Abstract
There are research questions whose answers require record linkage of multiple databases that may be characterized by limited options for full data sharing. For this purpose, the Open Data Infrastructure for Social Science and Economic Innovations (ODISSEI) consortium has supported the development of the ODISSEI Secure Supercomputer (OSSC) platform that allows researchers to link cohort data to data from Statistics Netherlands and run large-scale analyses in a high-performance computing (HPC) environment. Here, we report a successful record linkage genomewide association (GWA) study on expenditure for total health, mental health, primary and hospital care, and medication. Record linkage for genotype data from 16,726 participants from the Netherlands Twin Register (NTR) with data from Statistics Netherlands was accomplished in the secure OSSC platform, followed by gene-based tests and estimation of total and single nucleotide polymorphism (SNP)-based heritability. The total heritability of expenditure ranged between 29.4% (SE 0.8) and 37.5% (SE 0.8), but GWA analyses did not identify SNPs or genes that were genomewide significantly associated with health care expenditure. SNP-based heritability was between 0.0% (SE 3.5) and 5.4% (SE 4.0) and was different from zero for mental health care and primary care expenditure. We conclude that successfully linking genotype data to administrative health care expenditure data from Statistics Netherlands is feasible and demonstrates a series of analyses on health care expenditure. The OSSC platform offers secure possibilities for analyzing linked data in large scale and realizing sample sizes required for GWA studies, providing invaluable opportunities to answer many new research questions.
Highlights
Data collected for administrative or policy purposes often include detailed information at the individual level and are proving to be of great value for medical and scientific research
The platform is based on customizable, virtualized private clusters that are deployed on Cartesius, imposing strict security measures required by data owners, and facilitates access and record linkage (Private Cloud on a Compute Cluster [PCOCC], developed by CEA, https://github.com/ceahpc/pcocc.)
The results of the genomewide association (GWA) analyses summarized in Manhattan and Q-Q plots for health care expenditure are depicted in Figure 3 and Supplementary Figures S3−S6
Summary
Data collected for administrative or policy purposes often include detailed information at the individual level and are proving to be of great value for medical and scientific research. CBS is legally entitled to make these data, under strict terms, available for research purposes as well as link them to external data in a secure remote-access (RA) environment (www.cbs.nl/microdata). This RA environment does not offer high-performance computing facilities, which limits the scale of research projects. While the effect of environmental determinants related to overall health has been extensively studied, less is known about the genetic architecture of individual differences between people. We argue that investigating overall health, more objectively measured by health care expenditure, will lead to a better understanding of its genetic architecture
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