2615-790XTropical Animal Science JournalTrop. Anim. Sci. J.2615-790X2615-787XFaculty of Animal Science, IPB UniversityIndonesia10.5398/tasj.2027.50.1.21Association of IFN-γ Gene Polymorphisms with Avian Influenza-Related Immune Traits in Indonesian Local ChickensKuswandiW.IndonesiaSumantriC.IndonesiaListyariniK.IndonesiaDarwatiS.IndonesiaMulyonoR. H.IndonesiaMurtiniS.IndonesiaWiryawanProf. Dr. Komang GIndonesiaDepartment of Animal Production and Technology, Faculty of Animal ScienceIPB Universityhttps://ror.org/05smgpd89IndonesiaMedical Microbiology Division, School of Veterinary Medicine, and Biomedical SciencesIPB Universityhttps://ror.org/05smgpd89BogorIndonesiaTropical Animal Science Journal1210202612102026501Tropical Animal Science Journal (Issue in Progress)21322552026Copyright (c) 2026 Tropical Animal Science Journal2026Tropical Animal Science Journalhttp://creativecommons.org/licenses/by-sa/4.0/This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.Authors submitting manuscripts should understand and agree that copyright of manuscripts of the article shall be assigned/transferred to Tropical Animal Science Journal. The statement to release the copyright to Tropical Animal Science Journal is stated in Form A. This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License (CC BY-SA) where Authors and Readers can copy and redistribute the material in any medium or format, as well as remix, transform, and build upon the material for any purpose, but they must give appropriate credit (cite to the article or content), provide a link to the license, and indicate if changes were made. If you remix, transform, or build upon the material, you must distribute your contributions under the same license as the original.Association of IFN-γ Gene Polymorphisms with Avian Influenza-Related Immune Traits in Indonesian Local Chickens
Avian influenza (AI) remains a major threat to poultry production, and genetic improvement for disease resistance has emerged as a promising complementary strategy alongside vaccination and biosecurity measures. Interferon-gamma (IFN-γ) is a key cytokine involved in antiviral immunity; however, information regarding its genetic diversity and association with AI-related immune responses in Indonesian local chickens remains limited. This study aimed to identify single-nucleotide polymorphisms (SNPs) in the chicken IFN-γ gene and evaluate their associations with AI-related immune response traits. A total of 94 chickens, comprising IPB D1 (n = 60) and Merawangarab chickens (n = 34), were used. Blood samples were collected for DNA extraction, hematological analysis, quantification of immunoglobulin Y (IgY), and determination of avian influenza antibody titers. A 410 bp fragment of the IFN-γ gene was amplified and sequenced, revealing two SNPs located in exon 4, namely c.304C>G and c.390G>A. Genotype and allele frequencies were estimated, and association analyses were performed using a General Linear Model (GLM). Significant genotype effects were further compared using Tukey’s multiple comparison test. The C allele of c.304C>G and the G allele of c.390G>A were predominant in both chicken populations. Comparison of genotype distributions between populations revealed a significant difference for SNP c.304C>G (χ² = 8.713, p = 0.013), whereas no significant difference was observed for SNP c.390G>A (χ² = 3.784, p = 0.151). SNP c.304C>G was significantly associated with AI antibody titer, total leukocyte count, and eosinophil percentage, whereas SNP c.390G>A was significantly associated with AI antibody titer, total leukocyte count, heterophil percentage, and heterophil-to-lymphocyte ratio (p<0.05). These findings indicate that IFN-γ gene polymorphisms are associated with variation in AI-related immune responses in Indonesian local chickens and highlight IFN-γ as a candidate gene for marker-assisted selection to improve immune competence and disease resilience in poultry.
Avian influenzaIFN-γ geneimmune-related traitsIndonesian local chickenssingle nucleotide polymorphism (SNP)File created by JATS EditorJATS Editorissue-created-year2027INTRODUCTION
Avian influenza (AI) remains one of the most economically important viral diseases affecting the global poultry industry, particularly highly pathogenic avian influenza (HPAI) H5N1, which poses a major threat to poultry production and public health by causing severe disease and mortality in chickens (Ayuti et al., 2024); (Simancas-Racines et al., 2025). AI infection can cause respiratory distress, cyanosis, hemorrhage, sudden death, and damage to multiple organs (Kim et al., 2025). In Indonesia, the human AI case fatality rate (CFR) has reached approximately 84%, exceeding the regional average of 66.3% (Pawestri et al., 2020), while HPAI H5N1 outbreaks reported in several provinces in 2026 resulted in 14,125 poultry deaths (Ministry of Agriculture of the Republic of Indonesia, 2026). Despite vaccination, biosecurity, and molecular surveillance(Sajjadi et al., 2025), including strengthened vaccination programs, PCR-based detection, and One Health coordination during 2025–2026, AI control remains challenging due to viral mutation and antigenic drift. These challenges highlight the need for complementary and sustainable strategies, including marker-assisted selection (MAS) to identify favorable alleles associated with enhanced immune responsiveness and disease resistance. In this context, the genetic diversity and adaptability of Indonesian chickens provide valuable resources for identifying immune-related genetic markers to support breeding programs aimed at improving disease resistance.
The application of genetic approaches to disease resistance requires chicken populations with diverse genetic backgrounds and well-characterized phenotypic traits. In this regard, IPB D1 and Merawangarab chickens represent relevant Indonesian genetic resources for investigating variation in immune-related traits. IPB D1 is a composite breed derived from F1 PS (Pelung × Sentul) males and F1 KM (Kampung × Cobb) females, comprising approximately 75% indigenous germplasm and 25% commercial broiler ancestry, and was officially recognized under Ministry of Agriculture Decree No. 693/KPTS/PK.230/M/9/2019. IPB D1 chickens reach approximately 1.2–1.3 kg body weight at 12 weeks of age (Prawira et al., 2026) and have demonstrated protective antibody titers against Newcastle disease (≥3.5 log₂ HI) (Putri et al., 2022). Merawangarab chickens, derived from crosses between Merawang and Arabian chickens (Irmaya et al., 2024), attain an average body weight of 1,014.49 ± 35.98 g at 12 weeks of age and 1,154.11 ± 94.42 g at 16 weeks of age, with an average egg weight of 42.68 ± 1.16 g (Depison et al., 2025).
