Screening and instability of <i>FMR1</i> alleles in a prospective sample of 24,449 mother–newborn pairs from the general population
Bibliographic record
Abstract
To study the instability of FMR1 triplet repeats in the general population, we screened a prospective sample of 24,449 anonymized mother-offspring pairs and analyzed transmissions of intermediate-size (45-54 triplets) and premutation-size (55-200 triplets) alleles. We screened all mothers for alleles > or = 45 triplets by Southern blot and studied transmission of 545 maternal alleles to their offspring using polymerase chain reaction. Out of 21,411 maternal samples with conclusive results, we identified 250 carriers of at least one intermediate-size allele and 39 carrying a premutation-size allele. Out of a subsample of 430 transmissions of normal-size alleles (< 45 triplets), we observed four (< 1%) unstable transmissions. There were 6/90 intermediate-size unstable alleles (7%) and 11/25 unstable premutation-size alleles (44%). Two mothers transmitted a typical full mutation. The incidence of fragile X syndrome was thus 1/12,225 newborns (upper limit of 95% confidence interval: 1/4638 newborns), but larger in males (1/6209) than females (none detected in over 12,000 newborn females). Intermediate-size alleles were more unstable than normal-size alleles (p = 0.0027), but more stable (about sixfold) than premutation-size alleles (p < 0.0001). Unstable premutation-size alleles harbored the major fragile X haplotype (T50-T42-T62), and this haplotype appeared to be a good predictor of instability in premutations (p = 0.02). Incidence and instability are important to determine the feasibility and cost effectiveness of putative FMR1 screening programs. Carriers of FMR1 alleles of 55+ triplets with no family history of the disease may have a significant risk of expansion to a full mutation in a single generation.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 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".