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PubMed2025

Structural variation detection and association analysis of whole-genome-sequence data from 16,543 Alzheimer's disease sequencing project subjects.

INTRODUCTION: The role of structural variations (SVs) in Alzheimer's disease (AD) remains understudied. METHODS: We analyzed whole-genome sequencing data from the Alzheimer's Disease Sequencing Project (N = 16,543) and identified 400,234 (168,223 high-quality) SVs. Laboratory validation yielded a sensitivity of 82% (85% for high-quality). RESULTS: We found a burden of singletons (odds ratio [OR] = 1.07, p = 0.0017) and homozygous deletions (OR = 1.14, p < 0.0001) in cases. On AD genes, we observed the ultra-rare SVs associated with the disease, including protein-altering SVs in ABCA7, APP, PLCG2, and SORL1. Twenty-one SVs are in linkage disequilibrium (LD) with known AD-risk variants, exemplified by a 5k deletion in LD (R2 = 0.99) with rs143080277 in NCK2. We identified a rare deletion near RNA5SP293 associated with AD (OR = 1.99, p = 1.3 × 10-5), which was replicated using an independent dataset. DISCUSSION: This study highlights the pivotal role of SVs in AD genetics. HIGHLIGHTS: Observed a significant burden of singletons and homozygous deletions in Alzheimer's disease (AD) patients. Identified rare protein-altering structural variations (SVs) in ABCA7, APP, PLCG2, and SORL1. Established linkages between SVs and AD risk-associated single nucleotide variants (SNVs). Discovered a novel deletion near RNA5SP293 linked to AD, replicated independently. Uncovered over-representation of SVs in neuronal function pathways.

Humans↗

PubMed2026

The value of structural variants to conservation genomics in the pangenome era.

Structural variants (SVs) comprise an axis of genetic diversity with strong consequences for phenotype and fitness, making them a potentially important target for conservation genomics. Here, we review how and why SVs can play a role in conservation genomics; the different types of SVs and how they can affect phenotype; and how pangenomes and long-read sequencing are illuminating their evolution in populations, including small populations and those of conservation concern. SVs comprise multinucleotide mutations including insertions, deletions, transpositions, inversions, and other multinucleotide mutations, often overlapping genes and other functional genome regions. As a result, SVs often play important roles in phenotypic evolution and local adaptation and can contribute substantially to genetic load in inbred populations. However, our understanding of the factors influencing SV diversity in populations is still in its infancy and is complicated by the vast range of sizes, effects, and mechanisms of formation of these mutations. We argue that SVs are an important axis of genetic diversity which should be characterized alongside more traditional metrics of genetic diversity in conservation contexts. There are a number of analytical challenges to detecting and studying SVs, but analyses aimed at understanding the role of SVs in inbreeding load and population health are rapidly becoming realizable goals, accelerated by new technologies and analytical approaches. New tools, including population-scale long-read sequencing and pangenome approaches, are beginning to make SVs accessible in ways which can be readily applied in conservation settings.

Genomic Structural Variation↗

PubMed2025

Structural variants linked to Alzheimer's disease and other common age-related clinical and neuropathologic traits.

