Combinations of Variations in Multiple Genes Are Associated With Hypertension
Bibliographic record
Abstract
The genetic analysis of hypertension has revealed complex and inconsistent results, making it difficult to draw clear conclusions regarding the impact of specific genes on blood pressure regulation in diverse human populations. Some of the confusion from previous studies is probably due to undetected gene-gene interactions. Instead of focusing on the effects of single genes on hypertension, we examined the effects of interactions of alleles at 4 candidate loci. Three of the loci are in the renin-angiotensin-system, angiotensinogen, ACE, and angiotensin II type 1 receptor, and they have been associated with hypertension in at least 1 previous study. The fourth locus studied is a previously undescribed locus, named FJ. In total, 7 polymorphic sites at these loci were analyzed for their association with hypertension in 51 normotensive and 126 hypertensive age-matched individuals. There were no significant differences between the 2 phenotypic classes with respect to either allele or genotype frequencies. However, when we tested for nonallelic associations (linkage disequilibrium), we found that of the 120 multilocus comparisons, 16 deviated significantly from random in the hypertensive class, but there were no significant deviations in the normotensive group. These findings suggest that genetic interactions between multiple loci rather than variants of a single gene underlie the genetic basis of hypertension in our study subjects. We hypothesize that such interactions may account for the inconsistent findings in previous studies because, unlike our study, prior studies almost always examined single-locus effects and did not consider the effects of variation at other potentially interacting loci.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".