Large-scale Structural Variations Research Articles

SINE jumping contributes to large-scale polymorphisms in the pig genomes

BackgroundMolecular markers based on retrotransposon insertion polymorphisms (RIPs) have been developed and are widely used in plants and animals. Short interspersed nuclear elements (SINEs) exert wide impacts on gene activity and even on phenotypes. However, SINE RIP profiles in livestock remain largely unknown, and not be revealed in pigs.ResultsOur data revealed that SINEA1 displayed the most polymorphic insertions (22.5 % intragenic and 26.5 % intergenic), followed by SINEA2 (10.5 % intragenic and 9 % intergenic) and SINEA3 (12.5 % intragenic and 5.0 % intergenic). We developed a genome-wide SINE RIP mining protocol and obtained a large number of SINE RIPs (36,284), with over 80 % accuracy and an even distribution in chromosomes (14.5/Mb), and 74.34 % of SINE RIPs generated by SINEA1 element. Over 65 % of pig SINE RIPs overlap with genes, most of them (> 95 %) are in introns. Overall, about one forth (23.09 %) of the total genes contain SINE RIPs. Significant biases of SINE RIPs in the transcripts of protein coding genes were observed. Nearly half of the RIPs are common in these pig breeds. Sixteen SINE RIPs were applied for population genetic analysis in 23 pig breeds, the phylogeny tree and cluster analysis were generally consistent with the geographical distributions of native pig breeds in China.ConclusionsOur analysis revealed that SINEA1–3 elements, particularly SINEA1, are high polymorphic across different pig breeds, and generate large-scale structural variations in the pig genomes. And over 35,000 SINE RIP markers were obtained. These data indicate that young SINE elements play important roles in creating new genetic variations and shaping the evolution of pig genome, and also provide strong evidences to support the great potential of SINE RIPs as genetic markers, which can be used for population genetic analysis and quantitative trait locus (QTL) mapping in pig.

A high-quality genome assembly of Morinda officinalis, a famous native southern herb in the Lingnan region of southern China

Morinda officinalis is a well-known medicinal and edible plant that is widely cultivated in the Lingnan region of southern China. Its dried roots (called bajitian in traditional Chinese medicine) are broadly used to treat various diseases, such as impotence and rheumatism. Here, we report a high-quality chromosome-scale genome assembly of M. officinalis using Nanopore single-molecule sequencing and Hi-C technology. The assembled genome size was 484.85 Mb with a scaffold N50 of 40.97 Mb, and 90.77% of the assembled sequences were anchored on eleven pseudochromosomes. The genome includes 27,698 protein-coding genes, and most of the assemblies are repetitive sequences. Genome evolution analysis revealed that M. officinalis underwent core eudicot γ genome triplication events but no recent whole-genome duplication (WGD). Likewise, comparative genomic analysis showed no large-scale structural variation after species divergence between M. officinalis and Coffea canephora. Moreover, gene family analysis indicated that gene families associated with plant–pathogen interactions and sugar metabolism were significantly expanded in M. officinalis. Furthermore, we identified many candidate genes involved in the biosynthesis of major active components such as anthraquinones, iridoids and polysaccharides. In addition, we also found that the DHQS, GGPPS, TPS-Clin, TPS04, sacA, and UGDH gene families—which include the critical genes for active component biosynthesis—were expanded in M. officinalis. This study provides a valuable resource for understanding M. officinalis genome evolution and active component biosynthesis. This work will facilitate genetic improvement and molecular breeding of this commercially important plant.

