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.

Probable digenic inheritance of Diamond-Blackfan anemia.

A 26-year-old female proband with a clinical diagnosis and consistent phenotype of Diamond-Blackfan anemia (DBA, OMIM 105650) without an identified genotype was referred to the Undiagnosed Diseases Network. DBA is classically associated with monoallelic variants that have an autosomal-dominant or -recessive mode of inheritance. Intriguingly, her case was solved by a detection of a digenic interaction between non-allelic RPS19 and RPL27 variants. This was confirmed with a machine learning structural model, co-segregation analysis, and RNA sequencing. This is the first report of DBA caused by a digenic effect of two non-allelic variants demonstrated by machine learning structural model. This case suggests that atypical phenotypic presentations of DBA may be caused by digenic inheritance in some individuals. We also conclude that a machine learning structural model can be useful in detecting digenic models of possible interactions between products encoded by alleles of different genes inherited from non-affected carrier parents that can result in DBA with an unrealized 25% recurrence risk.

Novel variant in CADM3 causes Charcot-Marie-Tooth disease.

CADM3 has been recently reported causing a rare axonal Charcot-Marie-Tooth disease in three independent Caucasian families carrying a recurrent change. We describe the first alternative causative mutation in CADM3 in a family from black African and also observed de novo in a patient of Caucasian ancestry. The disease inheritance was consistent with autosomal dominant and sporadic patterns, respectively. Eight patients and their relatives were enroled from both families. The mean age at diagnosis was 33.9 years, and walking difficulty was commonly the first symptom. Neurological examination showed distal muscle weakness and atrophy, sensory loss and foot and hand deformities. A high clinical variability was noted, but as seen in CADM3-associated neuropathy, symptoms were more pronounced in the arms in some patients. Nerve conduction studies showed no response in most of the examined nerves, and an axonal type of neuropathy, where recorded. Whole exome sequencing revealed a novel missense variant (c.1102G>T; Gly368Cys) in CADM3, segregating with the disease. Functional analyses showed a significant decrease in CADM3-Gly368Cys protein levels in the membrane and major structural changes in its predicted secondary structure. Therefore, we extend the genotype spectrum of CADM3, underlining the need for genetic studies in underrepresented populations like in Africa.

A medical odyssey of a 72-year-old man with Charcot-Marie-Tooth disease type 2 newly diagnosed with biallelic variants in SORD gene causing sorbitol dehydrogenase deficiency.

A 72-year-old man was referred to the Undiagnosed Diseases Network (UDN) because of gradual progressive weakness in both lower extremities for the past 45 years. He was initially diagnosed as having Charcot-Marie-Tooth disease type 2 (CMT2) without a defined molecular genetic cause. Exome sequencing (ES) failed to detect deleterious neuromuscular variants. Very recently, biallelic variants in sorbitol dehydrogenase (SORD) were discovered to be a novel cause of inherited neuropathies including CMT2 or distal hereditary motor neuropathy (dHMN) referred to as Sorbitol Dehydrogenase Deficiency with Peripheral Neuropathy (SORDD, OMIM 618912). The most common variant identified was c.757delG; p.A253Qfs*27. Through the Vanderbilt UDN clinical site, this patient was formally diagnosed with SORDD after the identification of homozygosity for the above SORD frameshift through UDN Genome Sequencing (GS). His medical odyssey was solved by GS and detection of extremely high levels of sorbitol. The diagnosis provided him the opportunity to receive potential treatment with an investigational drug in a clinical trial for SORDD. We suggest that similar studies be considered in other individuals thought to possibly have CMT2 or dHMN.

Heterozygous rare variants in NR2F2 cause a recognizable multiple congenital anomaly syndrome with developmental delays.

Nuclear receptor subfamily 2 group F member 2 (NR2F2 or COUP-TF2) encodes a transcription factor which is expressed at high levels during mammalian development. Rare heterozygous Mendelian variants in NR2F2 were initially identified in individuals with congenital heart disease (CHD), then subsequently in cohorts of congenital diaphragmatic hernia (CDH) and 46,XX ovotesticular disorders/differences of sexual development (DSD); however, the phenotypic spectrum associated with pathogenic variants in NR2F2 remains poorly characterized. Currently, less than 40 individuals with heterozygous pathogenic variants in NR2F2 have been reported. Here, we review the clinical and molecular details of 17 previously unreported individuals with rare heterozygous NR2F2 variants, the majority of which were de novo. Clinical features were variable, including intrauterine growth restriction (IUGR), CHD, CDH, genital anomalies, DSD, developmental delays, hypotonia, feeding difficulties, failure to thrive, congenital and acquired microcephaly, dysmorphic facial features, renal failure, hearing loss, strabismus, asplenia, and vascular malformations, thus expanding the phenotypic spectrum associated with NR2F2 variants. The variants seen were predicted loss of function, including a nonsense variant inherited from a mildly affected mosaic mother, missense and a large deletion including the NR2F2 gene. Our study presents evidence for rare, heterozygous NR2F2 variants causing a highly variable syndrome of congenital anomalies, commonly associated with heart defects, developmental delays/intellectual disability, dysmorphic features, feeding difficulties, hypotonia, and genital anomalies. Based on the new and previous cases, we provide clinical recommendations for evaluating individuals diagnosed with an NR2F2-associated disorder.

