Genome-wide detection and characterization of mating asymmetry in human populations
Bibliographic record
Abstract
The study of the genetic component of early-onset diseases requires investigation into parental genetic effects, particularly those mediated by the mother who can influence the offspring's risk of disease through the effects of her genes acting directly on the intrauterine milieu or indirectly through maternal-gene child-gene interaction effects. An important source of bias that can arise in feto-maternal association testing is the possibility of confounding due to mating asymmetry (MA). However, there is little information on the levels of MA present in human populations and the impact on maternal association testing. In this study, we developed a novel approach to measuring MA and, using HapMap mate-pairs of European and African descent, carried out a genome-wide investigation and characterization of MA. We further investigated the impact of observed levels of MA on maternal association tests through simulation experiments. For the first time, we showed that non-negligible levels of MA are detected in human populations, such that subtle genotype frequency differences between individuals mating in the population are sufficient to induce spurious maternal genotype associations. Though the underlying mechanisms driving the asymmetry within these populations remain elusive, our findings provide consequential evidence for the occurrence of MA in humans and highlight the importance of controlling for MA in maternal association testing.
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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 | 0.009 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".