What Has the Undiagnosed Diseases Network Taught Us About the Clinical Applications of Genomic Testing?

Genetic testing has undergone a revolution in the last decade, particularly with the advent of next-generation sequencing and its associated reductions in costs and increases in efficiencies. The Undiagnosed Diseases Network (UDN) has been a leader in the application of such genomic testing for rare disease diagnosis. This review discusses the current state of genomic testing performed within the UDN, with a focus on the strengths and limitations of whole-exome and whole-genome sequencing in clinical diagnostics and the importance of ongoing data reanalysis. The role of emerging technologies such as RNA and long-read sequencing to further improve diagnostic rates in the UDN is also described. This review concludes with a discussion of the challenges faced in insurance coverage of comprehensive genomic testing as well as the opportunities for a larger role of testing in clinical medicine.

Identifying digenic disease genes via machine learning in the Undiagnosed Diseases Network.

Rare diseases affect millions of people worldwide, and discovering their genetic causes is challenging. More than half of the individuals analyzed by the Undiagnosed Diseases Network (UDN) remain undiagnosed. The central hypothesis of this work is that many of these rare genetic disorders are caused by multiple variants in more than one gene. However, given the large number of variants in each individual genome, experimentally evaluating combinations of variants for potential to cause disease is currently infeasible. To address this challenge, we developed the digenic predictor (DiGePred), a random forest classifier for identifying candidate digenic disease gene pairs by features derived from biological networks, genomics, evolutionary history, and functional annotations. We trained the DiGePred classifier by using DIDA, the largest available database of known digenic-disease-causing gene pairs, and several sets of non-digenic gene pairs, including variant pairs derived from unaffected relatives of UDN individuals. DiGePred achieved high precision and recall in cross-validation and on a held-out test set (PR area under the curve > 77%), and we further demonstrate its utility by using digenic pairs from the recent literature. In contrast to other approaches, DiGePred also appropriately controls the number of false positives when applied in realistic clinical settings. Finally, to enable the rapid screening of variant gene pairs for digenic disease potential, we freely provide the predictions of DiGePred on all human gene pairs. Our work enables the discovery of genetic causes for rare non-monogenic diseases by providing a means to rapidly evaluate variant gene pairs for the potential to cause digenic disease.

A qualitative evaluation of patient and parent experiences with an undiagnosed diseases program.

Previous studies have explored patient experiences before being seen or at the beginning of their evaluation by undiagnosed diseases programs. This study provides additional insight into experiences after participation through in-depth, qualitative evaluation, allowing for reflection of current practice and patient/parent needs. Semi-structured interviews were conducted with patients and parents of patients seen at the University of Alabama at Birmingham (UAB)'s unique, clinically focused Undiagnosed Diseases Program (UDP). Analysis of the interviews was guided by a thematic approach. Participants had undergone a diagnostic odyssey before being evaluated by the UDP and remained hopeful for a diagnosis. They appreciated the opportunity to be seen by the UDP. However, perception of experiences differed based on whether evaluation by the UDP led to a diagnosis. Additionally, while participants were pleased with initial communication, they indicated that there were unmet needs regarding follow-up. Patients and parents of patients believe that participation in an undiagnosed diseases program is the best option for diagnosis. The findings of this study provide a general overview of patient experiences and highlight strengths of the UAB UDP while also emphasizing areas to focus the improvement to optimize the benefit to patients and families with undiagnosed and rare diseases, which could be used helpful in the development of similar clinics.

Undiagnosed Disease Network collaborative approach in diagnosing rare disease in a patient with a mosaic CACNA1D variant.

