Cerebrofaciothoracic dysplasia: four new patients with a recurrent TMCO1 pathogenic variant.

Biallelic loss of function variants in the TMCO1 gene have been previously demonstrated to result in cerebrofaciothoracic dysplasia (CFTD; MIM #213980). The phenotype of this condition includes severe intellectual disability, as well as distinctive craniofacial features, including brachycephaly, synophrys, arched eyebrows, "cupid's bow" upper lip, and microdontia. In addition, nonspecific skeletal anomalies are common, including bifid ribs, scoliosis, and spinal fusion. Only 19 molecularly confirmed patients have been previously described. Here, we present four patients with CFTD, including three brothers from a Pakistani background and an additional unrelated white Scottish patient. All share the characteristic craniofacial appearance, with severe intellectual disability and skeletal abnormalities. We further define the phenotype with comparison to the published literature, and present images to define the dysmorphic features in a previously unreported ethnic group. All of our patient series are homozygous for the same c.292_293del (p.Ser98*) TMCO1 pathogenic variant, which has been previously reported only in an isolated Amish population. Thus we provide evidence that CFTD may be more common than previously thought. The patients presented here further delineate the phenotypic spectrum of CFTD and provide evidence for a recurrent pathogenic variant in TMCO1

Cross-ancestry genome-wide association analysis of corneal thickness strengthens link between complex and Mendelian eye diseases

Central corneal thickness (CCT) is a highly heritable trait associated with complex eye diseases such as keratoconus and glaucoma. We perform a genome-wide association meta-analysis of CCT and identify 19 novel regions. In addition to adding support for known connective tissue-related pathways, pathway analyses uncover previously unreported gene sets. Remarkably, >20% of the CCT-loci are near or within Mendelian disorder genes. These included FBN1, ADAMTS2 and TGFB2 which associate with connective tissue disorders (Marfan, Ehlers-Danlos and Loeys-Dietz syndromes), and the LUM-DCN-KERA gene complex involved in myopia, corneal dystrophies and cornea plana. Using index CCT-increasing variants, we find a significant inverse correlation in effect sizes between CCT and keratoconus (r =-0.62, P = 5.30 × 10-5) but not between CCT and primary open-angle glaucoma (r =-0.17, P = 0.2). Our findings provide evidence for shared genetic influences between CCT and keratoconus, and implicate candidate genes acting in collagen and extracellular matrix regulation

EffUnet-SpaGen:An efficient and spatial generative approach to glaucoma detection

Current research in automated disease detection focuses on making algorithms “slimmer” reducing the need for large training datasets and accelerating recalibration for new data while achieving high accuracy. The development of slimmer models has become a hot research topic in medical imaging. In this work, we develop a two-phase model for glaucoma detection, identifying and exploiting a redundancy in fundus image data relating particularly to the geometry. We propose a novel algorithm for the cup and disc segmentation “EffUnet” with an efficient convolution block and combine this with an extended spatial generative approach for geometry modelling and classification, termed “SpaGen” We demonstrate the high accuracy achievable by EffUnet in detecting the optic disc and cup boundaries and show how our algorithm can be quickly trained with new data by recalibrating the EffUnet layer only. Our resulting glaucoma detection algorithm, “EffUnet-SpaGen”, is optimized to significantly reduce the computational burden while at the same time surpassing the current state-of-art in glaucoma detection algorithms with AUROC 0.997 and 0.969 in the benchmark online datasets ORIGA and DRISHTI, respectively. Our algorithm also allows deformed areas of the optic rim to be displayed and investigated, providing explainability, which is crucial to successful adoption and implementation in clinical settings. © 2021 by the authors. Licensee MDPI, Basel, Switzerland