Epitope Effect Prevalence in Affinity-based pQTL studies
Bibliographic record
Abstract
Abstract Affinity-based proteomics platforms Olink and SomaScan have enabled population-wide human proteogenomic studies, linking genetic variants to protein abundances. However, sequence alterations in the binding site of the detection reagent may introduce platform-specific measurement bias unrelated to protein levels, referred to as the epitope effect. In this study, we investigated the prevalence of epitope effects using cis protein quantitative trait loci (pQTL) discovered in three of the largest proteogenomic studies: UK Biobank, deCODE, and Fenland. Across 5,817 protein targets assayed in these studies, cis -pQTL were identified for 914 proteins by both platforms, 301 (33%) of which were linked to a missense variant. We identified 37 proteins with opposing effect directions in two platforms for the same missense pQTL, and 85 proteins where a missense pQTL was significant in only one platform. We present examples where such discrepancies reflect differences in isoform or proteoform targeting, as well as examples where the discordance appears to result from true platform-specific detection bias. Further structural analyses reveal that missense cis -pQTL are more likely to be detected when they alter residues located on accessible protein surfaces - regions most likely to interfere with reagent binding in affinity-based assays. Overall, our findings suggest that missense-mediated epitope effects influence only a minority (12% or less) of cis -pQTL results. We also highlight the need for detailed assay annotations and structural context to improve result interpretation in proteogenomic studies.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.010 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".