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Unlocking the genetic code: a comprehensive Genome-Wide association study and gene set enrichment analysis of cell-mediated immunity in chickens

BMC Genomics volume 26, Article number: 337 (2025) Cite this article

Abstract

Background

The poultry immune system is essential for protecting against infectious diseases and maintaining health and productivity. Cell-mediated immune responses (CMIs) protect organisms against intracellular pathogens. This study aimed to enrich the findings of genome-wide association studies (GWAS) by including several systematic gene set enrichment analyses (GSEA) related to cell-mediated immune responses in chickens.

Methods

To investigate the function of the cellular immune system, phenotypic data were collected based on the differences in skin thickness before and after impregnation with dinitrochlorobenzene (DNCB) solution. Additionally, 312 hybrid birds of the F2 generation of Arian broiler chickens and Urmia native chickens were genotyped using the Illumina 60k SNP BeadChip. A general linear model (GLM) with an FDR < 5% was used for the association analysis. Functional enrichment analysis of the identified candidate genes was performed using the Enrichr database. A protein‒protein interaction (PPI) network was constructed using the STRING database. In addition, colocalization analysis was applied to identify QTLs related to the immune system.

Results

GWAS revealed 147 SNPs associated with the CMI trait, which were related to 1363 genes. These genes were significantly enriched in eight KEGG pathways, 22 Reactome pathways, and 18 biological processes. PPI network analysis led to the identification of 26 hub genes. The three hub genes PSMA3, PSMC2 and PSMB4 were enriched in almost all pathways related to cellular immunity, including the proteasome, interleukin-1 signaling, and programmed cell death pathways, which makes them important candidates involved in CMI. In addition, the MAP3K8, NLRC5, UBB, CASP6, DAPK2, TNFRSF6B, TNFSF15, and PIK3CD genes were identified as key genes in several functional pathways. A total of 10 SNPs were found in interferon-gamma QTLs, and two SNPs were found in the cell-mediated immune response QTL region, leading to the identification of 12 cellular immune response-related genes that were reported as important candidates in previous studies.

Conclusion

The post-GWAS analysis in this study led to the identification of key genes that regulate the biological processes of cellular immunity in chickens. Therefore, selecting birds that excel in expressing such genes can improve immunity in chickens.

Peer Review reports

Introduction

Poultry farming is a major source of protein for humans, and it has emerged as a significant industry worldwide [1]. The success of poultry farming depends on various factors, including genetics, nutrition, management, and disease control. The immune system plays a crucial role in maintaining the health and productivity of poultry [2].

Every year, animal diseases cause significant economic losses to the livestock and poultry industry and involve various costs, such as veterinary costs, drugs, vaccinations, and biosecurity measures. Previous studies have provided insights into the overall cost of disease outbreaks. The total economic loss due to chicken coccidiosis, including the cost of the vaccine, treatment, and other prevention measures, was calculated at £99.2 million in 2016 [3]. Therefore, it is essential to amplify the immune system to reduce the economic costs imposed on production units in the poultry industry [4].

The immune system of poultry comprises both innate and adaptive components, which work together to protect birds from infection and disease. The innate immune system provides the first line of defense against pathogens, while the adaptive immune system develops a specific response to a particular pathogen. Adaptive immunity involves both humoral and cell-mediated immune (CMI) responses [5]. The CMI response is effective against intracellular antigens, such as exogenous antigens or proteins produced inside the cell (viral proteins and proteins derived from the neoplastic transformation of cells). This process is carried out through the activation of macrophages and natural killer (NK) cells, the production of antigen-specific cytotoxic lymphocytes, and the release of cytotoxins [6].

Genome-wide association studies (GWASs) are used to identify genetic factors affecting polygenic traits. Previous studies have identified chromosomal regions linked to cellular immune response phenotypes. Thompson-Crispi et al. [7] performed a GWAS to determine differences in genetic profiles among Holstein cattle classified as high or low for CMI and found 21 SNP markers. In one study, a GWAS of the effects of CMI on leishmaniosis in dogs was carried out. As a result, 110,165 SNPs were scanned to identify chromosomal regions associated with the Leishmania skin test, lymphocyte replication measurement, and cytokine responses [8]. Raeesi et al. [9] identified six genomic areas linked to CMI in chicks through GWAS analysis. However, the cellular immune system in poultry is not yet fully understood, and it is possible that other important genes and biological mechanisms may affect CMI function but were not accounted for in their study due to GWAS limitations.

