Next-generation phenotyping using computer vision algorithms in rare genomic neurodevelopmental disorders.
Abnormalities, Multiple
/ diagnosis
Adolescent
Adult
Algorithms
Child
Child, Preschool
Chromosome Deletion
Chromosomes, Human, Pair 17
/ genetics
Craniofacial Abnormalities
/ diagnosis
Facial Recognition
Female
Genomics
Humans
Image Processing, Computer-Assisted
Infant
Intellectual Disability
/ diagnosis
Intracellular Signaling Peptides and Proteins
/ genetics
Male
Middle Aged
Muscular Atrophy
/ diagnosis
Musculoskeletal Abnormalities
/ diagnosis
Mutation, Missense
/ genetics
Neurodevelopmental Disorders
/ diagnosis
Phenotype
Protein Phosphatase 2C
/ genetics
Vesicular Transport Proteins
/ genetics
Young Adult
facial image processing
facial phenotyping
phenotyping
Journal
Genetics in medicine : official journal of the American College of Medical Genetics
ISSN: 1530-0366
Titre abrégé: Genet Med
Pays: United States
ID NLM: 9815831
Informations de publication
Date de publication:
08 2019
08 2019
Historique:
received:
02
07
2018
accepted:
03
12
2018
pubmed:
21
12
2018
medline:
7
2
2020
entrez:
21
12
2018
Statut:
ppublish
Résumé
The interpretation of genetic variants after genome-wide analysis is complex in heterogeneous disorders such as intellectual disability (ID). We investigate whether algorithms can be used to detect if a facial gestalt is present for three novel ID syndromes and if these techniques can help interpret variants of uncertain significance. Facial features were extracted from photos of ID patients harboring a pathogenic variant in three novel ID genes (PACS1, PPM1D, and PHIP) using algorithms that model human facial dysmorphism, and facial recognition. The resulting features were combined into a hybrid model to compare the three cohorts against a background ID population. We validated our model using images from 71 individuals with Koolen-de Vries syndrome, and then show that facial gestalts are present for individuals with a pathogenic variant in PACS1 (p = 8 × 10 Our results show that analysis of facial photos can be used to detect previously unknown facial gestalts for novel ID syndromes, which will facilitate both clinical and molecular diagnosis of rare and novel syndromes.
Identifiants
pubmed: 30568311
doi: 10.1038/s41436-018-0404-y
pii: S1098-3600(21)01612-9
pmc: PMC6752476
doi:
Substances chimiques
Intracellular Signaling Peptides and Proteins
0
PACS1 protein, human
0
PHIP protein, human
0
Vesicular Transport Proteins
0
PPM1D protein, human
EC 3.1.3.16
Protein Phosphatase 2C
EC 3.1.3.16
Types de publication
Journal Article
Research Support, Non-U.S. Gov't
Langues
eng
Sous-ensembles de citation
IM
Pagination
1719-1725Subventions
Organisme : Medical Research Council
ID : MR/M014568/1
Pays : United Kingdom
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