@article {810, title = {Missed diagnoses: Clinically relevant lessons learned through medical mysteries solved by the Undiagnosed Diseases Network.}, journal = {Mol Genet Genomic Med}, volume = {8}, year = {2020}, month = {2020 10}, pages = {e1397}, abstract = {

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.

}, keywords = {Adolescent, Child, Child, Preschool, Databases, Factual, Diagnosis, Computer-Assisted, Female, Genetic Diseases, Inborn, Genetic Testing, Humans, Information Dissemination, Male, Middle Aged, Missed Diagnosis, National Institutes of Health (U.S.), Phenotype, Precision Medicine, Undiagnosed Diseases, United States, Young Adult}, issn = {2324-9269}, doi = {10.1002/mgg3.1397}, author = {Cope, Heidi and Spillmann, Rebecca and Rosenfeld, Jill A and Brokamp, Elly and Signer, Rebecca and Schoch, Kelly and Kelley, Emily G and Sullivan, Jennifer A and Macnamara, Ellen and Lincoln, Sharyn and Golden-Grant, Katie and Orengo, James P and Clark, Gary and Burrage, Lindsay C and Posey, Jennifer E and Punetha, Jaya and Robertson, Amy and Cogan, Joy and Phillips, John A and Martinez-Agosto, Julian and Shashi, Vandana} } @article {428, title = {"Matching" consent to purpose: The example of the Matchmaker Exchange.}, journal = {Hum Mutat}, volume = {38}, year = {2017}, month = {2017 10}, pages = {1281-1285}, abstract = {

The Matchmaker Exchange (MME) connects rare disease clinicians and researchers to facilitate the sharing of data from undiagnosed patients for the purpose of novel gene discovery. Such sharing raises the odds that two or more similar patients with candidate genes in common may be found, thereby allowing their condition to be more readily studied and understood. Consent considerations for data sharing in MME included both the ethical and legal differences between clinical and research settings and the level of privacy risk involved in sharing varying amounts of rare disease patient data to enable patient matches. In this commentary, we discuss these consent considerations and the resulting MME Consent Policy as they may be relevant to other international data sharing initiatives.

}, keywords = {Databases, Genetic, Genetic Association Studies, Genetic Diseases, Inborn, Genomics, Humans, Information Dissemination, Patient Selection, Physicians, Rare Diseases, Research Personnel, Translational Medical Research}, issn = {1098-1004}, doi = {10.1002/humu.23278}, author = {Dyke, Stephanie O M and Knoppers, Bartha M and Hamosh, Ada and Firth, Helen V and Hurles, Matthew and Brudno, Michael and Boycott, Kym M and Philippakis, Anthony A and Rehm, Heidi L} } @article {105, title = {Gene discovery for Mendelian conditions via social networking: de novo variants in KDM1A cause developmental delay and distinctive facial features.}, journal = {Genet Med}, volume = {18}, year = {2016}, month = {2016 08}, pages = {788-95}, abstract = {

PURPOSE: The pace of Mendelian gene discovery is slowed by the "n-of-1 problem"-the difficulty of establishing the causality of a putatively pathogenic variant in a single person or family. Identification of an unrelated person with an overlapping phenotype and suspected pathogenic variant in the same gene can overcome this barrier, but it is often impeded by lack of a convenient or widely available way to share data on candidate variants/genes among families, clinicians, and researchers.

METHODS: Social networking among families, clinicians, and researchers was used to identify three children with variants of unknown significance in KDM1A and similar phenotypes.

RESULTS: De novo variants in KDM1A underlie a new syndrome characterized by developmental delay and distinctive facial features.

CONCLUSION: Social networking is a potentially powerful strategy to discover genes for rare Mendelian conditions, particularly those with nonspecific phenotypic features. To facilitate the efforts of families to share phenotypic and genomic information with each other, clinicians, and researchers, we developed the Repository for Mendelian Genomics Family Portal (RMD-FP; http://uwcmg.org/$\#$/family). Design and development of MyGene2 (http://www.mygene2.org), a Web-based tool that enables families, clinicians, and researchers to search for gene matches based on analysis of phenotype and exome data deposited into the RMD-FP, is under way.Genet Med 18 8, 788-795.

