Integrated single-cell chromatin and transcriptomic analyses of human scalp reveal etiological insights into genetic risk for hair and skin disease
Bibliographic record
Abstract
Genome-wide association studies (GWAS) of hair and skin disease have identified many genetic variants associated with disease phenotypes, but identifying causal variants and interpreting their function requires deciphering gene-regulatory networks in disease-relevant cell types. To this end, we generated matched scRNA- and scATAC-seq profiles of human scalp biopsies, identifying diverse cell types of the hair follicle niche. By interrogating the integrated datasets across multiple levels of cellular resolution, we infer 50-100% more enhancer-gene links than prior approaches, and show that the aggregate accessibility at linked enhancers for highly-regulated genes predicts expression. We use these gene-regulatory maps to prioritize cell types, genes, and causal variants implicated in the pathobiology of androgenetic alopecia (AGA), eczema, and other complex traits. AGA GWAS signals are strongly and specifically enriched in dermal papilla cell open chromatin regions, supporting the role of these cells as key drivers of AGA pathogenesis. Finally, we trained machine-learning models to nominate SNPs that affect gene expression through disruption of specific transcription factor binding motifs, identifying candidate functional SNPs linked to expression of WNT10A in AGA and IL18RAP in eczema. Together, this work reveals principles of gene regulation and identifies gene regulatory consequences of natural genetic variation in complex skin and hair diseases.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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".