Characterizing complex structural variation in germline and somatic genomes

Detection and analysis of complex structural variation in human genomes across populations and in brains of donors with psychiatric disorders

Complex structural variations (cxSVs) are often overlooked in genome analyses due to detection challenges. We developed ARC-SV, a probabilistic and machine-learning-based method that enables accurate detection and reconstruction of cxSVs from standard datasets. By applying ARC-SV across 4,262 genomes representing all continental populations, we identified cxSVs as a significant source of natural human genetic variation. Rare cxSVs have a propensity to occur in neural genes and loci that underwent rapid human-specific evolution, including those regulating corticogenesis. By performing single-nucleus multiomics in postmortem brains, we discovered cxSVs associated with differential gene expression and chromatin accessibility across various brain regions and cell types. Additionally, cxSVs detected in brains of psychiatric cases are enriched for linkage with psychiatric GWAS risk alleles detected in the same brains. Furthermore, our analysis revealed significantly decreased brain-region- and cell-type-specific expression of cxSV genes, specifically for psychiatric cases, implicating cxSVs in the molecular etiology of major neuropsychiatric disorders.

FindCSV: a long-read based method for detecting complex structural variations

BACKGROUND

Structural variations play a significant role in genetic diseases and evolutionary mechanisms. Extensive research has been conducted over the past decade to detect simple structural variations, leading to the development of well-established detection methods. However, recent studies have highlighted the potentially greater impact of complex structural variations on individuals compared to simple structural variations. Despite this, the field still lacks precise detection methods specifically designed for complex structural variations. Therefore, the development of a highly efficient and accurate detection method is of utmost importance.

RESULT

In response to this need, we propose a novel method called FindCSV, which leverages deep learning techniques and consensus sequences to enhance the detection of SVs using long-read sequencing data. Compared to current methods, FindCSV performs better in detecting complex and simple structural variations.

CONCLUSIONS

FindCSV is a new method to detect complex and simple structural variations with reasonable accuracy in real and simulated data. The source code for the program is available at https://github.com/nwpuzhengyan/FindCSV .

A comprehensive review of deep learning-based variant calling methods

Genome sequencing data have become increasingly important in the field of personalized medicine and diagnosis. However, accurately detecting genomic variations remains a challenging task. Traditional variation detection methods rely on manual inspection or predefined rules, which can be time-consuming and prone to errors. Consequently, deep learning-based approaches for variation detection have gained attention due to their ability to automatically learn genomic features that distinguish between variants. In our review, we discuss the recent advancements in deep learning-based algorithms for detecting small variations and structural variations in genomic data, as well as their advantages and limitations.

Exome copy number variant detection, analysis, and classification in a large cohort of families with undiagnosed rare genetic disease

Copy number variants (CNVs) are significant contributors to the pathogenicity of rare genetic diseases and, with new innovative methods, can now reliably be identified from exome sequencing. Challenges still remain in accurate classification of CNV pathogenicity. CNV calling using GATK-gCNV was performed on exomes from a cohort of 6,633 families (15,759 individuals) with heterogeneous phenotypes and variable prior genetic testing collected at the Broad Institute Center for Mendelian Genomics of the Genomics Research to Elucidate the Genetics of Rare Diseases consortium and analyzed using the seqr platform. The addition of CNV detection to exome analysis identified causal CNVs for 171 families (2.6%). The estimated sizes of CNVs ranged from 293 bp to 80 Mb. The causal CNVs consisted of 140 deletions, 15 duplications, 3 suspected complex structural variants (SVs), 3 insertions, and 10 complex SVs, the latter two groups being identified by orthogonal confirmation methods. To classify CNV variant pathogenicity, we used the 2020 American College of Medical Genetics and Genomics/ClinGen CNV interpretation standards and developed additional criteria to evaluate allelic and functional data as well as variants on the X chromosome to further advance the framework. We interpreted 151 CNVs as likely pathogenic/pathogenic and 20 CNVs as high-interest variants of uncertain significance. Calling CNVs from existing exome data increases the diagnostic yield for individuals undiagnosed after standard testing approaches, providing a higher-resolution alternative to arrays at a fraction of the cost of genome sequencing. Our improvements to the classification approach advances the systematic framework to assess the pathogenicity of CNVs.

Accurate identification of structural variations from cancer samples

Structural variations (SVs) are commonly found in cancer genomes. They can cause gene amplification, deletion and fusion, among other functional consequences. With an average read length of hundreds of kilobases, nano-channel-based optical DNA mapping is powerful in detecting large SVs. However, existing SV calling methods are not tailored for cancer samples, which have special properties such as mixed cell types and sub-clones. Here we propose the Cancer Optical Mapping for detecting Structural Variations (COMSV) method that is specifically designed for cancer samples. It shows high sensitivity and specificity in benchmark comparisons. Applying to cancer cell lines and patient samples, COMSV identifies hundreds of novel SVs per sample.

Exome copy number variant detection, analysis and classification in a large cohort of families with undiagnosed rare genetic disease

Copy number variants (CNVs) are significant contributors to the pathogenicity of rare genetic diseases and with new innovative methods can now reliably be identified from exome sequencing. Challenges still remain in accurate classification of CNV pathogenicity. CNV calling using GATK-gCNV was performed on exomes from a cohort of 6,633 families (15,759 individuals) with heterogeneous phenotypes and variable prior genetic testing collected at the Broad Institute Center for Mendelian Genomics of the GREGoR consortium. Each family's CNV data was analyzed using the seqr platform and candidate CNVs classified using the 2020 ACMG/ClinGen CNV interpretation standards. We developed additional evidence criteria to address situations not covered by the current standards. The addition of CNV calling to exome analysis identified causal CNVs for 173 families (2.6%). The estimated sizes of CNVs ranged from 293 bp to 80 Mb with estimates that 44% would not have been detected by standard chromosomal microarrays. The causal CNVs consisted of 141 deletions, 15 duplications, 4 suspected complex structural variants (SVs), 3 insertions and 10 complex SVs, the latter two groups being identified by orthogonal validation methods. We interpreted 153 CNVs as likely pathogenic/pathogenic and 20 CNVs as high interest variants of uncertain significance. Calling CNVs from existing exome data increases the diagnostic yield for individuals undiagnosed after standard testing approaches, providing a higher resolution alternative to arrays at a fraction of the cost of genome sequencing. Our improvements to the classification approach advances the systematic framework to assess the pathogenicity of CNVs.

