Parent-of-origin effects in MS
Bibliographic record
Abstract
BACKGROUND: Multiple sclerosis (MS) is a complex neurologic disease of unknown etiology and inheritance pattern, but with increasing incidence among females. The study of aunt/uncle-niece/nephew (AUNN) pairs has potential to shed light on the on complex trait inheritance as this group can be divided into eight different pair types by gender, MS status, and parent of origin. METHODS: Using a cohort of 807 avuncular MS families with 938 affected AUNN pairs ascertained from a longitudinal, population-based Canadian database, we examined differential MS transmission by separating affected pairs into likely maternal and paternal trait origin. RESULTS: We observed an increased number of avuncular pairs connected through unaffected mothers compared to unaffected fathers (p = 0.008). To restrict confounders introduced by families with multiple pairs the overall number of maternal and paternal families were compared, to reveal a significantly higher number of maternal families (p = 0.038). Female-to-male sex ratios were higher among affected nieces/nephews when compared to the sex ratio for aunts/uncles (0.00042). CONCLUSIONS: This observation independently confirms previous findings of a "maternal parent-of-origin" effect in multiple sclerosis (MS) susceptibility. These findings highlight the special contribution that can be derived from avuncular pairs. These underutilized pairings can compare transmission by the gender of affected aunt-uncle, the unaffected transmitting parent, and by that of the affected offspring. This strategy may be especially profitable in diseases where parent-of-origin effects are being sought. These findings also independently confirm the increasing rate of MS in females, demonstrating that familial cases are influenced by the same environmental factors as the general MS population.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".