Autoimmune Inflammatory Risk and Your Genetics
Written by Scott Peeples, BS Biomedical Sciences · ExomeDNA Founder Reviewed by ExomeDNA Editorial Process Last reviewed: 2026-05-29
For informational purposes only. Consult a healthcare provider for clinical guidance.
Genomic science has revealed a striking pattern: certain genetic regions that influence susceptibility to severe COVID-19 also overlap with regions linked to systemic lupus erythematosus (SLE), a chronic autoimmune condition. This convergence, identified through a multi-trait analysis approach called MTAG (Multi-Trait Analysis of GWAS), points to shared biological pathways governing how the immune system balances protective inflammation against damaging self-directed responses. Understanding these overlapping genetic signals offers a window into individual variation in immune regulation — not as a certainty, but as one piece of a complex biological picture. Your ExomeDNA Autoimmune Inflammatory Risk result draws on this research to highlight genetic factors that may shape how your immune system navigates these challenges. The result does not indicate whether either condition is present or likely to develop. It reflects population-level patterns in genetic data, contextualized to your specific variant profile.
What is autoimmune inflammatory risk?
Autoimmune inflammatory risk, as used here, refers to the spectrum of genetic variation that population studies associate with altered responses to inflammatory triggers — including those that arise when the immune system encounters a severe viral infection or begins to misrecognize the body's own tissues. Systemic lupus erythematosus is a condition in which the immune system attacks healthy tissue, producing widespread inflammation affecting the skin, joints, kidneys, and other organs. Severe COVID-19, at its most serious, involves a dysregulated immune cascade — sometimes called a cytokine storm — where inflammatory signaling amplifies beyond what is needed to clear the virus.
At first glance, these two conditions seem unrelated. One is a chronic autoimmune disease; the other is an acute infectious complication. But both involve failures of immune regulation, and genomic research has shown they share overlapping genetic architecture. The MTAG approach used in the study underlying this trait (PMID 36762574) was specifically designed to identify these shared signals, boosting statistical power by analyzing both phenotypes simultaneously.
For anyone exploring this result, it is worth holding two ideas at once: genetics can meaningfully shape how the immune system is wired, and yet environment, prior infections, medications, and many other factors determine how that wiring actually expresses itself across a lifetime.
The genetics behind autoimmune inflammatory risk
Several genomic regions stand out from the GWAS data underpinning this trait. The top-ranked gene by Open Targets Locus-to-Gene (L2G) scoring is CCR1 (L2G = 0.84), followed closely by CCR3 (L2G = 0.58) and FUT2 (L2G = 0.58). These scores reflect the computational confidence that a given gene, rather than a neighboring gene, is the functional target of the associated genetic variants.
CCR1 and CCR3 encode chemokine receptors — proteins embedded in the surface of immune cells that detect and respond to chemical distress signals called chemokines. Chemokines are the molecular messaging system immune cells use to recruit reinforcements, direct cell movement, and coordinate inflammatory responses. When a severe infection occurs, chemokine receptors like CCR1 and CCR3 help orchestrate which immune cells move where and how vigorously. Variants in these genes can shift the sensitivity and magnitude of this response.
FUT2 encodes an enzyme called fucosyltransferase 2, which adds a specific sugar modification to cell-surface molecules and secreted proteins. FUT2 is best known for its role in susceptibility to norovirus infection, but research has increasingly linked it to gut microbiome composition, mucosal immunity, and systemic inflammatory tone. The convergence of FUT2 in a COVID-19/lupus MTAG is biologically coherent: mucosal immune calibration may influence systemic inflammatory thresholds.
Additional ranked genes include SPPL2C and MAMSTR (both on chromosome 19), as well as MAPT, STH, and KANSL1 from a chromosome 17 locus. The HLA region genes HLA-DQA1 and HLA-DRB1 appear in the gene sample — the HLA system is among the most powerful genetic determinants of autoimmune susceptibility across dozens of conditions.
L2G = 0.84 for CCR1 — The Open Targets Locus-to-Gene score for the top-ranked gene in this trait, reflecting high computational confidence that CCR1 is the functional target of the chromosome 3 association signal.[1]
What the research says
The foundational study for this trait (PMID 36762574) applied MTAG methodology to genome-wide association data from both severe COVID-19 cases and systemic lupus erythematosus cohorts. MTAG is a statistical framework that simultaneously analyzes correlated traits to increase power for detecting shared genetic signals — signals that individual trait GWASs might miss at standard significance thresholds.
