Advances in Exercise, Fitness, and Performance Genomics in 2011
Bibliographic record
Abstract
This review of the exercise genomics literature emphasizes the highest quality articles published in 2011. Given this emphasis on the best publications, only a small number of published articles are reviewed. One study found that physical activity levels were significantly lower in patients with mitochondrial DNA mutations compared with controls. A two-stage fine-mapping follow-up of a previous linkage peak found strong associations between sequence variation in the activin A receptor, type-1B (ACVRIB) gene and knee extensor strength, with rs2854464 emerging as the most promising candidate polymorphism. The association of higher muscular strength with the rs2854464 A allele was confirmed in two separate cohorts. A study using a combination of transcriptomic and genomic data identified a comprehensive map of the transcriptomic features important for aerobic exercise training-induced improvements in maximal oxygen consumption, but no genetic variants derived from candidate transcripts were associated with trainability. A large-scale de novo meta-analysis confirmed that the effect of sequence variation in the fat mass and obesity-associated (FTO) gene on the risk of obesity differs between sedentary and physically active adults. Evidence for gene-physical activity interactions on type 2 diabetes risk was found in two separate studies. A large study of women found that physical activity modified the effect of polymorphisms in the lipoprotein lipase (LPL), hepatic lipase (LIPC), and cholesteryl ester transfer protein (CETP) genes, identified in previous genome-wide association study reports, on HDL cholesterol. We conclude that a strong exercise genomics corpus of evidence would not only translate into powerful genomic predictors but also have a major effect on exercise biology and exercise behavior research.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.003 |
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".