Dataset Information

Atypical face shape and genomic structural variants in epilepsy.

ABSTRACT: Many pathogenic structural variants of the human genome are known to cause facial dysmorphism. During the past decade, pathogenic structural variants have also been found to be an important class of genetic risk factor for epilepsy. In other fields, face shape has been assessed objectively using 3D stereophotogrammetry and dense surface models. We hypothesized that computer-based analysis of 3D face images would detect subtle facial abnormality in people with epilepsy who carry pathogenic structural variants as determined by chromosome microarray. In 118 children and adults attending three European epilepsy clinics, we used an objective measure called Face Shape Difference to show that those with pathogenic structural variants have a significantly more atypical face shape than those without such variants. This is true when analysing the whole face, or the periorbital region or the perinasal region alone. We then tested the predictive accuracy of our measure in a second group of 63 patients. Using a minimum threshold to detect face shape abnormalities with pathogenic structural variants, we found high sensitivity (4/5, 80% for whole face; 3/5, 60% for periorbital and perinasal regions) and specificity (45/58, 78% for whole face and perinasal regions; 40/58, 69% for periorbital region). We show that the results do not seem to be affected by facial injury, facial expression, intellectual disability, drug history or demographic differences. Finally, we use bioinformatics tools to explore relationships between facial shape and gene expression within the developing forebrain. Stereophotogrammetry and dense surface models are powerful, objective, non-contact methods of detecting relevant face shape abnormalities. We demonstrate that they are useful in identifying atypical face shape in adults or children with structural variants, and they may give insights into the molecular genetics of facial development.

SUBMITTER: Chinthapalli K

PROVIDER: S-EPMC3470710 | biostudies-literature | 2012 Oct

REPOSITORIES: biostudies-literature

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Atypical face shape and genomic structural variants in epilepsy.

Chinthapalli Krishna K Bartolini Emanuele E Novy Jan J Suttie Michael M Marini Carla C Falchi Melania M Fox Zoe Z Clayton Lisa M S LM Sander Josemir W JW Guerrini Renzo R Depondt Chantal C Hennekam Raoul R Hammond Peter P Sisodiya Sanjay M SM

Brain : a journal of neurology 20120913 Pt 10

Many pathogenic structural variants of the human genome are known to cause facial dysmorphism. During the past decade, pathogenic structural variants have also been found to be an important class of genetic risk factor for epilepsy. In other fields, face shape has been assessed objectively using 3D stereophotogrammetry and dense surface models. We hypothesized that computer-based analysis of 3D face images would detect subtle facial abnormality in people with epilepsy who carry pathogenic struct ...[more]

