Genome-wide association study of white matter hyperintensity volume in elderly persons without dementia
Bibliographic record
Abstract
White matter hyperintensity has been correlated with cognitive disorders and its genetic predictors remain unclear. Here we conducted a genome-wide association study to identify novel genetic determinants that were correlated with white matter hyperintensity volume (WMHV) among non-demented elders. Three hundred and fifty non-Hispanic Caucasian subjects aged 55–80 years were included from the Alzheimer's Disease Neuroimaging Initiative cohort. Associations of WMHV with genetic polymorphisms were explored using multiple linear regression under an additive genetic model. Further studies were conducted to explore the influence of genetic variants on cognition-related phenotypes. Rs7220676 near HS3ST3A1 and MIR548H3 genes was associated with WMHV levels at genome-wide significance (P = 2.96 × 10−8). Single nucleotide polymorphisms comprising rs9675262 (near HS3ST3A1 and MIR548H3 genes, P = 1.15 × 10−7), rs9820240 (in DCLK3 gene, P = 2.23 × 10−7), rs10916409 (near ISCA1P2 gene, P = 4.55 × 10−6), and rs540422 (in PICALM gene, P = 9.68 × 10−6) were identified as suggestive loci linked to WMHV levels. The minor allele of rs7220676 (C) showed association with lower log (WMHV) in a dose-dependent manner. Besides, rs7220676 was correlated with rates of cognitive decline assessed by Mini-mental State Examination and memory scores. A novel locus near HS3ST3A1 and MIR548H3 genes was associated with WMHV levels and it may be involved in neurodegenerative diseases.
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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 | 0.000 | 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".