Despite their distinct genetic backgrounds and favorable production characteristics, the genetic basis underlying immune responses to avian influenza (AI) in IPB D1 and Merawangarab chickens remains poorly understood. Interferon-gamma (IFN-γ), primarily produced by T helper 1 (Th1) and natural killer (NK) cells, plays a central role in antiviral immunity by regulating macrophage activation, antigen presentation, and T-cell and NK-cell activity (Frank & Paust, 2020). Experimental evidence supports its antiviral activity against avian influenza viruses. Chicken IFN-γ (chIFN-γ) has been shown to inhibit viral RNA replication and enhance interferon-stimulated genes and type I interferons (Yuk et al., 2016), while exerting stronger inhibitory effects on H9N2 replication than chicken IFN-α and IFN-β (Song et al., 2022). These findings support IFN-γ as a candidate gene for investigating genetic variation associated with antiviral immune responses.
Genetic variation in the chicken IFN-γ gene has been characterized through single-nucleotide polymorphisms (SNPs). According to(Ji et al., 2015), 52 SNPs were identified within a 1,137-bp region spanning the promoter and all four exons, including the −316A/G polymorphism associated with antibody responses following H5N1 vaccination in Red Junglefowl, with the AA genotype exhibiting higher antibody titers than the GG genotype. Several coding-region polymorphisms, including a novel missense mutation, were also identified, although their associations with immune-related traits remain insufficiently characterized. The chicken IFN-γ gene is located on chromosome 1, spans approximately 4,154 bp, and comprises four exons and three introns, with exons 2–4 encoding most of the mature protein (Kaiser et al., 1998). Exon 4 encodes the C-terminal region of mature IFN-γ and contains the translation termination codon, followed by the 3′ untranslated region (3′UTR). Because nucleotide variation within exon 4 may affect protein-coding or regulatory functions, this region represents a biologically relevant target for investigating polymorphisms associated with immune response traits.
However, information on coding-region polymorphisms of the IFN-γ gene and their associations with immune-related traits remains limited, particularly in Indonesian chicken populations. To the best of our knowledge, the association of IFN-γ polymorphisms with avian influenza (AI)-related hematological and immune response traits has not been investigated in IPB D1 and Merawangarab chickens. Therefore, this study aimed to identify single-nucleotide polymorphisms (SNPs) within exon 4 of the IFN-γ gene and evaluate their associations with hematological parameters and AI-related immune response traits. The findings may provide insights into the genetic basis of antiviral immune responsiveness and facilitate the identification of potential molecular markers for marker-assisted selection toward improved disease resistance in Indonesian chicken breeding programs.
MATERIALS AND METHODSEthical Approval
All procedures in this study were conducted in accordance with animal welfare principles and were approved by the Animal Ethics Committee of the School of Veterinary Medicine and Biomedical Sciences, IPB University, under approval number 209/KEH/SKE/IV/2024.
Experimental Animals and Management
A total of 94 chickens, approximately 2 years of age, were included in this study, comprising 60 IPB D1 chickens (15 males and 45 females) and 34 Merawangarab chickens (10 males and 24 females) (Figure 1). All birds were reared under intensive management at the IPB D1 Chicken Breeding Center, IPB University, Indonesia, in well-ventilated 3 × 3 m litter-floor pens at a stocking density of 15 birds per pen (approximately 0.6 m² per bird). Standard management and biosecurity practices, including routine pen sanitation, restricted access to the farm area, and regular health monitoring, were maintained throughout the study. The chickens were fed a commercial layer diet (324 KJ, PT Charoen Pokphand Indonesia Tbk, Indonesia) twice daily, containing 17.5%–18.5% crude protein, ≥3.0% crude fat, ≤7.0% crude fiber, ≤14.0% ash, 3.25%–4.25% calcium, and ≥0.55% phosphorus. None of the chickens had received avian influenza or other vaccinations before sampling, nor were they experimentally challenged with avian influenza virus. Therefore, the antibody titers measured in this study were considered to reflect naturally acquired immune responses under routine farm conditions.
Phenotypic appearance of female IPB D1 (A), male IPB D1 (B), female Merawangarab (C), and male Merawangarab (D)
ImageSample Collection
Blood samples were collected from the brachial vein (wing vein) using sterile 3 mL syringes fitted with 23-gauge needles. Approximately 2 mL of blood was collected from each bird without anesthesia following disinfection of the venipuncture area with 70% alcohol. Blood samples were transferred into two types of tubes: EDTA-containing tubes for hematological analysis and DNA extraction, and non-anticoagulant tubes for serum collection used in Immunoglobulin Y (IgY) analysis and avian influenza antibody titers.
Blood samples collected in EDTA tubes were gently homogenized to ensure thorough mixing with the anticoagulant, then stored at approximately 4°C until further analysis. Hematological analyses were performed according to standard laboratory procedures.
Hematological Analysis
Erythrocyte count. Erythrocyte counts were determined using an Improved Double Neubauer counting chamber (Germany). Blood samples collected in EDTA tubes were diluted with Hayem’s solution (France) and gently homogenized using a figure-eight motion. The diluted samples were subsequently loaded into a hemocytometer and observed under a microscope at 10× magnification, as described by (Pujiastuti et al., 2017).
Hemoglobin and hematocrit measurement. Hemoglobin concentration and hematocrit values were measured using a Mindray BC-2800 Vet Auto Hematology Analyzer (Mindray, China). Blood samples were introduced into the analyzer via the designated nozzle, and the measurement results were automatically recorded.
