Abstract
Among the biggest challenges in the post-GWAS (genome-wide association studies) era is the interpretation of disease-associated genetic variants in non-coding genomic regions. Enhancers have emerged as key players in mediating the effect of genetic variants on complex traits and diseases. Their activity is regulated by a combination of transcription factors (TFs), epigenetic changes and genetic variants. Several approaches exist to link enhancers to their target genes, and others that infer TF-gene connections. However, we currently lack a framework that systematically integrates enhancers into TF-gene regulatory networks. Furthermore, we lack an unbiased way of assessing whether inferred regulatory interactions are biologically meaningful. Here we present two methods, implemented as user-friendly R packages: GRaNIE (Gene Regulatory Network Inference including Enhancers) for building enhancer-based gene regulatory networks (eGRNs) and GRaNPA (Gene Regulatory Network Performance Analysis) for evaluating GRNs. GRaNIE jointly infers TF-enhancer, enhancer-gene and TF-gene interactions by integrating open chromatin data such as ATAC-Seq or H3K27ac with RNA-seq across a set of samples (e.g. individuals), and optionally also Hi-C data. GRaNPA is a general framework for evaluating the biological relevance of TF-gene GRNs by assessing their performance for predicting cell-type specific differential expression. We demonstrate the power of our tool-suite by investigating gene regulatory mechanisms in macrophages that underlie their response to infection and cancer, their involvement in common genetic diseases including autoimmune diseases, and identify the TF PURA as putative regulator of pro-inflammatory macrophage polarisation.
Availability
Competing Interest Statement
The authors have declared no competing interest.
Footnotes
The main changes are: 1. We added five additional layers of molecular / utility evidence for GRaNIE-inferred eGRNs: The results demonstrate that GRaNIE-inferred eGRNs capture molecular evidence from ChIP-seq (1.), eQTL (2.), CAGE data (3.), TF K/O data (4.) and that they are useful to study TF- driven processes in a cell type-specific manner (5.). 2. We performed extensive additional benchmarking of other GRNs. The additional evaluation of cell-type specific TF K/O data highlights the importance of cell type-specific eGRNs such as those from GRaNIE, which performed better or on par with the best other networks. 3. We followed-up on one of the TFs (PURA) predicted by GRaNPA to have a role in macrophage polarisation. These additional analyses support a role of PURA in macrophage proinflammatory programs, for which it previously was not known. This highlights the use of GRaNPA for finding novel TFs that may establish a specific expression response. 4. GRaNIE is now available on Bioconductor with further improved usability. Overall, this ensures easy installation, technical robustness, high-quality code and maintenance.
