Accurate distinction of pathogenic from benign CNVs in mental retardation

TADeus2: a web server facilitating the clinical diagnosis by pathogenicity assessment of structural variations disarranging 3D chromatin structure

In recent years great progress has been made in identification of structural variants (SV) in the human genome. However, the interpretation of SVs, especially located in non-coding DNA, remains challenging. One of the reasons stems in the lack of tools exclusively designed for clinical SVs evaluation acknowledging the 3D chromatin architecture. Therefore, we present TADeus2 a web server dedicated for a quick investigation of chromatin conformation changes, providing a visual framework for the interpretation of SVs affecting topologically associating domains (TADs). This tool provides a convenient visual inspection of SVs, both in a continuous genome view as well as from a rearrangement’s breakpoint perspective. Additionally, TADeus2 allows the user to assess the influence of analyzed SVs within flaking coding/non-coding regions based on the Hi-C matrix. Importantly, the SVs pathogenicity is quantified and ranked using TADA, ClassifyCNV tools and sampling-based P-value. TADeus2 is publicly available at https://tadeus2.mimuw.edu.pl.

DeepSVP: integration of genotype and phenotype for structural variant prioritization using deep learning

MOTIVATION

Structural genomic variants account for much of human variability and are involved in several diseases. Structural variants are complex and may affect coding regions of multiple genes, or affect the functions of genomic regions in different ways from single nucleotide variants. Interpreting the phenotypic consequences of structural variants relies on information about gene functions, haploinsufficiency or triplosensitivity and other genomic features. Phenotype-based methods to identifying variants that are involved in genetic diseases combine molecular features with prior knowledge about the phenotypic consequences of altering gene functions. While phenotype-based methods have been applied successfully to single nucleotide variants as well as short insertions and deletions, the complexity of structural variants makes it more challenging to link them to phenotypes. Furthermore, structural variants can affect a large number of coding regions, and phenotype information may not be available for all of them.

RESULTS

We developed DeepSVP, a computational method to prioritize structural variants involved in genetic diseases by combining genomic and gene functions information. We incorporate phenotypes linked to genes, functions of gene products, gene expression in individual cell types and anatomical sites of expression, and systematically relate them to their phenotypic consequences through ontologies and machine learning. DeepSVP significantly improves the success rate of finding causative variants in several benchmarks and can identify novel pathogenic structural variants in consanguineous families.

AVAILABILITY AND IMPLEMENTATION

https://github.com/bio-ontology-research-group/DeepSVP.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Chromosomal variants accumulate in genomes of the spontaneous aborted fetuses revealed by chromosomal microarray analysis

Spontaneous abortion is an impeding factor for the success rates of human assistant reproductive technology (ART). Causes of spontaneous abortion include not only the pregnant mothers’ health conditions and lifestyle habits, but also the fetal development potential. Evidences had shown that fetal chromosome aneuploidy is associated with fetal spontaneous abortion, however, it is still not definite that whether other genome variants, like copy number variations (CNVs) or loss of heterozygosity (LOHs) is associated with the spontaneous abortion. To assess the relationship between the fetal genome variants and abortion during ART, a chromosomal microarray data including chromosomal information of 184 spontaneous aborted fetuses, 147 adult female patients and 78 adult male patients during ART were collected. We firstly analyzed the relationship of fetal aneuploidy with maternal ages and then compared the numbers and lengths of CNVs (< 4Mbp) and LOHs among adults and aborted fetuses. In addition to the already known association between chromosomal aneuploidy and maternal ages, from the chromosomal microarray data we found that the numbers and the accumulated lengths of short CNVs and LOHs in the aborted fetuses were significantly larger or longer than those in adults. Our findings indicated that the increased numbers and accumulated lengths of CNVs or LOHs might be associated with the spontaneous abortion during ART.

Copy number variation analysis of patients with intellectual disability from North-West Spain

Intellectual disability (ID) is a complex and phenotypically heterogeneous neurodevelopmental disorder characterized by significant deficits in cognitive and adaptive skills, debuting during the developmental period. In the last decade, microarray-based copy number variation (CNV) analysis has been proved as a strategy particularly useful in the discovery of loci and candidate genes associated with these phenotypes and is widely used in the clinics with a diagnostic purpose. In this study, we evaluated the usefulness of two genome-wide high density SNP microarrays -Cytogenetics Whole-Genome 2.7M SNP array (n=126 patients; Group 1) and CytoScan High-Density SNP array (n=447 patients; Group 2)- in the detection of clinically relevant CNVs in a cohort of ID patients from Galicia (NW Spain). In 159 (27.7%) patients, we detected 186 rare exonic chromosomal imbalances, that were grouped into the following classes: Clinically relevant (67/186; 36.0%), of unknown clinical significance (93/186; 50.0%) and benign (26/186; 14.0%). The 67 pathogenic CNVs were identified in 64 patients, which means an overall diagnostic yield of 11.2%. Overall, we confirmed that ID is a genetically heterogeneous condition and emphasized the importance of using genome-wide high density SNP microarrays in the detection of its genetic causes. Additionally, we provided clinical and molecular data of patients with pathogenic or likely pathogenic CNVs and discussed the potential implication in neurodevelopmental disorders of genes located within these variants.

Diagnostic interpretation of array data using public databases and internet sources

The range of commercially available array platforms and analysis software packages is expanding and their utility is improving, making reliable detection of copy-number variants (CNVs) relatively straightforward. Reliable interpretation of CNV data, however, is often difficult and requires expertise. With our knowledge of the human genome growing rapidly, applications for array testing continuously broadening, and the resolution of CNV detection increasing, this leads to great complexity in interpreting what can be daunting data. Correct CNV interpretation and optimal use of the genotype information provided by single-nucleotide polymorphism probes on an array depends largely on knowledge present in various resources. In addition to the availability of host laboratories' own datasets and national registries, there are several public databases and Internet resources with genotype and phenotype information that can be used for array data interpretation. With so many resources now available, it is important to know which are fit-for-purpose in a diagnostic setting. We summarize the characteristics of the most commonly used Internet databases and resources, and propose a general data interpretation strategy that can be used for comparative hybridization, comparative intensity, and genotype-based array data.

