Chapter 6: Structural Variation and Medical Genomics

Differences between individual human genomes, or between human and cancer genomes, range in scale from single nucleotide variants (SNVs) through intermediate and large-scale duplications, deletions, and rearrangements of genomic segments. The latter class, called structural variants (SVs), have received considerable attention in the past several years as they are a previously under appreciated source of variation in human genomes. Much of this recent attention is the result of the availability of higher-resolution technologies for measuring these variants, including both microarray-based techniques, and more recently, high-throughput DNA sequencing. We describe the genomic technologies and computational techniques currently used to measure SVs, focusing on applications in human and cancer genomics.

What to Learn in This Chapter

• Current knowledge about the prevalence of structural variation in human and cancer genomes.
• Strategies for using microarray and high-throughput DNA sequencing technologies to measure structural variation.
• Computational techniques to detect structural variants from DNA sequencing data.

This article is part of the “Translational Bioinformatics” collection for PLOS Computational Biology.

     

Challenges in studying genomic structural variant formation mechanisms: the short-read dilemma and beyond

Next-generation sequencing (NGS) technologies have revolutionised the analysis of genomic structural variants (SVs), providing significant insights into SV de novo formation based on analyses of rearrangement breakpoint junctions. The short DNA reads generated by NGS, however, have also created novel obstacles by biasing the ascertainment of SVs, an aspect that we refer to as the 'short-read dilemma'. For example, recent studies have found that SVs are often complex, with SV formation generating large numbers of breakpoints in a single event (multi-breakpoint SVs) or structurally polymorphic loci having multiple allelic states (multi-allelic SVs). This complexity may be obscured in short reads, unless the data is analysed and interpreted within its wider genomic context. We discuss how novel approaches will help to overcome the short-read dilemma, and how integration of other sources of information, including the structure of chromatin, may help in the future to deepen the understanding of SV formation processes.     

Robust Benchmark Structural Variant Calls of An Asian Using State-of-the-art Long-read Sequencing Technologies

The importance of structural variants (SVs) for human phenotypes and diseases is now recognized. Although a variety of SV detection platforms and strategies that vary in sensitivity and specificity have been developed, few benchmarking procedures are available to confidently assess their performances in biological and clinical research. To facilitate the validation and application of these SV detection approaches, we established an Asian reference material by characterizing the genome of an Epstein-Barr virus (EBV)-immortalized B lymphocyte line along with identified benchmark regions and high-confidence SV calls. We established a high-confidence SV callset with 8938 SVs by integrating four alignment-based SV callers, including 109× Pacific Biosciences (PacBio) continuous long reads (CLRs), 22× PacBio circular consensus sequencing (CCS) reads, 104× Oxford Nanopore Technologies (ONT) long reads, and 114× Bionano optical mapping platform, and one de novo assembly-based SV caller using CCS reads. A total of 544 randomly selected SVs were validated by PCR amplification and Sanger sequencing, demonstrating the robustness of our SV calls. Combining trio-binning-based haplotype assemblies, we established an SV benchmark for identifying false negatives and false positives by constructing the continuous high-confidence regions (CHCRs), which covered 1.46 gigabase pairs (Gb) and 6882 SVs supported by at least one diploid haplotype assembly. Establishing high-confidence SV calls for a benchmark sample that has been characterized by multiple technologies provides a valuable resource for investigating SVs in human biology, disease, and clinical research.     

Assessing structural variation in a personal genome—towards a human reference diploid genome

Background

Characterizing large genomic variants is essential to expanding the research and clinical applications of genome sequencing. While multiple data types and methods are available to detect these structural variants (SVs), they remain less characterized than smaller variants because of SV diversity, complexity, and size. These challenges are exacerbated by the experimental and computational demands of SV analysis. Here, we characterize the SV content of a personal genome with Parliament, a publicly available consensus SV-calling infrastructure that merges multiple data types and SV detection methods.

