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Diagnostic Exome Sequencing in 100 Consecutive Patients With Both Epilepsy and Intellectual Disability Snoeijen-Schouwenaars FM, van Ool JS, Verhoeven JS, et al. Epilepsia. 2019;60(1):155-164. doi:10.1111/epi.14618. Epub December 7, 2018. PMID: 30525188.Objective:Epilepsy is highly prevalent among patients with intellectual disability (ID), and seizure control is often difficult. Identification of the underlying etiology in this patient group is important for daily clinical care. We assessed the diagnostic yield of whole-exome sequencing (WES). In addition, we evaluated which clinical characteristics influence the likelihood of identifying a genetic cause and we assessed the potential impact of the genetic diagnosis on (antiepileptic) treatment strategy.Methods:One hundred patients with both unexplained epilepsy and (borderline) ID (intelligence quotient ≤85) were included. All patients were evaluated by a clinical geneticist, a (pediatric) neurologist, and/or a specialist ID physician. Whole-exome sequencing analysis was performed in 2 steps. In step 1, analysis was restricted to the latest versions of ID and/or epilepsy gene panels. In step 2, exome analysis was extended to all genes (so-called full exome analysis). The results were classified according to the American College of Medical Genetics and Genomics guidelines. Results: In 58 patients, the diagnostic WES analysis reported one or more variant(s). In 25 of the 100 patients, these were classified as (likely) pathogenic, in 24 patients as variants of uncertain significance, and in the remaining patients the variant was most likely not related to the phenotype. In 10 (40%) of 25 patients with a (likely) pathogenic variant, the genetic diagnosis might have an impact on the treatment strategy in the future.Significance:This study illustrates the clinical diagnostic relevance of WES for patients with both epilepsy and ID. It also demonstrates that implementing WES diagnostics might have impact on the (antiepileptic) treatment strategy in this population. Confirmation of variants of uncertain significance in (candidate) genes may further increase the yield. Diagnostic Yield of Genetic Tests in Epilepsy: A Meta-Analysis and Cost-Effectiveness Study Sánchez Fernández I, Loddenkemper T, Gaínza-Lein M, Sheidley BR, Poduri A. Neurology. 2019. Epub ahead of print. pii: 10.1212/WNL.0000000000006850. doi:10.1212/WNL.0000000000006850. PMID: 30610098 Objective:To compare the cost-effectiveness of genetic testing strategies in patients with epilepsy of unknown etiology.Methods:This meta-analysis and cost-effectiveness study compared strategies involving 3 genetic tests: chromosomal microarray (CMA), epilepsy panel (EP) with deletion/duplication testing, and whole-exome sequencing (WES) in a cost-effectiveness model, using “no genetic testing”“ as a point of comparison.Results:Twenty studies provided information on the diagnostic yield of CMA (8 studies), EP (9 studies), and WES (6 studies). The diagnostic yield was highest for WES: 0.45 (95% confidence interval [CI]: 0.33-0.57; 0.32 [95% CI: 0.22-0.44] adjusting for potential publication bias), followed by EP: 0.23 (95% CI: 0.18-0.29) and CMA: 0.08 (95% CI: 0.06-0.12). The most cost-effective test was WES with an incremental cost-effectiveness ratio (ICER) of US$15 000/diagnosis. However, after adjusting for potential publication bias, the most cost-effective test was EP (ICER: US$15 848/diagnosis) followed by WES (ICER: US$34 500/diagnosis). Among combination strategies, the most cost-effective strategy was WES, then if nondiagnostic, EP, then if nondiagnostic, CMA (ICER: US$15 336/diagnosis); although adjusting for potential publication bias, the most cost-effective strategy was EP ± CMA ± WES (ICER: US$18 385/diagnosis). Although the cost-effectiveness of individual tests and testing strategies overlapped, CMA was consistently less cost-effective than WES and EP.Conclusion:Whole-exome sequencing and EP are the most cost-effective genetic tests for epilepsy. Our analyses support for a broad population of patients with unexplained epilepsy, starting with these tests. Although less expensive, CMA has lower yield, and its use as the first-tier test is thus not supported from a cost-effectiveness perspective.

Currently, there is a lack of software for detecting copy number variations and constructing copy number profile for the whole genome from single-cell DNA sequencing data, which are often of low coverage and high technical noises. Here we introduce a new toolkit, SCNV, which features an efficient bin-free segmentation approach and provides the highest resolution possible for breakpoint detection and the subsequent copy number calling. SCNV can auto-tune parameters based on a set of normal cells from the same batch to adjust for the technical noise level of the data, facilitating its application to data gathered from different platforms and different studies.

