Bibliographic record
Abstract
PURPOSE OF REVIEW: Both environmental (e.g., nutrition) and genetic factors contribute to adult height variation in the general population. However, heritability studies have shown that most of the variation in height is genetically controlled. Although height, a classic polygenic trait, has been studied for more than 100 years, the genetic factors that influence its variation remained, prior to 2007, unknown. The identification of genes that regulate human height would greatly enhance our understanding of human growth and height-associated human syndromes. RECENT FINDINGS: Genome-wide association studies have become a powerful tool to identify genes that are associated with complex human diseases and traits. Recent large meta-analyses of genome-wide association studies for height have yielded 47 loci robustly associated with height variation. The effect of each of these height single nucleotide polymorphisms is small, yet in aggregate they can correctly assign individuals to the lower or upper tail of the height distribution. Interestingly, some of these height loci include genes that have been previously implicated by Mendelian genetics in tall or short stature syndromes, confirming the hypothesis that genes that cause syndromes can also harbor common alleles with a weaker effect on stature. Finally, the recent findings highlight biological pathways (e.g., hedgehog signaling, microRNA, chromatin structure) involved in human growth. SUMMARY: This review summarizes the recent progress made using genome-wide association studies on the identification of common genetic variants that contribute to adult height variation in the general population.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".