Large-Scale Gene-Smoking Interactions and Fine Mapping Study Identifies Multiple Novel Blood Pressure Loci in over 1 Million Individuals
Bibliographic record
Abstract
Abstract Cigarette smoking influences blood pressure (BP) levels. Studying and accounting for potential gene-smoking interactions can help discover novel loci and provide insights into biological pathways for smoking-associated BP regulation. We conducted a genome-wide association meta-analysis involving 1,188,241 individuals from 66 studies in five ancestry groups, analyzing systolic BP, diastolic BP, and pulse pressure while considering interactions between genetic variants and three smoking exposures: smoking status, cigarettes per day, and pack years. These analyses identified twelve novel loci for BP at genome-wide significance ( P < 5 × 10 −9 ), and highlighted biological processes including tight junction integrity, mitochondrial health, vascular relaxation, and endothelial function. In smoking status-stratified analyses, smoking modifies the genetic effect of six variants on BP. To prioritize likely causal, we developed and applied SuSiEgxe, a fine-mapping method based on a two-degree-of-freedom joint test using gene-environment interaction summary statistics. Fine-mapped loci uncovered immune-related pathway for smoking-associated BP 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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".