Exome/Genome Sequencing in Undiagnosed Syndromes.

Exome sequencing (ES) and genome sequencing (GS) have radically transformed the diagnostic approach to undiagnosed rare/ultrarare Mendelian diseases. Next-generation sequencing (NGS), the technology integral for ES, GS, and most large (100+) gene panels, has enabled previously unimaginable diagnoses, changes in medical management, new treatments, and accurate reproductive risk assessments for patients, as well as new disease gene discoveries. Yet, challenges remain, as most individuals remain undiagnosed with current NGS. Improved NGS technology has resulted in long-read sequencing, which may resolve diagnoses in some patients who do not obtain a diagnosis with current short-read ES and GS, but its effectiveness is unclear, and it is expensive. Other challenges that persist include the resolution of variants of uncertain significance, the urgent need for patients with ultrarare disorders to have access to therapeutics, the need for equity in patient access to NGS-based testing, and the study of ethical concerns. However, the outlook for undiagnosed disease resolution is bright, due to continual advancements in the field.

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.

The best of both worlds: Blending cutting-edge research with clinical processes for a productive exome clinic.

Genomic medicine has been transformed by next-generation sequencing (NGS), inclusive of exome sequencing (ES) and genome sequencing (GS). Currently, ES is offered widely in clinical settings, with a less prevalent alternative model consisting of hybrid programs that incorporate research ES along with clinical patient workflows. We were among the earliest to implement a hybrid ES clinic, have provided diagnoses to 45% of probands, and have identified several novel candidate genes. Our program is enabled by a cost-effective investment by the health system and is unique in encompassing all the processes that have been variably included in other hybrid/clinical programs. These include careful patient selection, utilization of a phenotype-agnostic bioinformatics pipeline followed by manual curation of variants and phenotype integration by clinicians, close collaborations between the clinicians and the bioinformatician, pursuit of interesting variants, communication of results to patients in categories that are predicated upon the certainty of a diagnosis, and tracking changes in results over time and the underlying mechanisms for such changes. Due to its effectiveness, scalability to GS and its resource efficiency, specific elements of our paradigm can be incorporated into existing clinical settings, or the entire hybrid model can be implemented within health systems that have genomic medicine programs, to provide NGS in a scientifically rigorous, yet pragmatic setting.

Prevalence of Individuals With Multiple Diagnosed Genetic Diseases in the Undiagnosed Diseases Network.

Report the prevalence of multiple genetic diseases in the Undiagnosed Diseases Network (UDN) cohort in the post-exome-sequencing era. UDN subjects underwent genome sequencing before inclusion in the cohort. Records of all UDN subjects until January 2024 were analyzed. The number of diagnoses, proportion of molecular versus nonmolecular (i.e., not attributable to a discretely identifiable genetic change) diagnoses, and the inheritance patterns of the genetic diagnoses were determined. Of 2799 subjects, 766 (27.4%) had diagnoses. Of these 766, 95.4% had one diagnosis, 4.0% had two diagnoses, and 0.5% had three diagnoses. Of the diagnosed subjects, 93.4% had a genetic disease, and 6.5% had a nonmolecular disease. Of subjects with two diagnoses, both diagnoses were molecular in 90.3%, while 9.7% had one molecular and one nonmolecular diagnosis. All four subjects with three diagnoses had three molecular diagnoses. 4.2% of diagnosed subjects in the UDN had more than one molecular diagnosis, with four individuals having three concurrent Mendelian diagnoses. Additionally, three subjects had concurrent molecular and nonmolecular diagnoses. Given that numerous UDN subjects had a negative genome sequence prior to UDN enrollment, multiple molecular diagnoses may contribute to diagnostic uncertainty even with genome sequencing, as may concurrent nonmolecular disease.

Reanalysis of RNA sequencing data ends diagnostic odyssey and expands the phenotypic spectrum of congenital titinopathy.