Erythrocyte indices. Erythrocyte indices were calculated to evaluate red blood cell (RBC) characteristics and to assist in the detection of anemia-related conditions. The calculated indices included mean corpuscular volume (MCV), mean corpuscular hemoglobin (MCH), and mean corpuscular hemoglobin concentration (MCHC), following the method described by (Sarma, 1990) using the following formulas:
Total leukocyte count. Total leukocyte counts were determined using an Improved Neubauer hemocytometer following the method of (Maheshwari et al., 2017). Blood samples were diluted with Rees and Ecker solution, homogenized, and loaded into the counting chamber. Leukocytes were counted manually under a microscope at 400× magnification in four large squares of the chamber. The total leukocyte concentration was calculated using a standard dilution factor.
Differential leukocyte count. Differential leukocyte counts were performed using Giemsa-stained blood smears. Blood smears were fixed with methanol, stained with Giemsa solution, and examined under a microscope at 100× magnification. Leukocyte types, including lymphocytes, monocytes, heterophils, eosinophils, and basophils, were identified and counted using the battlement method. At least 100 leukocytes were counted per slide, and the percentage of each leukocyte type was calculated as described by (Thiam et al., 2021).
Heterophil-to-Lymphocyte (H/L) ratio. The heterophil-to-lymphocyte (H/L) ratio was calculated by comparing the number of heterophils to the number of lymphocytes obtained from the differential leukocyte count. The H/L ratio was calculated using the following formula:
H/L= Heterophils (%) / Limphocyte count (%)
Total serum IgY concentration. Total serum immunoglobulin Y (IgY) concentration was measured using an indirect enzyme-linked immunosorbent assay (ELISA) adapted from (Indriyani et al., 2024). Anti-IgY was diluted in 0.05 M carbonate-bicarbonate buffer (pH 9.6) to a final concentration of 2.5 μg/mL, and 100 μL was added to each well and incubated overnight at 4 °C. The plates were washed four times with PBS containing 0.05% Tween-20 (PBS-T) and blocked with 5% skim milk in PBS at 37 °C for 2 h. Serum samples diluted 1:100 were then added and incubated at 37 °C for 1 h, followed by incubation with 100 μL of HRP-conjugated anti-IgY diluted 1:25,000 at 37 °C for 1 h. After washing, 100 μL of tetramethylbenzidine (TMB) substrate was added, and the reaction was incubated for 20–30 min. Optical density was measured at 650 nm using an ELISA reader, and IgY concentrations were determined using an IgY control standard.
Hemagglutination Inhibition (HI) Assay for Avian Influenza
Avian Influenza (AI) antibody titers were measured using the hemagglutination inhibition (HI) assay, as described by (Novitasari & Anwar, 2020). Briefly, 50 μL of phosphate-buffered saline (PBS) and 50 μL of the serum sample were added to a 96-well microplate, and the sample was serially diluted up to the 11th well. Subsequently, 50 μL of AI antigen containing 4 HAU was added and incubated at room temperature for 30 min, followed by the addition of 50 μL of 0.5%–1% chicken red blood cell (RBC) suspension. Hemagglutination inhibition was observed after incubation at room temperature for 30–45 min, and antibody titers were expressed as log₂ HI values.
DNA Extraction
Genomic DNA was extracted from blood samples using the Promega Wizard® Genomic DNA Purification Kit following the manufacturer’s protocol with minor modifications. Briefly, 50 µL of fresh blood was transferred to a 1.5 mL microtube and mixed with 250 µL of phosphate-buffered saline (PBS). Samples were lysed with cell lysis solution, then with nucleus lysis solution, and finally treated with RNase. Protein precipitation was performed with protein precipitation solution, and DNA was precipitated with isopropanol, washed with 70% ethanol, and air-dried at room temperature. The DNA pellet was rehydrated in DNA rehydration solution, incubated overnight at 4 °C, and stored at −20 °C until further analysis.
Amplification and Sequencing
Amplification of the chicken Interferon-Gamma (IFN-γ) gene was performed using a self-designed primer pair generated with Primer3 (https://primer3.ut.ee/) and evaluated using Primer Stats (https://www.bioinformatics.org/sms2/pcrprimer_stats.html) based on the reference sequence GenBank accession Y07922.1. The primer pair was designed to amplify a 410 bp fragment spanning exon 4 of the IFN-γ gene. The forward primer sequence was 5′-GGCATCTCAAACCAGCAATG-3′, and the reverse primer sequence was 5′-ATACTTCACCACACCACCTC-3′. The PCR reaction was prepared in a total volume of 26 μL, consisting of 2 μL of genomic DNA template, 0.3 μL of forward primer, 0.3 μL of reverse primer, 12.5 μL of Red Master Mix, and 10.9 μL of nuclease-free water. The PCR program consisted of a pre-denaturation step at 95 °C for 1 minute, followed by denaturation at 95 °C for 15 seconds, annealing at 64 °C for 15 seconds, and extension at 72 °C for 10 seconds, for 35 cycles, with a final extension at 72 °C for 5 minutes. The amplification products were visualized on a 1.5% agarose gel by electrophoresis, stained with a DNA stain, and observed under a UV transilluminator. Successfully amplified PCR products were subsequently sequenced using the Sanger sequencing method by Macrogen (Singapore).
Data Analysis
Single-nucleotide polymorphisms (SNPs) were identified from sequencing chromatograms using FinchTV version 1.4.0 (http://www.geospiza.com), with double peaks considered heterozygous. The IFN-γ gene sequences were aligned with the reference sequence (GenBank accession no. Y07922.1) using Molecular Evolutionary Genetics Analysis (MEGA) version X software. Descriptive analyses of immune-related and hematological parameters were conducted to summarize the general characteristics of the study data. Allele and genotype frequencies were calculated using the following formulas, following (Nei & Kumar, 2000):
Image
where Xi represents the allele frequency, Xii represents the genotype frequency, nii is the number of individuals with genotype ii, nij is the number of individuals with genotype ij, and N is the total population size.
