Genome-wide association study identified ATP6V1H locus influencing cerebrospinal fluid BACE activity
Bibliographic record
Abstract
The activity of cerebrospinal fluid (CSF) β-site APP cleaving enzyme (BACE) is a potential diagnostic biomarker for Alzheimer disease (AD). A total of 340 non-Hispanic Caucasian participants from the Alzheimer’s Disease Neuroimaging Initiative cohort (ADNI) database were included in this study with quality-controlled CSF BACE and genotype data. Association of CSF BACE with the genetic variants of single nucleotide polymorphisms (SNPs) was assessed using PLINK under the additive genetic model. The P values of all SNPs for CSF BACE were adjusted for multiple comparisons. One SNP (rs1481950) in the ATP6V1H gene reached genome-wide significance for associations with CSF BACE ( P = 4.88 × 10 − 9 ). The minor allele (G) of rs1481950 was associated with higher CSF BACE activity. Although seven SNPs in SNX31, RORA, CDH23, RGS20, LRRC4C, MAPK6PS1 and LOC105378355 did not reach genome-wide significance ( P < 10 − 8 ), they were identified as suggestive loci ( P < 10 − 5 ). This study identified rs1481950 within ATP6V1H influencing human CSF BACE activity, which indicated that ATP6V1H gene may play some roles in the pathogenesis of neurodegenerative diseases such as AD.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".