The contribution of mosaicism to genetic diseases and de novo pathogenic variants.

The contribution of mosaicism to diagnosed genetic disease and presumed de novo variants (DNV) is under investigated. We determined the contribution of mosaic genetic disease (MGD) and diagnosed parental mosaicism (PM) in parents of offspring with reported DNV (in the same variant) in the (1) Undiagnosed Diseases Network (UDN) (N = 1946) and (2) in 12,472 individuals electronic health records (EHR) who underwent genetic testing at an academic medical center. In the UDN, we found 4.51% of diagnosed probands had MGD, and 2.86% of parents of those with DNV exhibited PM. In the EHR, we found 6.03% and 2.99% and (of diagnosed probands) had MGD detected on chromosomal microarray and exome/genome sequencing, respectively. We found 2.34% (of those with a presumed pathogenic DNV) had a parent with PM for the variant. We detected mosaicism (regardless of pathogenicity) in 4.49% of genetic tests performed. We found a broad phenotypic spectrum of MGD with previously unknown phenotypic phenomena. MGD is highly heterogeneous and provides a significant contribution to genetic diseases. Further work is required to improve the diagnosis of MGD and investigate how PM contributes to DNV risk.

A Case Study of Dysfunctional Nicotinamide Metabolism in a 20-Year-Old Male.

We present a case study of a 20-year-old male with an unknown neurodegenerative disease who was referred to the Undiagnosed Diseases Network Vanderbilt Medical Center site. A previous metabolic panel showed that the patient had a critical deficiency in nicotinamide intermediates that are generated during the biosynthesis of NAD(H). We followed up on these findings by evaluating the patient's ability to metabolize nicotinamide. We performed a global metabolic profiling analysis of plasma samples that were collected: (1) under normal fed conditions (baseline), (2) after the patient had fasted, and (3) after he was challenged with a 500 mg nasogastric tube bolus of nicotinamide following the fast. Our findings showed that the patient's nicotinamide N-methyltransferase (NNMT), a key enzyme in NAD(H) biosynthesis and methionine metabolism, was not functional under normal fed or fasting conditions but was restored in response to the nicotinamide challenge. Altered levels of metabolites situated downstream of NNMT and in neighboring biochemical pathways provided further evidence of a baseline defect in NNMT activity. To date, this is the only report of a critical defect in NNMT activity manifesting in adulthood and leading to neurodegenerative disease. Altogether, this study serves as an important reference in the rare disease literature and also demonstrates the utility of metabolomics as a diagnostic tool for uncharacterized metabolic diseases.

The Genetic Landscape of Familial Pulmonary Fibrosis.

Rationale and Objectives: Up to 20% of idiopathic interstitial lung disease is familial, referred to as familial pulmonary fibrosis (FPF). An integrated analysis of FPF genetic risk was performed by comprehensively evaluating for genetic rare variants (RVs) in a large cohort of FPF kindreds. Methods: Whole-exome sequencing and/or candidate gene sequencing from affected individuals in 569 FPF kindreds was performed, followed by cosegregation analysis in large kindreds, gene burden analysis, gene-based risk scoring, cell-type enrichment analysis, and coexpression network construction. Measurements and Main Results: It was found that 14.9-23.4% of genetic risk in kindreds could be explained by RVs in genes previously linked to FPF, predominantly telomere-related genes. New candidate genes were identified in a small number of families-including SYDE1, SERPINB8, GPR87, and NETO1-and tools were developed for evaluation and prioritization of RV-containing genes across kindreds. Several pathways were enriched for RV-containing genes in FPF, including focal adhesion and mitochondrial complex I assembly. By combining single-cell transcriptomics with prioritized candidate genes, expression of RV-containing genes was discovered to be enriched in smooth muscle cells, type II alveolar epithelial cells, and endothelial cells. Conclusions: In the most comprehensive FPF genetic study to date, the prevalence of RVs in known FPF-related genes was defined, and new candidate genes and pathways relevant to FPF were identified. However, new RV-containing genes shared across multiple kindreds were not identified, thereby suggesting that heterogeneous genetic variants involving a variety of genes and pathways mediate genetic risk in most FPF kindreds.

Personalized structural biology reveals the molecular mechanisms underlying heterogeneous epileptic phenotypes caused by de novo KCNC2 variants.