The Undiagnosed Disease Network (UDN) is comprised of clinical and research experts collaborating to diagnose rare disease. The UDN is funded by the National Institutes of Health and includes 12 different clinical sites (About Us, 2022). Here we highlight the success of collaborative efforts within the UDN Clinical Site at Vanderbilt University Medical Center (VUMC) in utilizing a cohort of experts in bioinformatics, structural biology, and genetics specialists in diagnosing rare disease. Our UDN team identified a de novo mosaic CACNA1D variant c.2299T>C in a 5-year-old female with a history of global developmental delay, dystonia, dyskinesis, and seizures. Using a collaborative multidisciplinary approach, our VUMC UDN team diagnosed the participant with Primary Aldosteronism, Seizures, and Neurologic abnormalities (PASNA) OMIM: 615474 due to a rare mosaic CACNA1D variant (O'Neill, 2013). Interestingly, this patient was mosaic, a phenotypic trait previously unreported in PASNA cases. This report highlights the importance of a multidisciplinary approach in diagnosing rare disease.

Occult liver disease: A multinational perspective.

Occult liver disease refers to the presence of unrecognized chronic liver disease and cirrhosis. Liver disease is currently the eleventh cause of death globally, representing 4% of all deaths in the world. Alcohol consumption is the leading cause of cirrhosis globally, accounting for approximately 60% of cases. The estimated global prevalence of non-alcoholic fatty liver disease (NAFLD) is 32.4% and has been steadily increasing over the last years. Viral hepatitis B and C accounted for 1.3 million deaths in 2020. Several studies in populations at high risk of chronic liver disease (elevated liver enzymes, type 2 diabetes, excessive alcohol consumption) have found an elevated prevalence of occult liver disease. Attempts should be made to assess the prevalence of occult liver disease in Latin America, a region with one of the highest rates of metabolic diseases and excessive alcohol consumption. Screening for NAFLD in high-risk subjects and screening for excessive drinking and alcohol use disorders at every level of medical care is relevant. Efforts should also focus on the early treatment of occult liver disease to try to reduce liver disease burden and, in the case of occult viral hepatitis infection, prevent further spreading.

Implementation of genomic medicine for rare disease in a tertiary healthcare system: Mayo Clinic Program for Rare and Undiagnosed Diseases (PRaUD).

BACKGROUND: In the United States, rare disease (RD) is defined as a condition that affects fewer than 200,000 individuals. Collectively, RD affects an estimated 30 million Americans. A significant portion of RD has an underlying genetic cause; however, this may go undiagnosed. To better serve these patients, the Mayo Clinic Program for Rare and Undiagnosed Diseases (PRaUD) was created under the auspices of the Center for Individualized Medicine (CIM) aiming to integrate genomics into subspecialty practice including targeted genetic testing, research, and education. METHODS: Patients were identified by subspecialty healthcare providers from 11 clinical divisions/departments. Targeted multi-gene panels or custom exome/genome-based panels were utilized. To support the goals of PRaUD, a new clinical service model, the Genetic Testing and Counseling (GTAC) unit, was established to improve access and increase efficiency for genetic test facilitation. The GTAC unit includes genetic counselors, genetic counseling assistants, genetic nurses, and a medical geneticist. Patients receive abbreviated point-of-care genetic counseling and testing through a partnership with subspecialty providers. RESULTS: Implementation of PRaUD began in 2018 and GTAC unit launched in 2020 to support program expansion. Currently, 29 RD clinical indications are included in 11 specialty divisions/departments with over 142 referring providers. To date, 1152 patients have been evaluated with an overall solved or likely solved rate of 17.5% and as high as 66.7% depending on the phenotype. Noteworthy, 42.7% of the solved or likely solved patients underwent changes in medical management and outcome based on genetic test results. CONCLUSION: Implementation of PRaUD and GTAC have enabled subspecialty practices advance expertise in RD where genetic counselors have not historically been embedded in practice. Democratizing access to genetic testing and counseling can broaden the reach of patients with RD and increase the diagnostic yield of such indications leading to better medical management as well as expanding research opportunities.

A concurrent dual analysis of genomic data augments diagnoses: Experiences of 2 clinical sites in the Undiagnosed Diseases Network.

