Structurally divergent and recurrently mutated regions of primate genomes.

We sequenced and assembled using multiple long-read sequencing technologies the genomes of chimpanzee, bonobo, gorilla, orangutan, gibbon, macaque, owl monkey, and marmoset. We identified 1,338,997 lineage-specific fixed structural variants (SVs) disrupting 1,561 protein-coding genes and 136,932 regulatory elements, including the most complete set of human-specific fixed differences. We estimate that 819.47 Mbp or ∼27% of the genome has been affected by SVs across primate evolution. We identify 1,607 structurally divergent regions wherein recurrent structural variation contributes to creating SV hotspots where genes are recurrently lost (e.g., CARD, C4, and OLAH gene families) and additional lineage-specific genes are generated (e.g., CKAP2, VPS36, ACBD7, and NEK5 paralogs), becoming targets of rapid chromosomal diversification and positive selection (e.g., RGPD gene family). High-fidelity long-read sequencing has made these dynamic regions of the genome accessible for sequence-level analyses within and between primate species.

Comparison of structural variant callers for massive whole-genome sequence data.

BACKGROUND: Detecting structural variations (SVs) at the population level using next-generation sequencing (NGS) requires substantial computational resources and processing time. Here, we compared the performances of 11 SV callers: Delly, Manta, GridSS, Wham, Sniffles, Lumpy, SvABA, Canvas, CNVnator, MELT, and INSurVeyor. These SV callers have been recently published and have been widely employed for processing massive whole-genome sequencing datasets. We evaluated the accuracy, sequence depth, running time, and memory usage of the SV callers. RESULTS: Notably, several callers exhibited better calling performance for deletions than for duplications, inversions, and insertions. Among the SV callers, Manta identified deletion SVs with better performance and efficient computing resources, and both Manta and MELT demonstrated relatively good precision regarding calling insertions. We confirmed that the copy number variation callers, Canvas and CNVnator, exhibited better performance in identifying long duplications as they employ the read-depth approach. Finally, we also verified the genotypes inferred from each SV caller using a phased long-read assembly dataset, and Manta showed the highest concordance in terms of the deletions and insertions. CONCLUSIONS: Our findings provide a comprehensive understanding of the accuracy and computational efficiency of SV callers, thereby facilitating integrative analysis of SV profiles in diverse large-scale genomic datasets.

NPSV-deep: a deep learning method for genotyping structural variants in short read genome sequencing data.

MOTIVATION: Structural variants (SVs) play a causal role in numerous diseases but can be difficult to detect and accurately genotype (determine zygosity) with short-read genome sequencing data (SRS). Improving SV genotyping accuracy in SRS data, particularly for the many SVs first detected with long-read sequencing, will improve our understanding of genetic variation. RESULTS: NPSV-deep is a deep learning-based approach for genotyping previously reported insertion and deletion SVs that recasts this task as an image similarity problem. NPSV-deep predicts the SV genotype based on the similarity between pileup images generated from the actual SRS data and matching SRS simulations. We show that NPSV-deep consistently matches or improves upon the state-of-the-art for SV genotyping accuracy across different SV call sets, samples and variant types, including a 25% reduction in genotyping errors for the Genome-in-a-Bottle (GIAB) high-confidence SVs. NPSV-deep is not limited to the SVs as described; it improves deletion genotyping concordance a further 1.5 percentage points for GIAB SVs (92%) by automatically correcting imprecise/incorrectly described SVs. AVAILABILITY AND IMPLEMENTATION: Python/C++ source code and pre-trained models freely available at https://github.com/mlinderm/npsv2.

Comparison of Optical Genome Mapping With Conventional Diagnostic Methods for Structural Variant Detection in Hematologic Malignancies.

