Genotype-Epigenome-Phenotype Integration Reveals the Contributions of Peripheral Immune Cells to Bipolar Disorder Pathogenesis, Phenotypic Heterogeneity, and Therapy
Bibliographic record
Abstract
Abstract Immune dysfunctions are believed to contribute to bipolar disorder (BD), yet their mechanistic basis remains unclear. To address this, we systematically characterize BD-associated epigenomic and genetic variation in peripheral blood immune cells by profiling and integrating 833 genome-wide maps of five histone modification marks across 180 individuals (88 Type I BD patients, 92 controls), coupled with whole-genome sequencing data and rich medical records. We annotate 450k candidate cis - regulatory elements (CREs) and identify differential CREs (dCREs) in BD patients, suggesting down-regulated adaptive and up-regulated innate immune response. We predict candidate BD driver genes in the circulating immune system, which frequently show matched brain activity mainly related to calcium signaling and endoplasmic reticulum (ER) transport, suggesting dysregulated synaptic transmission, neuronal plasticity, and ER stress. We find that candidate driver genes are often linked to BD GWAS variants through blood-specific eQTLs not found in any brain cell types, indicating potential causal roles of circulating immune cells in bipolar disorder. We then infer 24 latent factors of BD-differential CRE variation and use them to group the patients into five epigenomic subtypes, which also show distinct disease phenotypes, including infection and inflammation, osmotic laxative use and glucose intolerance, quetiapine use, and hypertension. We next associate immune-partitioned BD polygenic risk scores with patient epigenomic subtypes, revealing the genetic basis of BD patient heterogeneity captured by blood epigenomics. Lastly, by analyzing transcriptional responses to known pharmacological interventions in hematopoietic cells that enrich BD patient group-specific dysregulated genes, we identify drugs/compounds that could be repurposed for ameliorating BD-associated immune dysfunction in a patient group-dependent manner. Overall, based on our study of genotype-epigenome-phenotype integration, we infer a potentially causal role of immune cells in BD, offering insights into biomarkers, subtypes, and precision medicine interventions targeting peripheral immune dysfunction and thus advancing precision medicine in BD.
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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".