Author Correction: GWAS for male-pattern baldness identifies 71 susceptibility loci explaining 38% of the risk
Bibliographic record
Abstract
We have been alerted that in our recent Article the calculations used to transform the heritability from the observed scale to the liability scale did not take into account the individuals in category 2 of the baldness scale, who were removed in our original analysis. This led to an overestimation of the heritability on the liability scale, which should have been 0.62 instead of 0.94. Moreover, in the Title and in the Abstract, we report that we can explain 38% of the risk, while in fact that is the proportion of heritability explained by the loci we discovered. These errors do not substantially change the paper or its conclusions apart from the statement MBP is therefore probably one of the most heritable complex traits. Genome-wide significant associations and pathway analyses are not affected in any way and male-pattern baldness remains less genetically complex than other complex traits. We wish to thank Yap et al. for bringing this to our attention.
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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 | 0.051 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.004 | 0.009 |
| Insufficient payload (model declined to judge) | 0.077 | 0.034 |
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".