Comparison of Two Genome-Wide Association Studies for Heart Rate Response to Exercise from the UK Biobank
Bibliographic record
Abstract
Abstract The short-term changes in heart rate (HR) during and after exercise are important physiologic traits mediated via the autonomic nervous system. Variations in these traits are associated with mortality from cardiovascular causes. We conducted a systematic review of genome-wide association studies for these traits (with >10,000 participants) with the aim of comparing Polygenic Risk Scores (PRS) from different studies. Additionally, we applied the ST rengthening of R eporting of G enetic A ssociation Studies (STREGA) statement for assessing the completeness of reporting of evidence. Our systematic search yielded two studies (Verweij et al. and Ramirez et al.) that met our inclusion criteria. Both were conducted on the UK Biobank. Both defined their exercise traits as the difference between resting HR and the maximum HR during exercise. Their recovery traits were defined differently. Verweij et al. defined 5 recovery traits as the differences between the peak HR during exercise and the HRs at 10-50 sec post exercise cessation. Ramirez et al. defined their recovery trait as the difference between peak HR during exercise and the minimum HR during the minute post exercise cessation. While Ramirez et al. divided their sample into discovery and replication subsets, Verweij et al. analyzed the whole sample together. In terms of results, there were several common SNPs identified between studies and traits. There was evidence for the phenomenon of winner’s curse operating for a SNP from the Ramirez study’s HR recovery analysis. Many of the SNPs were mutually exclusive between the studies. However, there was a good agreement of PRS from the studies. The differences in the results could be attributed to the different exclusion criteria, analytic approaches, and definitions of traits used. Both studies had an under-representation of individuals of non-European ancestry compared to those of European ancestry. Further studies with proportionate representation of individuals of all ancestries would help address this gap. Graphical Abstract
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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 | 0.016 | 0.097 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.007 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".