Sparse Genome-Wide Polygenic Prediction of plasma protein levels.
The ProteinPRS portal enables users to query, visualize, and download SNPBoost-derived PRS models for serum protein concentration.
These models, trained to capture both local cis and distant trans-regulatory mechanisms, identify the most informative variants for protein levels through multivariable regression within a boosting framework, aiding in the discovery of independent pQTL loci.
Furthermore, the models are instrumental for inferring genetically driven proteomic levels in independent datasets, thus supporting proteome-wide association studies (PWAS) by associating imputed protein levels with specific phenotypes.
Methods
Data
Olink measurements for nearly 3,000 plasma proteins across more than 50,000 UK Biobank samples, predominantly of European ancestry.
Algorithm
SNPBoost produces sparse polygenic models from genome-wide data, accounting for the joint effect of several variants rather than marginal associations.
Interpretation
Cis variants may be pQTLs or their LD proxies. Distant variants can reflect transcription-factor effects or protein–protein interactions on concentration.
Limitations
Plasma concentration only — no tissue or cell specificity. Performance may drop in populations other than the training population.
Contact
We are actively refining PRS models to enhance protein level predictions and elucidate genetic loci implicated in regulatory mechanisms of protein expression.
For questions and collaboration inquiries, please contact Dr. Maj: [email protected]
Protein PRS Models
Genome-wide model R² against cis-model R² for every protein, coloured by model sparsity.