Tissue context determines the penetrance of regulatory DNA variation

Jessica M. Halow, Rachel Byron, Megan S. Hogan, Raquel Ordoñez, Mark Groudine, M. A. Bender, John A. Stamatoyannopoulos () and Matthew T. Maurano ()
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Jessica M. Halow: University of Washington
Rachel Byron: Fred Hutchinson Cancer Research Center
Megan S. Hogan: New York University Langone Medical Center
Raquel Ordoñez: New York University Langone Medical Center
Mark Groudine: Fred Hutchinson Cancer Research Center
M. A. Bender: Fred Hutchinson Cancer Research Center
John A. Stamatoyannopoulos: University of Washington
Matthew T. Maurano: University of Washington

Nature Communications, 2021, vol. 12, issue 1, 1-11

Abstract: Abstract Functional assessment of disease-associated sequence variation at non-coding regulatory elements is complicated by their high degree of context sensitivity to both the local chromatin and nuclear environments. Allelic profiling of DNA accessibility across individuals has shown that only a select minority of sequence variation affects transcription factor (TF) occupancy, yet low sequence diversity in human populations means that no experimental assessment is available for the majority of disease-associated variants. Here we describe high-resolution in vivo maps of allelic DNA accessibility in liver, kidney, lung and B cells from 5 increasingly diverged strains of F1 hybrid mice. The high density of heterozygous sites in these hybrids enables precise quantification of effect size and cell-type specificity for hundreds of thousands of variants throughout the mouse genome. We show that chromatin-altering variants delineate characteristic sensitivity profiles for hundreds of TF motifs. We develop a compendium of TF-specific sensitivity profiles accounting for genomic context effects. Finally, we link maps of allelic accessibility to allelic transcript levels in the same samples. This work provides a foundation for quantitative prediction of cell-type specific effects of non-coding variation on TF activity, which will facilitate both fine-mapping and systems-level analyses of common disease-associated variation in human genomes.

Date: 2021
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DOI: 10.1038/s41467-021-23139-3

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