PURPOSE: Next-generation sequencing (NGS) has revolutionized the diagnostic process for rare/ultrarare conditions. However, diagnosis rates differ between analytical pipelines. In the National Institutes of Health-Undiagnosed Diseases Network (UDN) study, each individual's NGS data are concurrently analyzed by the UDN sequencing core laboratory and the clinical sites. We examined the outcomes of this practice. METHODS: A retrospective review was performed at 2 UDN clinical sites to compare the variants and diagnoses/candidate genes identified with the dual analyses of the NGS data. RESULTS: In total, 95 individuals had 100 diagnoses/candidate genes. There was 59% concordance between the UDN sequencing core laboratories and the clinical sites in identifying diagnoses/candidate genes. The core laboratory provided more diagnoses, whereas the clinical sites prioritized more research variants/candidate genes (P < .001). The clinical sites solely identified 15% of the diagnoses/candidate genes. The differences between the 2 pipelines were more often because of variant prioritization disparities than variant detection. CONCLUSION: The unique dual analysis of NGS data in the UDN synergistically enhances outcomes. The core laboratory provided a clinical analysis with more diagnoses and the clinical sites prioritized more research variants/candidate genes. Implementing such concurrent dual analyses in other genomic research studies and clinical settings can improve both variant detection and prioritization.

Risks and benefits of anesthesia for combined pediatric procedures in the NIH undiagnosed diseases program.

PURPOSE: The NIH Undiagnosed Diseases Program (UDP) aims to provide diagnoses to patients who have previously received exhaustive evaluations yet remain undiagnosed. Patients undergo procedural anesthesia for deep phenotyping for analysis with genomic testing. METHODS: A retrospective chart review was performed to determine the safety and benefit of procedural anesthesia in pediatric patients in the UDP. Adverse perioperative events were classified as anesthesia-related complications or peri-procedural complications. The contribution of procedures performed under anesthesia to arriving at a diagnosis was also determined. RESULTS: From 2008 to 2020, 249 pediatric patients in the UDP underwent anesthesia for diagnostic procedures. The majority had a severe systemic disease (American Society for Anesthesiology status III, 79%) and/or a neurologic condition (91%). Perioperative events occurred in 45 patients; six of these were attributed to anesthesia. All patients recovered fully without sequelae. Nearly half of the 249 patients (49%) received a diagnosis, and almost all these diagnoses (88%) took advantage of information gleaned from procedures performed under anesthesia. CONCLUSIONS: The benefits of anesthesia involving multiple diagnostic procedures in a well-coordinated, multidisciplinary, research setting, such as in the pediatric UDP, outweigh the risks.

Assessment of whole-exome sequencing results in neurogenetic diseases.

Neurogenetic diseases are rare genetic diseases in which neurological findings are prominent. Whole exome sequencing (WES) has led to great advances in the understanding of the causes of neurogenetic diseases. Etiological research ends with the WES method in many patients. This etiological research is called a "diagnostic odyssey" for many families. Here, we present the results of 168 patients who were previously undiagnosed and underwent WES with the suspicion of neurogenetic disease. A total of 168 cases, 94 males and 74 females, with suspected undiagnosed neurogenetic disease were included in the study. We presented the WES results of the patients. The mean age of patients at the time of WES request was 11 years (range 0.25-68 years). Seventy percent (n = 117) of the patients were born from consanguineous marriage. Most of the patients were children (n = 145). Patients were grouped according to age at the time of examination. Patients younger than 18 years of age at the time of examination were classified as children, otherwise adults. Seventy-eight patients had either a pathogenic variant or a likely pathogenic variant so the diagnostic rate for WES in our cohort was %46. Our experience showing the high diagnostic rate of WES, supports its use in undiagnosed neurogenetic diseases. It also affects medical treatment, prognosis and family planning by enabling early diagnosis in patients.

Neighborhood-Level Risk Factors for Severe Hyperglycemia among Emergency Department Patients without a Prior Diabetes Diagnosis.

