Genetic variants of accessory proteins and G proteins in human genetic disease
Bibliographic record
Abstract
We present a series of three articles on the genetics and pharmacogenetics of G protein- coupled receptors (GPCR). In the first article, we discuss genetic variants of the G protein subunits and accessory proteins that are associated with human phenotypes; in the second article, we build upon this to discuss “G protein-coupled receptor (GPCR) gene variants and human genetic disease” and in the third article, we survey “G protein-coupled receptor pharmacogenomics”. In the present article, we review the processes of ligand binding, GPCR activation, inactivation, and receptor trafficking to the membrane in the context of human genetic disease resulting from pathogenic variants of accessory proteins and G proteins. Pathogenic variants of the genes encoding G protein α and β subunits are examined in diverse phenotypes. Variants in the genes encoding accessory proteins that modify or organize G protein coupling have been associated with disease; these include the contribution of variants of the regulator of G protein signaling (RGS) to hypertension; the role of variants of activator of G protein signaling type III in phenotypes such as hypoxia; the contribution of variation at the RGS10 gene to short stature and immunological compromise; and the involvement of variants of G protein-coupled receptor kinases (GRKs), such as GRK4, in hypertension. Variation in genes that encode proteins involved in GPCR signaling are outlined in the context of the changes in structure and function that may be associated with human 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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.003 |
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".