The immune system’s power comes from its genetic diversity, allowing myriad threats to be neutralized through first recognizing foreign antigens. That diversity is also what makes the immune system so difficult to study. Recent advances in sequencing technology and computational biology, however, are giving researchers new tools to understand immune responses and immune-related diseases in greater detail.
This convergence of genetics, immunology, and computation has become a discipline of its own called immunogenomics. With this exploration of the immune system, researchers can start to determine how an individual might respond to specific treatments and dangers such as pathogens or cancer cells. “Large-scale genetic studies have identified many disease-associated variants in immune-related regions, while immune-repertoire sequencing has enabled researchers to examine the diversity and clonal expansion of T- and B-cell receptors,” says Shi-qi An, Director of Science Communications at Novogene Europe.
Genetic variation of the immune system
There are multiple genetic aspects of the immune system that researchers can interrogate to gain greater insights into health and disease.
There are several gene families central to the immune system’s ability to protect the body from pathogens and other foreign substances, including human leukocyte antigen (HLA), killer immunoglobulin-like receptor (KIR), immunoglobulin (IG), and T-cell receptor (TCR) families.1 Of these, HLA is the most polymorphic and gene-dense region of the human genome.2 Exploring variation in these gene regions and beyond can illuminate underlying genetic contributors to disease, provide an understanding of cellular dynamics during infection or other disease processes, and assess self versus non-self recognition.
T-cell and B-cell receptors, meanwhile, bind foreign antigens and differ from one lymphocyte to the next. That diversity arises through V(D)J recombination, which reshuffles variable (V), diversity (D), and joining (J) gene segments to allow interaction with a vast range of antigens.3 Receptor repertoire sequencing can help researchers identify and track unique variants associated with various diseases, potentially leading to the discovery of useful predictive, diagnostic, or monitoring biomarkers.
Sequencing as the key immunogenomics tool
Advances in whole-genome sequencing availability have driven much of recent progress in immunogenomics. Scientists can use either short-read or long-read approaches, depending on which aspect of immune variation they are studying. “Short-read sequencing remains well suited to high-throughput studies, large cohorts, quantitative gene-expression analysis, and deep measurement of immune-repertoire diversity. It is supported by mature workflows and can provide a cost-effective option when many samples or large numbers of cells must be analyzed,” says An.
When full sequence content is important, researchers may prefer long-read sequencing to obtain reads that span full genes or receptor sequences and illuminate structural variants and differing isoforms. However, short- and long-read sequencing can be complementary. “Short reads may provide scalable depth and quantification, while long reads resolve regions or molecular structures that are difficult to reconstruct unambiguously from fragmented sequences alone,” adds An.
With either sequencing method, interrogating samples at the single-cell level is bringing novel insights to the immunogenomics field. Single-cell sequencing connects an individual immune cell’s identity, its transcriptional or functional state, and its paired receptor sequence. “Single-cell sequencing is extremely powerful, as it allows us to see the exact immune repertoire expressed by each immune cell in the sample,” says Andrea O’Hara, Senior Product Manager of Multiomics and Synthesis Solutions at Genewiz. “Because each cell is barcoded individually, we can see the exact TCR or BCR sequence of every cell, while utilizing short-read sequencing for the read out. If we opt for targeted sequencing approaches, long-read sequencing is ideal so we can read the full end-to-end V(D)J sequence and chain pairing among the cell population,” she adds.
The resolution gained from single-cell sequencing is especially valuable for detecting rare populations, reconstructing developmental relationships, and understanding heterogeneous responses to infection, vaccination, or immunotherapy, according to An.
Immunogenomics applications
Immunogenomics shapes research and practice across precision medicine, cancer immunotherapy, vaccine development, infectious disease, transplantation, and autoimmune disease. “Immunogenomics allows us to unlock information regarding how our immune system functions when we are healthy, when we are sick, and how we may respond to different treatments, which can ultimately impact our individual standard of care and our widespread use of personalized medicine,” says O’Hara.