PMID: 22975390

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Project description:Mutations in more than 500 genes have been associated with intellectual disability (ID) and related disorders of cognitive function, such as autism and schizophrenia. Here we aimed to unravel the molecular epidemiology of non-specific ID in a genetic isolate using a combination of population and molecular genetic approaches. A large multigenerational pedigree was ascertained within a Dagestan Genetic Heritage research program in a genetic isolate of indigenous ethnics. Clinical characteristics of the affected members were based on combining diagnoses from regional psychiatric hospitals with our own clinical assessment, using a Russian translation of the structured psychiatric interviews, the Diagnostic Interview for Genetic Studies and the Family Interview for Genetic Studies, based on DSM-IV criteria. Weber/CHLC 9.0 STRs set was used for multipoint parametric linkage analyses (Simwalk2.91). Next, we checked CNVs and LOH (based on Affymetrix SNP 5.0 data) in the linked with ID genomic regions with the aim to identify candidate genes associated with mutations in linked regions. The number of statistically significant (p ? 0.05) suggestive linkage peaks with 1.3 < LOD < 3.0 we detected in a total of 10 genomic regions: 1q41, 2p25.3-p24.2, 3p13-p12.1, 4q13.3, 10p11, 11q23, 12q24.22-q24.31, 17q24.2-q25.1, 21q22.13 and 22q12.3-q13.1. Three significant linkage signals with LOD >3 were obtained at 2p25.3-p24.2 under the dominant model, with a peak at 21 cM flanked by loci D2S2976 and D2S2952; at 12q24.22-q24.31 under the recessive model, with a peak at -120 cM flanked by marker D12S2070 and D12S395 and at 22q12.3 under the dominant model, with a peak at 32 cM flanked by marker D22S683 and D22S445. After a set of genes had been designated as possible candidates in these specific chromosomal regions,we conducted an exploratory search for LOH and CNV based on microarray data to detect structural genomic variants within five ID-linked regions with LOD scores between 2.0 and 3.9. In these selected regions we obtained 173 ROH segments and 98 CN segments. Further analysis of region 2p25.3-p24.2 revealed deletions within genes encoding MYTL, SNTG2 and TPO among five of 21 affected cases at 2p25.3-p24.2. In the ID-linked region at 12q24.22-12q24.31 19 out of 21 ID cases carried segmental CNV and 20 of 21 them displayed ROH segments with mean size lengths for ID cases 2512 kb (500-6,472 kb) and for healthy control 682 kb (531-986 kb), including the genes MED13L, HRK, FBXW8, TESC, CDK2AP1 and SBNO1. Seven of 21 affected pedigree members displayed segmental deletions at 22q12.3 that includes the gene LARGE. Eight affected pedigree members carried ROH segments and 6 CN segments at 10p11.23-p11.21 containing the genes ZEB1, c10orf68 and EPC1. Our linkage and structural genomic variation analyses in a remote highland genetic isolate with aggregation of ID demonstrated that even highly isolated single kindred ID has oligo/polygenic pathogenesis. The results obtained implicate 10 genomic regions linked with ID that contain some of previously reported candidate genes, including HRK, FBXW8, TESC, CDK2AP1 and SBNO1 at 12q24 that were shown in recent studies as associated with brain measures derived from MRI scans.

Project description:BackgroundWhole genome sequencing is effective at identification of small variants, but because it is based on short reads, assessment of structural variants (SVs) is limited. The advent of Optical Genome Mapping (OGM), which utilizes long fluorescently labeled DNA molecules for de novo genome assembly and SV calling, has allowed for increased sensitivity and specificity in SV detection. However, compared to small variant annotation tools, OGM-based SV annotation software has seen little development, and currently available SV annotation tools do not provide sufficient information for determination of variant pathogenicity.ResultsWe developed an R-based package, nanotatoR, which provides comprehensive annotation as a tool for SV classification. nanotatoR uses both external (DGV; DECIPHER; Bionano Genomics BNDB) and internal (user-defined) databases to estimate SV frequency. Human genome reference GRCh37/38-based BED files are used to annotate SVs with overlapping, upstream, and downstream genes. Overlap percentages and distances for nearest genes are calculated and can be used for filtration. A primary gene list is extracted from public databases based on the patient's phenotype and used to filter genes overlapping SVs, providing the analyst with an easy way to prioritize variants. If available, expression of overlapping or nearby genes of interest is extracted (e.g. from an RNA-Seq dataset, allowing the user to assess the effects of SVs on the transcriptome). Most quality-control filtration parameters are customizable by the user. The output is given in an Excel file format, subdivided into multiple sheets based on SV type and inheritance pattern (INDELs, inversions, translocations, de novo, etc.). nanotatoR passed all quality and run time criteria of Bioconductor, where it was accepted in the April 2019 release. We evaluated nanotatoR's annotation capabilities using publicly available reference datasets: the singleton sample NA12878, mapped with two types of enzyme labeling, and the NA24143 trio. nanotatoR was also able to accurately filter the known pathogenic variants in a cohort of patients with Duchenne Muscular Dystrophy for which we had previously demonstrated the diagnostic ability of OGM.ConclusionsThe extensive annotation enables users to rapidly identify potential pathogenic SVs, a critical step toward use of OGM in the clinical setting.

Dataset Information

Atypical face shape and genomic structural variants in epilepsy.

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Atypical face shape and genomic structural variants in epilepsy.

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