Genome-wide association studies (GWAS) in crop genetics commonly use single nucleotide polymorphisms (SNPs) to connect genomic variation with quantitative traits. SNP-based approaches remain highly useful, but they do not always capture the full spectrum of inherited variation. Structural variants (SVs), including deletions, insertions, duplications, inversions, copy number variants (CNVs), and some transposable element (TE) insertions, can affect gene content, dosage, regulatory regions, and chromosome structure. When a functional structural allele is absent from the reference genome or only weakly tagged by nearby SNPs, a SNP-only association analysis may fail to test that allele directly. SV-GWAS extends association testing to structural-variant genotypes, helping researchers evaluate whether structural variation contributes information beyond conventional SNP markers. Importantly, structural variation is one possible contributor to unexplained genetic variation; association signals still require careful statistical interpretation and independent validation before causality can be assigned.

Key takeaways

• Structural variants can represent biologically important alleles that are incompletely captured by SNP-only marker sets, particularly when local linkage disequilibrium (LD) is weak or the sequence is absent from a single reference genome.
• SVs can influence phenotype through gene presence-absence variation, gene dosage, breakpoint disruption, regulatory changes, or altered chromosome organization, but many SVs are neutral or only weakly associated with traits.
• Graph pangenomes and multi-assembly references can reduce reference bias and improve population-scale SV representation, but SV-GWAS can also be performed using validated SV calls generated against a linear reference.
• Directly testing structural alleles can narrow candidate intervals and highlight breakpoint-level candidates, although an association signal alone does not establish a causal variant.
• Conditional association, LD analysis, functional annotation, independent populations, and targeted validation can help determine whether an associated SV provides evidence beyond nearby linked SNPs.

SNP-GWAS versus SV-GWAS: a structural comparison

Why can a conventional SNP-GWAS miss a relevant genetic signal? Crop genomes are often repetitive, structurally diverse, and shaped by domestication, introgression, polyploidy, or large haplotype differences among breeding lines. Short-read mapping to one linear reference can characterize millions of SNPs and small indels, but complex insertions, highly divergent haplotypes, copy-number states, and non-reference sequence may be represented incompletely. In those situations, an SV genotype matrix can provide an additional layer of variation for association testing.

For foundational guidance on interpreting quantitative association signals and the limitations of standard GWAS, consult how to interpret GWAS data in agriculture. Researchers planning a structural-variant association project can also review our structural variant association for agricultural trait discovery service.

When should you consider SV-GWAS?

SV-GWAS is most useful when the biological question, available reference resources, and population design suggest that structural alleles may not be represented adequately by the current SNP marker set. It should complement rather than automatically replace SNP-GWAS.

• Substantial unexplained genetic variation: A trait shows meaningful heritable variation, yet SNP-based association signals explain only a limited fraction of the observed genetic differences. Structural variation is one possible source worth evaluating alongside rare SNPs, allelic heterogeneity, epistasis, genotype-by-environment effects, and phenotype quality.
• Known presence-absence or copy-number biology: Candidate genes or pathways are expected to vary in copy number, gene content, tandem duplication, or genomic presence across accessions.
• Domestication or improvement loci: Some crop traits have been linked to TE insertions, gene deletions, copy-number changes, promoter rearrangements, or other structural changes that may be poorly represented by nearby SNPs.
• Large assembly differences among cultivars: Comparative assemblies or pangenome analyses reveal non-reference sequence, inversions, or large haplotype differences in regions relevant to the target trait.
• Broad or difficult association intervals: SNP-GWAS identifies a long LD block or a region with suppressed recombination, and structural variation is a plausible explanation for the unresolved local architecture.

For an overview of how mapping approaches differ across agricultural projects, review crop trait mapping methods, or see the role of GWAS in agriculture.

Major structural variant classes and their biological mechanisms

Structural variants are commonly grouped by molecular configuration. Their phenotypic effects are highly context dependent: some alter genes or regulatory sequences, while others are neutral, rare, or simply linked to a functional allele. Variant size alone does not determine biological importance.

1. Deletions and presence-absence variations (PAVs)

Deletions can remove part of a regulatory element, one gene, or a larger genomic segment. Gene-level presence-absence variation can change pathway capacity or remove a susceptibility, resistance, metabolic, or developmental gene. However, a deletion observed near a trait locus should not be assumed to be functional without evidence from segregation, expression, independent populations, or targeted validation.

2. Transposable element insertions and novel sequence insertions

TE insertions and other novel sequence insertions can alter coding sequence, promoter architecture, chromatin state, or transcriptional regulation. Their effects vary with insertion position, orientation, local sequence context, and the regulatory state of the affected locus. Some crop domestication and adaptation studies have identified functionally important insertions, but most insertions in a population are not expected to have large phenotypic effects.

3. Copy number variations and tandem duplications

CNVs and tandem duplications alter the number of copies of a genomic segment. When the affected region contains an expressed gene, dosage changes may influence transcript or protein abundance, but the relationship is not always linear because dosage compensation and regulatory feedback can occur. Copy-number states also require careful genotyping because multi-allelic CNVs cannot always be represented accurately as simple diploid presence-absence calls.