Using pathway or gene set analysis for single nucleotide polymorphism (SNP) data is a valid approach for addressing these limitations for several reasons [10]. Gene set enrichment analysis (GSEA) may be useful for phenotypes where known genetic variants explain a small percentage of the phenotypic variance, as the individual effect of associated SNPs is small, and grouping of small-effect SNPs can contribute to new findings [11]. On the other hand, significant SNPs may be located in genomic regions without a unified biological context [12]. Therefore, the functions of many SNPs may not be well known [13]. Instead of performing analysis for single SNPs or individual genes, GSEA tests the association of phenotype with genetic variants in a group of functionally related genes, such as genes belonging to a biological pathway [10]. Moreover, recent developments in animal breeding that focus on gene editing approaches require some sort of filtration in the causal genes to precisely knock on the most important ones. Therefore, gene set enrichment analysis on top of GWAS findings may be a step forward in this filtration direction.

In the present study, we used GSEA to identify genes whose effect alone was not significant on the desired trait but whose cumulative effect affected the trait. Furthermore, new potential pathways that are effectively related to CMI can be identified through gene-gene interactions in gene sets, which GWAS analysis cannot detect. Furthermore, by screening genes using gene ontology methods and protein-protein network analysis, we sought to identify a set of genes that have the most functional relationships with each other and then with the desired trait. Therefore, this research was conducted to identify SNP markers, candidate genes, biological pathways, and their molecular interactions related to cell-mediated immunity in chickens.

Materials and methods

Ethics statement

All procedures were conducted in accordance with the protocols approved by the Laboratory Animal Care Advisory Committee of Tarbiat Modares University with proposal id: 1,588,878.

Experimental design and data collection

The data used in this study were from the F2 source population of chickens from a cross between the fast-growing Arian male meat line (AA) and the native Iranian Urmia chicken (NN). The Arian broiler line was established in 1990 by importing four different lines, A, B, C, and D, from the Netherlands to Babol Kenar, Babol, Iran [14]. Male lines (B and A) were selected for growth performance, feed conversion ratio, and body composition characteristics. For the female lines (C and D), the selection was based mainly on reproductive traits and relatively on growth traits. All lines were selected for approximately 23 generations. The Urmia chicken is a native Iranian bird with slow growth and high immunity and is scattered in northwestern Iran. These birds are classified as dual-purpose breeds and are used for both egg and meat production. F1 birds were produced by mating AA ♂ NN ♀ and NN ♂ AA ♀ birds. F1 males from each reciprocal cross were mated with 4–8 females from other families. In total, 312 F2 chicks produced from six different hatches were used for experimentation [15].

In this study, DNCB was used to measure the CMI responses of birds based on the protocol described by Verma et al. (2004). DNCB induced contact sensitization is widely considered to be a CMI rather than an antibody in several studies [16, 17]. DNCB modified macromolecules are then internalized by local APCs, such as skin Langerhans cells, dermal dendritic cells and macrophages, processed and presented to T cells for activation [18, 19]. Approximately twenty-four hours after subsequent exposure to DNCB (often referred to as “challenges”), macrophages accumulate at the site of DNCB exposure [20], leading to dermatitis such as ear and skin swelling. T cells and macrophages are the main components of the cellular immune response.

Dinitrochlorobenzene (DNCB) was used to investigate the function of the cellular immune system as follows: At the age of 10 weeks, the thickness of the skin was measured and recorded in three repetitions in the nonfeathered part under the right wing in a cross-sectional area of 7 cm2 using an electronic caliper. Then, the desired skin surface was smeared with 0.1 ml of DNCB solution, and after 24 h, the skin thickness was measured again in three repetitions. The average increase in skin thickness in each bird was obtained from the difference in thickness before and after the challenge, and based on this, cellular immunity was calculated [9].