Prioritizing Clinically Relevant Copy Number Variation from Genetic Interactions and Gene Function Data

It is becoming increasingly necessary to develop computerized methods for identifying the few disease-causing variants from hundreds discovered in each individual patient. This problem is especially relevant for Copy Number Variants (CNVs), which can be cheaply interrogated via low-cost hybridization arrays commonly used in clinical practice. We present a method to predict the disease relevance of CNVs that combines functional context and clinical phenotype to discover clinically harmful CNVs (and likely causative genes) in patients with a variety of phenotypes. We compare several feature and gene weighing systems for classifying both genes and CNVs. We combined the best performing methodologies and parameters on over 2,500 Agilent CGH 180k Microarray CNVs derived from 140 patients. Our method achieved an F-score of 91.59%, with 87.08% precision and 97.00% recall. Our methods are freely available at https://github.com/compbio-UofT/cnv-prioritization. Our dataset is included with the supplementary information.

Mechanisms of Origin, Phenotypic Effects and Diagnostic Implications of Complex Chromosome Rearrangements

Complex chromosome rearrangements (CCRs) are currently defined as structural genome variations that involve more than 2 chromosome breaks and result in exchanges of chromosomal segments. They are thought to be extremely rare, but their detection rate is rising because of improvements in molecular cytogenetic technology. Their population frequency is also underestimated, since many CCRs may not elicit a phenotypic effect. CCRs may be the result of fork stalling and template switching, microhomology-mediated break-induced repair, breakage-fusion-bridge cycles, or chromothripsis. Patients with chromosomal instability syndromes show elevated rates of CCRs due to impaired DNA double-strand break responses during meiosis. Therefore, the putative functions of the proteins encoded by ATM, BLM, WRN, ATR, MRE11, NBS1, and RAD51 in preventing CCRs are discussed. CCRs may exert a pathogenic effect by either (1) gene dosage-dependent mechanisms, e.g. haploinsufficiency, (2) mechanisms based on disruption of the genomic architecture, such that genes, parts of genes or regulatory elements are truncated, fused or relocated and thus their interactions disturbed - these mechanisms will predominantly affect gene expression - or (3) mixed mutation mechanisms in which a CCR on one chromosome is combined with a different type of mutation on the other chromosome. Such inferred mechanisms of pathogenicity need corroboration by mRNA sequencing. Also, future studies with in vitro models, such as inducible pluripotent stem cells from patients with CCRs, and transgenic model organisms should substantiate current inferences regarding putative pathogenic effects of CCRs. The ramifications of the growing body of information on CCRs for clinical and experimental genetics and future treatment modalities are briefly illustrated with 2 cases, one of which suggests KDM4C (JMJD2C) as a novel candidate gene for mental retardation.

The variation game: Cracking complex genetic disorders with NGS and omics data

Tremendous advances in Next Generation Sequencing (NGS) and high-throughput omics methods have brought us one step closer towards mechanistic understanding of the complex disease at the molecular level. In this review, we discuss four basic regulatory mechanisms implicated in complex genetic diseases, such as cancer, neurological disorders, heart disease, diabetes, and many others. The mechanisms, including genetic variations, copy-number variations, posttranscriptional variations, and epigenetic variations, can be detected using a variety of NGS methods. We propose that malfunctions detected in these mechanisms are not necessarily independent, since these malfunctions are often found associated with the same disease and targeting the same gene, group of genes, or functional pathway. As an example, we discuss possible rewiring effects of the cancer-associated genetic, structural, and posttranscriptional variations on the protein-protein interaction (PPI) network centered around P53 protein. The review highlights multi-layered complexity of common genetic disorders and suggests that integration of NGS and omics data is a critical step in developing new computational methods capable of deciphering this complexity.

Performance of case-control rare copy number variation annotation in classification of autism

BACKGROUND

A substantial proportion of Autism Spectrum Disorder (ASD) risk resides in de novo germline and rare inherited genetic variation. In particular, rare copy number variation (CNV) contributes to ASD risk in up to 10% of ASD subjects. Despite the striking degree of genetic heterogeneity, case-control studies have detected specific burden of rare disruptive CNV for neuronal and neurodevelopmental pathways. Here, we used machine learning methods to classify ASD subjects and controls, based on rare CNV data and comprehensive gene annotations. We investigated performance of different methods and estimated the percentage of ASD subjects that could be reliably classified based on presumed etiologic CNV they carry.

RESULTS

We analyzed 1,892 Caucasian ASD subjects and 2,342 matched controls. Rare CNVs (frequency 1% or less) were detected using Illumina 1M and 1M-Duo BeadChips. Conditional Inference Forest (CF) typically performed as well as or better than other classification methods. We found a maximum AUC (area under the ROC curve) of 0.533 when considering all ASD subjects with rare genic CNVs, corresponding to 7.9% correctly classified ASD subjects and less than 3% incorrectly classified controls; performance was significantly higher when considering only subjects harboring de novo or pathogenic CNVs. We also found rare losses to be more predictive than gains and that curated neurally-relevant annotations (brain expression, synaptic components and neurodevelopmental phenotypes) outperform Gene Ontology and pathway-based annotations.

CONCLUSIONS

CF is an optimal classification approach for case-control rare CNV data and it can be used to prioritize subjects with variants potentially contributing to ASD risk not yet recognized. The neurally-relevant annotations used in this study could be successfully applied to rare CNV case-control data-sets for other neuropsychiatric disorders.

