Genome-wide Association Study of Liking for Several Types of Physical Activity in the UK Biobank and Two Replication Cohorts.
Bibliographic record
Abstract
INTRODUCTION: A lack of physical activity (PA) is one of the most pressing health issues today. Our individual propensity for PA is influenced by genetic factors. Stated liking of different PA types may help capture additional and informative dimensions of PA behavior genetics. METHODS: In over 157,000 individuals from the UK Biobank, we performed genome-wide association studies of five items assessing the liking of different PA types, plus an additional derived trait of overall PA-liking. We attempted to replicate significant associations in the Netherlands Twin Register (NTR) and TwinsUK. Additionally, polygenic scores (PGS) were trained in the UK Biobank for each PA-liking item and for self-reported PA behavior, and tested for association with PA in the NTR. RESULTS: We identified a total of 19 unique significant loci across all five PA-liking items and the overall PA-liking trait, and these showed strong directional consistency in the replication cohorts. Four of these loci were previously identified for PA behavior, including CADM2 , which was associated with three PA-liking items. The PA-liking items were genetically correlated with self-reported ( rg = 0.38-0.80) and accelerometer ( rg = 0.26-0.49) PA measures, and with a wide range of health-related traits. Each PA-liking PGS significantly predicted the same PA-liking item in NTR. The PGS of liking for going to the gym predicted PA behavior in the NTR ( r2 = 0.40%) nearly as well as a PGS based on self-reported PA behavior ( r2 = 0.42%). Combining the two PGS into a single model increased the r2 to 0.59%, suggesting that PA-liking captures distinct and relevant dimensions of PA behavior. CONCLUSIONS: We have identified the first loci associated with PA-liking and extended our understanding of the genetic basis of PA behavior.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".