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      hReg-CNCC reconstructs a regulatory network in human cranial neural crest cells and annotates variants in a developmental context

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          Abstract

          Cranial Neural Crest Cells (CNCC) originate at the cephalic region from forebrain, midbrain and hindbrain, migrate into the developing craniofacial region, and subsequently differentiate into multiple cell types. The entire specification, delamination, migration, and differentiation process is highly regulated and abnormalities during this craniofacial development cause birth defects. To better understand the molecular networks underlying CNCC, we integrate paired gene expression & chromatin accessibility data and reconstruct the genome-wide human Regulatory network of CNCC (hReg-CNCC). Consensus optimization predicts high-quality regulations and reveals the architecture of upstream, core, and downstream transcription factors that are associated with functions of neural plate border, specification, and migration. hReg-CNCC allows us to annotate genetic variants of human facial GWAS and disease traits with associated cis-regulatory modules, transcription factors, and target genes. For example, we reveal the distal and combinatorial regulation of multiple SNPs to core TF ALX1 and associations to facial distances and cranial rare disease. In addition, hReg-CNCC connects the DNA sequence differences in evolution, such as ultra-conserved elements and human accelerated regions, with gene expression and phenotype. hReg-CNCC provides a valuable resource to interpret genetic variants as early as gastrulation during embryonic development. The network resources are available at https://github.com/AMSSwanglab/hReg-CNCC.

          Abstract

          Zhanying Feng et al. present hReg-CNCC, a high-quality gene regulatory network for human cranial neural crest cells (CNCCs) constructed by consensus optimization modeling. It may be useful in interpreting genetic variants involved in embryonic development by linking the cis-regulatory sequences in this network with GWAS SNPs, disease risk loci, and evolutionarily-conserved regions of the genome.

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          Most cited references66

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          Functional mapping and annotation of genetic associations with FUMA

          A main challenge in genome-wide association studies (GWAS) is to pinpoint possible causal variants. Results from GWAS typically do not directly translate into causal variants because the majority of hits are in non-coding or intergenic regions, and the presence of linkage disequilibrium leads to effects being statistically spread out across multiple variants. Post-GWAS annotation facilitates the selection of most likely causal variant(s). Multiple resources are available for post-GWAS annotation, yet these can be time consuming and do not provide integrated visual aids for data interpretation. We, therefore, develop FUMA: an integrative web-based platform using information from multiple biological resources to facilitate functional annotation of GWAS results, gene prioritization and interactive visualization. FUMA accommodates positional, expression quantitative trait loci (eQTL) and chromatin interaction mappings, and provides gene-based, pathway and tissue enrichment results. FUMA results directly aid in generating hypotheses that are testable in functional experiments aimed at proving causal relations.
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            GREAT improves functional interpretation of cis-regulatory regions.

            We developed the Genomic Regions Enrichment of Annotations Tool (GREAT) to analyze the functional significance of cis-regulatory regions identified by localized measurements of DNA binding events across an entire genome. Whereas previous methods took into account only binding proximal to genes, GREAT is able to properly incorporate distal binding sites and control for false positives using a binomial test over the input genomic regions. GREAT incorporates annotations from 20 ontologies and is available as a web application. Applying GREAT to data sets from chromatin immunoprecipitation coupled with massively parallel sequencing (ChIP-seq) of multiple transcription-associated factors, including SRF, NRSF, GABP, Stat3 and p300 in different developmental contexts, we recover many functions of these factors that are missed by existing gene-based tools, and we generate testable hypotheses. The utility of GREAT is not limited to ChIP-seq, as it could also be applied to open chromatin, localized epigenomic markers and similar functional data sets, as well as comparative genomics sets.
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              Activity-by-Contact model of enhancer-promoter regulation from thousands of CRISPR perturbations

              Enhancer elements in the human genome control how genes are expressed in specific cell types and harbor thousands of genetic variants that influence risk for common diseases 1–4 . Yet, we still do not know how enhancers regulate specific genes, and we lack general rules to predict enhancer-gene connections across cell types 5,6 . We developed an experimental approach, CRISPRi-FlowFISH, to perturb enhancers in the genome and applied it to test >3,500 potential enhancer-gene connections for 30 genes. We found that a simple Activity-by-Contact (ABC) model substantially outperformed previous methods at predicting the complex connections in our CRISPR dataset. This ABC model allows us to construct genome-wide maps of enhancer-gene connections in a given cell type based on chromatin state measurements. Together, CRISPRi-FlowFISH and the ABC model provide a systematic approach to map and predict which enhancers regulate which genes, and will help to interpret the functions of the thousands of disease risk variants in the noncoding genome.
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                Author and article information

