Dataset Information

GestaltMatcher Database - A global reference for the facial phenotypic variability of rare human diseases.

ABSTRACT: Dysmorphologists sometimes encounter challenges in recognizing disorders due to phenotypic variability influenced by factors such as age and ethnicity. Moreover, the performance of Next Generation Phenotyping Tools such as GestaltMatcher is dependent on the diversity of the training set. Therefore, we developed GestaltMatcher Database (GMDB) - a global reference for the phenotypic variability of rare diseases that complies with the FAIR-principles. We curated dysmorphic patient images and metadata from 2,224 publications, transforming GMDB into an online dynamic case report journal. To encourage clinicians worldwide to contribute, each case can receive a Digital Object Identifier (DOI), making it a citable micro-publication. This resulted in a collection of 2,312 unpublished images, partly with longitudinal data. We have compiled a collection of 10,189 frontal images from 7,695 patients representing 683 disorders. The web interface enables gene- and phenotype-centered queries for registered users (https://db.gestaltmatcher.org/). Despite the predominant European ancestry of most patients (59%), our global collaborations have facilitated the inclusion of data from frequently underrepresented ethnicities, with 17% Asian, 4% African, and 6% with other ethnic backgrounds. The analysis has revealed a significant enhancement in GestaltMatcher performance across all ethnic groups, incorporating non-European ethnicities, showcasing a remarkable increase in Top-1-Accuracy by 31.56% and Top-5-Accuracy by 12.64%. Importantly, this improvement was achieved without altering the performance metrics for European patients. GMDB addresses dysmorphology challenges by representing phenotypic variability and including underrepresented groups, enhancing global diagnostic rates and serving as a vital clinician reference database.

SUBMITTER: Lesmann H

PROVIDER: S-EPMC10371103 | biostudies-literature | 2024 Mar

REPOSITORIES: biostudies-literature

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GestaltMatcher Database - A global reference for facial phenotypic variability in rare human diseases.

Lesmann Hellen H Hustinx Alexander A Moosa Shahida S Klinkhammer Hannah H Marchi Elaine E Caro Pilar P Abdelrazek Ibrahim M IM Pantel Jean Tori JT Hagen Merle Ten MT Thong Meow-Keong MK Mazlan Rifhan Azwani Binti RAB Tae Sok Kun SK Kamphans Tom T Meiswinkel Wolfgang W Li Jing-Mei JM Javanmardi Behnam B Knaus Alexej A Uwineza Annette A Knopp Cordula C Tkemaladze Tinatin T Elbracht Miriam M Mattern Larissa L Jamra Rami Abou RA Velmans Clara C Strehlow Vincent V Jacob Maureen M Peron Angela A Dias Cristina C Nunes Beatriz Carvalho BC Vilella Thainá T Pinheiro Isabel Furquim IF Kim Chong Ae CA Melaragno Maria Isabel MI Weiland Hannah H Kaptain Sophia S Chwiałkowska Karolina K Kwasniewski Miroslaw M Saad Ramy R Wiethoff Sarah S Goel Himanshu H Tang Clara C Hau Anna A Barakat Tahsin Stefan TS Panek Przemysław P Nabil Amira A Suh Julia J Braun Frederik F Gomy Israel I Averdunk Luisa L Ekure Ekanem E Bergant Gaber G Peterlin Borut B Graziano Claudio C Gaboon Nagwa N Fiesco-Roa Moisés M Spinelli Alessandro Mauro AM Wilpert Nina-Maria NM Phowthongkum Prasit P Güzel Nergis N Haack Tobias B TB Bitar Rana R Tzschach Andreas A Rodriguez-Palmero Agusti A Brunet Theresa T Rudnik-Schöneborn Sabine S Contreras-Capetillo Silvina Noemi SN Oberlack Ava A Samango-Sprouse Carole C Sadeghin Teresa T Olaya Margaret M Platzer Konrad K Borovikov Artem A Schnabel Franziska F Heuft Lara L Herrmann Vera V Oegema Renske R Elkhateeb Nour N Kumar Sheetal S Komlosi Katalin K Mohamed Khoushoua K Kalantari Silvia S Sirchia Fabio F Martinez-Monseny Antonio F AF Höller Matthias M Toutouna Louiza L Mohamed Amal A Lasa-Aranzasti Amaia A Sayer John A JA Ehmke Nadja N Danyel Magdalena M Sczakiel Henrike H Schwartzmann Sarina S Boschann Felix F Zhao Max M Adam Ronja R Einicke Lara L Horn Denise D Chew Kee Seang KS Kam Choy Chen CC Karakoyun Miray M Pode-Shakked Ben B Eliyahu Aviva A Rock Rachel R Carrion Teresa T Chorin Odelia O Zarate Yuri A YA Conti Marcelo Martinez MM Karakaya Mert M Tung Moon Ley ML Chandra Bharatendu B Bouman Arjan A Lumaka Aime A Wasif Naveed N Shinawi Marwan M Blackburn Patrick R PR Wang Tianyun T Niehues Tim T Schmidt Axel A Roth Regina Rita RR Wieczorek Dagmar D Hu Ping P Waikel Rebekah L RL Ledgister Hanchard Suzanna E SE Elmakkawy Gehad G Safwat Sylvia S Ebstein Frédéric F Krüger Elke E Küry Sébastien S Bézieau Stéphane S Arlt Annabelle A Olinger Eric E Marbach Felix F Li Dong D Dupuis Lucie L Mendoza-Londono Roberto R Houge Sofia Douzgou SD Weis Denisa D Chung Brian Hon-Yin BH Mak Christopher C Y CCY Kayserili Hülya H Elcioglu Nursel N Aykut Ayca A Şimşek-Kiper Peli Özlem PÖ Bögershausen Nina N Wollnik Bernd B Bentzen Heidi Beate HB Kurth Ingo I Netzer Christian C Jezela-Stanek Aleksandra A Devriendt Koen K Gripp Karen W KW Mücke Martin M Verloes Alain A Schaaf Christian P CP Nellåker Christoffer C Solomon Benjamin D BD Nöthen Markus M MM Abdalla Ebtesam E Lyon Gholson J GJ Krawitz Peter M PM Hsieh Tzung-Chien TC