SNP arrays: comparing diagnostic yields for four platforms in children with developmental delay

Background

Molecular karyotyping is now the first-tier genetic test for patients affected with unexplained intellectual disability (ID) and/or multiple congenital anomalies (MCA), since it identifies a pathogenic copy number variation (CNV) in 10-14% of them. High-resolution microarrays combining molecular karyotyping and single nucleotide polymorphism (SNP) genotyping were recently introduced to the market. In addition to identifying CNVs, these platforms detect loss of heterozygosity (LOH), which can indicate the presence of a homozygous mutation or uniparental disomy. Since these abnormalities can be associated with ID and/or MCA, their detection is of particular interest for patients whose phenotype remains unexplained. However, the diagnostic yield obtained with these platforms is not confirmed, and the real clinical value of LOH detection has not been established.

Methods

We selected 21 children affected with ID, with or without congenital malformations, for whom standard genetic analyses failed to provide a diagnosis. We performed high-resolution SNP array analysis with four platforms (Affymetrix Genome-Wide Human SNP Array 6.0, Affymetrix Cytogenetics Whole-Genome 2.7 M array, Illumina HumanOmni1-Quad BeadChip, and Illumina HumanCytoSNP-12 DNA Analysis BeadChip) on whole-blood samples obtained from children and their parents to detect pathogenic CNVs and LOHs, and compared the results with those obtained on a moderate resolution array-based comparative genomic hybridization platform (NimbleGen CGX-12 Cytogenetics Array), already used in the clinical setting.

Results

We identified a total of four pathogenic CNVs in three patients, and all arrays successfully detected them. With the SNP arrays, we also identified a LOH containing a gene associated with a recessive disorder consistent with the patient’s phenotype (i.e., an informative LOH) in four children (including two siblings). A homozygous mutation within the informative LOH was found in three of these patients. Therefore, we were able to increase the diagnostic yield from 14.3% to 28.6% as a result of the information provided by LOHs.

Conclusions

This study shows the clinical usefulness of SNP arrays in children with ID, since they successfully detect pathogenic CNVs, identify informative LOHs that can lead to the diagnosis of a recessive disorder. It also highlights some challenges associated with the use of SNP arrays in a clinical laboratory.

Electronic supplementary material

The online version of this article (doi:10.1186/s12920-014-0070-0) contains supplementary material, which is available to authorized users.

Deciphering the pathogenic consequences of chromosomal aberrations in human genetic disease

Chromosomal aberrations include translocations, deletions, duplications, inversions, aneuploidies and complex rearrangements. They underlie genetic disease in roughly 15% of patients with multiple congenital abnormalities and/or mental retardation (MCA/MR). In genetic diagnostics, the pathogenicity of chromosomal aberrations in these patients is typically assessed based on criteria such as phenotypic similarity to other patients with the same or overlapping aberration, absence in healthy individuals, de novo occurrence, and protein coding gene content. However, a thorough understanding of the molecular mechanisms that lead to MCA/MR as a result of chromosome aberrations is often lacking. Chromosome aberrations can affect one or more genes in a complex manner, such as by changing the regulation of gene expression, by disrupting exons, and by creating fusion genes. The precise delineation of breakpoints by whole-genome sequencing enables the construction of local genomic architecture and facilitates the prediction of the molecular determinants of the patient's phenotype. Here, we review current methods for breakpoint identification and their impact on the interpretation of chromosome aberrations in patients with MCA/MR. In addition, we discuss opportunities to dissect disease mechanisms based on large-scale genomic technologies and studies in model organisms.

In silico molecular cytogenetics: a bioinformatic approach to prioritization of candidate genes and copy number variations for basic and clinical genome research

Background

The availability of multiple in silico tools for prioritizing genetic variants widens the possibilities for converting genomic data into biological knowledge. However, in molecular cytogenetics, bioinformatic analyses are generally limited to result visualization or database mining for finding similar cytogenetic data. Obviously, the potential of bioinformatics might go beyond these applications. On the other hand, the requirements for performing successful in silico analyses (i.e. deep knowledge of computer science, statistics etc.) can hinder the implementation of bioinformatics in clinical and basic molecular cytogenetic research. Here, we propose a bioinformatic approach to prioritization of genomic variations that is able to solve these problems.

Results

Selecting gene expression as an initial criterion, we have proposed a bioinformatic approach combining filtering and ranking prioritization strategies, which includes analyzing metabolome and interactome data on proteins encoded by candidate genes. To finalize the prioritization of genetic variants, genomic, epigenomic, interactomic and metabolomic data fusion has been made. Structural abnormalities and aneuploidy revealed by array CGH and FISH have been evaluated to test the approach through determining genotype-phenotype correlations, which have been found similar to those of previous studies. Additionally, we have been able to prioritize copy number variations (CNV) (i.e. differentiate between benign CNV and CNV with phenotypic outcome). Finally, the approach has been applied to prioritize genetic variants in cases of somatic mosaicism (including tissue-specific mosaicism).

Conclusions

In order to provide for an in silico evaluation of molecular cytogenetic data, we have proposed a bioinformatic approach to prioritization of candidate genes and CNV. While having the disadvantage of possible unavailability of gene expression data or lack of expression variability between genes of interest, the approach provides several advantages. These are (i) the versatility due to independence from specific databases/tools or software, (ii) relative algorithm simplicity (possibility to avoid sophisticated computational/statistical methodology) and (iii) applicability to molecular cytogenetic data because of the chromosome-centric nature. In conclusion, the approach is able to become useful for increasing the yield of molecular cytogenetic techniques.

Clinical interpretation of CNVs with cross-species phenotype data

BACKGROUND

Clinical evaluation of CNVs identified via techniques such as array comparative genome hybridisation (aCGH) involves the inspection of lists of known and unknown duplications and deletions with the goal of distinguishing pathogenic from benign CNVs. A key step in this process is the comparison of the individual's phenotypic abnormalities with those associated with Mendelian disorders of the genes affected by the CNV. However, because often there is not much known about these human genes, an additional source of data that could be used is model organism phenotype data. Currently, almost 6000 genes in mouse and zebrafish are, when knocked out, associated with a phenotype in the model organism, but no disease is known to be caused by mutations in the human ortholog. Yet, searching model organism databases and comparing model organism phenotypes with patient phenotypes for identifying novel disease genes and medical evaluation of CNVs is hindered by the difficulty in integrating phenotype information across species and the lack of appropriate software tools.

