SORDD: mutation frequency and phenotype in predominantly axonal Charcot-Marie-Tooth disease of undefined genetic cause.

Pathogenic, biallelic variants in SORD were identified in 2020 as a novel cause for autosomal-recessive Charcot-Marie-Tooth disease (CMT) type 2, an inherited neuropathy. SORD codes for the enzyme sorbitol dehydrogenase. Loss of this enzyme's activity leads to an increase of sorbitol in serum. We retrospectively screened 166 patients with axonal neuropathy (predominantly CMT type 2, but including intermediate form of CMT and distal hereditary motor neuropathy (dHMN)) without identified genetic etiology for SORD mutations at a single large German neuromuscular center. Clinical and electrophysiology exam findings were analyzed for genotype-phenotype correlation. Five patients of the total cohort of 166 patients harbored pathogenic variants in SORD (3%). The homozygous frameshift variant c.757delG (p.Ala253Glnfs*27) was the most common (4/5). One additional case carried this variant on one allele only and an additional pathogenic missense variant c.458C > A (p.Ala153Asp) on the other allele. Age of onset ranged from early infancy to mid-twenties, and phenotypes comprised axonal CMT (4) and dHMN (1). Our findings strengthen the importance of screening for pathogenic variants in SORD, especially in patients with genetically unconfirmed axonal neuropathy, especially CMT type 2 and dHMN.

GestaltMatcher Database - A global reference for facial phenotypic variability in rare human diseases.

The most important factor that complicates the work of dysmorphologists is the significant phenotypic variability of the human face. Next-Generation Phenotyping (NGP) tools that assist clinicians with recognizing characteristic syndromic patterns are particularly challenged when confronted with patients from populations different from their training data. To that end, we systematically analyzed the impact of genetic ancestry on facial dysmorphism. For that purpose, we established the GestaltMatcher Database (GMDB) as a reference dataset for medical images of patients with rare genetic disorders from around the world. We collected 10,980 frontal facial images - more than a quarter previously unpublished - from 8,346 patients, representing 581 rare disorders. Although the predominant ancestry is still European (67%), data from underrepresented populations have been increased considerably via global collaborations (19% Asian and 7% African). This includes previously unpublished reports for more than 40% of the African patients. The NGP analysis on this diverse dataset revealed characteristic performance differences depending on the composition of training and test sets corresponding to genetic relatedness. For clinical use of NGP, incorporating non-European patients resulted in a profound enhancement of GestaltMatcher performance. The top-5 accuracy rate increased by +11.29%. Importantly, this improvement in delineating the correct disorder from a facial portrait was achieved without decreasing the performance on European patients. By design, GMDB complies with the FAIR principles by rendering the curated medical data findable, accessible, interoperable, and reusable. This means GMDB can also serve as data for training and benchmarking. In summary, our study on facial dysmorphism on a global sample revealed a considerable cross ancestral phenotypic variability confounding NGP that should be counteracted by international efforts for increasing data diversity. GMDB will serve as a vital reference database for clinicians and a transparent training set for advancing NGP technology.