Curation and expansion of the Human Phenotype Ontology for systemic autoinflammatory diseases improves phenotype-driven disease-matching.

Introduction: Accurate and standardized phenotypic descriptions are essential in diagnosing rare diseases and discovering new diseases, and the Human Phenotype Ontology (HPO) system was developed to provide a rich collection of hierarchical phenotypic descriptions. However, although the HPO terms for inborn errors of immunity have been improved and curated, it has not been investigated whether this curation improves the diagnosis of systemic autoinflammatory disease (SAID) patients. Here, we aimed to study if improved HPO annotation for SAIDs enhanced SAID identification and to demonstrate the potential of phenotype-driven genome diagnostics using curated HPO terms for SAIDs. Methods: We collected HPO terms from 98 genetically confirmed SAID patients across eight different European SAID expertise centers and used the LIRICAL (Likelihood Ratio Interpretation of Clinical Abnormalities) computational algorithm to estimate the effect of HPO curation on the prioritization of the correct SAID for each patient. Results: Our results show that the percentage of correct diagnoses increased from 66% to 86% and that the number of diagnoses with the highest ranking increased from 38 to 45. In a further pilot study, curation also improved HPO-based whole-exome sequencing (WES) analysis, diagnosing 10/12 patients before and 12/12 after curation. In addition, the average number of candidate diseases that needed to be interpreted decreased from 35 to 2. Discussion: This study demonstrates that curation of HPO terms can increase identification of the correct diagnosis, emphasizing the high potential of HPO-based genome diagnostics for SAIDs.

Implementing Individually Tailored Prescription of Physical Activity in Routine Clinical Care: Protocol of the Physicians Implement Exercise = Medicine (PIE=M) Development and Implementation Project.

BACKGROUND: The prescription of physical activity (PA) in clinical care has been advocated worldwide. This "exercise is medicine" (E=M) concept can be used to prevent, manage, and cure various lifestyle-related chronic diseases. Due to several challenges, E=M is not yet routinely implemented in clinical care. OBJECTIVE: This paper describes the rationale and design of the Physicians Implement Exercise = Medicine (PIE=M) study, which aims to facilitate the implementation of E=M in hospital care. METHODS: PIE=M consists of 3 interrelated work packages. First, levels and determinants of PA in different patient and healthy populations will be investigated using existing cohort data. The current implementation status, facilitators, and barriers of E=M will also be investigated using a mixed-methods approach among clinicians of participating departments from 2 diverse university medical centers (both located in a city, but one serving an urban population and one serving a more rural population). Implementation strategies will be connected to these barriers and facilitators using a systematic implementation mapping approach. Second, a generic E=M tool will be developed that will provide tailored PA prescription and referral. Requirements for this tool will be investigated among clinicians and department managers. The tool will be developed using an iterative design process in which all stakeholders reflect on the design of the E=M tool. Third, we will pilot-implement the set of implementation strategies, including the E=M tool, to test its feasibility in routine care of clinicians in these 2 university medical centers. An extensive learning process evaluation will be performed among clinicians, department managers, lifestyle coaches, and patients using a mixed-methods design based on the RE-AIM framework. RESULTS: This project was approved and funded by the Dutch grant provider ZonMW in April 2018. The project started in September 2018 and continues until December 2020 (depending on the course of the COVID-19 crisis). All data from the first work package have been collected and analyzed and are expected to be published in 2021. Results of the second work package are described. The manuscript is expected to be published in 2021. The third work package is currently being conducted in clinical practice in 4 departments of 2 university medical hospitals among clinicians, lifestyle coaches, hospital managers, and patients. Results are expected to be published in 2021. CONCLUSIONS: The PIE=M project addresses the potential of providing patients with PA advice to prevent and manage chronic disease, improve recovery, and enable healthy ageing by developing E=M implementation strategies, including an E=M tool, in routine clinical care. The PIE=M project will result in a blueprint of implementation strategies, including an E=M screening and referral tool, which aims to improve E=M referral by clinicians to improve patients' health, while minimizing the burden on clinicians.

Rapid Targeted Genomics in Critically Ill Newborns.