Observed heterozygosity (Ho) and expected heterozygosity (He) were calculated using the following formulas according to (Allendorf et al., 2010):
Ho = Nh / N
He = 1 - Σpi2
where Ho is the observed heterozygosity, Nh is the number of heterozygous individuals, He is the expected heterozygosity, and is the frequency of the i-th allele.
Hardy–Weinberg equilibrium was evaluated using the Chi-square (χ2) test according to (Hartl & Clark, 1997):
Image
where χ2 represents the Chi-square value, O is the observed value, and E is the expected value.Associations between each identified SNP in the IFN-γ gene and immune-related traits were analyzed independently using the General Linear Model (GLM) in Minitab 19. The statistical model was adapted from (Amusan et al., 2013), and Tukey’s post hoc test was applied for multiple comparisons among genotype means.
Yijk= μ + Bi + Gj + eijk
where Yij is the observed phenotypic value of the measured trait, μ is the overall mean, Bi is the fixed effect of chicken breed (i= IPB D1 and Merawangarab), Gj is the fixed effect of genotype, and eij is the random residual error assumed to be independent and normally distributed with mean zero and variance σe2.
RESULTSIFN-γ Gene Amplification and SNP Identification
The primer pair successfully amplified the expected 410 bp fragment encompassing exon 4 of the chicken IFN-γ gene. Agarose gel electrophoresis showed a single, distinct PCR product without non-specific amplification (Figure 2), confirming the specificity of the designed primers. Subsequent Sanger sequencing identified two single-nucleotide polymorphisms (SNPs), namely c.304C>G and c.390G>A, within the amplified region of the IFN-γ gene (Figure 3). The c.304C>G polymorphism was identified as a synonymous variant within the coding region, whereas the c.390G>A polymorphism was located in the 3′ untranslated region (3′UTR). The sequence chromatograms exhibited clear homozygous and heterozygous nucleotide peaks, confirming the presence of genetic variation at both loci.
Descriptive Statistics of Avian Influenza-Related Immune Response Traits in Indonesian Local Chickens
The descriptive statistics of hematological and avian influenza (AI)-related immune response traits in IPB D1 and Merawangarab chickens are presented in Table 1. Mean AI antibody titers were 1.37 ± 0.13 log₂ HI in IPB D1 and 1.00 ± 0.09 log₂ HI in Merawangarab chickens, while serum IgY concentrations were 12.71 ± 0.52 and 12.46 ± 0.47 mg/mL, respectively. Erythrocyte counts were 2.84 ± 0.07 × 10⁶ cells/mm³ in IPB D1 and 2.84 ± 0.13 × 10⁶ cells/mm³ in Merawangarab chickens, with corresponding hemoglobin concentrations of 8.79 ± 0.14 and 8.99 ± 0.30 g/dL and hematocrit values of 30.40 ± 0.65% and 28.85 ± 0.98%. The erythrocyte indices, including MCV, MCH, and MCHC, were comparable between the two populations. Total leukocyte counts were 19.56 ± 0.37 × 10³ cells/mm³ in IPB D1 and 17.36 ± 0.42 × 10³ cells/mm³ in Merawangarab chickens. Lymphocytes were the predominant leukocyte population in IPB D1 and Merawangarab chickens (48.13 ± 0.53% and 48.85 ± 0.80%, respectively), followed by heterophils (31.21 ± 0.47% and 29.52 ± 0.66%, respectively). Eosinophils, monocytes, and basophils ranged from 10.87–11.99%, 8.87–9.01%, and 0.77–0.78%, respectively. The H/L ratio was 0.65 ± 0.02 in IPB D1 and 0.61 ± 0.02 in Merawangarab chickens.
PCR amplification of the IFN-γ gene in IPB D1 and Merawangarab chickens showing a 410-bp amplified fragment. Lanes 1–8 represent IPB D1 chickens, lanes 9–16 represent Merawangarab chickens, and M represents the 100-bp DNA ladder.
Image
Chromatogram showing mutations (SNPs) in the chicken IFN-γ gene
Image
Descriptive statistics of hematological and immune response traits in IPB D1 and Merawangarab chickens
Variables
IPB D1 chickens (n=60)
Merawangarab chickens (n=34)
Reference value
Mean ± SE
Mean ± SE
Immune response
Avian influenza antibody titer (log₂ HI)
1.37 ± 0.13
1.00 ± 0.09
Seropositive titer ≥4 log₂
HI (1)
IgY (mg/mL)
12.71 ± 0.52
12.46 ± 0.47
5-15 (2)
Erythrocyte profile
Erythrocytes (×10⁶ cells/mm³)
2.84 ± 0.07
2.84 ± 0.13
2.5-3.9 (³)
Hemoglobin (g/dL)
8.79 ± 0.14
8.99 ± 0.30
10.2-15.1 (³)
Hematocrit (%)
30.40 ± 0.65
28.85 ± 0.98
24-43 ( ³ )
MCV (fL)
107.37 ± 3.30
105.97 ± 4.31
90-140⁽⁴⁾
MCH (pg)
32.16 ± 1.03
31.33 ± 1.49
33-47⁽⁴⁾
MCHC (g/dL)
29.69 ± 0.69
31.62 ± 1.00
26-35⁽⁴⁾
Leukocyte profile
Total leukocytes (×10³ cells/mm³)
19.56 ± 0.37
17.36 ± 0.42
12-30⁽⁶⁾
Differential leukocyte count (%)
Lymphocytes (%)
48.13 ± 0.53
48.85 ± 0.80
45-70⁽⁴⁾
Heterophils (%)
31.21 ± 0.47
29.52 ± 0.66
15-40⁽⁴⁾
Eosinophils (%)
10.87 ± 0.25
11.99 ± 0.37
0-7 (³)
Monocytes (%)
9.01 ± 0.31
8.87 ± 0.34
1-8⁽⁷⁾
Basophils (%)
0.78 ± 0.01
0.77 ± 0.01
0-1 (³)
Heterophil/Lymphocyte ratio
0.65 ± 0.02
0.61 ± 0.02
0.27-1.54⁵
Note: x̄ = mean; SE = standard error; n = sample size; Min = minimum value; Max = maximum value. Reference values: (1)(Animal Health, 2021), (2)(Gaetani et al., 2017), (3)(Samour, 2015), (4)(Jain, 1993), (5)(Talebi et al., 2005), (6)(Weiss & Wardrop, 2006), (7)(Baudouin et al., 2021).