METHODS

Here, we present an integrated ranking scheme based on phenotypic matching, degree of overlap with known benign or pathogenic CNVs and the haploinsufficiency score for the prioritisation of CNVs responsible for a patient's clinical findings.

RESULTS

We show that this scheme leads to significant improvements compared with rankings that do not exploit phenotypic information. We provide a software tool called PhenogramViz, which supports phenotype-driven interpretation of aCGH findings based on multiple data sources, including the integrated cross-species phenotype ontology Uberpheno, in order to visualise gene-to-phenotype relations.

CONCLUSIONS

Integrating and visualising cross-species phenotype information on the affected genes may help in routine diagnostics of CNVs.

Deletions of chromosomal regulatory boundaries are associated with congenital disease

Background

Recent data from genome-wide chromosome conformation capture analysis indicate that the human genome is divided into conserved megabase-sized self-interacting regions called topological domains. These topological domains form the regulatory backbone of the genome and are separated by regulatory boundary elements or barriers. Copy-number variations can potentially alter the topological domain architecture by deleting or duplicating the barriers and thereby allowing enhancers from neighboring domains to ectopically activate genes causing misexpression and disease, a mutational mechanism that has recently been termed enhancer adoption.

Results

We use the Human Phenotype Ontology database to relate the phenotypes of 922 deletion cases recorded in the DECIPHER database to monogenic diseases associated with genes in or adjacent to the deletions. We identify combinations of tissue-specific enhancers and genes adjacent to the deletion and associated with phenotypes in the corresponding tissue, whereby the phenotype matched that observed in the deletion. We compare this computationally with a gene-dosage pathomechanism that attempts to explain the deletion phenotype based on haploinsufficiency of genes located within the deletions. Up to 11.8% of the deletions could be best explained by enhancer adoption or a combination of enhancer adoption and gene-dosage effects.

Conclusions

Our results suggest that enhancer adoption caused by deletions of regulatory boundaries may contribute to a substantial minority of copy-number variation phenotypes and should thus be taken into account in their medical interpretation.

Electronic supplementary material

The online version of this article (doi:10.1186/s13059-014-0423-1) contains supplementary material, which is available to authorized users.

Drosophila models of early onset cognitive disorders and their clinical applications

The number of genes known to cause human monogenic diseases is increasing rapidly. For the extremely large, genetically and phenotypically heterogeneous group of intellectual disability (ID) disorders, more than 600 causative genes have been identified to date. However, knowledge about the molecular mechanisms and networks disrupted by these genetic aberrations is lagging behind. The fruit fly Drosophila has emerged as a powerful model organism to close this knowledge gap. This review summarizes recent achievements that have been made in this model and envisions its future contribution to our understanding of ID genetics and neuropathology. The available resources and efficiency of Drosophila place it in a position to tackle the main challenges in the field: mapping functional modules of ID genes to provide conceptually novel insights into the genetic control of cognition, tailored functional studies to improve 'next-generation' diagnostics, and identification of reversible ID phenotypes and medication. Drosophila's behavioral repertoire and powerful genetics also open up perspectives for modeling genetically complex forms of ID and neuropsychiatric disorders, which overlap in their genetic etiologies. In conclusion, Drosophila provides many opportunities to advance future medical genomics of early onset cognitive disorders.

Human molecular cytogenetics: From cells to nucleotides

The field of cytogenetics has focused on studying the number, structure, function and origin of chromosomal abnormalities and the evolution of chromosomes. The development of fluorescent molecules that either directly or via an intermediate molecule bind to DNA has led to the development of fluorescent in situ hybridization (FISH), a technology linking cytogenetics to molecular genetics. This technique has a wide range of applications that increased the dimension of chromosome analysis. The field of cytogenetics is particularly important for medical diagnostics and research as well as for gene ordering and mapping. Furthermore, the increased application of molecular biology techniques, such as array-based technologies, has led to improved resolution, extending the recognized range of microdeletion/microduplication syndromes and genomic disorders. In adopting these newly expanded methods, cytogeneticists have used a range of technologies to study the association between visible chromosome rearrangements and defects at the single nucleotide level. Overall, molecular cytogenetic techniques offer a remarkable number of potential applications, ranging from physical mapping to clinical and evolutionary studies, making a powerful and informative complement to other molecular and genomic approaches. This manuscript does not present a detailed history of the development of molecular cytogenetics; however, references to historical reviews and experiments have been provided whenever possible. Herein, the basic principles of molecular cytogenetics, the technologies used to identify chromosomal rearrangements and copy number changes, and the applications for cytogenetics in biomedical diagnosis and research are presented and discussed.

Clinical significance of de novo and inherited copy-number variation

Copy-number variations (CNVs) are a common cause of intellectual disability and/or multiple congenital anomalies (ID/MCA). However, the clinical interpretation of CNVs remains challenging, especially for inherited CNVs. Well-phenotyped patients (5,531) with ID/MCA were screened for rare CNVs using a 250K single-nucleotide polymorphism array platform in order to improve the understanding of the contribution of CNVs to a patients phenotype. We detected 1,663 rare CNVs in 1,388 patients (25.1%; range 0-5 per patient) of which 437 occurred de novo and 638 were inherited. The detected CNVs were analyzed for various characteristics, gene content, and genotype-phenotype correlations. Patients with severe phenotypes, including organ malformations, had more de novo CNVs (P < 0.001), whereas patient groups with milder phenotypes, such as facial dysmorphisms, were enriched for both de novo and inherited CNVs (P < 0.001), indicating that not only de novo but also inherited CNVs can be associated with a clinically relevant phenotype. Moreover, patients with multiple CNVs presented with a more severe phenotype than patients with a single CNV (P < 0.001), pointing to a combinatorial effect of the additional CNVs. In addition, we identified 20 de novo single-gene CNVs that directly indicate novel genes for ID/MCA, including ZFHX4, ANKH, DLG2, MPP7, CEP89, TRIO, ASTN2, and PIK3C3.