Association between Intestinal Colonization and Extraintestinal Infection with Carbapenem-Resistant Klebsiella pneumoniae in Children

Carbapenem-resistant Klebsiella pneumoniae (CRKP) has become a critical public health threat. However, the association between intestinal colonization and parenteral infection among pediatric patients has not been elucidated. We collected 8 fecal CRKP strains and 10 corresponding CRKP strains responsible for extraintestinal infection from eight patients who did not manifest infection upon admission to the hospital. Paired isolates showed identical resistance to antimicrobials and identical virulence in vitro and in vivo. wzi capsule typing, multilocus sequence typing, and whole-genome sequencing (WGS) indicated high similarity between paired colonizing and infecting isolates. Mutations between colonizing and infecting isolate pairs found by WGS had a distinctive molecular signature of a high proportion of complex structural variants. The mutated genes were involved in pathways associated with infection-related physiological and pathogenic functions, including antibiotic resistance, virulence, and response to the extracellular environment. The latter is important for bacterial infection of environmental niches. Various mutations related to antibiotic resistance, virulence, and colonization that were not associated with any particular mutational hot spot correlated with an increased risk of extraintestinal infection. Notably, novel subclone carbapenem-resistant hypervirulent K. pneumoniae (CR-hvKP) KL19-ST15 exhibited hypervirulence in experimental assays that reflected the severe clinical symptoms of two patients infected with the clonal strains. Taken together, our findings indicate the association between CRKP intestinal colonization and extraintestinal infection, suggesting that active screening for colonization on admission could decrease infection risk in children. IMPORTANCE Carbapenem-resistant Klebsiella pneumoniae (CRKP) causes an increasing number of nosocomial infections, which can be life-threatening, as carbapenems are last-resort antibiotics. K. pneumoniae is part of the healthy human microbiome, and this provides a potential advantage for infection. This study demonstrated that CRKP intestinal colonization is strongly linked to extraintestinal infection, based on the evidence given by whole-genome sequencing data and phenotypic assays of antimicrobial resistance and virulence. Apart from these findings, our in-depth analysis of point mutations and chromosome structural variants in patient-specific infecting isolates compared with colonizing isolates may contribute insights into bacterial adaptation underlying CRKP infection. In addition, a novel subclone of carbapenem-resistant hypervirulent K. pneumoniae (CR-hvKP) was observed in the study. This finding highlights the importance of CRKP active surveillance among children, targeting in particular the novel high-risk CR-hvKP clone.

DelInsCaller: An Efficient Algorithm for Identifying Delins and Estimating Haplotypes from Long Reads with High Level of Sequencing Errors

Delins, as known as complex indel, is a combined genomic structural variation formed by deleting and inserting DNA fragments at a common genomic location. Recent studies emphasized the importance of delins in cancer diagnosis and treatment. Although the long reads from PacBio CLR sequencing significantly facilitate delins calling, the existing approaches still encounter computational challenges from the high level of sequencing errors, and often introduce errors in genotyping and phasing delins. In this paper, we propose an efficient algorithmic pipeline, named delInsCaller, to identify delins on haplotype resolution from the PacBio CLR sequencing data. delInsCaller design a fault-tolerant method by calculating a variation density score, which helps to locate the candidate mutational regions under a high-level of sequencing errors. It adopts a base association-based contig splicing method, which facilitates contig splicing in the presence of false-positive interference. We conducted a series of experiments on simulated datasets, and the results showed that delInsCaller outperformed several state-of-the-art approaches, e.g., SVseq3, across a wide range of parameter settings, such as read depth, sequencing error rates, etc. delInsCaller often obtained higher f-measures than other approaches; specifically, it was able to maintain advantages at ~15% sequencing errors. delInsCaller was able to significantly improve the N50 values with almost no loss of haplotype accuracy compared with the existing approach as well.

Complex genomic rearrangements: an underestimated cause of rare diseases

Complex genomic rearrangements (CGRs) are known contributors to disease but are often missed during routine genetic screening. Identifying CGRs requires (i) identifying copy number variants (CNVs) concurrently with inversions, (ii) phasing multiple breakpoint junctions incis, as well as (iii) detecting and resolving structural variants (SVs) within repeats. We demonstrate how combining cytogenetics and new sequencing methodologies is being successfully applied to gain insights into the genomic architecture of CGRs. In addition, we review CGR patterns and molecular features revealed by studying constitutional genomic disorders. These data offer invaluable lessons to individuals interested in investigating CGRs, evaluating their clinical relevance and frequency, as well as assessing their impact(s) on rare genetic diseases.

SVision: a deep learning approach to resolve complex structural variants

Complex structural variants (CSVs) encompass multiple breakpoints and are often missed or misinterpreted. We developed SVision, a deep-learning-based multi-object-recognition framework, to automatically detect and haracterize CSVs from long-read sequencing data. SVision outperforms current callers at identifying the internal structure of complex events and has revealed 80 high-quality CSVs with 25 distinct structures from an individual genome. SVision directly detects CSVs without matching known structures, allowing sensitive detection of both common and previously uncharacterized complex rearrangements.

Multiplex structural variant detection by whole-genome mapping and nanopore sequencing

Identification of structural variants (SVs) breakpoints is important in studying mutations, mutagenic causes, and functional impacts. Next-generation sequencing and whole-genome optical mapping are extensively used in SV discovery and characterization. However, multiple platforms and computational approaches are needed for comprehensive analysis, making it resource-intensive and expensive. Here, we propose a strategy combining optical mapping and cas9-assisted targeted nanopore sequencing to analyze SVs. Optical mapping can economically and quickly detect SVs across a whole genome but does not provide sequence-level information or precisely resolve breakpoints. Furthermore, since only a subset of all SVs is known to affect biology, we attempted to type a subset of all SVs using targeted nanopore sequencing. Using our approach, we resolved the breakpoints of five deletions, five insertions, and an inversion, in a single experiment.

Similarity-Based Analysis of Allele Frequency Distribution among Multiple Populations Identifies Adaptive Genomic Structural Variants

Structural variants have a considerable impact on human genomic diversity. However, their evolutionary history remains mostly unexplored. Here, we developed a new method to identify potentially adaptive structural variants based on a similarity-based analysis that incorporates genotype frequency data from 26 populations simultaneously. Using this method, we analyzed 57,629 structural variants and identified 576 structural variants that show unusual population differentiation. Of these putatively adaptive structural variants, we further showed that 24 variants are multiallelic and overlap with coding sequences, and 20 variants are significantly associated with GWAS traits. Closer inspection of the haplotypic variation associated with these putatively adaptive and functional structural variants reveals deviations from neutral expectations due to: 1) population differentiation of rapidly evolving multiallelic variants, 2) incomplete sweeps, and 3) recent population-specific negative selection. Overall, our study provides new methodological insights, documents hundreds of putatively adaptive variants, and introduces evolutionary models that may better explain the complex evolution of structural variants.

Rapid Cis-Trans Coevolution Driven by a Novel Gene Retroposed from a Eukaryotic Conserved CCR4-NOT Component in Drosophila

Young, or newly evolved, genes arise ubiquitously across the tree of life, and they can rapidly acquire novel functions that influence a diverse array of biological processes. Previous work identified a young regulatory duplicate gene in Drosophila, Zeus that unexpectedly diverged rapidly from its parent, Caf40, an extremely conserved component in the CCR4-NOT machinery in post-transcriptional and post-translational regulation of eukaryotic cells, and took on roles in the male reproductive system. This neofunctionalization was accompanied by differential binding of the Zeus protein to loci throughout the Drosophila melanogaster genome. However, the way in which new DNA-binding proteins acquire and coevolve with their targets in the genome is not understood. Here, by comparing Zeus ChIP-Seq data from D. melanogaster and D. simulans to the ancestral Caf40 binding events from D. yakuba, a species that diverged before the duplication event, we found a dynamic pattern in which Zeus binding rapidly coevolved with a previously unknown DNA motif, which we term Caf40 and Zeus-Associated Motif (CAZAM), under the influence of positive selection. Interestingly, while both copies of Zeus acquired targets at male-biased and testis-specific genes, D. melanogaster and D. simulans proteins have specialized binding on different chromosomes, a pattern echoed in the evolution of the associated motif. Using CRISPR-Cas9-mediated gene knockout of Zeus and RNA-Seq, we found that Zeus regulated the expression of 661 differentially expressed genes (DEGs). Our results suggest that the evolution of young regulatory genes can be coupled to substantial rewiring of the transcriptional networks into which they integrate, even over short evolutionary timescales. Our results thus uncover dynamic genome-wide evolutionary processes associated with new genes.

