Link: https://www.ncbi.nlm.nih.gov/pubmed/30430034

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.
Callaway DA1, Campbell IM2, Stover SR3, Hernandez-Garcia A3, Jhangiani SN3,4, Punetha J3, Paine IS3, Posey JE3, Muzny D3,4, Lally KP5, Lupski JR3,4,6, Shaw CA3, Fernandes CJ6, Scott DA3,7.
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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

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