Although next-generation sequencing has enabled diagnoses for many patients with Mendelian disorders, the majority remain undiagnosed. Here, we present a sibling pair who were clinically diagnosed with Escobar syndrome, however targeted gene testing was negative. Exome sequencing (ES), and later genome sequencing (GS), revealed compound heterozygous TTN variants in both siblings, a maternally inherited frameshift variant [(NM_133378.4):c.36812del; p.(Asp12271Valfs*10)], and a paternally inherited missense variant [(NM_133378.4):c.12322G > A; p.(Asp4108Asn)]. This result was considered nondiagnostic due to poor clinical fit and limited pathogenicity evidence for the missense variant of uncertain significance (VUS). Following initial nondiagnostic RNA sequencing (RNAseq) on muscle and further pursuit of other variants detected on the ES/GS, a reanalysis of noncanonical splice sites in the muscle transcriptome identified an out-of-frame exon retraction in TTN, near the known VUS. Interim literature included reports of patients with similar TTN variants who had phenotypic concordance with the siblings, and a diagnosis of a congenital titinopathy was given 4 years after the TTN variants had been initially reported. This report highlights the value of reanalysis of RNAseq with a different approach, expands the phenotypic spectrum of congenital titinopathy and also illustrates how a perceived phenotypic mismatch, and failure to consider known variants, can result in a prolongation of the diagnostic journey.

Advancing Equity in Rare Disease Research: Insights From the Undiagnosed Disease Network.

Rare diseases affect 6%-8% of the population and present diagnostic challenges, particularly for historically marginalized ethnic and racial groups. The Undiagnosed Diseases Network (UDN) aims to enhance diagnosis rates and research participation among such minoritized groups. A retrospective review was conducted from 2015 to 2023, analyzing 2235 UDN participants to evaluate its progress toward this objective. Data on demographics, disease phenotypes, diagnostic outcomes, and socioeconomic factors were collected and statistical analyses assessed differences among ethnic and racial groups. This demonstrated that Hispanic and Black non-Hispanic groups were underrepresented, while White non-Hispanic participants were overrepresented in the UDN compared to the US population. Individuals whose primary language was not English were also significantly underrepresented. Diagnosis rates varied, with the highest rates among Asian non-Hispanic (39.5%) and Hispanic (35.3%) groups and the lowest rate in the White non-Hispanic group (26.8%) (p < 0.001). Binomial logistic regression found, however, that only participant age and disease phenotype predicted the likelihood of receiving a diagnosis (p < 0.001). Persistent ethnic and racial disparities in UDN participation appear to be associated with major differences in application rates. Under-enrollment of historically marginalized ethnic and racial groups may be due to economic hardships and language barriers. No differences in the diagnostic yield among ethnic and racial groups were observed after controlling for other factors. This work highlights the value of comprehensive genetic evaluations for addressing healthcare disparities and suggests priorities for advancing inclusion in rare disease research.

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.

Development and internal validation of a screening tool for chronic prostatitis (S-CP).

PURPOSE: We developed a simple self-checkable screening tool for chronic prostatitis (S-CP) and internally validated it to encourage men (in the general population) with possible chronic prostatitis to consult urologists. METHODS: The expert panel proposed the S-CP, which comprises three domains: Area of pain or discomfort (6 components), accompanying Symptom (6 components), and Trigger for symptom flares (4 components). We employed logistic regression to predict chronic prostatitis prevalence with the S-CP. We evaluated the predictive performance using data from a representative national survey of Japanese men aged 20 to 84. We calculated the optimism-adjusted area under the curve using bootstrapping. We assessed sensitivity/specificity, likelihood ratio, and predictive value for each cutoff of the S-CP. RESULTS: Data were collected for 5,010 men-71 (1.4%) had a chronic prostatitis diagnosis. The apparent and adjusted area under the curve for the S-CP was 0.765 [95% confidence interval (CI) 0.702, 0.829] and 0.761 (0.696, 0.819), respectively. When the cutoff was two of the three domains being positive, sensitivity and specificity were 62.0% (95% CI 49.7, 73.2) and 85.4% (95% CI 84.4, 86.4), respectively. The positive/negative likelihood ratios were 4.2 (95% CI 3.5, 5.2) and 0.45 (95% CI 0.33, 0.60), respectively. The positive/negative predictive values were 5.7 (95% CI 4.2, 7.6) and 99.4 (95% CI 99.1, 99.6), respectively. CONCLUSION: The reasonable predictive performance of the S-CP indicated that patients (in the general population) with chronic prostatitis were screened as a first step. Further research would develop another tool for diagnostic support in actual clinical settings.

