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.