Exome copy number variant detection, analysis, and classification in a large cohort of families with undiagnosed rare genetic disease.

Copy number variants (CNVs) are significant contributors to the pathogenicity of rare genetic diseases and, with new innovative methods, can now reliably be identified from exome sequencing. Challenges still remain in accurate classification of CNV pathogenicity. CNV calling using GATK-gCNV was performed on exomes from a cohort of 6,633 families (15,759 individuals) with heterogeneous phenotypes and variable prior genetic testing collected at the Broad Institute Center for Mendelian Genomics of the Genomics Research to Elucidate the Genetics of Rare Diseases consortium and analyzed using the seqr platform. The addition of CNV detection to exome analysis identified causal CNVs for 171 families (2.6%). The estimated sizes of CNVs ranged from 293 bp to 80 Mb. The causal CNVs consisted of 140 deletions, 15 duplications, 3 suspected complex structural variants (SVs), 3 insertions, and 10 complex SVs, the latter two groups being identified by orthogonal confirmation methods. To classify CNV variant pathogenicity, we used the 2020 American College of Medical Genetics and Genomics/ClinGen CNV interpretation standards and developed additional criteria to evaluate allelic and functional data as well as variants on the X chromosome to further advance the framework. We interpreted 151 CNVs as likely pathogenic/pathogenic and 20 CNVs as high-interest variants of uncertain significance. Calling CNVs from existing exome data increases the diagnostic yield for individuals undiagnosed after standard testing approaches, providing a higher-resolution alternative to arrays at a fraction of the cost of genome sequencing. Our improvements to the classification approach advances the systematic framework to assess the pathogenicity of CNVs.

Loss-of-function variants in ZEB1 cause dominant anomalies of the corpus callosum with favourable cognitive prognosis.

BACKGROUND: The neurodevelopmental prognosis of anomalies of the corpus callosum (ACC), one of the most frequent brain malformations, varies extremely, ranging from normal development to profound intellectual disability (ID). Numerous genes are known to cause syndromic ACC with ID, whereas the genetics of ACC without ID remains poorly deciphered. METHODS: Through a collaborative work, we describe here ZEB1, a gene previously involved in an ophthalmological condition called type 3 posterior polymorphous corneal dystrophy, as a new dominant gene of ACC. We report a series of nine individuals with ACC (including three fetuses terminated due to ACC) carrying a ZEB1 heterozygous loss-of-function (LoF) variant, identified by exome sequencing. RESULTS: In five cases, the variant was inherited from a parent with a normal corpus callosum, which illustrates the incomplete penetrance of ACC in individuals with an LoF in ZEB1. All patients reported normal schooling and none of them had ID. Neuropsychological assessment in six patients showed either normal functioning or heterogeneous cognition. Moreover, two patients had a bicornuate uterus, three had a cardiovascular anomaly and four had macrocephaly at birth, which suggests a larger spectrum of malformations related to ZEB1. CONCLUSION: This study shows ZEB1 LoF variants cause dominantly inherited ACC without ID and extends the extraocular phenotype related to this gene.

Exome copy number variant detection, analysis and classification in a large cohort of families with undiagnosed rare genetic disease.

Copy number variants (CNVs) are significant contributors to the pathogenicity of rare genetic diseases and with new innovative methods can now reliably be identified from exome sequencing. Challenges still remain in accurate classification of CNV pathogenicity. CNV calling using GATK-gCNV was performed on exomes from a cohort of 6,633 families (15,759 individuals) with heterogeneous phenotypes and variable prior genetic testing collected at the Broad Institute Center for Mendelian Genomics of the GREGoR consortium. Each family's CNV data was analyzed using the seqr platform and candidate CNVs classified using the 2020 ACMG/ClinGen CNV interpretation standards. We developed additional evidence criteria to address situations not covered by the current standards. The addition of CNV calling to exome analysis identified causal CNVs for 173 families (2.6%). The estimated sizes of CNVs ranged from 293 bp to 80 Mb with estimates that 44% would not have been detected by standard chromosomal microarrays. The causal CNVs consisted of 141 deletions, 15 duplications, 4 suspected complex structural variants (SVs), 3 insertions and 10 complex SVs, the latter two groups being identified by orthogonal validation methods. We interpreted 153 CNVs as likely pathogenic/pathogenic and 20 CNVs as high interest variants of uncertain significance. Calling CNVs from existing exome data increases the diagnostic yield for individuals undiagnosed after standard testing approaches, providing a higher resolution alternative to arrays at a fraction of the cost of genome sequencing. Our improvements to the classification approach advances the systematic framework to assess the pathogenicity of CNVs.

Improved Thermal and Electrical Properties of Nanocomposites Based on Poly(vinyl pyrrolidone)/Poly(acrylonitrile)/Multiwalled Carbon Nanotubes.

Poly(vinyl pyrrolidone)/Poly(acrylonitrile)/multi-walled carbon nanotubes composites (PVP/PAN/MWNTs) were fabricated by a simple solution cast technique with a wide composition range from 0 to 3.12 wt% of MWNTs. The nanocomposites were characterized by fourier transform infrared (FT-IR) spectroscopy and scanning electron microscopy (SEM). A percolated MWNTs network structure and relatively good dispersion of MWNTs are evident in PVP/PAN/MWNTs nanocomposites with 3.12 wt% of MWNTs. The thermal properties of the polymer blend with MWNTs were carried out by means of different scanning calorimetry (DSC). It indicated that the incorporation of MWNTs had a significant influence on crystallization and melting behaviors for the polymer nanocomposites. The four point probe method was used to measure the electrical conductivity, and the result showed ionic conductivity of the order of 2.87 × 10-4 S/cm to 1.91 × 10-2 S/cm. The effect of the concentration of the filler on the conductivity of the polymer nanocomposite was discussed. Nanocomposites based on PVP/PAN and MWNTs as filler show a significant enhancement in the electrical conductivity as a function of temperature. The low percolation threshold in PVP/PAN/MWNTs nanocomposites was explained by high aspect ratio of the nanotubes and their distribution inside the polymer blend.