Gene expression and complex phenotypes are determined by the activity of cis-regulatory elements. However, an understanding of how extant genetic variants affect cis regulation remains limited. Here, we investigated the consequences of cis-regulatory diversity using single-cell genomics of more than 0.7 million nuclei across 172Zea mays(maize) inbreds. Our analyses pinpointed cis-regulatory elements distinct to domesticated maize and revealed how historical transposon activity has shaped the cis-regulatory landscape. Leveraging population genetics principles, we fine-mapped about 22,000 chromatin accessibility–associated genetic variants with widespread cell type–specific effects. Variants in TEOSINTE BRANCHED1/CYCLOIDEA/PROLIFERATING CELL FACTOR–binding sites were the most prevalent determinants of chromatin accessibility. Finally, integrating chromatin accessibility–associated variants, organismal trait variation, and population differentiation revealed how local adaptation has rewired regulatory networks in unique cellular contexts to alter maize flowering.
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Integrating regulatory DNA sequence and gene expression to predict genome-wide chromatin accessibility across cellular contexts
Abstract Motivation Genome-wide profiles of chromatin accessibility and gene expression in diverse cellular contexts are critical to decipher the dynamics of transcriptional regulation. Recently, convolutional neural networks have been used to learn predictive cis-regulatory DNA sequence models of context-specific chromatin accessibility landscapes. However, these context-specific regulatory sequence models cannot generalize predictions across cell types. Results We introduce multi-modal, residual neural network architectures that integrate cis-regulatory sequence and context-specific expression of trans-regulators to predict genome-wide chromatin accessibility profiles across cellular contexts. We show that the average accessibility of a genomic region across training contexts can be a surprisingly powerful predictor. We leverage this feature and employ novel strategies for training models to enhance genome-wide prediction of shared and context-specific chromatin accessible sites across cell types. We interpret the models to reveal insights into cis- and trans-regulation of chromatin dynamics across 123 diverse cellular contexts. Availability and implementation The code is available at https://github.com/kundajelab/ChromDragoNN. Supplementary information Supplementary data are available at Bioinformatics online.
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- Award ID(s):
- 1730158
- PAR ID:
- 10171052
- Date Published:
- Journal Name:
- Bioinformatics
- Volume:
- 35
- Issue:
- 14
- ISSN:
- 1367-4803
- Page Range / eLocation ID:
- i108 to i116
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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