Defining Disease, Diagnosis, and Translational Medicine within a Homeostatic Perturbation Paradigm: The National Institutes of Health Undiagnosed Diseases Program Experience.

Traditionally, the use of genomic information for personalized medical decisions relies on prior discovery and validation of genotype-phenotype associations. This approach constrains care for patients presenting with undescribed problems. The National Institutes of Health (NIH) Undiagnosed Diseases Program (UDP) hypothesized that defining disease as maladaptation to an ecological niche allows delineation of a logical framework to diagnose and evaluate such patients. Herein, we present the philosophical bases, methodologies, and processes implemented by the NIH UDP. The NIH UDP incorporated use of the Human Phenotype Ontology, developed a genomic alignment strategy cognizant of parental genotypes, pursued agnostic biochemical analyses, implemented functional validation, and established virtual villages of global experts. This systematic approach provided a foundation for the diagnostic or non-diagnostic answers provided to patients and serves as a paradigm for scalable translational research.

Distributed Cognition and Process Management Enabling Individualized Translational Research: The NIH Undiagnosed Diseases Program Experience.

The National Institutes of Health Undiagnosed Diseases Program (NIH UDP) applies translational research systematically to diagnose patients with undiagnosed diseases. The challenge is to implement an information system enabling scalable translational research. The authors hypothesized that similar complex problems are resolvable through process management and the distributed cognition of communities. The team, therefore, built the NIH UDP integrated collaboration system (UDPICS) to form virtual collaborative multidisciplinary research networks or communities. UDPICS supports these communities through integrated process management, ontology-based phenotyping, biospecimen management, cloud-based genomic analysis, and an electronic laboratory notebook. UDPICS provided a mechanism for efficient, transparent, and scalable translational research and thereby addressed many of the complex and diverse research and logistical problems of the NIH UDP. Full definition of the strengths and deficiencies of UDPICS will require formal qualitative and quantitative usability and process improvement measurement.

Complex translocation disrupting TCF4 and altering TCF4 isoform expression segregates as mild autosomal dominant intellectual disability.

BACKGROUND: Mutations of TCF4, which encodes a basic helix-loop-helix transcription factor, cause Pitt-Hopkins syndrome (PTHS) via multiple genetic mechanisms. TCF4 is a complex locus expressing multiple transcripts by alternative splicing and use of multiple promoters. To address the relationship between mutation of these transcripts and phenotype, we report a three-generation family segregating mild intellectual disability with a chromosomal translocation disrupting TCF4. RESULTS: Using whole genome sequencing, we detected a complex unbalanced karyotype disrupting TCF4 (46,XY,del(14)(q23.3q23.3)del(18)(q21.2q21.2)del(18)(q21.2q21.2)inv(18)(q21.2q21.2)t(14;18)(q23.3;q21.2)(14pter®14q23.3::18q21.2®18q21.2::18q21.1®18qter;18pter®18q21.2::14q23.3®14qter). Subsequent transcriptome sequencing, qRT-PCR and nCounter analyses revealed that cultured skin fibroblasts and peripheral blood had normal expression of genes along chromosomes 14 or 18 and no marked changes in expression of genes other than TCF4. Affected individuals had 12-33 fold higher mRNA levels of TCF4 than did unaffected controls or individuals with PTHS. Although the derivative chromosome generated a PLEKHG3-TCF4 fusion transcript, the increased levels of TCF4 mRNA arose from transcript variants originating distal to the translocation breakpoint, not from the fusion transcript. CONCLUSIONS: Although validation in additional patients is required, our findings suggest that the dysmorphic features and severe intellectual disability characteristic of PTHS are partially rescued by overexpression of those short TCF4 transcripts encoding a nuclear localization signal, a transcription activation domain, and the basic helix-loop-helix domain.

Computational evaluation of exome sequence data using human and model organism phenotypes improves diagnostic efficiency.

PURPOSE: Medical diagnosis and molecular or biochemical confirmation typically rely on the knowledge of the clinician. Although this is very difficult in extremely rare diseases, we hypothesized that the recording of patient phenotypes in Human Phenotype Ontology (HPO) terms and computationally ranking putative disease-associated sequence variants improves diagnosis, particularly for patients with atypical clinical profiles. METHODS: Using simulated exomes and the National Institutes of Health Undiagnosed Diseases Program (UDP) patient cohort and associated exome sequence, we tested our hypothesis using Exomiser. Exomiser ranks candidate variants based on patient phenotype similarity to (i) known disease-gene phenotypes, (ii) model organism phenotypes of candidate orthologs, and (iii) phenotypes of protein-protein association neighbors. RESULTS: Benchmarking showed Exomiser ranked the causal variant as the top hit in 97% of known disease-gene associations and ranked the correct seeded variant in up to 87% when detectable disease-gene associations were unavailable. Using UDP data, Exomiser ranked the causative variant(s) within the top 10 variants for 11 previously diagnosed variants and achieved a diagnosis for 4 of 23 cases undiagnosed by clinical evaluation. CONCLUSION: Structured phenotyping of patients and computational analysis are effective adjuncts for diagnosing patients with genetic disorders.Genet Med 18 6, 608-617.

PhenomeCentral: a portal for phenotypic and genotypic matchmaking of patients with rare genetic diseases.

The discovery of disease-causing mutations typically requires confirmation of the variant or gene in multiple unrelated individuals, and a large number of rare genetic diseases remain unsolved due to difficulty identifying second families. To enable the secure sharing of case records by clinicians and rare disease scientists, we have developed the PhenomeCentral portal (https://phenomecentral.org). Each record includes a phenotypic description and relevant genetic information (exome or candidate genes). PhenomeCentral identifies similar patients in the database based on semantic similarity between clinical features, automatically prioritized genes from whole-exome data, and candidate genes entered by the users, enabling both hypothesis-free and hypothesis-driven matchmaking. Users can then contact other submitters to follow up on promising matches. PhenomeCentral incorporates data for over 1,000 patients with rare genetic diseases, contributed by the FORGE and Care4Rare Canada projects, the US NIH Undiagnosed Diseases Program, the EU Neuromics and ANDDIrare projects, as well as numerous independent clinicians and scientists. Though the majority of these records have associated exome data, most lack a molecular diagnosis. PhenomeCentral has already been used to identify causative mutations for several patients, and its ability to find matching patients and diagnose these diseases will grow with each additional patient that is entered.