Non-linear genetic regulation of the blood plasma proteome

Abstract

Although thousands of genetic variants are linked to human traits and diseases, the underlying mechanisms influencing these traits remain largely unexplored. One important aspect is to understand how proteins are regulated by the genome by identifying protein quantitative trait loci (pQTLs). Beyond this, there is a need to understand the role of complex genetics effects such as dominance and epistasis that regulate plasma proteins and protein biomarkers. Therefore, we developed EIR-auto-GP, a deep learning-based approach, to identify such effects. Our results complement the additive genetic regulation identified in previous pQTLs screens by adding a nuanced view of the complex genetic regulation of plasma proteins. Applying this method to the UK Biobank proteomics cohort of 48,594 individuals, we identified 138 proteins that were regulated by non-linear effects, including non-linear covariates (123) as well as genetic dominance and epistasis (15). We uncovered a novel epistatic interaction between the ABO and FUT3 loci, and demonstrated dominance effects of the ABO locus on plasma levels of pathogen recognition receptors CD209 and CLEC4M. Furthermore, we replicated these findings and the methodology across Olink and mass spectrometry-based cohorts and concluded that large sample sizes are needed to discover more complex genetic effects. Our approach presents a systematic, large-scale attempt to identify complex effects of plasma protein levels and can be applied to study other tissues or molecular QTLs.