Young adults with a 22q11.2 microdeletion and the cost of aging with complexity in a population-based context

PURPOSE

Information about the impact on the adult health care system is limited for complex rare pediatric diseases, despite their increasing collective prevalence that has paralleled advances in clinical care of children. Within a population-based health care context, we examined costs and multimorbidity in adults with an exemplar of contemporary genetic diagnostics.

METHODS

We estimated direct health care costs over an 18-year period for adults with molecularly confirmed 22q11.2 microdeletion (cases) and matched controls (total 60,459 person-years of data) by linking the case cohort to health administrative data for the Ontario population (∼15 million people). We used linear regression to compare the relative ratio (RR) of costs and to identify baseline predictors of higher costs.

RESULTS

Total adult (age ≥ 18) health care costs were significantly higher for cases compared with population-based (RR 8.5, 95% CI 6.5-11.1) controls, and involved all health care sectors. At study end, when median age was <30 years, case costs were comparable to population-based individuals aged 72 years, likelihood of being within the top 1st percentile of health care costs for the entire (any age) population was significantly greater for cases than controls (odds ratio [OR], for adults 17.90, 95% CI 7.43-43.14), and just 8 (2.19%) cases had a multimorbidity score of zero (vs 1483 (40.63%) controls). The 22q11.2 microdeletion was a significant predictor of higher overall health care costs after adjustment for baseline variables (RR 6.9, 95% CI 4.6-10.5).

CONCLUSION

The findings support the possible extension of integrative models of complex care used in pediatrics to adult medicine and the potential value of genetic diagnostics in adult clinical medicine.

Exome sequencing improves the molecular diagnostics of paediatric unexplained neurodevelopmental disorders

BACKGROUND

Neurodevelopmental disorders (NDDs) and/or associated multiple congenital abnormalities (MCAs) represent a genetically heterogeneous group of conditions with an adverse prognosis for the quality of intellectual and social abilities and common daily functioning. The rapid development of exome sequencing (ES) techniques, together with trio-based analysis, nowadays leads to up to 50% diagnostic yield. Therefore, it is considered as the state-of-the-art approach in these diagnoses.

RESULTS

In our study, we present the results of ES in a cohort of 85 families with 90 children with severe NDDs and MCAs. The interconnection of the in-house bioinformatic pipeline and a unique algorithm for variant prioritization resulted in a diagnostic yield of up to 48.9% (44/90), including rare and novel causative variants (41/90) and intragenic copy-number variations (CNVs) (3/90). Of the total number of 47 causative variants, 53.2% (25/47) were novel, highlighting the clinical benefit of ES for unexplained NDDs. Moreover, trio-based ES was verified as a reliable tool for the detection of rare CNVs, ranging from intragenic exon deletions (GRIN2A, ZC4H2 genes) to a 6-Mb duplication. The functional analysis using PANTHER Gene Ontology confirmed the involvement of genes with causative variants in a wide spectrum of developmental processes and molecular pathways, which form essential structural and functional components of the central nervous system.

CONCLUSION

Taken together, we present one of the first ES studies of this scale from the central European region. Based on the high diagnostic yield for paediatric NDDs in this study, 48.9%, we confirm trio-based ES as an effective and reliable first-tier diagnostic test in the genetic evaluation of children with NDDs.

Clinical utility of periodic reinterpretation of CNVs of uncertain significance: an 8-year retrospective study

BACKGROUND

Array-CGH is the first-tier genetic test both in pre- and postnatal developmental disorders worldwide. Variants of uncertain significance (VUS) represent around 10~15% of reported copy number variants (CNVs). Even though VUS reanalysis has become usual in practice, no long-term study regarding CNV reinterpretation has been reported.

METHODS

This retrospective study examined 1641 CGH arrays performed over 8 years (2010-2017) to demonstrate the contribution of periodically re-analyzing CNVs of uncertain significance. CNVs were classified using AnnotSV on the one hand and manually curated on the other hand. The classification was based on the 2020 American College of Medical Genetics (ACMG) criteria.

