Association of genotypes at single-nucleotide polymorphisms in IL-28B, TGF-β1, TP53, MTHFR and GSTP1 gene and their combinations with ischemic stroke: A case-control study

Abstract

Objective

Ischemic stroke is a complex pathological condition in which the brain is damaged due to blockage of blood vessel(s) supplying blood to the brain. This study investigates the genotype and allelic frequency at the single-nucleotide polymorphism (SNP) rs8099917 in IL-28B, rs1800470 and rs1800472 in TGF-β1, rs1042522 in TP53, rs180133 in MTHFR and rs1695 in GSTP1 gene and correlates them individually or in various combinations with the incidence of ischemic stroke.

Methods

A total of 156 clinically confirmed ischemic stroke patients and 156 age and sex matched controls were enrolled from Multan District in Pakistan during September 2024 till February 2025. Tetra-primer amplification refractory mutation system-polymerase chain reaction (T-ARMS PCR) approach was used to determine the genotype at the screened SNPs.

Results

Significant associations of polymorphic TT genotype at rs8099917 in IL-28B, wildtype CC genotype at rs1800472 in TGF-β1, polymorphic TT and heterozygous CT genotypes at rs1800472 in TGF-β1, wild genotype GG at rs8099917 in IL-28B, polymorphic CC at rs1042522 in TP53 and wild genotype CC at rs180133 in MTHFR gene were observed with ischemic stroke. Smokers were more prone to the disease. While genotypes at rs1800470 in TGF-β1 and at rs1695 in GSTP1 as well as age and sex of the subject were not associated with the incidence of ischemic stroke.

Conclusion

Our results indicated that genotypes at the analyzed SNPs were associated with ischemic stroke in the screened Pakistani population.

1. Introduction

Ischemic stroke is a complex pathological condition in which artery carrying blood to the brain gets blocked by a clot resulting in reduced or no blood flow causing brain damage (Osler et al., 2022). It is global health constrain and according to 2021 estimate, the global incidence of ischemic stroke was 7.8 million cases marking an 88 % increase since 1990 (Hou et al., 2024). Genetic predisposition, environmental factors and lifestyle choices and exposure to certain conditions significantly influence the risk of ischemic stroke (Zhang et al., 2023). Prevalence of ischemic stroke is higher in subjects having family history. Variations in genes having diverse functions have been linked with the pathogenesis of stroke including the genes that are associated with vascular integrity, thrombosis and inflammation (Alabduljabbar et al., 2025). There is an increasing trend to screen single nucleotide polymorphisms (SNPs) in genes as they are the disease modifiers that affect its severity and clinical course as susceptibility to disease development varies from person to person (Kim, 2016).

Ischemic stroke results in inflammation that disturbs the immune system by disrupting the cytokine production that contributes to both local and systemic immune dysregulation (Chavda et al., 2021). In addition, it is also known that genetic variations in genes that are associated with immune response can modulate the expression of immune mediators and increases the risk of ischemic stroke (Zhang et al., 2021). IL-28B gene, also known as IFN-λ, have important role in defense mechanism against infection and it can regulate the innate and adaptive immune system against chronic inflammation (Heidari et al., 2017). The gene encoding the IL-28B cytokine is located at 19q13.13 (Witte et al., 2010) It has been established that genetic variations such as SNPs at or near the IL-28B gene can cause ischemic stroke as these variations can change expression or activity of type III interferons (IFN-λ) altering the vascular inflammation, endothelial function, immune cell recruitment, or coagulation; the processes that contribute to atherosclerosis, plaque instability, thrombosis and thus ischemic stroke risk (Traylor et al., 2012).