miRNA dysregulation is an emerging modulator of genomic instability

Genomic instability consists of a range of genetic alterations within the genome that contributes to tumor heterogeneity and drug resistance. It is a well-established characteristic of most cancer cells. Genome instability induction results from defects in DNA damage surveillance mechanisms, mitotic checkpoints and DNA repair machinery. Accumulation of genetic alterations ultimately sets cells towards malignant transformation. Recent studies suggest that miRNAs are key players in mediating genome instability. miRNAs are a class of small RNAs expressed in most somatic tissues and are part of the epigenome. Importantly, in many cancers, miRNA expression is dysregulated. Consequently, this review examines the role of miRNA dysregulation as a causal step for induction of genome instability and subsequent carcinogenesis. We focus specifically on mechanistic studies assessing miRNA(s) and specific subtypes of genome instability or known modes of genome instability. In addition, we provide insight on the existing knowledge gaps within the field and possible ways to address them.

Complex structural variations in non-human primate hepatitis B virus

BACKGROUND

Recent genome sequence technology has revealed a novel type of genetic rearrangement referred to as complex structural variations (SVs). Previous studies have elucidated the complex SVs in human hepatitis B viruses (HBVs). In this study, we investigated the existence of complex SVs in HBVs from non-human primates (NHPs).

METHODS

Searches for nucleotide sequences of NHP HBV were conducted using the PubMed, and genetic sequences were retrieved from databases. The candidate genetic sequences harboring complex SVs were analyzed using the CLUSTALW program and MAFFT. Additional bioinformatical analyses were performed to determine strains with complex SVs and to elucidate characteristics of NHP HBV strains.

RESULTS

One hundred and fifty-four HBV strains from NHPs were identified from databases. SVs and complex SVs were observed in 11 (7.1%) strains. Three gibbon HBV (GiHBV) strains showed complex SVs consisting of an insertion and a deletion in the pre-S1 region. One GiHBV strain possessed a 6-nt insertion, which are normally specific to human HBV genotype A (HBV/A) in the Core region, and further analyses clarified that the 6-nt insertion was not caused by recombination, but rather by simple insertion. Another chimpanzee HBV strain showed complex SVs in the pre-S1 region, which were composed of human HBV/E, G-specific polymorphic SV, and an additional 6-nt insertion.

CONCLUSIONS

In this study, complex SVs were observed in HBV strains from NHPs, in addition to human HBV strains, as shown in previous studies. These data suggest that complex SVs could also be found in other members of hepadnaviruses, and may play a role in their genetic diversity.

Mako: A Graph-based Pattern Growth Approach to Detect Complex Structural Variants

Complex structural variants (CSVs) are genomic alterations that have more than two breakpoints and are considered as the simultaneous occurrence of simple structural variants. However, detecting the compounded mutational signals of CSVs is challenging through a commonly used model-match strategy. As a result, there has been limited progress for CSV discovery compared with simple structural variants. Here, we systematically analyzed the multi-breakpoint connection feature of CSVs, and proposed Mako, utilizing a bottom-up guided model-free strategy, to detect CSVs from paired-end short-read sequencing. Specifically, we implemented a graph-based pattern growth approach, where the graph depicts potential breakpoint connections, and pattern growth enables CSV detection without pre-defined models. Comprehensive evaluations on both simulated and real datasets revealed that Mako outperformed other algorithms. Notably, validation rates of CSVs on real data based on experimental and computational validations as well as manual inspections are around 70%, where the medians of experimental and computational breakpoint shift are 13 bp and 26 bp, respectively. Moreover, the Mako CSV subgraph effectively characterized the breakpoint connections of a CSV event and uncovered a total of 15 CSV types, including two novel types of adjacent segment swap and tandem dispersed duplication. Further analysis of these CSVs also revealed the impact of sequence homology on the formation of CSVs. Mako is publicly available at https://github.com/xjtu-omics/Mako.

Machine-learning predicts genomic determinants of meiosis-driven structural variation in a eukaryotic pathogen

Species harbor extensive structural variation underpinning recent adaptive evolution. However, the causality between genomic features and the induction of new rearrangements is poorly established. Here, we analyze a global set of telomere-to-telomere genome assemblies of a fungal pathogen of wheat to establish a nucleotide-level map of structural variation. We show that the recent emergence of pesticide resistance has been disproportionally driven by rearrangements. We use machine learning to train a model on structural variation events based on 30 chromosomal sequence features. We show that base composition and gene density are the major determinants of structural variation. Retrotransposons explain most inversion, indel and duplication events. We apply our model to Arabidopsis thaliana and show that our approach extends to more complex genomes. Finally, we analyze complete genomes of haploid offspring in a four-generation pedigree. Meiotic crossover locations are enriched for new rearrangements consistent with crossovers being mutational hotspots. The model trained on species-wide structural variation accurately predicts the position of >74% of newly generated variants along the pedigree. The predictive power highlights causality between specific sequence features and the induction of chromosomal rearrangements. Our work demonstrates that training sequence-derived models can accurately identify regions of intrinsic DNA instability in eukaryotic genomes.

Novel Genetic Rearrangements in Hepatitis B Virus: Complex Structural Variations and Structural Variation Polymorphisms

Chronic hepatitis B virus (HBV) causes serious clinical problems, such as liver cirrhosis and hepatocellular carcinoma. Current antiviral treatments suppress HBV; however, the clinical cure rate remains low. Basic research on HBV is indispensable to eradicate and cure HBV. Genetic alterations are defined by nucleotide substitutions and canonical forms of structural variations (SVs), such as insertion, deletion and duplication. Additionally, genetic changes inconsistent with the canonical forms have been reported, and these have been termed complex SVs. Detailed analyses of HBV using bioinformatical applications have detected complex SVs in HBV genomes. Sequence gaps and low sequence similarity have been observed in the region containing complex SVs. Additionally, insertional motif sequences have been observed in HBV strains with complex SVs. Following the analyses of complex SVs in the HBV genome, the role of SVs in the genetic diversity of orthohepadnavirus has been investigated. SV polymorphisms have been detected in comparisons of several species of orthohepadnaviruses. As mentioned, complex SVs are composed of multiple SVs. On the contrary, SV polymorphisms are observed as insertions of different SVs. Up to a certain point, nucleotide substitutions cause genetic differences. However, at some point, the nucleotide sequences are split into several particular patterns. These SVs have been observed as polymorphic changes. Different species of orthohepadnaviruses possess SVs which are unique and specific to a certain host of the virus. Studies have shown that SVs play an important role in the HBV genome. Further studies are required to elucidate their virologic and clinical roles.