RESULTS

Of the 1641 array-CGH analyzed, 259 (15.7%) showed at least one CNV initially reported as of uncertain significance. After reinterpretation, 106 of the 259 patients (40.9%) changed categories, and 12 of 259 (4.6%) had a VUS reclassified to likely pathogenic or pathogenic. Six were predisposing factors for neurodevelopmental disorder/autism spectrum disorder (ASD). CNV type (gain or loss) does not seem to impact the reclassification rate, unlike the length of the CNV: 75% of CNVs downgraded to benign or likely benign are less than 500 kb in size.

CONCLUSIONS

This study's high rate of reinterpretation suggests that CNV interpretation has rapidly evolved since 2010, thanks to the continuous enrichment of available databases. The reinterpreted CNV explained the phenotype for ten patients, leading to optimal genetic counseling. These findings suggest that CNVs should be reinterpreted at least every 2 years.

Genome-Wide Identification of Variants Associated with Antifungal Drug Resistance

Genomic approaches are widely applied to study the genetic basis of antifungal drug resistance in clinical isolates and experimental studies. Whole-genome sequencing of clinical isolates can comprehensively identify mutations associated with drug resistance and their frequency across fungal populations. In addition, genome comparison of serially collected isolates, such as from patient samples or in vitro drug selection experiments, will identify a small number of changes that can be evaluated for association with drug resistance. Here, we provide a detailed protocol for the computational analysis of genome sequences to identify variants associated with drug resistance.

Difference of copy number variation in blood of patients with lung cancer

BACKGROUND

Lung cancer is the leading cause of cancer-related deaths worldwide. Copy number variation (CNV) in several genetic regions correlate with cancer susceptibility. Hence, this study evaluated the association between CNV and non-small cell lung cancer (NSCLC) in the peripheral blood.

METHODS

Blood samples of 150 patients with NSCLC and 150 normal controls were obtained from a bioresource center (Seoul, Korea). Through an epigenome-wide analysis using the MethylationEPIC BeadChip method, we extracted CNVs by using an SVS8 software-supplied multivariate method. We compared CNV frequencies between the NSCLC and controls, and then performed stratification analyses according to smoking status.

RESULTS

We acquired 979 CNVs, with 582 and 967 copy-number gains and losses, respectively. We identified five nominally significant associations (ACOT1, NAA60, GSDMD, HLA-DPA1, and SLC35B3 genes). Among the current smokers, the NSCLC group had more CNV losses and gains at the GSDMD gene in chromosome 8 (P=0.02) and at the ACOT1 gene in chromosome 14 (P=0.03) than the control group. It also had more CNV losses at the NAA60 gene in chromosome 16 (P=0.03) among non-smokers. In the NSCLC group, current smokers had more CNV gains and losses at the ACOT1 gene in chromosome 14 (P=0.003) and at HLA-DPA1 gene in chromosome 6 (P=0.02), respectively, than non-smokers.

CONCLUSION

Five nominally significant associations were found between the NSCLC and CNVs. CNVs are associated with the mechanism of lung cancer development. However, the role of CNVs in lung cancer development needs further investigation.

A highly sensitive and specific workflow for detecting rare copy-number variants from exome sequencing data

Background

Exome sequencing (ES) is a first-tier diagnostic test for many suspected Mendelian disorders. While it is routine to detect small sequence variants, it is not a standard practice in clinical settings to detect germline copy-number variants (CNVs) from ES data due to several reasons relating to performance. In this work, we comprehensively characterized one of the most sensitive ES-based CNV tools, ExomeDepth, against SNP array, a standard of care test in clinical settings to detect genome-wide CNVs.

Methods

We propose a modified ExomeDepth workflow by excluding exons with low mappability prior to variant calling to drastically reduce the false positives originating from the repetitive regions of the genome, and an iterative variant calling framework to assess the reproducibility. We used a cohort of 307 individuals with clinical ES data and clinical SNP array to estimate the sensitivity and false discovery rate of the CNV detection using exome sequencing. Further, we performed targeted testing of the STRC gene in 1972 individuals. To reduce the number of variants for downstream analysis, we performed a large-scale iterative variant calling process with random control cohorts to assess the reproducibility of the CNVs.