Background: Structural variants (SVs) are currently analyzed using a combination of conventional methods; however, this approach has limitations. Optical genome mapping (OGM), an emerging technology for detecting SVs using a single-molecule strategy, has the potential to replace conventional methods. We compared OGM with conventional diagnostic methods for detecting SVs in various hematologic malignancies. Methods: Residual bone marrow aspirates from 27 patients with hematologic malignancies in whom SVs were observed using conventional methods (chromosomal banding analysis, FISH, an RNA fusion panel, and reverse transcription PCR) were analyzed using OGM. The concordance between the OGM and conventional method results was evaluated. Results: OGM showed concordance in 63% (17/27) and partial concordance in 37% (10/27) of samples. OGM detected 76% (52/68) of the total SVs correctly (concordance rate for each type of SVs: aneuploidies, 83% [15/18]; balanced translocation, 80% [12/15] unbalanced translocation, 54% [7/13] deletions, 81% [13/16]; duplications, 100% [2/2] inversion 100% [1/1]; insertion, 100% [1/1]; marker chromosome, 0% [0/1]; isochromosome, 100% [1/1]). Sixteen discordant results were attributed to the involvement of centromeric/telomeric regions, detection sensitivity, and a low mapping rate and coverage. OGM identified additional SVs, including submicroscopic SVs and novel fusions, in five cases. Conclusions: OGM shows a high level of concordance with conventional diagnostic methods for the detection of SVs and can identify novel variants, suggesting its potential utility in enabling more comprehensive SV analysis in routine diagnostics of hematologic malignancies, although further studies and improvements are required.

A sequence-aware merger of genomic structural variations at population scale.

Merging structural variations (SVs) at the population level presents a significant challenge, yet it is essential for conducting comprehensive genotypic analyses, especially in the era of pangenomics. Here, we introduce PanPop, a tool that utilizes an advanced sequence-aware SV merging algorithm to efficiently merge SVs of various types. We demonstrate that PanPop can merge and optimize the majority of multiallelic SVs into informative biallelic variants. We show its superior precision and lower rates of missing data compared to alternative software solutions. Our approach not only enables the filtering of SVs by leveraging multiple SV callers for enhanced accuracy but also facilitates the accurate merging of large-scale population SVs. These capabilities of PanPop will help to accelerate future SV-related studies.

Pangenome-genotyped structural variation improves molecular phenotype mapping in cattle.

Expression and splicing quantitative trait loci (e/sQTL) are large contributors to phenotypic variability. Achieving sufficient statistical power for e/sQTL mapping requires large cohorts with both genotypes and molecular phenotypes, and so, the genomic variation is often called from short-read alignments, which are unable to comprehensively resolve structural variation. Here we build a pangenome from 16 HiFi haplotype-resolved cattle assemblies to identify small and structural variation and genotype them with PanGenie in 307 short-read samples. We find high (>90%) concordance of PanGenie-genotyped and DeepVariant-called small variation and confidently genotype close to 21 million small and 43,000 structural variants in the larger population. We validate 85% of these structural variants (with MAF > 0.1) directly with a subset of 25 short-read samples that also have medium coverage HiFi reads. We then conduct e/sQTL mapping with this comprehensive variant set in a subset of 117 cattle that have testis transcriptome data, and find 92 structural variants as causal candidates for eQTL and 73 for sQTL. We find that roughly half of the top associated structural variants affecting expression or splicing are transposable elements, such as SV-eQTL for STN1 and MYH7 and SV-sQTL for CEP89 and ASAH2 Extensive linkage disequilibrium between small and structural variation results in only 28 additional eQTL and 17 sQTL discovered when including SVs, although many top associated SVs are compelling candidates.

A 25-year odyssey of genomic technology advances and structural variant discovery.

This perspective focuses on advances in genome technology over the last 25 years and their impact on germline variant discovery within the field of human genetics. The field has witnessed tremendous technological advances from microarrays to short-read sequencing and now long-read sequencing. Each technology has provided genome-wide access to different classes of human genetic variation. We are now on the verge of comprehensive variant detection of all forms of variation for the first time with a single assay. We predict that this transition will further transform our understanding of human health and biology and, more importantly, provide novel insights into the dynamic mutational processes shaping our genomes.

Small polymorphisms are a source of ancestral bias in structural variant breakpoint placement.

