Novel early‐onset Alzheimer‐associated genes influence risk through dysregulation of glutamate, immune activation, and intracellular signaling pathways
Bibliographic record
Abstract
INTRODUCTION: Most genetic studies for Alzheimer's disease (AD) have been focused on late-onset AD (LOAD). There are no large genetic studies on early-onset AD (EOAD). METHODS: We performed a multi-ancestry (non-Hispanic European, African, and East Asian) genome-wide association study (GWAS) including a total of 7,349 cases and 17,887 control. Cases with age at onset younger than 70 years were included. Sensitivity analysis including cases with onset <65 was performed. Only controls older than 70 were included to decrease the risk of developing LOAD. RESULTS: We identified eight novel significant loci: six in the ancestry-specific analyses and two in the trans-ancestry analysis. By integrating gene-based analysis, expression quantitative trait loci (eQTL), protein quantitative trait loci (pQTL), and functional annotations, we nominate eight novel genes that are involved in microglia activation, glutamate production, and signaling pathways. DISCUSSION: EOAD, although sharing genes with LOAD, harbors unique genes and pathways that could be used to create better prediction models or target identification. HIGHLIGHTS: We performed the largest and first multi-ethnic genetic screening for early-onset Alzheimer's disease (AD). We identified eight novel significant loci: six in the ancestry-specific analyses and two in the trans-ancestry analysis. The novel genes are implicated microglia activation, glutamate production, and signaling pathways. EOAD, although sharing many genes with LOAD, harbors unique genes and pathways that could be used to create better prediction models or target identification for this type of AD.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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 | 0.000 | 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".