Diversity and impact of rare variants in genes encoding the platelet G protein-coupled receptors
Bibliographic record
Abstract
Platelet responses to activating agonists are influenced by common population variants within or near G protein-coupled receptor (GPCR) genes that affect receptor activity. However, the impact of rare GPCR gene variants is unknown. We describe the rare single nucleotide variants (SNVs) in the coding and splice regions of 18 GPCR genes in 7,595 exomes from the 1,000-genomes and Exome Sequencing Project databases and in 31 cases with inherited platelet function disorders (IPFDs). In the population databases, the GPCR gene target regions contained 740 SNVs (318 synonymous, 410 missense, 7 stop gain and 6 splice region) of which 70 % had global minor allele frequency (MAF) < 0.05 %. Functional annotation using six computational algorithms, experimental evidence and structural data identified 156/740 (21 %) SNVs as potentially damaging to GPCR function, most commonly in regions encoding the transmembrane and C-terminal intracellular receptor domains. In 31 index cases with IPFDs (Gi-pathway defect n=15; secretion defect n=11; thromboxane pathway defect n=3 and complex defect n=2) there were 256 SNVs in the target regions of 15 stimulatory platelet GPCRs (34 unique; 12 with MAF< 1 % and 22 with MAF≥ 1 %). These included rare variants predicting R122H, P258T and V207A substitutions in the P2Y12 receptor that were annotated as potentially damaging, but only partially explained the platelet function defects in each case. Our data highlight that potentially damaging variants in platelet GPCR genes have low individual frequencies, but are collectively abundant in the population. Potentially damaging variants are also present in pedigrees with IPFDs and may contribute to complex laboratory phenotypes.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".