Genotyping and quality control

DNA was extracted from chicken blood samples using an optimized salting-out method and was genotyped with an Illumina 60k SNP chip commercial panel. For each sample, 55,329 SNP markers were genotyped [15]. In the first step, SNP IDs and genotype map positions were updated according to the GRCg6a chicken reference genome assembly by PLINK v1.9 [21]. Quality control was applied by excluding samples with more than 10% missing data per marker (geno) or individual (mind), a minor allele frequency lower than 5%, and a Hardy–Weinberg equilibrium lower than 1 × 10− 6.

Genome-wide association analysis

The generalized linear model (GLM) was used to conduct a GWAS analysis for traits related to cell-mediated immunity.

The fitted equation was as follows:

$$\eqalign{{y_{ijml}} = & {b_0} + {b_1}se{x_i} + {b_2}hatc{h_j} \cr & + {b_3}SN{P_m} + {e_{ijml}} \cr} $$

where yijml is the vector of CMI phenotypes; b0 is the overall mean; sexi, hatchj, and SNPm are the effects of sex, hatch, and the additive effect of SNP, respectively; b1, b2, b3 are regression coefficients for related effects; and e is the residual effect. In this model, sex and hatch were included as fixed effects.

In this study, an F2 design was used to minimize population stratification, which allows each individual to have the same probability of being assigned to each parental genotype compared to every other individual, so individuals are essentially randomized to genotypes, and we have the equivalent of a true experiment with randomization. Since it is unclear which systematic effect (e.g., population stratification, sex, batch effects, etc.) the pc component represents, we did not incorporate it as a population structure correction in our analysis [22].

For each SNP, an adjusted \(\:P\)-value was computed using the false discovery rate (FDR) procedure as \(\:{P}_{FDR}=(i/m)\times\:Q\), where, \(\:i\) is the rank of \(\:P\)-value, \(\:m\) is the number of total SNPs and \(\:Q\) is the false discovery rate [23]. Based on this procedure, an adjusted threshold value of \(\:{P}_{FDR}=2.76\times\:{10}^{-3}\:\left(\right(2369/42794)\times\:0.05)\), with \(\:-\text{log}\left({P}_{FDR}\right)=2.55\) for genome-wide levels of significance was applied to declare significant association between CMI and marker.

Manhattan and quantile‒quantile (Q‒Q) plots were generated using the ‘qqman’ package of R software version 4.3.2 [24].

Annotation of candidates

Genes within 500 kb upstream and downstream of the significant SNPs were considered candidate genes according to the GALLO R package [25] and based on the GRCg6a assembly supported by Ensembl (http://asia.ensembl.org/Gallus_gallus/Info/Index).

Functional enrichment and gene network analysis

The Enrichr tool was used to provide comprehensive information about the biological functions and pathways enriched in the candidate genes [26]. Biological process (BP), Kyoto Encyclopedia of Gene Pathways and Genomes (KEGG), and Reactome pathway with FDR < 0.05 were considered to indicate statistical significance.

In addition, protein‒protein interaction analysis was performed using the Search Tool for the Retrieval of Interacting Genes/Proteins (STRING) (V11.5) database [27] for candidate genes. The minimum required interaction score was determined to be 0.4 (a commonly used threshold). The constructed network was clustered with a k-means algorithm to define functional modules. Cytoscape software (version 3.9.1) was used to visualize the integrated networks [28]. Hub gene detection in the network was performed based on the method proposed by Bakhtiarizadeh et al. [29]. Briefly, 12 centrality methods were applied, including maximum click centrality (MCC), the density of maximum neighborhood component (DMNC), maximum neighborhood component (MNC), degree method, edge percolated component (EPC), bottleneck, ecCentricity, closeness, radiality, betweenness, stress, and clustering coefficient analysis, using the CytoHubba plugin in Cytoscape [30]. The top 50 important genes in the PPI network were identified using each of the above methodologies. A consensus rank of important genes was created based on the Monte Carlo cross-entropy algorithm and the genetic algorithm using the RankAggreg R package (version 0.6.5) [31]. Finally, overlapping genes between these two methods were defined as important hub genes. Then, the identified hub genes were subjected to functional analysis using the Enrichr tool. To explore similarly connected gene clusters (modules) in the network, module analysis was performed using k-means = 3 in the STRING database. Then, functional enrichment analysis of the identified modules was performed using the Enrichr database. These modules were considered candidate functional modules if their corresponding genes were significantly enriched in GO, Reactome or KEGG pathways related to CMI. Cytoscape software (version 3.9.1) was used to visualize the PPI network.