}, keywords = {Child, Child, Preschool, Databases, Factual, Developmental Disabilities, Female, Genetic Association Studies, Histone Demethylases, Humans, Information Dissemination, Male, Mutation, Missense, Phenotype, Social Networking, Web Browser}, issn = {1530-0366}, doi = {10.1038/gim.2015.161}, author = {Chong, Jessica X and Yu, Joon-Ho and Lorentzen, Peter and Park, Karen M and Jamal, Seema M and Tabor, Holly K and Rauch, Anita and Saenz, Margarita Sifuentes and Boltshauser, Eugen and Patterson, Karynne E and Nickerson, Deborah A and Bamshad, Michael J} } @article {139, title = {GeneMatcher: a matching tool for connecting investigators with an interest in the same gene.}, journal = {Hum Mutat}, volume = {36}, year = {2015}, month = {2015 Oct}, pages = {928-30}, abstract = {

Here, we describe an overview and update on GeneMatcher (http://www.genematcher.org), a freely accessible Web-based tool developed as part of the Baylor-Hopkins Center for Mendelian Genomics. We created GeneMatcher with the goal of identifying additional individuals with rare phenotypes who had variants in the same candidate disease gene. We also wanted to facilitate connections to basic scientists working on orthologous genes in model systems with the goal of connecting their work to human Mendelian phenotypes. Meeting these goals will enhance the identification of novel Mendelian genes. Launched in September, 2013, GeneMatcher now has 2,178 candidate genes from 486 submitters spread across 38 countries entered in the database (June 1, 2015). GeneMatcher is also part of the Matchmaker Exchange (http://matchmakerexchange.org/) with an Application Programing Interface enabling submitters to query other databases of genetic variants and phenotypes without having to create accounts and data entries in multiple systems.

}, keywords = {Computational Biology, Databases, Genetic, Genetic Predisposition to Disease, Genetic Variation, Humans, Information Dissemination, Phenotype, Rare Diseases, Software, User-Computer Interface, Web Browser}, issn = {1098-1004}, doi = {10.1002/humu.22844}, author = {Sobreira, Nara and Schiettecatte, Fran{\c c}ois and Valle, David and Hamosh, Ada} } @article {146, title = {GeneMatcher aids in the identification of a new malformation syndrome with intellectual disability, unique facial dysmorphisms, and skeletal and connective tissue abnormalities caused by de novo variants in HNRNPK.}, journal = {Hum Mutat}, volume = {36}, year = {2015}, month = {2015 Oct}, pages = {1009-1014}, abstract = {

We report a new syndrome due to loss-of-function variants in the heterogeneous nuclear ribonucleoprotein K gene (HNRNPK). We describe two probands: one with a de novo frameshift (NM_002140.3: c.953+1dup), and the other with a de novo splice donor site variant (NM_002140.3: c.257G>A). Both probands have intellectual disability, a shared unique craniofacial phenotype, and connective tissue and skeletal abnormalities. The identification of this syndrome was made possible by a new online tool, GeneMatcher, which facilitates connections between clinicians and researchers based on shared interest in candidate genes. This report demonstrates that new Web-based approaches can be effective in helping investigators solve exome sequencing projects, and also highlights the newer paradigm of "reverse phenotyping," where characterization of syndromic features follows the identification of genetic variants.

}, keywords = {Abnormalities, Multiple, Adolescent, Child, Craniofacial Abnormalities, Databases, Genetic, Genetic Predisposition to Disease, Heterogeneous-Nuclear Ribonucleoprotein K, Humans, Information Dissemination, Intellectual Disability, Male, Muscular Atrophy, Phenotype, Polymorphism, Single Nucleotide, Ribonucleoproteins, Software, Web Browser}, issn = {1098-1004}, doi = {10.1002/humu.22837}, author = {Au, P Y Billie and You, Jing and Caluseriu, Oana and Schwartzentruber, Jeremy and Majewski, Jacek and Bernier, Francois P and Ferguson, Marcia and Valle, David and Parboosingh, Jillian S and Sobreira, Nara and Innes, A Micheil and Kline, Antonie D} } @article {131, title = {The Matchmaker Exchange: a platform for rare disease gene discovery.}, journal = {Hum Mutat}, volume = {36}, year = {2015}, month = {2015 Oct}, pages = {915-21}, abstract = {

There are few better examples of the need for data sharing than in the rare disease community, where patients, physicians, and researchers must search for "the needle in a haystack" to uncover rare, novel causes of disease within the genome. Impeding the pace of discovery has been the existence of many small siloed datasets within individual research or clinical laboratory databases and/or disease-specific organizations, hoping for serendipitous occasions when two distant investigators happen to learn they have a rare phenotype in common and can "match" these cases to build evidence for causality. However, serendipity has never proven to be a reliable or scalable approach in science. As such, the Matchmaker Exchange (MME) was launched to provide a robust and systematic approach to rare disease gene discovery through the creation of a federated network connecting databases of genotypes and rare phenotypes using a common application programming interface (API). The core building blocks of the MME have been defined and assembled. Three MME services have now been connected through the API and are available for community use. Additional databases that support internal matching are anticipated to join the MME network as it continues to grow.