Results

We demonstrate Parliament’s efficacy via integrated analyses of data from whole-genome array comparative genomic hybridization, short-read next-generation sequencing, long-read (Pacific BioSciences RSII), long-insert (Illumina Nextera), and whole-genome architecture (BioNano Irys) data from the personal genome of a single subject (HS1011). From this genome, Parliament identified 31,007 genomic loci between 100 bp and 1 Mbp that are inconsistent with the hg19 reference assembly. Of these loci, 9,777 are supported as putative SVs by hybrid local assembly, long-read PacBio data, or multi-source heuristics. These SVs span 59 Mbp of the reference genome (1.8%) and include 3,801 events identified only with long-read data. The HS1011 data and complete Parliament infrastructure, including a BAM-to-SV workflow, are available on the cloud-based service DNAnexus.

Conclusions

HS1011 SV analysis reveals the limits and advantages of multiple sequencing technologies, specifically the impact of long-read SV discovery. With the full Parliament infrastructure, the HS1011 data constitute a public resource for novel SV discovery, software calibration, and personal genome structural variation analysis.

Electronic supplementary material

The online version of this article (doi:10.1186/s12864-015-1479-3) contains supplementary material, which is available to authorized users.     

Evolutionary Genomics of Structural Variation in Asian Rice (Oryza sativa) Domestication

Structural variants (SVs) are a largely unstudied feature of plant genome evolution, despite the fact that SVs contribute substantially to phenotypes. In this study, we discovered SVs across a population sample of 347 high-coverage, resequenced genomes of Asian rice (Oryza sativa) and its wild ancestor (O. rufipogon). In addition to this short-read data set, we also inferred SVs from whole-genome assemblies and long-read data. Comparisons among data sets revealed different features of genome variability. For example, genome alignment identified a large (∼4.3 Mb) inversion in indica rice varieties relative to japonica varieties, and long-read analyses suggest that ∼9% of genes from the outgroup (O. longistaminata) are hemizygous. We focused, however, on the resequencing sample to investigate the population genomics of SVs. Clustering analyses with SVs recapitulated the rice cultivar groups that were also inferred from SNPs. However, the site-frequency spectrum of each SV type—which included inversions, duplications, deletions, translocations, and mobile element insertions—was skewed toward lower frequency variants than synonymous SNPs, suggesting that SVs may be predominantly deleterious. Among transposable elements, SINE and mariner insertions were found at especially low frequency. We also used SVs to study domestication by contrasting between rice and O. rufipogon. Cultivated genomes contained ∼25% more derived SVs and mobile element insertions than O. rufipogon, indicating that SVs contribute to the cost of domestication in rice. Peaks of SV divergence were enriched for known domestication genes, but we also detected hundreds of genes gained and lost during domestication, some of which were enriched for traits of agronomic interest.     

Structural variation in two human genomes mapped at single-nucleotide resolution by whole genome de novo assembly

Here we use whole-genome de novo assembly of second-generation sequencing reads to map structural variation (SV) in an Asian genome and an African genome. Our approach identifies small- and intermediate-size homozygous variants (1-50 kb) including insertions, deletions, inversions and their precise breakpoints, and in contrast to other methods, can resolve complex rearrangements. In total, we identified 277,243 SVs ranging in length from 1-23 kb. Validation using computational and experimental methods suggests that we achieve overall <6% false-positive rate and <10% false-negative rate in genomic regions that can be assembled, which outperforms other methods. Analysis of the SVs in the genomes of 106 individuals sequenced as part of the 1000 Genomes Project suggests that SVs account for a greater fraction of the diversity between individuals than do single-nucleotide polymorphisms (SNPs). These findings demonstrate that whole-genome de novo assembly is a feasible approach to deriving more comprehensive maps of genetic variation.     

Characterizing complex structural variation in germline and somatic genomes

Genome structural variation (SV) is a major source of genetic diversity in mammals and a hallmark of cancer. Although SV is typically defined by its canonical forms (duplication, deletion, insertion, inversion and translocation), recent breakpoint mapping studies have revealed a surprising number of 'complex' variants that evade simple classification. Complex SVs are defined by clustered breakpoints that arose through a single mutation but cannot be explained by one simple end-joining or recombination event. Some complex variants exhibit profoundly complicated rearrangements between distinct loci from multiple chromosomes, whereas others involve more subtle alterations at a single locus. These diverse and unpredictable features present a challenge for SV mapping experiments. Here, we review current knowledge of complex SV in mammals, and outline techniques for identifying and characterizing complex variants using next-generation DNA sequencing.     

