Overcoming attenuation bias in regressions using polygenic indices

Hans Kippersluis (), Pietro Biroli, Rita Dias Pereira, Titus Galama, Stephanie Hinke, S. Fleur W. Meddens, Dilnoza Muslimova, Eric A. W. Slob, Ronald Vlaming and Cornelius A. Rietveld
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Hans Kippersluis: Erasmus University Rotterdam
Pietro Biroli: University of Bologna
Rita Dias Pereira: Erasmus University Rotterdam
Stephanie Hinke: Erasmus University Rotterdam
S. Fleur W. Meddens: Erasmus University Rotterdam
Dilnoza Muslimova: Erasmus University Rotterdam
Eric A. W. Slob: Erasmus University Rotterdam
Ronald Vlaming: Tinbergen Institute
Cornelius A. Rietveld: Erasmus University Rotterdam

Nature Communications, 2023, vol. 14, issue 1, 1-16

Abstract: 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

Date: 2023
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DOI: 10.1038/s41467-023-40069-4

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