Conserved mammalian modularity of quantitative trait loci revealed human functional orthologs in blood pressure control
Bibliographic record
Abstract
Genome-wide association studies (GWAS) have routinely detected human quantitative trait loci (QTLs) for complex traits. Viewing that most GWAS single nucleotide polymorphisms (SNPs) are found in non-coding regions unrelated to the physiology of a polygenic trait of interest, a vital question to answer is whether or not any of these SNPs can functionally alter the phenotype with which it is associated. The study of blood pressure (BP) is a case in point. Conserved mechanisms in controlling BP by modularity is now unifying differing mammalian orders in that understanding mechanisms in rodents is tantamount to revealing the same in humans, while overcoming experimental limitations imposed by human studies. As a proof of principle, we used BP QTLs from Dahl salt-sensitive rats (DSS) as substitutes to capture distinct human functional orthologs. 3 DSS BP QTLs are located into distinct genome regions and correspond to several human GWAS genes. Each of the QTLs independently exerted a major impact on BP in vivo. BP was functionally changed by normotensive alleles from each of these QTLs, and yet, the human GWAS SNPs do not exist in the rat. They cannot be responsible for physiological alterations in BP caused by these QTLs. These SNPs are genome emblems for QTLs nearby, rather than being QTLs per se, since they only emerged during primate evolution after BP-regulating mechanisms have been established. We then identified specific mutated coding domains that are conserved between rodents and humans and that may implicate different steps of a common pathway or separate pathways.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".