Cytokines are the signaling molecules that are known to play important role in progression of ischemic stroke as they are key mediators of the inflammatory cascade and their activation and release contribute to the progression of ischemic damage (Doll et al., 2014). Transforming growth factor beta 1 is encoded by TGF-β1 gene that is located at 19q13.1–13.3. This gene is known to play role during development as well as during potential recovery from ischemic stroke. TGF-β1 can protect neurons from damage caused by ischemia and may reduce infarct size (Li et al., 2024). Altered TGF-β signaling can disrupt endothelial function by changing cell phenotype, increasing vascular permeability, and upregulating adhesion molecules. It also influences arterial remodeling by modifying plaque composition, stability, and wall thickness: processes that are central to atherothrombosis and the development of ischemic stroke (Zhang and Yang, 2020). TGF-β1 production differs among peoples and depending on the TGF- β1 gene polymorphisms and hence leads to variety of pathological conditions including ischemic stroke (Tao et al., 2010).

Tp53 is an important gene that is involve in cell apoptosis process when a cell under stress conditions releases excessive amounts of mitochondrial ROS and a stress-induced apoptosis mechanism is initiated (Mustafa et al., 2024). It has been an established fact that p53 protein stops the cell cycle when the DNA gets damaged and keeps it halted until either the DNA is repaired or cell death is provoked and hence this protein prevents the proliferation of mutated cells (Wohak et al., 2019). p53 interacts with NF-κB and cytokine signaling pathways, and SNPs that weaken its regulatory control can promote chronic vascular inflammation, thereby accelerating atherosclerosis. In addition, p53 influences antioxidant defenses, and dysfunctional variants may exacerbate oxidative injury during ischemia (Hernández Borrero and El-Deiry, 2021).

The MTHFR gene (located on chromosome 1p36.3) gives rise to methylenetetrahydrofolate reductase enzyme that is required for the folate and homocysteine metabolism, specifically converting folate to its active form and processing homocysteine to methionine (Alkhiary et al., 2021). Variations (SNPs) in the MTHFR gene can lead to reduced enzyme activity, potentially resulting in elevated homocysteine levels that are known risk factor for the development of ischemic stroke (Jin et al., 2022). Elevated Homocysteine reduces nitric oxide bioavailability, leading to impaired vasodilation, while also promoting endothelial cell injury and making blood vessels more susceptible to atherosclerosis (Toole et al., 2004).

We are provided with a variety of enzymes, proteins and non-proteins in order to cope with the oxidative stress. Glutathione S-transferases (GSTs) are a group of enzymes that act as antioxidants and involve in detoxification of a variety of compounds (Arshad et al., 2023). In order to get protected from oxidative damage, we are provided with seven classes of GST that are known to play a protective role against ischemic stroke by reducing oxidative stress and promoting cell survival (Türkanoğlu et al., 2010). Glutathione S-transferase pi 1 (GSTP1) is a phase II detoxification enzyme that protects cells from damage caused by reactive chemicals and carcinogens by conjugating them with glutathione (Aloke et al., 2024). SNPs in GSTP1 reduce the enzyme’s antioxidant capacity, leading to excessive accumulation of reactive oxygen species (ROS). This oxidative stress damages endothelial cells, decreases nitric oxide bioavailability, and promotes vascular stiffness, which impairs vasodilation and accelerates atherosclerosis (Qi et al., 2015).

The estimated annual incidence of ischemic stroke in Pakistan is around 250 per 100,000 people with approximately 350,000 new stroke cases occurring each year. The high prevalence of this disease in Pakistan is attributed to widespread prevalence of risk factors such as hypertension, diabetes, obesity, high cholesterol as well as smoking (Javed et al., 2024). Genetic basis of ischemic stroke has been explored among local population. Literature review revealed the association of genotypes at PDE4D, ACE, AAD1, E-selectin, and PON1 genes with ischemic stroke (Saleheen et al., 2005, Nomani et al., 2017).While several studies has also reported that association of SNPs in IL-28B, TGF-β1, TP53, MTHFR and GSTP1 gene with ischemic stroke but no such information is available for Pakistani population. We assumed that SNPs in these genes can be used as markers for the early detection of ischemic stroke and the current study is the first investigation that has been conducted on this issue.

2. Materials and methods

2.1. Ethical approval

All the experimental details and subject handling procedures were approved by the Institutional Review Board of Nishter Medical University via letter number 212133/NMU (2024) following guidelines provided in the Declaration of Helsinki.

