Discovering Genetics‐related Biomarkers of Neuronal Vulnerability Using Post‐mortem Datasets from Multiple Large Brain Banks
Bibliographic record
Abstract
BACKGROUND: Neuropsychiatric disorders are linked to changes in brain composition, such as cortical neuron loss in late-onset Alzheimer's disease (AD). GABAergic cell types have been implicated in dysregulated neural interactions, contributing to pronounced neurodegeneration. However, the inaccessibility of human brain biopsies hampers understanding of cell type proportion changes in neuropsychiatric disorders. This study aims to identify genetic factors governing cell-type vulnerabilities and their links to neuropsychiatric traits. We hypothesize that the selective vulnerability of neuronal subtypes is associated with distinct genetic risk factors. METHOD: Cell-type deconvolution algorithms for bulk RNAseq data from the Religious Orders Study/Memory and Aging Project (ROSMAP) study (n = 912) were benchmarked and validated against single-nucleus RNA sequencing (snRNA-seq) data from the same subjects. A genome-wide association study (GWAS) was conducted to identify genetic variants linked to changes in cell type proportions (CTPs). RESULT: We found that no single deconvolution method is universally effective across all cell types and datasets. The MarkerGeneProfile (MGP) method demonstrated relatively high accuracy in predicting cell type proportions. Incorporating approaches to account for technical covariates in RNAseq data significantly improved the power of GWAS analyses, identifying genetic variants associated with changes in CTPs. CONCLUSION: This research demonstrates that brain cell type proportions can be reliably inferred from bulk brain tissue RNAseq datasets. Furthermore, our findings reveal that these proportions are partially governed by genetic factors, advancing our understanding of neuronal vulnerability in neuropsychiatric disorders.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".