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65th Annual Meeting of the German Association for Medical Informatics, Biometry and Epidemiology (GMDS), Meeting of the Central European Network (CEN: German Region, Austro-Swiss Region and Polish Region) of the International Biometric Society (IBS)

06.09. - 09.09.2020, Berlin (online conference)

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Population Stratification in Polygenic Risk Prediction Models for Coronary Artery Disease

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  • Damian Gola - Institut für Medizinische Biometrie und Statistik, Universität zu Lübeck, Universitätsklinikum Schleswig-Holstein, Campus Lübeck, Lübeck, Germany; German Center for Cardiovascular Research, Partner Site Hamburg/Kiel/Lübeck, Lübeck, Germany
  • Jeanette Erdmann - Institute for Cardiogenetics, Universität zu Lübeck, Lübeck, Germany; German Center for Cardiovascular Research, Partner Site Hamburg/Kiel/Lübeck, Lübeck, Germany
  • Bertram Müller-Myhsok - Department of Translational Research in Psychiatry, Max Planck Institute of Psychiatry, München, Germany; Munich Cluster of Systems Neurology, SyNergy, München, Germany; Institute of Translational Medicine, University of Liverpool, Liverpool, United Kingdom
  • Heribert Schunkert - Deutsches Herzzentrum München, Technische Universität München, München, Germany; German Center for Cardiovascular Research, Partner Site München, München, Germany
  • Inke R. König - Institut für Medizinische Biometrie und Statistik, Universität zu Lübeck, Universitätsklinikum Schleswig-Holstein, Campus Lübeck, Lübeck, Germany; German Center for Cardiovascular Research, Partner Site Hamburg/Kiel/Lübeck, Lübeck, Germany

Deutsche Gesellschaft für Medizinische Informatik, Biometrie und Epidemiologie. 65th Annual Meeting of the German Association for Medical Informatics, Biometry and Epidemiology (GMDS), Meeting of the Central European Network (CEN: German Region, Austro-Swiss Region and Polish Region) of the International Biometric Society (IBS). Berlin, 06.-09.09.2020. Düsseldorf: German Medical Science GMS Publishing House; 2021. DocAbstr. 41

doi: 10.3205/20gmds262, urn:nbn:de:0183-20gmds2621

Published: February 26, 2021

© 2021 Gola et al.
This is an Open Access article distributed under the terms of the Creative Commons Attribution 4.0 License. See license information at http://creativecommons.org/licenses/by/4.0/.

Outline

Text

Background: Individual risk prediction based on genome-wide polygenic risk scores (PGRS) utilizing millions of genetic variants has attracted much attention. It is under debate whether PGRS models can be applied – without loss of precision – to populations of similar ethnic but different geographical background than the one the scores were trained on. Here, we examine how PGRS trained in one population-specific but European data set perform in other European subpopulations.

Methods: We used data from UK and Estonian biobanks as well as case-control data from the German population to construct population specific PGRS. All PGRS were tested on independent testing data from all populations under consideration.

Results: We show that PGRS have the highest performance in their corresponding population testing data sets, whereas their performance significantly drops if applied to testing data sets from different European populations.

Conclusion: This result has direct impact on the clinical usability of PGRS for risk prediction models utilizing PGRS: a population effect must be kept in mind when applying risk estimation models which are based on additional genetic information even for individuals from different European populations of the same ethnicity.

The authors declare that they have no competing interests.

The authors declare that an ethics committee vote is not required.