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Overcoming attenuation bias in regressions using polygenic indices

Author

Listed:
  • Hans Kippersluis

    (Erasmus University Rotterdam
    Tinbergen Institute)

  • Pietro Biroli

    (University of Bologna)

  • Rita Dias Pereira

    (Erasmus University Rotterdam
    Tinbergen Institute)

  • Titus J. Galama

    (Erasmus University Rotterdam
    Tinbergen Institute
    Vrije Universiteit Amsterdam
    University of Southern California)

  • Stephanie Hinke

    (Erasmus University Rotterdam
    Tinbergen Institute
    University of Bristol)

  • S. Fleur W. Meddens

    (Erasmus University Rotterdam
    Statistics Netherlands)

  • Dilnoza Muslimova

    (Erasmus University Rotterdam
    Tinbergen Institute)

  • Eric A. W. Slob

    (Erasmus University Rotterdam
    Cambridge University
    Erasmus University Rotterdam Institute for Behavior and Biology)

  • Ronald Vlaming

    (Tinbergen Institute
    Vrije Universiteit Amsterdam)

  • Cornelius A. Rietveld

    (Erasmus University Rotterdam
    Tinbergen Institute
    Erasmus University Rotterdam Institute for Behavior and Biology)

Abstract

Measurement error in polygenic indices (PGIs) attenuates the estimation of their effects in regression models. We analyze and compare two approaches addressing this attenuation bias: Obviously Related Instrumental Variables (ORIV) and the PGI Repository Correction (PGI-RC). Through simulations, we show that the PGI-RC performs slightly better than ORIV, unless the prediction sample is very small (N

Suggested Citation

  • Hans Kippersluis & Pietro Biroli & Rita Dias Pereira & Titus J. Galama & Stephanie Hinke & S. Fleur W. Meddens & Dilnoza Muslimova & Eric A. W. Slob & Ronald Vlaming & Cornelius A. Rietveld, 2023. "Overcoming attenuation bias in regressions using polygenic indices," Nature Communications, Nature, vol. 14(1), pages 1-16, December.
  • Handle: RePEc:nat:natcom:v:14:y:2023:i:1:d:10.1038_s41467-023-40069-4
    DOI: 10.1038/s41467-023-40069-4
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