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      Rare single‐nucleotide variants in oculo‐auriculo‐vertebral spectrum (OAVS)

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          Abstract

          Background

          Oculo‐auriculo‐vertebral spectrum (OAVS) is a craniofacial developmental disorder that affects structures derived from the first and second pharyngeal arches. The clinically heterogeneous phenotype involves mandibular, oral, and ear development anomalies. Etiology is complex and poorly understood. Genetic factors have been associated, evidenced by chromosomal abnormalities affecting different genomic regions and genes. However, known pathogenic single‐nucleotide variants (SNVs) have only been identified in MYT1 in a restricted number of patients. Therefore, investigations of SNVs on candidate genes may reveal other pathogenic mechanisms.

          Methods

          In a cohort of 73 patients, coding and untranslated regions (UTR) of 10 candidate genes ( CRKL, YPEL1, MAPK1, NKX3‐2, HMX1, MYT1, OTX2, GSC, PUF60, HOXA2) were sequenced. Rare SNVs were selected and in silico predictions were performed to ascertain pathogenicity. Likely pathogenic variants were validated by Sanger sequencing and heritability was assessed when possible.

          Results

          Four likely pathogenic variants in heterozygous state were identified in different patients. Two SNVs were located in the 5’UTR of YPEL1; one in the 3’UTR of CRKL and one in the 3’UTR of OTX2.

          Conclusion

          Our work described variants in candidate genes for OAVS and supported the genetic heterogeneity of the spectrum.

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          Most cited references 34

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          Gene prioritization through genomic data fusion.

          The identification of genes involved in health and disease remains a challenge. We describe a bioinformatics approach, together with a freely accessible, interactive and flexible software termed Endeavour, to prioritize candidate genes underlying biological processes or diseases, based on their similarity to known genes involved in these phenomena. Unlike previous approaches, ours generates distinct prioritizations for multiple heterogeneous data sources, which are then integrated, or fused, into a global ranking using order statistics. In addition, it offers the flexibility of including additional data sources. Validation of our approach revealed it was able to efficiently prioritize 627 genes in disease data sets and 76 genes in biological pathway sets, identify candidates of 16 mono- or polygenic diseases, and discover regulatory genes of myeloid differentiation. Furthermore, the approach identified a novel gene involved in craniofacial development from a 2-Mb chromosomal region, deleted in some patients with DiGeorge-like birth defects. The approach described here offers an alternative integrative method for gene discovery.
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            DANN: a deep learning approach for annotating the pathogenicity of genetic variants.

            Annotating genetic variants, especially non-coding variants, for the purpose of identifying pathogenic variants remains a challenge. Combined annotation-dependent depletion (CADD) is an algorithm designed to annotate both coding and non-coding variants, and has been shown to outperform other annotation algorithms. CADD trains a linear kernel support vector machine (SVM) to differentiate evolutionarily derived, likely benign, alleles from simulated, likely deleterious, variants. However, SVMs cannot capture non-linear relationships among the features, which can limit performance. To address this issue, we have developed DANN. DANN uses the same feature set and training data as CADD to train a deep neural network (DNN). DNNs can capture non-linear relationships among features and are better suited than SVMs for problems with a large number of samples and features. We exploit Compute Unified Device Architecture-compatible graphics processing units and deep learning techniques such as dropout and momentum training to accelerate the DNN training. DANN achieves about a 19% relative reduction in the error rate and about a 14% relative increase in the area under the curve (AUC) metric over CADD's SVM methodology. All data and source code are available at https://cbcl.ics.uci.edu/public_data/DANN/. © The Author 2014. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.
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              Mouse Otx2 functions in the formation and patterning of rostral head.

              The anterior part of the vertebrate head expresses a group of homeo box genes in segmentally restricted patterns during embryogenesis. Among these, Otx2 expression covers the entire fore- and midbrains and takes place earliest. To examine its role in development of the rostral head, a mutation was introduced into this locus. The homozygous mutants did not develop structures anterior to rhombomere 3, indicating an essential role of Otx2 in the formation of the rostral head. In contrast, heterozygous mutants displayed craniofacial malformations designated as otocephaly; affected structures appeared to correspond to the most posterior and most anterior domains of Otx expression where Otx1 is not expressed. The homo- and heterozygous mutant phenotypes suggest Otx2 functions as a gap-like gene in the rostral head where Hox code is not present. The evolutionary significance of Otx2 mutant phenotypes was discussed for the innovation of the neurocranium and the jaw.
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                Author and article information

                Contributors
                melaragno.maria@unifesp.br
                Journal
                Mol Genet Genomic Med
                Mol Genet Genomic Med
                10.1002/(ISSN)2324-9269
                MGG3
                Molecular Genetics & Genomic Medicine
                John Wiley and Sons Inc. (Hoboken )
                2324-9269
                30 August 2019
                October 2019
                : 7
                : 10 ( doiID: 10.1002/mgg3.v7.10 )
                Affiliations
                [ 1 ] Genetics Division, Department of Morphology and Genetics Universidade Federal de São Paulo São Paulo Brazil
                Author notes
                [* ] Correspondence

                Maria Isabel Melaragno, Department of Morphology and Genetics, Universidade Federal de São Paulo, Rua Botucatu, São Paulo, SP, Brazil.

                Email: melaragno.maria@ 123456unifesp.br

                Article
                MGG3959
                10.1002/mgg3.959
                6785430
                31469246
                © 2019 The Authors. Molecular Genetics & Genomic Medicine published by Wiley Periodicals, Inc.

                This is an open access article under the terms of the http://creativecommons.org/licenses/by-nc/4.0/ License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited and is not used for commercial purposes.

                Page count
                Figures: 0, Tables: 2, Pages: 8, Words: 5784
                Product
                Funding
                Funded by: Fundação de Amparo à Pesquisa do Estado de São Paulo
                Award ID: 2014/11572‐8
                Funded by: Coordenação de Aperfeiçoamento de Pessoal de Nível Superior
                Categories
                Original Article
                Original Articles
                Custom metadata
                2.0
                mgg3959
                October 2019
                Converter:WILEY_ML3GV2_TO_NLMPMC version:5.7.0 mode:remove_FC converted:09.10.2019

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