2.2. Patients and sample collection

The sample size was calculated using the following formula: N = Z² P(1-P)/e², where “N” is the required sample size, “Z” is the Z-score corresponding to the desired confidence level, “P” is the expected prevalence, and “e” is the acceptable margin of error (Jones et al., 2003). Based on this calculation, the required sample size was determined to be 150. Clinically confirmed ischemic stroke patients (n = 156), that were brought in the primary stroke center of Nishtar Medical University and Hospital Multan for their check and treatment up during September 2024 till February 2025, were enrolled in this study following the informed verbal consent from them or their family member’s. The subjects included 92 males and 64 females with ages ranging from 19 years to 95 years. The patients were thoroughly examined by the trained neurologist and only the subjects suffering from ischemic stroke were included in this study. Patients suffering either from hemorrhagic stroke, severe head trauma and pregnant females were excluded from this investigation. A non-contrast computed tomography scan was used for the ischemic stroke patients to identify the location and nature of a stroke (Meyer et al., 1999). Patients were further examined by magnetic resonance imaging to reveal the typical cortical pattern of ischemic enhancement (Vymazal et al., 2012). The age and sex distribution matched control group (n = 156) was also enrolled from same area with subjects having same ethnic background as cases but they were not suffering from stroke based on physical/neurological examination performed by a qualified physician. The control group also included 92 males and 64 females with their ages ranging from 25 years to 90 years. Individuals with a prior history of cerebrovascular disease, transient ischemic attack, or neurological deficits were excluded.

A blood sample (3–5 ml) was collected from each subject from their median cubital vein in a labelled EDTA containing collection tube. A questionnaire was completed for each enrolled subject to gather the epidemiological and clinical data associated with periodontal status (Sex, age, diabetes, hypertension, smoking, stroke history and ischemic heart disease).

2.3. DNA extraction

Genomic DNA was extracted from whole blood using a DNA extraction protocol as described previously (Arshad et al., 2023). DNA concentration and purity were measured using a T80 UV/VIS Spectrophotometer (PG Instruments, UK) and only samples with acceptable A260/280 ratios were included in the study. The extracted DNA was stored at –20 °C until further analysis.

2.4. Tetra ARMS PCR based amplification of rs8099917 in IL-28B

A T-ARMS–PCR based analysis was performed to investigate the rs8099917 (G/T) in IL-28B gene. A reaction mixture of 50 μl was prepared containing 50 ng of template DNA, 5 μl of 10X PCR buffer, 1.5 mM MgCl₂, 0.125 mM of each dNTP, 0.6 mM of each primer, 1Ul of Taq DNA polymerase (AbClonal, USA) balanced by double distilled water. The thermo-profile consisted of initial denaturation for 7 min at 95 ^ᵒC followed by 25 cycles of denaturation for 45 s at 95 ^◦C, annealing for 45 s at 59 ^ᵒC, elongation for 30 sec at 72 ^ᵒC and the final extension for 7 min at 72 ^ᵒC. PCR products were held at 4 ^ᵒC until gel electrophoresis was carried out (Heidari et al., 2017).

2.5. Tetra ARMS PCR based amplification of rs1800470 in TGF-β1 gene

A Tetra-Primer Amplification Refractory Mutation System Polymerase Chain Reaction (T-ARMS–PCR) based analysis was performed to investigate the rs1800470 (C/T) in TGF-β1 gene. A reaction mixture of 50 µl was prepared containing 50 ng of template DNA, 5 µl of 10X PCR buffer, 1.5 mM MgCl₂, 0.125 mM of each dNTP, 0.6 mM of each primer, 1Ul of Taq DNA polymerase (AbClonal, USA) balanced by double distilled water. The thermo-profile consisted of initial denaturation for 5 min at 95 °C followed by 30 cycles of denaturation for 30 s at 95 °C, annealing for 30 s at 65 °C, elongation for 30 s at 72 °C and the final extension for 10 min at 72 °C. PCR products were held at 4 ° C until gel electrophoresis was carried out (Heidari et al., 2013)(Heidari et al., 2013).