Fusion of large-scale genomic knowledge and frequency data computationally prioritizes variants in epilepsy

Curation and interpretation of copy number variants identified by genome-wide testing is challenged by the large number of events harbored in each personal genome. Conventional determination of phenotypic relevance relies on patterns of higher frequency in affected individuals versus controls; however, an increasing amount of ascertained variation is rare or private to clans. Consequently, frequency data have less utility to resolve pathogenic from benign. One solution is disease-specific algorithms that leverage gene knowledge together with variant frequency to aid prioritization. We used large-scale resources including Gene Ontology, protein-protein interactions and other annotation systems together with a broad set of 83 genes with known associations to epilepsy to construct a pathogenicity score for the phenotype. We evaluated the score for all annotated human genes and applied Bayesian methods to combine the derived pathogenicity score with frequency information from our diagnostic laboratory. Analysis determined Bayes factors and posterior distributions for each gene. We applied our method to subjects with abnormal chromosomal microarray results and confirmed epilepsy diagnoses gathered by electronic medical record review. Genes deleted in our subjects with epilepsy had significantly higher pathogenicity scores and Bayes factors compared to subjects referred for non-neurologic indications. We also applied our scores to identify a recently validated epilepsy gene in a complex genomic region and to reveal candidate genes for epilepsy. We propose a potential use in clinical decision support for our results in the context of genome-wide screening. Our approach demonstrates the utility of integrative data in medical genomics.

Pathogenic or not? Assessing the clinical relevance of copy number variants

The availability of commercially produced genomic microarrays has resulted in the wide spread implementation of genomic microarrays, often as a first-tier diagnostic test for copy number variant (CNV) screening of patients who are suspected for chromosomal aberrations. Patients with intellectual disability (ID) and/or multiple congenital anomalies (MCA) were traditionally the main focus for this microarray-based CNV screening, but the application of microarrays to other (neurodevelopmental) disorders and tumor diagnostics has also been explored and implemented. The diagnostic workflow for patients with ID is now well established, relying on the identification of rare CNVs and determining their inheritance patterns. However, experience gained through screening large numbers of samples has revealed many subtleties and complexities of CNV interpretation. This has resulted in a better understanding of the contribution of CNVs to genomic disorders not only via de novo occurrence, but also via X-linked and recessive inheritance models as well as through models taking into account mosaicisms, imprinting, and digenic inheritance. In this review, we discuss CNV interpretation within the context of these different genetic disease models and common pitfalls that can occur when searching for supportive evidence that a CNV is clinically relevant.

Building a genome analysis pipeline to predict disease risk and prevent disease

Reduced costs and increased speed and accuracy of sequencing can bring the genome-based evaluation of individual disease risk to the bedside. While past efforts have identified a number of actionable mutations, the bulk of genetic risk remains hidden in sequence data. The biggest challenge facing genomic medicine today is the development of new techniques to predict the specifics of a given human phenome (set of all expressed phenotypes) encoded by each individual variome (full set of genome variants) in the context of the given environment. Numerous tools exist for the computational identification of the functional effects of a single variant. However, the pipelines taking advantage of full genomic, exomic, transcriptomic (and other) sequences have only recently become a reality. This review looks at the building of methodologies for predicting "variome"-defined disease risk. It also discusses some of the challenges for incorporating such a pipeline into everyday medical practice.

PECONPI: a novel software for uncovering pathogenic copy number variations in non-syndromic sensorineural hearing loss and other genetically heterogeneous disorders

This report describes an algorithm developed to predict the pathogenicity of copy number variants (CNVs) in large sample cohorts. CNVs (genomic deletions and duplications) are found in healthy individuals and in individuals with genetic diagnoses, and differentiation of these two classes of CNVs can be challenging and usually requires extensive manual curation. We have developed PECONPI, an algorithm to assess the pathogenicity of CNVs based on gene content and CNV frequency. This software was applied to a large cohort of patients with genetically heterogeneous non-syndromic hearing loss to score and rank each CNV based on its relative pathogenicity. Of 636 individuals tested, we identified the likely underlying etiology of the hearing loss in 14 (2%) of the patients (1 with a homozygous deletion, 7 with a deletion of a known hearing loss gene and a point mutation on the trans allele and 6 with a deletion larger than 1 Mb). We also identified two probands with smaller deletions encompassing genes that may be functionally related to their hearing loss. The ability of PECONPI to determine the pathogenicity of CNVs was tested on a second genetically heterogeneous cohort with congenital heart defects (CHDs). It successfully identified a likely etiology in 6 of 355 individuals (2%). We believe this tool is useful for researchers with large genetically heterogeneous cohorts to help identify known pathogenic causes and novel disease genes.

Multiple samples aCGH analysis for rare CNVs detection

Background

DNA copy number variations (CNV) constitute an important source of genetic variability. The standard method used for CNV detection is array comparative genomic hybridization (aCGH).

Results

We propose a novel multiple sample aCGH analysis methodology aiming in rare CNVs detection. In contrast to the majority of previous approaches, which deal with cancer datasets, we focus on constitutional genomic abnormalities identified in a diverse spectrum of diseases in human. Our method is tested on exon targeted aCGH array of 366 patients affected with developmental delay/intellectual disability, epilepsy, or autism. The proposed algorithms can be applied as a post–processing filtering to any given segmentation method.

Conclusions

Thanks to the additional information obtained from multiple samples, we could efficiently detect significant segments corresponding to rare CNVs responsible for pathogenic changes. The robust statistical framework applied in our method enables to eliminate the influence of widespread technical artifact termed ‘waves’.

