From genetics to genomics of epilepsy

NeuroArray: A Customized aCGH for the Analysis of Copy Number Variations in Neurological Disorders

BACKGROUND

Neurological disorders are a highly heterogeneous group of pathological conditions that affect both the peripheral and the central nervous system. These pathologies are characterized by a complex and multifactorial etiology involving numerous environmental agents and genetic susceptibility factors. For this reason, the investigation of their pathogenetic basis by means of traditional methodological approaches is rather arduous. High-throughput genotyping technologies, including the microarray-based comparative genomic hybridization (aCGH), are currently replacing classical detection methods, providing powerful molecular tools to identify genomic unbalanced structural rearrangements and explore their role in the pathogenesis of many complex human diseases.

METHODS

In this report, we comprehensively describe the design method, the procedures, validation, and implementation of an exon-centric customized aCGH (NeuroArray 1.0), tailored to detect both single and multi-exon deletions or duplications in a large set of multi- and monogenic neurological diseases. This focused platform enables a targeted measurement of structural imbalances across the human genome, targeting the clinically relevant genes at exon-level resolution.

CONCLUSION

An increasing use of the NeuroArray platform may offer new insights in investigating potential overlapping gene signatures among neurological conditions and defining genotype-phenotype relationships.

Retrospective genotype-phenotype analysis in a 305 patient cohort referred for testing of a targeted epilepsy panel

PURPOSE

Epilepsy is a diverse neurological condition with extreme genetic and phenotypic heterogeneity. The introduction of next-generation sequencing into the clinical laboratory has made it possible to investigate hundreds of associated genes simultaneously for a patient, even in the absence of a clearly defined syndrome. This has resulted in the detection of rare and novel mutations at a rate well beyond our ability to characterize their effects. This retrospective study reviews genotype data in the context of available phenotypic information on 305 patients spanning the epileptic spectrum to identify established and novel patterns of correlation.

METHODS

Our epilepsy panel comprising 377 genes was used to sequence 305 patients referred for genetic testing. Qualifying variants were annotated with phenotypic data obtained from either the test requisition form or supporting clinical documentation. Observed phenotypes were compared with established phenotypes in OMIM, published literature and the ILAEs 2010 report on genetic testing to assess congruity with known gene aberrations.

RESULTS

We identified a number of novel and recognized genetic variants consistent with established epileptic phenotypes. Forty-one pathogenic or predicted deleterious variants were detected in 39 patients with accompanying clinical documentation. Twenty-five of these variants across 15 genes were novel. Furthermore, evaluation of phenotype data for 194 patients with variants of unknown significance in genes with autosomal dominant and X-linked disease inheritance elucidated potentially disease-causing variants that were not currently characterized in the literature.

CONCLUSIONS

Assessment of key genotype-phenotype correlations from our cohort provide insight into variant classification, as well as the importance of including ILAE recommended genes as part of minimum panel content for comprehensive epilepsy tests. Many of the reported VUSs are likely genuine pathogenic variants driving the observed phenotypes, but not enough evidence is available for assertive classifications. Similar studies will provide more utility via mounting independent genotype-phenotype data from unrelated patients. The possible outcome would be a better molecular diagnostic product, with fewer indeterminate reports containing only VUSs.

Annotating pathogenic non-coding variants in genic regions

Identifying the underlying causes of disease requires accurate interpretation of genetic variants. Current methods ineffectively capture pathogenic non-coding variants in genic regions, resulting in overlooking synonymous and intronic variants when searching for disease risk. Here we present the Transcript-inferred Pathogenicity (TraP) score, which uses sequence context alterations to reliably identify non-coding variation that causes disease. High TraP scores single out extremely rare variants with lower minor allele frequencies than missense variants. TraP accurately distinguishes known pathogenic and benign variants in synonymous (AUC = 0.88) and intronic (AUC = 0.83) public datasets, dismissing benign variants with exceptionally high specificity. TraP analysis of 843 exomes from epilepsy family trios identifies synonymous variants in known epilepsy genes, thus pinpointing risk factors of disease from non-coding sequence data. TraP outperforms leading methods in identifying non-coding variants that are pathogenic and is therefore a valuable tool for use in gene discovery and the interpretation of personal genomes.

While non-coding synonymous and intronic variants are often not under strong selective constraint, they can be pathogenic through affecting splicing or transcription. Here, the authors develop a score that uses sequence context alterations to predict pathogenicity of synonymous and non-coding genetic variants, and provide a web server of pre-computed scores.