2.6. Tetra ARMS PCR based amplification of rs1800472 in TGF-β1 gene

A T-ARMS–PCR based analysis was performed to investigate the rs1800472 (C/T) in TGF-β1 gene. A reaction mixture of 50 μl was prepared containing 50 ng of template DNA, 5 μl of 10X PCR buffer, 1.5 mM MgCl₂, 0.125 mM of each dNTP, 0.6 mM of each primer, 1Ul of Taq DNA polymerase (AbClonal, USA) balanced by double distilled water. The thermo-profile of Heidari et al (Heidari et al., 2013). was followed to amplify TGF-β1 gene fragment as following: initial denaturation for 5 min at 95ᵒC followed by 30 cycles of denaturation for 30 s at 95ᵒC, annealing for 30 s at 63 ᵒC, elongation for 30 s at 72 ᵒC and the final extension for 10 min at 72 ᵒC.

2.7. Tetra ARMS PCR based amplification of rs1042522 in TP53 gene

A T-ARMS–PCR analysis was performed to investigate the rs1042522 (G/C) in the TP53 gene A reaction mixture of 25 µl was prepared that contained 13 mM Tris–HCl (pH 8.3), 65 mM KCl, 1.5 mM MgCl₂, 300 µM of each dNTP, 1 U of DNA Polymerase (AbClonal, USA), 0.5 µM of each primer and 5 µl of template DNA. The thermo-profile consisted of an initial denaturing for 10 min at 95ᵒC followed by 35 cycles of denaturation for 45 s at 95ᵒC, annealing for 30 s at 56ᵒC, elongation for 45 s at 72ᵒC and the final extension for 10 min at 72ᵒC (Asadi et al., 2017).

2.8. Tetra ARMS PCR based amplification of rs180133 in MTHFR

A T-ARMS–PCR based analysis was performed to investigate the rs180133 (C/T) in MTHFR gene following El-Khawaga et al (El-Khawaga et al., 2024). The primers used in this T-ARMS PCR are shown in Supplementary Table 1. A reaction mixture of 50 µ1 was prepared containing 50 ng of template DNA, 5 µl of 10X PCR buffer, 1.5Mm MgCl_2, 0.125 mM of each dNTP, 0.6 mM of each primer, 1 µl of Taq DNA polymerase (AbClonal, USA) balanced with double distilled water. The thermo-profile consisted of an initial denaturing for 7 min at 95 ^ᵒC followed by 25 cycles of denaturation for 30 s at 95 ^ᵒC, annealing for 40 s at 60 ^ᵒC, elongation for 35 s at 72 ^ᵒC and the final extension for 10 min at 72 ^ᵒC. PCR products were held at 4 ^ᵒC until gel electrophoresis was carried out (El-Khawaga et al., 2024).

2.9. Tetra ARMS PCR based amplification of rs1695 in GSTP1 gene

A Tetra-Primer Amplification Refractory Mutation System Polymerase Chain Reaction (T-ARMS–PCR) based analysis was performed to investigate the rs1695 (A/G) single nucleotide polymorphism (SNP) in the GSTP1 gene in all the subjects (case and control) following Youssef et al (Youssef et al., 2021). The primers and conditions used in this investigation are shown in supplementary table 1. A reaction mixture of 25 µ1 was prepared containing 50 ng of template DNA, 5 µl of 10X PCR buffer, 2 mM MgCl_2, 0.125 mM of each dNTP, 0.6 mM of each primer, 2 µl of Taq DNA polymerase (AbClonal, USA) balanced with double distilled water. The thermo-profile consisted of an initial denaturing for 5 min at 95 ^ᵒC followed by 25 cycles of denaturation for 30 s at 94 ^ᵒC, annealing for 45 s at 60 ^ᵒC, elongation for 30 s at 72 ^ᵒC and the final extension for 5 min at 72 ^ᵒC. PCR products were held at 4 ^ᵒC until gel electrophoresis was carried out (Youssef et al., 2021).

