Better together against genetic effect heterogeneity: a sex-combined interaction analysis of testosterone levels in the UK Biobank data
Bibliographic record
Abstract
ABSTRACT The effect of a genetic variant on a complex trait may differ between female and male, and in the presence of such genetic effect heterogeneity, sex-stratified analysis is often used. For example, genetic effects are sex-specific for testosterone levels, and sex-stratified analysis of testosterone in literature provided easy-to-interpret, sex-specific effect size estimates. However, from the perspective of association testing power, sex-stratified analysis may not be the best approach. As sex-specific genetic effect implies SNP×Sex interaction effect, jointly testing SNP main and SNP×Sex interaction effects may be more powerful than sex-stratified analysis or the standard main-effect testing approach. Moreover, since individual data may be unavailable, it is then of interest to study if the interaction analysis can be derived from sex-stratified summary statistics. We considered several different sex-combined methods and evaluated them through extensive simulation studies. We observed that a) the joint SNP main and SNP×Sex interaction analysis is most robust to a wide range of genetic models, and b) this joint interaction testing result can be obtained by quadratically combining sex-stratified summary statistics (i.e. squared sum of the sex-stratified summary statistics). We then reanalysed the testosterone levels of the UK Biobank data using sex-combined interaction analysis, which identified 27 new loci that were missed by the sex-stratified approach and the standard sex-combined analysis. Finally, we provide supporting association evidence for nine new loci, uniquely identified by the sex-combined interaction analysis, from earlier association studies of either testosterone level or steroid biosynthesis pathway where testosterone is synthesized. We thus recommend sex-combined interaction analysis, particularly for traits with known sex differences, for most powerful association testing, then followed by sex-stratified analysis for effect size estimation and interpretation.
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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 | 0.022 | 0.057 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.006 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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 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".