Many patients remain without a diagnosis despite extensive medical evaluation. The Undiagnosed Diseases Network (UDN) was established to apply a multidisciplinary model in the evaluation of the most challenging cases and to identify the biologic characteristics of newly discovered diseases. The UDN, which is funded by the National Institutes of Health, was formed in 2014 as a network of seven clinical sites, two sequencing cores, and a coordinating center. Later, a central biorepository, a metabolomics core, and a model organisms screening center were added. We evaluated patients who were referred to the UDN over a period of 20 months. The patients were required to have an undiagnosed condition despite thorough evaluation by a health care provider. We determined the rate of diagnosis among patients who subsequently had a complete evaluation, and we observed the effect of diagnosis on medical care. A total of 1519 patients (53% female) were referred to the UDN, of whom 601 (40%) were accepted for evaluation. Of the accepted patients, 192 (32%) had previously undergone exome sequencing. Symptoms were neurologic in 40% of the applicants, musculoskeletal in 10%, immunologic in 7%, gastrointestinal in 7%, and rheumatologic in 6%. Of the 382 patients who had a complete evaluation, 132 received a diagnosis, yielding a rate of diagnosis of 35%. A total of 15 diagnoses (11%) were made by clinical review alone, and 98 (74%) were made by exome or genome sequencing. Of the diagnoses, 21% led to recommendations regarding changes in therapy, 37% led to changes in diagnostic testing, and 36% led to variant-specific genetic counseling. We defined 31 new syndromes. The UDN established a diagnosis in 132 of the 382 patients who had a complete evaluation, yielding a rate of diagnosis of 35%. (Funded by the National Institutes of Health Common Fund.)

Purpose: This report describes the National Institutes of Health Undiagnosed Diseases Program, details the Program’s application of genomic technology to establish diagnoses, and details the Program’s success rate during its first 2 years. Methods: Each accepted study participant was extensively phenotyped. A subset of participants and selected family members (29 patients and 78 unaffected family members) was subjected to an integrated set of genomic analyses including high-density single-nucleotide polymorphism arrays and whole exome or genome analysis. Results: Of 1,191 medical records reviewed, 326 patients were accepted and 160 were admitted directly to the National Institutes of Health Clinical Center on the Undiagnosed Diseases Program service. Of those, 47% were children, 55% were females, and 53% had neurologic disorders. Diagnoses were reached on 39 participants (24%) on clinical, biochemical, pathologic, or molecular grounds; 21 diagnoses involved rare or ultra-rare diseases. Three disorders were diagnosed based on single-nucleotide polymorphism array analysis and three others using whole exome sequencing and filtering of variants. Two new disorders were discovered. Analysis of the single-nucleotide polymorphism array study cohort revealed that large stretches of homozygosity were more common in affected participants relative to controls. Conclusion: The National Institutes of Health Undiagnosed Diseases Program addresses an unmet need, i.e., the diagnosis of patients with complex, multisystem disorders. It may serve as a model for the clinical application of emerging genomic technologies and is providing insights into the characteristics of diseases that remain undiagnosed after extensive clinical workup. Genet Med 2012:14(1):51–59

The technologies and chemistries underlying next-generation sequencing of DNA are evolving rapidly. This review describes the three main approaches to obtaining a genetic diagnosis through next-generation sequencing, their advantages, and their limitations.

Disease gene discovery has been transformed by affordable sequencing of exomes and genomes. Identification of disease‐causing mutations requires sifting through a large number of sequence variants. A subset of the variants are unlikely to be good candidates for disease causation based on one or more of the following criteria: (1) being located in genomic regions known to be highly polymorphic, (2) having characteristics suggesting assembly misalignment, and/or (3) being labeled as variants based on misleading reference genome information. We analyzed exome sequence data from 118 individuals in 29 families seen in the NIH Undiagnosed Diseases Program (UDP) to create lists of variants and genes with these characteristics. Specifically, we identified several groups of genes that are candidates for provisional exclusion during exome analysis: 23,389 positions with excess heterozygosity suggestive of alignment errors and 1,009 positions in which the hg18 human genome reference sequence appeared to contain a minor allele. Exclusion of such variants, which we provide in supplemental lists, will likely enhance identification of disease‐causing mutations using exome sequence data. Hum Mutat 33:609–613, 2012. © 2012 Wiley Periodicals, Inc. . 1 This article is a US Government work and, as such, is in the public domain of the United States of America.

NMDA receptors (NMDARs), ligand-gated ion channels, play important roles in various neurological disorders, including epilepsy. Here we show the functional analysis of a de novo missense mutation (L812M) in a gene encoding NMDAR subunit GluN2A (GRIN2A). The mutation, identified in a patient with early-onset epileptic encephalopathy and profound developmental delay, is located in the linker region between the ligand-binding and transmembrane domains. Electrophysiological recordings revealed that the mutation enhances agonist potency, decreases sensitivity to negative modulators including magnesium, protons and zinc, prolongs the synaptic response time course and increases single-channel open probability. The functional changes of this amino acid apply to all other NMDAR subunits, suggesting an important role of this residue on the function of NMDARs. Taken together, these data suggest that the L812M mutation causes overactivation of NMDARs and drives neuronal hyperexcitability. We hypothesize that this mechanism underlies the patient’s epileptic phenotype as well as cerebral atrophy.