Analysis of whole genome sequence data shows association of Alzheimer’s disease with rare coding variants in <i>ABCA7, PSEN1, SORL1</i> and <i>TREM2</i>
Bibliographic record
Abstract
Abstract Previous studies have reported associations between risk of Alzheimer’s disease (AD) or dementia and rare coding variants in a number of genes. A two stage strategy was used in which a previously released whole exome sequenced sample was used to prioritise 100 genes showing the strongest evidence for association with AD. These genes were then analysed in a newly released whole genome sequenced sample to identify those which showed statistically significant evidence for rare coding variant association. Association analysis of loss of function (LOF) and nonsynonymous variants was carried out in 18,998 protein coding genes using 11,188 controls and 5,808 cases, with nonsynonymous variants being annotated using 45 different pathogenicity predictors. The 100 genes showing strongest evidence for association were then analysed in a new sample of 27,749 controls and 13,234 cases using only the pathogenicity predictor which had performed best in the first sample. Four genes were statistically significant after correction for multiple testing: ABCA7, PSEN1, SORL1 and TREM2 . The association of different categories of variant with AD was characterised and the pattern was seen to vary between genes. This study quantifies the contribution of different types of variant within each gene to AD risk. In general these variants are probably too rare to be clinically useful for assessing individual risk of AD. Further research into the mechanisms whereby the products of these genes affect AD pathogenesis may aid development of novel therapeutic strategies.
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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) | 0.002 | 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".