InGAP-family: Accurate Detection of Meiotic Recombination Loci and Causal Mutations by Filtering Out Artificial Variants due to Genome Complexities

Accurately identifying DNA polymorphisms can bridge the gap between phenotypes and genotypes and is essential for molecular marker assisted genetic studies. Genome complexities, including large-scale structural variations, bring great challenges to bioinformatic analysis for obtaining high-confidence genomic variants, as sequence differences between non-allelic loci of two or more genomes can be misinterpreted as polymorphisms. It is important to correctly filter out artificial variants to avoid false genotyping or estimation of allele frequencies. Here, we present an efficient and effective framework, inGAP-family, to discover, filter, and visualize DNA polymorphisms and structural variants (SVs) from alignment of short reads. Applying this method to polymorphism detection on real datasets shows that elimination of artificial variants greatly facilitates the precise identification of meiotic recombination points as well as causal mutations in mutant genomes or quantitative trait loci. In addition, inGAP-family provides a user-friendly graphical interface for detecting polymorphisms and SVs, further evaluating predicted variants and identifying mutations related to genotypes. It is accessible at https://sourceforge.net/projects/ingap-family/.

HiTea: a computational pipeline to identify non-reference transposable element insertions in Hi-C data.

HiTea is available at https://github.com/parklab/HiTea and as a Docker image. Supplementary data are available at Bioinformatics online.

Opposing Evolutionary Pressures Drive Clonal Evolution and Health Outcomes in the Aging Blood System

The age-associated accumulation of somatic mutations and large-scale structural variants (SVs) in the early hematopoietic hierarchy have been linked to premalignant stages for cancer and cardiovascular disease (CVD). However, only a small proportion of individuals harboring these mutations progress to disease, and mechanisms driving the transformation to malignancy remains unclear. Hematopoietic evolution, and cancer evolution more broadly, has largely been studied through a lens of adaptive evolution and the contribution of functionally neutral or mildly damaging mutations to early disease-associated clonal expansions has not been well characterised despite comprising the majority of the mutational burden in healthy or tumoural tissues. Through combining deep learning with population genetics, we interrogate the hematopoietic system to capture signatures of selection acting in healthy and pre-cancerous blood populations. Here, we leverage high-coverage sequencing data from healthy and pre-cancerous individuals from the European Prospective Investigation into Cancer and Nutrition Study (n=477) and dense genotyping from the Canadian Partnership for Tomorrow's Health (n=5,000) to show that blood rejects the paradigm of strictly adaptive or neutral evolution and is subject to pervasive negative selection. We observe clear age associations across hematopoietic populations and the dominant class of selection driving evolutionary dynamics acting at an individual level. We find that both the location and ratio of passenger to driver mutations are critical in determining if positive selection acting on driver mutations is able to overwhelm regulated hematopoiesis and allow clones harbouring disease-predisposing mutations to rise to dominance. Certain genes are enriched for passenger mutations in healthy individuals fitting purifying models of evolution, suggesting that the presence of passenger mutations in a subset of genes might confer a protective role against disease-predisposing clonal expansions. Finally, we find that the density of gene disruption events with known pathogenic associations in somatic SVs impacts the frequency at which the SV segregates in the population with variants displaying higher gene disruption density segregating at lower frequencies. Understanding how blood evolves towards malignancy will allow us to capture cancer in its earliest stages and identify events initiating departures from healthy blood evolution. Further, as the majority of mutations are passengers, studying their contribution to tumorigenesis, will unveil novel therapeutic targets thus enabling us to better understand patterns of clonal evolution in order to diagnose and treat disease in its infancy. Disclosures Dick: Bristol-Myers Squibb/Celgene: Research Funding.

DeepC: predicting 3D genome folding using megabase-scale transfer learning.

Predicting the impact of non-coding genetic variation requires interpreting it in the context of 3D genome architecture. We have developed deepC, a transfer learning based deep neural network that accurately predicts genome folding from megabase-scale DNA sequence. DeepC predicts domain boundaries at high-resolution, learns the sequence determinants of genome folding and predicts the impact of both large-scale structural and single base pair variations.

Hierarchical natural move Monte Carlo refines flexible RNA structures into cryo-EM densities.