Colocalization analysis

The current version of the chicken QTLdb contains 18,414 QTLs representing 372 different traits. To investigate whether the identified SNP sets were significantly enriched in QTLs relevant to the immune system, colocalization analysis was performed. To do this, one hundred and seventy-six QTLs associated with cellular immunity-related traits, such as cell-mediated immune response, immunoglobulin Y level, antibody response to SRBC antigen, and interferon-gamma level, were obtained from the Animal QTL database [32]. Colocalization between the candidate genes and the genomic regions containing QTLs related to the immune system was assessed using a permutation test according to the Regione R package. In this approach, the mean distance of the genes from the nearest QTLs was compared to the distribution of distances of random samples in the whole genome. A total of 200 random samples were generated by shuffling the genomic regions. The distribution of means was used to calculate the P values.

Results

Descriptive statistics

According to the quality control (QC) criteria, 182 variants were removed due to missing genotype data, 2524 variants due to the Hardy‒Weinberg exact test, and 8522 variants due to minor allele threshold(s). Therefore, 42,794 variants and 312 samples remained for further analysis. The data structure and descriptive statistics are summarized in Table 1.

Table 1 Descriptive summary of phenotypic data
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To identify significant loci associated with cellular immune traits, we performed a GWAS with 42,794 SNPs that met our filtering criteria. SNPs with an FDR < 0.05 were considered significant SNPs and were located on chromosomes 1–12, 15, 17, 19, 20, 21, 24, 25, and 27. The QQ and Manhattan plots are shown in Fig. 1.

Pathway analysis

A total of 147 SNPs related to the CMI trait were identified (FDR < 0.05), which overlapped with 1,363 genes. To better understand the functional roles of these genes, functional annotation analysis was performed, which led to the identification of 28 KEGG terms, 536 GO (BP) terms, and 219 Reactome pathways (P value < 0.05). Of these, eight KEGG, 22 Reactome pathway, and 18 biological process terms were found to be directly associated with immune traits at a P value < 0.05 (Fig. 2).

Protein‒protein interaction (PPI) network analysis

A PPI network including 301 (genes) nodes and 699 (interaction) edges and a P value of 4.49e-07 was constructed. Furthermore, the cluster analysis revealed a network with a clustering coefficient of 0.176 and a degree of 0.726 (Fig. 3). This strategy led to three clusters (referred to as clusters A, B, and C) with maximum and minimum sizes of 150 and 63 genes, respectively (Fig. 3). Functional enrichment analysis was performed for the genes in each cluster. Twelve significant (FDR < 0.05) KEGG pathways were identified in cluster A, nine of which were related to the cellular immune response. Thirty of the 204 significant pathways (FDR < 0.05) in the Reactome analysis were related to the cellular immune response. Furthermore, 237 significant biological processes (FDR < 0.05) were found, 25 of which were directly connected to the cellular immune response. Cluster A had 150 members and was enriched in genes involved in regulating apoptosis and the positive regulation of macrophage migration (Additional File 1, Fig. S1). Gene enrichment analysis revealed 20 KEGG pathways, 64 Reactome pathways, and 165 biological process terms (FDR < 0.05) in cluster B. Among them, five KEGG pathways, nine Reactome pathways, and 15 biological process terms were associated with the cellular immune system (FDR < 0.05). Cluster B consisted of 63 genes that were found to be associated with processes such as cytokine signaling in the immune system, antigen processing, and presentation via MHC class I (Additional File 1, Fig. S2). In cluster C, 30 KEGG pathways, 52 Reactome pathways, and 220 biological process terms (FDR < 0.05) were found. Overall, 14 KEGG pathways, 14 Reactome pathways, and 37 biological process terms were associated with the cellular immune response. Cluster C, with 70 members, was enriched in biological processes related to the positive regulation of cytokine production and the immune system (Additional File 1, Fig. S3).