Genetic Diversity of IFN-γ Gene Polymorphisms
Two SNPs were identified within exon 4 of the IFN-γ gene, namely c.304C>G and c.390G>A. The genotype and allele frequencies of both SNPs are presented in Tables 2 and 4, whereas the observed and expected genotype frequencies under Hardy–Weinberg equilibrium are shown in Tables 3 and 5. For SNP c.304C>G, three genotypes (CC, CG, and GG) were detected in both populations. The CC genotype was the predominant genotype in IPB D1 (0.73) and Merawangarab chickens (0.97), whereas the GG genotype occurred at relatively low frequencies in both populations. Consistently, the C allele was the major allele, accounting for 83% and 97% of alleles in IPB D1 and Merawangarab chickens, respectively (Table 2).
Similarly, SNP c.390G>A exhibited three genotypes (GG, AG, and AA). The GG genotype was the most prevalent genotype in both IPB D1 (0.75) and Merawangarab chickens (0.74). The G allele was the predominant allele, with frequencies of 0.84 and 0.78 in IPB D1 and Merawangarab chickens, respectively (Table 4). The observed and expected genotype frequencies under the Hardy–Weinberg equilibrium for both SNPs are presented in Tables 3 and 5. Comparison of genotype distributions between IPB D1 and Merawangarab chickens revealed a significant difference for SNP c.304C>G (χ² = 8.713, p = 0.013), indicating population-specific variation at this locus. In contrast, no significant difference was detected for SNP c.390G>A (χ² = 3.784, p = 0.151), suggesting comparable genotype distributions between the two populations.
Genotype and allele frequencies of the IFN-γ gene SNP c.304C>G in IPB D1 and Merawangarab chickens
Breed
N
Genotype count
Genotype frequency IFN-γ
Allele frequency IFN-γ
CC
CG
GG
CC
CG
GG
C
G
IPB D1 Chicken and
60
44
11
5
0.73
0.18
0.08
0.83
0.18
Merawangarab chickens
34
33
0
1
0.97
0.00
0.03
0.97
0.03
Total
77
11
6
0.82
0.12
0.06
0.88
0.12
Note: N = number of chickens; CC, CG, and GG represent genotypes; C and G represent allele frequencies.
Observed (O) and expected (E) genotype frequencies of the IFN-γ gene SNP c.304C>G in IPB D1 and Merawangarab chickens
Breed
N
Genotype IFN-γ (c.304C>G )
χ²
P-value
CC
CG
GG
8.713
0.0128*
O
E
O
E
O
E
IPB D1 Chickens
60
44
40.83
11
17.32
5
1.83
Merawangarab chickens
34
33
32.02
0
1.94
1
0.02
77
11
6
Note: O = observed genotype count; E = expected genotype count under Hardy–Weinberg equilibrium, χ² = chi-square test statistic; P = probability value. p<0.05 indicates a statistically significant deviation from the Hardy–Weinberg equilibrium.
Genotype and allele frequencies of the IFN-γ gene polymorphism (c.390G>A) in IPB D1 and Merawangarab chickens
Breed
N
Genotype Count
Genotype Frequency IFN-γ
Allele Frequency IFN-γ
GG
AG
AA
GG
AG
AA
G
A
IPB D1 Chickens
60
45
11
4
0.75
0.18
0.07
0.84
0.16
Merawangarab chickens
34
25
3
6
0.74
0.09
0.18
0.78
0.22
Total
70
14
10
0.74
0.15
0.11
0.82
0.18
Note: N = number of chickens; GG, AG, and AA represent genotypes; G and A represent allele frequencies.
Observed (O) and expected (E) genotype frequencies of the IFN-γ gene SNP c.390G>A in IPB D1 and Merawangarab chickens
Breed
N
Genotype IFN-γ (c.390G>A)
χ²
P-value
GG
AG
AA
3.784
0.151
O
E
O
E
O
E
IPB D1 Chickens
60
45
42.50
11
15.99
4
1.50
Merawangarab chickens
34
25
20.65
3
11.69
6
1.65
70
14
10
Note: O = observed genotype count; E = expected genotype count under Hardy–Weinberg equilibrium; χ² = chi-square test statistic; P = P-value. p≥0.05 indicates no statistically significant deviation from the Hardy–Weinberg equilibrium.
Association of IFN-γ Gene SNP c.304C>G with Avian Influenza-Related Immune Response Traits
The associations between the IFN-γ gene SNP c.304C>G and avian influenza-related immune response traits are presented in Table 6. Significant genotype effects were observed for avian influenza (AI) antibody titer (p<0.001), total leukocyte count (p<0.001), and eosinophil percentage (p = 0.021). In contrast, no significant associations were observed for serum IgY concentration, hematocrit, MCV, MCH, MCHC, lymphocyte percentage, heterophil percentage, monocyte percentage, basophil percentage, or the heterophil-to-lymphocyte ratio (p>0.05).
Association of IFN-γ Gene SNP c.390G>A with Avian Influenza-Related Immune Response Traits
The associations between IFN-γ gene SNP c.390G>A and avian influenza-related immune response traits are presented in Table 7. Significant genotype effects were observed for AI antibody titer (p = 0.001), total leukocyte count (p = 0.009), heterophil percentage (p = 0.001), and the heterophil-to-lymphocyte (H/L) ratio (p = 0.014). Chickens carrying the GG genotype exhibited the highest AI antibody titer, whereas those carrying the AA genotype exhibited the lowest. In contrast, no significant associations were detected for serum IgY concentration, erythrocyte count, hemoglobin concentration, hematocrit, MCV, MCH, MCHC, lymphocyte percentage, eosinophil percentage, monocyte percentage, or basophil percentage (p>0.05).
