A blue-print for genome diagnostics for patients with rare diseases in Germany (genomDE).
Most individuals with rare diseases first contact primary care physicians. Although efficient diagnostic routines exist for a subset of rare diseases, ultra-rare entities often require expert clinical knowledge or comprehensive genetic diagnostics, which poses structural challenges to public healthcare systems. To address these challenges, a novel structured diagnostic concept based on the presence of multidisciplinary expertise at centers for rare diseases (CRDs) that have been established at German university hospitals in recent years, was evaluated in a prospective study (TRANSLATE-NAMSE). Between January 2018 and December 2020, 5652 patients were enrolled in the study and were comprehensively assessed by multidisciplinary teams (MDTs) at ten CRDs. Exome sequencing (ES) was initiated for 268 adult and 1309 pediatric patients and partially complemented by additional molecular tests. Conclusive diagnoses were established in 497 individuals, covering 400 diagnostic-grade genes, suggesting ultra-rare disorders were enriched in this cohort. In addition, we describe 56 novel gene-phenotype associations, mainly in individuals with neurodevelopmental delay. A subcohort of 211 individuals was analyzed with the artificial intelligence–based PEDIA protocol, which integrates next-generation phenotyping on medical imaging and sequencing data. With the entire cohort data, we developed a tool to predict the diagnostic yield from the clinical features of a patient if advanced molecular testing strategies are applied.
What is the Selektivvertrag (§140 SGBV) “Exom-Sequenzierung bei Seltenen Erkrankungen”?
Since the evaluation of TRANSLATE NAMSE was positive, the concept is continued as a Selektivvertrag. That basically means the parties that can participate are paid a little bit more money for Wissensgenerierende Krankenversorgung, that is they continue with research until a case is solved. A crucial part of solving a case that is due to a novel disease (otherwise it would have been simple to diagnose it), is sharing data. The members of the Selektivvertrag share variant of unknown clinical significance (VUCS) and clinical features of a patient, e.g. intellectual disability, in HPO terminology, in order to identify similar cases. A new monogenic disorder requires at least three cases with similar mutations e.g. in the same gene, and similar phenotype, e.g. a facial gestalt that’s alike. This is first done on the level of the members of the Selektivvertrag (national level) and if this is not sufficient on a global level via the Match Maker Exchange network. If your IP address is already on the whitelist, you can reach Exome AG server here:
If you are a member of the Selektivvertrag and would like to be onboarded, please contact firstname.lastname@example.org and email@example.com, or call +49 171 7889198.
Can an exome solve your case? YieldPred knows!
The diagnostic yield is highly dependent on the disease group, the clinical features, and the genetic test. Based on the TRANSLATE NAMSE cohort the tool YieldPred was developed to predict the diagnostic yield that can be achieved with exome sequencing (ES). YieldPred is based on least absolute shrinkage and selection operator (LASSO), which is a multi-regression analysis that we performed on the HPO annotations of all cases. Users can specify the age, sex, and assigned HPO terms of their patient while the remaining confounders are estimated as the mean effect of the TRANSLATE NAMSE cohort. The service provides a point estimate of the diagnostic yield as well as a relation to the densities of the diagnostic yields of TRANSLATE NAMSE patients resulting from the model.
The TRANSLATE NAMSE ES cohort (n=1,577) was randomly divided into a training set incorporating 1,256 cases (399 solved, 32%) and a test set incorporating 321 cases (99 solved, 31 %). For each of the 49 phenotypic groups (cf. clinical and laboratory phenotype data), we defined a binary predictor being 1 if the patient was assigned at least one HPO term of the respective group and 0 otherwise. The binary status of a case (1=solved, 0=not solved) was regressed on those 49 phenotypic predictors using LASSO for binary outcomes with the logit function as a link function (R package glmnet, version 4.1-4) and controlling for age (adult/child), sex (male/female), sequencing laboratory, and the use of the PEDIA workflow. Variable selection was applied only on the 49 phenotypic groups. The model was fitted on the training data and the penalty parameter was tuned via ten-fold cross-validation. The resulting model was then applied to the test set, and its predictive performance was evaluated using the Receiver Operator Characteristics (ROC) curve. The model was then refitted on the whole ES cohort of 1,577 cases and made the results available in the predictor of diagnostic yield web application.
What is the most efficient way to analyze your variants? Using PEDIA!
PEDIA is a workflow that adds the results of computer-assisted evaluation of a portrait to the interpretation of variants (Prioritization of Exome Data by Image Analysis). The PEDIA approach showed superior performance compared to standard workflows that only work with molecular- and feature-based scores. The Image analysis is done by the AI GestaltMatcher, which is open-source and provided by AGD e.V. (link). PEDIA can be integrated in frameworks for variant analysis such as e.g. VarFish or GeneTalk, which are user-friendly web applications for the quality control, filtering, prioritization, analysis, and user-based annotation of DNA variant data with a focus on rare disease genetics. VarFish was originally developed in Berlin by CUBI (Core Unit Bioinformatics, BIH) together with the Institute of Medical and Human Genetics, Charite Berlin. Currently, a growing number of genetics institutes and TRANSLATE NAMSE members are is using VarFish and are working on extensions and improvements. You can find out more on the CUBI Website.
Code availability and Software Demos
- TNAMSE Code Repository
- PEDIA is provided as a Webservice by GeneTalk. If you are interested in a customized integration of the PEDIA protocol into your diagnostics workflow, please contact firstname.lastname@example.org.
- VarFish Demo shows the features of the VarFish software including user-based commenting of variants. This is the “classic” mode where data is uploaded using a back-end API by computational staff.
- VarFish Kiosk allows users to upload their own cases as VCF files and perform an analysis.
Hsieh, TC., Bar-Haim, A., Moosa, S. et al., GestaltMatcher facilitates rare disease matching using facial phenotype descriptors. Nat Genet 54, 349–357 (2022). https://doi.org/10.1038/s41588-021-01010-xChengyao Peng, Simon Dieck, Alexander Schmid, Ashar Ahmad, Alexej Knaus, Maren Wenzel, Laura Mehnert, Birgit Zirn, Tobias Haack, Stephan Ossowski, Matias Wagner, Theresa Brunet, Nadja Ehmke, Magdalena Danyel, Stanislav Rosnev, Tom Kamphans, Guy Nadav, Nicole Fleischer, Holger Fröhlich, Peter Krawitz, CADA: phenotype-driven gene prioritization based on a case-enriched knowledge graph, NAR Genomics and Bioinformatics, Volume 3, Issue 3, September 2021. https://doi.org/10.1093/nargab/lqab078
Holtgrewe, M.; Stolpe, O.; Nieminen, M.; Mundlos, S.; Knaus, A.; Kornak, U.; Seelow, D.; Segebrecht, L.; Spielmann, M.; Fischer-Zirnsak, B.; Boschann, F.; Scholl, U.; Ehmke, N.; Beule, D. VarFish: Comprehensive DNA Variant Analysis for Diagnostics and Research. Nucleic Acids Research 2020, gkaa241. https://doi.org/10.1093/nar/gkaa241.
Tzung-Chien Hsieh, Martin Atta Mensah et al., PEDIA: Prioritization of Exome Data by Image Analysis, Genetics in Medicine, June 5, 2019. https://doi.org/10.1038/s41436-019-0566-2
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