Dynamic genetic linkage of intermediate blood pressure phenotypes during postural adaptations in a founder population
Bibliographic record
Abstract
Blood pressure (BP) is a dynamic phenotype that varies rapidly to adjust to changing environmental conditions. Standing upright is a recent evolutionary trait, and genetic factors that influence postural adaptations may contribute to BP variability. We studied the effect of posture on the genetics of BP and intermediate BP phenotypes. We included 384 sib-pairs in 64 sib-ships from families ascertained by early-onset hypertension and dyslipidemia. Blood pressure, three hemodynamic and seven neuroendocrine intermediate BP phenotypes were measured with subjects lying supine and standing upright. The effect of posture on estimates of heritability and genetic covariance was investigated in full pedigrees. Linkage was conducted on 196 candidate genes by sib-pair analyses, and empirical estimates of significance were obtained. A permutation algorithm was implemented to study the postural effect on linkage. ADRA1A, APO, CAST, CORIN, CRHR1, EDNRB, FGF2, GC, GJA1, KCNB2, MMP3, NPY, NR3C2, PLN, TGFBR2, TNFRSF6, and TRHR showed evidence of linkage with any phenotype in the supine position and not upon standing, whereas AKR1B1, CD36, EDNRA, F5, MMP9, PKD2, PON1, PPARG, PPARGC1A, PRKCA, and RET were specifically linked to standing phenotypes. Genetic profiling was undertaken to show genetic interactions among intermediate BP phenotypes and genes specific to each posture. When investigators perform genetic studies exclusively on a single posture, important genetic components of BP are missed. Supine and standing BPs have distinct genetic signatures. Standardized maneuvers influence the results of genetic investigations into BP, thus reflecting its dynamic regulation.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".