Complex genetic effects linked to plasma protein abundance in the UK Biobank

TL;DR

This paper introduces a deep learning method to uncover complex genetic effects on plasma protein levels using data from the UK Biobank.

Contribution

The study presents EIR-auto-GP, a novel deep learning approach for identifying non-linear and complex genetic effects on plasma proteins.

Findings

123 proteins were found to correlate with non-linear covariates in the UK Biobank cohort.

15 proteins showed genetic dominance and epistasis effects.

A novel interaction between ABO and FUT3 loci was identified, along with dominance effects on CD209 and CLEC4M.

Abstract

Understanding genetic associations of proteins is important for studying the molecular effect of genetic variation. A key component of this is to understand the role of complex genetic effects such as dominance and epistasis that are associated with plasma proteins. Therefore, we develop EIR-auto-GP, a deep learning-based approach, to identify complex effects that are associated with protein quantitative trait loci (pQTLs). Applying this method to the UK Biobank proteomics cohort of 48,594 individuals, we identify 123 proteins that are correlated with non-linear covariates and 15 with genetic dominance and epistasis. We uncover a novel interaction between the ABO and FUT3 loci and demonstrate dominance effects of the ABO locus on plasma levels of pathogen recognition receptors CD209 and CLEC4M. Furthermore, we replicate these findings and the methodology across Olink and mass spectrometry-based cohorts. Our approach presents a systematic, large-scale attempt to identify complex effects of plasma protein levels.

The authors present a deep learning approach to uncover complex genetic effects on circulating protein levels. They reveal new interactions and dominance patterns using UK Biobank proteomics data.

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