Integrative multi-omics data from early development to identify the genes and cell types underlying attention-deficit/hyperactivity disorder
Bibliographic record
Abstract
BACKGROUND: Genome-wide association studies (GWASs) have identified numerous loci significantly associated with attention-deficit/hyperactivity disorder (ADHD); however, the majority of these loci are located in non-coding regions, limiting our understanding of the disorder's underlying pathogenesis. METHODS: We applied the summary data-based Mendelian randomization (SMR) approach to integrate expression quantitative trait loci (eQTL) data derived from bulk post-mortem tissues, fetal brain tissues, and single-cell types from induced pluripotent stem cell (iPSC)-derived neurons and post-mortem brain samples with ADHD GWAS data. Additionally, we performed cell-type enrichment analysis to identify specific cell types implicated in ADHD. RESULTS: Our integrative analysis identified LSM6 and RPS26 as significantly associated with ADHD, based on eQTL data from fetal brain and iPSC-derived neurons. Genes highlighted in fetal brain and iPSC-derived neurons showed high expression levels during early development, whereas genes identified from post-mortem brain samples tended to be expressed at low levels before the peak onset period of ADHD. Furthermore, cell-type enrichment analysis revealed that SNP-based heritability for ADHD was predominantly enriched in excitatory glutamatergic neurons, with relatively lower enrichment observed in glial cells. CONCLUSIONS: The findings highlight the importance of considering developmental gene expression dynamics in integrative analyses. Genetic variants may contribute to ADHD pathogenesis by modulating gene expression in the fetal brain, thereby impacting early neurodevelopmental processes.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".