Genetic analyses identify widespread sex-differential participation bias
Bibliographic record
Abstract
Abstract Genetic association results are often interpreted with the assumption that study participation does not affect downstream analyses. Understanding the genetic basis of this participation bias is challenging as it requires the genotypes of unseen individuals. However, we demonstrate that it is possible to estimate comparative biases by performing GWAS contrasting one subgroup versus another. For example, we show that sex exhibits autosomal heritability in the presence of sex-differential participation bias. By performing a GWAS of sex in ~3.3 million males and females, we identify over 158 autosomal loci significantly associated with sex and highlight complex traits underpinning differences in study participation between sexes. For example, the body mass index (BMI) increasing allele at the FTO locus was observed at higher frequency in males compared to females (OR 1.02 [1.02-1.03], P=4.4×10 −36 ). Finally, we demonstrate how these biases can potentially lead to incorrect inferences in downstream analyses and propose a conceptual framework for addressing such biases. Our findings highlight a new challenge that genetic studies may face as sample sizes continue to grow.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".