A variant in <i>PPP4R3A</i> protects against alzheimer‐related metabolic decline
Bibliographic record
Abstract
Objectives A reduction in glucose metabolism in the posterior cingulate cortex (PCC) predicts conversion to Alzheimer's disease (AD) and tracks disease progression, signifying its importance in AD. We aimed to use decline in PCC glucose metabolism as a proxy for the development and progression of AD to discover common genetic variants associated with disease vulnerability. Methods We performed a genome‐wide association study (GWAS) of decline in PCC fludeoxyglucose F 18 ([18F] FDG) positron emission tomography measured in Alzheimer's Disease Neuroimaging Initiative participants (n = 606). We then performed follow‐up analyses to assess the impact of significant single‐nucleotide polymorphisms (SNPs) on disease risk and longitudinal cognitive performance in a large independent data set (n = 870). Last, we assessed whether significant SNP influence gene expression using two RNA sequencing data sets (n = 210 and n = 159). Results We demonstrate a novel genome‐wide significant association between rs2273647‐T in the gene, PPP4R3A, and reduced [18F] FDG decline (p = 4.44 × 10−8). In a follow‐up analysis using an independent data set, we demonstrate a protective effect of this variant against risk of conversion to MCI or AD (p = 0.038) and against cognitive decline in individuals who develop dementia (p = 3.41 × 10−15). Furthermore, this variant is associated with altered gene expression in peripheral blood and altered PPPP4R3A transcript expression in temporal cortex, suggesting a role at the molecular level. Interpretations PPP4R3A is a gene involved in AD risk and progression. Given the protective effect of this variant, PPP4R3A should be further investigated as a gene of interest in neurodegenerative diseases and as a potential target for AD therapies. Ann Neurol 2017;82:900–911
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 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) | 0.002 | 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".