Results

The modified workflow presented in this paper reduced the number of total variants identified by one third while retaining a higher sensitivity of 97% and resulted in an improved false discovery rate of 11.4% compared to the default ExomeDepth pipeline. The exclusion of exons with low mappability removes 4.5% of the exons, including a subset of exons (0.6%) in disease-associated genes which are intractable by short-read next-generation sequencing (NGS). Results from the reproducibility analysis showed that the clinically reported variants were reproducible 100% of the time and that the modified workflow can be used to rank variants from high to low confidence. Targeted testing of 30 CNVs identified in STRC, a challenging gene to ascertain by NGS, showed a 100% validation rate.

Conclusions

In summary, we introduced a modification to the default ExomeDepth workflow to reduce the false positives originating from the repetitive regions of the genome, created a large-scale iterative variant calling framework for reproducibility, and provided recommendations for implementation in clinical settings.

High-resolution chromosomal microarray analysis for copy-number variations in high-functioning autism reveals large aberration typical for intellectual disability

Copy-number variants (CNVs), in particular rare, small and large ones (< 1% frequency) and those encompassing brain-related genes, have been shown to be associated with neurodevelopmental disorders like autism spectrum disorders (ASDs), attention deficit hyperactivity disorder (ADHD), and intellectual disability (ID). However, the vast majority of CNV findings lack specificity with respect to autistic or developmental-delay phenotypes. Therefore, the aim of the study was to investigate the size and frequency of CNVs in high-functioning ASD (HFA) without ID compared with a random population sample and with published findings in ASD and ID. To investigate the role of CNVs for the "core symptoms" of high-functioning autism, we included in the present exploratory study only patients with HFA without ID. The aim was to test whether HFA have similar large rare (> 1 Mb) CNVs as reported in ASD and ID. We performed high-resolution chromosomal microarray analysis in 108 children and adolescents with HFA without ID. There was no significant difference in the overall number of rare CNVs compared to 124 random population samples. However, patients with HFA carried significantly more frequently CNVs containing brain-related genes. Surprisingly, six HFA patients carried very large CNVs known to be typically present in ID. Our findings provide new evidence that not only small, but also large CNVs affecting several key genes contribute to the genetic etiology/risk of HFA without affecting their intellectual ability.

VRN1-ratio test for polyploid wheat

The duplications of the dominantVrn-A1alleles as well as theVRN-B1gene, revealed for the first time, are new sources of polymorphism in polyploid wheat at these agronomically valuable genomic locations. Flowering time is an important trait in wheat breeding. In spring wheat, this feature is mainly determined by the variants and number of the homoeologous dominant VRN1 alleles. Previously, multiplication of the recessive vrn-A1 allele was shown for winter hexaploid wheat (Würschum et al., BMC Genet 29:16-96, 2015). In the present study, VRN1 gene copy-number variation as well as the copy number of VRN-A1 with the alternative exon 4 haplotype were investigated in spring and winter accessions of different tetraploid and hexaploid wheat species. Two ratio tests were optimized based on end-point quantification of PCR fragments and results were verified by a qPCR assay. It was defined that since the genomic environment affects the accessibility of amplified VRN1 regions, the DNA template should be fragmented for proper quantification of VRN1 copy number during PCR-based assays. For the first time, it was shown that the dominant Vrn-A1 alleles are most often duplicated in hexaploid wheat. In tetraploid wheat, both the dominant and recessive alleles were represented as a single haploid copy, and in only two accessions of T. dicoccum, vrn-A1b.3 was duplicated. Multiplication of VRN-A1 was often associated with awnless spikes. Five haploid combinations of the recessive vrn-A1 copies with alternative exon 4 were identified in hexaploid wheat. Finally for the first time, duplication of VRN-B1 was found in hexaploid wheat of T. compactum and T. spelta. These results expand our knowledge of the genetic diversity of VRN1 genes in wheat and provide additional strategies for the manipulation of flowering time in this strategic crop.

DECA: scalable XHMM exome copy-number variant calling with ADAM and Apache Spark

Background

XHMM is a widely used tool for copy-number variant (CNV) discovery from whole exome sequencing data but can require hours to days to run for large cohorts. A more scalable implementation would reduce the need for specialized computational resources and enable increased exploration of the configuration parameter space to obtain the best possible results.

