Removing genetic effects on plasma proteins enhances their utility as disease biomarkers
Fusco, D.; Yang, Z.; Viippola, E.; Cajuso, T.; Corbetta, A.; Caime, C.; German, J.; Fu, M.; Argentieri, M. A.; FinnGen, ; Nakanishi, T.; Yang, Z.; Ganna, A.
Show abstract
Plasma protein levels are influenced by both genetic and non-genetic factors and can serve as early disease biomarkers. When a protein is correlated with, but not causally linked to, disease, its genetic determinants can add unwanted variability to protein-disease associations. In such cases, removing the genetic component may improve their predictive performances. Here, we tested this hypothesis by genetically adjusting 94 highly heritable proteins spanning diverse biological pathways and evaluating their associations with the onset of 37 diseases in 39,871 UK Biobank participants. Genetically adjusted proteins showed stronger associations in 88% of 1,312 significant protein-disease pairs, equivalent to a 30% median reduction in required sample size for comparable power. Of 96 protein-disease pairs with significant differences, all but one showed larger effects for adjusted proteins. Most proteins also showed consistently stronger associations with environmental and lifestyle factors once genetic effects were removed. Finally, we constructed multi-protein scores from genetically adjusted proteins and demonstrated that they significantly improve prediction for 7 diseases compared to unadjusted proteins. These findings demonstrate that removing genetic effects from plasma proteins is an effective strategy to increase power for biomarker discovery and clinical trial design, consistent with the largely non-causal role of most plasma proteins in disease risk.
Matching journals
The top 4 journals account for 50% of the predicted probability mass.
Similar papers in this journal
Similar papers in this journal
- Machine learning-guided deconvolution of plasma protein levels 96%
- A tissue-aware machine learning framework enhances the mechanistic understanding and genetic diagnosis of Mendelian and rare diseases 95%
- Metabolic reaction fluxes as amplifiers and buffers of risk alleles for coronary artery disease 94%
Similar papers in this journal
Similar papers in this journal
- Interaction molecular QTL mapping discovers cellular and environmental modifiers of genetic regulatory effects 96%
- Leveraging phenotypic variability to identify genetic interactions in human phenotypes 95%
- ExPRSweb - An Online Repository with Polygenic Risk Scores for Common Health-related Exposures 95%
Similar papers in this journal
- Proteome-wide Mendelian randomization in global biobank meta-analysis reveals multi-ancestry drug targets for common diseases 97%
- Genetic associations with ratios between protein levels detect new pQTLs and reveal protein-protein interactions 96%
- Polygenic scores capture genetic modification of the adiposity-cardiometabolic risk factor relationship 95%
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.