Hub gene identification

Hub genes were identified by 12 topological analysis methods, in which the top 50 genes were selected for each method (Additional File 1, Table S1). Overall, 26 genes that overlapped between the two methods of the Monte Carlo mutual entropy algorithm and genetic algorithm were considered important hub genes (Table 2). Functional enrichment analysis was performed using the hub genes. In this regard, 12 KEGG pathways, 172 Reactome pathways, and 37 BP pathways were significant (FDR < 0.05), and two KEGG pathways, 48 Reactome pathways, and five BP pathways were linked with CMI including Cellular Responses to Stimuli, ER‒Phagosome Pathway, NIK to Noncanonical NF-kB Signaling, Regulation Of Apoptosis, Negative Regulation Of NOTCH4 Signaling, ABC Transporter Disorders, TCR Signaling, Interleukin-1 Family Signaling, Class I MHC Mediated Antigen Processing And Presentation etc. (Fig. 4). Among the identified hub genes, the hub genes PSMC2, PSMA3, SEC61A2, and PSMB4 were significantly enriched in most of the mentioned pathways.

Table 2 List of hub genes identified using 12 CytoHubba methods
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Colocalization analysis

An investigation was conducted using the colocalization method to determine whether the identified SNPs were significantly colocalized with QTLs associated with the immune system. The results showed that 42 out of 147 SNPs were located in QTL regions that were linked to the immune system, indicating a significant association (P value = 0.005 and z_ score = -5.48). Next, 42 genes associated with SNPs located in the QTL regions were extracted (Fig. 5). The results of the functional analysis of the genes detected in the colocalization analysis revealed 14 KEGG pathways, 57 Reactome pathways, and 188 significant biological process terms, of which 7 KEGG pathways, 10 Reactome pathways, and 20 biological process terms were directly related to CMI. The identified pathways included the Wnt signaling pathway, the Notch signaling pathway, Th1 and Th2 cell differentiation, interleukin-3, interleukin-5, GM-CSF signaling, and macroautophagy (Additional File 1, Fig. S4).

Discussion

The present study identified 26 hub genes related to the cellular immune response. most of the identified genes were directly or indirectly involved in pathways related to cellular immune responses.

The gene MAP3K8 was identified in all analytical methods used in this study. Therefore, it can be concluded that this gene likely plays a key role in cellular immune responses. It involves in neutrophil activation, leukocyte migration, and adhesion in glioma and positively correlated with immune system infiltration [33]. MAPK cascades are very important intracellular signaling pathways for the development and maintenance of the immune system. In the early stages of infection, due to the stimulation of TLRs, they are responsible for the production of cytokines such as TNF-α, IL-1, or IFN-γ and participate in the pathways induced by these cytokines. In addition, in the differentiation and maturation of T cells in the thymus, the activation of T cells and B cells and the direction of the immune response via the Th-1 or Th-2 pathway are essential [34]. This pathway is activated in macrophages infected with the avian influenza virus, which regulates proinflammatory cytokines and inhibits apoptosis [35]. Additionally, a microRNA that modulates the immune response in chicks with necrotic enteritis through the MAPK signaling pathway has been identified [36].

