Genetic loci in nasal polyposis and asthma map immune tissues

Upper and lower airway diseases occupy shared immunologic terrain, yet the causal threads connecting chronic rhinosinusitis with nasal polyps and asthma have been difficult to disentangle. Large-scale human genetics can clarify that link by measuring how common variants aggregate risk within biological systems. In this meta-analytic framework, a comprehensive genome-wide association study coupled to pathway and tissue enrichment identifies 131 risk loci and concentrates heritability within immune and epithelial contexts. By integrating gene expression, chromatin accessibility, and cell-type signatures, the analysis refines candidate effector genes and indicates that both mucosal barrier and inflammatory pathways are central to disease.

Cohorts, methods, and genetic architecture

The analytic strategy follows a now-standard but powerful arc for complex disease genetics. First, it aggregates case-control and quantitative phenotypes from multiple cohorts to increase power and ensure robust signal discovery. Rigorous quality control harmonizes variant representation, imputation reference panels, ancestry assignment, and phenotype definitions. Meta-analysis across contributing datasets boosts effective sample size and moderates cohort-specific idiosyncrasies, while sensitivity analyses gauge heterogeneity of effect estimates across ancestry strata and ascertainment sources.

Signals are discovered via genome-wide association testing with control for population structure and cryptic relatedness, typically using linear or logistic mixed models. Clumping and conditional analyses then separate independent signals within loci. Fine-mapping narrows credible sets, aiming to identify a small subset of variants that explain observed associations given local linkage disequilibrium. Although credible sets rarely pinpoint single causal variants, they prioritize specific nucleotides and regulatory elements for functional validation.

Downstream, the framework maps variants to genes using multiple lines of evidence: distance to transcription start sites, colocalization with expression quantitative trait loci in disease-relevant tissues, chromatin interaction data, and gene-based association tests. This multi-evidence approach mitigates the limitations of any single mapping rule and helps to separate coincident from causal gene assignments. Notably, because nasal polyps and asthma involve epithelial, stromal, and immune compartments, tissue and cell-type specificity is crucial when aligning regulatory variants with candidate effector genes.

At the architecture level, several features are typical and consistent with airway inflammation. First, polygenicity is evident: many loci each contribute small effects, collectively explaining a modest fraction of variance. Second, enrichment of association signals in immune and epithelial annotations reflects the biology of mucosal surfaces, where barrier integrity, pathogen sensing, and cytokine signaling intersect. Third, partial sharing of signals across upper and lower airway phenotypes suggests a common inflammatory backbone, with additional tissue-context modifiers shaping clinical presentation.

Heritability partitioning tools such as stratified LD score regression quantify how much of the trait variance is captured by variants annotated to specific chromatin states or expressed in particular tissues. In airway disease, enrichment typically appears in open chromatin of airway epithelium, fibroblasts, and multiple immune lineages, including type 2 cytokine-responsive cells. The present analysis aligns with that expectation, focusing attention on epithelial barrier programs, innate cytokine alarmins, and downstream effector arms that orchestrate eosinophilic inflammation.

Genetic correlation analysis evaluates the shared polygenic basis between nasal polyposis and asthma. A positive correlation indicates that many variants contribute risk to both phenotypes, while locus-by-locus concordance testing can identify shared versus distinct signals. Given clinical co-occurrence and overlapping immunologic features, a positive and significant correlation is biologically plausible. Distinctions likely remain, with some loci exerting larger effects in one phenotype, reflecting tissue-specific regulatory architecture or environmental exposure interactions (for example, differential microbiome niches in nasal versus bronchial mucosa).

Colocalization analysis, when applied to disease signals and eQTLs in nasal, bronchial, and blood tissues, helps infer whether the same causal variant drives both expression and disease association. High posterior probabilities of colocalization in airway epithelium strengthen the case for epithelial effector genes. Conversely, colocalization in immune tissues may implicate cytokine receptors, signaling adaptors, or transcriptional regulators active in lymphoid or myeloid cells. This variant-to-gene mapping is critical for prioritizing perturbation targets in cellular models and for nominating biomarkers.

Finally, pathway analysis using gene-set libraries (GO, Reactome, KEGG) evaluates whether implicated genes cluster within particular biological processes. In the airway context, recurrent themes include epithelial differentiation and repair, tight junction and barrier function, extracellular matrix organization, cytokine signaling cascades, and antigen presentation. Such results provide a mechanistic scaffold that can be tested in vitro and in vivo, bridging statistical association to experimental biology.

