High-efficiency likelihood inference of shared proteomic architectures across 50 complex human traits

Advancements in genetic correlation estimation have elucidated genome-wide pleiotropys influence on phenotypic correlations among human complex traits and diseases. However, the role of proteomic domains in these correlations remains underexplored. Traditional genetic correlation analysis assumptions, including the minute effects of SNPs and their linkage disequilibrium, do not suit proteomic data. We present a novel method, Likelihood-based Estimation for Proteomic Correlation (LEAP), tailored to provide unbiased estimation of shared proteomic architectures between trait pairs. LEAP notably decreases computational demands by approximately 1000-fold compared to conventional bivariate linear mixed models. We applied LEAP to data from the UK Biobank Pharma Proteomics Project, identifying 585 significant proteomic correlations among 1,225 pairs of 50 biochemical, anthropometric, and behavioral traits. Furthermore, we quantified the distinct proteomic and genetic contributions to phenotypic correlations, highlighting significant gender differences. This study provides a comprehensive computational approach for proteomic correlation estimation, clarifying the specific roles of genomics and proteomics in complex trait correlations. Our findings not only advance the understanding of proteomic contributions to phenotypic traits but also suggest potential applications for evaluating shared omics architectures in other domains such as transcriptomics and metabolomics.