BACKGROUND: Alzheimer's disease (AD) is a complex neurodegenerative disorder with substantial genetic influence. While genome-wide association studies (GWAS) have identified numerous risk loci for late-onset AD (LOAD), the functional mechanisms underlying most of these associations remain unresolved. Large genomic rearrangements, known as structural variants (SVs), represent a promising avenue for elucidating such mechanisms within some of these loci. METHODS: By leveraging data from two ongoing cohort studies of aging and dementia, the Religious Orders Study and Rush Memory and Aging Project (ROS/MAP), we performed genome-wide association analysis testing 20,205 common SVs from 1088 participants with whole genome sequencing (WGS) data. A range of Alzheimer's disease and other common age-related clinical and neuropathologic traits were examined. RESULTS: First, we mapped SVs across 81 AD risk loci and discovered 22 SVs in linkage disequilibrium (LD) with GWAS lead variants and directly associated with the phenotypes tested. The strongest association was a deletion of an Alu element in the 3'UTR of the TMEM106B gene, in high LD with the respective AD GWAS locus and associated with multiple AD and AD-related disorders (ADRD) phenotypes, including tangles density, TDP-43, and cognitive resilience. The deletion of this element was also linked to lower TMEM106B protein abundance. We also found a 22-kb deletion associated with depression in ROS/MAP and bearing similar association patterns as GWAS SNPs at the IQCK locus. In addition, we leveraged our catalog of SV-GWAS to replicate and characterize independent findings in SV-based GWAS for AD and five other neurodegenerative diseases. Among these findings, we highlight the replication of genome-wide significant SVs for progressive supranuclear palsy (PSP), including markers for the 17q21.31 MAPT locus inversion and a 1483-bp deletion at the CYP2A13 locus, along with other suggestive associations, such as a 994-bp duplication in the LMNTD1 locus, suggestively linked to AD and a 3958-bp deletion at the DOCK5 locus linked to Lewy body disease (LBD) (P = 3.36 × 10-4). CONCLUSIONS: While still limited in sample size, this study highlights the utility of including analysis of SVs for elucidating mechanisms underlying GWAS loci and provides a valuable resource for the characterization of the effects of SVs in neurodegenerative disease pathogenesis.

Humans↗

PubMed2026

RAG-mediated structural variation and its impact on relapse risk in acute lymphoblastic leukemia.

Relapse during treatment of B-cell acute lymphoblastic leukemia (B-ALL) is a harbinger of poor outcomes. Identifying biomarkers for subsequent relapse risk which are detectable at B-ALL diagnosis remains a priority. Off-target recombination-activating gene (RAG)-mediated structural variants (SVs) generate genomic instability that drives leukemogenesis and may underlie treatment resistance. Leveraging sequencing data in 1,496 pediatric B-ALL patients enriched for relapse status (relapse n=532; non-relapse n=964), we characterized RAG-mediated SVs across B-ALL molecular subtypes and examined their association with patient characteristics and their impact on clinical outcomes. Off-target RAG-mediated SVs were overall frequent, particularly in ETV6::RUNX1, ETV6::RUNX1-like, and Ph-like B-ALL subtypes, while increasing age-at-diagnosis was positively associated with burden of off-target RAG-mediated SVs (P<.001). Off-target RAG-mediated SVs with a recombination signal sequence (RSS) at one breakpoint, a hallmark of off-target RAG activity, were significantly more frequent at diagnosis in patients who subsequently relapsed (P=.001). This association remained significant in multivariable regression analysis (per SV odds ratio [OR]:1.08, 95%CI:1.04-1.12), in minimal residual disease (MRD)-negative patients (OR:1.09, 95%CI:1.04-1.14) and across subtypes. Excluding deletions, MRD-negative ETV6::RUNX1 patients with ≥3 off-target RAG-mediated SVs had a >3-fold risk of relapse (hazard ratio:3.47, 95% CI:1.86-6.49). RAG-mediated SVs were also associated with relapse risk in T-cell ALL patients. Off-target RAG-mediated SV burden at diagnosis is a risk factor of relapse in pediatric ALL across molecular subtypes and independent of MRD status.

Journal Article↗

PubMed2026

Comparisons Between Large-Scale Genomic Variants and SNPs in Driving Population Divergence and Local Adaptation.