Association of the IFN-γ Gene SNP c.304C>G with Avian Influenza-Related Immune Responses in IPB D1 and Merawangarab chickens
Variables
IPB D1 chickens (x̄ ± SE)
Merawangarab chickens (x̄ ± SE)
CC (44)
GC (11)
GG (5)
CC (33)
GC (1)
GG(0)
P- Value
Immune response
Avian influenza antibody titer (log₂ HI)
1.14±0.19ᵃ
1.82 ± 0.42ᵃ
2.40 ± 0.68ᵃ
0.27 ± 0.09ᵇ
1 ± 0.00
-
0.000*
IgY (mg/mL)
12.19 ± 0.61
13.35 ± 1.00
15.88 ± 2.02
12.41 ± 0.49
13.93 ± 0.00
-
0.260
Erythrocyte Profile
Erythrocytes (×10⁶ cells/mm³)
2.74 ± 0.08
3.11 ± 0.15
3.03 ± 0.17
2.87 ± 0.13
1.98 ± 0.00
-
0.227
Hemoglobin (g/dL)
8.74 ± 0.18
8.73 ± 0.25
9.36 ± 0.31
8.87 ± 0.29
13.00 ± 0.00
-
0.786
Hematocrit (%)
30.73 ± 0.89
30.09 ± 0.85
28.20 ± 1.07
28.85 ± 0.99
29.00 ± 0.00
-
0.606
MCV (fL)
110.79 ± 4.01
99.61 ± 6.68
94.31 ± 7.28
104.74 ± 4.19
146.46 ± 0.00
-
0.202
MCH (pg)
33.18 ± 1.32
28.46 ± 1.11
31.30 ± 2.30
31.16 ± 0.98
65.66 ± 0.00
-
0.238
MCHC (g/dL)
29.39 ± 0.97
29.27 ± 1.29
33.23 ± 0.45
31.22 ± 0.94
44.83 ± 0.00
-
0.315
Leukocyte profile
-
Total leukocytes (×10³ cells/mm³)
18.98 ± 0.47a
21.29 ± 0.61a
20.85 ± 1.46a
17.17 ± 0.38b
23.50 ± 0.00b
-
0.000*
Differential leukocyte count (%)
-
Lymphocytes (%)
48.53 ± 0.60
48.03 ± 1.41
44.83 ± 1.55
48.74 ± 0.82
52.48 ± 0.00
-
0.334
Heterophils (%)
31.00 ± 0.56
31.18 ± 1.29
33.07 ± 0.94
29.73 ± 0.64
22.70 ± 0.00
-
0.059
Eosinophils (%)
10.54 ± 0.27b
11.62 ± 0.51ab
12.11 ± 0.98ab
11.94 ± 0.38a
13.48 ± 0.00
-
0.021*
Monocytes (%)
9.14 ± 0.35
8.39 ± 0.90
9.25 ± 1.29
8.81 ± 0.35
10.64 ± 0.00
-
0.799
Basophils (%)
0.79 ± 0.01
0.78 ± 0.02
0.74 ± 0.02
0.78 ± 0.01
0.70 ± 0.00
-
0.374
Heterophil/Lymphocyte ratio
0.65 ± 0.02
0.66 ± 0.05
0.74 ± 0.05
0.62 ± 0.02
0.43 ± 0.00
-
0.148
Note: x̄ = mean; SE = standard error. Means within the same row bearing different superscript letters (a, b) differ significantly (P < 0.05). Numbers in parentheses indicate the sample size for each genotype. CC, CG, and GG represent genotypes of the IFN-γ gene SNP c.304C>G. (*) Significant at p<0.05.
Association between the IFN-γ Gene SNP c.390G>A and Avian Influenza-Related Immune Responses in IPB D1 and Merawangarab chickens
Variables
IPB D1 Chicken (x̄ ± SE)
Merawangarab chickens (x̄ ± SE)
P-value
GG (45)
AG (11)
AA (4)
GG (25)
AG (3)
AA (6)
Immune response
Avian influenza antibody titer (log₂ HI)
1.53 ± 0.21ᵃ
0.91 ± 0.34 ab
0.75 ± 0.75 ab
0.40 ± 0.11ᵇ
0.00 ± 0.00ᵇ
0.00 ± 0.00ᵇ
0.001*
IgY (mg/mL)
12.37 ± 0.52
13.70 ± 1.10
13.83 ± 1.37
12.42 ± 0.61
12.90 ± 1.65
12.40 ± 0.48
0.891
Erythrocyte profile
Erythrocytes (×10⁶ cells/mm³)
2.85 ± 0.07
2.67 ± 0.22
3.16 ± 0.22
2.97 ± 0.17
2.23 ± 0.11
2.61 ± 0.19
0.248
Hemoglobin (g/dL)
8.71 ± 0.15
8.85 ± 0.30
9.55 ± 1.01
9.38 ± 0.38
7.13 ± 0.13
8.33 ± 0.26
0.058
Hematocrit (%)
29.80 ± 0.74
32.00 ± 2.08
32.75 ± 0.75
29.80 ± 1.19
28.00 ± 2.00
25.33 ± 1.52
0.195
MCV (fL)
104.69 ± 3.15
119.10 ± 12.10
105.15 ± 8.12
105.68 ± 5.55
125.30 ± 2.66
97.51 ± 2.17
0.365
MCH (pg)
31.37 ± 0.95
36.17 ± 3.97
29.99 ± 1.16
33.21 ± 1.98
32.08 ± 0.94
32.41 ± 1.46
0.449
MCHC (g/dL)
29.97 ± 0.85
28.69 ± 1.79
29.31 ± 3.47
31.93 ± 1.23
25.66 ± 1.27
33.28 ± 1.58
0.276
Leukocyte profile
Total leukocytes (×10³ cells/mm³)
19.57 ± 0.45ab
18.88 ± 0.87abc
21.22 ± 1.03a
17.73 ± 0.49c
16.21 ± 1.17bc
16.38 ± 0.99c
0.009*
Differential leukocyte (%)
Lymphocytes (%)
48.03 ± 0.62
47.22 ± 1.27
51.73 ± 0.80
48.40 ± 0.90
46.79 ± 3.04
51.76 ± 1.90
0.182
Heterophils (%)
31.17 ± 0.55a
32.36 ± 0.95a
28.49 ± 1.97ab
29.78 ± 0.66ab
34.96 ± 1.76a
25.75 ± 0.89b
0.001*
Eosinophils (%)
10.87 ± 0.27
11.40 ± 0.64
9.42 ± 1.04
12.23 ± 0.40
10.95 ± 1.55
11.48 ± 1.08
0.060
Monocytes (%)
9.16 ± 0.37
8.22 ± 0.74
9.60 ± 1.01
8.81 ± 0.42
6.55 ± 0.30
10.26 ± 0.22
0.216
Basophils (%)
0.78 ± 0.01
0.79 ± 0.03
0.77 ± 0.01
0.78 ± 0.01
0.76 ± 0.02
0.75 ± 0.00
0.796
Heterophil/Lymphocyte ratio
0.66 ± 0.02ᵃ
0.69 ± 0.04 ab
0.55 ± 0.04ᵇ
0.63 ± 0.02ᵃ
0.76 ± 0.08ᵃ
0.50 ± 0.04ᵇ
0.014*
Note: x̄ = mean; SE = standard error. Means within the same row bearing different superscript letters (a, b,c) differ significantly (p<0.05). Numbers in parentheses indicate the sample size for each genotype. GG, AG, and AA represent genotypes of the IFN-γ gene SNP c.390G>A. (*) Significant at p<0.05.