High-quality genome assemblies and sophisticated algorithms have increased sensitivity for a wide range of variant types, and breakpoint accuracy for structural variants (SVs, ≥50 bp) has improved to near base pair precision. Despite these advances, many SV breakpoint locations are subject to systematic bias affecting variant representation. To understand why SV breakpoints are inconsistent across samples, we reanalyzed 64 phased haplotypes constructed from long-read assemblies released by the Human Genome Structural Variation Consortium (HGSVC). We identify 882 SV insertions and 180 SV deletions with variable breakpoints not anchored in tandem repeats (TRs) or segmental duplications (SDs). SVs called from aligned sequencing reads increase breakpoint disagreements by 2×-16×. Sequence accuracy had a minimal impact on breakpoints, but we observe a strong effect of ancestry. We confirm that SNP and indel polymorphisms are enriched at shifted breakpoints and are also absent from variant callsets. Breakpoint homology increases the likelihood of imprecise SV calls and the distance they are shifted, and tandem duplications are the most heavily affected SVs. Because graph genome methods normalize SV calls across samples, we investigated graphs generated by two different methods and find the resulting breakpoints are subject to other technical biases affecting breakpoint accuracy. The breakpoint inconsistencies we characterize affect ∼5% of the SVs called in a human genome and can impact variant interpretation and annotation. These limitations underscore a need for algorithm development to improve SV databases, mitigate the impact of ancestry on breakpoints, and increase the value of callsets for investigating breakpoint features.

Genomic structural variation is associated with hypoxia adaptation in high-altitude zokors.

Zokors, an Asiatic group of subterranean rodents, originated in lowlands and colonized high-elevational zones following the uplift of the Qinghai-Tibet plateau about 3.6 million years ago. Zokors live at high elevation in subterranean burrows and experience hypobaric hypoxia, including both hypoxia (low oxygen concentration) and hypercapnia (elevated partial pressure of CO2). Here we report a genomic analysis of six zokor species (genus Eospalax) with different elevational ranges to identify structural variants (deletions and inversions) that may have contributed to high-elevation adaptation. Based on an assembly of a chromosome-level genome of the high-elevation species, Eospalax baileyi, we identified 18 large inversions that distinguished this species from congeners native to lower elevations. Small-scale structural variants in the introns of EGLN1, HIF1A, HSF1 and SFTPD of E. baileyi were associated with the upregulated expression of those genes. A rearrangement on chromosome 1 was associated with altered chromatin accessibility, leading to modified gene expression profiles of key genes involved in the physiological response to hypoxia. Multigene families that underwent copy-number expansions in E. baileyi were enriched for autophagy, HIF1 signalling and immune response. E. baileyi show a significantly larger lung mass than those of other Eospalax species. These findings highlight the key role of structural variants underlying hypoxia adaptation of high-elevation species in Eospalax.

SVvalidation: A long-read-based validation method for genomic structural variation.

Although various methods have been developed to detect structural variations (SVs) in genomic sequences, few are used to validate these results. Several commonly used SV callers produce many false positive SVs, and existing validation methods are not accurate enough. Therefore, a highly efficient and accurate validation method is essential. In response, we propose SVvalidation-a new method that uses long-read sequencing data for validating SVs with higher accuracy and efficiency. Compared to existing methods, SVvalidation performs better in validating SVs in repeat regions and can determine the homozygosity or heterozygosity of an SV. Additionally, SVvalidation offers the highest recall, precision, and F1-score (improving by 7-16%) across all datasets. Moreover, SVvalidation is suitable for different types of SVs. The program is available at https://github.com/nwpuzhengyan/SVvalidation.

Short-Read Sequencing Detects Large Structural Variants in Cancer Genomes.

Most large structural variants (SV) can be detected by short-read sequencing (SRS) of cancer genomes.

The landscape of genomic structural variation in Indigenous Australians.

