Arginase deficiency in Bulgaria: first cases and potential endemic region for the disorder.

Arginase deficiency is an autosomal recessive urea cycle disorder caused by pathogenic variants in the ARG1 gene. The clinical features of the disease include spasticity, tremour, ataxia, hypotonia, microcephaly and seizures. Growth delay can also be observed in the affected individuals. Here we describe the results from molecular-genetic analysis of two patients with arginase deficiency. In the first case, we reported a novel homozygous missense variant c.775G>A p.(Gly259Ser) in a patient with Bulgarian ethnic origin. In the second case, a novel homozygous splice site variant c.329+1G>A was detected in a patient from a consanguineous family of Roma ethnic origin. A hundred samples of newborns of Roma origin were screened for variant c.329+1G>A and one individual was found to be a heterozygous carrier of variant c.329+1G> A. The results from this study indicated the necessity for screening of the Roma population with respect to the disease arginase deficiency in Bulgaria.

PEDIA: prioritization of exome data by image analysis.

PURPOSE: Phenotype information is crucial for the interpretation of genomic variants. So far it has only been accessible for bioinformatics workflows after encoding into clinical terms by expert dysmorphologists. METHODS: Here, we introduce an approach driven by artificial intelligence that uses portrait photographs for the interpretation of clinical exome data. We measured the value added by computer-assisted image analysis to the diagnostic yield on a cohort consisting of 679 individuals with 105 different monogenic disorders. For each case in the cohort we compiled frontal photos, clinical features, and the disease-causing variants, and simulated multiple exomes of different ethnic backgrounds. RESULTS: The additional use of similarity scores from computer-assisted analysis of frontal photos improved the top 1 accuracy rate by more than 20-89% and the top 10 accuracy rate by more than 5-99% for the disease-causing gene. CONCLUSION: Image analysis by deep-learning algorithms can be used to quantify the phenotypic similarity (PP4 criterion of the American College of Medical Genetics and Genomics guidelines) and to advance the performance of bioinformatics pipelines for exome analysis.