@article {920, title = {KMT2B-related disorders: expansion of the phenotypic spectrum and long-term efficacy of deep brain stimulation.}, journal = {Brain}, volume = {143}, year = {2020}, month = {2020 12 05}, pages = {3242-3261}, abstract = {

Heterozygous mutations in KMT2B are associated with an early-onset, progressive and often complex dystonia (DYT28). Key characteristics of typical disease include focal motor features at disease presentation, evolving through a caudocranial pattern into generalized dystonia, with prominent oromandibular, laryngeal and cervical involvement. Although KMT2B-related disease is emerging as one of the most common causes of early-onset genetic dystonia, much remains to be understood about the full spectrum of the disease. We describe a cohort of 53 patients with KMT2B mutations, with detailed delineation of their clinical phenotype and molecular genetic features. We report new disease presentations, including atypical patterns of dystonia evolution and a subgroup of patients with a non-dystonic neurodevelopmental phenotype. In addition to the previously reported systemic features, our study has identified co-morbidities, including the risk of status dystonicus, intrauterine growth retardation, and endocrinopathies. Analysis of this study cohort (n = 53) in tandem with published cases (n = 80) revealed that patients with chromosomal deletions and protein truncating variants had a significantly higher burden of systemic disease (with earlier onset of dystonia) than those with missense variants. Eighteen individuals had detailed longitudinal data available after insertion of deep brain stimulation for medically refractory dystonia. Median age at deep brain stimulation was 11.5 years (range: 4.5-37.0 years). Follow-up after deep brain stimulation ranged from 0.25 to 22 years. Significant improvement of motor function and disability (as assessed by the Burke Fahn Marsden{\textquoteright}s Dystonia Rating Scales, BFMDRS-M and BFMDRS-D) was evident at 6 months, 1 year and last follow-up (motor, P = 0.001, P = 0.004, and P = 0.012; disability, P = 0.009, P = 0.002 and P = 0.012). At 1 year post-deep brain stimulation, >50\% of subjects showed BFMDRS-M and BFMDRS-D improvements of >30\%. In the long-term deep brain stimulation cohort (deep brain stimulation inserted for >5 years, n = 8), improvement of >30\% was maintained in 5/8 and 3/8 subjects for the BFMDRS-M and BFMDRS-D, respectively. The greatest BFMDRS-M improvements were observed for trunk (53.2\%) and cervical (50.5\%) dystonia, with less clinical impact on laryngeal dystonia. Improvements in gait dystonia decreased from 20.9\% at 1 year to 16.2\% at last assessment; no patient maintained a fully independent gait. Reduction of BFMDRS-D was maintained for swallowing (52.9\%). Five patients developed mild parkinsonism following deep brain stimulation. KMT2B-related disease comprises an expanding continuum from infancy to adulthood, with early evidence of genotype-phenotype correlations. Except for laryngeal dysphonia, deep brain stimulation provides a significant improvement in quality of life and function with sustained clinical benefit depending on symptoms distribution.

