Synthetic Phenotype Assisted Linear Mixed Models Improve Proteome-Wide Genetic Discovery in Incomplete Biobank Data

<title>Abstract</title> <p>The UK Biobank Pharma Proteomics Project (UKB-PPP) generated plasma proteomic data for 54,219 participants. However, proteomic measurements remain unavailable for approximately 90% of the UK Biobank participants. This substantial missingness significantly limits the power for proteome-wide genetic discovery, and downstream genome-wide association studies (GWAS) using traditional imputation methods can yield spurious associations if prediction models are misspecified. We propose the Synthetic Phenotype Assisted Linear Mixed models (Syn-PALM), a robust and computationally scalable synthetic data-assisted framework for GWAS in the presence of partially observed measurements. Syn-PALM jointly analyzes partially observed proteomic measurements and complete synthetic data predicted for all samples using machine learning models, while accounting for cryptic relatedness and population structure using mixed models. Syn-PALM is robust to prediction model misspecification and increases statistical power as prediction accuracy improves. Through simulations, ablation analyses, and GWAS analysis of the UKB-PPP data, Syn-PALM improves power over existing methods and boosts proteome-wide genetic discovery.</p>