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Clin Genet. 2018 Feb;93(2):378-381. doi: 10.1111/cge.13087. Epub 2017 Nov 29.

Next generation phenotyping in Emanuel and Pallister-Killian syndrome using computer-aided facial dysmorphology analysis of 2D photos.

Author information

1
Jena University Hospital, Friedrich Schiller University, Institute of Human Genetics, Jena, Germany.
2
Pallister-Killian Syndrome Foundation of Australia, Myaree, Australia.
3
Chromosome 22 Central, Timmins, Canada.
4
Chromosome 22 Central, Fuquay-Varina, North Carolina.
5
FDNA Inc., Boston, Massachusetts.
6
Center for Ambulant Medicine, Human Genetics, Jena, Germany.

Abstract

High throughput approaches are continuously progressing and have become a major part of clinical diagnostics. Still, the critical process of detailed phenotyping and gathering clinical information has not changed much in the last decades. Forms of next generation phenotyping (NGP) are needed to increase further the value of any kind of genetic approaches, including timely consideration of (molecular) cytogenetics during the diagnostic quest. As NGP we used in this study the facial dysmorphology novel analysis (FDNA) technology to automatically identify facial phenotypes associated with Emanuel (ES) and Pallister-Killian Syndrome (PKS) from 2D facial photos. The comparison between ES or PKS and normal individuals expressed a full separation between the cohorts. Our results show that NPG is able to help in the clinic, and could reduce the time patients spend in diagnostic odyssey. It also helps to differentiate ES or PKS from each other and other patients with small supernumerary marker chromosomes, especially in countries with no access to more sophisticated genetic approaches apart from banding cytogenetics. Inclusion of more facial pictures of patient with sSMC, like isochromosome-18p-, cat-eye-syndrome or others may contribute to higher detection rates in future.

KEYWORDS:

Emanuel syndrome; OMIM 609029; Face2Gene; Pallister-Killian syndrome; OMIM 601803; facial dysmorphology novel analysis technology; small supernumerary marker chromosome

PMID:
28661575
DOI:
10.1111/cge.13087

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