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J Pediatr Genet. 2018 Dec;7(4):164-173. doi: 10.1055/s-0038-1655755. Epub 2018 May 30.

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

Author information

1
Department of Pediatrics, Baylor College of Medicine, Houston, Texas, United States.
2
Division of Human Genetics, Children's Hospital of Philadelphia, Philadelphia, Pennsylvania, United States.
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.
5
Department of Pediatric Surgery, McGovern Medical School at UT Health, Houston, Texas, United States.
6
Division of Neonatology, Department of Pediatrics, Baylor College of Medicine, Houston, Texas, United States.
7
Department of Molecular Physiology and Biophysics, Baylor College of Medicine, Houston, Texas, United States.

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

PMID:
30430034
PMCID:
PMC6234038
[Available on 2019-12-01]
DOI:
10.1055/s-0038-1655755

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