Genes PSMA3, PSMB4, PSMC2, SEC61A2 and USP10 stood out because they appeared in at least three of the four gene set enrichment methodologies, practiced in this study. This repeated detection suggests that these genes are critical in how they manage CMI, likely influencing processes such as fighting infections or regulating immunity.

studies showed that PSMA3 is highly expressed in esophageal squamous cell carcinoma (ESCC) tumor tissues and negatively regulates the infiltration of CD8 + cells [37]. PSMB4 plays an antiviral role against porcine reproductive and respiratory syndrome virus (PRRSV) by inducing the degradation of the PRRSV Nsp1α protein and promoting the production of type I interferon [38]. Brehm et al. [39] indicated that mutations in this gene cause defects in the induction of the type I interferon response, strong expression of IFN-induced genes, and an increase in chemokines and cytokines. SEC61A2 gene is crucial for protein translocation into the ER membrane, ribosome binding, and secretory protein translocation [40]. PSMC2 is involved in the modulation of signaling pathways that are critical for T cell activation, such as the MAPK and STAT5 pathways, which are essential for T cell proliferation and cytokine secretion [41]. Furthermore, the balance of Th1 and Th2 cytokines, which are influenced by PSMC2, is important for maintaining appropriate T-cell responses and preventing excessive activation that can lead to autoimmune conditions [41].

Modulation of TCR signaling by USP10 is critical for effective T cell responses and ensures that T cells can proliferate and differentiate appropriately in response to infections [42, 43]. Furthermore, the interaction of USP10 with Id proteins (Id2 and Id3) is essential for the differentiation and persistence of memory T cells and influences their proliferation during secondary immune responses [44].

The first 3 genes above were significantly enriched in several pathways related to the immune responses in chicken. Class I MHC-mediated antigen processing and presentation, antigen processing ubiquitination and proteasome degradation, interleukin-1 signaling, programmed cell death and the TNFR2 noncanonical NF-kB pathway were among pathways that these genes are involved.

They also related to the negative immune response regulation of NOTCH4 signaling and MAPK family signaling cascades. The NOTCH4 signaling pathway is a conserved pathway that regulates cell proliferation, apoptosis, and fate decisions during development and adult tissue homeostasis. Furthermore, it plays a role in the development of the immune system, the activation of T and B cells, the regulatory function of T cells, and the differentiation of helper T cells. Notch signaling is also regulated by TLRs, which enable macrophages and dendritic cells to rapidly respond to pathogen infections [45, 46]. Lu et al. [47] showed that activation of NOTCH4signaling contributes to the expression of inflammatory cytokines in porcine alveolar macrophages during infection with porcine reproductive and respiratory syndrome virus (HP-PRRSV).

Major histocompatibility complex (MHC) class I molecules involved in intracellular events such as viral infection or antigen release [48]. They present peptides to cytotoxic CD8 + T cells, supporting their immune surveillance. Peptides from normal proteins are ignored by CD8 + T cells, while peptides from mutant proteins and non-self-proteins elicit an adaptive immune response. MHC class I molecules also acting as ligands for immunoglobulin receptors on NK cells [49]. They bind cytosolically derived peptides within the endoplasmic reticulum (ER) and present them at the cell surface to cytotoxic T cells [50].

S Lamont [51] reported that MHC class I exerts major control over disease resistance in chickens. MHC class I presents intracellular antigens to cytotoxic T cells, allowing the immune system to detect infections. In addition, research shows that resistance and sensitivity to Salmonella enteritidis (SE) depend on different MHC haplotypes, so haplotype BC is resistant, and B18 and B15 are susceptible to SE disease [52].

Interleukin 1 is a cytokine produced by macrophages during defense reactions that enhances the immune system. It induces fever, promotes lymphocyte function, and increases B and T lymphocyte proliferation. It also stimulates the production of chemokines and inflammatory cytokines [53].

UBB and NLRC5 genes are revealed in 3 enrichment methodologies. Ubiquitin B (UBB) gene encodes ubiquitin, one of the most conserved proteins plays a major role in targeting cellular proteins for degradation by the 26 S proteasome [54]. In addition to regulating ubiquitin enzymes, ubiquitination is a critical immune mechanism that causes dendritic cell maturation and T-cell growth and is essential for NF-kB activation and cell death regulation [55]. NLRC5 is a master regulator of MHC-I, whose expression increases in response to various stimuli, including pathogen-associated molecular patterns (PAMPs) [56]. In chicken macrophages, NLRC5 expression is significantly increased following stimulation with lipopolysaccharides (LPS), suggesting its role in the immune response [57]. This gene and its related genes are involved in the antiviral immune response to avian leukosis virus subtype J infection [58]. In addition, a mutation in its promoter was reported to affect NF-κB signaling and immune responses to Salmonella enteritidis infection in chickens [59].

