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

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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… Click to show full 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: congenital diaphragmatic; diaphragmatic hernia; machine learning; critical region; learning algorithm; region

Journal Title: Journal of pediatric genetics
Year Published: 2018

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