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ABSTRACT Background Acromesomelic chondrodysplasias are a rare subgroup of the clinically and genetically heterogeneous osteochondrodysplasias that are characterised by abnormalities in the limb development and short stature. Here, we report a 2‐year‐old boy, offspring of consanguineous parents, with acromesomelic dysplasia and postaxial polydactyly in which exome sequencing identified a novel homozygous missense variant in BMPR1B. The patient showed skeletal malformation of both hands and feet that included complex brachydactyly with the thumbs most severely affected, postaxial polydactyly of both hands, shortened toes as well as a bilateral hypoplasia of the fibula. Methods Whole trio exome sequencing was conducted to identify potential genetic variants in the patient. Results The analysis identified the biallelic variant NM_001203.3:c.821A > G;p.(Gln274Arg) in BMPR1B, a gene encoding bone morphogenetic protein receptor 1B. Conclusion The skeletal phenotype can be brought in line with the phenotypes of previously reported cases of BMPR1B‐associated chondrodysplasias. However, the postaxial polydactyly described here is a novel clinical finding in a BMPR1B‐related case; notably, it has previously been reported in other acromesomelic dysplasia cases caused by homozygous pathogenic variants in GDF5—a gene which encodes for growth differentiation factor 5, a high‐affinity ligand to BMPR1B.

Abstract Genetic variants in relevant genes coexisting with MRI lesions in children with drug‐resistant epilepsy (DRE) can negatively influence epilepsy surgery outcomes. Still, presurgical evaluation does not include genetic diagnostics routinely. Here, we report our presurgical evaluation algorithm that includes routine genetic testing. We analyzed retrospectively the data of 68 children with DRE operated at a mean age of 7.8 years (IQR: 8.1 years) at our center. In 49 children, genetic test results were available. We identified 21 gene variants (ACMG III: n = 7, ACMG IV: n = 2, ACMG V: n = 12) in 19 patients (45.2%) in the genes TSC1, TSC2, MECP2, DEPDC5, HUWE1, GRIN1, ASH1I, TRIO, KIF5C, CDON, ANKD11, TGFBR2, ATN1, COL4A1, JAK2, KCNQ2, ATP1A2, and GLI3 by whole‐exome sequencing as well as deletions and duplications by array CGH in six patients. While the results did not change the surgery indication, they supported counseling with respect to postoperative chance of seizure freedom and weaning of antiseizure medication (ASM). The presence of genetic findings leads to the postoperative retention of at least one ASM. In our cohort, the International League against Epilepsy (ILAE) seizure outcome did not differ between patients with and without abnormal genetic findings. However, in the 7/68 patients with an unsatisfactory ILAE seizure outcome IV or V 12 months postsurgery, 2 had an abnormal or suspicious genetic finding as a putative explanation for persisting seizures postsurgery, and 3 had received palliative surgery including one TSC patient. This study highlights the importance of genetic testing in children with DRE to address putative underlying germline variants as genetic epilepsy causes or predisposing factors that guide patient and/or parent counseling on a case‐by‐case with respect to their individual chance of postoperative seizure freedom and ASM weaning. Plain Language Summary Genetic variants in children with drug‐resistant epilepsy (DRE) can negatively influence epilepsy surgery outcomes. However, presurgical evaluation does not include genetic diagnostics routinely. This retrospective study analyzed the genetic testing results of the 68 pediatric patients who received epilepsy surgery in our center. We identified 21 gene variants by whole‐exome sequencing as well as deletions and duplications by array CGH in 6 patients. These results highlight the importance of genetic testing in children with DRE to guide patient and/or parent counseling on a case‐by‐case with respect to their individual chance of postoperative seizure freedom and ASM weaning.

BackgroundWhile characteristic facial features provide important clues for finding the correct diagnosis in genetic syndromes, valid assessment can be challenging. The next-generation phenotyping algorithm DeepGestalt analyzes patient images and provides syndrome suggestions. GestaltMatcher matches patient images with similar facial features. The new D-Score provides a score for the degree of facial dysmorphism. ObjectiveWe aimed to test state-of-the-art facial phenotyping tools by benchmarking GestaltMatcher and D-Score and comparing them to DeepGestalt. MethodsUsing a retrospective sample of 4796 images of patients with 486 different genetic syndromes (London Medical Database, GestaltMatcher Database, and literature images) and 323 inconspicuous control images, we determined the clinical use of D-Score, GestaltMatcher, and DeepGestalt, evaluating sensitivity; specificity; accuracy; the number of supported diagnoses; and potential biases such as age, sex, and ethnicity. ResultsDeepGestalt suggested 340 distinct syndromes and GestaltMatcher suggested 1128 syndromes. The top-30 sensitivity was higher for DeepGestalt (88%, SD 18%) than for GestaltMatcher (76%, SD 26%). DeepGestalt generally assigned lower scores but provided higher scores for patient images than for inconspicuous control images, thus allowing the 2 cohorts to be separated with an area under the receiver operating characteristic curve (AUROC) of 0.73. GestaltMatcher could not separate the 2 classes (AUROC 0.55). Trained for this purpose, D-Score achieved the highest discriminatory power (AUROC 0.86). D-Score’s levels increased with the age of the depicted individuals. Male individuals yielded higher D-scores than female individuals. Ethnicity did not appear to influence D-scores. ConclusionsIf used with caution, algorithms such as D-score could help clinicians with constrained resources or limited experience in syndromology to decide whether a patient needs further genetic evaluation. Algorithms such as DeepGestalt could support diagnosing rather common genetic syndromes with facial abnormalities, whereas algorithms such as GestaltMatcher could suggest rare diagnoses that are unknown to the clinician in patients with a characteristic, dysmorphic face.