Whole-exome sequencing (WES) in the clinic has identified several rare monogenic developmental and epileptic encephalopathies (DEE) caused by ion channel variants. However, WES often fails to provide actionable insight for rare diseases, such as DEEs, due to the challenges of interpreting variants of unknown significance (VUS). Here, we describe a "personalized structural biology" (PSB) approach that leverages recent innovations in the analysis of protein 3D structures to address this challenge. We illustrate this approach in an Undiagnosed Diseases Network (UDN) individual with DEE symptoms and a de novo VUS in KCNC2 (p.V469L), the Kv3.2 voltage-gated potassium channel. A nearby KCNC2 variant (p.V471L) was recently suggested to cause DEE-like phenotypes. Computational structural modeling suggests that both affect protein function. However, despite their proximity, the p.V469L variant is likely to sterically block the channel pore, while the p.V471L variant is likely to stabilize the open state. Biochemical and electrophysiological analyses demonstrate heterogeneous loss-of-function and gain-of-function effects, as well as differential response to 4-aminopyridine treatment. Molecular dynamics simulations illustrate that the pore of the p.V469L variant is more constricted, increasing the energetic barrier for K+ permeation, whereas the p.V471L variant stabilizes the open conformation. Our results implicate variants in KCNC2 as causative for DEE and guide the interpretation of a UDN individual. They further delineate the molecular basis for the heterogeneous clinical phenotypes resulting from two proximal pathogenic variants. This demonstrates how the PSB approach can provide an analytical framework for individualized hypothesis-driven interpretation of protein-coding VUS.

The Association between Exposures and Disease Characteristics in Familial Pulmonary Fibrosis.

Rationale: Heterogeneous characteristics are observed in familial pulmonary fibrosis (FPF), suggesting that nongenetic factors contribute to disease manifestations. Objectives: To determine the relationship between environmental exposures and disease characteristics of FPF, including the morphological characteristics on chest computed tomography (CT) scan, and timing of FPF symptom onset, lung transplantation, or death. Methods: Subjects with FPF with an exposure questionnaire and chest CT were selected from a prospective cohort at Vanderbilt. Disease characteristics were defined by lung parenchymal findings on chest CT associated with fibrotic hypersensitivity pneumonitis (fHP) or usual interstitial pneumonia (UIP) and by time from birth to symptom onset or a composite of lung transplantation or death. After assessing the potential for confounding by sex or smoking, adjusted logistic or Cox proportional hazards regression models identified exposures associated with fHP or UIP CT findings. Findings were validated in a cohort of patients with sporadic pulmonary fibrosis enrolled in the LTRC (Lung Tissue Research Consortium) study. Results: Among 159 subjects with FPF, 98 (61.6%) were males and 96 (60.4%) were ever-smokers. Males were less likely to have CT features of fHP, including mosaic attenuation (FPF: adjusted [for sex and smoking] odds ratio [aOR], 0.27; 95% confidence interval [CI], 0.09-0.76; P = 0.01; LTRC: aOR, 0.35; 95% CI, 0.21-0.61; P = 0.0002). Organic exposures, however, were not consistently associated with fHP features in either cohort. Smoking was a risk factor for honeycombing in both cohorts (FPF: aOR, 2.19; 95% CI, 1.12-4.28; P = 0.02; LTRC: aOR, 1.69; 95% CI, 1.22-2.33; P = 0.002). Rock dust exposure may also be associated with honeycombing, although the association was not statistically-significant when accounting for sex and smoking (FPF: aOR, 2.27; 95% CI, 0.997-5.15; P = 0.051; LTRC: aOR, 1.51; 95% CI, 0.97-2.33; P = 0.07). In the FPF cohort, ever-smokers experienced a shorter transplant-free survival (adjusted hazard ratio, 1.64; 95% CI, 1.07-2.52; P = 0.02), whereas sex was not associated with differential survival (male adjusted hazard ratio, 0.75; 95% CI, 0.50-1.14; P = 0.18). Conclusions: In FPF, smoking contributes to shortened transplant-free survival and development of honeycombing, a finding that is also likely applicable to sporadic pulmonary fibrosis. Females are more likely to manifest CT features of fHP (mosaic attenuation), a finding that was incompletely explained by sex differences in exposures. These findings may have implications for pulmonary fibrosis classification and management.

Lessons learned: next-generation sequencing applied to undiagnosed genetic diseases.