Genomic variations, such as indels (2-49 bp) and structural variants (SVs, ≥50 bp), are larger-scale mutations than single nucleotide polymorphisms (SNPs) and can substantially impact evolutionary processes, including speciation, adaptation, and phenotypes. Despite their functional importance, integrative population genetic analyses that jointly consider genome-wide SNPs, indels, and SVs remain under-explored. The ground tit (Pseudopodoces humilis), an endemic species to the Qinghai-Tibet Plateau (QTP), exhibits divergence across distinct glacial refugia, accompanied by habitat and morphological divergence, making it an excellent example for investigating how different types of genomic variants contribute to population divergence and local adaptation. Here, by retrieving 81 whole-genome sequence data, over 13 million SNPs, 2 million indels, and 22,101 SVs were identified. Variants were unevenly distributed across the genome, characterized by distinct hotspot regions. Indels and SVs revealed four genetic clusters consistent with previous SNP-based results, thereby validating the reliability of our variant datasets. FST and genotype-environment association (GEA) analyses independently revealed numerous candidate indels and SVs; each showed minimal overlap with previously identified SNPs, and were enriched in similar functional pathways such as signal transduction, skeletal muscle development, water transport, DNA repair, reproduction, nervous system development, and immunity. Collectively, our results demonstrated that indels and SVs could capture additional signatures besides SNPs. Furthermore, similar but distinct gene functions among different types of genomic variants collectively and complementarily drive genomic divergence across environmental gradients in such a high-elevation endemic species, underscoring its evolutionary relevance in local adaptation.

indels↗

PubMed2026

Structural variant discovery and diagnostic impact in rare diseases from short-read and long-read sequencing.

Rare diseases collectively affect 1 in 10 individuals, yet current genetic testing fails to identify a causal variant for most cases. At present, cytogenetic methods and/or sequencing approaches such as exome (ES) or short-read genome sequencing (srGS) represent the state-of-the-art for comprehensive clinical discovery of sequence and structural variants (SVs), including copy number variants, balanced SVs, complex SVs, and tandem repeats (TRs). Recently, long-read genome sequencing (lrGS), coupled with multiomics data, has presented great promise to resolve variation in genomic regions recalcitrant to characterization by srGS such as highly repetitive simple repeat sequences and segmental duplications. However, there are few guidelines to enable clinical interpretation of genetic variation in these highly repetitive genomic regions, and the enthusiasm of the field in adopting lrGS has made it difficult to assess the true added diagnostic yield of this technology due to widely variable and inconsistently applied analytic pipelines and variable degrees of pre-screening by ES or srGS. Here, we investigated the contribution of SVs to rare diseases using srGS as a front-line strategy when paired with highly sensitive SV discovery and evaluate the added diagnostic yield of incorporating lrGS for a subset of cases. Our srGS analysis encompassed 1,462 families (3,450 individuals) recruited through the Broad Institute Center for Mendelian Genetics and the Genomics Research to Elucidate the Genetics of Rare Diseases (GREGoR) programs. Diagnostic SVs were identified in 5.4% of cases (79/1,462), of which 80% were uniquely detectable by srGS compared to standard cytogenetic techniques. For 96 families (including 10 families with a heterozygous variant observed in a known recessive gene of clinical relevance), we performed lrGS with methylation profiling, as well as long-read transcriptomic analyses in a subset of 20 trios. Analyses with lrGS yielded over 25,000 SVs per genome, 63% of which were not captured by srGS, along with an additional ~200 rare SNV/indels per genome not previously captured and 12 differentially methylated regions per genome. Among these, we identified only one diagnostic variant not interpreted by srGS, an apparently mosaic de novo SNV in CASK that was absent in the srGS callset due to allelic imbalance. No new diagnoses were supported by long-read transcriptomics or episignatures. In this well characterized rare disease cohort, the added diagnostic yield was thus 1.04% (1/96 families). Following a systematic literature review of prior lrGS studies, we find that most reported diagnoses were detectable by srGS and that our added diagnostic yield is consistent with those prior studies. These studies emphasize the significant impact of comprehensive SV discovery in rare disease cases and further demonstrate the power for increased discovery of novel genomic variation and episignatures from lrGS. Nonetheless, they also serve to temper expectations of dramatic diagnostic advances in rare disease patients until there is more extensive annotation of the functional and clinical impact of all coding and noncoding variation uniquely accessible to lrGS with extensive reference databases spanning highly repetitive genomic sequencing that could be enabled by this transformative technology.

Journal Article↗

PubMed2026

Chromosome-scale genome remodeling in tumor evolution: Copy number alterations and structural variants as two sides of the same coin.

