ArticleGenome biology2025
Genotype inference from aggregated chromatin accessibility data reveals genetic regulatory mechanisms.
Article in Genome biology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- Machine learning and statistical methods for molecular quantitative trait loci.Nature reviews. Genetics · 2026Review
- Chromatin Accessibility Reveals Adaptive Regulatory Variation in a Keystone Freshwater Fish.Molecular ecology · 2026Article
- A meta-analysis of chromatin-associated loci provides insights into mechanistic interpretations of trait heritability.bioRxiv : the preprint server for biology · 2026Article
- Expanded chromatin accessibility mapping explains genetic variation associated with complex traits in liver.American journal of human genetics · 2026Article
- Fine-tuning sequence to function deep learning models on large-scale proteomic data improves the accuracy of variant effect prediction.bioRxiv : the preprint server for biology · 2025Article
- Expanded Chromatin Accessibility Mapping Explains Genetic Variation Associated with Complex Traits in Liver.medRxiv : the preprint server for health sciences · 2025Article
- Genotype inference from aggregated chromatin accessibility data reveals genetic regulatory mechanisms.Genome biology · 2025Article
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Abstract
backgroundUnderstanding the genetic causes underlying variability in chromatin accessibility can shed light on the molecular mechanisms through which genetic variants may affect complex traits. Thousands of ATAC-seq samples have been collected that hold information about chromatin accessibility across diverse cell types and contexts, but most of these are not paired with genetic information and come from distinct projects and laboratories.
resultsWe report here joint genotyping, chromatin accessibility peak calling, and discovery of quantitative trait loci which influence chromatin accessibility (caQTLs), demonstrating the capability of performing caQTL analysis on a large scale in a diverse sample set without pre-existing genotype information. Using 10,293 profiling samples representing 1454 unique donor individuals across 653 studies from public databases, we catalog 24,159 caQTLs in total. After joint discovery analysis, we cluster samples based on accessible chromatin profiles to identify context-specific caQTLs. We find that caQTLs are strongly enriched for annotations of gene regulatory elements across diverse cell types and tissues and are often linked with genetic variation associated with changes in expression (eQTLs), indicating that caQTLs can mediate genetic effects on gene expression. We demonstrate sharing of causal variants for chromatin accessibility across human traits, enabling a more complete picture of the genetic mechanisms underlying complex human phenotypes.
conclusionsOur work provides a proof of principle for caQTL calling from previously ungenotyped samples and represents one of the largest, most diverse caQTL resources currently available, informing mechanisms of genetic regulation of gene expression and contribution to disease.
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