DNA analysis of distinct populations suggests multiple origins for the mutation causing Huntington disease
Bibliographic record
Abstract
Results of association studies can be significantly biased if the ancestry of the control population is not similar to that of the affected population. One approach to overcome such a bias is to use distinct populations where controls and affected individuals are likely to be of similar descent. We have examined homogeneous populations of French, Danish and Swedish ancestry for nonrandom allelic association between Huntington disease (HD) and several markers previously shown to be in association with HD. No evidence for nonrandom allelic association between HD and these markers was shown in these populations. The demonstration of association in a United Kingdom (UK) sample of similar size, and lack of significant differences in allele frequencies between the French, Danish, Swedish and UK populations suggested that the absence of association was not predominantly a consequence of allele frequencies or sample size. To investigate further the number of potential HD chromosomes, DNA haplotypes were constructed for the Danish, French, Swedish and UK populations. The minimum of two HD haplotypes observed in each of the French, Danish and Swedish populations, compared to the one haplotype in the UK population of a similar size, is an important factor accounting for the absence of association between HD and the DNA markers in these populations. Furthermore, these data are in favour of multiple independent origins for the mutation causing HD.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.000 | 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 teacher head, 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".