medRxiv : the preprint server for health sciences 20241008

The most important factor that complicates the work of dysmorphologists is the significant phenotypic variability of the human face. Next-Generation Phenotyping (NGP) tools that assist clinicians with recognizing characteristic syndromic patterns are particularly challenged when confronted with patients from populations different from their training data. To that end, we systematically analyzed the impact of genetic ancestry on facial dysmorphism. For that purpose, we established the GestaltMatc ...[more]

PMID: 37503210

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Project description:BackgroundThe ability to discover genetic variants in a patient runs far ahead of the ability to interpret them. Databases with accurate descriptions of the causal relationship between the variants and the phenotype are valuable since these are critical tools in clinical genetic diagnostics. Here, we introduce a comprehensive and global genotype-phenotype database focusing on rare diseases.MethodsThis database (CentoMD ®) is a browser-based tool that enables access to a comprehensive, independently curated system utilizing stringent high-quality criteria and a quickly growing repository of genetic and human phenotype ontology (HPO)-based clinical information. Its main goals are to aid the evaluation of genetic variants, to enhance the validity of the genetic analytical workflow, to increase the quality of genetic diagnoses, and to improve evaluation of treatment options for patients with hereditary diseases. The database software correlates clinical information from consented patients and probands of different geographical backgrounds with a large dataset of genetic variants and, when available, biomarker information. An automated follow-up tool is incorporated that informs all users whenever a variant classification has changed. These unique features fully embedded in a CLIA/CAP-accredited quality management system allow appropriate data quality and enhanced patient safety.ResultsMore than 100,000 genetically screened individuals are documented in the database, resulting in more than 470 million variant detections. Approximately, 57% of the clinically relevant and uncertain variants in the database are novel. Notably, 3% of the genetic variants identified and previously reported in the literature as being associated with a particular rare disease were reclassified, based on internal evidence, as clinically irrelevant.ConclusionsThe database offers a comprehensive summary of the clinical validity and causality of detected gene variants with their associated phenotypes, and is a valuable tool for identifying new disease genes through the correlation of novel genetic variants with specific, well-defined phenotypes.

Project description:While rare diseases (RDs) are by definition of low prevalence, the total number of patients suffering from an RD is high, and the majority of them have neurologic manifestations, involving central, peripheral nerve, and muscle. In 2017, 24 European Reference Networks (ERNs), each focusing on a specific group of rare or low-prevalence complex diseases, were formed to improve the care for patients with an RD. One major aim is to have "the knowledge travel instead of the patient," which has been put into practice by the implementation of the Clinical Patient Management System (CPMS) that enables clinicians to perform pan-European virtual consultations. The European Reference Network for Rare Neurological Diseases (ERN-RND) provides an infrastructure for knowledge sharing and care coordination for patients affected by a rare neurological disease (RND) involving the most common central nervous system pathological conditions. It covers the following disease groups: (i) Cerebellar Ataxias and Hereditary Spastic Paraplegias; (ii) Huntington's disease and Other Choreas; (iii) Frontotemporal dementia; (iv) Dystonia, (non-epileptic) paroxysmal disorders, and Neurodegeneration with Brain Iron Accumulation; (v) Leukoencephalopathies; and (vi) Atypical Parkinsonian Syndromes. At the moment, it unites 32 expert centers and 10 affiliated partners in 21 European countries, as well as patient representatives, but will soon cover nearly all countries of the European Union as a result of the ongoing expansion process. Disease expert groups developed and consented on diagnostic flowcharts and disease scales to assess the different aspects of RNDs. ERN-RND has started to discuss diagnostically unclear patients in the CPMS, is one of four ERNs that serve as foundation of Solve-RD, and has established an RND training and education program. The network will facilitate trial readiness through the establishment of an ERN-RND registry with a minimal data of all patients seen at the ERN-RND centers, thus providing a unique overview of existing genotype-based cohorts. The overall aim of the ERNs is to improve access for patients with RDs to quality diagnosis, care, and treatment. Based on this objective, ERNs are monitored by the European Commission on a regular basis to provide transparency and reassurance to the RD community and the general public.

Dataset Information

GestaltMatcher Database - A global reference for the facial phenotypic variability of rare human diseases.

Publications

GestaltMatcher Database - A global reference for facial phenotypic variability in rare human diseases.

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