SV-AUTOPILOT: optimized,automated construction of structural variation discovery and benchmarking pipelines

Background

Many tools exist to predict structural variants (SVs), utilizing a variety of algorithms. However, they have largely been developed and tested on human germline or somatic (e.g. cancer) variation. It seems appropriate to exploit this wealth of technology available for humans also for other species. Objectives of this work included:

1. Creating an automated, standardized pipeline for SV prediction.
2. Identifying the best tool(s) for SV prediction through benchmarking.
3. Providing a statistically sound method for merging SV calls.

Results

The SV-AUTOPILOT meta-tool platform is an automated pipeline for standardization of SV prediction and SV tool development in paired-end next-generation sequencing (NGS) analysis. SV-AUTOPILOT comes in the form of a virtual machine, which includes all datasets, tools and algorithms presented here. The virtual machine easily allows one to add, replace and update genomes, SV callers and post-processing routines and therefore provides an easy, out-of-the-box environment for complex SV discovery tasks. SV-AUTOPILOT was used to make a direct comparison between 7 popular SV tools on the Arabidopsis thaliana genome using the Landsberg (Ler) ecotype as a standardized dataset. Recall and precision measurements suggest that Pindel and Clever were the most adaptable to this dataset across all size ranges while Delly performed well for SVs larger than 250 nucleotides. A novel, statistically-sound merging process, which can control the false discovery rate, reduced the false positive rate on the Arabidopsis benchmark dataset used here by >60%.

Conclusion

SV-AUTOPILOT provides a meta-tool platform for future SV tool development and the benchmarking of tools on other genomes using a standardized pipeline. It optimizes detection of SVs in non-human genomes using statistically robust merging. The benchmarking in this study has demonstrated the power of 7 different SV tools for analyzing different size classes and types of structural variants. The optional merge feature enriches the call set and reduces false positives providing added benefit to researchers planning to validate SVs. SV-AUTOPILOT is a powerful, new meta-tool for biologists as well as SV tool developers.

Electronic supplementary material

The online version of this article (doi:10.1186/s12864-015-1376-9) contains supplementary material, which is available to authorized users.     

Genetic variation and domestication of horses revealed by 10 chromosome-level genomes and whole-genome resequencing

Understanding the genetic variations of the horse (Equus caballus) genome will improve breeding conservation and welfare. However, genetic variations in long segments, such as structural variants (SVs), remain understudied. We de novo assembled 10 chromosome-level three-dimensional horse genomes, each representing a distinct breed, and analysed horse SVs using a multi-assembly approach. Our findings suggest that SVs with the accumulation of mammalian-wide interspersed repeats related to long interspersed nuclear elements might be a horse-specific mechanism to modulate genome-wide gene regulatory networks. We found that olfactory receptors were commonly loss and accumulated deleterious mutations, but no purge of deleterious mutations occurred during horse domestication. We examined the potential effects of SVs on the spatial structure of chromatin via topologically associating domains (TADs). Breed-specific TADs were significantly enriched by breed-specific SVs. We identified 4199 unique breakpoint-resolved novel insertions across all chromosomes that account for 2.84 Mb sequences missing from the reference genome. Several novel insertions might have potential functional consequences, as 519 appeared to reside within 449 gene bodies. These genes are primarily involved in pathogen recognition, innate immune responses and drug metabolism. Moreover, 37 diverse horses were resequenced. Combining this with public data, we analysed 97 horses through a comparative population genomics approach to identify the genetic basis underlying breed characteristics using Thoroughbreds as a case study. We provide new scientific evidence for horse domestication, an understanding of the genetic mechanism underlying the phenotypic evolution of horses, and a comprehensive genetic variation resource for further genetic studies of horses.     