This approach identified genomic loci with consistent effects across both phenotypes, suggesting that the same underlying biology influences susceptibility to both conditions. The authors prioritized immune-regulatory genes, particularly those involved in innate immune signaling, cytokine production, and antigen presentation.
For additional scientific context on how ExomeDNA evaluates and applies GWAS findings, see our methodology page.
It is important to interpret these findings carefully. GWAS identifies statistical associations between genetic variants and outcomes measured across large populations. The variants themselves are often common, and most people carrying them will not develop either severe COVID-19 or lupus. The associations reflect modest shifts in population-level odds, not individual destiny. The confidence tier for this trait is moderate, reflecting the biological plausibility of the findings and the MTAG methodology's ability to amplify real signals — while acknowledging that a single contributing study limits the overall evidence weight.
4 credible sets analyzed — ExomeDNA's gene prioritization for this trait drew on 4 GWAS credible sets, all with L2G gene assignments, using Open Targets L2G v25 methodology.[2]
How autoimmune inflammatory risk affects you
For anyone thinking about what this result means day-to-day, the most honest framing is one of biological tendency rather than biological fate. Immune systems exist on a spectrum. Some are wired toward stronger, faster responses — effective at clearing pathogens, but also more prone to inflammatory overshoot. Others are calibrated toward more measured responses — less likely to overreact, but potentially slower to mobilize.
The genes in this trait — particularly the chemokine receptor cluster on chromosome 3 — are plausibly involved in setting that calibration. People with higher genetic loading in this direction may, at a population level, experience more robust inflammatory responses to certain triggers. This can be protective in some contexts (rapid pathogen clearance) and potentially costly in others (inflammatory complications during severe infections, or elevated risk of immune self-targeting over time).
This does not translate into a concrete action item for most people. The ExomeDNA result is informational: it contextualizes population-level research within your individual genetic profile. It is not a clinical tool, it does not replace assessment by a qualified healthcare provider, and it does not account for the many non-genetic factors — lifestyle, environment, prior medical history, medications — that strongly shape immune outcomes.
For people with a personal or family history of autoimmune conditions, or those who have experienced severe inflammatory events, this result may be worth discussing with a rheumatologist or immunologist as one data point among many.
Working with your immune profile
Genetic information about immune function is most valuable when held alongside other sources of health information. A few evidence-informed considerations for anyone interested in immune health:
Sleep and immune regulation. Sleep is one of the most robust modulators of immune function studied in humans. Chronic sleep deprivation elevates inflammatory markers including IL-6 and CRP, and impairs the balance between pro- and anti-inflammatory signaling. For anyone with genetic tendencies toward elevated inflammatory tone, protecting sleep quality is a foundational, modifiable lever.
Diet and mucosal immunity. The FUT2 connection in this trait is a reminder that gut and mucosal health intersect with systemic immune calibration. Diets high in fiber and fermented foods are associated with greater gut microbiome diversity and lower systemic inflammatory markers in population studies. This is not a therapeutic claim — it is general health context.
Physical activity and inflammation. Regular moderate-intensity exercise is consistently associated with lower levels of inflammatory cytokines in observational research. Prolonged or very high-intensity exercise without adequate recovery can temporarily elevate inflammatory markers, so balance matters.
Monitoring and clinical engagement. For people with known autoimmune conditions or immune-related health history, periodic laboratory monitoring (CRP, ESR, complement levels, ANA titers where indicated) provides clinical-grade signal that genetic data alone cannot offer. Consult a healthcare provider to determine what monitoring, if any, is appropriate for your situation.
No supplement, dietary protocol, or behavioral program has been proven to specifically modify the genetic pathways underlying this trait. The above reflects general evidence on immune health, not a treatment plan.
Related traits and genes
The biology surfaced in this trait connects to a broader web of immune and inflammatory phenotypes that ExomeDNA covers. Relevant related areas include:
Rheumatoid arthritis genetic risk shares substantial HLA-region overlap with lupus and involves many of the same antigen-presentation pathways. HLA-DQA1 and HLA-DRB1 — both present in the gene list for this trait — are among the strongest known genetic associations for rheumatoid arthritis.