The proteasome was a significant pathway identified in three enrichment analyses, all genes, cluster 1, and hub genes. Proteasomes are large protein complexes responsible for cellular protein degradation and consist of two subunits: the 20 S catalytic core particle (CP) and the 19 S terminal regulatory particle (RP) [60]. They play a crucial role in protein quality control, transcription, immune responses, cell signaling, and apoptosis. Immunoproteasomes are crucial for priming CD8 T-cell-mediated immune responses and regulating immune cell function [61]. PSMA3 and PSMB4 are the key genes in this pathway.

Programmed cell death was another important pathway identified in three enrichment analyses of all genes, cluster 1, and hub genes. The hub genes PSMB4, PSMA3, and PSMC2 were enriched in this pathway. Moreover, the DAPK2, UBB, and CASP6 genes were identified as effective candidate genes in this pathway. Programmed cell death is a crucial process in the immune system that ends a cell’s vital capacity. It helps maintain tissue homeostasis by killing infected and abnormal cells and removing dead cell fragments. T cells play a significant role in this process; they selectively kill infected or dysfunctional cells [62]. The CASP6 gene identified in this pathway encodes a member of the family of cysteine proteases (caspases), which are the main mediators of apoptosis and inflammation [63]. Furthermore, the DAPK2 gene regulates mTORC1 activity and autophagy, processes that are important in the immune response [64].

Tumor necrosis factor alpha (TNFα) was identified as one of the pathways related to the cellular immune response. The hub genes PSMA3, PSMB4, and PSMC2, as well as the TNFRSF6B and TNFSF15 genes, were found to be effective genes in this pathway. Interestingly, TNF and its syntenic genes are located on chicken chromosome 16 in the major histocompatibility complex (MHC) region. This situation suggests that avian TNF plays a role in the regulation of immune responses [65]. The immune system TNFs bind their physiological receptors, regulate the apoptotic process, and respond to tumor necrosis factors related to cellular immune responses [66]. The TNFRSF6B gene, which encodes tumor necrosis factor receptor-related factor 6 (TRAF6), plays an important role in regulating the immune response. The TRAF6 receptor is involved in dendritic cell maturation, cytokine production, and T-cell stimulation, as well as in the development, function, and homeostasis of various immune cells, including B cells, T cells, macrophages, and osteoclasts [67].

PIK3CD was identified as a candidate gene involved in Fc gamma R-mediated phagocytosis. Fc gamma R-mediated phagocytosis pathway was identified in three enrichment analyses involving all genes and clusters 1 and 2. The Fc gamma receptor is an essential participant in many immune system effector functions, such as opsonized phagocytosis, inflammatory mediator release, and antibody-dependent cellular cytotoxicity. Fc receptor-mediated phagocytosis is pivotal for the clearance of respiratory virus infections, including influenza [68]. In addition, the development of T cells, NK cells, neutrophil chemotaxis, and mast cells is influenced by the PIK3CD gene [69].

The hub genes PSMA3, PSMB4, PSMC2, SEC61A2, RPL12, RPL37A, RPL3 USP10, MCM5, RPL27A, and MRPS2, which are members of Cluster A in the PPI network, were all enriched in the ribosome pathway. Studies have shown that ribosomal immunotherapy stimulates various immune functions, including the production of specific antibodies and the activation of cytokine networks [70]. Interestingly, the 20 S proteasome cleaves translation initiation factors and affects the assembly of ribosomal complexes and the translation of various mRNAs [71].

Conclusion

In our study, the identified candidate SNPs led to the identification of genes that are strongly associated with the cell-mediated immune response. Moreover, we identified 26 hub genes that are enriched in several pathways related to the cellular response, including the proteasome, major histocompatibility complex (MHC) class I, interleukin-1 signaling, Fc gamma R-mediated phagocytosis pathway, and MAPK family signaling cascades. These pathways may be suitable candidates for identifying markers associated with the immune response in poultry. We also identified genes that interacted with other genes in functional modules and played a very significant role in the cellular immune response. A large number of SNPs belonging to different genes/genomic regions located in genetic regions with known functions in the immune response were identified. These SNPs were also mapped to QTL regions associated with cellular immunity. Future studies by integrating RNA-seq data with the SNPs used in this study may help identify genes and SNPs that have a significant impact on the cellular immune response. Additionally, validation of the reported candidate genes seems necessary.