Locus discovery, tissues, and pathway interpretation

The headline quantitative result is the discovery of 131 genetic loci associated with nasal polyposis and asthma. This scale of locus discovery offers several advantages. First, it increases coverage across the causal graph, improving the chance that multiple nodes within a single pathway are tagged. Second, it permits cross-phenotype analyses to distinguish shared versus phenotype-specific components. Third, it enables more precise and statistically stable enrichment estimates for tissues and pathways.

Interpreting these loci through tissue- and cell-specific lenses is essential. Expression enrichment of implicated genes in airway epithelium points toward barrier-related mechanisms: differentiation of basal cells, ciliated cell function, mucus production, and junctional integrity. Variants affecting enhancers active in epithelial cells can alter alarmin production and cytokine responsiveness, modulating the initiation of type 2 inflammation. In nasal polyps, chronic epithelial injury and remodeling are hallmarks; genetic predisposition that shifts epithelial set points could predispose to persistent polypoid growth and mucous stasis.

Immune tissue enrichment complements this epithelial narrative. Signals mapping to genes involved in cytokine signaling and leukocyte trafficking suggest modulation of Th2 polarization, eosinophil recruitment, and mast cell activation. In the asthmatic lower airway, these pathways contribute to airway hyperresponsiveness and mucus hypersecretion; in nasal polyposis, they drive tissue edema, polyp formation, and recurrent obstruction. The shared enrichment across immune cell types underscores the systemic nature of mucosal immunity linking sinuses and bronchi.

Partitioned heritability concentrated in enhancers and promoters accessible in airway epithelial cells and in key immune lineages indicates that regulatory variants, rather than coding changes, dominate risk. This is consistent with the broader landscape of complex inflammatory diseases, where noncoding variation tunes gene expression programs in a context-dependent manner. Functional assays that deploy CRISPR perturbations in primary airway epithelial cells, organoids, or air-liquid interface cultures can directly test causal regulatory hypotheses nominated by the fine-mapped variants.

Multi-ancestry meta-analysis, when available, improves fine-mapping resolution by leveraging differences in linkage disequilibrium. It also evaluates transferability of loci and polygenic scores, an important consideration because allele frequencies and LD structure vary across populations. Where effect estimates are consistent across ancestries, confidence increases in the generalizability of mechanistic inferences. Conversely, ancestry-specific effects can illuminate context-dependent regulation or gene-environment interactions, informing diverse population health relevance and avoiding one-size-fits-all translational claims.

The gene prioritization pipeline commonly integrates several criteria: (1) gene proximity, (2) colocalized eQTLs in nasal or bronchial epithelium and immune tissues, (3) chromatin interaction proximity via promoter capture Hi-C in airway cells, (4) single-cell expression specificity, and (5) protein interaction and pathway membership. Genes satisfying multiple independent lines of evidence ascend as high-confidence effectors. These candidates can be triaged for experimental validation and druggability assessment, including ligand-receptor axes and intracellular signaling nodes amenable to small molecules or biologics.

Pathway and ontology analysis often surfaces coherent themes in type 2 airway disease: epithelial alarmins and their receptors, eosinophil chemotaxis, IgE-mediated responses, barrier maintenance, and extracellular matrix dynamics. In nasal polyposis, additional emphasis on tissue remodeling, mesenchymal activation, and fibrin turnover is biologically plausible; in asthma, airway smooth muscle contractility and neural regulation of bronchomotor tone may be reflected in a subset of signals. The balance of shared versus distinct enrichment elements can guide phenotype-specific therapeutic hypotheses while reinforcing the core shared immunologic axis.

Collectively, the findings provide a layered evidence map: from variant-level association to gene-level mapping, to tissue and cell-type specificity, and finally to pathway-level interpretation. This map allows targeted hypothesis generation. For example, if a locus colocalizes with expression of a cytokine receptor in epithelial cells and shows enhancer activity in those cells, perturbing that receptor pathway in epithelial organoids could test the causal chain. If successively validated, such a chain strengthens the rationale for therapeutic modulation.

Translational implications, limitations, and priorities for next steps

From a translational standpoint, an expanded catalog of loci has several immediate utilities. First, it refines polygenic models for risk stratification, particularly when combined with clinical and biomarker data. While polygenic scores alone rarely achieve clinical deployment in complex inflammatory diseases, integration with eosinophil counts, fractional exhaled nitric oxide, or specific IgE profiles could support earlier identification of individuals at risk for severe or polyp-prone trajectories. Second, gene and pathway prioritization helps align therapeutic targets with genetic evidence, a factor associated with higher success rates in drug development.

