Identification and replication of sex-dimorphic protein quantitative trait loci across multiple ancestries and their associations with diseases
Bibliographic record
Abstract
Males and females exhibit differences in proteome profiles associated with disease risk. However, sex-dimorphic protein quantitative trait loci (SD-pQTL) and their effects on sex differences in health disorders have not been thoroughly investigated. We conducted a sex-stratified, genome-wide association study on 2,922 proteins using data from 30,272 individuals of Caucasian ancestry from the UK Biobank and compared the estimated effects on protein levels of these variants in the men and women to identify SD-pQTLs. The identified SD-pQTLs were replicated using data from two Japanese cohorts (comprising 2,886 and 1,394 individuals, respectively), as well as from 1,990 Finnish, 630 South Asian, and 662 Black ancestry individuals. Sex-dimorphic pleiotropy and the causal relationship between protein levels and health disorders were assessed using the identified SD-pQTLs. We identified 113 SD-pQTLs associated with 65 proteins. Of the 113 SD-pQTLs, 52 were significant in both sexes, five were not significant in either sex, and 42 and 14 were significant only in males and females, respectively. Variant rs2270416 was significantly associated with the CDH15 protein in both sexes but showed opposite effect direction in men and women. Of the 113 SD-pQTLs identified, a total of 41 were replicated in a meta-analysis encompassing Japanese, South Asian, and Black ancestry individuals. SD-pQTLs for proteins APOE (rs157581) and SNAP25 (rs4420638) exhibited sex-dimorphic associations with dementia, indicating sex dimorphic pleiotropy in both proteins and health disorders. From sex-stratified Mendelian randomization using the SD-pQTLs, proteins NCAM1 and PZP showed significant causal relationship with dementia in males and females, respectively. The present study provides evidence of sex-dimorphic genetic architecture in protein-level regulation, elucidating the proteo-genetic architecture for sex differences in human variation.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".