Chromosome-scale genomic rearrangements are a dominant force in tumor evolution. Copy-number alterations (CNAs) and structural variants (SVs) constitute two complementary axes of this process. Although detection technologies now deliver near-comprehensive catalogs, technical resolution has outpaced conceptual integration. In this review, we frame CNAs and SVs as inextricable facets of chromosomal aberrations. They reshape cancer genomes through altered gene dosage and three-dimensional regulatory rewiring. CNAs quantify the gene-dosage imbalance, yet arise through mechanistically distinct routes. Segmental CNAs typically require chromosomal breakage, and therefore often coincide with SV junctions. By contrast, whole-chromosome aneuploidy and whole-genome doubling (WGD) primarily reflect mitotic or cytokinetic failure and can occur without local breakpoints, while nevertheless reshaping the karyotypic landscape and seeding subsequent structural complexity. SVs, in turn, range from unbalanced events that alter copy number to ostensibly balanced exchanges that predominantly rewire regulatory architecture. Despite their diverse and sometimes catastrophic architectures, SVs are ultimately rooted in double-strand break formation and error-prone resolution. By integrating CNAs and SVs within a unified mechanistic and functional framework, we aim to convert catalogs into concepts and distill the organizing principles that govern tumor genome evolution.

Humans↗

PubMed2026

Analysis of deep-resequencing data of 984 soybean accessions reveals structural variations underlying agronomic traits.

Genomic structural variants (SVs) are major sources of genetic variation and have profound impacts on phenotypic traits. However, their functional effects remain largely unexplored in soybean. Here, we resequence 940 soybean accessions. Together with 44 publicly available datasets, we identify 602,281 SVs. Using a graph-based genome, we detect an additional 58,760 presence/absence variations (PAVs) that broadly affect gene expression. Population genomic analyses reveal that SVs serve as a core driving force for soybean domestication and improvement. Integrating SVs with QTLs for oil and protein content, and performing GWAS on 27 traits, we identify key functional SVs. These include transposable element insertions altering seed coat color, multiple insertions within a cytochrome P450 gene modifying flower and hypocotyl color, and a GmMATE1 deletion enhancing seed size. Together, our study establishes a comprehensive SV map of soybean, offering a valuable resource for dissecting the genetic basis of complex traits to accelerate molecular breeding.

Glycine max↗

PubMed2026

SynFlow: an interactive online genome structural variant viewer.

MOTIVATION: Structural variations (SVs), including inversions, translocations (TRAs), duplications, and large insertions or deletions, are key drivers of genome evolution and phenotypic diversity. With the increasing number of high-quality, chromosome-scale genome assemblies, the ability to detect and interpret SVs has become a crucial aspect of modern genomics. While SV detection has advanced, most visualization methods produce static plots that fall short when researchers, particularly in comparative genomics, need to interactively explore large datasets, zoom into specific genomic regions, or dynamically filter structural events in real time. RESULTS: To address this gap, we introduce SynFlow, a lightweight, web-based interactive application specifically designed for exploring and visualizing SVs identified by SyRI. We demonstrate that SynFlow can reproduce complex static synteny plots published in literature, but transforms them into dynamic, shareable visualizations that support real-time filtering, reordering, and deep exploration of specific SVs, including TRAs. SynFlow is available as a web server and offers multiple entry points: browsing precomputed datasets (e.g. banana and grapevine genomes), uploading user-provided SyRI outputs, or running an integrated workflow to produce and visualize SVs on the fly. AVAILABILITY AND IMPLEMENTATION: https://synflow.southgreen.fr; source code https://github.com/SouthGreenPlatform/synflow; preprocessing Snakemake workflow https://gitlab.cirad.fr/agap/cluster/snakemake/synflow.

Software↗

PubMed2025

Panorama of Chromosomal Instability in Lung Cancer.