Intronic Breakpoint Signatures Enhance Detection and Characterization of Clinically Relevant Germline Structural Variants

The relevance of large copy number variants (CNVs) to hereditary disorders has been long recognized, and population sequencing efforts have chronicled many common structural variants (SVs). However, limited data are available on the clinical contribution of rare germline SVs. Here, a detailed characterization of SVs identified using targeted next-generation sequencing was performed. Across 50 genes associated with hereditary cancer and cardiovascular disorders, a minimum of 828 unique SVs were reported, including 584 fully characterized SVs. Almost 40% of CNVs were <5 kb, with one in three deletions impacting a single exon. Additionally, 36 mid-range deletions/duplications (50 to 250 bp), 21 mobile element insertions, 6 inversions, and 27 complex rearrangements were detected. This data set was used to model SV detection in a bioinformatics pipeline solely relying on read depth, which revealed that genome sequencing (30×) allows detection of 71%, a 500× panel only targeting coding regions 53%, and exome sequencing (100×) <20% of characterized SVs. SVs accounted for 14.1% of all unique pathogenic variants, supporting the importance of SVs in hereditary disorders. Robust SV detection requires an ensemble of variant-calling algorithms that utilize sequencing of intronic regions. These algorithms should use distinct data features representative of each class of mutational mechanism, including recombination between two sequences sharing high similarity, covariants inserted between CNV breakpoints, and complex rearrangements containing inverted sequences.

Analysis of complex structural variants in the DMD gene in one family

This work describes a family with Duchenne muscular dystrophy (DMD) with a rare case of a symptomatic pregnant woman. The main aim was to perform prenatal molecular diagnosis to provide genetic counseling. The secondary aim was to suggest the molecular mechanisms causing the complex structural variant (cxSV) identified. To accomplish this, we used a multi-technique algorithm including segregation analysis, Multiplex Ligation-dependent Probe Amplification, PCR, X-chromosome inactivation studies, microarrays, whole genome sequencing and bioinformatics. We identified a duplication of exons 38-43 in the DMD gene in all affected and obligate carrier members, proving that this was the DMD-causing mutation. We also observed a skewed X-chromosome inactivation in the symptomatic woman that explained her symptomatology. In addition, we identified a cxSV (duplication of exons 38-43 and deletion of exons 45-54) in the affected boy. The molecular characterization and bioinformatic analyses of the breakpoint junctions allowed us to identify Double Strand Breaks stimulator motifs and suggested the replication-dependent Fork Stalling and Template Switching as the most probable mechanisms leading to the duplication. In addition, the de novo deletion might have been the result of a germline inter-chromosome non-allelic recombination involving the Non-Homologous End Joining mechanism. In conclusion, the diagnostic strategy used allowed us to provide accurate molecular diagnosis and genetic counseling. In addition, the familial molecular diagnosis together with the in-depth characterization of the cxSV helped to determine the chronology of the molecular events, and propose and understand the molecular mechanisms involved in the generation of this complex rearrangement.

Shiny-SoSV: A web-based performance calculator for somatic structural variant detection

Somatic structural variants are an important contributor to cancer development and evolution. Accurate detection of these complex variants from whole genome sequencing data is influenced by a multitude of parameters. However, there are currently no tools for guiding study design nor are there applications that could predict the performance of somatic structural variant detection. To address this gap, we developed Shiny-SoSV, a user-friendly web-based calculator for determining the impact of common variables on the sensitivity, precision and F1 score of somatic structural variant detection, including choice of variant detection tool, sequencing depth of coverage, variant allele fraction, and variant breakpoint resolution. Using simulation studies, we determined singular and combinatoric effects of these variables, modelled the results using a generalised additive model, allowing structural variant detection performance to be predicted for any combination of predictors. Shiny-SoSV provides an interactive and visual platform for users to easily compare individual and combined impact of different parameters. It predicts the performance of a proposed study design, on somatic structural variant detection, prior to the commencement of benchwork. Shiny-SoSV is freely available at https://hcpcg.shinyapps.io/Shiny-SoSV with accompanying user’s guide and example use-cases.

Complex Variant Discovery Using Discordant Cluster Normalization

Complex genomic structural variants (CGSVs) are abnormalities that present with three or more breakpoints, making their discovery a challenge. The majority of existing algorithms for structural variant detection are only designed to find simple structural variants (SSVs) such as deletions and inversions; they fail to find more complex events such as deletion-inversions or deletion-duplications, for example. In this study, we present an algorithm named CleanBreak that employs a clique partitioning graph-based strategy to identify collections of SSV clusters and then subsequently identifies overlapping SSV clusters to examine the search space of possible CGSVs, choosing the one that is most concordant with local read depth. We evaluated CleanBreak's performance on whole genome simulated data and a real data set from the 1000 Genomes Project. We also compared CleanBreak with another algorithm for CGSV discovery. The results demonstrate CleanBreak's utility as an effective method to discover CGSVs.

Mapping and characterization of structural variation in 17,795 human genomes

A key goal of whole-genome sequencing for studies of human genetics is to interrogate all forms of variation, including single-nucleotide variants, small insertion or deletion (indel) variants and structural variants. However, tools and resources for the study of structural variants have lagged behind those for smaller variants. Here we used a scalable pipeline1 to map and characterize structural variants in 17,795 deeply sequenced human genomes. We publicly release site-frequency data to create the largest, to our knowledge, whole-genome-sequencing-based structural variant resource so far. On average, individuals carry 2.9 rare structural variants that alter coding regions; these variants affect the dosage or structure of 4.2 genes and account for 4.0-11.2% of rare high-impact coding alleles. Using a computational model, we estimate that structural variants account for 17.2% of rare alleles genome-wide, with predicted deleterious effects that are equivalent to loss-of-function coding alleles; approximately 90% of such structural variants are noncoding deletions (mean 19.1 per genome). We report 158,991 ultra-rare structural variants and show that 2% of individuals carry ultra-rare megabase-scale structural variants, nearly half of which are balanced or complex rearrangements. Finally, we infer the dosage sensitivity of genes and noncoding elements, and reveal trends that relate to element class and conservation. This work will help to guide the analysis and interpretation of structural variants in the era of whole-genome sequencing.

Mapping and characterization of structural variation in 17,795 human genomes

A key goal of whole-genome sequencing for studies of human genetics is to interrogate all forms of variation, including single-nucleotide variants, small insertion or deletion (indel) variants and structural variants. However, tools and resources for the study of structural variants have lagged behind those for smaller variants. Here we used a scalable pipeline¹ to map and characterize structural variants in 17,795 deeply sequenced human genomes. We publicly release site-frequency data to create the largest, to our knowledge, whole-genome-sequencing-based structural variant resource so far. On average, individuals carry 2.9 rare structural variants that alter coding regions; these variants affect the dosage or structure of 4.2 genes and account for 4.0-11.2% of rare high-impact coding alleles. Using a computational model, we estimate that structural variants account for 17.2% of rare alleles genome-wide, with predicted deleterious effects that are equivalent to loss-of-function coding alleles; approximately 90% of such structural variants are noncoding deletions (mean 19.1 per genome). We report 158,991 ultra-rare structural variants and show that 2% of individuals carry ultra-rare megabase-scale structural variants, nearly half of which are balanced or complex rearrangements. Finally, we infer the dosage sensitivity of genes and noncoding elements, and reveal trends that relate to element class and conservation. This work will help to guide the analysis and interpretation of structural variants in the era of whole-genome sequencing.