A person's place of residence is a strong risk factor for important diagnosed chronic diseases such as diabetes. It is unclear whether neighborhood-level risk factors also predict the probability of undiagnosed disease. The objective of this study was to identify neighborhood-level variables associated with severe hyperglycemia among emergency department (ED) patients without a history of diabetes. We analyzed patients without previously diagnosed diabetes for whom a random serum glucose value was obtained in the ED. We defined random glucose values ≥ 200 mg/dL as severe hyperglycemia, indicating probable undiagnosed diabetes. Patient addresses were geocoded and matched with neighborhood-level socioeconomic measures from the American Community Survey and claims-based surveillance estimates of diabetes prevalence. Neighborhood-level exposure variables were standardized based on z-scores, and a series of logistic regression models were used to assess the association of selected exposures and hyperglycemia adjusting for biological and social individual-level risk factors for diabetes. Of 77,882 ED patients without a history of diabetes presenting in 2021, 1,715 (2.2%) had severe hyperglycemia. Many geospatial exposures were associated with uncontrolled hyperglycemia, even after controlling for individual-level risk factors. The most strongly associated neighborhood-level variables included lower markers of educational attainment, higher percentage of households where limited English is spoken, lower rates of white-collar employment, and higher rates of Medicaid insurance. Including these geospatial factors in risk assessment models may help identify important subgroups of patients with undiagnosed disease.

Lessons learnt from multifaceted diagnostic approaches to the first 150 families in Victoria's Undiagnosed Diseases Program.

BACKGROUND: Clinical exome sequencing typically achieves diagnostic yields of 30%-57.5% in individuals with monogenic rare diseases. Undiagnosed diseases programmes implement strategies to improve diagnostic outcomes for these individuals. AIM: We share the lessons learnt from the first 3 years of the Undiagnosed Diseases Program-Victoria, an Australian programme embedded within a clinical genetics service in the state of Victoria with a focus on paediatric rare diseases. METHODS: We enrolled families who remained without a diagnosis after clinical genomic (panel, exome or genome) sequencing between 2016 and 2018. We used family-based exome sequencing (family ES), family-based genome sequencing (family GS), RNA sequencing (RNA-seq) and high-resolution chromosomal microarray (CMA) with research-based analysis. RESULTS: In 150 families, we achieved a diagnosis or strong candidate in 64 (42.7%) (37 in known genes with a consistent phenotype, 3 in known genes with a novel phenotype and 24 in novel disease genes). Fifty-four diagnoses or strong candidates were made by family ES, six by family GS with RNA-seq, two by high-resolution CMA and two by data reanalysis. CONCLUSION: We share our lessons learnt from the programme. Flexible implementation of multiple strategies allowed for scalability and response to the availability of new technologies. Broad implementation of family ES with research-based analysis showed promising yields post a negative clinical singleton ES. RNA-seq offered multiple benefits in family ES-negative populations. International data sharing strategies were critical in facilitating collaborations to establish novel disease-gene associations. Finally, the integrated approach of a multiskilled, multidisciplinary team was fundamental to having diverse perspectives and strategic decision-making.

ModelMatcher: A scientist-centric online platform to facilitate collaborations between stakeholders of rare and undiagnosed disease research.

Next-generation sequencing is a prevalent diagnostic tool for undiagnosed diseases and has played a significant role in rare disease gene discovery. Although this technology resolves some cases, others are given a list of possibly damaging genetic variants necessitating functional studies. Productive collaborations between scientists, clinicians, and patients (affected individuals) can help resolve such medical mysteries and provide insights into in vivo function of human genes. Furthermore, facilitating interactions between scientists and research funders, including nonprofit organizations or commercial entities, can dramatically reduce the time to translate discoveries from bench to bedside. Several systems designed to connect clinicians and researchers with a shared gene of interest have been successful. However, these platforms exclude some stakeholders based on their role or geography. Here we describe ModelMatcher, a global online matchmaking tool designed to facilitate cross-disciplinary collaborations, especially between scientists and other stakeholders of rare and undiagnosed disease research. ModelMatcher is integrated into the Rare Diseases Models and Mechanisms Network and Matchmaker Exchange, allowing users to identify potential collaborators in other registries. This living database decreases the time from when a scientist or clinician is making discoveries regarding their genes of interest, to when they identify collaborators and sponsors to facilitate translational and therapeutic research.

