Overcoming Attenuation Bias in Regressions Using Polygenic Indices

Overview

Journal Nat Commun

Specialty Biology

Date 2023 Jul 25

PMID 37491308

Authors

Hans van Kippersluis

Pietro Biroli

Rita Dias Pereira

Titus J Galama

Stephanie von Hinke

S Fleur W Meddens

Dilnoza Muslimova

Eric A W Slob

Ronald de Vlaming

Cornelius A Rietveld

Affiliations

Soon will be listed here.

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 < 1000) or when there is considerable assortative mating. Within families, ORIV is the best choice since the PGI-RC correction factor is generally not available. We verify the empirical validity of the simulations by predicting educational attainment and height in a sample of siblings from the UK Biobank. We show that applying ORIV between families increases the standardized effect of the PGI by 12% (height) and by 22% (educational attainment) compared to a meta-analysis-based PGI, yet estimates remain slightly below the PGI-RC estimates. Furthermore, within-family ORIV regression provides the tightest lower bound for the direct genetic effect, increasing the lower bound for the standardized direct genetic effect on educational attainment from 0.14 to 0.18 (+29%), and for height from 0.54 to 0.61 (+13%) compared to a meta-analysis-based PGI.

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