Results

DECA is a horizontally scalable implementation of the XHMM algorithm using the ADAM framework and Apache Spark that incorporates novel algorithmic optimizations to eliminate unneeded computation. DECA parallelizes XHMM on both multi-core shared memory computers and large shared-nothing Spark clusters. We performed CNV discovery from the read-depth matrix in 2535 exomes in 9.3 min on a 16-core workstation (35.3× speedup vs. XHMM), 12.7 min using 10 executor cores on a Spark cluster (18.8× speedup vs. XHMM), and 9.8 min using 32 executor cores on Amazon AWS’ Elastic MapReduce. We performed CNV discovery from the original BAM files in 292 min using 640 executor cores on a Spark cluster.

Conclusions

We describe DECA’s performance, our algorithmic and implementation enhancements to XHMM to obtain that performance, and our lessons learned porting a complex genome analysis application to ADAM and Spark. ADAM and Apache Spark are a performant and productive platform for implementing large-scale genome analyses, but efficiently utilizing large clusters can require algorithmic optimizations and careful attention to Spark’s configuration parameters.

Next Generation Sequencing of Prenatal Structural Chromosomal Rearrangements Using Large-Insert Libraries

Precise tests for genomic structural variation (SV) are essential for accurate diagnosis of prenatal genome abnormalities. The two most ubiquitous traditional methods for prenatal SV assessment, karyotyping and chromosomal microarrays, do not provide sufficient resolution for some clinically actionable SVs. Standard whole-genome sequencing (WGS) overcomes shortcomings of traditional techniques by providing base-pair resolution of the entire accessible genome. However, while sequencing costs have continued to decline in recent years, conventional WGS costs remain high for most routine clinical applications. Here, we describe a specialized WGS technique using large inserts (liWGS; also known as "jumping libraries") to resolve large (>5000-10,000 nucleotides) SVs at kilobase-resolution in prenatal samples, and at a fraction of the cost of standard WGS. We explicate the protocols for generating liWGS libraries and supplement with an overview for processing and analyzing liWGS data.

CNV analysis using whole exome sequencing identified biallelic CNVs of VPS13B in siblings with intellectual disability

Cohen syndrome is an autosomal recessive disease characterized by myopia, retinal dystrophy, neutropenia, short stature, microcephaly, persistent hypotonia, intellectual disability (ID), and a distinct facial appearance. Cohen syndrome is caused by mutations, such as single nucleotide variants (SNVs) and small insertions/deletions, and copy number variations (CNVs) in vacuolar protein sorting 13 homolog B (VPS13B). Here, we report Japanese siblings with ID, who were subsequently diagnosed with Cohen syndrome by whole exome sequencing (WES). The older sister had hypotonia and mild to moderate ID. The younger sister had short stature, postnatal onset microcephaly, and developmental delay. No pathogenic mutations, including SNVs or small insertions/deletions, were found by WES. Comparative genomic hybridization (CGH)-array did not detect pathogenic copy-number variations. However, using log2-ratio values calculated from WES depth data, we detected pathogenic biallelic heterozygous CNVs in VPS13B in both sisters: a maternally-derived exons 8-15 deletion and a paternally-derived exons 32-33 deletion. Interestingly, the sisters did not show obvious clinical features suggestive of Cohen syndrome, including the distinct facial appearance. These results support the idea that the typical facial features of Cohen syndrome do not appear in early childhood, and that the late appearance of distinctive clinical features results in delayed diagnosis. Furthermore, these results show the possibility that CNV analysis using log2-ratio values calculated from WES depth data is a useful and effective method to detect CNVs, such as the deletion of multiple exons.

Electrical status epilepticus in sleep, a constitutive feature of Christianson syndrome?

Christianson syndrome (CS) is a X-linked neurodevelopmental disorder, including severe intellectual disability (ID), progressive microcephaly, ataxia, autistic behaviour (ASD), near absent speech, and epilepsy. Electrical status epilepticus in sleep (ESES) has been reported in two patients. We describe five male patients from three unrelated families with Christianson syndrome caused by a pathogenic nucleotide variation or a copy-number variation involving SLC9A6. ESES was present in three out of the five patients in the critical age window between 4 and 8 years. All patients presented with severe intellectual disability, autistic features, and hyperactivity. Epilepsy onset occurred within the first two years of life. Seizures were of various types. In the two boys with a 20-years follow-up, epilepsy was drug-resistant during childhood, and became less active in early adolescence. Psychomotor regression was noted in two patients presenting with ESES. It was difficult to assess to what extent ESES could have contributed to the pathophysiological process, leading to regression of the already very limited communication skills. The two published case reports and our observation suggests that ESES could be a constitutive feature of Christianson syndrome, as it has already been shown for other Mendelian epileptic disorders, such as GRIN2A and CNKSR2-related developmental epileptic encephalopathies. Sleep EEG should be performed in patients with Christianson syndrome between 4 and 8 years of age. ESES occurring in the context of ID, ASD and severe speech delay, could be helpful to make a diagnosis of CS.