Lung cancer is a highly heterogeneous disease primarily driven by tobacco smoking. About 20% of lung cancers occur among patients who have never smoked (LCINS) with differences in patient ancestry, sex, tumor histology, and clinical features. Our understanding of chromosomal instability in lung cancer, especially LCINS, is still limited. Here, we perform a comprehensive study of 182,429 somatic structural variations (SVs) detected in 1,209 whole-genome sequenced lung cancers, of which 864 LCINS. SVs are more abundant in tumors from patients who have smoked (LCSS); however, they are more complex and play more important roles in tumorigenesis in LCINS. EGFR mutations and KRAS mutations profoundly and independently shape the SV landscape. EGFR-mutant tumors have higher SV burden and more cancer-driving SVs. In contrast, KRAS mutations are associated with lower SV burden and less driver SVs. We decompose 16 SV signatures for both complex and simple SVs that likely represent divergent molecular mechanisms. The SV breakpoints have distinct distributions across the genome depending on the signatures due to mutagenic mechanisms and positive selection. Many established cancer-driving genes are recurrently rearranged by multiple SV signatures suggesting functional convergence of these genome instability mechanisms.

Journal Article↗

PubMed2025

Unveiling the Genetic Landscape of Coronary Artery Disease Through Common and Rare Structural Variants.

BACKGROUND: Genome-wide association studies have identified several hundred susceptibility single nucleotide variants for coronary artery disease (CAD). Despite single nucleotide variant-based genome-wide association studies improving our understanding of the genetics of CAD, the contribution of structural variants (SVs) to the risk of CAD remains largely unclear. METHOD AND RESULTS: We leveraged SVs detected from high-coverage whole genome sequencing data in a diverse group of participants from the National Heart Lung and Blood Institute's Trans-Omics for Precision Medicine program. Single variant tests were performed on 58 706 SVs in a study sample of 11 556 CAD cases and 42 907 controls. Additionally, aggregate tests using sliding windows were performed to examine rare SVs. One genome-wide significant association was identified for a common biallelic intergenic duplication on chromosome 6q21 (P=1.54E-09, odds ratio=1.34). The sliding window-based aggregate tests found 1 region on chromosome 17q25.3, overlapping USP36, to be significantly associated with coronary artery disease (P=1.03E-10). USP36 is highly expressed in arterial and adipose tissues while broadly affecting several cardiometabolic traits. CONCLUSIONS: Our results suggest that SVs, both common and rare, may influence the risk of coronary artery disease.

Humans↗

PubMed2026

Dissecting genetic architecture and improving machine learning‑based genomic prediction of flowering time in Osmanthus fragrans by integrating structural variants.

Sweet osmanthus (Osmanthus fragrans), a traditional ornamental plant in China, exhibits substantial variation in autumn flowering time, which significantly affects landscape application and cultivation efficiency. Here, we performed a genome-wide association study on 127 resequenced accessions classified into early, intermediate, and late flowering types, using a set of 2,325,410 single-nucleotide polymorphisms (SNPs) and 246,824 structural variants (SVs). By integrating SNP/insertion and deletion (Indel) and SV data with weighted gene co-expression network analysis, machine learning, and genomic prediction, we dissected the genetic architecture of flowering time. We identified 24 associated SNP/Indels and six SVs, mapping to 30 candidate genes, including known flowering regulators FLK, LOS1, Y14, MIF2, and GID1B. These genes showed tissue-specific expression, with some responding to low temperature. The two hub genes, GUX1 and LYG027904, were located within modules of the co-expression network associated with low-temperature treatment. Haplotype analysis revealed a specific three-SNP haplotype associated with late flowering and linked to LOS1, and epistatic interactions among combined genotypes contributed to phenotypic variation. Notably, integrating SVs with SNP/Indels improved genomic prediction accuracy; the gradient boosting decision tree model outperformed other machine learning algorithms, achieving a mean accuracy of 0.859 and an AUC > 0.8 (where AUC is area under receiver operating characteristic curve) for all flowering types. These findings provide insights into the genetic mechanisms underlying flowering time variation in O. fragrans, offer candidate genes and haplotypes for molecular breeding, and highlight the value of integrating SVs with machine learning for genomic prediction in woody ornamentals.

Machine Learning↗

PubMed2026

Charting host structural variations in cervical cancer by long-read sequencing pinpoints a functional deletion in PIAS1.

