The majority of monogenic disorders cause craniofacial abnormalities with characteristic facial morphology. These disorders can be diagnosed more e ciently with the support of computer-aided nextgeneration phenotyping tools, such as DeepGestalt. These tools have learned to associate facial phenotypes with the underlying syndrome through training on thousands of patient photographs. However, this "supervised" approach means that diagnoses are only possible if they were part of the training set. To improve recognition of ultra-rare diseases, we created GestaltMatcher, which uses a deep convolutional neural network based on the DeepGestalt framework. We used photographs of 21,836 patients with 1,362 rare disorders to de ne a "Clinical Face Phenotype Space". Distance between cases in the phenotype space de nes syndromic similarity, allowing test patients to be matched to a molecular diagnosis even when the disorder was not included in the training set. Similarities among patients with previously unknown disease genes can also be detected. Therefore, in concert with mutation data, GestaltMatcher could accelerate the clinical diagnosis of patients with ultra-rare disorders and facial dysmorphism.

The majority of monogenic disorders cause craniofacial abnormalities with characteristic facial morphology. These disorders can be diagnosed more efficiently with the support of computer-aided next-generation phenotyping tools, such as DeepGestalt. These tools have learned to associate facial phenotypes with the underlying syndrome through training on thousands of patient photographs. However, this “supervised” approach means that diagnoses are only possible if they were part of the training set. To improve recognition of ultra-rare diseases, we created GestaltMatcher, which uses a deep convolutional neural network based on the DeepGestalt framework. We used photographs of 21,836 patients with 1,362 rare disorders to define a “Clinical Face Phenotype Space”. Distance between cases in the phenotype space defines syndromic similarity, allowing test patients to be matched to a molecular diagnosis even when the disorder was not included in the training set. Similarities among patients with previously unknown disease genes can also be detected. Therefore, in concert with mutation data, GestaltMatcher could accelerate the clinical diagnosis of patients with ultra-rare disorders and facial dysmorphism.

Hemizygous loss-of-function variants in the non-POU domain-containing, octamerbinding gene, NONO, cause X-linked mental retardation syndrome 34 (MRXS34).Here, we describe the 12th patient in the literature with this rare syndrome, the first affected male from sub-Saharan Africa. This South African patient presented with dysmorphic features, congenital cardiac abnormalities (Ebstein's anomaly, left ventricular non-compaction, and a VSD), and developmental delay. He was enrolled in our "Undiagnosed Disease Programme." Exome sequencing identified a novel hemizygous 14bp deletion in NONO, which he inherited from his unaffected, healthy mother. His features overlap with the previous patients described, lending more support to the assertion that MRXS34 is a recognizable, albeit rare, syndrome. The cardiac anomalies are particularly distinctive, which combined with a variety of other associated features, should prompt the inclusion of NONO-associated MRXS34 in the differential diagnosis.