Next‐generation phenotyping in Nigerian children with Cornelia de Lange syndrome

Next‐generation phenotyping (NGP) can be used to compute the similarity of dysmorphic patients to known syndromic diseases. So far, the technology has been evaluated in variant prioritization and classification, providing evidence for pathogenicity if the phenotype matched with other patients with a...

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Bibliographic Details
Published in:American journal of medical genetics. Part A 2024-09, Vol.194 (9), p.e63641-n/a
Main Authors: Arlt, Annabelle, Knaus, Alexej, Hsieh, Tzung‐Chien, Klinkhammer, Hannah, Bhasin, Meghna Ahuja, Hustinx, Alexander, Moosa, Shahida, Krawitz, Peter, Ekure, Ekanem
Format: Article
Language:English
Subjects:
Artificial intelligence
Cornelia de Lange syndrome
De Lange's syndrome
facial dysmorphism
Limb malformations
next‐generation phenotyping
Nigeria
NIPBL
Pathogenicity
Phenotypes
Phenotyping
Pulmonary arteries
Pulmonary artery
Stenosis
West Africa
Whole genome sequencing
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Summary:Next‐generation phenotyping (NGP) can be used to compute the similarity of dysmorphic patients to known syndromic diseases. So far, the technology has been evaluated in variant prioritization and classification, providing evidence for pathogenicity if the phenotype matched with other patients with a confirmed molecular diagnosis. In a Nigerian cohort of individuals with facial dysmorphism, we used the NGP tool GestaltMatcher to screen portraits prior to genetic testing and subjected individuals with high similarity scores to exome sequencing (ES). Here, we report on two individuals with global developmental delay, pulmonary artery stenosis, and genital and limb malformations for whom GestaltMatcher yielded Cornelia de Lange syndrome (CdLS) as the top hit. ES revealed a known pathogenic nonsense variant, NM_133433.4: c.598C>T; p.(Gln200*), as well as a novel frameshift variant c.7948dup; p.(Ile2650Asnfs*11) in NIPBL. Our results suggest that NGP can be used as a screening tool and thresholds could be defined for achieving high diagnostic yields in ES. Training the artificial intelligence (AI) with additional cases of the same ethnicity might further increase the positive predictive value of GestaltMatcher.
ISSN:1552-4825
1552-4833
1552-4833
DOI:10.1002/ajmg.a.63641