Genetic architecture of the limbic white matter microstructure in aging and Alzheimer’s Disease
Bibliographic record
Abstract
Abstract Background Limbic white matter (WM) abnormalities are prevalent in aging and Alzheimer’s disease (AD), yet their underlying biological mechanisms remain unclear. This study aims to identify the genetic architecture of limbic WM microstructure in older adults by leveraging harmonized data from multiple cohorts, including those enriched for cognitively impaired individuals. Methods We analyzed diffusion MRI (dMRI) data from 2,614 non-Hispanic White older adults (mean age = 73.7 ± 9.8 years; 57% female; 26% cognitively impaired) across 7 harmonized aging cohorts. WM microstructure was assessed in 7 limbic tracts, including the cingulum, fornix, inferior longitudinal fasciculus (ILF), uncinate fasciculus (UF), and transcallosal tracts of the inferior, middle, and superior temporal gyri (ITG, MTG, STG) using advanced diffusion MRI metrics corrected for free-water (FW): fractional anisotropy (FA FWcorr ), axial diffusivity (AxD FWcorr ), mean diffusivity (MD FWcorr ), radial diffusivity (RD FWcorr ). We performed heritability estimations, genome-wide association studies (GWAS) and post-GWAS analyses (genetic covariance, gene-level and pathway analysis, transcriptome-wide association [TWAS] studies). The AD relevance of the discovered variants was explored using bulk RNA-seq data from caudate, dorsolateral prefrontal, and posterior cingulate cortex human brain tissues. Results Limbic WM microstructure demonstrated significant heritability (estimates between 0.26 and 0.60, p FDR < 0.05 for 15 of 35 tract-by-microstructure combinations). GWAS identified 6 genome-wide significant loci ( p < 5.0×10 −8 ) associated with WM microstructure. Notably, for MTG RD FWcorr , we identified a locus on chromosome 18 (lead SNP: rs12959877) comprising 38 SNPs that are eQTLs for CDH19 , a gene involved in cell adhesion and highly expressed in oligodendrocytes. Other significant associations involved SNPs near KC6, SENP5, RORA, FAM107B , and MIR548A1 . Bulk RNA-seq analyses revealed that brain tissue expression of RORA, FAM107B , and KC6 was significantly associated with cognitive decline and several AD pathologies ( p FDR < 0.05). Post-GWAS analyses identified the genes SERPINA12 and DNAJB14 , and highlighted the involvement of insulin signaling, immune response, and neurotrophic pathways. Genetic covariance analyses indicated shared genetic architecture between limbic WM and lipid profiles (e.g., HDL cholesterol), cardiovascular traits, and neurological conditions (e.g., multiple sclerosis) ( p FDR < 0.05). Conclusion This multi-cohort imaging genetics study identified several novel genes and biological pathways associated with limbic WM microstructure in an aging population enriched for cognitive impairment. The association of several identified genes with cognitive decline and AD pathology underscores their AD relevance. Our findings further suggest that the genetic underpinnings of limbic WM microstructure are linked to vascular health and inflammation, highlighting these pathways as promising avenues for future AD-related therapeutic development.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".