Balancing Selection at <i>HLA-G</i> Modulates Fetal Survival, Preeclampsia and Human Birth Sex Ratio
Bibliographic record
Abstract
Abstract The population sex ratio is thought to be maintained through balancing selection on rare phenotypes. However, empirical evidence for genetic influence has thus far proven elusive. We combined 1000 Genomes data and large cohorts to study human sex ratios. We found underrepresentation of male offspring in preeclampsia, a serious pregnancy disorder with uncertain pathogenesis. Genetic variation of fetal human leukocyte antigen G ( HLA-G ), regulating maternal anti-fetal immune responses, was found to be under balancing selection. Sex-linked downregulation of HLA-G and upregulation of interferon alpha-1 ( IFNA1 ) expression contribute to loss of fetal immunotolerance in preeclampsia and suggest hydroxychloroquine as a treatment option. Our findings indicate that an evolutionary trade-off between fetal immunotolerance and protection against infections promotes genetic diversity in HLA-G , thereby maintaining human sex ratios. One Sentence Summary Fetal HLA-G modulates human sex ratio.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".