Variants in Candidate Genes for Phenotype Heterogeneity in Patients with the 22q11.2 Deletion Syndrome

22q11.2 deletion syndrome (22q11.2DS) is a microdeletion syndrome with a broad and heterogeneous phenotype, even though most of the deletions present similar sizes, involving ∼3 Mb of DNA. In a relatively large population of a Brazilian 22q11.2DS cohort (60 patients), we investigated genetic variants that could act as genetic modifiers and contribute to the phenotypic heterogeneity, using a targeted NGS (Next Generation Sequencing) with a specific Ion AmpliSeq panel to sequence nine candidate genes (CRKL, MAPK1, HIRA, TANGO2, PI4KA, HDAC1, ZDHHC8, ZFPM2, and JAM3), mapped in and outside the 22q11.2 hemizygous deleted region. In silico prediction was performed, and the whole-genome sequencing annotation analysis package (WGSA) was used to predict the possible pathogenic effect of single nucleotide variants (SNVs). For the in silico prediction of the indels, we used the genomic variants filtered by a deep learning model in NGS (GARFIELD-NGS). We identified six variants, 4 SNVs and 2 indels, in MAPK1, JAM3, and ZFPM2 genes with possibly synergistic deleterious effects in the context of the 22q11.2 deletion. Our results provide the opportunity for the discovery of the co-occurrence of genetic variants with 22q11.2 deletions, which may influence the patients´ phenotype.

GestaltMatcher Database - A global reference for the facial phenotypic variability of rare human diseases

Dysmorphologists sometimes encounter challenges in recognizing disorders due to phenotypic variability influenced by factors such as age and ethnicity. Moreover, the performance of Next Generation Phenotyping Tools such as GestaltMatcher is dependent on the diversity of the training set. Therefore, we developed GestaltMatcher Database (GMDB) - a global reference for the phenotypic variability of rare diseases that complies with the FAIR-principles. We curated dysmorphic patient images and metadata from 2,224 publications, transforming GMDB into an online dynamic case report journal. To encourage clinicians worldwide to contribute, each case can receive a Digital Object Identifier (DOI), making it a citable micro-publication. This resulted in a collection of 2,312 unpublished images, partly with longitudinal data. We have compiled a collection of 10,189 frontal images from 7,695 patients representing 683 disorders. The web interface enables gene- and phenotype-centered queries for registered users (https://db.gestaltmatcher.org/). Despite the predominant European ancestry of most patients (59%), our global collaborations have facilitated the inclusion of data from frequently underrepresented ethnicities, with 17% Asian, 4% African, and 6% with other ethnic backgrounds. The analysis has revealed a significant enhancement in GestaltMatcher performance across all ethnic groups, incorporating non-European ethnicities, showcasing a remarkable increase in Top-1-Accuracy by 31.56% and Top-5-Accuracy by 12.64%. Importantly, this improvement was achieved without altering the performance metrics for European patients. GMDB addresses dysmorphology challenges by representing phenotypic variability and including underrepresented groups, enhancing global diagnostic rates and serving as a vital clinician reference database.