The genomic pool of standing structural variation outnumbers single nucleotide polymorphism by threefold in the marine teleost Chrysophrys auratus

Recent studies have highlighted an important role of structural variation (SV) in ecological and evolutionary processes, but few have studied nonmodel species in the wild. As part of our long‐term research programme on the nonmodel teleost fish Australasian snapper (Chrysophrys auratus), we aim to build one of the first catalogues of genomic variants (SNPs and indels, and deletions, duplications and inversions) in fishes and evaluate overlap of genomic variants with regions under putative selection (Tajima's D and π), and coding sequences (genes). For this, we analysed six males and six females from three locations in New Zealand and generated a high‐resolution genomic variation catalogue. We characterized 20,385 SVs and found they intersected with almost a third of all annotated genes. Together with small indels, SVs account for three times more variation in the genome in terms of bases affected compared to SNPs. We found that a sizeable portion of detected SVs was in the upper and lower genomic regions of Tajima's D and π, indicating that some of these have an effect on the phenotype. Together, these results shed light on the often neglected genomic variation that is produced by SVs and highlights the need to go beyond the mere measure of SNPs when investigating evolutionary processes, such as species diversification and adaptation.     

ezQTL: A Web Platform for Interactive Visualization and Colocalization of QTLs and GWAS Loci

Genome-wide association studies (GWAS) have identified thousands of genomic loci associated with complex diseases and traits, including cancer. The vast majority of common trait-associated variants identified via GWAS fall in non-coding regions of the genome, posing a challenge in elucidating the causal variants, genes, and mechanisms involved. Expression quantitative trait locus (eQTL) and other molecular QTL studies have been valuable resources in identifying candidate causal genes from GWAS loci through statistical colocalization methods. While QTL colocalization is becoming a standard analysis in post-GWAS investigation, an easy web tool for users to perform formal colocalization analyses with either user-provided or public GWAS and eQTL datasets has been lacking. Here, we present ezQTL, a web-based bioinformatic application to interactively visualize and analyze genetic association data such as GWAS loci and molecular QTLs under different linkage disequilibrium (LD) patterns (1000 Genomes Project, UK Biobank, or user-provided data). This application allows users to perform data quality control for variants matched between different datasets, LD visualization, and two-trait colocalization analyses using two state-of-the-art methodologies (eCAVIAR and HyPrColoc), including batch processing. ezQTL is a free and publicly available cross-platform web tool, which can be accessed online at https://analysistools.cancer.gov/ezqtl.     

The Global Landscape of SARS-CoV-2 Genomes,Variants, and Haplotypes in 2019nCoVR

On January 22, 2020, China National Center for Bioinformation (CNCB) released the 2019 Novel Coronavirus Resource (2019nCoVR), an open-access information resource for the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). 2019nCoVR features a comprehensive integration of sequence and clinical information for all publicly available SARS-CoV-2 isolates, which are manually curated with value-added annotations and quality evaluated by an automated in-house pipeline. Of particular note, 2019nCoVR offers systematic analyses to generate a dynamic landscape of SARS-CoV-2 genomic variations at a global scale. It provides all identified variants and their detailed statistics for each virus isolate, and congregates the quality score, functional annotation, and population frequency for each variant. Spatiotemporal change for each variant can be visualized and historical viral haplotype network maps for the course of the outbreak are also generated based on all complete and high-quality genomes available. Moreover, 2019nCoVR provides a full collection of SARS-CoV-2 relevant literature on the coronavirus disease 2019 (COVID-19), including published papers from PubMed as well as preprints from services such as bioRxiv and medRxiv through Europe PMC. Furthermore, by linking with relevant databases in CNCB, 2019nCoVR offers data submission services for raw sequence reads and assembled genomes, and data sharing with NCBI. Collectively, SARS-CoV-2 is updated daily to collect the latest information on genome sequences, variants, haplotypes, and literature for a timely reflection, making 2019nCoVR a valuable resource for the global research community. 2019nCoVR is accessible at https://bigd.big.ac.cn/ncov/.     