PatientMatcher: A customizable Python-based open-source tool for matching undiagnosed rare disease patients via the Matchmaker Exchange network.

The amount of data available from genomic medicine has revolutionized the approach to identify the determinants underlying many rare diseases. The task of confirming a genotype-phenotype causality for a patient affected with a rare genetic disease is often challenging. In this context, the establishment of the Matchmaker Exchange (MME) network has assumed a pivotal role in bridging heterogeneous patient information stored on different medical and research servers. MME has made it possible to solve rare disease cases by "matching" the genotypic and phenotypic characteristics of a patient of interest with patient data available at other clinical facilities participating in the network. Here, we present PatientMatcher (https://github.com/Clinical-Genomics/patientMatcher), an open-source Python and MongoDB-based software solution developed by Clinical Genomics facility at the Science for Life Laboratory in Stockholm. PatientMatcher is designed as a standalone MME server, but can easily communicate via REST API with external applications managing genetic analyses and patient data. The MME node is being implemented in clinical routine in collaboration with the Genomic Medicine Center Karolinska at the Karolinska University Hospital. PatientMatcher is written to implement the MME API and provides several customizable settings, including a custom-fit similarity score algorithm and adjustable matching results notifications.

Diagnostic sequencing to support genetically stratified medicine in a tertiary care setting.

PURPOSE: The goal of stratified medicine is to identify subgroups of patients with similar disease mechanisms and specific responses to treatments. To prepare for stratified clinical trials, genome-wide genetic analysis should occur across clinical areas to identify undiagnosed genetic diseases and new genetic causes of disease. METHODS: To advance genetically stratified medicine, we have developed and implemented broad exome sequencing infrastructure and research protocols at Columbia University Irving Medical Center/NewYork-Presbyterian Hospital. RESULTS: We enrolled 4889 adult and pediatric probands and identified a primary result in 572 probands. The cohort was phenotypically and demographically heterogeneous because enrollment occurred across multiple specialty clinics (eg, epilepsy, nephrology, fetal anomaly). New gene-disease associations and phenotypic expansions were discovered across clinical specialties. CONCLUSION: Our study processes have enabled the enrollment and exome sequencing/analysis of a phenotypically and demographically diverse cohort of patients within 1 tertiary care medical center. Because all genomic data are stored centrally with permission for longitudinal access to the electronic medical record, subjects can be recontacted with updated genetic diagnoses or for participation in future genotype-based clinical trials. This infrastructure has allowed for the promotion of genetically stratified clinical trial readiness within the Columbia University Irving Medical Center/NewYork-Presbyterian Hospital health care system.

Cost-effectiveness of genome sequencing for diagnosing patients with undiagnosed rare genetic diseases.

PURPOSE: To estimate the cost-effectiveness of genome sequencing (GS) for diagnosing critically ill infants and noncritically ill pediatric patients (children) with suspected rare genetic diseases from a United States health sector perspective. METHODS: A decision-analytic model was developed to simulate the diagnostic trajectory of patients. Parameter estimates were derived from a targeted literature review and meta-analysis. The model simulated clinical and economic outcomes associated with 3 diagnostic pathways: (1) standard diagnostic care, (2) GS, and (3) standard diagnostic care followed by GS. RESULTS: For children, costs of GS ($7284) were similar to that of standard care ($7355) and lower than that of standard care followed by GS pathways ($12,030). In critically ill infants, when cost estimates were based on the length of stay in the neonatal intensive care unit, the lowest cost pathway was GS ($209,472). When only diagnostic test costs were included, the cost per diagnosis was $17,940 for standard, $17,019 for GS, and $20,255 for standard care followed by GS. CONCLUSION: The results of this economic model suggest that GS may be cost neutral or possibly cost saving as a first line diagnostic tool for children and critically ill infants.

