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      p63 establishes epithelial enhancers at critical craniofacial development genes

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          Abstract

          Enhancers at critical craniofacial development genes are established by p63 and enriched for SNPs associated with CL/P.

          Abstract

          The transcription factor p63 is a key mediator of epidermal development. Point mutations in p63 in patients lead to developmental defects, including orofacial clefting. To date, knowledge on how pivotal the role of p63 is in human craniofacial development is limited. Using an inducible transdifferentiation model, combined with epigenomic sequencing and multicohort meta-analysis of genome-wide association studies data, we show that p63 establishes enhancers at craniofacial development genes to modulate their transcription. Disease-specific substitution mutation in the DNA binding domain or sterile alpha motif protein interaction domain of p63, respectively, eliminates or reduces establishment of these enhancers. We show that enhancers established by p63 are highly enriched for single-nucleotide polymorphisms associated with nonsyndromic cleft lip ± cleft palate (CL/P). These orthogonal approaches indicate a strong molecular link between p63 enhancer function and CL/P, illuminating molecular mechanisms underlying this developmental defect and revealing vital regulatory elements and new candidate causative genes.

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          Transposition of native chromatin for fast and sensitive epigenomic profiling of open chromatin, DNA-binding proteins and nucleosome position.

          We describe an assay for transposase-accessible chromatin using sequencing (ATAC-seq), based on direct in vitro transposition of sequencing adaptors into native chromatin, as a rapid and sensitive method for integrative epigenomic analysis. ATAC-seq captures open chromatin sites using a simple two-step protocol with 500-50,000 cells and reveals the interplay between genomic locations of open chromatin, DNA-binding proteins, individual nucleosomes and chromatin compaction at nucleotide resolution. We discovered classes of DNA-binding factors that strictly avoided, could tolerate or tended to overlap with nucleosomes. Using ATAC-seq maps of human CD4(+) T cells from a proband obtained on consecutive days, we demonstrated the feasibility of analyzing an individual's epigenome on a timescale compatible with clinical decision-making.
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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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              MAGMA: Generalized Gene-Set Analysis of GWAS Data

              By aggregating data for complex traits in a biologically meaningful way, gene and gene-set analysis constitute a valuable addition to single-marker analysis. However, although various methods for gene and gene-set analysis currently exist, they generally suffer from a number of issues. Statistical power for most methods is strongly affected by linkage disequilibrium between markers, multi-marker associations are often hard to detect, and the reliance on permutation to compute p-values tends to make the analysis computationally very expensive. To address these issues we have developed MAGMA, a novel tool for gene and gene-set analysis. The gene analysis is based on a multiple regression model, to provide better statistical performance. The gene-set analysis is built as a separate layer around the gene analysis for additional flexibility. This gene-set analysis also uses a regression structure to allow generalization to analysis of continuous properties of genes and simultaneous analysis of multiple gene sets and other gene properties. Simulations and an analysis of Crohn’s Disease data are used to evaluate the performance of MAGMA and to compare it to a number of other gene and gene-set analysis tools. The results show that MAGMA has significantly more power than other tools for both the gene and the gene-set analysis, identifying more genes and gene sets associated with Crohn’s Disease while maintaining a correct type 1 error rate. Moreover, the MAGMA analysis of the Crohn’s Disease data was found to be considerably faster as well.
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                Author and article information

                Journal
                Sci Adv
                Sci Adv
                SciAdv
                advances
                Science Advances
                American Association for the Advancement of Science
                2375-2548
                May 2019
                01 May 2019
                : 5
                : 5
                : eaaw0946
                Affiliations
                [1 ]Departments of Cell and Developmental Biology and Epigenetics Institute, Philadelphia, PA 19104, USA.
                [2 ]Biochemistry and Molecular Biophysics, Biomedical Sciences Graduate Program, University of Pennsylvania, Perelman School of Medicine, Philadelphia, PA 19104, USA.
                [3 ]Institute of Human Genetics, University Bonn, School of Medicine and University Hospital Bonn, Bonn, Germany.
                [4 ]Institute of Medical Biometry, Informatics and Epidemiology, University of Bonn, Bonn, Germany.
                Author notes
                [*]

                Present address: Department of Biological Sciences, State University of New York at Albany, Albany, NY 12222, USA.

                []Corresponding author. Email: bergers@ 123456pennmedicine.upenn.edu
                Author information
                http://orcid.org/0000-0001-5396-9614
                http://orcid.org/0000-0002-9290-7173
                http://orcid.org/0000-0002-3819-2576
                http://orcid.org/0000-0002-5329-1169
                http://orcid.org/0000-0002-8541-2519
                http://orcid.org/0000-0001-5398-4400
                Article
                aaw0946
                10.1126/sciadv.aaw0946
                6494499
                31049400
                687d2b3a-7901-4db3-825b-d957ffad8fd4
                Copyright © 2019 The Authors, some rights reserved; exclusive licensee American Association for the Advancement of Science. No claim to original U.S. Government Works. Distributed under a Creative Commons Attribution NonCommercial License 4.0 (CC BY-NC).

                This is an open-access article distributed under the terms of the Creative Commons Attribution-NonCommercial license, which permits use, distribution, and reproduction in any medium, so long as the resultant use is not for commercial advantage and provided the original work is properly cited.

                History
                : 16 November 2018
                : 19 March 2019
                Funding
                Funded by: doi http://dx.doi.org/10.13039/100000054, National Cancer Institute;
                Award ID: CA078831
                Categories
                Research Article
                Research Articles
                SciAdv r-articles
                Diseases and Disorders
                Genetics
                Genetics
                Custom metadata
                Jeanelle Ebreo

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