Ribonucleic acids (RNAs) play essential roles in living cells. Many of them fold into defined three-dimensional (3D) structures to perform functions. Recent advances in single-particle cryo-electron microscopy (cryo-EM) have enabled structure determinations of RNA to atomic resolutions. However, most RNA molecules are structurally flexible, limiting the resolution of their structures solved by cryo-EM. In modeling these molecules, several computational methods are limited by the requirement of massive computational resources and/or the low efficiency in exploring large-scale structural variations. Here we use hierarchical natural move Monte Carlo (HNMMC), which takes advantage of collective motions for groups of nucleic acid residues, to refine RNA structures into their cryo-EM maps, preserving atomic details in the models. After validating the method on a simulated density map of tRNA, we applied it to objectively obtain the model of the folding intermediate for the specificity domain of ribonuclease P from Bacillus subtilis and refine a flexible ribosomal RNA (rRNA) expansion segment from the Mycobacterium tuberculosis (Mtb) ribosome in different conformational states. Finally, we used HNMMC to model atomic details and flexibility for two distinct conformations of the complete genomic RNA (gRNA) inside MS2, a single-stranded RNA virus, revealing multiple pathways for its capsid assembly.

On Variant Discovery in Genomes of Fungal Plant Pathogens.

Comparative genome analyses of eukaryotic pathogens including fungi and oomycetes have revealed extensive variability in genome composition and structure. The genomes of individuals from the same population can exhibit different numbers of chromosomes and different organization of chromosomal segments, defining so-called accessory compartments that have been shown to be crucial to pathogenicity in plant-infecting fungi. This high level of structural variation confers a methodological challenge for population genomic analyses. Variant discovery from population sequencing data is typically achieved using established pipelines based on the mapping of short reads to a reference genome. These pipelines have been developed, and extensively used, for eukaryote genomes of both plants and animals, to retrieve single nucleotide polymorphisms and short insertions and deletions. However, they do not permit the inference of large-scale genomic structural variation, as this task typically requires the alignment of complete genome sequences. Here, we compare traditional variant discovery approaches to a pipeline based on de novo genome assembly of short read data followed by whole genome alignment, using simulated data sets with properties mimicking that of fungal pathogen genomes. We show that the latter approach exhibits levels of performance comparable to that of read-mapping based methodologies, when used on sequence data with sufficient coverage. We argue that this approach further allows additional types of genomic diversity to be explored, in particular as long-read third-generation sequencing technologies are becoming increasingly available to generate population genomic data.

Genomic Outcomes of Haploid Induction Crosses in Potato (Solanum tuberosum L.).

The challenges of breeding autotetraploid potato (Solanum tuberosum) have motivated the development of alternative breeding strategies. A common approach is to obtain uniparental dihaploids from a tetraploid of interest through pollination with S. tuberosum Andigenum Group (formerly S. phureja) cultivars. The mechanism underlying haploid formation of these crosses is unclear, and questions regarding the frequency of paternal DNA transmission remain. Previous reports have described aneuploid and euploid progeny that, in some cases, displayed genetic markers from the haploid inducer (HI). Here, we surveyed a population of 167 presumed dihaploids for large-scale structural variation that would underlie chromosomal addition from the HI, and for small-scale introgression of genetic markers. In 19 progeny, we detected 10 of the 12 possible trisomies and, in all cases, demonstrated the noninducer parent origin of the additional chromosome. Deep sequencing indicated that occasional, short-tract signals appearing to be of HI origin were better explained as technical artifacts. Leveraging recurring copy number variation patterns, we documented subchromosomal dosage variation indicating segregation of polymorphic maternal haplotypes. Collectively, 52% of the assayed chromosomal loci were classified as dosage variable. Our findings help elucidate the genomic consequences of potato haploid induction and suggest that most potato dihaploids will be free of residual pollinator DNA.