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.

Undiagnosed diabetes: Prevalence and cardiovascular risk profile in a population-based study of 52,856 individuals. The HUNT Study, Norway.

AIMS: We investigated the current extent of undiagnosed diabetes and prediabetes and their associated cardiovascular risk profile in a population-based study. METHODS: All residents aged ≥20 years in the Nord-Trøndelag region, Norway, were invited to the HUNT4 Survey in 2017-2019, and 54% attended. Diagnosed diabetes was self-reported, and in those reporting no diabetes HbA1c was used to classify undiagnosed diabetes (≥48 mmol/mol [6.5%]) and prediabetes (39-47 mmol/mol [5.7%-6.4%]). We estimated the age- and sex-standardized prevalence of these conditions and their age- and sex-adjusted associations with other cardiovascular risk factors. RESULTS: Among 52,856 participants, the prevalence of diabetes was 6.0% (95% CI 5.8, 6.2), of which 11.1% were previously undiagnosed (95% CI 10.1, 12.2). The prevalence of prediabetes was 6.4% (95% CI 6.2, 6.6). Among participants with undiagnosed diabetes, 58% had HbA1c of 48-53 mmol/mol (6.5%-7.0%), and only 14% (i.e., 0.1% of the total study population) had HbA1c >64 mmol/mol (8.0%). Compared with normoglycaemic participants, those with undiagnosed diabetes or prediabetes had higher body mass index, waist circumference, systolic blood pressure, triglycerides and C-reactive protein but lower low-density lipoprotein cholesterol (all p < 0.001). Participants with undiagnosed diabetes had less favourable values for every measured risk factor compared with those with diagnosed diabetes. CONCLUSIONS: The low prevalence of undiagnosed diabetes suggests that the current case-finding-based diagnostic practice is well-functioning. Few participants with undiagnosed diabetes had very high HbA1c levels indicating severe hyperglycaemia. Nonetheless, participants with undiagnosed diabetes had a poorer cardiovascular risk profile compared with participants with known or no diabetes.

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.

Disease Modeling of Rare Neurological Disorders in Zebrafish.

Rare diseases are those which affect a small number of people compared to the general population. However, many patients with a rare disease remain undiagnosed, and a large majority of rare diseases still have no form of viable treatment. Approximately 40% of rare diseases include neurologic and neurodevelopmental disorders. In order to understand the characteristics of rare neurological disorders and identify causative genes, various model organisms have been utilized extensively. In this review, the characteristics of model organisms, such as roundworms, fruit flies, and zebrafish, are examined, with an emphasis on zebrafish disease modeling in rare neurological disorders.

Clinical sites of the Undiagnosed Diseases Network: unique contributions to genomic medicine and science.

PURPOSE: The NIH Undiagnosed Diseases Network (UDN) evaluates participants with disorders that have defied diagnosis, applying personalized clinical and genomic evaluations and innovative research. The clinical sites of the UDN are essential to advancing the UDN mission; this study assesses their contributions relative to standard clinical practices. METHODS: We analyzed retrospective data from four UDN clinical sites, from July 2015 to September 2019, for diagnoses, new disease gene discoveries and the underlying investigative methods. RESULTS: Of 791 evaluated individuals, 231 received 240 diagnoses and 17 new disease-gene associations were recognized. Straightforward diagnoses on UDN exome and genome sequencing occurred in 35% (84/240). We considered these tractable in standard clinical practice, although genome sequencing is not yet widely available clinically. The majority (156/240, 65%) required additional UDN-driven investigations, including 90 diagnoses that occurred after prior nondiagnostic exome sequencing and 45 diagnoses (19%) that were nongenetic. The UDN-driven investigations included complementary/supplementary phenotyping, innovative analyses of genomic variants, and collaborative science for functional assays and animal modeling. CONCLUSION: Investigations driven by the clinical sites identified diagnostic and research paradigms that surpass standard diagnostic processes. The new diagnoses, disease gene discoveries, and delineation of novel disorders represent a model for genomic medicine and science.