DeviCNV: detection and visualization of exon-level copy number variants in targeted next-generation sequencing data

BACKGROUND

Targeted next-generation sequencing (NGS) is increasingly being adopted in clinical laboratories for genomic diagnostic tests.

RESULTS

We developed a new computational method, DeviCNV, intended for the detection of exon-level copy number variants (CNVs) in targeted NGS data. DeviCNV builds linear regression models with bootstrapping for every probe to capture the relationship between read depth of an individual probe and the median of read depth values of all probes in the sample. From the regression models, it estimates the read depth ratio of the observed and predicted read depth with confidence interval for each probe which is applied to a circular binary segmentation (CBS) algorithm to obtain CNV candidates. Then, it assigns confidence scores to those candidates based on the reliability and strength of the CNV signals inferred from the read depth ratios of the probes within them. Finally, it also provides gene-centric plots with confidence levels of CNV candidates for visual inspection. We applied DeviCNV to targeted NGS data generated for newborn screening and demonstrated its ability to detect novel pathogenic CNVs from clinical samples.

CONCLUSIONS

We propose a new pragmatic method for detecting CNVs in targeted NGS data with an intuitive visualization and a systematic method to assign confidence scores for candidate CNVs. Since DeviCNV was developed for use in clinical diagnosis, sensitivity is increased by the detection of exon-level CNVs.

Practical implementation of DNA methylation and copy-number-based CNS tumor diagnostics: the Heidelberg experience

Recently, we described a machine learning approach for classification of central nervous system tumors based on the analysis of genome-wide DNA methylation patterns [6]. Here, we report on DNA methylation-based central nervous system (CNS) tumor diagnostics conducted in our institution between the years 2015 and 2018. In this period, more than 1000 tumors from the neurosurgical departments in Heidelberg and Mannheim and more than 1000 tumors referred from external institutions were subjected to DNA methylation analysis for diagnostic purposes. We describe our current approach to the integrated diagnosis of CNS tumors with a focus on constellations with conflicts between morphological and molecular genetic findings. We further describe the benefit of integrating DNA copy-number alterations into diagnostic considerations and provide a catalog of copy-number changes for individual DNA methylation classes. We also point to several pitfalls accompanying the diagnostic implementation of DNA methylation profiling and give practical suggestions for recurring diagnostic scenarios.

PennCNV in whole-genome sequencing data

BACKGROUND

The use of high-throughput sequencing data has improved the results of genomic analysis due to the resolution of mapping algorithms. Although several tools for copy-number variation calling in whole genome sequencing have been published, the noisy nature of sequencing data is still a limitation for accuracy and concordance among such tools. To assess the performance of PennCNV original algorithm for array data in whole genome sequencing data, we processed mapping (BAM) files to extract coverage, representing log R ratio (LRR) of signal intensity, and B allele frequency (BAF).

RESULTS

We used high quality sample NA12878 from the recently reported NIST database and created 10 artificial samples with several CNVs spread along all chromosomes. We compared PennCNV-Seq with other tools with general deletions and duplications, as well as for different number of copies and copy-neutral loss-of-heterozygosity (LOH).

CONCLUSION

PennCNV-Seq was able to find correct CNVs and can be integrated in existing CNV calling pipelines to report accurately the number of copies in specific genomic regions.

Sacral agenesis: a pilot whole exome sequencing and copy number study

Background

Caudal regression syndrome (CRS) or sacral agenesis is a rare congenital disorder characterized by a constellation of congenital caudal anomalies affecting the caudal spine and spinal cord, the hindgut, the urogenital system, and the lower limbs. CRS is a complex condition, attributed to an abnormal development of the caudal mesoderm, likely caused by the effect of interacting genetic and environmental factors. A well-known risk factor is maternal type 1 diabetes.