Phenotypic overlap in the contribution of individual genes to CNV pathogenicity revealed by cross-species computational analysis of single-gene mutations in humans, mice and zebrafish

Numerous disease syndromes are associated with regions of copy number variation (CNV) in the human genome and, in most cases, the pathogenicity of the CNV is thought to be related to altered dosage of the genes contained within the affected segment. However, establishing the contribution of individual genes to the overall pathogenicity of CNV syndromes is difficult and often relies on the identification of potential candidates through manual searches of the literature and online resources. We describe here the development of a computational framework to comprehensively search phenotypic information from model organisms and single-gene human hereditary disorders, and thus speed the interpretation of the complex phenotypes of CNV disorders. There are currently more than 5000 human genes about which nothing is known phenotypically but for which detailed phenotypic information for the mouse and/or zebrafish orthologs is available. Here, we present an ontology-based approach to identify similarities between human disease manifestations and the mutational phenotypes in characterized model organism genes; this approach can therefore be used even in cases where there is little or no information about the function of the human genes. We applied this algorithm to detect candidate genes for 27 recurrent CNV disorders and identified 802 gene-phenotype associations, approximately half of which involved genes that were previously reported to be associated with individual phenotypic features and half of which were novel candidates. A total of 431 associations were made solely on the basis of model organism phenotype data. Additionally, we observed a striking, statistically significant tendency for individual disease phenotypes to be associated with multiple genes located within a single CNV region, a phenomenon that we denote as pheno-clustering. Many of the clusters also display statistically significant similarities in protein function or vicinity within the protein-protein interaction network. Our results provide a basis for understanding previously un-interpretable genotype-phenotype correlations in pathogenic CNVs and for mobilizing the large amount of model organism phenotype data to provide insights into human genetic disorders.

Developmental psychopathology: The role of structural variation in the genome

A wide range of developmental disorders present with characteristic psychopathologies and behaviors, with diagnoses including, inter alia, cognitive disorders and learning disabilities, epilepsies, autism, and schizophrenia. Each, to varying extent, has a genetic component to etiology and is associated with cytogenetic abnormalities. Technological developments, particularly array-based comparative genome hybridization and single nucleotide polymorphism chips, has revealed a wide range of rare recurrent and de novo copy number variants (CNVs) to be associated with disorder and psychopathology. It is surprising that many apparently similar CNVs are identified across two or more disorders hitherto considered unrelated. This article describes the characteristics of CNVs and current technological restrictions that make accurately identifying small events difficult. It summarizes the latest discoveries for individual diagnostic categories and considers the implications for a shared neurobiology. It examines likely developments in the knowledge base as well as addressing the clinical implications going forward.

Genetic counselling and ethical issues with chromosome microarray analysis in prenatal testing

Molecular karyotyping using chromosome microarray analysis (CMA) detects more pathogenic chromosomal anomalies than classical karyotyping, making CMA likely to become a first tier test for prenatal diagnosis. Detecting copy number variants of uncertain clinical significance raises ethical considerations. We consider the risk of harm to a woman or her fetus following the detection of a copy number variant of uncertain significance, whether it is ethically justifiable to withhold any test result information from a woman, what constitutes an 'informed choice' when women are offered CMA in pregnancy and whether clinicians are morally responsible for 'unnecessary' termination of pregnancy. Although we are cognisant of the distress associated with uncertain prenatal results, we argue in favour of the autonomy of women and their right to information from genome-wide CMA in order to make informed choices about their pregnancies. We propose that information material to a woman's decision-making process, including uncertain information, should not be withheld, and that it would be paternalistic for clinicians to try to take responsibility for women's decisions to terminate pregnancies. Non-directive pre-test and post-test genetic counselling is central to the delivery of these ethical objectives.

Single cell genomics of the brain: focus on neuronal diversity and neuropsychiatric diseases

Single cell genomics has made increasingly significant contributions to our understanding of the role that somatic genome variations play in human neuronal diversity and brain diseases. Studying intercellular genome and epigenome variations has provided new clues to the delineation of molecular mechanisms that regulate development, function and plasticity of the human central nervous system (CNS). It has been shown that changes of genomic content and epigenetic profiling at single cell level are involved in the pathogenesis of neuropsychiatric diseases (schizophrenia, mental retardation (intellectual/leaning disability), autism, Alzheimer's disease etc.). Additionally, several brain diseases were found to be associated with genome and chromosome instability (copy number variations, aneuploidy) variably affecting cell populations of the human CNS. The present review focuses on the latest advances of single cell genomics, which have led to a better understanding of molecular mechanisms of neuronal diversity and neuropsychiatric diseases, in the light of dynamically developing fields of systems biology and "omics".

Mouse genetic and phenotypic resources for human genetics

The use of model organisms to provide information on gene function has proved to be a powerful approach to our understanding of both human disease and fundamental mammalian biology. Large-scale community projects using mice, based on forward and reverse genetics, and now the pan-genomic phenotyping efforts of the International Mouse Phenotyping Consortium, are generating resources on an unprecedented scale, which will be extremely valuable to human genetics and medicine. We discuss the nature and availability of data, mice and embryonic stem cells from these large-scale programmes, the use of these resources to help prioritize and validate candidate genes in human genetic association studies, and how they can improve our understanding of the underlying pathobiology of human disease.

Genome-wide transcriptome profiling reveals the functional impact of rare de novo and recurrent CNVs in autism spectrum disorders

Copy-number variants (CNVs) are a major contributor to the pathophysiology of autism spectrum disorders (ASDs), but the functional impact of CNVs remains largely unexplored. Because brain tissue is not available from most samples, we interrogated gene expression in lymphoblasts from 244 families with discordant siblings in the Simons Simplex Collection in order to identify potentially pathogenic variation. Our results reveal that the overall frequency of significantly misexpressed genes (which we refer to here as outliers) identified in probands and unaffected siblings does not differ. However, in probands, but not their unaffected siblings, the group of outlier genes is significantly enriched in neural-related pathways, including neuropeptide signaling, synaptogenesis, and cell adhesion. We demonstrate that outlier genes cluster within the most pathogenic CNVs (rare de novo CNVs) and can be used for the prioritization of rare CNVs of potentially unknown significance. Several nonrecurrent CNVs with significant gene-expression alterations are identified (these include deletions in chromosomal regions 3q27, 3p13, and 3p26 and duplications at 2p15), suggesting that these are potential candidate ASD loci. In addition, we identify distinct expression changes in 16p11.2 microdeletions, 16p11.2 microduplications, and 7q11.23 duplications, and we show that specific genes within the 16p CNV interval correlate with differences in head circumference, an ASD-relevant phenotype. This study provides evidence that pathogenic structural variants have a functional impact via transcriptome alterations in ASDs at a genome-wide level and demonstrates the utility of integrating gene expression with mutation data for the prioritization of genes disrupted by potentially pathogenic mutations.