Can regular follow-up imaging contribute to the determination of appropriate timing of surgery in patients with undiagnosed mucinous cystic neoplasm? A multicenter retrospective study.

OBJECTIVE: Guidelines suggest that patients with undiagnosed pancreatic cystic lesions should be monitored despite a lack of evidence supporting surveillance for undiagnosed mucinous cystic neoplasms (MCNs). We aimed to investigate the pre- and post-operative clinical course of patients with MCN and the utility of follow-up for patients who were not diagnosed with MCN at initial examination. PATIENTS AND METHODS: This multicenter retrospective study enrolled 28 patients with resected pathology-proven MCN; 12 and 16 patients underwent surgery within and after 6 months from the initial examination (Groups A and B, respectively). Outcome measures included changes in imaging findings until surgery in Group B, pathological findings between both groups and differences in pathological findings between patients with and without regular follow-up imaging in Group B. RESULTS: In Group B, the median cyst size was 30 and 48 mm at the initial examination and immediately before surgery, respectively. The incidence of mural cysts, thickened walls and mural nodules were 25, 19 and 0%, respectively, at the initial examination and 69, 56 and 31%, respectively, immediately before surgery. There were no significant differences in the invasive carcinoma rates between Groups A and B (13 vs. 17%). Regular follow-up imaging was offered to Group B. Among these, invasive carcinoma was found in one patient exhibiting no recurrence. One patient without follow-up imaging had invasive carcinoma recurrence post-operatively. CONCLUSIONS: MCNs increased in size, and typical imaging findings appeared over time. For undiagnosed MCN, regular follow-up examination contributed to the determination of the appropriate surgical timing.

"Doctors can read about it, they can know about it, but they've never lived with it": How parents use social media throughout the diagnostic odyssey.

Parents of children with undiagnosed conditions struggle to obtain information about how to treat and support their children. It can be particularly challenging to find communities and other parents who share their experiences and can provide emotional and informational support. This study sought to characterize how parents use social media, both throughout the diagnostic odyssey and post-diagnosis, to meet their informational, social, and emotional support needs. We conducted qualitative semi-structured interviews with 14 parents from the Stanford site of the Undiagnosed Diseases Network (UDN), including five whose children had received a diagnosis through study participation. Interview recordings were analyzed using inductive, team-based coding and thematic analysis based in grounded theory using Dedoose qualitative analysis software. Through this process, we identified four key themes related to social media use. First, parents struggled to find the "right" community, often seeking out groups of similar patients based on symptoms or similar conditions. Second, though they found much valuable information through social media about caring for their child, they also struggled to interpret the relevance of the information to their own child's condition. Third, the social support and access to other patients' and families' lived experiences were described as both highly valued and emotionally challenging, particularly in the case of poor outcomes for similar families. Finally, parents expressed the need to balance concerns about their child's privacy with the value of transparency and data sharing for diagnosis. Our results suggest that the needs and experiences of undiagnosed patients and families differ from those with diagnosed diseases and highlight the need for support in best utilizing social media resources at different stages of the diagnostic odyssey.

Early Detection of Public Health Emergencies of International Concern through Undiagnosed Disease Reports in ProMED-Mail.

We conducted a retrospective analysis of all reports in ProMED-mail that were initially classified as undiagnosed diseases during 2007-2018. We identified 371 cases reported in ProMED-mail; 34% were later diagnosed. ProMED-mail could be used to supplement other undiagnosed disease surveillance systems worldwide.

