With the popularization of genome sequencing technology, the emergence of massive genetic variation data has brought unprecedented opportunities and challenges for precision medicine. Among the numerous variants, only a small number have demonstrated clear clinical or scientific research value. The core proposition of modern medicine is to identify key pathogenic loci from the "data noise" and analyze their molecular mechanisms. Variant analysis transforms raw data into actionable biological knowledge through three major steps: annotation, prioritization, and effect prediction, bridging the gap between basic research and clinical practice. Currently, non-coding region annotation, algorithm overfitting, and data heterogeneity are still technical bottlenecks. Nonetheless, the convergence of deep learning, cloud computing, and other technologies is propelling this field toward real-time intelligence. Meanwhile, multi-omics data integration and causal reasoning based on graph knowledge systems are reshaping the paradigm of variant interpretation to provide dynamic decision support for individualized diagnosis and treatment.
This paper systematically discusses the technical framework of variant analysis, its core tools, and its translational applications in clinical and scientific research. It also looks forward to the future prospects of comprehensive variant knowledge graphs in precision medicine.
The Importance of Variant Analysis
Among the huge amount of variant data generated by genome sequencing, only a few have clinical or scientific research value. Variant analysis transforms raw data into actionable biological knowledge through annotation, sequencing, and effect prediction, becoming the core link between basic research and clinical practice, and the technical cornerstone for individualized diagnosis and treatment in precision medicine.
Dissecting genetic variation: annotation, sequencing, and effect prediction
Variant annotation aims to decode the biological significance of variants, and its core tasks include 1) predicting functional impacts (e.g., disruption of protein structure by missense mutations); 2) integrating population frequency, evolutionary conservatism, and disease-associated data; and 3) accomplishing automated annotation using tools (e.g., ANNOVAR, Ensembl VEP). However, non-coding region annotation is still the main technical bottleneck at present.
Diagram of Ensembl VEP showing the location of each display term relative to the transcript structure (Ref: ENSEMBL database)
Prioritization targets disease-causing candidates through multistage screening: first excluding high-frequency benign variants, followed by focusing on functionally destructive mutations (e.g. nonsense mutations) and incorporating disease-specific criteria (e.g. cancer drug-gene interaction information). This process significantly improves the efficiency of identifying disease-causing variants from "data noise".
Effect prediction relies on algorithmic models to assess molecular consequences, e.g., SIFT/PolyPhen-2 for protein effects and SpliceAI for splicing effects. Despite continuous optimization of the tools, the prediction of gain-of-function mutations and haplotype effects has limitations that directly affect the reliability of subsequent experimental and clinical interpretations.
Annotation technology reshapes clinical and research decisions
In clinical diagnostics, annotation techniques can quickly distinguish benign variants from disease-causing mutations. For example, nonsense mutations in BRCA1/2 are categorized as "possibly pathogenic" according to the ACMG/AMP guidelines, which directly guides breast cancer treatment decisions; the Tier classification system maps variants such as EGFR exon 19 deletions to targeted drugs, shortening the path to diagnosis and treatment.
In scientific research, annotation data drives experimental validation (e.g., CRISPR editing of candidate splice sites) and multi-omics integration (e.g., RNA-seq validation of non-coding variants), accelerating the analysis of pathogenic mechanisms. The introduction of standardized frameworks (e.g., VariO) further enhances data comparability.
Current challenges center on data heterogeneity, algorithm overfitting, and non-coding variant resolution. Deep learning models (e.g., Enformer) and cloud platforms (e.g., Google Genomics) are revolutionizing the annotation process towards real-time and intelligence, providing new paths to break through bottlenecks.
Tools and Databases for Variant Annotation
Variant annotation is the cornerstone of genomic data analysis, aiming to filter out biologically or clinically significant variants from massive sequencing data. This process relies on authoritative databases to provide standardized references and efficient tools to automate the analysis. Currently, mainstream databases (e.g., dbSNP, ClinVar, gnomAD) and tools (e.g., SnpEff, ANNOVAR, Ensembl VEP) have formed a complementary system to support the analysis of the whole chain from basic research to clinical diagnosis.
Functional positioning and synergistic application of core databases
As the world's largest genetic variant archive, dbSNP contains a wide range of variant types including SNPs and indels, and seamlessly connects with NCBI genomic resources to provide frequency data for population genetics research. However, some of its low-frequency variants lack clinical validation and need to be cross-validated with other databases.