DISCUSSION
Descriptive statistics indicated that IPB D1 and Merawangarab chickens exhibited relatively favorable physiological conditions, with erythrocyte counts of 2.84 × 10⁶ cells/mm³, total leukocyte counts ranging from 17.36 to 19.56 × 10³ cells/mm³, and heterophil-to-lymphocyte (H/L) ratios of 0.61–0.65 (Table 1). Hematological parameters, particularly erythrocyte and leukocyte profiles, are useful indicators of physiological condition and immunological status in chickens (Kuswandi et al., 2025). Regarding humoral immune status, serum IgY concentrations were comparable between IPB D1 (12.71 mg/mL) and Merawangarab (12.46 mg/mL) chickens, consistent with the 12.60 mg/mL reported for IPB D1 chickens by (Lestari et al., 2025). These concentrations were higher than those reported in Sentul hens (9.55 ± 1.25 mg/mL) (Setyawati et al., 2019) and remained within the normal range of serum IgY concentrations in chickens (5–15 mg/mL) (Gaetani et al., 2017). In contrast, mean AI antibody titers were relatively low in both populations, ranging from 1.00 to 1.37 log₂ HI. According to (Animal Health, 2021), an HI titer of ≥4 log₂ (1:16) is considered positive for AI antibodies when 4 HAU of antigen is used, whereas titers <4 log₂ are below the positive threshold. The titers observed in the present study were also below the protective levels reported in previous studies(Mirzaie et al., 2020); (Ikhsan, 2021). As the chickens were neither vaccinated against AI nor experimentally challenged with the virus, the detected antibodies likely reflected naturally acquired immune responses following environmental exposure. This interpretation is consistent with (Rouygari et al., 2025), who reported higher and more sustained HI antibody responses in vaccinated than in non-immunized chickens. Collectively, these findings indicate that despite relatively favorable physiological conditions and adequate baseline humoral immunity, AI-specific antibody responses remained low in both populations, suggesting that variation in AI-related immune responses may also be influenced by intrinsic host genetic factors.
The comparable physiological and immunological conditions of the two populations suggest that variation in avian influenza (AI)-related immune responses may be associated with host genetic factors. In this study, two polymorphisms were identified in exon 4 of the IFN-γ gene: c.304C>G, a synonymous coding variant, and c.390G>A, located in the 3′ untranslated region (3′UTR). Variations within these regions may influence gene regulation or expression without directly altering protein structure. The biological relevance of these findings is supported by the established role of IFN-γ in antiviral immunity, particularly through the induction of interferon-stimulated genes (ISGs) and activation of antiviral signaling pathways that suppress AI virus replication (Freier et al., 2025); (Guo et al., 2025). Thus, the identification of exon 4 polymorphisms extends current knowledge of IFN-γ genetic variation potentially associated with AI-related immune responses in Indonesian chickens.
Genotype distributions at c.304C>G differed between IPB D1 and Merawangarab chickens, whereas no significant difference was observed at c.390G>A. The predominance of the CC genotype and C allele in Merawangarab chickens indicates greater genetic homogeneity at this locus than in IPB D1 chickens. This pattern may reflect differences in the genetic backgrounds and breeding histories of the two populations, with IPB D1 developed through a structured breeding program and Merawangarab derived from crosses between Merawang and Arabian chickens. Consistent with this interpretation, (Wang et al., 2025) reported that demographic history, genetic introgression, and population differentiation can shape genetic structure and allele frequency variation in chicken populations. Therefore, the observed differences in IFN-γ genotype distribution may reflect genetic differentiation between IPB D1 and Merawangarab chickens and potentially contribute to variation in AI-related immune responses.
