Coding mutations in <scp><i>SORL</i></scp><i>1</i> and <scp>A</scp>lzheimer disease
Bibliographic record
Abstract
OBJECTIVE: Common single nucleotide polymorphisms in the SORL1 gene have been associated with late onset Alzheimer disease (LOAD), but causal variants have not been fully characterized nor has the mechanism been established. The study was undertaken to identify functional SORL1 mutations in patients with LOAD. METHODS: This was a family- and cohort-based genetic association study. Caribbean Hispanics with familial and sporadic LOAD and similarly aged controls were recruited from the United States and the Dominican Republic, and patients with sporadic disease of Northern European origin were recruited from Canada. Prioritized coding variants in SORL1 were detected by targeted resequencing and validated by genotyping in additional family members and unrelated healthy controls. Variants transfected into human embryonic kidney 293 cell lines were tested for Aβ40 and Aβ42 secretion, and the amount of the amyloid precursor protein (APP) secreted at the cell surface was determined. RESULTS: Seventeen coding exonic variants were significantly associated with disease. Two rare variants (rs117260922-E270K and rs143571823-T947M) with minor allele frequency (MAF) < 1% and 1 common variant (rs2298813-A528T) with MAF = 14.9% segregated within families and were deemed deleterious to the coding protein. Transfected cell lines showed increased Aβ40 and Aβ42 secretion for the rare variants (E270K and T947M) and increased Aβ42 secretion for the common variant (A528T). All mutants increased the amount of APP at the cell surface, although in slightly different ways, thereby failing to direct full-length APP into the retromer-recycling endosome pathway. INTERPRETATION: Common and rare variants in SORL1 elevate the risk of LOAD by directly affecting APP processing, which in turn can result in increased Aβ40 and Aβ42 secretion.
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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) | 0.001 | 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) | 0.006 | 0.001 |
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".