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

• 发表时间: Dec 2018
• 来源期刊: J Pediatr Genet
• DOI: 10.1055/s-0038-1655755
• PMID: 30430034
• 文章类型: Case Reports
• 作者列表: Callaway DA,Campbell IM,Stover SR,Hernandez-Garcia A,Jhangiani SN,Punetha J,Paine IS,Posey JE,Muzny D,Lally KP,Lupski JR,Shaw CA,Fernandes CJ,Scott DA

关键词: Wolf–Hirschhorn syndrome; congenital diaphragmatic hernia; machine-learning algorithm

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.