Fig. 1
figure 1

a Quantile-quantile plots of the generalized linear model (GLM) were analyzed for chicken cell-mediated immune response (CMI) traits. The x-axis shows the expected P values under the null hypothesis, and the y-axis shows the observed P values. The value of the genomic control inflation factor λ was 1.30279. b Manhattan plot of the cell-mediated immune response in chickens. The x-axis is the SNP position on the chromosome, and the y-axis is the -log10 P value

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Fig. 2
figure 2

Functional enrichment analysis results for GO-biological process (a), Reactome pathway (b), and KEGG pathway (c) terms. The colors and sizes of the points represent the -log10(P value) and gene count associated with each term, respectively

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Fig. 3
figure 3

Cluster analysis of 302 predicted genes using STRING. The detected clusters are marked in blue (A), pink (B), and green (C). Edges represent the interconnections between nodes. Hub genes in the PPI network are indicated using larger nodes. The size of the nodes indicates the rank of the node

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Fig. 4
figure 4

Functional enrichment analysis results for the hub genes, Gene Ontology (a), Reactome pathway (b), and KEGG pathway (c) terms. The colors and sizes of the points indicate the FDR and the number of genes associated with each expression, respectively

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Fig. 5
figure 5

Circos plot for the distribution of SNPs identified with immunity-related QTLs; the outermost ring shows chromosome numbers. The positions of the identified SNPs in QTL regions related to cellular immunity (blue points) are shown in the inner yellow ring. Additionally, the vertical gray lines in the inner green ring indicate the positions of the QTLs

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Data availability

The datasets analyzed during the current study are available in the [Mendeley] repository, [Ehsani, Alireza (2024), “F2_chicken_Tarbiat_modares_university_Iran”, Mendeley Data, V1, https://doi.org/10.17632/vscgspfpj9.1]

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Acknowledgements

The authors gratefully acknowledge the Tarbiat Modares University, Tehran, Iran for financial supporting (D82/1049), Staff of Arian Line Breeding Center and West Azarbayjan Native Fowls breeding Center for providing parents of F1 chickens. Genotyping of the birds was supported by Aarhus University, Denmark. The authors would like to thank Dr. Just Jensen for financial support through GenSAP grant no 0603-00519B from the Danish Innovation Fund for the bird’s genotyping.

Funding

Tarbiat Modares University, D82/1049.

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Authors and Affiliations

  1. Department of Animal Science, Faculty of Agriculture, Tarbiat Modares University, Tehran, Iran

    Somayeh Kianpoor, Alireza Ehsani, Rasoul Vaez Torshizi & Ali Akbar Masoudi

  2. Department of Animal and Poultry Science, College of Aburaihan, University of Tehran, Tehran, Iran

    Mohammad Reza Bakhtiarizadeh

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Contributions

1. S. K.: Writing-Original Draft, Investigation. 2. A. E.: Project administration, Investigation, Supervision. Reviewing and Editing, Methodologies 3. R. V. T.: Reviewing and Editing, Supervision. 4. A. A. M.: Reviewing and Editing. 5. M. R. B.: Reviewing and Editing, Methodology. All authors commented on previous versions of the manuscript, and read and approved the final manuscript.

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Correspondence to Alireza Ehsani.

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All experimental procedures were approved by the Animal Care Committee of Tarbiat Modares University.

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Kianpoor, S., Ehsani, A., Torshizi, R.V. et al. Unlocking the genetic code: a comprehensive Genome-Wide association study and gene set enrichment analysis of cell-mediated immunity in chickens. BMC Genomics 26, 337 (2025). https://doi.org/10.1186/s12864-025-11538-5

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BMC Genomics

ISSN: 1471-2164

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