A genome scan in a single pedigree with a high prevalence of multiple sclerosis
Bibliographic record
Abstract
BACKGROUND: Multiple sclerosis (MS) is a disease that is widely believed to be autoimmune in nature. Genetic-epidemiological studies implicate susceptibility genes in the pathogenesis of MS, although non-MHC susceptibility linkages have been difficult to confirm. Insight into pathways that are intrinsic to other complex diseases has come from the genetic analysis of large, autosomal-dominant kindreds. Here, we present a genetic study of a large and unique kindred in which MS appears to follow an autosomal-dominant pattern of inheritance, with consistent penetrance in four generations. METHODS: Eighty-two individuals of this 370-member family were genotyped with 681 microsatellite markers spanning the genome, with an average spacing of 5.3 cM. RESULTS: Parametric linkage analysis was performed and no significant LOD score (LOD >3.3) was observed. For a rare dominant disease model with reduced penetrance, 99.6% of the genome was excluded at a LOD score <-1 and 96% at a LOD score <-2. The HLA-DRB1 candidate gene was also genotyped by allele-specific methods. In each instance where at least one parent was positive for HLA-DRB1*15, one or more HLA-DRB1*15 alleles were transmitted to the affected offspring (11/11). HLA-DRB1*15 was transmitted equally from both the familial and the married-in parents and therefore this locus does not appear to be an autosomal-dominant acting gene in this family but an important modifier of risk. CONCLUSIONS: These results further stress the importance of the HLA-DRB1*15-bearing haplotype in determining MS susceptibility. Furthermore, this study highlights the complexity of MS genetics, even in the presence of a single family, seemingly segregating MS as an autosomal-dominant trait.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".