Mechanical and metabolic interactions in cortical bone development

OBJECTIVES

Anthropological studies of cortical bone often aim to reconstruct either habitual activities or health of past populations. During development, mechanical loading and metabolism simultaneously shape cortical bone structure; yet, few studies have investigated how these factors interact. Understanding their relative morphological effects is essential for assessing human behavior from skeletal samples, as previous studies have suggested that interaction effects may influence the interpretation from cortical structure of physical activity or metabolic status.

MATERIAL AND METHODS

This study assesses cross-sectional geometric and histomorphometric features in bones under different loading regimes (femur, humerus, rib) and compares these properties among individuals under different degrees of metabolic stress. The study sample consists of immature humans from a late medieval Lithuanian cemetery (Alytus, 14th-18th centuries AD). Analyses are based on the hypothesis that metabolic bone loss is distributed within the skeleton in a way that optimizes mechanical competency.

RESULTS

Results suggest mechanical compensation for metabolic bone loss in the cross-sectional properties of all three bones (especially ribs), suggesting a mechanism for conserving adequate bone strength for different loads across the skeleton. Microscopic bone loss is restricted to stronger bones under high loads, which may mitigate fracture risk in areas of the skeleton that are more resistive to loading, although alternative explanations are examined.

DISCUSSION

Distributions of metabolic bone loss and subsequent structural adjustments appear to preserve strength. Nevertheless, both mechanics and metabolism have a detectable influence on morphology, and potential implications for behavioral interpretations in bioculturally stressed samples due to this interaction are explored. Am J Phys Anthropol 160:317-333, 2016. © 2016 Wiley Periodicals, Inc.

Epileptic syndromes: From clinic to genetic

Epilepsy is one of the most common neurological disorders. Studies have demonstrated that genetic factors have a strong role in etiology of epilepsy. Mutations in genes encoding ion channels, neurotransmitters and other proteins involved in the neuronal biology have been recognized in different types of this disease. Moreover, some chromosomal aberration including ring chromosomes will result in epilepsy. In this review, we intend to highlight the role of molecular genetic in etiology of epilepsy syndromes, inspect the most recent classification of International League against Epilepsy and discuss the role of genetic counseling and genetic testing in management of epilepsy syndromes. Furthermore, we emphasize on collaboration of neurologists and geneticists to improve diagnosis and management.

SDS, a structural disruption score for assessment of missense variant deleteriousness

We have developed a novel structure-based evaluation for missense variants that explicitly models protein structure and amino acid properties to predict the likelihood that a variant disrupts protein function. A structural disruption score (SDS) is introduced as a measure to depict the likelihood that a case variant is functional. The score is constructed using characteristics that distinguish between causal and neutral variants within a group of proteins. The SDS score is correlated with standard sequence-based deleteriousness, but shows promise for improving discrimination between neutral and causal variants at less conserved sites. The prediction was performed on 3-dimentional structures of 57 gene products whose homozygous SNPs were identified as case-exclusive variants in an exome sequencing study of epilepsy disorders. We contrasted the candidate epilepsy variants with scores for likely benign variants found in the EVS database, and for positive control variants in the same genes that are suspected to promote a range of diseases. To derive a characteristic profile of damaging SNPs, we transformed continuous scores into categorical variables based on the score distribution of each measurement, collected from all possible SNPs in this protein set, where extreme measures were assumed to be deleterious. A second epilepsy dataset was used to replicate the findings. Causal variants tend to receive higher sequence-based deleterious scores, induce larger physico-chemical changes between amino acid pairs, locate in protein domains, buried sites or on conserved protein surface clusters, and cause protein destabilization, relative to negative controls. These measures were agglomerated for each variant. A list of nine high-priority putative functional variants for epilepsy was generated. Our newly developed SDS protocol facilitates SNP prioritization for experimental validation.

Mutation in SNAP25 as a novel genetic cause of epilepsy and intellectual disability

Whole exome sequencing using a parent-child trio design to identify de novo mutations provides an efficient method to identify novel genes for rare diseases with low reproductive fitness that are difficult to study by more classical genetic methods of linkage analysis. We describe a 15 y old female with severe static encephalopathy, intellectual disability, and generalized epilepsy. After extensive metabolic and genetic testing, whole exome sequencing identified a novel de novo variant in Synaptosomal-associated protein-25 (SNAP25), c.142G > T p.Phe48Val alteration. This variant is predicted to be damaging by all prediction algorithms. SNAP25 is part of the soluble N-ethylmaleimide-sensitive factor attachment protein receptor (SNARE) protein complex which is involved in exocytotic release of neurotransmitters. Genetic alterations in Snap25 in animal models can cause anxiety-related behavior, ataxia and seizures. We suggest that SNAP25 mutations in humans are a novel genetic cause of intellectual disability and epilepsy.