Alström syndrome (AS) is a monogenic syndromic ciliopathy caused by mutations in the ALMS1 (Alström Syndrome 1) gene. A total of 21 subjects with AS from 20 unrelated Chinese families were recruited. Our cohort consists of 9 females and 12 males, between 5 months and 20 years old. The first symptom(s) appeared between 3 and 24 months. They were recorded to be either visual impairments (83%) or dilated cardiomyopathy (17%). Median time from symptom onset to seeking medical attention was 6 months (3-36 months) and the median time needed to reach the final molecular diagnosis is 54 months (6-240 months). System involvement at the time of the survey was as follows: visual symptoms (100%), hearing Impairment (67%), endocrine symptoms (43%), neurological symptoms (19%), hepatic symptoms (14%), and renal Involvement (14%). These findings are comparable to data reported in the literature. However, the proportion of subjects with cognitive impairment (33%) and behavioral problems (19%) were higher. Thirty-three unique mutations were identified in the ALMS1 gene, of which 18 are novel mutations classified as pathogenic/likely pathogenic according to the American College of Medical Genetics (ACMG) guideline. Four recurrent mutations were identified in the cohort, in particular; c.2084C>A, p. (Ser695Ter), is suggestive to be a founder mutation in people of Chinese ancestry. The participation of AS subjects of differing ethnicities is essential to improve the algorithm in facial recognition/phenotyping, as well as to understand the mutation spectrum beyond than just those of European ancestry.
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 We collected 10,980 frontal facial images - more than a quarter previously unpublished - from 8,346 patients, representing 581 rare disorders.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.
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.