Safe Linkage of Cohort and Population-Based Register Data in a Genomewide Association Study on Health Care Expenditure.

Record linkage genomewide association study health care expenditure register-based data safely interconnected data

Journal

Twin research and human genetics : the official journal of the International Society for Twin Studies
ISSN: 1832-4274
Titre abrégé: Twin Res Hum Genet
Pays: England
ID NLM: 101244624

Informations de publication

Date de publication:
04 2021
Historique:
entrez: 2 7 2021
pubmed: 3 7 2021
medline: 18 9 2021
Statut: ppublish

Résumé

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.

Identifiants

pubmed: 34213412
pii: S1832427421000189
doi: 10.1017/thg.2021.18
doi:

Types de publication

Journal Article Research Support, Non-U.S. Gov't

Langues

eng

Sous-ensembles de citation

IM

Pagination

103-109

Auteurs

Eveline L de Zeeuw (EL)

Department of Biological Psychology, Vrije Universiteit, Amsterdam, the Netherlands.

Lykle Voort (L)

SURF, Amsterdam, the Netherlands.

Ruurd Schoonhoven (R)

Statistics Netherlands, Den Haag/Heerlen, the Netherlands.

Michel G Nivard (MG)

Department of Biological Psychology, Vrije Universiteit, Amsterdam, the Netherlands.
Amsterdam Public Health Research Institute, Amsterdam, the Netherlands.

Thomas Emery (T)

Netherlands Interdisciplinary Demographic Institute, Den Haag, the Netherlands.

Jouke-Jan Hottenga (JJ)

Department of Biological Psychology, Vrije Universiteit, Amsterdam, the Netherlands.

Gonneke A H M Willemsen (GAHM)

Department of Biological Psychology, Vrije Universiteit, Amsterdam, the Netherlands.
Amsterdam Public Health Research Institute, Amsterdam, the Netherlands.

Pearl A Dykstra (PA)

Department of Public Administration and Sociology, Erasmus University, Rotterdam, the Netherlands.

Narges Zarrabi (N)

SURF, Amsterdam, the Netherlands.

John D Kartopawiro (JD)

Statistics Netherlands, Den Haag/Heerlen, the Netherlands.

Dorret I Boomsma (DI)

Department of Biological Psychology, Vrije Universiteit, Amsterdam, the Netherlands.
Amsterdam Public Health Research Institute, Amsterdam, the Netherlands.
Amsterdam Reproduction and Development Research Institute, Amsterdam, the Netherlands.

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