Host structural variations (SVs) are critical in cancer development but their landscape and interaction with HPV integration in cervical carcinogenesis remain unclear. In this study, we performed Nanopore long-read sequencing on five HPV-positive cervical cancer tissues and two cell lines to profile host SVs. We identified thousands of SVs and statistically demonstrated their significant enrichment in genomic windows ±25 to ±50 kb from HPV integration sites. Cross-sample analysis revealed 60 shared SVs, including a recurrent deletion within the PIAS1 gene. Multi-omics integration (Hi-C, H3K27ac ChIP-seq, and TCGA data) showed that this deletion is associated with reduced PIAS1 expression, disruption of local topologically associating domains, advanced pathological tumor stage, and poorer overall survival. Functional assays confirmed that PIAS1 deficiency inhibits cervical cancer cell proliferation and migration. Our findings identify a PIAS1 deletion as a candidate driver event, and underscore the pivotal role of host genomic instability in HPV-associated oncogenesis.

Cervical cancer↗

PubMed2025

Sawfish: improving long-read structural variant discovery and genotyping with local haplotype modeling.

MOTIVATION: Structural variants (SVs) play an important role in evolutionary and functional genomics but are challenging to characterize. High-accuracy, long-read sequencing can substantially improve SV characterization when coupled with effective calling methods. While state-of-the-art long-read SV callers are highly accurate, further improvements are achievable by systematically modeling local haplotypes during SV discovery and genotyping. RESULTS: We describe sawfish, an SV caller for mapped high-quality long reads incorporating systematic SV haplotype modeling to improve accuracy and resolution. Assessment against the draft Genome in a Bottle (GIAB) SV benchmark from the T2T-HG002-Q100 diploid assembly shows that sawfish has the highest accuracy among state-of-the-art long-read SV callers across every tested SV size group. Additionally, sawfish maintains the highest accuracy at every tested depth level from 10- to 32-fold coverage, such that other callers required at least 30-fold coverage to match sawfish accuracy at 15-fold coverage. Sawfish also shows the highest accuracy in the GIAB challenging medically relevant genes benchmark, demonstrating improvements in both comprehensive and medically relevant contexts.When joint-genotyping seven samples from CEPH-1463, sawfish has over 9000 more pedigree-concordant calls than other state-of-the-art SV callers, with the highest proportion of concordant SVs (81%). Sawfish's quality model enables selection for an even higher proportion of concordant SVs (88%), while still calling nearly 5000 more pedigree-concordant SVs than other callers. These results demonstrate that sawfish improves on the state-of-the-art for long-read SV calling accuracy across both individual and joint-sample analyses. AVAILABILITY AND IMPLEMENTATION: Sawfish source code, pre-compiled Linux binaries, and documentation are released on GitHub: https://github.com/PacificBiosciences/sawfish.

Haplotypes↗

PubMed2025

insilicoSV: a flexible grammar-based framework for structural variant simulation and placement.

SUMMARY: Structural variants (SVs) are key drivers of genetic variation and disease in the genome. Their discovery remains challenging, however, in large part due to the scarcity of validated SV callsets and comprehensive benchmarks, which are essential for method development and evaluation. The growing number of data-driven learning-based approaches for SV discovery, in particular, requires large, diverse, and well-balanced training datasets to achieve reliable performance. To address this need, SV simulation has served as a key tool for assessing method performance and training SV models. However, existing SV simulators only support a fixed and limited set of SV classes and do not provide fine-grained control over the placement of SVs within specific contexts of the genome. Here we present insilicoSV, a versatile framework for SV simulation, which models SVs using a simple and flexible grammar, allowing users to easily define standard and custom arbitrary genome rearrangements, as well as encode genome placement constraints. This design allows insilicoSV to naturally support new and bespoke SV types, such as the complex rearrangements of cancer genomes. In addition to grammar-based modeling, insilicoSV provides built-in support for 26 predefined SV types, placement of user-provided SVs, small variant simulation, streamlined workflows for the simulation of genome evolution and genome mixtures, read simulation, alignment, and visualization. These features enable the creation of comprehensive genomic datasets for a variety of downstream applications, such as in-depth benchmarking of alignment and variant calling methods, as well as training of data-driven learning-based approaches for SV detection. AVAILABILITY AND IMPLEMENTATION: insilicoSV is available under the MIT license at https://github.com/PopicLab/insilicoSV and https://doi.org/10.5281/zenodo.17402009.

