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      Genome-Wide Association Studies of Conotruncal Heart Defects with Normally Related Great Vessels in the United States

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          Abstract

          Conotruncal defects with normally related great vessels (CTD-NRGVs) occur in both patients with and without 22q11.2 deletion syndrome (22q11.2DS), but it is unclear to what extent the genetically complex etiologies of these heart defects may overlap across these two groups, potentially involving variation within and/or outside of the 22q11.2 region. To explore this potential overlap, we conducted genome-wide SNP-level, gene-level, and gene set analyses using common variants, separately in each of five cohorts, including two with 22q11.2DS ( N = 1472 total cases) and three without 22q11.2DS ( N = 935 total cases). Results from the SNP-level analyses were combined in meta-analyses, and summary statistics from these analyses were also used in gene and gene set analyses. Across all these analyses, no association was significant after correction for multiple comparisons. However, several SNPs, genes, and gene sets with suggestive evidence of association were identified. For common inherited variants, we did not identify strong evidence for shared genomic mechanisms for CTD-NRGVs across individuals with and without 22q11.2 deletions. Nevertheless, several of our top gene-level and gene set results have been linked to cardiogenesis and may represent candidates for future work.

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          The reactome pathway knowledgebase

          Abstract The Reactome Knowledgebase (https://reactome.org) provides molecular details of signal transduction, transport, DNA replication, metabolism and other cellular processes as an ordered network of molecular transformations in a single consistent data model, an extended version of a classic metabolic map. Reactome functions both as an archive of biological processes and as a tool for discovering functional relationships in data such as gene expression profiles or somatic mutation catalogs from tumor cells. To extend our ability to annotate human disease processes, we have implemented a new drug class and have used it initially to annotate drugs relevant to cardiovascular disease. Our annotation model depends on external domain experts to identify new areas for annotation and to review new content. New web pages facilitate recruitment of community experts and allow those who have contributed to Reactome to identify their contributions and link them to their ORCID records. To improve visualization of our content, we have implemented a new tool to automatically lay out the components of individual reactions with multiple options for downloading the reaction diagrams and associated data, and a new display of our event hierarchy that will facilitate visual interpretation of pathway analysis results.
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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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              GWAMA: software for genome-wide association meta-analysis

              Background Despite the recent success of genome-wide association studies in identifying novel loci contributing effects to complex human traits, such as type 2 diabetes and obesity, much of the genetic component of variation in these phenotypes remains unexplained. One way to improving power to detect further novel loci is through meta-analysis of studies from the same population, increasing the sample size over any individual study. Although statistical software analysis packages incorporate routines for meta-analysis, they are ill equipped to meet the challenges of the scale and complexity of data generated in genome-wide association studies. Results We have developed flexible, open-source software for the meta-analysis of genome-wide association studies. The software incorporates a variety of error trapping facilities, and provides a range of meta-analysis summary statistics. The software is distributed with scripts that allow simple formatting of files containing the results of each association study and generate graphical summaries of genome-wide meta-analysis results. Conclusions The GWAMA (Genome-Wide Association Meta-Analysis) software has been developed to perform meta-analysis of summary statistics generated from genome-wide association studies of dichotomous phenotypes or quantitative traits. Software with source files, documentation and example data files are freely available online at http://www.well.ox.ac.uk/GWAMA.
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                Author and article information

                Contributors
                Role: Academic Editor
                Journal
                Genes (Basel)
                Genes (Basel)
                genes
                Genes
                MDPI
                2073-4425
                01 July 2021
                July 2021
                : 12
                : 7
                : 1030
                Affiliations
                [1 ]Department of Epidemiology, Human Genetics and Environmental Sciences, UTHealth School of Public Health, Houston, TX 77030, USA; omobola.o.oluwafemi@ 123456uth.tmc.edu (O.O.O.); Fadi.I.Musfee@ 123456uth.tmc.edu (F.I.M.); Laura.E.Mitchell@ 123456uth.tmc.edu (L.E.M.)
                [2 ]Division of Cardiology, Children’s Hospital of Philadelphia, Philadelphia, PA 19104, USA; goldmuntz@ 123456chop.edu
                [3 ]Department of Pediatrics, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA 19104, USA; hakonarson@ 123456email.chop.edu (H.H.); taylordm@ 123456chop.edu (D.M.T.); emanuel@ 123456chop.edu (B.S.E.)
                [4 ]Department of Biomedical and Health Informatics, Children’s Hospital of Philadelphia, Philadelphia, PA 19104, USA; xiem1@ 123456chop.edu
                [5 ]Center for Applied Genomics, Children’s Hospital of Philadelphia, Philadelphia, PA 19104, USA
                [6 ]Department of Genetics, Albert Einstein College of Medicine, Bronx, NY 10461, USA; bernice.morrow@ 123456einsteinmed.org (B.E.M.); tingweiguo@ 123456gmail.com (T.G.); MCGINN@ 123456chop.edu (D.M.M.-M.)
                [7 ]Division of Human Genetics, Children’s Hospital of Philadelphia, Philadelphia, PA 19104, USA
                Author notes
                Author information
                https://orcid.org/0000-0003-2814-7461
                Article
                genes-12-01030
                10.3390/genes12071030
                8306129
                2cb463ae-a7bb-4d94-b035-84976441ab3a
                © 2021 by the authors.

                Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ( https://creativecommons.org/licenses/by/4.0/).

                History
                : 27 April 2021
                : 30 June 2021
                Categories
                Article

                heart defects,congenital,genome-wide association study

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