Sex-specific genetic regulation of proteomics in cerebrospinal fluid uncovers genetic causes for sex differences in neurodegeneration
Bibliographic record
Abstract
Abstract Sex-specific genetic regulation of cerebrospinal fluid (CSF) protein levels may contribute to differential vulnerability to neurodegenerative diseases. To systematically identify sex differences in the genetic regulation of CSF proteome and their link to neurodegeneration, we performed sex-stratified pQTL analysis of 6,361 proteins in 1,713 males and 1,640 females, separately. We identified 1,729 pQTLs significant in either sex. They included 407 sex-specific pQTLs (genetic regulation in only one sex) and 159 sex-biased pQTLs (regulation in both sexes, but with different magnitudes of regulation between sexes). The HLA locus on chromosome 6 and the APOE locus on chromosome 19, two known pleiotropic regions, regulated several proteins in a sex-dependent way. Pathway enrichment revealed several biological processes that were shared and distinctive of sex. Using proteome-wide association study (PWAS) and colocalization, we identified 22 proteins associated and colocalized with AD risk loci. TMEM106B and ACE proteins were identified in only one sex. Four proteins were associated and colocalized with PD risk loci. These findings provide insights into dissecting the underlying mechanisms contributing to sex differences in neurodegeneration.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".