Next‐generation phenotyping contributing to the identification of a 4.7 kb deletion in KANSL1 causing Koolen‐de Vries syndrome

Abstract

Next‐generation phenotyping (NGP) is an application of advanced methods of computer vision on medical imaging data such as portrait photos of individuals with rare disorders. NGP on portraits results in gestalt scores that can be used for the selection of appropriate genetic tests, and for the interpretation of the molecular data. Here, we report on an exceptional case of a young girl that was presented at the age of 8 and 15 and enrolled in NGP diagnostics on the latter occasion. The girl had clinical features associated with Koolen‐de Vries syndrome (KdVS) and a suggestive facial gestalt. However, chromosomal microarray (CMA), Sanger sequencing, multiplex ligation‐dependent probe analysis (MLPA), and trio exome sequencing remained inconclusive. Based on the highly indicative gestalt score for KdVS, the decision was made to perform genome sequencing to also evaluate noncoding variants. This analysis revealed a 4.7 kb de novo deletion partially affecting intron 6 and exon 7 of the KANSL1 gene. This is the smallest reported structural variant to date for this phenotype. The case illustrates how NGP can be integrated into the iterative diagnostic process of test selection and interpretation of sequencing results.

Next-generation phenotyping integrated in a national framework for patients with ultra-rare disorders improves genetic diagnostics and yields new molecular findings

“…A total of 224 of the 1,577 patients had also provided written informed consent for the evaluation of their facial images with the AI tool GestaltMatcher 40 and the use of the results (gestalt scores) in exome variant interpretation (PEDIA, prioritization of exome data by image analysis) 41 . In 94 of these PEDIA subcohort cases, a molecular diagnosis was established.…”

“…Prioritization of exome data by image analysis (PEDIA) integrated the facial image and clinical feature analysis with exome data analysis 41 . For each patient, a frontal facial image, clinical features encoded in HPO terminology, and exome sequencing data were available for analysis.…”

“…Subsequently, the proportion of cases detected with the correct disease-associated gene (sensitivity) was shown as a function of the number of disease-associated genes considered, beginning at the top score. The following four approaches for variant prioritization were tested in solved cases from the PEDIA cohort (n=94): 1) only a molecular pathogenicity score (CADD 86 ) with top-10 accuracy of 48%; 2) feature-based score (CADA85 ) in addition to CADD with top-10 accuracy of 68%; 3 and 4) A gestalt score from facial image analysis (GestaltMatcher 40 ) alone or in addition to both CADD and CADA referred to as PEDIA score41 with top-10 accuracy of 82%. Note that bootstrapped 95% confidence intervals are indicated by the lighter shading around the lines.…”

Facilitating the Molecular Diagnosis of Rare Genetic Disorders Through Facial Phenotypic Scores

“…In the past, such analyses have usually been done by integrating Human Phenotype Ontology (HPO) terminology into the prioritization (Köhler et al, 2021;Smedley et al, 2015). The facial phenotypic scores quantified by DeepGestalt (Gurovich et al, 2019) and GestaltMatcher (Hsieh et al, 2022) can further assist the prioritization of variants with facial image analysis (Brand et al, 2022;Forwood et al, 2023;Hsieh et al, 2019).…”

Computational facial analysis for rare Mendelian disorders

“…Moreover, GestaltMatcher could suggest to the clinician to perform the whole genome sequencing when no pathogenic variants were found. Brand et al presented a patient with a 4.7 kb deletion in KANSL1 (Brand et al, 2022). Initially, the authors did not find the pathogenic variants with exome sequencing.…”