}, keywords = {Database Management Systems, Databases, Genetic, Genetic Association Studies, Genetic Predisposition to Disease, Humans, Information Dissemination, Rare Diseases, Software}, issn = {1098-1004}, doi = {10.1002/humu.22858}, author = {Philippakis, Anthony A and Azzariti, Danielle R and Beltran, Sergi and Brookes, Anthony J and Brownstein, Catherine A and Brudno, Michael and Brunner, Han G and Buske, Orion J and Carey, Knox and Doll, Cassie and Dumitriu, Sergiu and Dyke, Stephanie O M and den Dunnen, Johan T and Firth, Helen V and Gibbs, Richard A and Girdea, Marta and Gonzalez, Michael and Haendel, Melissa A and Hamosh, Ada and Holm, Ingrid A and Huang, Lijia and Hurles, Matthew E and Hutton, Ben and Krier, Joel B and Misyura, Andriy and Mungall, Christopher J and Paschall, Justin and Paten, Benedict and Robinson, Peter N and Schiettecatte, Fran{\c c}ois and Sobreira, Nara L and Swaminathan, Ganesh J and Taschner, Peter E and Terry, Sharon F and Washington, Nicole L and Z{\"u}chner, Stephan and Boycott, Kym M and Rehm, Heidi L} } @article {136, title = {The Matchmaker Exchange API: automating patient matching through the exchange of structured phenotypic and genotypic profiles.}, journal = {Hum Mutat}, volume = {36}, year = {2015}, month = {2015 Oct}, pages = {922-7}, abstract = {

Despite the increasing prevalence of clinical sequencing, the difficulty of identifying additional affected families is a key obstacle to solving many rare diseases. There may only be a handful of similar patients worldwide, and their data may be stored in diverse clinical and research databases. Computational methods are necessary to enable finding similar patients across the growing number of patient repositories and registries. We present the Matchmaker Exchange Application Programming Interface (MME API), a protocol and data format for exchanging phenotype and genotype profiles to enable matchmaking among patient databases, facilitate the identification of additional cohorts, and increase the rate with which rare diseases can be researched and diagnosed. We designed the API to be straightforward and flexible in order to simplify its adoption on a large number of data types and workflows. We also provide a public test data set, curated from the literature, to facilitate implementation of the API and development of new matching algorithms. The initial version of the API has been successfully implemented by three members of the Matchmaker Exchange and was immediately able to reproduce previously identified matches and generate several new leads currently being validated. The API is available at https://github.com/ga4gh/mme-apis.

}, keywords = {Algorithms, Computational Biology, Databases, Genetic, Genetic Predisposition to Disease, Genotype, Humans, Information Dissemination, Phenotype, Rare Diseases, Web Browser}, issn = {1098-1004}, doi = {10.1002/humu.22850}, author = {Buske, Orion J and Schiettecatte, Fran{\c c}ois and Hutton, Benjamin and Dumitriu, Sergiu and Misyura, Andriy and Huang, Lijia and Hartley, Taila and Girdea, Marta and Sobreira, Nara and Mungall, Chris and Brudno, Michael} } @article {304, title = {Practices and policies of clinical exome sequencing providers: analysis and implications.}, journal = {Am J Med Genet A}, volume = {161A}, year = {2013}, month = {2013 May}, pages = {935-50}, abstract = {

Exome and whole genome sequencing (ES/WGS) offer potential advantages over traditional approaches to diagnostic genetic testing. Consequently, use of ES/WGS in clinical settings is rapidly becoming commonplace. Yet there are myriad moral, ethical, and perhaps legal implications attached to the use of ES and health care professionals and institutions will need to consider these implications in the context of the varied practices and policies of ES service providers. We developed "core elements" of content and procedures for informed consent, data sharing, and results management and a quantitative scale to assess the extent to which research protocols met the standards established by these core elements. We then used these tools to evaluate the practices and policies of each of the 6 U.S. CLIA-certified labs offering clinical ES. Approaches toward informed consent, data sharing, and results return vary widely among ES providers as do the overall potential merits and disadvantages of each, and more importantly, the balance between the two.

}, keywords = {Exome, Humans, Information Dissemination, Informed Consent, Morals, Organizational Policy, Practice Patterns, Physicians{\textquoteright}, Sequence Analysis, DNA, United States}, issn = {1552-4833}, doi = {10.1002/ajmg.a.35942}, author = {Jamal, Seema M and Yu, Joon-Ho and Chong, Jessica X and Dent, Karin M and Conta, Jessie H and Tabor, Holly K and Bamshad, Michael J} }