ClinVar focuses on the clinical interpretation of variants and integrates multiple sources of evidence (e.g., laboratory reports, ACMG guidelines) through a five-level classification system (pathogenic to benign), which serves as a key reference for the diagnosis of genetic diseases. For example, known pathogenic mutations in BRCA1 can be directly associated with breast cancer risk. However, 83% of clinically important mutations in ClinVar are rare variants, requiring continuous updating of evidence to improve interpretation reliability.
With more than 100,000 genome-wide data, gnomAD provides a population baseline for variant filtering, and its population diversity design (covering African American, East Asian, etc.) effectively excludes high-frequency benign variants. The new version adds structural variant annotation, but underrepresentation of rare ethnic groups (e.g., Pacific Islanders) may affect the accuracy of the analysis.
As a multifunctional annotation tool, Ensembl VEP not only predicts the impact of variants on transcripts, but also integrates ENCODE regulatory regions and ClinVar pathogenicity information to support non-coding variant resolution. With parameter optimization (e.g. --most_severe), high-priority variants can be quickly targeted. Its plug-in system allows users to extend localized databases to adapt to individual analysis needs.
These databases cascade from basic archiving (dbSNP), clinical association (ClinVar), population filtering (gnomAD) to functional prediction (VEP), building a standardized framework for variant annotation.
Technical Characterization and Scenario Adaptation of Functional Annotation Tools
Known for its fast annotation and cross-species support, SnpEff comes with 38,000+ species databases pre-installed and is suitable for non-model organism research (e.g. crop genome analysis). However, its non-coding region annotation relies on third-party plug-ins, limiting the efficiency of parsing complex regulatory variants.
ANNOVAR's advantage lies in its flexible multi-database co-filtering, e.g., combining ClinVar pathogenicity tags with gnomAD frequency thresholds (AF < 0.1%) to accurately screen low-frequency pathogenic loci. However, its non-human genome annotations need to be manually configured, and delays in database updates may affect rare variant analysis.
Ensembl VEP excels in clinical integration, allowing simultaneous assessment of pathogenic mechanisms and therapeutic associations of variants by docking COSMIC and ClinVar. Compared to the first two, VEP is more comprehensive in annotating non-coding regions, but is slightly less computationally fast than Java or C-optimized tools.
Variant Impact Prediction and Disease Association
Pathogenicity assessment of genomic variants is the cornerstone of precision medicine. Through computational modeling to predict the impact of variants on protein function or gene regulation, researchers can filter key pathogenic sites from massive data.CADD and SIFT, as the two core tools, provide important support for rare disease diagnosis and cancer research through genome-wide coverage and coding region analysis, respectively.
Relationship of scaled C scores and categorical variant consequences (Kircher et al., 2014)
Algorithmic principle: multidimensional data-driven functional prediction
CADD integrates multivariate information such as evolutionary conservatism and the degree of functional region destruction, and outputs a C-score (0-100) to quantify the deleteriousness of variants through machine learning. Its advantage is that it is applicable to annotate both coding regions (e.g., BRCA1 missense mutations) and non-coding regions (e.g., promoter variants), and variant anomalies with C-score>20 are prioritized for validation. For example, in rare disease research, CADD rapidly screens for double allele mutations associated with epileptic encephalopathies.
SIFT, on the other hand, focuses on coding region missense mutations, and assesses the probability of amino acid substitutions disrupting protein function by multiple sequence comparisons, with scores <0.05 suggesting high risk of pathogenicity. For example, SIFT in combination with PolyPhen-2 accurately identified mutations in the BEST1 gene that cause macular degeneration. The two are complementary: CADD provides a global view, SIFT focuses on protein function, and emerging tools such as AlphaMissense are further optimizing prediction accuracy through deep learning.
A proposed computational mutation analysis workflow (Pires et al., 2016)
Application Scenario: Accelerating Disease Mechanism Analysis
In rare disease diagnosis, CADD and SIFT have significantly shortened the cycle of identifying the causative loci: CADD has helped to target double allele mutations in CAD-deficient disorders and guide the treatment of ouabain, while SIFT has corrected the false-positive results of deafness genetic screening and improved the diagnostic accuracy to 95%. However, challenges remain in non-coding variant prediction, such as CADD's insufficient specificity for intronic variants, which needs to be validated in combination with CRISPR experiments.