Method

Whole exome sequencing and copy number variation (CNV) analyses were conducted on 4 Caucasian trios to identify de novo and inherited rare mutations.

Results

In this pilot study, exome sequencing and copy number variation (CNV) analyses implicate a number of candidate genes, including SPTBN5, MORN1, ZNF330, CLTCL1 and PDZD2. De novo mutations were found in SPTBN5, MORN1 and ZNF330 and inherited predicted damaging mutations in PDZD2 (homozygous) and CLTCL1 (compound heterozygous). Importantly, predicted damaging mutations in PTEN (heterozygous), in its direct regulator GLTSCR2 (compound heterozygous) and in VANGL1 (heterozygous) were identified. These genes had previously been linked with the CRS phenotype.

Two CNV deletions, one de novo (chr3q13.13) and one homozygous (chr8p23.2), were detected in one of our CRS patients. These deletions overlapped with CNVs previously reported in patients with similar phenotype.

Conclusion

Despite the genetic diversity and the complexity of the phenotype, this pilot study identified genetic features common across CRS patients.

Electronic supplementary material

The online version of this article (doi:10.1186/s12881-016-0359-2) contains supplementary material, which is available to authorized users.

Representing and decomposing genomic structural variants as balanced integer flows on sequence graphs

Background

The study of genomic variation has provided key insights into the functional role of mutations. Predominantly, studies have focused on single nucleotide variants (SNV), which are relatively easy to detect and can be described with rich mathematical models. However, it has been observed that genomes are highly plastic, and that whole regions can be moved, removed or duplicated in bulk. These structural variants (SV) have been shown to have significant impact on phenotype, but their study has been held back by the combinatorial complexity of the underlying models.

Results

We describe here a general model of structural variation that encompasses both balanced rearrangements and arbitrary copy-number variants (CNV).

Conclusions

In this model, we show that the space of possible evolutionary histories that explain the structural differences between any two genomes can be sampled ergodically.

Testing neutrality at copy-number-variable loci under the finite-allele and finite-site models

Copy-number variation (CNV) is an important form of DNA structural variation because a certain proportion of genomes in many eukaryotic species can contribute to such variations. Owing to the differences between CNVs and single nucleotide polymorphisms (SNPs) in size, mutation rate and maintaining mechanism, it is more realistic to characterize CNV evolution under the finite-allele and finite-site models. Here, we propose a method to test multiple CNVs neutrality under the finite-allele and finite-site models and the assumption of mutation-drift process. The statistical property of the method is evaluated through Monte Carlo simulations under the effects of the sample size, the scaled mutation rates, the number of CNVs, the population demographic change, and selection. Different from Tajima's D test, a bootstrap or a permutation approach is suggested to conduct a neutrality test. Application of this method is illustrated using the diploid CNV genotypes measured in discrete copy numbers in 11 HapMap phase III populations. The results show that the mutation-drift process can explain the variation of genome-wide CNVs among 1184 individuals (856 CNVs, ∼0.02Mb on average in size), irrespective of the historical demographic changes. Patterns from allele-frequency-spectrum analysis also support the hypothesis of neutral CNVs. Our results suggest that most human chromosomal changes in healthy individuals via unbalanced rearrangements of the segments with certain sizes are neutral.

Sequential model selection-based segmentation to detect DNA copy number variation

Array-based CGH experiments are designed to detect genomic aberrations or regions of DNA copy-number variation that are associated with an outcome, typically a state of disease. Most of the existing statistical methods target on detecting DNA copy number variations in a single sample or array. We focus on the detection of group effect variation, through simultaneous study of multiple samples from multiple groups. Rather than using direct segmentation or smoothing techniques, as commonly seen in existing detection methods, we develop a sequential model selection procedure that is guided by a modified Bayesian information criterion. This approach improves detection accuracy by accumulatively utilizing information across contiguous clones, and has computational advantage over the existing popular detection methods. Our empirical investigation suggests that the performance of the proposed method is superior to that of the existing detection methods, in particular, in detecting small segments or separating neighboring segments with differential degrees of copy-number variation.