Indigenous Australians harbour rich and unique genomic diversity. However, Aboriginal and Torres Strait Islander ancestries are historically under-represented in genomics research and almost completely missing from reference datasets1-3. Addressing this representation gap is critical, both to advance our understanding of global human genomic diversity and as a prerequisite for ensuring equitable outcomes in genomic medicine. Here we apply population-scale whole-genome long-read sequencing4 to profile genomic structural variation across four remote Indigenous communities. We uncover an abundance of large insertion-deletion variants (20-49 bp; n = 136,797), structural variants (50 b-50 kb; n = 159,912) and regions of variable copy number (>50 kb; n = 156). The majority of variants are composed of tandem repeat or interspersed mobile element sequences (up to 90%) and have not been previously annotated (up to 62%). A large fraction of structural variants appear to be exclusive to Indigenous Australians (12% lower-bound estimate) and most of these are found in only a single community, underscoring the need for broad and deep sampling to achieve a comprehensive catalogue of genomic structural variation across the Australian continent. Finally, we explore short tandem repeats throughout the genome to characterize allelic diversity at 50 known disease loci5, uncover hundreds of novel repeat expansion sites within protein-coding genes, and identify unique patterns of diversity and constraint among short tandem repeat sequences. Our study sheds new light on the dimensions and dynamics of genomic structural variation within and beyond Australia.

Indigenous Australian genomes show deep structure and rich novel variation.

The Indigenous peoples of Australia have a rich linguistic and cultural history. How this relates to genetic diversity remains largely unknown because of their limited engagement with genomic studies. Here we analyse the genomes of 159 individuals from four remote Indigenous communities, including people who speak a language (Tiwi) not from the most widespread family (Pama-Nyungan). This large collection of Indigenous Australian genomes was made possible by careful community engagement and consultation. We observe exceptionally strong population structure across Australia, driven by divergence times between communities of 26,000-35,000 years ago and long-term low but stable effective population sizes. This demographic history, including early divergence from Papua New Guinean (47,000 years ago) and Eurasian groups1, has generated the highest proportion of previously undescribed genetic variation seen outside Africa and the most extended homozygosity compared with global samples. A substantial proportion of this variation is not observed in global reference panels or clinical datasets, and variation with predicted functional consequence is more likely to be homozygous than in other populations, with consequent implications for medical genomics2. Our results show that Indigenous Australians are not a single homogeneous genetic group and their genetic relationship with the peoples of New Guinea is not uniform. These patterns imply that the full breadth of Indigenous Australian genetic diversity remains uncharacterized, potentially limiting genomic medicine and equitable healthcare for Indigenous Australians.

Insight into chloroplast genome structural variation of the Mongolian endemic species Adonis mongolica (Ranunculaceae) in the Adonideae tribe.

Adonis mongolica is a threatened species that is endemic to Mongolia. It is a medicinal plant from the Adonis genus and has been used to treat heart diseases. However, the genomics and evolution of this species have not been thoroughly studied. We sequenced the first complete plastome of A. mongolica and compared it with ten Adonideae species to describe the plastome structure and infer phylogenetic relationships. The complete plastome of A. mongolica was 157,521 bp long and had a typical quadripartite structure with numerous divergent regions. The plastomes of Adonideae had relatively constant genome structures and sizes, except for those of Adonis. The plastome structure was consistent across Adonis. We identified a 44.8 kb large-scale inversion within the large single-copy region and rpl32 gene loss in the Adonis plastomes compared to other members of the Adonideae tribe. Additionally, Adonis had a smaller plastome size (156,917-157,603 bp) than the other genera within the tribe (159,666-160,940 bp), which was attributed to deletions of intergenic regions and partial and complete gene losses. These results suggested that an intramolecular mutation occurred in the ancestor of the Adonis genus. Based on the phylogenetic results, Adonis separated earlier than the other genera within the Adonideae tribe. The genome structures and divergences of specific regions in the Adonis genus were unique to the Adonideae tribe. This study provides fundamental knowledge for further genomic research in Mongolia and a better understanding of the evolutionary history of endemic plants.

Most large structural variants in cancer genomes can be detected without long reads.