                Contributors
                liufan@big.ac.cn
                whwong@stanford.edu
                ywang@amss.ac.cn
                Journal
                Commun Biol
                Commun Biol
                Communications Biology
                Nature Publishing Group UK (London )
                2399-3642
                6 April 2021
                6 April 2021
                2021
                : 4
                : 442
                Affiliations
                [1 ]GRID grid.458463.8, ISNI 0000 0004 0489 6406, CEMS, NCMIS, MDIS, Academy of Mathematics and Systems Science, National Center for Mathematics and Interdisciplinary Sciences, Chinese Academy of Sciences, ; Beijing, China
                [2 ]GRID grid.410726.6, ISNI 0000 0004 1797 8419, School of Mathematics, , University of Chinese Academy of Sciences, Chinese Academy of Sciences, ; Beijing, China
                [3 ]GRID grid.26090.3d, ISNI 0000 0001 0665 0280, Center for Human Genetics, Department of Genetics and Biochemistry, , Clemson University, ; Greenwood, SC USA
                [4 ]GRID grid.168010.e, ISNI 0000000419368956, Department of Statistics, Department of Biomedical Data Science, Bio-X Program, , Stanford University, ; Stanford, CA USA
                [5 ]GRID grid.5645.2, ISNI 000000040459992X, Department of Genetic Identification, , Erasmus MC University Medical Center Rotterdam, ; Rotterdam, Netherlands
                [6 ]GRID grid.5645.2, ISNI 000000040459992X, Department of Epidemiology, , Erasmus MC University Medical Center Rotterdam, ; Rotterdam, Netherlands
                [7 ]GRID grid.464209.d, ISNI 0000 0004 0644 6935, CAS Key Laboratory of Genomic and Precision Medicine, Beijing Institute of Genomics, Chinese Academy of Sciences, ; Beijing, China
                [8 ]GRID grid.419092.7, ISNI 0000 0004 0467 2285, Key Laboratory of Computational Biology, CAS-MPG Partner Institute for Computational Biology, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, ; Shanghai, China
                [9 ]GRID grid.9227.e, ISNI 0000000119573309, Center for Excellence in Animal Evolution and Genetics, Chinese Academy of Sciences, ; Kunming, China
                [10 ]GRID grid.9227.e, ISNI 0000000119573309, China National Center for Bioinformation, Chinese Academy of Sciences, ; Beijing, China
                [11 ]GRID grid.410726.6, ISNI 0000 0004 1797 8419, Key Laboratory of Systems Biology, Hangzhou Institute for Advanced Study, , University of Chinese Academy of Sciences, Chinese Academy of Sciences, ; Hangzhou, China
                Author information
                http://orcid.org/0000-0002-5727-3929
                http://orcid.org/0000-0001-6961-7867
                http://orcid.org/0000-0001-9241-8161
                http://orcid.org/0000-0001-7466-2339
                http://orcid.org/0000-0003-0695-5273
                Article
                1970
                10.1038/s42003-021-01970-0
                8024315
                33824393
                2c11a5d6-3b7d-493b-87eb-ab12b14ed390
                © The Author(s) 2021

                Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.

                History
                : 30 August 2020
                : 9 March 2021
                Funding
                Funded by: FundRef https://doi.org/10.13039/501100001809, National Natural Science Foundation of China (National Science Foundation of China);
                Award ID: 12025107, 11871463, 61621003, and 91651507
                Award Recipient :
                Funded by: Shanghai Municipal Science and Technology Major Project with grant number 2017SHZDZX01 CAS "Light of West China" Program with grant number xbzg-zdsys-201913 "CAS Interdisciplinary Innovation Team" project, National Key R&D Program of China with grant number 2017YFC0908400 and 2020YFA0712402 Strategic Priority Research Program of Chinese Academy of Sciences with grant number XDC01000000 and XDB38010400.
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                © The Author(s) 2021

                gene regulation,gene regulatory networks
                gene regulation, gene regulatory networks

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