SVSR: A Program to Simulate Structural Variations and Generate Sequencing Reads for Multiple Platforms

Structural variation accounts for a major fraction of mutations in the human genome and confers susceptibility to complex diseases. Next generation sequencing along with the rapid development of computational methods provides a cost-effective procedure to detect such variations. Simulation of structural variations and sequencing reads with real characteristics is essential for benchmarking the computational methods. Here, we develop a new program, SVSR, to simulate five types of structural variations (indels, tandem duplication, CNVs, inversions, and translocations) and SNPs for the human genome and to generate sequencing reads with features from popular platforms (Illumina, SOLiD, 454, and Ion Torrent). We adopt a selection model trained from real data to predict copy number states, starting from the first site of a particular genome to the end. Furthermore, we utilize references of microbial genomes to produce insertion fragments and design probabilistic models to imitate inversions and translocations. Moreover, we create platform-specific errors and base quality profiles to generate normal, tumor, or normal-tumor mixture reads. Experimental results show that SVSR could capture more features that are realistic and generate datasets with satisfactory quality scores. SVSR is able to evaluate the performance of structural variation detection methods and guide the development of new computational methods.

Quantitative evaluation of the molecular marker using droplet digital PCR

Transposable elements (TEs) constitute approximately half of Bovine genome. They can be a powerful species-specific marker without regression mutations by the structure variation (SV) at the time of genomic evolution. In a previous study, we identified the Hanwoo-specific SV that was generated by a TE–association deletion event using traditional PCR method and Sanger sequencing validation. It could be used as a molecular marker to distinguish different cattle breeds (i.e., Hanwoo vs. Holstein). However, PCR is defective with various final copy quantifications from every sample. Thus, we applied to the droplet digital PCR (ddPCR) platform for accurate quantitative detection of the Hanwoo-specific SV. Although samples have low allele frequency variation within Hanwoo population, ddPCR could perform high sensitive detection with absolute quantification. We aimed to use ddPCR for more accurate quantification than PCR. We suggest that the ddPCR platform is applicable for the quantitative evaluation of molecular markers.

MVSC: A Multi-variation Simulator of Cancer Genome

BACKGROUND

Many forms of variations exist in the genome, which are the main causes of individual phenotypic differences. The detection of variants, especially those located in the tumor genome, still faces many challenges due to the complexity of the genome structure. Thus, the performance assessment of variation detection tools using next-generation sequencing platforms is urgently needed.

METHOD

We have created a software package called the Multi-Variation Simulator of Cancer genomes (MVSC) to simulate common genomic variants, including single nucleotide polymorphisms, small insertion and deletion polymorphisms, and structural variations (SVs), which are analogous to human somatically acquired variations. Three sets of variations embedded in genomic sequences in different periods were dynamically and sequentially simulated one by one.

RESULTS

In cancer genome simulation, complex SVs are important because this type of variation is characteristic of the tumor genome structure. Overlapping variations of different sizes can also coexist in the same genome regions, adding to the complexity of cancer genome architecture. Our results show that MVSC can efficiently simulate a variety of genomic variants that cannot be simulated by existing software packages.

CONCLUSION

The MVSC-simulated variants can be used to assess the performance of existing tools designed to detect SVs in next-generation sequencing data, and we also find that MVSC is memory and time-efficient compared with similar software packages.

Structural variant identification and characterization

Structural variant (SV) differences between human genomes can cause germline and mosaic disease as well as inter-individual variation. De-regulation of accurate DNA repair and genomic surveillance mechanisms results in a large number of SVs in cancer. Analysis of the DNA sequences at SV breakpoints can help identify pathways of mutagenesis and regions of the genome that are more susceptible to rearrangement. Large-scale SV analyses have been enabled by high-throughput genome-level sequencing on humans in the past decade. These studies have shed light on the mechanisms and prevalence of complex genomic rearrangements. Recent advancements in both sequencing and other mapping technologies as well as calling algorithms for detection of genomic rearrangements have helped propel SV detection into population-scale studies, and have begun to elucidate previously inaccessible regions of the genome. Here, we discuss the genomic organization of simple and complex SVs, the molecular mechanisms of their formation, and various ways to detect them. We also introduce methods for characterizing SVs and their consequences on human genomes.

Structural variation in the sequencing era

Identifying structural variation (SV) is essential for genome interpretation but has been historically difficult due to limitations inherent to available genome technologies. Detection methods that use ensemble algorithms and emerging sequencing technologies have enabled the discovery of thousands of SVs, uncovering information about their ubiquity, relationship to disease and possible effects on biological mechanisms. Given the variability in SV type and size, along with unique detection biases of emerging genomic platforms, multiplatform discovery is necessary to resolve the full spectrum of variation. Here, we review modern approaches for investigating SVs and proffer that, moving forwards, studies integrating biological information with detection will be necessary to comprehensively understand the impact of SV in the human genome.

Challenges in identifying large germline structural variants for clinical use by long read sequencing

Genomic structural variations, previously considered rare events, are widely recognized as a major source of inter-individual variability and hence, a major hurdle in optimum patient stratification and disease management. Herein, we focus on large complex germline structural variations and present challenges towards target treatment via the synergy of state-of-the-art approaches and information technology tools. A complex structural variation detection remains challenging, as there is no gold standard for identifying such genomic variations with long reads, especially when the chromosomal rearrangement in question is a few Mb in length. A clinical case with a large complex chromosomal rearrangement serves as a paradigm. We feel that functional validation and data interpretation are of outmost importance for information growth to be translated into knowledge growth and hence, new working practices are highlighted.

Long-read sequencing in deciphering human genetics to a greater depth

Through four decades' development, DNA sequencing has inched into the era of single-molecule sequencing (SMS), or the third-generation sequencing (TGS), as represented by two distinct technical approaches developed independently by Pacific Bioscience (PacBio) and Oxford Nanopore Technologies (ONT). Historically, each generation of sequencing technologies was marked by innovative technological achievements and novel applications. Long reads (LRs) are considered as the most advantageous feature of SMS shared by both PacBio and ONT to distinguish SMS from next-generation sequencing (NGS, or the second-generation sequencing) and Sanger sequencing (the first-generation sequencing). Long reads overcome the limitations of NGS and drastically improves the quality of genome assembly. Besides, ONT also contributes several unique features including ultra-long reads (ULRs) with read length above 300 kb and some close to 1 million bp, direct RNA sequencing and superior portability as made possible by pocket-sized MinION sequencer. Here, we review the history of DNA sequencing technologies and associated applications, with a special focus on the advantages as well as the limitations of ULR sequencing in genome assembly.

Novel Genetic Rearrangements Termed "Structural Variation Polymorphisms" Contribute to the Genetic Diversity of Orthohepadnaviruses

The genetic diversity of orthohepadnaviruses is not yet fully understood. This study was conducted to investigate the role of structural variations (SVs) in their diversity. Genetic sequences of orthohepadnaviruses were retrieved from databases. The positions of sequence gaps were investigated, since they were found to be related to SVs, and they were further used to search for SVs. Then, a combination of pair-wise and multiple alignment analyses was performed to analyze the genomic structure. Unique patterns of SVs were observed; genetic sequences at certain genomic positions could be separated into multiple patterns, such as no SV, SV pattern 1, SV pattern 2, and SV pattern 3, which were observed as polymorphic changes. We provisionally referred to these genetic changes as SV polymorphisms. Our data showed that higher frequency of sequence gaps and lower genetic identity were observed in the pre-S1-S2 region of various types of HBVs. Detailed examination of the genetic structure in the pre-S region by a combination of pair-wise and multiple alignment analyses showed that the genetic diversity of orthohepadnaviruses in the pre-S1 region could have been also induced by SV polymorphisms. Our data showed that novel genetic rearrangements provisionally termed SV polymorphisms were observed in various orthohepadnaviruses.

