While much progress has been made generating high quality chromatin state and accessibility data from the ENCODE and Roadmap consortia, accurately identifying cell-type specific enhancers from these data remains a significant challenge. We have recently developed a computational approach (gkmSVM) to predict regulatory elements from DNA sequence, and we have shown that when gkmSVM is trained on DHS data from each of the human and mouse ENCODE and Roadmap cells and tissues, it can predict both cell specific enhancer activity and the impact of regulatory variants (deltaSVM) with greater precision than alternative approaches. The gkmSVM model encapsulates a set of cell-type specific weights describing the regulatory binding site vocabulary controlling chromatin accessibility in each cell type. A striking observation is that the significant gkmSVM weights are generally identifiable with a small (~20) set of TF binding sites which vary by cell-type, consistent with the hypothesis that cell-type specific expression programs are controlled by a small set of core factors tightly coupled in mutually interacting regulatory circuits. Perturbations of these core regulators enable transitions between stable differentiated cell-type states of this genetic circuit. Here, we will use gkmSVM to systematically identify the core regulatory circuitry in all existing ENCODE and Roadmap human and mouse cell lines and tissues, and produce DNA sequence based genomic regulatory maps and fine-scale predictions of core regulator binding sites within predicted regulatory regions. We will generate binding site models for core regulators in each cell type, assess the accuracy of our predictions through direct experimental validation. The value of this map critically depends on its accuracy, so we demonstrate that gkmSVM predictions consistently outperform alternative methods in massively parallel enhancer reporter and luciferase validation assays, in blind community assessments of regulatory element predictions (CAGI), and in predicting validated causal disease associated variants. In contrast, we show that methods using PWM descriptions of TF binding sites are significantly less accurate. Finally, we will use our predictions of regulatory mutation impact to identify causal variants in GWAS and recently produced GTEx expression trait loci detected in a wide range of human tissues. Our regulatory maps will help design and inform focused experiments probing regulatory mechanisms, and aid in the interpretation of disease associated non-coding variants.

Public Health Relevance

We propose to develop improved gkmSVM models from ENCODE chromatin accessibility data and produce both genome wide and fine-scale maps of cell-specific enhancers and the core regulator binding sites within them. We will assess the activity of these predictions by direct experiments in human ENCODE cell lines and predict causal regulatory SNPs in GTEx expression trait loci. This project will significantly contribute to our understanding of regulatory element function and how sequence variation impacts disease.

National Institute of Health (NIH)
National Human Genome Research Institute (NHGRI)
Research Project--Cooperative Agreements (U01)
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Special Emphasis Panel (ZHG1)
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Gilchrist, Daniel A
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Johns Hopkins University
Biomedical Engineering
Schools of Medicine
United States
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Gate, Rachel E; Cheng, Christine S; Aiden, Aviva P et al. (2018) Genetic determinants of co-accessible chromatin regions in activated T cells across humans. Nat Genet 50:1140-1150
Migeon, Barbara R; Beer, Michael A; Bjornsson, Hans T (2017) Embryonic loss of human females with partial trisomy 19 identifies region critical for the single active X. PLoS One 12:e0170403
Beer, Michael A (2017) Predicting enhancer activity and variant impact using gkm-SVM. Hum Mutat 38:1251-1258
Kreimer, Anat; Zeng, Haoyang; Edwards, Matthew D et al. (2017) Predicting gene expression in massively parallel reporter assays: A comparative study. Hum Mutat 38:1240-1250