}, keywords = {Adolescent, Adult, Child, Child, Preschool, Chromosome Deletion, Cohort Studies, Computer Simulation, Deep Brain Stimulation, Disease Progression, Dystonic Disorders, Endocrine System Diseases, Female, Fetal Growth Retardation, Gait Disorders, Neurologic, Histone-Lysine N-Methyltransferase, Humans, Laryngeal Diseases, Male, Mutation, Mutation, Missense, Phenotype, Quality of Life, Treatment Outcome, Young Adult}, issn = {1460-2156}, doi = {10.1093/brain/awaa304}, author = {Cif, Laura and Demailly, Diane and Lin, Jean-Pierre and Barwick, Katy E and Sa, Mario and Abela, Lucia and Malhotra, Sony and Chong, Wui K and Steel, Dora and Sanchis-Juan, Alba and Ngoh, Adeline and Trump, Natalie and Meyer, Esther and Vasques, Xavier and Rankin, Julia and Allain, Meredith W and Applegate, Carolyn D and Attaripour IsfAHni, Sanaz and Baleine, Julien and Balint, Bettina and Bassetti, Jennifer A and Baple, Emma L and Bhatia, Kailash P and Blanchet, Catherine and Burglen, Lydie and Cambonie, Gilles and Seng, Emilie Chan and Bastaraud, Sandra Chantot and Cyprien, Fabienne and Coubes, Christine and d{\textquoteright}Hardemare, Vincent and Doja, Asif and Dorison, Nathalie and Doummar, Diane and Dy-Hollins, Marisela E and Farrelly, Ellyn and FitzPatrick, David R and Fearon, Conor and Fieg, Elizabeth L and Fogel, Brent L and Forman, Eva B and Fox, Rachel G and Gahl, William A and Galosi, Serena and Gonzalez, Victoria and Graves, Tracey D and Gregory, Allison and Hallett, Mark and Hasegawa, Harutomo and Hayflick, Susan J and Hamosh, Ada and Hully, Marie and Jansen, Sandra and Jeong, Suh Young and Krier, Joel B and Krystal, Sidney and Kumar, Kishore R and Laurencin, Chlo{\'e} and Lee, Hane and Lesca, Gaetan and Fran{\c c}ois, Laurence Lion and Lynch, Timothy and MAHnt, Neil and Martinez-Agosto, Julian A and Milesi, Christophe and Mills, Kelly A and Mondain, Michel and Morales-Briceno, Hugo and Ostergaard, John R and Pal, Swasti and Pallais, Juan C and Pavillard, Fr{\'e}d{\'e}rique and Perrigault, Pierre-Francois and Petersen, Andrea K and Polo, Gustavo and Poulen, Gaetan and Rinne, Tuula and Roujeau, Thomas and Rogers, Caleb and Roubertie, Agathe and SAHgian, Michelle and Schaefer, Elise and Selim, Laila and Selway, Richard and Sharma, Nutan and Signer, Rebecca and Soldatos, Ariane G and Stevenson, David A and Stewart, Fiona and Tchan, Michel and Verma, Ishwar C and de Vries, Bert B A and Wilson, Jenny L and Wong, Derek A and Zaitoun, Raghda and Zhen, Dolly and Znaczko, Anna and Dale, Russell C and de Gusm{\~a}o, Claudio M and Friedman, Jennifer and Fung, Victor S C and King, Mary D and Mohammad, Shekeeb S and Rohena, Luis and Waugh, Jeff L and Toro, Camilo and Raymond, F Lucy and Topf, Maya and Coubes, Philippe and Gorman, Kathleen M and Kurian, Manju A} } @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 {149, title = {The Genetic Basis of Mendelian Phenotypes: Discoveries, Challenges, and Opportunities.}, journal = {Am J Hum Genet}, volume = {97}, year = {2015}, month = {2015 Aug 06}, pages = {199-215}, abstract = {

Discovering the genetic basis of a Mendelian phenotype establishes a causal link between genotype and phenotype, making possible carrier and population screening and direct diagnosis. Such discoveries also contribute to our knowledge of gene function, gene regulation, development, and biological mechanisms that can be used for developing new therapeutics. As of February 2015, 2,937 genes underlying 4,163 Mendelian phenotypes have been discovered, but the genes underlying \~{}50\% (i.e., 3,152) of all known Mendelian phenotypes are still unknown, and many more Mendelian conditions have yet to be recognized. This is a formidable gap in biomedical knowledge. Accordingly, in December 2011, the NIH established the Centers for Mendelian Genomics (CMGs) to provide the collaborative framework and infrastructure necessary for undertaking large-scale whole-exome sequencing and discovery of the genetic variants responsible for Mendelian phenotypes. In partnership with 529 investigators from 261 institutions in 36 countries, the CMGs assessed 18,863 samples from 8,838 families representing 579 known and 470 novel Mendelian phenotypes as of January 2015. This collaborative effort has identified 956 genes, including 375 not previously associated with human health, that underlie a Mendelian phenotype. These results provide insight into study design and analytical strategies, identify novel mechanisms of disease, and reveal the extensive clinical variability of Mendelian phenotypes. Discovering the gene underlying every Mendelian phenotype will require tackling challenges such as worldwide ascertainment and phenotypic characterization of families affected by Mendelian conditions, improvement in sequencing and analytical techniques, and pervasive sharing of phenotypic and genomic data among researchers, clinicians, and families.