Six years' accomplishment of the Initiative on Rare and Undiagnosed Diseases: nationwide project in Japan to discover causes, mechanisms, and cures.

The identification of causative genetic variants for hereditary diseases has revolutionized clinical medicine and an extensive collaborative framework with international cooperation has become a global trend to understand rare disorders. The Initiative on Rare and Undiagnosed Diseases (IRUD) was established in Japan to provide accurate diagnosis, discover causes, and ultimately provide cures for rare and undiagnosed diseases. The fundamental IRUD system consists of three pillars: IRUD diagnostic coordination, analysis centers (IRUD-ACs), and a data center (IRUD-DC). IRUD diagnostic coordination consists of clinical centers (IRUD-CLs) and clinical specialty subgroups (IRUD-CSSs). In addition, the IRUD coordinating center (IRUD-CC) manages the entire IRUD system and temporarily operates the IRUD resource center (IRUD-RC). By the end of March 2021, 6301 pedigrees consisting of 18,136 individuals were registered in the IRUD. The whole-exome sequencing method was completed in 5136 pedigrees, and a final diagnosis was established in 2247 pedigrees (43.8%). The total number of aberrated genes and pathogenic variants was 657 and 1718, among which 1113 (64.8%) were novel. In addition, 39 novel disease entities or phenotypes with 41 aberrated genes were identified. The 6-year endeavor of IRUD has been an overwhelming success, establishing an all-Japan comprehensive diagnostic and research system covering all geographic areas and clinical specialties/subspecialties. IRUD has accurately diagnosed diseases, identified novel aberrated genes or disease entities, discovered many candidate genes, and enriched phenotypic and pathogenic variant databases. Further promotion of the IRUD is essential for determining causes and developing cures for rare and undiagnosed diseases.

Undiagnosed disease program in South Africa: Results from first 100 exomes.

The Undiagnosed Disease Program in South Africa (UDP) sought to prospectively evaluate the clinical utility of exome sequencing (ES) in a phenotypically diverse, multi-ethnic cohort of South African patients with suspected rare genetic disorders. ES was undertaken in 100 sequential patients (93 singletons, 3 duos, and 4 trios) recruited to the UDP at Stellenbosch University. The data were analyzed through two separate bioinformatics pipelines (EVIDENCE from 3 billion and our in-house pipeline). A definitive diagnosis could be reached in 51% (51/100) patients, with 46% (46/100) patients having either pathogenic or likely pathogenic single-nucleotide variants/indels (SNVs/indels), and 5 patients with likely-pathogenic copy number variants (CNVs) (5/100). The CNVs were subsequently confirmed on microarray or MLPA analysis. Detailed phenotyping and HPO terms enabled analysis and variant identification. Twenty-five novel variants in 22 genes are reported here. We provide data from the first year of this UDP and show that even amongst mainly singletons from an understudied, diverse African population, ES is a valuable diagnostic tool, especially if it includes CNV analysis. The remaining undiagnosed patients present a unique opportunity for further research and novel gene discovery.

Diagnosis and discovery: Insights from the NIH Undiagnosed Diseases Program.

