The limb-girdle muscular dystrophies (LGMDs) are a diverse group of genetic neuromuscular conditions that usually manifest in the proximal muscles of the hip and shoulder girdles. Since the identification of the first gene associated with the phenotype in 1994, an extensive body of research has identified the genetic defects responsible for over 30 LGMD subtypes, revealed an increasingly varied phenotypic spectrum, and exposed the need to move towards a systems-based understanding of the molecular pathways affected. New sequencing technologies, including whole-exome and whole-genome sequencing, are continuing to expand the range of genes and phenotypes associated with the LGMDs, and new computational approaches are helping clinicians to adapt to this new genomic medicine paradigm. However, 60 years on from the first description of LGMD, no curative therapies exist, and systematic exploration of the natural history is still lacking. To enable rapid translation of basic research to the clinic, well-phenotyped and genetically characterized patient cohorts are a necessity, and appropriate outcome measures and biomarkers must be developed through natural history studies. Here, we review the international collaborations that are addressing these translational research issues, and the lessons learned from large-scale LGMD sequencing programmes.

Prognostic Level III. See Instructions for Authors for a complete description of levels of evidence.

For the first time in Europe hundreds of rare disease (RD) experts team up to actively share and jointly analyse existing patient’s data. Solve-RD is a Horizon 2020-supported EU flagship project bringing together >300 clinicians, scientists, and patient representatives of 51 sites from 15 countries. Solve-RD is built upon a core group of four European Reference Networks (ERNs; ERN-ITHACA, ERN-RND, ERN-Euro NMD, ERN-GENTURIS) which annually see more than 270,000 RD patients with respective pathologies. The main ambition is to solve unsolved rare diseases for which a molecular cause is not yet known. This is achieved through an innovative clinical research environment that introduces novel ways to organise expertise and data. Two major approaches are being pursued (i) massive data re-analysis of >19,000 unsolved rare disease patients and (ii) novel combined -omics approaches. The minimum requirement to be eligible for the analysis activities is an inconclusive exome that can be shared with controlled access. The first preliminary data re-analysis has already diagnosed 255 cases form 8393 exomes/genome datasets. This unprecedented degree of collaboration focused on sharing of data and expertise shall identify many new disease genes and enable diagnosis of many so far undiagnosed patients from all over Europe.

Hip dysplasia is common in children with cerebral palsy (CP), especially in those children with notable functional impairment. Severity of hip dysplasia has been shown to correlate with higher Gross Motor Function Classification System levels. Migration percentage measured on AP pelvis radiographs is the key radiographic measure quantifying hip displacement in CP. Hip surveillance programs for children with CP exist in Europe, Australia, and parts of Canada and have been adopted as standard of care. These programs have demonstrated improved detection of hip subluxation and appropriate early intervention with a resultant decrease in the number of painful dislocations. Hip surveillance programs provide healthcare providers with guidance for a schedule of obtaining hip radiographs based on patients' age, Gross Motor Function Classification System level, and migration percentage. Although systematic surveillance programs have yet to be adopted in the United States, several centers and organizations are currently investigating the potential and efficacy of hip screening in CP.

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Patient registries are an essential tool to increase current knowledge regarding rare diseases. Understanding these data is a vital step to improve patient treatments and to create the most adequate tools for personalized medicine. However, the growing number of disease-specific patient registries brings also new technical challenges. Usually, these systems are developed as closed data silos, with independent formats and models, lacking comprehensive mechanisms to enable data sharing. To tackle these challenges, we developed a Semantic Web based solution that allows connecting distributed and heterogeneous registries, enabling the federation of knowledge between multiple independent environments. This semantic layer creates a holistic view over a set of anonymised registries, supporting semantic data representation, integrated access, and querying. The implemented system gave us the opportunity to answer challenging questions across disperse rare disease patient registries. The interconnection between those registries using Semantic Web technologies benefits our final solution in a way that we can query single or multiple instances according to our needs. The outcome is a unique semantic layer, connecting miscellaneous registries and delivering a lightweight holistic perspective over the wealth of knowledge stemming from linked rare disease patient registries.

Rare disease patients are more likely to receive a rapid molecular diagnosis nowadays thanks to the wide adoption of next-generation sequencing. However, many cases remain undiagnosed even after exome or genome analysis, because the methods used missed the molecular cause in a known gene, or a novel causative gene could not be identified and/or confirmed. To address these challenges, the RD-Connect Genome-Phenome Analysis Platform (GPAP) facilitates the collation, discovery, sharing, and analysis of standardized genome-phenome data within a collaborative environment. Authorized clinicians and researchers submit pseudonymised phenotypic profiles encoded using the Human Phenotype Ontology, and raw genomic data which is processed through a standardized pipeline. After an optional embargo period, the data are shared with other platform users, with the objective that similar cases in the system and queries from peers may help diagnose the case.Additionally, the platform enables bidirectional discovery of similar cases in other databases from the Matchmaker Exchange network. To facilitate genome-phenome

Level IV-case series.