Genomic regions associated with microdeletion/microduplication syndromes exhibit extreme diversity of structural variation

Segmental duplications (SDs) are a class of long, repetitive DNA elements whose paralogs share a high level of sequence similarity with each other. SDs mediate chromosomal rearrangements that lead to structural variation in the general population as well as genomic disorders associated with multiple congenital anomalies, including the 7q11.23 (Williams-Beuren Syndrome, WBS), 15q13.3, and 16p12.2 microdeletion syndromes. Population-level characterization of SDs has generally been lacking because most techniques used for analyzing these complex regions are both labor and cost intensive. In this study, we have used a high-throughput technique to genotype complex structural variation with a single molecule, long-range optical mapping approach. We characterized SDs and identified novel structural variants (SVs) at 7q11.23, 15q13.3, and 16p12.2 using optical mapping data from 154 phenotypically normal individuals from 26 populations comprising five super-populations. We detected several novel SVs for each locus, some of which had significantly different prevalence between populations. Additionally, we localized the microdeletion breakpoints to specific paralogous duplicons located within complex SDs in two patients with WBS, one patient with 15q13.3, and one patient with 16p12.2 microdeletion syndromes. The population-level data presented here highlights the extreme diversity of large and complex SVs within SD-containing regions. The approach we outline will greatly facilitate the investigation of the role of inter-SD structural variation as a driver of chromosomal rearrangements and genomic disorders

Automated syndrome diagnosis by three-dimensional facial imaging.

PurposeDeep phenotyping is an emerging trend in precision medicine for genetic disease. The shape of the face is affected in 30-40% of known genetic syndromes. Here, we determine whether syndromes can be diagnosed from 3D images of human faces.MethodsWe analyzed variation in three-dimensional (3D) facial images of 7057 subjects: 3327 with 396 different syndromes, 727 of their relatives, and 3003 unrelated, unaffected subjects. We developed and tested machine learning and parametric approaches to automated syndrome diagnosis using 3D facial images.ResultsUnrelated, unaffected subjects were correctly classified with 96% accuracy. Considering both syndromic and unrelated, unaffected subjects together, balanced accuracy was 73% and mean sensitivity 49%. Excluding unrelated, unaffected subjects substantially improved both balanced accuracy (78.1%) and sensitivity (56.9%) of syndrome diagnosis. The best predictors of classification accuracy were phenotypic severity and facial distinctiveness of syndromes. Surprisingly, unaffected relatives of syndromic subjects were frequently classified as syndromic, often to the syndrome of their affected relative.ConclusionDeep phenotyping by quantitative 3D facial imaging has considerable potential to facilitate syndrome diagnosis. Furthermore, 3D facial imaging of "unaffected" relatives may identify unrecognized cases or may reveal novel examples of semidominant inheritance

Automated syndrome diagnosis by three-dimensional facial imaging.

PurposeDeep phenotyping is an emerging trend in precision medicine for genetic disease. The shape of the face is affected in 30-40% of known genetic syndromes. Here, we determine whether syndromes can be diagnosed from 3D images of human faces.MethodsWe analyzed variation in three-dimensional (3D) facial images of 7057 subjects: 3327 with 396 different syndromes, 727 of their relatives, and 3003 unrelated, unaffected subjects. We developed and tested machine learning and parametric approaches to automated syndrome diagnosis using 3D facial images.ResultsUnrelated, unaffected subjects were correctly classified with 96% accuracy. Considering both syndromic and unrelated, unaffected subjects together, balanced accuracy was 73% and mean sensitivity 49%. Excluding unrelated, unaffected subjects substantially improved both balanced accuracy (78.1%) and sensitivity (56.9%) of syndrome diagnosis. The best predictors of classification accuracy were phenotypic severity and facial distinctiveness of syndromes. Surprisingly, unaffected relatives of syndromic subjects were frequently classified as syndromic, often to the syndrome of their affected relative.ConclusionDeep phenotyping by quantitative 3D facial imaging has considerable potential to facilitate syndrome diagnosis. Furthermore, 3D facial imaging of "unaffected" relatives may identify unrecognized cases or may reveal novel examples of semidominant inheritance

Predominant and novel de novo variants in 29 individuals with ALG13 deficiency: Clinical description, biomarker status, biochemical analysis, and treatment suggestions

Asparagine-linked glycosylation 13 homolog (ALG13) encodes a nonredundant, highly conserved, X-linked uridine diphosphate (UDP)-N-acetylglucosaminyltransferase required for the synthesis of lipid linked oligosaccharide precursor and proper N-linked glycosylation. De novo variants in ALG13 underlie a form of early infantile epileptic encephalopathy known as EIEE36, but given its essential role in glycosylation, it is also considered a congenital disorder of glycosylation (CDG), ALG13-CDG. Twenty-four previously reported ALG13-CDG cases had de novo variants, but surprisingly, unlike most forms of CDG, ALG13-CDG did not show the anticipated glycosylation defects, typically detected by altered transferrin glycosylation. Structural homology modeling of two recurrent de novo variants, p.A81T and p.N107S, suggests both are likely to impact the function of ALG13. Using a corresponding ALG13-deficient yeast strain, we show that expressing yeast ALG13 with either of the highly conserved hotspot variants rescues the observed growth defect, but not its glycosylation abnormality. We present molecular and clinical data on 29 previously unreported individuals with de novo variants in ALG13. This more than doubles the number of known cases. A key finding is that a vast majority of the individuals presents with West syndrome, a feature shared with other CDG types. Among these, the initial epileptic spasms best responded to adrenocorticotropic hormone or prednisolone, while clobazam and felbamate showed promise for continued epilepsy treatment. A ketogenic diet seems to play an important role in the treatment of these individuals