BACKGROUND: Rapid diagnostic whole-genome sequencing has been explored in critically ill newborns, hoping to improve their clinical care and replace time-consuming and/or invasive diagnostic testing. A previous retrospective study in a research setting showed promising results with diagnoses in 57%, but patients were highly selected for known and likely Mendelian disorders. The aim of our prospective study was to assess the speed and yield of rapid targeted genomic diagnostics for clinical application. METHODS: We included 23 critically ill children younger than 12 months in ICUs over a period of 2 years. A quick diagnosis could not be made after routine clinical evaluation and diagnostics. Targeted analysis of 3426 known disease genes was performed by using whole-genome sequencing data. We measured diagnostic yield, turnaround times, and clinical consequences. RESULTS: A genetic diagnosis was obtained in 7 patients (30%), with a median turnaround time of 12 days (ranging from 5 to 23 days). We identified compound heterozygous mutations in the EPG5 gene (Vici syndrome), the RMND1 gene (combined oxidative phosphorylation deficiency-11), and the EIF2B5 gene (vanishing white matter), and homozygous mutations in the KLHL41 gene (nemaline myopathy), the GFER gene (progressive mitochondrial myopathy), and the GLB1 gene (GM1-gangliosidosis). In addition, a 1p36.33p36.32 microdeletion was detected in a child with cardiomyopathy. CONCLUSIONS: Rapid targeted genomics combined with copy number variant detection adds important value in the neonatal and pediatric intensive care setting. It led to a fast diagnosis in 30% of critically ill children for whom the routine clinical workup was unsuccessful.

SORTA: a system for ontology-based re-coding and technical annotation of biomedical phenotype data.

There is an urgent need to standardize the semantics of biomedical data values, such as phenotypes, to enable comparative and integrative analyses. However, it is unlikely that all studies will use the same data collection protocols. As a result, retrospective standardization is often required, which involves matching of original (unstructured or locally coded) data to widely used coding or ontology systems such as SNOMED CT (clinical terms), ICD-10 (International Classification of Disease) and HPO (Human Phenotype Ontology). This data curation process is usually a time-consuming process performed by a human expert. To help mechanize this process, we have developed SORTA, a computer-aided system for rapidly encoding free text or locally coded values to a formal coding system or ontology. SORTA matches original data values (uploaded in semicolon delimited format) to a target coding system (uploaded in Excel spreadsheet, OWL ontology web language or OBO open biomedical ontologies format). It then semi- automatically shortlists candidate codes for each data value using Lucene and n-gram based matching algorithms, and can also learn from matches chosen by human experts. We evaluated SORTA's applicability in two use cases. For the LifeLines biobank, we used SORTA to recode 90 000 free text values (including 5211 unique values) about physical exercise to MET (Metabolic Equivalent of Task) codes. For the CINEAS clinical symptom coding system, we used SORTA to map to HPO, enriching HPO when necessary (315 terms matched so far). Out of the shortlists at rank 1, we found a precision/recall of 0.97/0.98 in LifeLines and of 0.58/0.45 in CINEAS. More importantly, users found the tool both a major time saver and a quality improvement because SORTA reduced the chances of human mistakes. Thus, SORTA can dramatically ease data (re)coding tasks and we believe it will prove useful for many more projects. Database URL: http://molgenis.org/sorta or as an open source download from http://www.molgenis.org/wiki/SORTA.

Pheno2Geno - High-throughput generation of genetic markers and maps from molecular phenotypes for crosses between inbred strains.

