing approaches have been reported. We describe the results of the three individuals with FATCO syndrome from exome and genome sequencing, all of which was nondiagnostic. Our study suggests that FATCO syndrome is not the result of a simple monogenic etiology.

Objective We sought to develop and evaluate an electronic health record (EHR) genetic testing tracking system to address the barriers and limitations of existing spreadsheet-based workarounds. Materials and Methods We evaluated the spreadsheet-based system using mixed effects logistic regression to identify factors associated with delayed follow up. These factors informed the design of an EHR-integrated genetic testing tracking system. After deployment we assessed the system in two ways. We analyzed EHR access logs and note data to assess patient outcomes and performed semi-structured interviews with system users to identify impact of the new system on work. Results We found that patient-reported race was a significant predictor of documented genetic testing follow up, indicating a possible inequity in care. We implemented a CDS system including a patient data capture form and management dashboard to facilitate important care tasks. The system significantly speeded review of results and significantly increased documentation of follow-up recommendations. Interviews with system users identified key team members ensuring success and revealed that the system addresses a number of sociotechnical factors that collectively result in safer and more efficient care. Discussion Our new tracking system ended decades of workarounds for identifying and communicating test results and improved clinical workflows. Interview participants related that the system decreased cognitive and time burden which allowed them to focus on direct patient interaction. Conclusion By assembling a multidisciplinary team, we designed a novel patient tracking system that improves genetic testing follow up. Similar approaches may be effective in other clinical settings.

Objective We sought to develop and evaluate an electronic health record (EHR) genetic testing tracking system to address the barriers and limitations of existing spreadsheet-based workarounds. Materials and Methods We evaluated the spreadsheet-based system using mixed effects logistic regression to identify factors associated with delayed follow up. These factors informed the design of an EHR-integrated genetic testing tracking system. After deployment, we assessed the system in 2 ways. We analyzed EHR access logs and note data to assess patient outcomes and performed semistructured interviews with users to identify impact of the system on work. Results We found that patient-reported race was a significant predictor of documented genetic testing follow up, indicating a possible inequity in care. We implemented a CDS system including a patient data capture form and management dashboard to facilitate important care tasks. The system significantly sped review of results and significantly increased documentation of follow-up recommendations. Interviews with key system users identified a range of sociotechnical factors (ie, tools, tasks, collaboration) that contribute to safer and more efficient care. Discussion Our new tracking system ended decades of workarounds for identifying and communicating test results and improved clinical workflows. Interview participants related that the system decreased cognitive and time burden which allowed them to focus on direct patient interaction. Conclusion By assembling a multidisciplinary team, we designed a novel patient tracking system that improves genetic testing follow up. Similar approaches may be effective in other clinical settings.