Identification of 16 novel Alzheimer's disease loci using multi‐ancestry meta‐analyses
Bibliographic record
Abstract
INTRODUCTION: Alzheimer's disease (AD) is the most prevalent form of dementia. While many AD-associated genetic determinants have been identified, few studies have analyzed individuals of non-European ancestry. METHODS: We conducted a multi-ancestry genome-wide association study (GWAS) of clinically diagnosed AD and AD-by-proxy using whole genome sequencing data from the National Institute on Aging Genetics of Alzheimer's Disease Data Storage Site (NIAGADS), National Institute of Mental Health, UK Biobank (UKB), and All of Us (AoU) consisting of 49,149 cases (12,074 clinically diagnosed and 37,075 AD-by-proxy) and 383,225 controls. Nearly half of NIAGADS and AoU participants were of non-European ancestry. RESULTS: For clinically diagnosed AD, we identified 14 new loci-five common (FBN2/SCL27A6, AC090115.1, DYM, KCNG1/AL121785.1, TIAM1) and nine rare (VWA5B1, RNU6-755P/LMX1A, MOB1A, MORC1-AS1, LINC00989, PDE4D, RNU2-49P/CDO1, NEO1, and SLC35G3/AC022916.1). Meta-analysis of UKB and AoU AD-by-proxy cases yielded two new rare loci (RPL23/LASP1 and CEBPA/AC008738.6), also nominally significant in NIAGADS. DISCUSSION: In summary, we provide evidence for 16 novel AD loci and advocate for more studies using whole genome sequencing-based GWAS of diverse cohorts. HIGHLIGHTS: We used whole-genome sequencing data from large and diverse cohorts. We found novel genome-wide association study findings based on whole-genome data. We performed a multiancestry meta-analysis and incorporated results from underrepresented groups.
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 | 0.015 | 0.021 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.029 |
| Bibliometrics | 0.005 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".