Rare genetic disorders, when considered together, are relatively common. Despite advancements in genetics and genomics technologies as well as increased understanding of genomic function and dysfunction, many genetic diseases continue to be difficult to diagnose. The goal of this Review is to increase the familiarity of genetic testing strategies for non-genetics providers. As genetic testing is increasingly used in primary care, many subspecialty clinics, and various inpatient settings, it is important that non-genetics providers have a fundamental understanding of the strengths and weaknesses of various genetic testing strategies as well as develop an ability to interpret genetic testing results. We provide background on commonly used genetic testing approaches, give examples of phenotypes in which the various genetic testing approaches are used, describe types of genetic and genomic variations, cover challenges in variant identification, provide examples in which next-generation sequencing (NGS) failed to uncover the variant responsible for a disease, and discuss opportunities for continued improvement in the application of NGS clinically. As genetic testing becomes increasingly a part of all areas of medicine, familiarity with genetic testing approaches and result interpretation is vital to decrease the burden of undiagnosed disease.

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.

Familial Autonomic Ganglionopathy Caused by Rare CHRNA3 Genetic Variants.

OBJECTIVE: To determine the molecular basis of a new monogenetic recessive disorder that results in familial autonomic ganglionopathy with diffuse autonomic failure. METHODS: Two adult siblings from one family (I-4 and I-5) and another participant from a second family (II-3) presented with severe neurogenic orthostatic hypotension (nOH), small nonreactive pupils, and constipation. All 3 affected members had low norepinephrine levels and diffuse panautonomic failure. RESULTS: Whole exome sequencing of DNA from I-4 and I-5 showed compound heterozygosity for c.907_908delCT (p.L303Dfs*115)/c.688 G>A (p.D230N) pathologic variants in the acetylcholine receptor, neuronal nicotinic, α3 subunit gene (CHRNA3). II-3 from the second family was homozygous for the same frameshift (fs) variant (p.L303Dfs*115//p.L303Dfs*115). CHRNA3 encodes a critical subunit of the nicotinic acetylcholine receptors (nAChRs) responsible for fast synaptic transmission in the autonomic ganglia. The fs variant is clearly pathogenic and the p.D230N variant is predicted to be damaging (SIFT)/probably damaging (PolyPhen2). The p.D230N variant lies on the interface between CHRNA3 and other nAChR subunits based on structural modeling and is predicted to destabilize the nAChR pentameric complex. CONCLUSIONS: We report a novel genetic disease that affected 3 individuals from 2 unrelated families who presented with severe nOH, miosis, and constipation. These patients had rare pathologic variants in the CHRNA3 gene that cosegregate with and are predicted to be the likely cause of their diffuse panautonomic failure.

Commonalities across computational workflows for uncovering explanatory variants in undiagnosed cases.

PURPOSE: Genomic sequencing has become an increasingly powerful and relevant tool to be leveraged for the discovery of genetic aberrations underlying rare, Mendelian conditions. Although the computational tools incorporated into diagnostic workflows for this task are continually evolving and improving, we nevertheless sought to investigate commonalities across sequencing processing workflows to reveal consensus and standard practice tools and highlight exploratory analyses where technical and theoretical method improvements would be most impactful. METHODS: We collected details regarding the computational approaches used by a genetic testing laboratory and 11 clinical research sites in the United States participating in the Undiagnosed Diseases Network via meetings with bioinformaticians, online survey forms, and analyses of internal protocols. RESULTS: We found that tools for processing genomic sequencing data can be grouped into four distinct categories. Whereas well-established practices exist for initial variant calling and quality control steps, there is substantial divergence across sites in later stages for variant prioritization and multimodal data integration, demonstrating a diversity of approaches for solving the most mysterious undiagnosed cases. CONCLUSION: The largest differences across diagnostic workflows suggest that advances in structural variant detection, noncoding variant interpretation, and integration of additional biomedical data may be especially promising for solving chronically undiagnosed cases.

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.

Missed diagnoses: Clinically relevant lessons learned through medical mysteries solved by the Undiagnosed Diseases Network.

BACKGROUND: Resources within the Undiagnosed Diseases Network (UDN), such as genome sequencing (GS) and model organisms aid in diagnosis and identification of new disease genes, but are currently difficult to access by clinical providers. While these resources do contribute to diagnoses in many cases, they are not always necessary to reach diagnostic resolution. The UDN experience has been that participants can also receive diagnoses through the thoughtful and customized application of approaches and resources that are readily available in clinical settings. METHODS: The UDN Genetic Counseling and Testing Working Group collected case vignettes that illustrated how clinically available methods resulted in diagnoses. The case vignettes were classified into three themes; phenotypic considerations, selection of genetic testing, and evaluating exome/GS variants and data. RESULTS: We present 12 participants that illustrate how clinical practices such as phenotype-driven genomic investigations, consideration of variable expressivity, selecting the relevant tissue of interest for testing, utilizing updated testing platforms, and recognition of alternate transcript nomenclature resulted in diagnoses. CONCLUSION: These examples demonstrate that when a diagnosis is elusive, an iterative patient-specific approach utilizing assessment options available to clinical providers may solve a portion of cases. However, this does require increased provider time commitment, a particular challenge in the current practice of genomics.

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.