4. Chromosomal inversions and translocations

Large inversions and translocations can change local recombination patterns and maintain extended haplotypes. In inversion heterozygotes, recombination within the inverted interval can generate unbalanced recombinant products, reducing effective recombination across that region. Some inversions therefore preserve multi-locus haplotypes and can contribute to supergene-like architectures, but this is not a universal property of all inversions. Breakpoint-resolved genotyping can help determine whether a rearrangement co-segregates with the target phenotype.

Graph pangenomes as one route to population-scale SV genotyping

Graph pangenomes are increasingly useful when a single linear reference does not represent the structural diversity of a crop population. They are not mandatory for every SV-GWAS project, but they can reduce reference bias and provide a consistent representation of alternative alleles discovered across multiple assemblies. A 2022 cucumber study constructed a graph-based pangenome from 12 chromosome-scale assemblies, identified 56,214 structural variants, and genotyped variation in a 115-line core collection to support trait-association analyses. More recent crop studies have extended this concept to larger assembly and population panels.

A three-stage pangenome-to-SV-GWAS workflow

• Stage 1: Representative SV discovery: Select accessions that capture major population groups, breeding founders, or divergent haplotypes. Long-read sequencing and high-quality assemblies can resolve insertions, inversions, CNVs, and other complex SVs that are difficult to discover from short reads alone.
• Stage 2: Variant integration and representation: Integrate validated SVs into a multi-assembly or graph representation. Tools such as Minigraph-Cactus can be used for graph construction, while the final representation should preserve stable coordinates, allele definitions, and links to gene annotations.
• Stage 3: Cohort-scale genotyping: Genotype the SV catalog across the association cohort using a method appropriate to the variant classes and available data. Depending on the project, this may involve graph-aware short-read genotyping, conventional SV callers, long-read genotyping, or targeted assays.

The number of representative assemblies and the sequencing depth required at each stage should be selected according to population diversity, genome complexity, ploidy, existing reference resources, expected SV classes, and the downstream genotyping method. There is no universal founder number or coverage threshold for all crop species.

Long-read callers such as Sniffles2 provide scalable frameworks for breakpoint-resolved and population-level SV calling. Smolka et al. (2024) demonstrated the method across long-read datasets and population-scale analyses, although crop-specific performance should still be evaluated against genome complexity, repeat content, ploidy, and sequencing design. For broader computational context, review pan-genome sequencing technologies and assembly tools and pan-genome research in agriculture case studies.

Crop evidence for structural-variant association

Recent crop studies demonstrate why structural variants can add information beyond conventional SNP datasets. Li et al. (2022) built a graph-based cucumber pangenome and identified SVs associated with agronomic traits including fruit wartiness, flowering time, and root-related traits. In soybean, Yano et al. (2025) constructed multiple long-read genome references and used gene-level SV analysis across 462 worldwide cultivars, linking structural variation to soybean population differentiation and candidate seed-trait loci.

The evidence has expanded further in 2026. Guan et al. analyzed 125 cultivated and wild cucumber assemblies, cataloged 135,597 SVs, and integrated structural variants into GWAS for 38 agronomic traits, identifying 172 QTLs and a rare long terminal repeat insertion associated with fruit length. Li et al. analyzed resequencing data from 984 soybean accessions, identified 602,281 SVs plus additional graph-derived PAVs, and performed GWAS across 27 traits. These studies show how population-scale SV catalogs can expose candidate alleles that may be missing or incompletely tagged in SNP-only analyses. They do not, however, imply that every significant SV is causal or that SV-based models will outperform SNP-based models for every trait.

Statistical workflows and candidate variant prioritization

Once structural variants are genotyped across an association panel, the statistical workflow should emphasize genotype quality, population structure, appropriate encoding, and independent interpretation of local LD.

1. Quality control and allele frequency filtering

SV datasets can contain biallelic presence-absence states, multi-allelic variants, breakpoint uncertainty, and copy-number values such as 0, 1, 2, 3+. Filtering should consider genotype confidence, missingness, Hardy-Weinberg expectations where appropriate, call consistency, allele frequency, and validation evidence. Numerical filters such as missingness or minor allele frequency thresholds are practical starting points rather than universal pass/fail rules. Rare functional SVs may fall below conventional MAF cutoffs, so filtering should be aligned with cohort size, statistical power, variant uncertainty, and the biological objective.

2. Association models and population structure control

Crop diversity panels frequently contain population stratification, family structure, and relatedness. Association frameworks may incorporate principal components, kinship or genomic relationship matrices, fixed covariates, and method-specific population-structure controls. Mixed linear models, FarmCPU, BLINK, and other GWAS frameworks can be adapted to SV genotypes, but model choice should match the cohort structure, trait architecture, variant encoding, and computational scale rather than being selected solely because it is commonly used for SNPs.

