Genome-wide screening for genetic variants in polyadenylation signal (PAS) sites in mouse selection lines for fatness and leanness
Bibliographic record
Abstract
Alternative polyadenylation (APA) determines mRNA stability, localisation, translation and protein function. Several diseases, including obesity, have been linked to APA. Studies have shown that single nucleotide polymorphisms in polyadenylation signals (PAS-SNPs) can influence APA and affect phenotype and disease susceptibility. However, these studies focussed on associations between single PAS-SNP alleles with very large effects and phenotype. Therefore, we performed a genome-wide screening for PAS-SNPs in the polygenic mouse selection lines for fatness and leanness by whole-genome sequencing. The genetic variants identified in the two lines were overlapped with locations of PAS sites obtained from the PolyASite 2.0 database. Expression data for selected genes were extracted from the microarray expression experiment performed on multiple tissue samples. In total, 682 PAS-SNPs were identified within 583 genes involved in various biological processes, including transport, protein modifications and degradation, cell adhesion and immune response. Moreover, 63 of the 583 orthologous genes in human have been previously associated with human diseases, such as nervous system and physical disorders, and immune, endocrine, and metabolic diseases. In both lines, PAS-SNPs have also been identified in genes broadly involved in APA, such as Polr2c, Eif3e and Ints11. Five PAS-SNPs within 5 genes (Car, Col4a1, Itga7, Lat, Nmnat1) were prioritised as potential functional variants and could contribute to the phenotypic disparity between the two selection lines. The developed PAS-SNPs catalogue presents a key resource for planning functional studies to uncover the role of PAS-SNPs in APA, disease susceptibility and fat deposition.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".