Next-generation phenotyping using computer vision algorithms in rare genomic neurodevelopmental disorders.

ABSTRACT: PURPOSE: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. METHODS: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. RESULTS: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?×?10-4), PPM1D (p?=?4.65?×?10-2), and PHIP (p?=?6.3?×?10-3). Moreover, two individuals with a de novo missense variant of uncertain significance in PHIP have significant similarity to the expected facial phenotype of PHIP patients (p?

SUBMITTER: van der Donk R 

PROVIDER: S-EPMC6752476 | biostudies-literature | 2019 Aug

REPOSITORIES: biostudies-literature