Comprehensive genomic characterization of breast tumors with BRCA1 and BRCA2 mutations

BACKGROUND

Germline mutations in the BRCA1 and BRCA2 genes predispose carriers to breast and ovarian cancer, and there remains a need to identify the specific genomic mechanisms by which cancer evolves in these patients. Here we present a systematic genomic analysis of breast tumors with BRCA1 and BRCA2 mutations.

METHODS

We analyzed genomic data from breast tumors, with a focus on comparing tumors with BRCA1/BRCA2 gene mutations with common classes of sporadic breast tumors.

RESULTS

We identify differences between BRCA-mutated and sporadic breast tumors in patterns of point mutation, DNA methylation and structural variation. We show that structural variation disproportionately affects tumor suppressor genes and identify specific driver gene candidates that are enriched for structural variation.

CONCLUSIONS

Compared to sporadic tumors, BRCA-mutated breast tumors show signals of reduced DNA methylation, more ancestral cell divisions, and elevated rates of structural variation that tend to disrupt highly expressed protein-coding genes and known tumor suppressors. Our analysis suggests that BRCA-mutated tumors are more aggressive than sporadic breast cancers because loss of the BRCA pathway causes multiple processes of mutagenesis and gene dysregulation.

Genomic Analysis in the Age of Human Genome Sequencing

Affordable genome sequencing technologies promise to revolutionize the field of human genetics by enabling comprehensive studies that interrogate all classes of genome variation, genome-wide, across the entire allele frequency spectrum. Ongoing projects worldwide are sequencing many thousands-and soon millions-of human genomes as part of various gene mapping studies, biobanking efforts, and clinical programs. However, while genome sequencing data production has become routine, genome analysis and interpretation remain challenging endeavors with many limitations and caveats. Here, we review the current state of technologies for genetic variant discovery, genotyping, and functional interpretation and discuss the prospects for future advances. We focus on germline variants discovered by whole-genome sequencing, genome-wide functional genomic approaches for predicting and measuring variant functional effects, and implications for studies of common and rare human disease.

Comprehensive, integrated, and phased whole-genome analysis of the primary ENCODE cell line K562

K562 is widely used in biomedical research. It is one of three tier-one cell lines of ENCODE and also most commonly used for large-scale CRISPR/Cas9 screens. Although its functional genomic and epigenomic characteristics have been extensively studied, its genome sequence and genomic structural features have never been comprehensively analyzed. Such information is essential for the correct interpretation and understanding of the vast troves of existing functional genomics and epigenomics data for K562. We performed and integrated deep-coverage whole-genome (short-insert), mate-pair, and linked-read sequencing as well as karyotyping and array CGH analysis to identify a wide spectrum of genome characteristics in K562: copy numbers (CN) of aneuploid chromosome segments at high-resolution, SNVs and indels (both corrected for CN in aneuploid regions), loss of heterozygosity, megabase-scale phased haplotypes often spanning entire chromosome arms, structural variants (SVs), including small and large-scale complex SVs and nonreference retrotransposon insertions. Many SVs were phased, assembled, and experimentally validated. We identified multiple allele-specific deletions and duplications within the tumor suppressor gene FHIT. Taking aneuploidy into account, we reanalyzed K562 RNA-seq and whole-genome bisulfite sequencing data for allele-specific expression and allele-specific DNA methylation. We also show examples of how deeper insights into regulatory complexity are gained by integrating genomic variant information and structural context with functional genomics and epigenomics data. Furthermore, using K562 haplotype information, we produced an allele-specific CRISPR targeting map. This comprehensive whole-genome analysis serves as a resource for future studies that utilize K562 as well as a framework for the analysis of other cancer genomes.

Common fragile site instability in normal cells: Lessons and perspectives

Mechanisms and events related to common fragile site (CFS) instability are well known in cancer cells. Here, we argue that normal cells remain an important experimental model to address questions related to CFS instability in the absence of alterations in cell cycle and DNA damage repair pathways, which are common features acquired in cancer. Furthermore, a major gap of knowledge concerns the stability of CFSs during gametogenesis. CFS instability in meiotic or postmeiotic stages of the germ cell line could generate chromosome deletions or large rearrangements. This in turn can lead to the functional loss of the several CFS-associated genes with tumor suppressor function. Our hypothesis is that such mutations can potentially result in genetic predisposition to develop cancer. Indirect evidence for CFS instability in human germ cells has been provided by genomic investigations in family pedigrees associated with genetic disease. The issue of CFS instability in the germ cell line should represent one of the future efforts, and may take advantage of the existence of sequence and functional conservation of CFSs between rodents and humans.

Complex structural variants in Mendelian disorders: identification and breakpoint resolution using short- and long-read genome sequencing

Background

Studies have shown that complex structural variants (cxSVs) contribute to human genomic variation and can cause Mendelian disease. We aimed to identify cxSVs relevant to Mendelian disease using short-read whole-genome sequencing (WGS), resolve the precise variant configuration and investigate possible mechanisms of cxSV formation.

Methods

We performed short-read WGS and analysis of breakpoint junctions to identify cxSVs in a cohort of 1324 undiagnosed rare disease patients. Long-read WGS and gene expression analysis were used to resolve one case.

Results

We identified three pathogenic cxSVs: a de novo duplication-inversion-inversion-deletion affecting ARID1B, a de novo deletion-inversion-duplication affecting HNRNPU and a homozygous deletion-inversion-deletion affecting CEP78. Additionally, a de novo duplication-inversion-duplication overlapping CDKL5 was resolved by long-read WGS demonstrating the presence of both a disrupted and an intact copy of CDKL5 on the same allele, and gene expression analysis showed both parental alleles of CDKL5 were expressed. Breakpoint analysis in all the cxSVs revealed both microhomology and longer repetitive elements.

Conclusions

Our results corroborate that cxSVs cause Mendelian disease, and we recommend their consideration during clinical investigations. We show that resolution of breakpoints can be critical to interpret pathogenicity and present evidence of replication-based mechanisms in cxSV formation.

Electronic supplementary material

The online version of this article (10.1186/s13073-018-0606-6) contains supplementary material, which is available to authorized users.

Characterization of hepatitis B virus with complex structural variations

BACKGROUND

Hepatitis B virus (HBV) infection is one of the most serious public health issues. Recent HBV genetic research has revealed novel genetic rearrangements termed complex structural variations (SVs), which are composed of combinations of SVs such as insertions, deletions, and duplications. An extensive search was made for complex SVs of HBV and their characteristics were analyzed.

RESULTS

Fifty-five HBV strains with complex SVs were identified by analyzing genetic sequences of HBV with bioinformatical tools. Along with 15 HBV strains with complex SVs in a previous report, a total of 70 HBV strains harboring complex SVs were analyzed. Complex SVs in the HBV genome were located frequently between nt 1500 and 2000. Insertions were observed in 65/70 (92.9%) of HBV strains with complex SVs. As insertional motif sequences, hepatocyte nuclear factor 1 binding site, a sequence complementary to part of box α in enhancer II, and insertions of unknown origins were observed. The complex SVs were classified into six groups, and combination of insertion and deletion was observed more frequently than other patterns.