Tissue prioritization informs experimental model selection. For epithelial-enriched signals, air-liquid interface cultures, primary nasal or bronchial epithelial cells, and epithelial organoids are appropriate systems to assay barrier integrity, ciliary function, and cytokine responses. For immune-enriched signals, co-culture systems and in vivo models that reproduce type 2 inflammation may be more informative. Nasal tissue is relatively accessible, enabling repeated sampling for eQTL mapping, chromatin profiling, and longitudinal transcriptomics to assess gene-environment interactions over time.

Functional annotation gaps remain a central limitation. Most credible sets still contain multiple noncoding variants, and the causal variant and effector gene relationships can be ambiguous. Colocalization depends on the availability and power of eQTL datasets in the correct cell state; many disease-relevant states, such as cytokine-stimulated epithelium or activated eosinophils, are underrepresented. Single-cell eQTL and perturbation QTL studies in stimulated airway cells could materially improve resolution, directly linking regulatory variation to transcriptional responses relevant to disease.

Phenotypic heterogeneity also complicates interpretation. Asthma encompasses multiple endotypes, including type 2-high and type 2-low inflammation, with divergent biology and therapeutic responsiveness. Nasal polyposis severity, recurrence, and response to corticosteroids or biologics vary widely. Harmonized deep phenotyping, including imaging, histopathology, and biomarker panels, would sharpen genotype-phenotype mapping and enable more specific genetic associations with sub-phenotypes, such as eosinophilic polyp burden or corticosteroid refractoriness.

Ancestry representation is another constraint that influences generalizability and fine-mapping resolution. Expanding recruitment and genomic resources for underrepresented populations is both an equity imperative and a scientific necessity. Differences in allele frequency and LD can materially improve causal resolution and reveal context-dependent effects. In parallel, rigorous assessment of transferability of polygenic scores and effect estimates across populations should be standard.

Environmental modifiers intersect with genetic risk in airway disease. Viral infections, allergen exposure, air pollution, microbiome composition, occupational irritants, and smoking history can modulate inflammatory tone and epithelial barrier function. Incorporating environmental and exposome data into interaction analyses, while statistically and logistically challenging, is likely to reveal additional variance and refine the conditions under which particular genetic risks manifest.

From a therapeutic perspective, the enrichment of risk within immune and epithelial pathways prioritizes two complementary strategies. One is modulation of inflammatory effectors and their receptors to dampen pathologic immune responses; the other is reinforcement of epithelial barrier integrity and repair. Genetic support for targets can guide development or repurposing decisions. Furthermore, loci that implicate ligand-receptor pairs present immediate mechanistic hypotheses that are testable in human primary cells and could align with existing biologics.

Methodologically, future work can extend beyond discovery and annotation toward causal inference and perturbation. Mendelian randomization can probe whether intermediate molecular traits (for example, epithelial gene expression or circulating cytokines) lie on causal paths to disease. Transcriptome-wide association studies, integrated with cell-state-specific expression models, can complement colocalization by aggregating variant effects into gene-level predictions. CRISPR tiling of fine-mapped enhancers in airway cells can resolve causal nucleotides and map the regulatory grammar responsive to inflammatory cues.

Clinical application implies building robust pipelines that translate genomic findings to patient stratification. For instance, integrating genetically prioritized pathways with biomarker panels could identify patients more likely to benefit from epithelial-targeted interventions versus immune-modulatory biologics. Nasal tissue, as a proxy for lower airway biology, may offer a less invasive window into disease state and treatment response, enabling repeated sampling matched to genetic risk to monitor pathway activity over time.

It is important to avoid overinterpretation. Statistical association does not guarantee mechanistic causation, and even colocalization can be confounded by unmodeled regulatory complexity. Tissue enrichment is informative at the group level but does not preclude roles for other tissues or cell states. Replication across independent cohorts and orthogonal functional validation remain the gold standards before asserting causal mechanisms or therapeutic implications.

In summary, the identification of 131 loci consolidates a polygenic architecture shared between nasal polyposis and asthma and localizes risk to immune and epithelial compartments. This multi-layered annotation framework provides a disciplined path from variant to pathway to tissue, enabling focused experimental work and more rational target selection. Expanding functional datasets in disease-relevant cell states, improving ancestry diversity, and deepening clinical phenotyping are the next steps to translate these genetic insights into clinical utility.