Gene expression as a quantitative trait: what about translation?
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVE: Much progress has been made in determining loci where gene expression at the steady-state mRNA level is controlled by genetic variants in cis. However, mRNA is only a proxy for what matters in phenotypic variation, which is protein level. We set out to review the evidence for exonic polymorphisms that affect translation of mRNA. METHODS: Informal literature review. RESULTS: There is ample literature on monogenic diseases caused by rare mutations in translationally active mRNA elements. Such mutations may eliminate or create AUG initiation codons or alter the Kozak sequences surrounding them, alter RNA secondary structure, destroy or enhance internal ribosomal entry sequences or Fe-response elements. By contrast, examples of complex phenotypes determined by common polymorphisms in these elements are scarce, although reports (to be confirmed) of functionally polymorphic miRNA binding sites are beginning to appear. CONCLUSIONS: Given the methodological limitations of detecting these translational effects, we posit that existing knowledge of such effects in common complex phenotypes represent the 'tip of the iceberg'. High-throughput quantitative proteomics is beginning to offer a promise to explore this possibility.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".