Parallel detection of single nucleotide variants and copy number variants with exome analysis: Validation in a cohort of 700 undiagnosed patients.

Copy number variants (CNVs) are significant causes of rare and undiagnosed diseases. Parallel detection of single nucleotide variants (SNVs) and CNVs with exome analysis, if feasible, would shorten the diagnostic closure in a timely manner. We validated such "parallel" approach through a cohort study of 791 undiagnosed patients. In addition to routine exome analysis, we applied an innovative algorithm EXCAVATOR2 which enhances sensitivity by paradoxically exploiting read depth data that covers nonexonic regions where baits were not originally intended to hybridize. About 48 patients had copy number variations, 42 deletions, and 6 duplications with a resolution of 0.51-14.7 mega base pairs. Importantly from a clinical standpoint, we identified three patients with "dual diagnosis" due to concurrent pathogenic CNV and SNV. We suggest "hitting two birds with one stone" approach to exome data is an efficient strategy in deciphering undiagnosed patients and may well be considered as a first-tier genetic test.

Limitations of exome sequencing in detecting rare and undiagnosed diseases.

While exome sequencing (ES) is commonly the final diagnostic step in clinical genetics, it may miss diagnoses. To clarify the limitations of ES, we investigated the diagnostic yield of genetic tests beyond ES in our Undiagnosed Diseases Network (UDN) participants. We reviewed the yield of additional genetic testing including genome sequencing (GS), copy number variant (CNV), noncoding variant (NCV), repeat expansion (RE), or methylation testing in UDN cases with nondiagnostic ES results. Overall, 36/54 (67%) of total diagnoses were based on clinical findings and coding variants found by ES and 3/54 (6%) were based on clinical findings only. The remaining 15/54 (28%) required testing beyond ES. Of these, 7/15 (47%) had NCV, 6/15 (40%) CNV, and 2/15 (13%) had a RE or a DNA methylation disorder. Thus 18/54 (33%) of diagnoses were not solved exclusively by ES. Several methods were needed to detect and/or confirm the functional effects of the variants missed by ES, and in some cases by GS. These results indicate that tests to detect elusive variants should be considered after nondiagnostic preliminary steps. Further studies are needed to determine the cost-effectiveness of tests beyond ES that provide diagnoses and insights to possible treatment.

Myopathy associated with homozygous PYROXD1 pathogenic variants detected by genome sequencing.

Biallelic pathogenic variants in the gene PYROXD1 have recently been described to cause early-onset autosomal recessive myopathy. Myopathy associated with PYROXD1 pathogenic variants is rare and reported in only 17 individuals. Known pathogenic variants in PYROXD1 include missense, insertion and essential splice-site variants. Here we describe a consanguineous family of individuals affected with late-onset myopathy and homozygous PYROXD1 missense variants (NM_024854.5:c.464A>G [p.Asn155Ser]) expanding our understanding of the possible disease phenotypes of PYROXD1-associated myopathy.

Building dialogues between clinical and biomedical research through cross-species collaborations.

Today, biomedical science is equipped with an impressive array of technologies and genetic resources that bolster our basic understanding of fundamental biology and enhance the practice of modern medicine by providing clinicians with a diverse toolkit to diagnose, prognosticate, and treat a plethora of conditions. Many significant advances in our understanding of disease mechanisms and therapeutic interventions have arisen from fruitful dialogues between clinicians and biomedical research scientists. However, the increasingly specialized scientific and medical disciplines, globalization of science and technology, and complex datasets often hinder the development of effective interdisciplinary collaborations between clinical medicine and biomedical research. The goal of this review is to provide examples of diverse strategies to enhance communication and collaboration across diverse disciplines. First, we discuss examples of efforts to foster interdisciplinary collaborations at institutional and multi-institutional levels. Second, we explore resources and tools for clinicians and research scientists to facilitate effective bi-directional dialogues. Third, we use our experiences in neurobiology and human genetics to highlight how communication between clinical medicine and biomedical research lead to effective implementation of cross-species model organism approaches to uncover the biological underpinnings of health and disease.