CONCLUSION

Through an extensive search of HBV sequences, new strains with complex SVs were identified in this study. Characteristics of HBV with complex SVs were clarified by the analysis of 70 HBV strains harboring complex SVs. Further investigation is required to elucidate its role in pathogenesis of HBV-related liver disease.

Genome-wide reconstruction of complex structural variants using read clouds

In read cloud approaches, microfluidic partitioning of long genomic DNA fragments and barcoding of shorter fragments derived from these fragments retains long-range information in short sequencing reads. This combination of short reads with long-range information represents a powerful alternative to single-molecule long-read sequencing. We develop Genome-wide Reconstruction of Complex Structural Variants (GROC-SVs) for SV detection and assembly from read cloud data and apply this method to Illumina-sequenced 10x Genomics sarcoma and breast cancer data sets. Compared with short-fragment sequencing, GROC-SVs substantially improves the specificity of breakpoint detection at comparable sensitivity. This approach also performs sequence assembly across multiple breakpoints simultaneously, enabling the reconstruction of events exhibiting remarkable complexity. We show that chromothriptic rearrangements occurred before copy number amplifications, and that rates of single-nucleotide variants and SVs are not correlated. Our results support the use of read cloud approaches to advance the characterization of large and complex structural variation.

Structural variants in genes associated with human Williams-Beuren syndrome underlie stereotypical hypersociability in domestic dogs

Although considerable progress has been made in understanding the genetic basis of morphologic traits (for example, body size and coat color) in dogs and wolves, the genetic basis of their behavioral divergence is poorly understood. An integrative approach using both behavioral and genetic data is required to understand the molecular underpinnings of the various behavioral characteristics associated with domestication. We analyze a 5-Mb genomic region on chromosome 6 previously found to be under positive selection in domestic dog breeds. Deletion of this region in humans is linked to Williams-Beuren syndrome (WBS), a multisystem congenital disorder characterized by hypersocial behavior. We associate quantitative data on behavioral phenotypes symptomatic of WBS in humans with structural changes in the WBS locus in dogs. We find that hypersociability, a central feature of WBS, is also a core element of domestication that distinguishes dogs from wolves. We provide evidence that structural variants in GTF2I and GTF2IRD1, genes previously implicated in the behavioral phenotype of patients with WBS and contained within the WBS locus, contribute to extreme sociability in dogs. This finding suggests that there are commonalities in the genetic architecture of WBS and canine tameness and that directional selection may have targeted a unique set of linked behavioral genes of large phenotypic effect, allowing for rapid behavioral divergence of dogs and wolves, facilitating coexistence with humans.

Linked read sequencing resolves complex genomic rearrangements in gastric cancer metastases

Background

Genome rearrangements are critical oncogenic driver events in many malignancies. However, the identification and resolution of the structure of cancer genomic rearrangements remain challenging even with whole genome sequencing.

Methods

To identify oncogenic genomic rearrangements and resolve their structure, we analyzed linked read sequencing. This approach relies on a microfluidic droplet technology to produce libraries derived from single, high molecular weight DNA molecules, 50 kb in size or greater. After sequencing, the barcoded sequence reads provide long range genomic information, identify individual high molecular weight DNA molecules, determine the haplotype context of genetic variants that occur across contiguous megabase-length segments of the genome and delineate the structure of complex rearrangements. We applied linked read sequencing of whole genomes to the analysis of a set of synchronous metastatic diffuse gastric cancers that occurred in the same individual.

Results

When comparing metastatic sites, our analysis implicated a complex somatic rearrangement that was present in the metastatic tumor. The oncogenic event associated with the identified complex rearrangement resulted in an amplification of the known cancer driver gene FGFR2. With further investigation using these linked read data, the FGFR2 copy number alteration was determined to be a deletion-inversion motif that underwent tandem duplication, with unique breakpoints in each metastasis. Using a three-dimensional organoid tissue model, we functionally validated the metastatic potential of an FGFR2 amplification in gastric cancer.

Conclusions

Our study demonstrates that linked read sequencing is useful in characterizing oncogenic rearrangements in cancer metastasis.

Electronic supplementary material

The online version of this article (doi:10.1186/s13073-017-0447-8) contains supplementary material, which is available to authorized users.

Novel non-canonical genetic rearrangements termed "complex structural variations" in HBV genome

BACKGROUNDS AND AIMS

Chronic hepatitis B virus (HBV) infection is an important worldwide public health issue. Further knowledge on the characteristics of HBV will facilitate its eradication. Genome structural variations (SVs) are defined by its canonical form such as duplication, deletion, and insertion. However, recent studies have reported complex SVs that cannot be explained by those canonical SVs. A HBV strain (UK2) with an unusual genome structure rearrangement that was completely different from known mutations or rearrangements was previously reported. Thus, this study was conducted to confirm the rearrangement in UK2 as a novel complex SV, and to find additional HBV strains with complex SVs. Further, the contribution of complex SVs in hepadnavirus variability was investigated.

METHODS

The genome rearrangement pattern in UK2 was analyzed. Further, a search of online databases retrieved additional HBV strains which were candidates to harbor complex SVs. The architecture of each rearrangement in the candidate strains was analyzed by bioinformatical tools. In addition, alignment of woolly monkey hepatitis virus (WMHV) and HBV from human and non-human primates was performed to investigate the contribution of complex SVs to variability of hepadnavirus.

RESULTS

The rearrangement in UK2 was confirmed as a complex SV. An additional 15 HBV strains were retrieved from databases, and confirmed as harboring complex SVs. Complex combinations of deletion, insertion, and duplication characterized the novel rearrangements. The complex SVs in six strains (37.5%) were composed of deletion, insertion, and duplication. The complex SVs in another six strains (37.5%) consisted of deletion and insertion, followed by insertions and duplication in three strains (18.8%), and deletion and duplication in one strain (6.3%). In addition, unique preS1 promoter insertions, which contained the hepatocyte nuclear factor 1 binding site, were observed in seven (43.8%) of 16 strains. Further, analysis of the genetic sequences of WMHV and HBV from human and non-human primates showed that complex combinations of deletions and insertions accounted for their genetic differences.

CONCLUSIONS

Non-canonical genetic rearrangements termed complex SVs were observed in HBV. Further, complex SVs accounted for the genetic differences of WMHV and HBV from human and non-human primates.

The impact of structural variation on human gene expression

Structural variants (SVs) are an important source of human genetic diversity, but their contribution to traits, disease and gene regulation remains unclear. We mapped cis expression quantitative trait loci (eQTLs) in 13 tissues via joint analysis of SVs, single-nucleotide variants (SNVs) and short insertion/deletion (indel) variants from deep whole-genome sequencing (WGS). We estimated that SVs are causal at 3.5-6.8% of eQTLs-a substantially higher fraction than prior estimates-and that expression-altering SVs have larger effect sizes than do SNVs and indels. We identified 789 putative causal SVs predicted to directly alter gene expression: most (88.3%) were noncoding variants enriched at enhancers and other regulatory elements, and 52 were linked to genome-wide association study loci. We observed a notable abundance of rare high-impact SVs associated with aberrant expression of nearby genes. These results suggest that comprehensive WGS-based SV analyses will increase the power of common- and rare-variant association studies.