Inflammatory bowel disease (IBD) risk intersects with FUT2 biology. FUT2 secretor status has been associated with altered microbiome composition and modified IBD susceptibility in independent studies, illustrating how a single gene can participate in multiple immune phenotypes.
COVID-19 severity genetics as a standalone phenotype (separate from the MTAG approach used here) has been studied extensively since 2020. The chromosome 3 chemokine receptor cluster — CCR1, CCR3, and neighboring genes — has appeared in several COVID-19 GWAS, reinforcing the relevance of this locus to acute inflammatory outcomes.
Cytokine regulation traits such as IL-6 levels, TNF-alpha production, and interferon response variability reflect the downstream effectors that genes like CCR1 and CCR3 help regulate. These traits collectively describe how vigorously and how precisely the immune system mounts and resolves inflammatory responses.
The chemokine receptor gene cluster on chromosome 3 is notably gene-dense in the immune space. CCR1 and CCR3 share this region with CCR2, CCR5, and several other chemokine receptors — a genomic neighborhood shaped by evolutionary pressure from millennia of pathogen exposure.
Frequently asked questions
Q: Does this result mean I have lupus or am likely to develop it? A: No. This result reflects population-level genetic associations, not a clinical assessment of your individual health status. Having higher genetic loading in this direction does not indicate lupus, prior severe COVID-19, or that either condition is likely to occur. Many people with similar genetic profiles never develop either condition. A qualified healthcare provider is the appropriate person to evaluate clinical symptoms or health history.
Q: What is MTAG, and why does ExomeDNA use it as a data source? A: MTAG (Multi-Trait Analysis of GWAS) is a statistical method that analyzes multiple genetically correlated traits simultaneously to identify shared genetic signals with higher statistical power than single-trait GWAS. ExomeDNA uses MTAG-derived findings because they identify biologically coherent shared pathways — in this case, the immune regulatory overlap between severe COVID-19 and lupus — with greater confidence than either trait alone would provide.
Q: The gene list mentions HLA genes. What makes the HLA region special? A: The HLA (human leukocyte antigen) region on chromosome 6 encodes proteins that present fragments of pathogens — and sometimes self-proteins — to immune cells. It is the most polymorphic region of the human genome, meaning there is more genetic variation here between individuals than almost anywhere else. This variation underlies much of the individual-to-individual difference in autoimmune susceptibility. HLA-DQA1 and HLA-DRB1, both in the gene list for this trait, are among the best-characterized HLA genes in autoimmune GWAS research.
Q: My result is higher than average. Should I be worried? A: Not based on this result alone. A higher-than-average genetic loading on this trait reflects a statistical pattern in population data, not a personal health forecast. Many factors beyond genetics shape immune outcomes, and the effect sizes associated with common genetic variants in GWAS are typically modest at the individual level. Anyone with existing health concerns should discuss them with a qualified healthcare provider, not act on genetic population data in isolation.
Q: Can I do anything to reduce my autoimmune inflammatory risk? A: Genetics establishes a biological context, not a fixed outcome. Evidence-based lifestyle factors — consistent sleep, a diet rich in fiber and anti-inflammatory foods, regular moderate exercise, avoiding smoking, and managing chronic stress — are broadly associated with more favorable immune and inflammatory markers in population research. None of these have been demonstrated to specifically counter the genetic pathways in this trait, but they represent modifiable factors with consistent evidence across immune and inflammatory health outcomes. For clinical guidance specific to your situation, consult a healthcare provider.
This page is for educational purposes only. ExomeDNA does not provide clinical guidance. For health-related questions, please consult a qualified healthcare provider.
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References
- Mountjoy E, et al. An open approach to systematically prioritize causal variants and genes at all published human GWAS trait-associated loci. Nat Genet. 2021;53(11):1527–1533. PMID 34662886.
- GWAS MTAG study: Severe COVID-19 or systemic lupus erythematosus (MTAG). PMID 36762574.
- Open Targets Platform release 25.12. Locus-to-Gene model v25. Accessed 2026-05-20.