Software↗

PubMed2026

needLR: long-read structural variant annotation with population-scale frequency estimation.

SUMMARY: We present needLR, a structural variant (SV) annotation tool that can be used for filtering and prioritization of candidate pathogenic SVs from long-read sequencing data using population allele frequencies, annotations for genomic context, and gene-phenotype associations. When using population data from 500 presumably healthy individuals to evaluate nine test cases with known pathogenic SVs, needLR assigned allele frequencies to over 97.5% of all detected SVs and reduced the average number of novel genic SVs to 121 per case while retaining all known pathogenic variants. AVAILABILITY AND IMPLEMENTATION: needLR is implemented in bash with dependencies including Truvari v4.2.2, BEDTools v2.31.1, and BCFtools v1.19. Source code, documentation, and pre-computed population allele frequency data are freely available at https://github.com/jgust1/needLR under an MIT license and archived on Zenodo at https://zenodo.org/records/19463479.

Software↗

PubMed2025

Pan-Genome Analysis Reveals Local Adaptation to Climate Driven by Introgression in Oak Species.

The genetic base of local adaptation has been extensively studied in natural populations. However, a comprehensive genome-wide perspective on the contribution of structural variants (SVs) and adaptive introgression to local adaptation remains limited. In this study, we performed de novo assembly and annotation of 22 representative accessions of Quercus variabilis, identifying a total of 543,372 SVs. These SVs play crucial roles in shaping genomic structure and influencing gene expression. By analyzing range-wide genomic data, we identified both SNPs and SVs associated with local adaptation in Q. variabilis and Quercus acutissima. Notably, SV-outliers exhibit selection signals that did not overlap with SNP-outliers, indicating that SNP-based analyses may not detect the same candidate genes associated with SV-outliers. Remarkably, 29%-37% of candidate SNPs were located in a 250 kb region on chromosome 9, referred to as Chr9-ERF. This region contains 8 duplicated ethylene-responsive factor (ERF) genes, which may have contributed to local adaptation of Q. variabilis and Q. acutissima. We also found that a considerable number of candidate SNPs were shared between Q. variabilis and Q. acutissima in the Chr9-ERF region, suggesting a pattern of repeated selection. We further demonstrated that advantageous variants in this region were introgressed from western populations of Q. acutissima into Q. variabilis, providing compelling evidence that introgression facilitates local adaptation. This study offers a valuable genomic resource for future studies on oak species and highlights the importance of pan-genome analysis in understating mechanism driving adaptation and evolution.

Quercus↗

PubMed2026

Long-read based detection of large copy number variants with potential functional significance using the ContextSV structural variant caller.

Long-read sequencing enables improved detection of structural variants (SVs) in the human genome due to its substantially increased read lengths. However, currently widely used long-read SV callers primarily rely on alignment-based evidence, limiting their ability to detect large and complex SVs and potentially missing disease-relevant events. To address these limitations, we developed ContextSV, a framework that integrates alignment evidence with copy number predictions derived from sequencing coverage and single-nucleotide variant allele frequencies to improve SV detection, particularly for large copy number variants (CNVs). We additionally developed ContextScore, a machine learning-based classification model to assign SV confidence scores based on genomic context features and integrated it within ContextSV. Through benchmarking analyses on both simulated and real datasets, we demonstrate that ContextSV improves detection of large CNVs and inversions that may be missed by existing long-read SV callers. We further illustrate its utility by identifying and experimentally validating multiple large SVs in the KOLF2.1J reference stem cell line that were not detected by other methods. Collectively, our results demonstrate that ContextSV serves as a valuable complement to existing long-read SV detection approaches by improving sensitivity for large and clinically relevant SVs.

Humans↗