In infectious disease and vaccine research, scientists use immunogenomics to examine the immune response following vaccination or pathogen exposure.4 For example, single-cell V(D)J sequencing to analyze T-cell repertoires of patients with COVID-19 highlighted decreased T-cell receptor clone diversity in cases of disease. The researchers found certain VJ pairs that were increased or decreased in patients compared to healthy controls, leading to a greater understanding of the immune response against SARS-CoV-2.5
Autoimmune disease results from a breakdown of immune tolerance, which creates self-reactive T and B cells. Immunogenomic approaches can link immune cell populations to disease-associated variants and help identify abnormal clones, which helps researchers pinpoint what is going awry in the immune response. These diseases are often complex, and single-cell sequencing makes it possible to identify paired TCR and BCR chains alongside a cell’s gene expression profile.6
In transplantation, HLA typing and immune repertoire monitoring help assess donor-recipient compatibility, track immune reconstitution, and monitor for signs of rejection. Genotyping key NK receptors from donor tissue can also shed light on relapse risk after transplantation, as was shown in a recent long-read KIR genotyping study that linked donor KIR and HLA polymorphisms to post-transplant relapse in T-cell malignancies.7
A standout application of immunogenomics is within the field of oncology. “Immunogenomics can be used to learn more about the cancer itself by capturing the complex heterogeneity of the tumor microenvironment and identification of non-responsive cancer cells for additional targeting,” notes O’Hara. “It can also be used to assess predictive biomarkers to guide potential treatments or even as a means of identification and creation of new immunotherapies to target tumor-specific mutations to activate an immune response for personalized medicine.” For example, because higher tumor mutational burden (TMB) has been associated with improved response to immune checkpoint blockade in some settings, TMB has become one of several biomarkers used to guide treatment decisions.8
Future perspectives
With advancing technologies, researchers will continue to gain novel insights through immunogenomics. Already, the addition of spatial information is complementing single-cell data, allowing scientists to analyze immune cells within tissues.9 In the future, combining robust sequencing data with protein measurements, longitudinal sampling, and more will broaden the scientific community’s appreciation of the immune response during health, disease, and treatment.
The future of immunogenomics
“As we continue to learn more about the interplay of specific biomarkers, we will continue to develop more advanced and target drugs. Personalized medicine is already driving a number of therapeutics in oncology today but I think we will see a continued shift toward targeted therapies as we streamline the downstream targeted therapy development.”–Andrea O’Hara, Senior Product Manager of Multiomics and Synthesis Solutions at Genewiz
“Over the next five to ten years, immunogenomics is likely to move beyond describing immune-cell populations and receptor repertoires toward connecting clonotype, antigen specificity, functional state, tissue location and clinical outcome … Lower sequencing costs, greater multiplexing, and laboratory automation should also make larger, more diverse cohorts more practical. AI and machine learning will support multimodal data integration, cell-state classification, repertoire pattern discovery, and biomarker development, although strong study design and biological validation will remain essential.” –Shi-qi An, Director of Science Communications at Novogene Europe
References
1. Wang S, et al. A Scalable Framework for Comprehensive Typing of Polymorphic Immune Genes from Long‐Read Data. Advanced Science. 2026;13(22):e21531.
2. Robinson J, et al. The IPD and IMGT/HLA database: allele variant databases. Nucleic Acids Research. 2014;43(D1):423-D431.
3. Hou D, et al. High-Throughput Sequencing-Based Immune Repertoire Study during Infectious Disease. Frontiers in Immunology. 2016;7:336.
4. He J, et al. Research progress on application of single-cell TCR/BCR sequencing technology to the tumor immune microenvironment, autoimmune diseases, and infectious diseases. Frontiers in Immunology. 2022;13:969808.
5. Wang P, Jin X, Zhou W, et al. Comprehensive analysis of TCR repertoire in COVID-19 using single cell sequencing. Genomics. 2020;113(2):456-462.
6. Wang S, et al. Decoding autoimmune disease with single-cell immune repertoire and transcriptome sequencing: mechanisms and therapeutic opportunities. Frontiers in Immunology. 2026;17:1827384.
7. Morita M, et al. Long-read KIR genotyping reveals donor KIR/HLA polymorphisms linked to posttransplant relapse in T-cell malignancies. Blood Advances. 2026;10(13):4563-4572.
8. Chan TA, et al. Development of tumor mutation burden as an immunotherapy biomarker: utility for the oncology clinic. Annals of Oncology. 2018;30(1):44-56.
9. Ståhl PL, et al. Visualization and analysis of gene expression in tissue sections by spatial transcriptomics. Science. 2016;353(6294):78-82.
About the author: Niki Spahich, Ph.D., earned her Ph.D. in genetics and genomics from Duke University, where she studied Haemophilus influenzae membrane proteins that contribute to respiratory infections. She later explored Staphylococcus aureus metabolism during her postdoctoral fellowship in the Department of Microbiology and Immunology at the University of North Carolina - Chapel Hill. In 2016, Niki co-founded Science Riot, a nonprofit dedicated to providing entertaining and engaging science outreach events, and in 2020, she launched The Scientist Speaks, a popular podcast series for researchers at the bench. Niki has led custom content creation for marketers seeking to reach life scientists since 2019.