Mutational signatures in tumours induced by high and low energy radiation in Trp53 deficient mice

Ionising radiation (IR) is a recognised carcinogen responsible for cancer development in patients previously treated using radiotherapy, and in individuals exposed as a result of accidents at nuclear energy plants. However, the mutational signatures induced by distinct types and doses of radiation are unknown. Here, we analyse the genetic architecture of mammary tumours, lymphomas and sarcomas induced by high (56Fe-ions) or low (gamma) energy radiation in mice carrying Trp53 loss of function alleles. In mammary tumours, high-energy radiation is associated with induction of focal structural variants, leading to genomic instability and Met amplification. Gamma-radiation is linked to large-scale structural variants and a point mutation signature associated with oxidative stress. The genomic architecture of carcinomas, sarcomas and lymphomas arising in the same animals are significantly different. Our study illustrates the complex interactions between radiation quality, germline Trp53 deficiency and tissue/cell of origin in shaping the genomic landscape of IR-induced tumours.

The Power of Human Cancer Genetics as Revealed by Low-Grade Gliomas.

The human brain contains a vast number of cells and shows extraordinary cellular diversity to facilitate the many cognitive and automatic commands governing our bodily functions. This complexity arises partly from large-scale structural variations in the genome, evolutionary processes to increase brain size, function, and cognition. Not surprisingly given recent technical advances, low-grade gliomas (LGGs), which arise from the glia (the most abundant cell type in the brain), have undergone a recent revolution in their classification and therapy, especially in the pediatric setting. Next-generation sequencing has uncovered previously unappreciated diverse LGG entities, unraveling genetic subgroups and multiple molecular alterations and altered pathways, including many amenable to therapeutic targeting. In this article we review these novel entities, in which oncogenic processes show striking age-related neuroanatomical specificity (highlighting their close interplay with development); the opportunities they provide for targeted therapies, some of which are already practiced at the bedside; and the challenges of implementing molecular pathology in the clinic.

Therapeutic Implication of Genomic Landscape of Adult Metastatic Sarcoma.

This study investigated therapeutic potential of integrated genome and transcriptome profiling of metastatic sarcoma, a rare but extremely heterogeneous group of aggressive mesenchymal malignancies with few systemic therapeutic options. Forty-three adult patients with advanced or metastatic non-GI stromal tumor sarcomas of various histology subtypes who were enrolled in the Personalized OncoGenomics program at BC Cancer were included in this study. Fresh tumor tissues along with blood samples underwent whole-genome and transcriptome sequencing. The most frequent genomic alterations in this cohort are large-scale structural variation and somatic copy number variation. Outlier RNA expression as well as somatic copy number variations, structural variations, and small mutations together suggest the presence of one or more potential therapeutic targets in the majority of patients in our cohort. Point mutations or deletions in known targetable cancer genes are rare; for example, tuberous sclerosis complex 2 provides a rationale for targeting the mammalian target of rapamycin pathway, resulting in a few patients with exceptional clinical benefit from everolimus. In addition, we observed recurrent 17p11-12 amplifications, which seem to be a sarcoma-specific event. This may suggest that this region harbors an oncogene(s) that is significant for sarcoma tumorigenesis. Furthermore, some sarcoma tumors carrying a distinct mutational signature suggestive of homologous recombination deficiency seem to demonstrate sensitivity to double-strand DNA-damaging agents. Integrated large-scale genomic analysis may provide insights into potential therapeutic targets as well as novel biologic features of metastatic sarcomas that could fuel future experimental and clinical research and help design biomarker-driven basket clinical trials for novel therapeutic strategies.

A chromosome-level assembly of the Atlantic herring genome-detection of a supergene and other signals of selection.

The Atlantic herring is a model species for exploring the genetic basis for ecological adaptation, due to its huge population size and extremely low genetic differentiation at selectively neutral loci. However, such studies have so far been hampered because of a highly fragmented genome assembly. Here, we deliver a chromosome-level genome assembly based on a hybrid approach combining a de novo Pacific Biosciences (PacBio) assembly with Hi-C-supported scaffolding. The assembly comprises 26 autosomes with sizes ranging from 12.4 to 33.1 Mb and a total size, in chromosomes, of 726 Mb, which has been corroborated by a high-resolution linkage map. A comparison between the herring genome assembly with other high-quality assemblies from bony fishes revealed few inter-chromosomal but frequent intra-chromosomal rearrangements. The improved assembly facilitates analysis of previously intractable large-scale structural variation, allowing, for example, the detection of a 7.8-Mb inversion on Chromosome 12 underlying ecological adaptation. This supergene shows strong genetic differentiation between populations. The chromosome-based assembly also markedly improves the interpretation of previously detected signals of selection, allowing us to reveal hundreds of independent loci associated with ecological adaptation.

