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J Pediatr Genet 2018; 07(04): 164-173
DOI: 10.1055/s-0038-1655755
Case Report
Georg Thieme Verlag KG Stuttgart · New York

Prioritization of Candidate Genes for Congenital Diaphragmatic Hernia in a Critical Region on Chromosome 4p16 using a Machine-Learning Algorithm

Danielle A. Callaway
1   Department of Pediatrics, Baylor College of Medicine, Houston, Texas, United States
,
Ian M. Campbell
2   Division of Human Genetics, Children's Hospital of Philadelphia, Philadelphia, Pennsylvania, United States
,
Samantha R. Stover
3   Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, Texas, United States
,
Andres Hernandez-Garcia
3   Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, Texas, United States
,
Shalini N. Jhangiani
3   Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, Texas, United States
4   Human Genome Sequencing Center, Baylor College of Medicine, Houston, Texas, United States
,
Jaya Punetha
3   Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, Texas, United States
,
Ingrid S. Paine
3   Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, Texas, United States
,
Jennifer E. Posey
3   Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, Texas, United States
,
Donna Muzny
3   Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, Texas, United States
4   Human Genome Sequencing Center, Baylor College of Medicine, Houston, Texas, United States
,
Kevin P. Lally
5   Department of Pediatric Surgery, McGovern Medical School at UT Health, Houston, Texas, United States
,
James R. Lupski
3   Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, Texas, United States
4   Human Genome Sequencing Center, Baylor College of Medicine, Houston, Texas, United States
6   Division of Neonatology, Department of Pediatrics, Baylor College of Medicine, Houston, Texas, United States
,
Chad A. Shaw
3   Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, Texas, United States
,
Caraciolo J. Fernandes
6   Division of Neonatology, Department of Pediatrics, Baylor College of Medicine, Houston, Texas, United States
,
Daryl A. Scott
3   Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, Texas, United States
7   Department of Molecular Physiology and Biophysics, Baylor College of Medicine, Houston, Texas, United States
› Author Affiliations Funding This project was supported by the National Institutes of Health/Eunice Kennedy Shriver National Institute of Child Health and Human Development (R01 HD064667 to DAS), National Institutes of Health/National Institute of Neurological Disease and Stroke (F30 NS083159 to IMC), the United States National Human Genome Research Institute/National Heart Blood and Lung Institute (UM1 HG006542 to the Baylor-Hopkins Center for Mendelian Genomics), the National Human Genome Research Institute (K08 HG008986 to JEP), and the Ting Tsung and Wei Fong Chao Foundation (Physician-Scientist Award to JEP).
Further Information

Publication History

01 December 2017

10 April 2018

Publication Date:
30 May 2018 (online)

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Abstract

Wolf–Hirschhorn syndrome (WHS) is caused by partial deletion of the short arm of chromosome 4 and is characterized by dysmorphic facies, congenital heart defects, intellectual/developmental disability, and increased risk for congenital diaphragmatic hernia (CDH). In this report, we describe a stillborn girl with WHS and a large CDH. A literature review revealed 15 cases of WHS with CDH, which overlap a 2.3-Mb CDH critical region. We applied a machine-learning algorithm that integrates large-scale genomic knowledge to genes within the 4p16.3 CDH critical region and identified FGFRL1, CTBP1, NSD2, FGFR3, CPLX1, MAEA, CTBP1-AS2, and ZNF141 as genes whose haploinsufficiency may contribute to the development of CDH.

Keywords

Wolf–Hirschhorn syndrome - congenital diaphragmatic hernia - machine-learning algorithm

Supplementary Material

 

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