Linking Genetics of Brain Changes to Alzheimer's Disease: Sparse Whole Genome Association Scan of Regional MRI Volumes in the ADNI and AddNeuroMed Cohorts
Bibliographic record
Abstract
BACKGROUND: Alzheimer's disease (AD) is a highly heritable disease, but until recently few replicated genetic markers have been identified. Markers identified so far are likely to account for only a tiny fraction of the heritability of AD and many more genetic risk alleles are thought to be undiscovered. OBJECTIVE: Identifying genetic markers for AD using combined analysis of genetics and brain imaging data. METHODS: Imaging quantitative trait loci (iQTLs) has recently emerged as an interesting research area for linking genetics of brain changes to AD. We consider a genome-wide association scan of 109 brain-wide regional imaging phenotypes to identify genetic susceptibility loci for AD from a combined set of 1,045 subjects from the Alzheimer's Disease Neuroimaging Initiative (ADNI) and the AddNeuroMed studies. We use one-SNP-at-a-time as well as multi-SNP Hyperlasso based iQTL methods for the analysis. RESULTS: We identified several novel markers associated with AD, namely HOMER2 (rs1256429; intronic, p = 8.7 × 10⁻¹⁰), EOMES (rs2724509; flanking), JAM2 (rs2829841; intronic), and WEE1 (rs10770042; coding). The SNP rs1256429 (HOMER2) was one of the top hits in Hyperlasso as well as in the single-SNP analysis showing an association with the volume of the right thalamus and AD, a brain region reported to be linked with AD in several studies. CONCLUSION: We believe that the markers identified in this study are novel additions to the existing list of genetic variants associated with AD which can be validated in future replicated studies.
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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".