Epistatic partners of neurogenic genes modulate Drosophila olfactory behavior

The extent to which epistasis affects the genetic architecture of complex traits is difficult to quantify, and identifying variants in natural populations with epistatic interactions is challenging. Previous studies in Drosophila implicated extensive epistasis between variants in genes that affect neural connectivity and contribute to natural variation in olfactory response to benzaldehyde. In this study, we implemented a powerful screen to quantify the extent of epistasis as well as identify candidate interacting variants using 203 inbred wild‐derived lines with sequenced genomes of the Drosophila melanogaster Genetic Reference Panel (DGRP). We crossed the DGRP lines to P[GT1]‐element insertion mutants in Sema‐5c and neuralized (neur), two neurodevelopmental loci which affect olfactory behavior, and to their coisogenic wild‐type control. We observed significant variation in olfactory responses to benzaldehyde among F₁ genotypes and for the DGRP line by mutant genotype interactions for both loci, showing extensive nonadditive genetic variation. We performed genome‐wide association analyses to identify the candidate modifier loci. None of these polymorphisms were in or near the focal genes; therefore, epistasis is the cause of the nonadditive genetic variance. Candidate genes could be placed in interaction networks. Several candidate modifiers are associated with neural development. Analyses of mutants of candidate epistatic partners with neur (merry‐go‐round (mgr), prospero (pros), CG10098, Alhambra (Alh) and CG12535) and Sema‐5c (CG42540 and bruchpilot (brp)) showed aberrant olfactory responses compared with coisogenic controls. Thus, integrating genome‐wide analyses of natural variants with mutations at defined genomic locations in a common coisogenic background can unmask specific epistatic modifiers of behavioral phenotypes.     

The fine-scale architecture of structural variants in 17 mouse genomes

Background

Accurate catalogs of structural variants (SVs) in mammalian genomes are necessary to elucidate the potential mechanisms that drive SV formation and to assess their functional impact. Next generation sequencing methods for SV detection are an advance on array-based methods, but are almost exclusively limited to four basic types: deletions, insertions, inversions and copy number gains.

Results

By visual inspection of 100 Mbp of genome to which next generation sequence data from 17 inbred mouse strains had been aligned, we identify and interpret 21 paired-end mapping patterns, which we validate by PCR. These paired-end mapping patterns reveal a greater diversity and complexity in SVs than previously recognized. In addition, Sanger-based sequence analysis of 4,176 breakpoints at 261 SV sites reveal additional complexity at approximately a quarter of structural variants analyzed. We find micro-deletions and micro-insertions at SV breakpoints, ranging from 1 to 107 bp, and SNPs that extend breakpoint micro-homology and may catalyze SV formation.

Conclusions

An integrative approach using experimental analyses to train computational SV calling is essential for the accurate resolution of the architecture of SVs. We find considerable complexity in SV formation; about a quarter of SVs in the mouse are composed of a complex mixture of deletion, insertion, inversion and copy number gain. Computational methods can be adapted to identify most paired-end mapping patterns.     

Towards a comprehensive structural variation map of an individual human genome

Background

Several genomes have now been sequenced, with millions of genetic variants annotated. While significant progress has been made in mapping single nucleotide polymorphisms (SNPs) and small (<10 bp) insertion/deletions (indels), the annotation of larger structural variants has been less comprehensive. It is still unclear to what extent a typical genome differs from the reference assembly, and the analysis of the genomes sequenced to date have shown varying results for copy number variation (CNV) and inversions.

Results

We have combined computational re-analysis of existing whole genome sequence data with novel microarray-based analysis, and detect 12,178 structural variants covering 40.6 Mb that were not reported in the initial sequencing of the first published personal genome. We estimate a total non-SNP variation content of 48.8 Mb in a single genome. Our results indicate that this genome differs from the consensus reference sequence by approximately 1.2% when considering indels/CNVs, 0.1% by SNPs and approximately 0.3% by inversions. The structural variants impact 4,867 genes, and >24% of structural variants would not be imputed by SNP-association.

Conclusions

Our results indicate that a large number of structural variants have been unreported in the individual genomes published to date. This significant extent and complexity of structural variants, as well as the growing recognition of their medical relevance, necessitate they be actively studied in health-related analyses of personal genomes. The new catalogue of structural variants generated for this genome provides a crucial resource for future comparison studies.     

