Applying the FAIR principles to data in a hospital: challenges and opportunities in a pandemic.

BACKGROUND: The COVID-19 pandemic has challenged healthcare systems and research worldwide. Data is collected all over the world and needs to be integrated and made available to other researchers quickly. However, the various heterogeneous information systems that are used in hospitals can result in fragmentation of health data over multiple data 'silos' that are not interoperable for analysis. Consequently, clinical observations in hospitalised patients are not prepared to be reused efficiently and timely. There is a need to adapt the research data management in hospitals to make COVID-19 observational patient data machine actionable, i.e. more Findable, Accessible, Interoperable and Reusable (FAIR) for humans and machines. We therefore applied the FAIR principles in the hospital to make patient data more FAIR. RESULTS: In this paper, we present our FAIR approach to transform COVID-19 observational patient data collected in the hospital into machine actionable digital objects to answer medical doctors' research questions. With this objective, we conducted a coordinated FAIRification among stakeholders based on ontological models for data and metadata, and a FAIR based architecture that complements the existing data management. We applied FAIR Data Points for metadata exposure, turning investigational parameters into a FAIR dataset. We demonstrated that this dataset is machine actionable by means of three different computational activities: federated query of patient data along open existing knowledge sources across the world through the Semantic Web, implementing Web APIs for data query interoperability, and building applications on top of these FAIR patient data for FAIR data analytics in the hospital. CONCLUSIONS: Our work demonstrates that a FAIR research data management plan based on ontological models for data and metadata, open Science, Semantic Web technologies, and FAIR Data Points is providing data infrastructure in the hospital for machine actionable FAIR Digital Objects. This FAIR data is prepared to be reused for federated analysis, linkable to other FAIR data such as Linked Open Data, and reusable to develop software applications on top of them for hypothesis generation and knowledge discovery.

Identification of DIO2 as a new susceptibility locus for symptomatic osteoarthritis.

Osteoarthritis [MIM 165720] is a common late-onset articular joint disease for which no pharmaceutical intervention is available to attenuate the cartilage degeneration. To identify a new osteoarthritis susceptibility locus, a genome-wide linkage scan and combined linkage association analysis were applied to 179 affected siblings and four trios with generalized osteoarthritis (The GARP study). We tested, for confirmation by association, 1478 subjects who required joint replacement and 734 controls in a UK population. Additional replication was tested in 1582 population-based females from the Rotterdam study that contained 94 cases with defined hip osteoarthritis and in 267 Japanese females with symptomatic hip osteoarthritis and 465 controls. Suggested evidence for linkage in the GARP study was observed on chromosome 14q32.11 (log of odds = 3.03, P = 1.9 x 10(-4)). Genotyping tagging single-nucleotide polymorphisms covering three important candidate genes revealed a common coding variant (rs225014; Thr92Ala) in the iodothyronine-deiodinase enzyme type 2 (D2) gene (DIO2 [MIM 601413]) which significantly explained the linkage signal (P = 0.006). Confirmation and replication by association in the additional osteoarthritis studies indicated a common DIO2 haplotype, exclusively containing the minor allele of rs225014 and common allele of rs12885300, with a combined recessive odds ratio of 1.79, 95% confidence interval (CI) 1.37-2.34 with P = 2.02 x 10(-5) in female cases with advanced/symptomatic hip osteoarthritis. The gene product of this DIO2 converts intracellular pro-hormone-3,3',5,5'-tetraiodothyronine (T4) into the active thyroid hormone 3,3',5-triiodothyronine (T3) thereby regulating intracellular levels of active T3 in target tissues such as the growth plate. Our results indicate a new susceptibility gene (DIO2) conferring risk to osteoarthritis.

Meta-analysis of four new genome scans for lipid parameters and analysis of positional candidates in positive linkage regions.

Lipid levels in plasma strongly influence the risk for coronary heart disease. To localise and subsequently identify genes affecting lipid levels, we performed four genome-wide linkage scans followed by combined linkage/association analysis. Genome-scans were performed in 701 dizygotic twin pairs from four samples with data on plasma levels of HDL- and LDL-cholesterol and their major protein constituents, apolipoprotein AI (ApoAI) and Apolipoprotein B (ApoB). To maximise power, the genome scans were analysed simultaneously using a well-established meta-analysis method that was newly applied to linkage analysis. Overall LOD scores were estimated using the means of the sample-specific quantitative trait locus (QTL) effects inversely weighted by the standard errors obtained using an inverse regression method. Possible heterogeneity was accounted for with a random effects model. Suggestive linkage for HDL-C was observed on 8p23.1 and 12q21.2 and for ApoAI on 1q21.3. For LDL-C and ApoB, linkage regions frequently coincided (2p24.1, 2q32.1, 19p13.2 and 19q13.31). Six of the putative QTLs replicated previous findings. After fine mapping, three maximum LOD scores mapped within 1 cM of major candidate genes, namely APOB (LOD=2.1), LDLR (LOD=1.9) and APOE (LOD=1.7). APOB haplotypes explained 27% of the QTL effect observed for LDL-C on 2p24.1 and reduced the LOD-score by 0.82. Accounting for the effect of the LDLR and APOE haplotypes did not change the LOD score close to the LDLR gene but abolished the linkage signal at the APOE gene. In conclusion, application of a new meta-analysis approach maximised the power to detect QTLs for lipid levels and improved the precision of their location estimate.