Exome sequencing offers an efficient and affordable method to interrogate genetic factors involved in human disease. Performing exome sequencing of monozygotic twins discordant for VACTERL (Vertebral anomalies, Anal atresia, Cardiac malformations, Tracheo-Esophageal fistula, Renal anomalies, and Limb abnormalities) association-type congenital malformations was hypothesized to potentially reveal discordant variants that could demonstrate disease cause(s). After demonstrating monozygosity, we applied high-density microarrays and exome sequencing to 2 twin pairs in which 1 twin had features of VACTERL association while the other was phenotypically normal (demonstrated through comprehensive clinical and radiological evaluation). No obvious discordant genotypic results were found that would explain phenotypic discordance. We conclude that VACTERL association is a complex disease, and while performing microarray analysis and exome sequencing on phenotypically discordant monozygotic twins may hypothetically reveal genetic causes of disorders, challenges remain in applying these methods in this circumstance.

The VATER/VACTERL association is typically defined by the presence of at least 3 of the following congenital malformations: Vertebral anomalies, Anal atresia, Cardiac malformations, Tracheo-Esophageal fistula, Renal anomalies, and Limb abnormalities. The involvement of genetic factors in the development of this rare association is suggested by reports of familial occurrence, the increased prevalence of component features among first-degree relatives of affected individuals, high concordance rates among monozygotic twins, chromosomal (micro-)aberrations or single gene mutations in individuals with the VATER/VACTERL phenotype, as well as murine knock-out models. Despite substantial efforts over the past decade, the genetic etiology of the VATER/VACTERL association in most instances remains elusive. The application of new genomic technologies such as high-resolution copy number variation studies or next-generation exome sequencing might lead to the identification of some of these causes.

Current variant discovery approaches often rely on an initial read mapping to the reference sequence. Their effectiveness is limited by the presence of gaps, potential misassemblies, regions of duplicates with a high-sequence similarity and regions of high-sequence divergence in the reference. Also, mapping-based approaches are less sensitive to large INDELs and complex variations and provide little phase information in personal genomes. A few de novo assemblers have been developed to identify variants through direct variant calling from the assembly graph, micro-assembly and whole-genome assembly, but mainly for whole-genome sequencing (WGS) data. We developed SGVar, a de novo assembly workflow for haplotype-based variant discovery from whole-exome sequencing (WES) data. Using simulated human exome data, we compared SGVar with five variation-aware de novo assemblers and with BWA-MEM together with three haplotype- or local de novo assembly-based callers. SGVar outperforms the other assemblers in sensitivity and tolerance of sequencing errors. We recapitulated the findings on whole-genome and exome data from a Utah residents with Northern and Western European ancestry (CEU) trio, showing that SGVar had high sensitivity both in the highly divergent human leukocyte antigen (HLA) region and in non-HLA regions of chromosome 6. In particular, SGVar is robust to sequencing error, k-mer selection, divergence level and coverage depth. Unlike mapping-based approaches, SGVar is capable of resolving long-range phase and identifying large INDELs from WES, more prominently from WGS. We conclude that SGVar represents an ideal platform for WES-based variant discovery in highly divergent regions and across the whole genome.

Linkage analysis was developed to detect excess co-segregation of the putative alleles underlying a phenotype with the alleles at a marker locus in family data. Many different variations of this analysis and corresponding study design have been developed to detect this co-segregation. Linkage studies have been shown to have high power to detect loci that have alleles (or variants) with a large effect size, i.e. alleles that make large contributions to the risk of a disease or to the variation of a quantitative trait. However, alleles with a large effect size tend to be rare in the population. In contrast, association studies are designed to have high power to detect common alleles which tend to have a small effect size for most diseases or traits. Although genome-wide association studies have been successful in detecting many new loci with common alleles of small effect for many complex traits, these common variants often do not explain a large proportion of disease risk or variation of the trait. In the past, linkage studies were successful in detecting regions of the genome that were likely to harbor rare variants with large effect for many simple Mendelian diseases and for many complex traits. However, identifying the actual sequence variant(s) responsible for these linkage signals was challenging because of difficulties in sequencing the large regions implicated by each linkage peak. Current ‘next-generation’ DNA sequencing techniques have made it economically feasible to sequence all exons or the whole genomes of a reasonably large number of individuals. Studies have shown that rare variants are quite common in the general population, and it is now possible to combine these new DNA sequencing methods with linkage studies to identify rare causal variants with a large effect size. A brief review of linkage methods is presented here with examples of their relevance and usefulness for the interpretation of whole-exome and whole-genome sequence data.