Large-scale objective association of mouse phenotypes with human symptoms through structural variation identified in patients with developmental disorders

Copy number variants (CNVs) are thought to underlie many human developmental abnormalities. However, it is unclear how many of these CNVs exert their pathogenic effects or, in particular, how distinct CNVs at dispersed loci can give rise to the same abnormality. We hypothesize that the mouse orthologs of genes whose copy number change gives rise to the same human abnormality might also yield a similar phenotype when disrupted in mice. Thus, by bringing together a large number of disparate CNVs, we may be able to identify an unusually overrepresented phenotype among the affected genes' mouse orthologs. We obtained 1,624 de novo CNVs identified in patients with developmental abnormalities from Database of Chromosomal Imbalance and Phenotype in Humans Using Ensembl Resources and European Cytogeneticists Association Register of Unbalanced Chromosome Aberrations database. Forming CNV sets for each of 1,088 distinct human abnormalities, we were able to associate a total of 143 (13%) human abnormalities with mouse model phenotypes. Although many mouse phenotypes are readily comparable to their associated human abnormality, others are less so, generating novel biological hypotheses. Of the 2,086 candidate genes that contribute to these associations, 65% have not been previously associated with human disease in Online Mendelian Inheritance in Man, and their distribution suggests both extensive pleiotropy and epistasis while also proposing a small number of simple additive consequences.

Phenotypic information in genomic variant databases enhances clinical care and research: the International Standards for Cytogenomic Arrays Consortium experience

Whole-genome analysis, now including whole-genome sequencing, is moving rapidly into the clinical setting, leading to detection of human variation on a broader scale than ever before. Interpreting this information will depend on the availability of thorough and accurate phenotype information, and the ability to curate, store, and access data on genotype-phenotype relationships. This idea has already been demonstrated within the context of chromosomal microarray (CMA) testing. The International Standards for Cytogenomic Arrays (ISCA) Consortium promotes standardization of variant interpretation for this technology through its initiatives, including the formation of a publicly available database housing clinical CMA data. Recognizing that phenotypic data are essential for the interpretation of genomic variants, the ISCA Consortium has developed tools to facilitate the collection of these data and its deposition in a standardized structured format within the ISCA Consortium database. This rich source of phenotypic data can also be used within broader applications such as developing phenotypic profiles of emerging genomic disorders, identification of candidate regions for particular phenotypes, or creation of tools for use in clinical practice. We summarize the ISCA experience as a model for ongoing efforts incorporating phenotype data with genotype data to improve the quality of research and clinical care in human genetics.

High resolution array in the clinical approach to chromosomal phenotypes

Array genomic hybridization (AGH) has recently been implemented as a diagnostic tool for the detection of submicroscopic copy number variants (CNVs) in patients with developmental disorders. However, there is no consensus regarding the choice of the platform, the minimal resolution needed and systematic interpretation of CNVs. We report our experience in the clinical diagnostic use of high resolution AGH up to 100 kb on 131 patients with chromosomal phenotypes but previously normal karyotype. We evaluated the usefulness in our clinics and laboratories by the detection rate of causal CNVs and CNVs of unknown clinical significance and to what extent their interpretation would challenge the systematic use of high-resolution arrays in clinical application. Prioritizing phenotype-genotype correlation in our interpretation strategy to criteria previously described, we identified 33 (25.2%) potentially pathogenic aberrations. 16 aberrations were confirmed pathogenic (16.4% syndromic, 8.5% non-syndromic patients); 9 were new and individual aberrations, 3 of them were pathogenic although inherited and one is as small as approx 200 kb. 13 of 16 further CNVs of unknown significance were classified likely benign, for 3 the significance remained unclear. High resolution array allows the detection of up to 12.2% of pathogenic aberrations in a diagnostic clinical setting. Although the majority of aberrations are larger, the detection of small causal aberrations may be relevant for family counseling. The number of remaining unclear CNVs is limited. Careful phenotype-genotype correlations of the individual CNVs and clinical features are challenging but remain a hallmark for CNV interpretation.

Structural genomic variation in intellectual disability

The genetic causes of mental retardation are highly heterogeneous and for a large proportion unknown. Mutations as well as large chromosomal abnormalities are known to contribute to mental retardation, and recently more subtle structural genomic variations have been shown to contribute significantly to this common and complex disorder. Genomic microarrays with increasing resolution levels have revealed the presence of rare de novo CNVs in approximately 15% of all mentally retarded patients. Microarray-based CNV screening is rapidly replacing conventional karyotyping in the diagnostic workflow, resulting in an increased diagnostic yield as well as biological insight into this disorder. In this chapter, an overview is given of the detection and interpretation of copy number variations in mental retardation, with a focus on diagnostic applications. In addition, a detailed protocol is provided for the diagnostic interpretation of copy-number variations in mental retardation.