Living with an undiagnosed medical condition places a tremendous burden on patients, their families, and their healthcare providers. The Undiagnosed Diseases Program (UDP) was established at the National Institutes of Health (NIH) in 2008 with the primary goals of providing a diagnosis for patients with mysterious conditions and advancing medical knowledge about rare and common diseases. The program reviews applications from referring clinicians for cases that are considered undiagnosed despite a thorough evaluation. Those that are accepted receive clinical evaluations involving deep phenotyping and genetic testing that includes exome and genomic sequencing. Selected candidate gene variants are evaluated by collaborators using functional assays. Since its inception, the UDP has received more than 4500 applications and has completed evaluations on nearly 1300 individuals. Here we present six cases that exemplify the discovery of novel disease mechanisms, the importance of deep phenotyping for rare diseases, and how genetic diagnoses have led to appropriate treatment. The creation of the Undiagnosed Diseases Network (UDN) in 2014 has substantially increased the number of patients evaluated and allowed for greater opportunities for data sharing. Expansion to the Undiagnosed Diseases Network International (UDNI) has the possibility to extend this reach even farther. Together, networks of undiagnosed diseases programs are powerful tools to advance our knowledge of pathophysiology, accelerate accurate diagnoses, and improve patient care for patients with rare conditions.

Exome and genome sequencing in adults with undiagnosed disease: a prospective cohort study.

BACKGROUND: Exome and genome sequencing have been demonstrated to increase diagnostic yield in paediatric populations, improving treatment options and providing risk information for relatives. There are limited studies examining the clinical utility of these tests in adults, who currently have limited access to this technology. METHODS: Patients from adult and cancer genetics clinics across Toronto, Ontario, Canada were recruited into a prospective cohort study evaluating the diagnostic utility of exome and genome sequencing in adults. Eligible patients were ≥18 years of age and suspected of having a hereditary disorder but had received previous uninformative genetic test results. In total, we examined the diagnostic utility of exome and genome sequencing in 47 probands and 34 of their relatives who consented to participate and underwent exome or genome sequencing. RESULTS: Overall, 17% (8/47) of probands had a pathogenic or likely pathogenic variant identified in a gene associated with their primary indication for testing. The diagnostic yield for patients with a cancer history was similar to the yield for patients with a non-cancer history (4/18 (22%) vs 4/29 (14%)). An additional 24 probands (51%) had an inconclusive result. Secondary findings were identified in 10 patients (21%); three had medically actionable results. CONCLUSIONS: This study lends evidence to the diagnostic utility of exome or genome sequencing in an undiagnosed adult population. The significant increase in diagnostic yield warrants the use of this technology. The identification and communication of secondary findings may provide added value when using this testing modality as a first-line test.

Genetic counselor roles in the undiagnosed diseases network research study: Clinical care, collaboration, and curation.

Genetic counselors (GCs) are increasingly filling important positions on research study teams, but there is limited literature describing the roles of GCs in these settings. GCs on the Undiagnosed Diseases Network (UDN) study team serve in a variety of roles across the research network and provide an opportunity to better understand genetic counselor roles in research. To quantitatively characterize the tasks regularly performed and professional fulfillment derived from these tasks, two surveys were administered to UDN GCs in a stepwise fashion. Responses from the first, free-response survey elicited the scope of tasks which informed development of a second structured, multiple-select survey. In survey 2, respondents were asked to select which roles they performed. Across 19 respondents, roles in survey 2 received a total of 947 selections averaging approximately 10 selections per role. When asked to indicate what roles they performed, respondent selected a mean of 50 roles (range 22-70). Survey 2 data were analyzed via thematic coding of responses and hierarchical cluster analysis to identify patterns in responses. From the thematic analysis, 20 non-overlapping codes emerged in seven categories: clinical interaction and care, communication, curation, leadership, participant management, research, and team management. Three themes emerged from the categories that represented the roles of GCs in the UDN: clinical care, collaboration, and curation. Cluster analyses showed that responses were more similar among individuals at the same institution than between institutions. This study highlights the ways GCs apply their unique skill set in the context of a clinical translational research network. Additionally, findings from this study reinforce the wide applicability of core skills that are part of genetic counseling training. Clinical literacy, genomics expertise and analysis, interpersonal, psychosocial and counseling skills, education, professional practice skills, and an understanding of research processes make genetic counselors well suited for such roles and poised to positively impact research experiences and outcomes for participants.