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Introduction: In recent genome-wide association studies for psoriatic arthritis (PsA) and psoriasis vulgaris, common coding variants in the TRAF3IP2 gene were identified to contribute to susceptibility to both disease entities. The risk allele of p.Asp10Asn (rs33980500) proved to be most significantly associated and to encode a mutant protein with an almost completely disrupted binding property to TRAF6, supporting its impact as a main disease-causing variant and modulator of IL-17 signaling.
Methods: To identify further variants, exons 2-4 encoding both known TNF-receptor-associated factor (TRAF) binding domains were sequenced in 871 PsA patients. Seven missense variants and one three-base-pair insertion were identified in 0.06% to 1.02% of alleles. Five of these variants were also present in 931 control individuals at comparable frequency. Constructs containing full-length wild-type or mutant TRAF3IP2 were generated and used to analyze functionally all variants for TRAF6-binding in a mammalian two-hybrid assay.
Results: None of the newly found alleles, though, encoded proteins with different binding properties to TRAF6, or to the cytoplasmic tail of the IL-17-receptor α-chain, suggesting that they do not contribute to susceptibility.
Conclusions: Thus, the TRAF3IP2-variant p.Asp10Asn is the only susceptibility allele with functional impact on TRAF6 binding, at least in the German population.
Background The COVID-19 pandemic has spurred large-scale, inter-institutional research efforts. To enable these efforts, researchers must agree on dataset definitions that not only cover all elements relevant to the respective medical specialty but that are also syntactically and semantically interoperable. Following such an effort, the German Corona Consensus (GECCO) dataset has been developed previously as a harmonized, interoperable collection of the most relevant data elements for COVID-19-related patient research. As GECCO has been developed as a compact core dataset across all medical fields, the focused research within particular medical domains demands the definition of extension modules that include those data elements that are most relevant to the research performed in these individual medical specialties.
Objective To (i) specify a workflow for the development of interoperable dataset definitions that involves a close collaboration between medical experts and information scientists and to (ii) apply the workflow to develop dataset definitions that include data elements most relevant to COVID-19-related patient research in immunization, pediatrics, and cardiology.
Methods We developed a workflow to create dataset definitions that are (i) content-wise as relevant as possible to a specific field of study and (ii) universally usable across computer systems, institutions, and countries, i.e., interoperable. We then gathered medical experts from three specialties (immunization, pediatrics, and cardiology) to the select data elements most relevant to COVID-19-related patient research in the respective specialty. We mapped the data elements to international standardized vocabularies and created data exchange specifications using HL7 FHIR. All steps were performed in close interdisciplinary collaboration between medical domain experts and medical information scientists. The profiles and vocabulary mappings were syntactically and semantically validated in a two-stage process.
Results We created GECCO extension modules for the immunization, pediatrics, and cardiology domains with respect to the pandemic requests. The data elements included in each of these modules were selected according to the here developed consensus-based workflow by medical experts from the respective specialty to ensure that the contents are aligned with the respective research needs. We defined dataset specifications for a total number of 48 (immunization), 150 (pediatrics), and 52 (cardiology) data elements that complement the GECCO core dataset. We created and published implementation guides and example implementations as well as dataset annotations for each extension module.
Conclusions These here presented GECCO extension modules, which contain data elements most relevant to COVID-19-related patient research in immunization, pediatrics and cardiology, were defined in an interdisciplinary, iterative, consensus-based workflow that may serve as a blueprint for the development of further dataset definitions. The GECCO extension modules provide a standardized and harmonized definition of specialty-related datasets that can help to enable inter-institutional and cross-country COVID-19 research in these specialties.
Background: The COVID-19 pandemic has spurred large-scale, inter-institutional research efforts. To enable these efforts, the German Corona Consensus (GECCO) dataset has been developed previously as a harmonized, interoperable collection of the most relevant data elements for COVID-19-related patient research. As GECCO has been developed as a compact core dataset across all medical fields, the focused research within particular medical domains demanded the definition of extension modules that include those data elements that are most relevant to the research performed in these individual medical specialties.
Main body: We created GECCO extension modules for the immunization, pediatrics, and cardiology domains with respect to the pandemic requests. The data elements included in each of these modules were selected in a consensus-based process by working groups of medical experts from the respective specialty to ensure that the contents are aligned with the research needs of the specialty. The selected data elements were mapped to international standardized vocabularies and data exchange specifications were created using HL7 FHIR profiles on the appropriate resources. All steps were performed in close interdisciplinary collaboration between medical domain experts, medical information scientists and FHIR developers. The profiles and vocabulary mappings were syntactically and semantically validated in a two-stage process. In that way, we defined dataset specifications for a total number of 23 (immunization), 59 (pediatrics), and 50 (cardiology) data elements that augment the GECCO core dataset. We created and published implementation guides and example implementations as well as dataset annotations for each extension module.
Conclusions: We here present extension modules for the GECCO core dataset that contain data elements most relevant to COVID-19-related patient research in immunization, pediatrics and cardiology. These extension modules were defined in an interdisciplinary, iterative, consensus-based approach that may serve as a blueprint for the development of further dataset definitions and GECCO extension modules. The here developed GECCO extension modules provide a standardized and harmonized definition of specialty-related datasets that can help to enable inter-institutional and cross-country COVID-19 research in these specialties.
