Mutation analysis of disease causing genes in patients with early onset or familial forms of Alzheimer’s disease and frontotemporal dementia
Bibliographic record
Abstract
BACKGROUND: Most dementia disorders have a clear genetic background and a number of disease genes have been identified. Mutations in the tau gene (MAPT) lead to frontotemporal dementia (FTD), whereas mutations in the genes for the amyloid-β precursor protein (APP) and the presenilins (PSEN1, PSEN2) cause early-onset, dominantly inherited forms of Alzheimer's disease (AD). Even if mutations causing Mendelian forms of these diseases are uncommon, elucidation of the pathogenic effects of such mutations have proven important for understanding the pathogenic processes. Here, we performed a screen to identify novel pathogenic mutations in known disease genes among patients undergoing dementia investigation. RESULTS: Using targeted exome sequencing we have screened all coding exons in eleven known dementia genes (PSEN1, PSEN2, APP, MAPT, APOE, GRN, TARDBP, CHMP2B, TREM2, VCP and FUS) in 102 patients with AD, FTD, other dementia diagnoses or mild cognitive impairment. We found three AD patients with two previously identified pathogenic mutations in PSEN1 (Pro264Leu and Met146Val). In this screen, we also identified the recently reported APP mutation in two siblings with AD. This mutation, named the Uppsala mutation, consists of a six amino acid intra-amyloid β deletion. In addition, we found several potentially pathogenic mutations in PSEN2, FUS, MAPT, GRN and APOE. Finally, APOE ε4 was prevalent in this patient group with an allele frequency of 54%. CONCLUSIONS: Among the 102 screened patients, we found two disease causing mutations in PSEN1 and one in APP, as well as several potentially pathogenic mutations in other genes related to neurodegenerative disorders. Apart from giving important information to the clinical investigation, the identification of disease mutations can contribute to an increased understanding of disease mechanisms.
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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 | 0.001 |
| 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 | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".