3. Conditional association and LD disentanglement

When a significant SV is located near one or more associated SNPs, conditional analyses can help determine whether the structural allele adds information beyond local SNP LD. For example, the candidate SV can be fitted as a covariate and the surrounding association pattern re-evaluated; the reciprocal analysis can condition on the top SNP. If conditioning on the SV substantially reduces nearby SNP signals while conditioning on the top SNP leaves residual SV association, the SV becomes a stronger candidate for the underlying functional allele. Functional validation is still required before assigning causality.

4. Functional and breeding-oriented prioritization

High-priority SVs should be evaluated using multiple evidence layers. Useful criteria include overlap with coding sequence or promoters, effects on gene dosage, expression or regulatory evidence, consistency across independent populations, segregation with the target phenotype, and technical confirmation of the genotype. Breakpoint-resolved alleles can sometimes be converted into targeted genotyping assays, but assay development is more challenging for repetitive insertions, multi-allelic CNVs, and complex rearrangements.

When SV-GWAS may not add much beyond SNP-GWAS

Adding structural variation is not automatically the best next step for every association project. If the relevant SV is in strong LD with an existing SNP marker, SNP-GWAS may already capture most of the useful association information for that locus. Similarly, a small cohort can have inadequate power for rare SV alleles even when the variants are biologically interesting. Poor phenotype quality, strong environmental confounding, or unresolved population structure can also dominate the error budget; adding more variant classes will not correct those study-design limitations.

SV-GWAS may also provide limited value when structural genotypes are uncertain. Complex repeats, low-confidence breakpoints, inconsistent copy-number calls, and incomplete representation of population diversity can introduce false associations. Before expanding a SNP study to SV-GWAS, researchers should ask whether the current SNP analysis is limited by variant representation, whether a reliable SV catalog exists or can be generated, whether the cohort contains enough carriers of the structural alleles of interest, and whether there is a realistic path to orthogonal or functional validation. In some projects, improving phenotype replication, increasing cohort size, or refining population-structure correction will produce more value than immediately adding structural variants.

SV-GWAS project readiness checklist

• Population Power: The association cohort has sufficient sample size, phenotype quality, and allele representation for the expected SV frequency spectrum and target effect sizes.
• Representative SV Discovery: Existing assemblies, long-read samples, or validated SV catalogs capture the major genetic groups and breeding lineages relevant to the cohort.
• Genotype Reliability: Structural-variant genotypes are evaluated using confidence metrics, replicate concordance, or orthogonal validation for critical candidate loci.
• Variant Representation: Multi-allelic SVs, PAVs, CNVs, and complex rearrangements are encoded in a form compatible with the intended association model.
• Population Structure Control: Kinship, ancestry, family structure, and other major confounders are characterized before association testing.
• Multiple-Testing Strategy: FDR, Bonferroni, permutation, or other appropriate significance controls are planned according to marker count and study design.
• Functional Validation Path: The project has a defined strategy for validating high-priority SVs using targeted genotyping, expression evidence, independent populations, segregation analysis, or functional experiments.

To establish long-read datasets or build a crop pangenome, explore our long-read sequencing and plant pan-genome sequencing services, or review pan-genome analysis services, GWAS services, and structural variant association for agricultural trait discovery.

How CD Genomics can help

Transitioning from a SNP-only association study to structural-variant analysis may require additional decisions about SV discovery, reference representation, population-scale genotyping, association modeling, and candidate validation. CD Genomics supports agricultural research teams with long-read sequencing, genome assembly, pangenome analysis, population-scale variant genotyping, and integrated SNP/SV association workflows. Project design can be adapted to the available crop reference resources, population structure, ploidy, sequencing data, and downstream breeding objective rather than forcing every project into a single pangenome workflow. To review our analytical capabilities, visit structural variant association for agricultural trait discovery, agricultural long-read sequencing data analysis, or agricultural genomic data analysis. All laboratory and analytical services described herein are provided strictly for Research Use Only (RUO) in agricultural, plant, and animal genetics research and are not intended for clinical or diagnostic use.

Frequently asked questions (FAQ)

References

1. Smolka, Moritz, et al. "Detection of Mosaic and Population-Level Structural Variants with Sniffles2." Nature Biotechnology, vol. 42, no. 10, 2024, pp. 1571–1580.
2. Li, Hongbo, Shenhao Wang, Sen Chai, et al. "Graph-Based Pan-Genome Reveals Structural and Sequence Variations Related to Agronomic Traits and Domestication in Cucumber." Nature Communications, vol. 13, 2022, Article 682.
3. Yano, Ryoichi, Feng Li, Susumu Hiraga, et al. "The Genomic Landscape of Gene-Level Structural Variations in Japanese and Global Soybean Glycine max Cultivars." Nature Genetics, vol. 57, 2025, pp. 973–985.
4. Guan, Jiantao, Xiangsheng Li, Han Miao, et al. "Pangenome-Resolved Structural Variation Drives Adaptation and Trait Evolution in Cucumber." Nature Genetics, vol. 58, 2026, pp. 2040–2052.
5. Li, Jie, Wenhao Yue, Guangqi He, et al. "Analysis of Deep-Resequencing Data of 984 Soybean Accessions Reveals Structural Variations Underlying Agronomic Traits." Nature Communications, 2026.