Large-insert genome analysis technology detects structural variation in Pseudomonas aeruginosa clinical strains from cystic fibrosis patients

Large-insert genome analysis (LIGAN) is a broadly applicable, high-throughput technology designed to characterize genome-scale structural variation. Fosmid paired-end sequences and DNA fingerprints from a query genome are compared to a reference sequence using the Genomic Variation Analysis (GenVal) suite of software tools to pinpoint locations of insertions, deletions, and rearrangements. Fosmids spanning regions that contain new structural variants can then be sequenced. Clonal pairs of Pseudomonas aeruginosa isolates from four cystic fibrosis patients were used to validate the LIGAN technology. Approximately 1.5 Mb of inserted sequences were identified, including 743 kb containing 615 ORFs that are absent from published P. aeruginosa genomes. Six rearrangement breakpoints and 220 kb of deleted sequences were also identified. Our study expands the “genome universe” of P. aeruginosa and validates a technology that complements emerging, short-read sequencing methods that are better suited to characterizing single-nucleotide polymorphisms than structural variation.     

Novel chloroplast DNA polymorphism in a sympatric region of two pines1

Nine hundred and two individuals from two populations in a Pinus banksiana — P. contora sympatric region were classified by restriction fragment length polymorphisms of two polymorphic chloroplast DNA markers. A large number of novel chloroplast DNA variants were identified which have not been observed in the allopatric ranges of the two parental species. Three apparently recombinant chloroplast DNA genotypes were discovered, in each of which one marker was typical of P. banksiana and the other was typical of P. contorta. These unusual sympatric chloroplast DNA's are evidence that the genetic complexity and rare variants of hybrid zones are not limited to the nuclear genome.     

The Biological Significance of Multi-copy Regions and Their Impact on Variant Discovery

Identification of genetic variants via high-throughput sequencing (HTS) technologies has been essential for both fundamental and clinical studies. However, to what extent the genome sequence composition affects variant calling remains unclear. In this study, we identified 63,897 multi-copy sequences (MCSs) with a minimum length of 300 bp, each of which occurs at least twice in the human genome. The 151,749 genomic loci (multi-copy regions, or MCRs) harboring these MCSs account for 1.98% of the genome and are distributed unevenly across chromosomes. MCRs containing the same MCS tend to be located on the same chromosome. Gene Ontology (GO) analyses revealed that 3800 genes whose UTRs or exons overlap with MCRs are enriched for Golgi-related cellular component terms and various enzymatic activities in the GO biological function category. MCRs are also enriched for loci that are sensitive to neocarzinostatin-induced double-strand breaks. Moreover, genetic variants discovered by genome-wide association studies and recorded in dbSNP are significantly underrepresented in MCRs. Using simulated HTS datasets, we show that false variant discovery rates are significantly higher in MCRs than in other genomic regions. These results suggest that extra caution must be taken when identifying genetic variants in the MCRs via HTS technologies.     

BGVD:An Integrated Database for Bovine Sequencing Variations and Selective Signatures

Next-generation sequencing has yielded a vast amount of cattle genomic data for global characterization of population genetic diversity and identification of genomic regions under natural and artificial selection. However, efficient storage, querying, and visualization of such large datasets remain challenging. Here, we developed a comprehensive database, the Bovine Genome Variation Database (BGVD). It provides six main functionalities: gene search, variation search, genomic signature search, Genome Browser, alignment search tools, and the genome coordinate conversion tool. BGVD contains information on genomic variations comprising ~60.44 M SNPs, ~6.86 M indels, 76,634 CNV regions, and signatures of selective sweeps in 432 samples from modern cattle worldwide. Users can quickly retrieve distribution patterns of these variations for 54 cattle breeds through an interactive source of breed origin map, using a given gene symbol or genomic region for any of the three versions of the bovine reference genomes (ARS-UCD1.2, UMD3.1.1, and Btau 5.0.1). Signals of selection sweep are displayed as Manhattan plots and Genome Browser tracks. To further investigate and visualize the relationships between variants and signatures of selection, the Genome Browser integrates all variations, selection data, and resources, from NCBI, the UCSC Genome Browser, and Animal QTLdb. Collectively, all these features make the BGVD a useful archive for in-depth data mining and analyses of cattle biology and cattle breeding on a global scale. BGVD is publicly available at http://animal.nwsuaf.edu.cn/BosVar.