BACKGROUND: Genetic markers and maps are instrumental in quantitative trait locus (QTL) mapping in segregating populations. The resolution of QTL localization depends on the number of informative recombinations in the population and how well they are tagged by markers. Larger populations and denser marker maps are better for detecting and locating QTLs. Marker maps that are initially too sparse can be saturated or derived de novo from high-throughput omics data, (e.g. gene expression, protein or metabolite abundance). If these molecular phenotypes are affected by genetic variation due to a major QTL they will show a clear multimodal distribution. Using this information, phenotypes can be converted into genetic markers. RESULTS: The Pheno2Geno tool uses mixture modeling to select phenotypes and transform them into genetic markers suitable for construction and/or saturation of a genetic map. Pheno2Geno excludes candidate genetic markers that show evidence for multiple possibly epistatically interacting QTL and/or interaction with the environment, in order to provide a set of robust markers for follow-up QTL mapping. We demonstrate the use of Pheno2Geno on gene expression data of 370,000 probes in 148 A. thaliana recombinant inbred lines. Pheno2Geno is able to saturate the existing genetic map, decreasing the average distance between markers from 7.1 cM to 0.89 cM, close to the theoretical limit of 0.68 cM (with 148 individuals we expect a recombination every 100/148=0.68 cM); this pinpointed almost all of the informative recombinations in the population. CONCLUSION: The Pheno2Geno package makes use of genome-wide molecular profiling and provides a tool for high-throughput de novo map construction and saturation of existing genetic maps. Processing of the showcase dataset takes less than 30 minutes on an average desktop PC. Pheno2Geno improves QTL mapping results at no additional laboratory cost and with minimum computational effort. Its results are formatted for direct use in R/qtl, the leading R package for QTL studies. Pheno2Geno is freely available on CRAN under "GNU GPL v3". The Pheno2Geno package as well as the tutorial can also be found at: http://pheno2geno.nl .

Modifiers of mutant huntingtin aggregation: functional conservation of C. elegans-modifiers of polyglutamine aggregation.

Protein aggregation is a common hallmark of a number of age-related neurodegenerative diseases, including Alzheimer's, Parkinson's, and polyglutamine-expansion disorders such as Huntington's disease, but how aggregation-prone proteins lead to pathology is not known. Using a genome-wide RNAi screen in a C. elegans-model for polyglutamine aggregation, we previously identified 186 genes that suppress aggregation. Using an RNAi screen for human orthologs of these genes, we here present 26 human genes that suppress aggregation of mutant huntingtin in a human cell line. Among these are genes that have not been previously linked to mutant huntingtin aggregation. They include those encoding eukaryotic translation initiation, elongation and translation factors, and genes that have been previously associated with other neurodegenerative diseases, like the ATP-ase family gene 3-like 2 (AFG3L2) and ubiquitin-like modifier activating enzyme 1 (UBA1). Unravelling the role of these genes will broaden our understanding of the pathogenesis of Huntington's disease.

The MOLGENIS toolkit: rapid prototyping of biosoftware at the push of a button.

BACKGROUND: There is a huge demand on bioinformaticians to provide their biologists with user friendly and scalable software infrastructures to capture, exchange, and exploit the unprecedented amounts of new *omics data. We here present MOLGENIS, a generic, open source, software toolkit to quickly produce the bespoke MOLecular GENetics Information Systems needed. METHODS: The MOLGENIS toolkit provides bioinformaticians with a simple language to model biological data structures and user interfaces. At the push of a button, MOLGENIS' generator suite automatically translates these models into a feature-rich, ready-to-use web application including database, user interfaces, exchange formats, and scriptable interfaces. Each generator is a template of SQL, JAVA, R, or HTML code that would require much effort to write by hand. This 'model-driven' method ensures reuse of best practices and improves quality because the modeling language and generators are shared between all MOLGENIS applications, so that errors are found quickly and improvements are shared easily by a re-generation. A plug-in mechanism ensures that both the generator suite and generated product can be customized just as much as hand-written software. RESULTS: In recent years we have successfully evaluated the MOLGENIS toolkit for the rapid prototyping of many types of biomedical applications, including next-generation sequencing, GWAS, QTL, proteomics and biobanking. Writing 500 lines of model XML typically replaces 15,000 lines of hand-written programming code, which allows for quick adaptation if the information system is not yet to the biologist's satisfaction. Each application generated with MOLGENIS comes with an optimized database back-end, user interfaces for biologists to manage and exploit their data, programming interfaces for bioinformaticians to script analysis tools in R, Java, SOAP, REST/JSON and RDF, a tab-delimited file format to ease upload and exchange of data, and detailed technical documentation. Existing databases can be quickly enhanced with MOLGENIS generated interfaces using the 'ExtractModel' procedure. CONCLUSIONS: The MOLGENIS toolkit provides bioinformaticians with a simple model to quickly generate flexible web platforms for all possible genomic, molecular and phenotypic experiments with a richness of interfaces not provided by other tools. All the software and manuals are available free as LGPLv3 open source at http://www.molgenis.org.