Hi-C detects novel structural variants in HL-60 and HL-60/S4 cell lines

Cancer cell lines often have large structural variants (SVs) that evolve over time. There are many reported differences in large scale SVs between HL-60 and HL-60/S4, two cell lines derived from the same acute myeloid leukemia sample. However, the stability and variability of inter- and intra-chromosomal structural variants between different sources of the same cell line is unknown. Here, we used Hi-C and RNA-seq to identify and compare large SVs in HL-60 and HL-60/S4 cell lines. Comparisons with previously published karyotypes identified novel SVs in both cell lines. Hi-C was used to characterize the known expansion centered on the MYC locus. The MYC expansion was integrated into known locations in HL-60/S4, and a novel location (chr4) in HL-60. The HL-60 cell line has more within-line structural variation than the HL-60/S4 derivative cell line. Collectively we demonstrate the usefulness of Hi-C and with RNA-seq data for the identification and characterization of SVs.

CNspector: a web-based tool for visualisation and clinical diagnosis of copy number variation from next generation sequencing

Next Generation Sequencing is now routinely used in the practice of diagnostic pathology to detect clinically relevant somatic and germline sequence variations in patient samples. However, clinical assessment of copy number variations (CNVs) and large-scale structural variations (SVs) is still challenging. While tools exist to estimate both, their results are typically presented separately in tables or static plots which can be difficult to read and are unable to show the context needed for clinical interpretation and reporting. We have addressed this problem with CNspector, a multi-scale interactive browser that shows CNVs in the context of other relevant genomic features to enable fast and effective clinical reporting. We illustrate the utility of CNspector at different genomic scales across a variety of sample types in a range of case studies. We show how CNspector can be used for diagnosis and reporting of exon-level deletions, focal gene-level amplifications, chromosome and chromosome arm level amplifications/deletions and in complex genomic rearrangements. CNspector is a web-based clinical variant browser tailored to the clinical application of next generation sequencing for CNV assessment. We have demonstrated the utility of this interactive software in typical applications across a range of tissue types and disease contexts encountered in the context of diagnostic pathology. CNspector is written in R and the source code is available for download under the GPL3 Licence from https://github.com/PapenfussLab/CNspector.

Chromothripsis during telomere crisis is independent of NHEJ, and consistent with a replicative origin.

Telomere erosion, dysfunction, and fusion can lead to a state of cellular crisis characterized by large-scale genome instability. We investigated the impact of a telomere-driven crisis on the structural integrity of the genome by undertaking whole-genome sequence analyses of clonal populations of cells that had escaped crisis. Quantification of large-scale structural variants revealed patterns of rearrangement consistent with chromothripsis but formed in the absence of functional nonhomologous end-joining pathways. Rearrangements frequently consisted of short fragments with complex mutational patterns, with a repair topology that deviated from randomness showing preferential repair to local regions or exchange between specific loci. We find evidence of telomere involvement with an enrichment of fold-back inversions demarcating clusters of rearrangements. Our data suggest that chromothriptic rearrangements caused by a telomere crisis arise via a replicative repair process involving template switching.

MapOptics: a light-weight, cross-platform visualization tool for optical mapping alignment.

Bionano optical mapping is a technology that can assist in the final stages of genome assembly by lengthening and ordering scaffolds in a draft assembly by aligning the assembly to a genomic map. However, currently, tools for visualization are limited to use on a Windows operating system or are developed initially for visualizing large-scale structural variation. MapOptics is a lightweight cross-platform tool that enables the user to visualize and interact with the alignment of Bionano optical mapping data and can be used for in depth exploration of hybrid scaffolding alignments. It provides a fast, simple alternative to the large optical mapping analysis programs currently available for this area of research. MapOptics is implemented in Java 1.8 and released under an MIT licence. MapOptics can be downloaded from https://github.com/FadyMohareb/mapoptics and run on any standard desktop computer equipped with a Java Virtual Machine (JVM). Supplementary data are available at Bioinformatics online.