Short-read sequencing is the workhorse of cancer genomics yet is thought to miss many structural variants (SVs), particularly large chromosomal alterations. To characterize missing SVs in short-read whole genomes, we analyzed 'loose ends'-local violations of mass balance between adjacent DNA segments. In the landscape of loose ends across 1,330 high-purity cancer whole genomes, most large (>10-kb) clonal SVs were fully resolved by short reads in the 87% of the human genome where copy number could be reliably measured. Some loose ends represent neotelomeres, which we propose as a hallmark of the alternative lengthening of telomeres phenotype. These pan-cancer findings were confirmed by long-molecule profiles of 38 breast cancer and melanoma cases. Our results indicate that aberrant homologous recombination is unlikely to drive the majority of large cancer SVs. Furthermore, analysis of mass balance in short-read whole genome data provides a surprisingly complete picture of cancer chromosomal structure.

PhenoSV: interpretable phenotype-aware model for the prioritization of genes affected by structural variants.

Structural variants (SVs) represent a major source of genetic variation associated with phenotypic diversity and disease susceptibility. While long-read sequencing can discover over 20,000 SVs per human genome, interpreting their functional consequences remains challenging. Existing methods for identifying disease-related SVs focus on deletion/duplication only and cannot prioritize individual genes affected by SVs, especially for noncoding SVs. Here, we introduce PhenoSV, a phenotype-aware machine-learning model that interprets all major types of SVs and genes affected. PhenoSV segments and annotates SVs with diverse genomic features and employs a transformer-based architecture to predict their impacts under a multiple-instance learning framework. With phenotype information, PhenoSV further utilizes gene-phenotype associations to prioritize phenotype-related SVs. Evaluation on extensive human SV datasets covering all SV types demonstrates PhenoSV's superior performance over competing methods. Applications in diseases suggest that PhenoSV can determine disease-related genes from SVs. A web server and a command-line tool for PhenoSV are available at https://phenosv.wglab.org .

Applications of advanced technologies for detecting genomic structural variation.

Chromosomal structural variation (SV) encompasses a heterogenous class of genetic variants that exerts strong influences on human health and disease. Despite their importance, many structural variants (SVs) have remained poorly characterized at even a basic level, a discrepancy predicated upon the technical limitations of prior genomic assays. However, recent advances in genomic technology can identify and localize SVs accurately, opening new questions regarding SV risk factors and their impacts in humans. Here, we first define and classify human SVs and their generative mechanisms, highlighting characteristics leveraged by various SV assays. We next examine the first-ever gapless assembly of the human genome and the technical process of assembling it, which required third-generation sequencing technologies to resolve structurally complex loci. The new portions of that "telomere-to-telomere" and subsequent pangenome assemblies highlight aspects of SV biology likely to develop in the near-term. We consider the strengths and limitations of the most promising new SV technologies and when they or longstanding approaches are best suited to meeting salient goals in the study of human SV in population-scale genomics research, clinical, and public health contexts. It is a watershed time in our understanding of human SV when new approaches are expected to fundamentally change genomic applications.

Evolutionary origin of genomic structural variations in domestic yaks.

Yak has been subject to natural selection, human domestication and interspecific introgression during its evolution. However, genetic variants favored by each of these processes have not been distinguished previously. We constructed a graph-genome for 47 genomes of 7 cross-fertile bovine species. This allowed detection of 57,432 high-resolution structural variants (SVs) within and across the species, which were genotyped in 386 individuals. We distinguished the evolutionary origins of diverse SVs in domestic yaks by phylogenetic analyses. We further identified 334 genes overlapping with SVs in domestic yaks that bore potential signals of selection from wild yaks, plus an additional 686 genes introgressed from cattle. Nearly 90% of the domestic yaks were introgressed by cattle. Introgression of an SV spanning the KIT gene triggered the breeding of white domestic yaks. We validated a significant association of the selected stratified SVs with gene expression, which contributes to phenotypic variations. Our results highlight that SVs of different origins contribute to the phenotypic diversity of domestic yaks.