Global analysis of somatic structural genomic alterations and their impact on gene expression in diverse human cancers

Tumor genomes are mosaics of somatic structural variants (SVs) that may contribute to the activation of oncogenes or inactivation of tumor suppressors, for example, by altering gene copy number amplitude. However, there are multiple other ways in which SVs can modulate transcription, but the general impact of such events on tumor transcriptional output has not been systematically determined. Here we use whole-genome sequencing data to map SVs across 600 tumors and 18 cancers, and investigate the relationship between SVs, copy number alterations (CNAs), and mRNA expression. We find that 34% of CNA breakpoints can be clarified structurally and that most amplifications are due to tandem duplications. We observe frequent swapping of strong and weak promoters in the context of gene fusions, and find that this has a measurable global impact on mRNA levels. Interestingly, several long noncoding RNAs were strongly activated by this mechanism. Additionally, SVs were confirmed in telomere reverse transcriptase (TERT) upstream regions in several cancers, associated with elevated TERT mRNA levels. We also highlight high-confidence gene fusions supported by both genomic and transcriptomic evidence, including a previously undescribed paired box 8 (PAX8)-nuclear factor, erythroid 2 like 2 (NFE2L2) fusion in thyroid carcinoma. In summary, we combine SV, CNA, and expression data to provide insights into the structural basis of CNAs as well as the impact of SVs on gene expression in tumors.

Resolving complex structural genomic rearrangements using a randomized approach

Complex chromosomal rearrangements are structural genomic alterations involving multiple instances of deletions, duplications, inversions, or translocations that co-occur either on the same chromosome or represent different overlapping events on homologous chromosomes. We present SVelter, an algorithm that identifies regions of the genome suspected to harbor a complex event and then resolves the structure by iteratively rearranging the local genome structure, in a randomized fashion, with each structure scored against characteristics of the observed sequencing data. SVelter is able to accurately reconstruct complex chromosomal rearrangements when compared to well-characterized genomes that have been deeply sequenced with both short and long reads.

Vcfanno: fast, flexible annotation of genetic variants

The integration of genome annotations is critical to the identification of genetic variants that are relevant to studies of disease or other traits. However, comprehensive variant annotation with diverse file formats is difficult with existing methods. Here we describe vcfanno, which flexibly extracts and summarizes attributes from multiple annotation files and integrates the annotations within the INFO column of the original VCF file. By leveraging a parallel "chromosome sweeping" algorithm, we demonstrate substantial performance gains by annotating ~85,000 variants per second with 50 attributes from 17 commonly used genome annotation resources. Vcfanno is available at https://github.com/brentp/vcfanno under the MIT license.

Heterogeneity of a Constitutional Complex Chromosomal Rearrangement in 2q

Complex chromosome rearrangements (CCRs) are unusual structural chromosome alterations found in humans, and to date only a few have been characterized molecularly. New mechanisms, such as chromothripsis, have been proposed to explain the presence of the CCRs in cancer cells and in patients with congenital disorders and/or mental retardation. The aim of the present study was the molecular characterization of a constitutional CCR in a girl with multiple congenital disorders and intellectual disability in order to determine the genotype-phenotype relation and to clarify whether the CCR could have been caused by chromosomal catastrophic events. The present CCR was characterized by G-banding, high-resolution CGH, multiplex ligation-dependent probe amplification and subtelomeric 2q-FISH analyses. Preliminary results indicate that the de novo CCR is unbalanced showing a 2q37.3 deletion and 2q34q37.2 partial trisomy. Our patient shows some of the typical traits and intellectual disability described in patients with 2q37 deletion and also in carriers of 2q34q37.2 partial trisomy; thus, the clinical disorders could be explained by additional effects of both chromosome alterations (deletions and duplications). A posterior, sequential FISH study using BAC probes revealed the unexpected presence of at least 17 different reorganizations affecting 2q34q37.2, suggesting the existence of chromosome instability in this region. The present CCR is the first case described in the literature of heterogeneity of unbalanced CCRs affecting a small region of 2q, indicating that the mechanisms involved in constitutional chromosome rearrangement may be more complex than previously thought.

The Complete Genome Sequences, Unique Mutational Spectra, and Developmental Potency of Adult Neurons Revealed by Cloning

Somatic mutation in neurons is linked to neurologic disease and implicated in cell-type diversification. However, the origin, extent, and patterns of genomic mutation in neurons remain unknown. We established a nuclear transfer method to clonally amplify the genomes of neurons from adult mice for whole-genome sequencing. Comprehensive mutation detection and independent validation revealed that individual neurons harbor ∼100 unique mutations from all classes but lack recurrent rearrangements. Most neurons contain at least one gene-disrupting mutation and rare (0-2) mobile element insertions. The frequency and gene bias of neuronal mutations differ from other lineages, potentially due to novel mechanisms governing postmitotic mutation. Fertile mice were cloned from several neurons, establishing the compatibility of mutated adult neuronal genomes with reprogramming to pluripotency and development.

COSMOS: accurate detection of somatic structural variations through asymmetric comparison between tumor and normal samples

An important challenge in cancer genomics is precise detection of structural variations (SVs) by high-throughput short-read sequencing, which is hampered by the high false discovery rates of existing analysis tools. Here, we propose an accurate SV detection method named COSMOS, which compares the statistics of the mapped read pairs in tumor samples with isogenic normal control samples in a distinct asymmetric manner. COSMOS also prioritizes the candidate SVs using strand-specific read-depth information. Performance tests on modeled tumor genomes revealed that COSMOS outperformed existing methods in terms of F-measure. We also applied COSMOS to an experimental mouse cell-based model, in which SVs were induced by genome engineering and gamma-ray irradiation, followed by polymerase chain reaction-based confirmation. The precision of COSMOS was 84.5%, while the next best existing method was 70.4%. Moreover, the sensitivity of COSMOS was the highest, indicating that COSMOS has great potential for cancer genome analysis.

Structural Variant Detection by Large-scale Sequencing Reveals New Evolutionary Evidence on Breed Divergence between Chinese and European Pigs

In this study, we performed a genome-wide SV detection among the genomes of thirteen pigs from diverse Chinese and European originated breeds by next genetation sequencing, and constrcuted a single-nucleotide resolution map involving 56,930 putative SVs. We firstly identified a SV hotspot spanning 35 Mb region on the X chromosome specifically in the genomes of Chinese originated individuals. Further scrutinizing this region by large-scale sequencing data of extra 111 individuals, we obtained the confirmatory evidence on our initial finding. Moreover, thirty five SV-related genes within the hotspot region, being of importance for reproduction ability, rendered significant different evolution rates between Chinese and European originated breeds. The SV hotspot identified herein offers a novel evidence for assessing phylogenetic relationships, as well as likely explains the genetic difference of corresponding phenotypes and features, among Chinese and European pig breeds. Furthermore, we employed various SVs to infer genetic structure of individuls surveyed. We found SVs can clearly detect the difference of genetic background among individuals. This clues us that genome-wide SVs can capture majority of geneic variation and be applied into cladistic analyses. Characterizing whole genome SVs demonstrated that SVs are significantly enriched/depleted with various genomic features.