Subtle population structure remains a significant concern in genome-wide association studies. Using human height as an example, we show how quantile regression, a natural extension of linear regression, can better correct for subtle population structure due to its inherent ability to adjust for quantile-specific effects of covariates such as principal components. We utilize data from the UK Biobank and the SardiNIA/ProgeNIA project for demonstration.
Quantile-specific confounding: correction for subtle population stratification via quantile regression
Masala, MarcoSecondo
Membro del Collaboration Group
;Fiorillo, EdoardoMembro del Collaboration Group
;Devoto, MarcellaMembro del Collaboration Group
;Cucca, FrancescoMembro del Collaboration Group
;
2025
Abstract
Subtle population structure remains a significant concern in genome-wide association studies. Using human height as an example, we show how quantile regression, a natural extension of linear regression, can better correct for subtle population structure due to its inherent ability to adjust for quantile-specific effects of covariates such as principal components. We utilize data from the UK Biobank and the SardiNIA/ProgeNIA project for demonstration.File in questo prodotto:
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