@article {440, title = {Matchmaker Exchange.}, journal = {Curr Protoc Hum Genet}, volume = {95}, year = {2017}, month = {2017 10 18}, pages = {9.31.1-9.31.15}, abstract = {

In well over half of the individuals with rare disease who undergo clinical or research next-generation sequencing, the responsible gene cannot be determined. Some reasons for this relatively low yield include unappreciated phenotypic heterogeneity; locus heterogeneity; somatic and germline mosaicism; variants of uncertain functional significance; technically inaccessible areas of the genome; incorrect mode of inheritance investigated; and inadequate communication between clinicians and basic scientists with knowledge of particular genes, proteins, or biological systems. To facilitate such communication and improve the search for patients or model organisms with similar phenotypes and variants in specific candidate genes, we have developed the Matchmaker Exchange (MME). MME was created to establish a federated network connecting databases of genomic and phenotypic data using a common application programming interface (API). To date, seven databases can exchange data using the API (GeneMatcher, PhenomeCentral, DECIPHER, MyGene2, matchbox, Australian Genomics Health Alliance Patient Archive, and Monarch Initiative; the latter included for model organism matching). This article guides usage of the MME for rare disease gene discovery. {\textcopyright} 2017 by John Wiley \& Sons, Inc.

}, keywords = {Animals, Computational Biology, Databases, Genetic, Genetic Association Studies, Genetic Predisposition to Disease, Genomics, Humans, Rare Diseases, Software, Web Browser}, issn = {1934-8258}, doi = {10.1002/cphg.50}, author = {Sobreira, Nara L M and Arachchi, Harindra and Buske, Orion J and Chong, Jessica X and Hutton, Ben and Foreman, Julia and Schiettecatte, Fran{\c c}ois and Groza, Tudor and Jacobsen, Julius O B and Haendel, Melissa A and Boycott, Kym M and Hamosh, Ada and Rehm, Heidi L} } @article {199, title = {Assessment of incidental findings in 232 whole-exome sequences from the Baylor-Hopkins Center for Mendelian Genomics.}, journal = {Genet Med}, volume = {17}, year = {2015}, month = {2015 Oct}, pages = {782-8}, abstract = {

PURPOSE: In March 2013 the American College of Medical Genetics and Genomics published a list of 56 genes with the recommendation that pathogenic and likely pathogenic variants detected incidentally by clinical sequencing be reported to patients. As an initial step in determining the practical consequences of this recommendation in the research setting, we searched for variants in these genes in 232 whole-exome sequences from the Baylor-Hopkins Center for Mendelian Genomics.

METHODS: We identified rare, nonsynonymous, and splicing single-nucleotide variants and insertions/deletions and assessed variant classification using the Human Gene Mutation, Emory, and ClinVar databases. We analyzed the burden of mutation in each of the 56 genes and determined which variants should be reported to patients.

RESULTS: Our filtering resulted in 249 distinct variants, with a mean of 1.69 variants per individual. Half of these were novel missense mutations not classified by any of the three reference databases. Of 101 variants listed in the Human Gene Mutation Database, 48 were also in ClinVar and 3 were also in Emory; half of these shared variants were classified discordantly between databases. Some genes consistently had greater variation than others. In total, 0.86\% of individuals had a reportable incidental variant.

CONCLUSION: These observations demonstrate some current challenges of assessing phenotypic consequences of incidental variants for counseling patients.

}, keywords = {Databases, Nucleic Acid, Exome, Female, Genetic Diseases, Inborn, Genomics, High-Throughput Nucleotide Sequencing, Humans, Incidental Findings, Male, Mutation, Polymorphism, Single Nucleotide}, issn = {1530-0366}, doi = {10.1038/gim.2014.196}, author = {Jurgens, Julie and Ling, Hua and Hetrick, Kurt and Pugh, Elizabeth and Schiettecatte, Fran{\c c}ois and Doheny, Kimberly and Hamosh, Ada and Avramopoulos, Dimitri and Valle, David and Sobreira, Nara} } @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} }