In cancer research, CADD identified efficient inhibitors of KRAS G12V through virtual screening, while SIFT mass spectrometry (SIFT-MS) constructed a non-invasive screening model for lung cancer based on exhaled volatile organic compounds (AUC=0.95). In addition, SIFT combined with fluorescence imaging achieves precise identification of tumor margins during glioma surgery and promotes surgical innovation.
Toward Comprehensive Variant Knowledge Graphs
The core challenge of genomics research is to analyze the association between massive variant data and complex phenotypes. Traditional unidimensional analysis can no longer meet the needs of precision medicine, and the Variant Knowledge Graph provides a systematic framework for the annotation of variant functions and the analysis of disease-causing mechanisms by integrating genomic, epigenetic, expression profiling, and clinical data to build a multimodal association network.
Multi-omics data integration
Pathogenicity assessment of genetic variants needs to cross the divide between genomic, transcriptomic, epigenomic and phenotypic data. For example, variant mapping tools (e.g. Variant-to-Gene-to-Phenotype Contextualizer, VGC) visualize functional associations of non-coding variants with distal regulatory genes by integrating chromatin interactions (Hi-C), single-cell expression profiles, and electronic health records. In a study of congenital heart disease, mutations in the CTCF anchoring region disrupt TBX5 expression, leading to abnormal heart development. Epigenetic data (e.g., DNA methylation), on the other hand, enhance the spatiotemporal specificity of annotations, e.g., certain promoter mutations in cancer activate oncogenic pathways only at specific stages of differentiation.
Design and integration of VGC (Li et al., 2024)
Current technologies address data interoperability challenges through standardized frameworks (e.g., GA4GH), using knowledge graphs to dynamically associate databases such as ClinVar and GWAS. This integration significantly improves the efficiency of clinical decision-making: in the diagnosis of rare diseases, combining patients' RNA-seq data can verify the pathogenicity of splice variants and reduce the proportion of "variant of undetermined significance (VUS)" by 30%. Multi-omics fusion not only fills the blind spot of traditional annotation, but also builds a bridge from lab to clinic.
Graph Structures and Artificial Intelligence
Graph-based knowledge systems abstract genes, variants, and phenotypes as network nodes and support complex reasoning through edge relationships (e.g., "regulation" and "co-expression"). For example, in the analysis of BRCA1 variants in breast cancer, knowledge graphs can automatically correlate homologous recombination repair defects with PARP inhibitor efficacy, guiding the choice of treatment options. Graph neural network (GNN) further breaks through the limitations of traditional models and predicts the pathogenicity of new variants by aggregating the features of neighboring nodes, with an accuracy rate (AUC>0.92) that exceeds that of matrix analysis methods.
Artificial intelligence drives dynamic optimization of the knowledge graph. Deep learning models (e.g. DeepVariant) combine multi-omics data to identify cell type-specific variant effects, e.g. predicting the regulation of neuronal lysosomal function by LRRK2 non-coding variants. Reinforcement learning is then used to automatically correct contradictory evidence in the knowledge base (e.g., conflicting pathogenicity ratings in the literature) to improve system reliability. Cutting-edge causal inference models are used to distinguish mutational correlations (e.g., passenger mutations) from real drivers through counterfactual analysis, moving cancer research from "statistical correlation" to "mechanism analysis".
References
- Kircher, Martin et al. "A general framework for estimating the relative pathogenicity of human genetic variants." Nature genetics vol. 46,3 (2014): 310-5. doi:10.1038/ng.2892
- Li, Jennifer et al. "Variant graph craft (VGC): a comprehensive tool for analyzing genetic variation and identifying disease-causing variants." BMC bioinformatics vol. 25,1 288. 3 Sep. 2024, doi:10.1186/s12859-024-05875-7
- Pires, Douglas E V et al. "In silico functional dissection of saturation mutagenesis: Interpreting the relationship between phenotypes and changes in protein stability, interactions and activity." Scientific reports vol. 6 19848. 22 Jan. 2016, doi:10.1038/srep19848
- Ensembl VEP: https://grch37.ensembl.org/info/docs/tools/vep/index.html
