Genome‐wide survey of copy number variants finds MAPT duplications in progressive supranuclear palsy
Bibliographic record
Abstract
BACKGROUND: Progressive supranuclear palsy is a neurodegenerative tauopathy manifesting clinically as a progressive akinetic-rigid syndrome. In this study, we sought to identify genetic variants influencing PSP susceptibility through a genome-wide association analysis of a cohort of well-characterized patients who had participated in the Neuroprotection and Natural History in Parkinson Plus Syndromes and Blood Brain Barrier in Parkinson Plus Syndromes studies. METHODS: We genotyped single-nucleotide polymorphisms in 283 PSP cases from the United Kingdom, Germany, and France and compared these with genotypes from 4472 controls. Copy number variants were identified from genotyping data. RESULTS: ). The population-attributable risk from the MAPT, MOBP, and STX6 single-nucleotide polymorphisms was found to be 0.37, 0.26, and 0.08, respectively. In addition, we found 2 instances of copy number variants spanning the MAPT gene in patients with PSP. These copy number variants include tau but few other genes within the chromosome 17 haplotype region, providing additional support for the direct pathogenicity of MAPT in PSP. CONCLUSIONS: Clinicians should also be aware of MAPT duplication as a possible genetic cause of PSP, especially in patients presenting with young age at onset. © 2019 International Parkinson and Movement Disorder Society.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.002 | 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".