Mutation Landscape of Base Substitutions, Duplications, and Deletions in the Representative Current Cholera Pandemic Strain.

Pandemic cholera is a major concern for public health because of its high mortality and morbidity. Mutation accumulation (MA) experiments were performed on a representative strain of the current cholera pandemic. Although the base-pair substitution mutation rates in Vibrio cholerae (1.24 × 10−10 per site per generation for wild-type lines and 3.29 × 10−8 for mismatch repair deficient lines) are lower than that previously reported in other bacteria using MA analysis, we discovered specific high rates (8.31 × 10−8 site/generation for wild-type lines and 1.82 × 10−6 for mismatch repair deficient lines) of base duplication or deletion driven by large-scale copy number variations (CNVs). These duplication–deletions are located in two pathogenic islands, IMEX and the large integron island. Each element of these islands has discrepant rate in rapid integration and excision, which provides clues to the pandemicity evolution of V. cholerae. These results also suggest that large-scale structural variants such as CNVs can accumulate rapidly during short-term evolution. Mismatch repair deficient lines exhibit a significantly increased mutation rate in the larger chromosome (Chr1) at specific regions, and this pattern is not observed in wild-type lines. We propose that the high frequency of GATC sites in Chr1 improves the efficiency of MMR, resulting in similar rates of mutation in the wild-type condition. In addition, different mutation rates and spectra were observed in the MA lines under distinct growth conditions, including minimal media, rich media and antibiotic treatments.

Large-Scale Structural Variation Detection in Subterranean Clover Subtypes Using Optical Mapping

We selected two genetically diverse subspecies of the Trifolium model species, subterranean clover cvs. Daliak and Yarloop. The structural variations (SVs) discovered by Bionano optical mapping (BOM) were validated using Illumina short reads. In the analysis, BOM identified 12 large-scale regions containing deletions and 19 regions containing insertions in Yarloop. The 12 large-scale regions contained 71 small deletions when validated by Illumina short reads. The results suggest that BOM could detect the total size of deletions and insertions, but it could not precisely report the location and actual quantity of SVs in the genome. Nucleotide-level validation is crucial to confirm and characterize SVs reported by optical mapping. The accuracy of SV detection by BOM is highly dependent on the quality of reference genomes and the density of selected nickases.

Finding invisible quantitative trait loci with missing data.

SummaryEvolutionary processes during plant polyploidization and speciation have led to extensive presence–absence variation (PAV) in crop genomes, and there is increasing evidence that PAV associates with important traits. Today, high‐resolution genetic analysis in major crops frequently implements simple, cost‐effective, high‐throughput genotyping from single nucleotide polymorphism (SNP) hybridization arrays; however, these are normally not designed to distinguish PAV from failed SNP calls caused by hybridization artefacts. Here, we describe a strategy to recover valuable information from single nucleotide absence polymorphisms (SNaPs) by population‐based quality filtering of SNP hybridization data to distinguish patterns associated with genuine deletions from those caused by technical failures. We reveal that including SNaPs in genetic analyses elucidate segregation of small to large‐scale structural variants in nested association mapping populations of oilseed rape (Brassica napus), a recent polyploid crop with widespread structural variation. Including SNaP markers in genomewide association studies identified numerous quantitative trait loci, invisible using SNP markers alone, for resistance to two major fungal diseases of oilseed rape, Sclerotinia stem rot and blackleg disease. Our results indicate that PAV has a strong influence on quantitative disease resistance in B. napus and that SNaP analysis using cost‐effective SNP array data can provide extensive added value from ‘missing data’. This strategy might also be applicable for improving the precision of genetic mapping in many important crop species.