Genotype inference from aggregated chromatin accessibility data reveals genetic regulatory mechanisms
Abstract Background Understanding 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 accessib...
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BMC
2025-03-01
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| Series: | Genome Biology |
| Online Access: | https://doi.org/10.1186/s13059-025-03538-1 |
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| author | Brandon M. Wenz Yuan He Nae-Chyun Chen Joseph K. Pickrell Jeremiah H. Li Max F. Dudek Taibo Li Rebecca Keener Benjamin F. Voight Christopher D. Brown Alexis Battle |
| author_facet | Brandon M. Wenz Yuan He Nae-Chyun Chen Joseph K. Pickrell Jeremiah H. Li Max F. Dudek Taibo Li Rebecca Keener Benjamin F. Voight Christopher D. Brown Alexis Battle |
| author_sort | Brandon M. Wenz |
| collection | DOAJ |
| description | Abstract Background Understanding 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. Results We 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. Conclusions Our 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. |
| format | Article |
| id | doaj-art-7048702bf4e54252a6181b283cfaa407 |
| institution | OA Journals |
| issn | 1474-760X |
| language | English |
| publishDate | 2025-03-01 |
| publisher | BMC |
| record_format | Article |
| series | Genome Biology |
| spelling | doaj-art-7048702bf4e54252a6181b283cfaa4072025-08-20T02:25:40ZengBMCGenome Biology1474-760X2025-03-0126112610.1186/s13059-025-03538-1Genotype inference from aggregated chromatin accessibility data reveals genetic regulatory mechanismsBrandon M. Wenz0Yuan He1Nae-Chyun Chen2Joseph K. Pickrell3Jeremiah H. Li4Max F. Dudek5Taibo Li6Rebecca Keener7Benjamin F. Voight8Christopher D. Brown9Alexis Battle10Genetics and Epigenetics Program, Cell and Molecular Biology Graduate Group, Biomedical Graduate Studies, University of Pennsylvania—Perelman School of MedicineDepartment of Biomedical Engineering, Johns Hopkins UniversityDepartment of Computer Science, Johns Hopkins UniversityGencove, Inc.Gencove, Inc.Graduate Group in Genomics and Computational Biology, University of PennsylvaniaDepartment of Biomedical Engineering, Johns Hopkins UniversityDepartment of Biomedical Engineering, Johns Hopkins UniversityDepartment of Genetics, University of Pennsylvania—Perelman School of MedicineDepartment of Genetics, University of Pennsylvania—Perelman School of MedicineDepartment of Biomedical Engineering, Johns Hopkins UniversityAbstract Background Understanding 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. Results We 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. Conclusions Our 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.https://doi.org/10.1186/s13059-025-03538-1 |
| spellingShingle | Brandon M. Wenz Yuan He Nae-Chyun Chen Joseph K. Pickrell Jeremiah H. Li Max F. Dudek Taibo Li Rebecca Keener Benjamin F. Voight Christopher D. Brown Alexis Battle Genotype inference from aggregated chromatin accessibility data reveals genetic regulatory mechanisms Genome Biology |
| title | Genotype inference from aggregated chromatin accessibility data reveals genetic regulatory mechanisms |
| title_full | Genotype inference from aggregated chromatin accessibility data reveals genetic regulatory mechanisms |
| title_fullStr | Genotype inference from aggregated chromatin accessibility data reveals genetic regulatory mechanisms |
| title_full_unstemmed | Genotype inference from aggregated chromatin accessibility data reveals genetic regulatory mechanisms |
| title_short | Genotype inference from aggregated chromatin accessibility data reveals genetic regulatory mechanisms |
| title_sort | genotype inference from aggregated chromatin accessibility data reveals genetic regulatory mechanisms |
| url | https://doi.org/10.1186/s13059-025-03538-1 |
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