The significant association between SNP c.304C>G and avian influenza (AI) antibody titer suggests that this locus may contribute to variation in antiviral humoral immune responses. Chickens carrying the GC and GG genotypes exhibited higher AI antibody titers than those with the CC genotype, suggesting a potential association of the G allele with enhanced antibody responses. Antibody production is a key component of adaptive immunity (Lam et al., 2024), and IFN-γ may modulate humoral responses by enhancing antigen presentation, activating macrophages and other antigen-presenting cells, and regulating B-cell activation and differentiation (Burke & Young, 2019). Consistent with this mechanism, increased IFN-γ expression has been associated with enhanced antibody production and antiviral responses during avian influenza virus infection(Karpala et al., 2011); (Alqazlan et al., 2022); (Pan et al., 2023). Although c.304C>G is a synonymous variant that does not alter the IFN-γ amino acid sequence, synonymous variants may affect gene expression through changes in mRNA secondary structure, codon usage, translational efficiency, mRNA stability, or linkage disequilibrium with nearby functional variants (Oelschlaeger, 2024); (Lin et al., 2023); (Jiang et al., 2023). These findings support c.304C>G as a potential candidate molecular marker for AI-related immune responsiveness, although further functional validation is required before its application in marker-assisted selection.
Beyond the AI antibody titer, SNP c.304C>G was significantly associated with total leukocyte count and eosinophil percentage, further suggesting its involvement in both innate and adaptive immune responses. Genotype-dependent variation in leukocyte counts may reflect differences in immune responsiveness (Wang et al., 2023), while eosinophils contribute to antiviral defense through cytokine production and interactions with other immune cells (Arnold & Munitz, 2024).
SNP c.390G>A showed broader associations with immune-related traits, including AI antibody titer, total leukocyte count, heterophil percentage, and the heterophil-to-lymphocyte (H/L) ratio. Chickens carrying the GG genotype exhibited higher AI antibody titers, suggesting a potential association with enhanced humoral immune responsiveness. This finding is consistent with previous studies showing that polymorphisms in immune-related genes involved in cytokine signaling and antigen presentation contribute to variation in antibody responses in chickens (Luo et al., 2013); (Zhang et al., 2015); (Wang et al., 2020); (Lestari et al., 2023). The associations with leukocyte count, heterophil percentage, and H/L ratio further support the immunoregulatory role of IFN-γ in antiviral defense (He et al., 2024). Previous studies have similarly linked immune-related genetic variation, including IFNG variants, to H/L ratio (Wang et al., 2020), while variation in this ratio has been associated with immune responsiveness and inflammatory status (Krzysica et al., 2025). IFN-γ-related pathways may also regulate heterophil–lymphocyte balance through modulation of Th1/Th2 cytokine responses (Pu et al., 2023). Because c.390G>A is located within the 3′ untranslated region (3′UTR), these associations may involve regulatory effects on gene expression or linkage disequilibrium with nearby functional variants rather than direct alterations in protein structure. Thus, c.390G>A may contribute to variation in antiviral immune responsiveness, although its underlying functional mechanisms require further validation.
The immune-related associations observed in this study may also reflect differences in the genetic backgrounds of the two populations. IPB D1 was developed through a structured breeding program involving indigenous and commercial genetic resources and has previously been reported to exhibit relatively high IgY concentrations (Lestari et al., 2025). In the present study, IPB D1 also tended to exhibit higher AI antibody titers and total leukocyte counts than Merawangarab chickens, potentially reflecting differences in their genetic backgrounds and breeding histories. Nevertheless, the associations between IFN-γ polymorphisms and multiple immune-related traits support their potential as candidate markers for marker-assisted selection (MAS). However, because AI-related immune responsiveness is a complex trait influenced by both genetic and environmental factors, these SNPs should complement phenotypic selection and be validated in larger, genetically diverse populations before their application in breeding programs.
Several limitations should be considered when interpreting these findings. The relatively small sample size (n = 94) may limit statistical power and generalizability. Moreover, the chickens were neither vaccinated against AI nor experimentally challenged with the virus, and the measured antibody titers therefore reflected responses under natural exposure conditions rather than controlled immune responses. Accordingly, c.304C>G and c.390G>A should be regarded as candidate markers of immune responsiveness rather than direct markers of AI resistance or susceptibility. Furthermore, c.304C>G is a synonymous coding variant, whereas c.390G>A is located within the 3′ untranslated region (3′UTR), and the functional mechanisms underlying their associations with immune-related traits remain unclear. Further studies involving larger and genetically diverse populations, combined with gene expression and transcriptomic analyses, controlled vaccination or challenge experiments, and functional molecular assays, are needed to validate these associations and evaluate the potential application of these SNPs in marker-assisted selection.
CONCLUSION
Two SNPs (c.304C>G and c.390G>A) were identified in the IFN-γ gene of Indonesian local chickens and were significantly associated with several AI-related immune traits. The c.304C>G polymorphism was associated with avian influenza antibody titer, total leukocyte count, and eosinophil percentage, whereas c.390G>A was associated with avian influenza antibody titer, total leukocyte count, heterophil percentage, and the heterophil-to-lymphocyte ratio. These findings indicate that genetic variation within the IFN-γ gene is associated with variation in immune responses related to avian influenza in Indonesian local chickens. Therefore, IFN-γ may be a promising candidate gene for identifying molecular markers associated with AI-related immune responsiveness. Nevertheless, further validation in larger and independent populations, together with functional characterization and controlled challenge or vaccination studies, is required before these polymorphisms can be applied in marker-assisted breeding programs.
CONFLICT OF INTEREST
C. Sumantri is a member of the editorial board of the Tropical Animal Science Journal and was not involved in the evaluation or publication decision of this manuscript. The authors declare that there are no competing financial, personal, or institutional interests associated with this study.
DECLARATION OF GENERATIVE AI AND AI-ASSISTED TECHNOLOGIES IN THE WRITING PROCESS
The authors used Paperpal and Grammarly to assist with language editing, including grammar correction, vocabulary refinement, and improvement of sentence structure and overall manuscript clarity. All revisions were carefully reviewed by the authors, who take full responsibility for the accuracy, integrity, and scientific content of this publication.
How to Cite
Kuswandi, W., Sumantri, C., Listyarini, K., Darwati, S., Mulyono , R. H., & Murtini, S. (2026). Association of IFN-γ Gene Polymorphisms with Avian Influenza-Related Immune Traits in Indonesian Local Chickens. Tropical Animal Science Journal, 50(1), 21. https://doi.org/10.5398/tasj.2027.50.1.21
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