}, keywords = {Genetic Diseases, Inborn, Genetics, Medical, Humans, Phenotype, Proteins}, issn = {1537-6605}, doi = {10.1016/j.ajhg.2015.06.009}, author = {Chong, Jessica X and Buckingham, Kati J and Jhangiani, Shalini N and Boehm, Corinne and Sobreira, Nara and Smith, Joshua D and Harrell, Tanya M and McMillin, Margaret J and Wiszniewski, Wojciech and Gambin, Tomasz and Coban Akdemir, Zeynep H and Doheny, Kimberly and Scott, Alan F and Avramopoulos, Dimitri and Chakravarti, Aravinda and Hoover-Fong, Julie and Mathews, Debra and Witmer, P Dane and Ling, Hua and Hetrick, Kurt and Watkins, Lee and Patterson, Karynne E and Reinier, Frederic and Blue, Elizabeth and Muzny, Donna and Kircher, Martin and Bilguvar, Kaya and L{\'o}pez-Gir{\'a}ldez, Francesc and Sutton, V Reid and Tabor, Holly K and Leal, Suzanne M and G{\"u}nel, Murat and Mane, Shrikant and Gibbs, Richard A and Boerwinkle, Eric and Hamosh, Ada and Shendure, Jay and Lupski, James R and Lifton, Richard P and Valle, David and Nickerson, Deborah A and Bamshad, Michael J} } @article {193, title = {New tools for Mendelian disease gene identification: PhenoDB variant analysis module; and GeneMatcher, a web-based tool for linking investigators with an interest in the same gene.}, journal = {Hum Mutat}, volume = {36}, year = {2015}, month = {2015 Apr}, pages = {425-31}, abstract = {

Identifying the causative variant from among the thousands identified by whole-exome sequencing or whole-genome sequencing is a formidable challenge. To make this process as efficient and flexible as possible, we have developed a Variant Analysis Module coupled to our previously described Web-based phenotype intake tool, PhenoDB (http://researchphenodb.net and http://phenodb.org). When a small number of candidate-causative variants have been identified in a study of a particular patient or family, a second, more difficult challenge becomes proof of causality for any given variant. One approach to this problem is to find other cases with a similar phenotype and mutations in the same candidate gene. Alternatively, it may be possible to develop biological evidence for causality, an approach that is assisted by making connections to basic scientists studying the gene of interest, often in the setting of a model organism. Both of these strategies benefit from an open access, online site where individual clinicians and investigators could post genes of interest. To this end, we developed GeneMatcher (http://genematcher.org), a freely accessible Website that enables connections between clinicians and researchers across the world who share an interest in the same gene(s).

}, keywords = {Computational Biology, Databases, Genetic, Genetic Association Studies, Genetic Diseases, Inborn, Genetic Variation, Genomics, Internet, Phenotype, Software, Web Browser}, issn = {1098-1004}, doi = {10.1002/humu.22769}, author = {Sobreira, Nara and Schiettecatte, Fran{\c c}ois and Boehm, Corinne and Valle, David and Hamosh, Ada} } @article {310, title = {PhenoDB: a new web-based tool for the collection, storage, and analysis of phenotypic features.}, journal = {Hum Mutat}, volume = {34}, year = {2013}, month = {2013 Apr}, pages = {566-71}, abstract = {

To interpret whole exome/genome sequence data for clinical and research purposes, comprehensive phenotypic information, knowledge of pedigree structure, and results of previous clinical testing are essential. With these requirements in mind and to meet the needs of the Centers for Mendelian Genomics project, we have developed PhenoDB (http://phenodb.net), a secure, Web-based portal for entry, storage, and analysis of phenotypic and other clinical information. The phenotypic features are organized hierarchically according to the major headings and subheadings of the Online Mendelian Inheritance in Man (OMIM{\textregistered}) clinical synopses, with further subdivisions according to structure and function. Every string allows for a free-text entry. All of the approximately 2,900 features use the preferred term from Elements of Morphology and are fully searchable and mapped to the Human Phenotype Ontology and Elements of Morphology. The PhenoDB allows for ascertainment of relevant information from a case in a family or cohort, which is then searchable by family, OMIM number, phenotypic feature, mode of inheritance, genes screened, and so on. The database can also be used to format phenotypic data for submission to dbGaP for appropriately consented individuals. PhenoDB was built using Django, an open source Web development tool, and is freely available through the Johns Hopkins McKusick-Nathans Institute of Genetic Medicine (http://phenodb.net).

}, keywords = {Databases, Factual, Genomics, Humans, Internet, Medical Informatics, Phenotype, Software}, issn = {1098-1004}, doi = {10.1002/humu.22283}, author = {Hamosh, Ada and Sobreira, Nara and Hoover-Fong, Julie and Sutton, V Reid and Boehm, Corinne and Schiettecatte, Fran{\c c}ois and Valle, David} }