Epilepsy genetics: the ongoing revolution

Epilepsies have long remained refractory to gene identification due to several obstacles, including a highly variable inter- and intrafamilial expressivity of the phenotypes, a high frequency of phenocopies, and a huge genetic heterogeneity. Recent technological breakthroughs, such as array comparative genomic hybridization and next generation sequencing, have been leading, in the past few years, to the identification of an increasing number of genomic regions and genes in which mutations or copy-number variations cause various epileptic disorders, revealing an enormous diversity of pathophysiological mechanisms. The field that has undergone the most striking revolution is that of epileptic encephalopathies, for which most of causing genes have been discovered since the year 2012. Some examples are the continuous spike-and-waves during slow-wave sleep and Landau-Kleffner syndromes for which the recent discovery of the role of GRIN2A mutations has finally confirmed the genetic bases. These new technologies begin to be used for diagnostic applications, and the main challenge now resides in the interpretation of the huge mass of variants detected by these methods. The identification of causative mutations in epilepsies provides definitive confirmation of the clinical diagnosis, allows accurate genetic counselling, and sometimes permits the development of new appropriate and specific antiepileptic therapies. Future challenges include the identification of the genetic or environmental factors that modify the epileptic phenotypes caused by mutations in a given gene and the understanding of the role of somatic mutations in sporadic epilepsies.

Genetics of human sleep behavioral phenotypes

Quality sleep is critical for daily functions of human beings and thus the timing and duration of sleep are tightly controlled. However, rare genetic variants affecting sleep regulatory mechanisms can result in sleep phenotypes of extremely deviated sleep/wake onset time or duration. Using genetic analyses in families with multiple members expressing particular sleep phenotypes, these sleep-associated genetic variants can be identified. Deciphering the nature of these genetic variants using animal models or biochemical methods helps further our understanding of sleep processes. In this chapter, we describe the methods for studying genetics of human sleep behavioral phenotypes.

Comparative genomic hybridization shows complex genomic changes of plasmacytoid urothelial carcinoma

OBJECTIVES

To describe genomic imbalances in plasmacytoid urothelial carcinoma (PUC), which is a rare and aggressive variant of urothelial carcinoma (UC).

METHODS AND MATERIALS

In total, 25 formalin-fixed paraffin-embedded PUCs were analyzed by metaphase comparative genomic hybridization. Genomic imbalances were considered to be characteristic if they were detected in ≥ 20% of the cases. Chromosome regions deviating by ≥ 3 standard deviations from the average chromosome profile were scored as chromosomal gains or losses. Copy-number variations (CNVs) of CDH1 (16q 22.1), SNAI1 (20q 13.1), CCND1 (11q13.3), ERBB2 (17q12), and FOXO3 (6q21) were validated using quantitative polymerase chain reaction.

RESULTS

Chromosomal aberrations were detected in every PUC analyzed, and the average number of aberrations was 10.24 (ranging from 1-15). Characteristic aberrations were gains on 1q (48%), 3p (20%), 6p (32%), 11q (72%), 15q (36%), 16q (44%), 17p (76%), 17q (88%), and 20q (88%) and losses on 2q (24%) 4p (36%), 4q (84%), 5q (44%), 6q (68%), 13q (20%), and Xq (52%). polymerase chain reaction-based analysis of CNV for CCND1 (11q13) showed a deletion in 73% of the cases. CDH1 (16q22) was deleted in 72% and amplified in 5%. ERBB2 (17q12) displayed remarkably few copy-number alterations, with only 14% showing an amplification. SNAI1 (20q13) showed reduced gene copy numbers in 59.1% of the cases, whereas no copy-number gains were detected. FOXO3 (6q21) exhibited the lowest number of copy-number alterations, with 9% of all cases showing an amplification.

CONCLUSIONS

In PUCs, the frequency of aneuploidy and the complexity of genomic changes per tumor are greater than those described in conventional UC. The aberrations described in PUC involve the same regions that are associated with aggressive biological behavior in conventional UC. Gains on 11q, 17q, 17p, and 20q and losses on 4q and 6q affect most PUCs and seem to harbor important chromosomal regions for PUC carcinogenesis. Large-scale deletions on chromosome 9 were not detected. CNV analysis indicates heterozygous deletion of CDH1 as one underlying mechanism of loss of membranous E-cadherin in PUC. Loss of CCND1 and SNAI1 is a common molecular feature and could contribute to the aggressive biological behavior of PUC.