Genome arrays for the detection of copy number variations in idiopathic mental retardation, idiopathic generalized epilepsy and neuropsychiatric disorders: lessons for diagnostic workflow and research

We review the contributions and limitations of genome-wide array-based identification of copy number variants (CNVs) in the clinical diagnostic evaluation of patients with mental retardation (MR) and other brain-related disorders. In unselected MR referrals a causative genomic gain or loss is detected in 14-18% of cases. Usually, such CNVs arise de novo, are not found in healthy subjects, and have a major impact on the phenotype by altering the dosage of multiple genes. This high diagnostic yield justifies array-based segmental aneuploidy screening as the initial genetic test in these patients. This also pertains to patients with autism (expected yield about 5-10% in nonsyndromic and 10-20% in syndromic patients) and schizophrenia (at least 5% yield). CNV studies in idiopathic generalized epilepsy, attention-deficit hyperactivity disorder, major depressive disorder and Tourette syndrome indicate that patients have, on average, a larger CNV burden as compared to controls. Collectively, the CNV studies suggest that a wide spectrum of disease-susceptibility variants exists, most of which are rare (<0.1%) and of variable and usually small effect. Notwithstanding, a rare CNV can have a major impact on the phenotype. Exome sequencing in MR and autism patients revealed de novo mutations in protein coding genes in 60 and 20% of cases, respectively. Therefore, it is likely that arrays will be supplanted by next-generation sequencing methods as the initial and perhaps ultimate diagnostic tool in patients with brain-related disorders, revealing both CNVs and mutations in a single test.

Functional enrichment analysis with structural variants: pitfalls and strategies

Interpreting the phenotypic consequences of human structural variation remains challenging. Functional enrichment analysis, which can identify functional enrichments among genes affected by structural variants, is providing significant biological insights into the genotype-phenotype relationship. In this review, we discuss the different approaches and choices in the application of this technique to human structural variation. We consider the importance of choosing the right background distribution for detection, the significance of the gene selection criteria, the effects of tissue-specific gene length biases and discuss sources of functional annotations with a focus on Gene Ontology and mouse phenotypic resources. Throughout this review, we highlight potential sources of significant bias that are of particular concern to the analysis of structural variants, and illustrate the importance of examining the expectations upon which enrichment analysis techniques depend.

Identification of submicroscopic chromosomal aberrations in fetuses with increased nuchal translucency and apparently normal karyotype

OBJECTIVE

Fetal nuchal translucency (NT) is assessed by ultrasonography as a screening tool for aneuploidy at 11 to 13 + 6 weeks' gestation. Fetuses with increased NT but apparently normal karyotype are still at higher risk of structural abnormality and a range of genetic syndromes, which may be related to major and submicroscopic chromosomal abnormalities. The aim of this study was to report the prevalence of submicroscopic chromosomal abnormalities in a cohort of apparently euploid fetuses that presented with increased NT.

METHODS

DNA was extracted from stored chorionic villus samples from fetuses found to have increased NT (> 3.5 mm) during first-trimester screening. These samples were examined by microarray-based comparative genomic hybridization (aCGH) using a 44K oligonucleotide array specifically constructed for prenatal screening. Variations in copy number (CNVs) were reported after excluding known non-pathogenic variants and after validation with multiplex ligation-dependent probe amplification (MLPA) or real-time quantitative polymerase chain reaction (qPCR). The prevalence of pathogenic CNVs is reported and the association with NT and other ultrasound findings described.

RESULTS

CNVs were reported in 6/48 (12.5%) cases by aCGH and the microdeletions or microduplications ranged from 1.1 to 7.9 Mb. Five of these were validated by MLPA/real-time qPCR and four (8.3%) were considered to be pathogenic and clinically significant. The incidence of pathogenic CNVs was 20.0% (2/10) among those cases with other sonographic anomalies and 5.3% (2/38) among those without.

CONCLUSION

aCGH allows detection of submicroscopic chromosomal abnormalities, the prevalence of which may be increased in fetuses with NT > 3.5 mm and an apparently normal karyotype.

Characterization of copy number-stable regions in the human genome

In the past few years the number of copy number variants (CNVs) identified in the human genome has increased significantly, but our understanding of the functional impact of CNVs is still limited. Clinically significant variations cannot easily be distinguished from benign, complicating interpretation of patient data. Multiple studies have focused on analysis of regions that vary in copy number in specific disorders. Here we use the opposite strategy and focus our analysis on regions that never seem to vary in the general population, hypothesizing that these are copy number stable because variations within them are deleterious. Our results show that copy number stable regions are characterized by correlation with a number of genomic features, allowing us to define a list of genomic regions that are dosage sensitive in humans. We find that these dosage-sensitive regions show significant overlap with de novo CNVs identified in patients with intellectual disability or autism. There is also a significant association between copy number stable regions and rare inherited variants in autism patients, but not in controls. Based on this predictive power, we propose that copy number stable regions can be used to complement maps of known CNVs to facilitate interpretation of patient data.

CNV-WebStore: online CNV analysis, storage and interpretation

Background

Microarray technology allows the analysis of genomic aberrations at an ever increasing resolution, making functional interpretation of these vast amounts of data the main bottleneck in routine implementation of high resolution array platforms, and emphasising the need for a centralised and easy to use CNV data management and interpretation system.

Results

We present CNV-WebStore, an online platform to streamline the processing and downstream interpretation of microarray data in a clinical context, tailored towards but not limited to the Illumina BeadArray platform. Provided analysis tools include CNV analsyis, parent of origin and uniparental disomy detection. Interpretation tools include data visualisation, gene prioritisation, automated PubMed searching, linking data to several genome browsers and annotation of CNVs based on several public databases. Finally a module is provided for uniform reporting of results.

Conclusion

CNV-WebStore is able to present copy number data in an intuitive way to both lab technicians and clinicians, making it a useful tool in daily clinical practice.

Classification of pathogenic or benign status of CNVs detected by microarray analysis

Multiple lines of evidence indicated that microarray analysis has a better diagnostic yield of clinically significant genetic changes than do conventional methods in patients with constitutional abnormalities. However, interpretation of microarray data is complicated by the presence of both novel and recurrent copy number variants (CNVs) of unknown significance. To address this issue, Hehir-Kwa et al. described a new computational method for determining the pathogenicity between benign and mental retardation (MR)-associated CNVs among patients with MR. This study demonstrated the value of objectively prioritizing MR-associated CNVs using structural and functional genomic features in diagnostics. In this regard, we discuss an evidence-based summary of how to classify pathogenic or benign status of a CNV in clinical genetics and advocate that there is a need for algorithmic adjustment between constitutional cytogenetic and prenatal diagnosis settings.