2.10. Agarose gel electrophoresis and documentation

All the genotyping was performed with appropriate negative controls to monitor contamination. A random 10 % of the samples were re-genotyped in duplicate to confirm reproducibility, and the concordance rate was > 99 %. All the amplified PCR products were separated through agarose gel electrophoresis according by using 1.8 % agarose gel. PCR products were separated in parallel with a 100 bp DNA ladder (Bio Labs, New England). Gel was run at 120 Volts and 90 mA for 30 min and visualized on a UV illuminator (Biostep, Germany) and the results were documented with the help of a digital camera.

2.11. Statistical analysis

Statistical Package Minitab (Minitab, USA) was used for data analysis during present study. Significance level was set as P ≤ 0.05. Chi square test was applied to compare the genotype and allelic frequency between case and controls and to report the association of epidemiological factors with observed genotypes at a specific single nucleotide polymorphism. Odds ratios (ORs) with 95 % confidence intervals (95 % CI) were calculated from 2 × 2 contingency tables using the Woolf logit method. Hardy Weinberg Equilibrium was calculated for to estimate the genetic diversity among the studied population.

3. Results

3.1. Genotypic and allelic frequency at rs8099917 in IL-28B gene and their association with Ischemic stroke

Tetra-ARMS PCR based amplification of rs8099917 in IL28B gene resulted in the generation of 437 base pairs fragment from the outer primer pair in all the enrolled subjects. While the primers specific for homozygous wild (GG) and homozygous mutant (TT) genotype amplified products of 197 and 295 base pairs respectively. Subjects having heterozygous genotype (GT) at rs8099917 amplified both 197 and 295 base pair amplicons along with 437 base pair fragment amplified by outer primers.

Data analysis revealed that at rs8099917 wild (GG) genotype was more common (88 %) in controls followed by heterozygous (GT, 10 %) and homozygous mutant (TT, 2 %) respectively. While for the Ischemic stroke patients, the genotype pattern at rs8099917 was: wild (GG, 43 %) > mutant (TT, 41 %) > heterozygous (GT, 16 %). Chi square test results revealed that the genotypic frequency at rs8099917 varied significant (P = 0.05) when compared between cases and controls. Cases had higher heterozygous and polymorphic genotypes when compared between ischemic stroke patients and control group and had 1.62 and 1.69 times higher risk of developing Ischemic stroke respectively, Ischemic stroke while the controls had higher wild genotype indicating it to be safe combination at rs8099917. For allelic frequency among the controls, it was observed that G (wild type) allele was more common (62 %) followed by T (polymorphic) allele (38 %). Similar trend was observed in Ischemic stroke patients enrolled during present study as G allele was common (51 %) followed by T allele (49 %). Chi square test results revealed that allelic frequency at rs8099917 varied significant (P = 0.005) with between the enrolled subjects. Upon comparison, it was observed that cases had higher polymorphic “T” while controls had higher wild “G” allele indicating that cases with polymorphic allele has 1.58 times higher risk of Ischemic stroke than controls (Table 1).

Table 1.

Genotype and allelic frequency distribution at single nucleotide polymorphism rs8099917 in IL28B, rs1800470 and rs1800472 in TGF-β1, rs1042522 in TP53, rs180133 in MTHFR and rs1695 in GSTP1 gene among case and controls enrolled during present study and their possible association with Ischemic stroke. Chi-square test was applied to compare the genotype and allelic frequency between case and controls.

Gene/SNP	Treatment ˅		Genotypic frequency				Chi square value	P-value	Allelic frequency			Chi square value	P-value
IL−28B (rs8099917)	Genotype ˃	GG	GT	OR (95 % CI)	TT	OR (95 % CI)		0.05*	G	T	OR (95 % CI)		0.005**
	Control	87 (88 %)	20 (10 %)	1.62 (0.83–3.17)	49 (2 %)	1.69 (1.02 – 2.81)	5.144		194 (62 %)	118 (38 %)	1.58 (1.14–2.19)	7.991
	Case	67 (43 %)	25 (16 %)		64 (41 %)				159 (51 %)	153 (49 %)
TGFB1(rs1800470)	Genotypes ˃	CC	CT	OR (95 % CI)	TT	OR (95 % CI)			C	T	OR (95 % CI)
	Controls	57 (37 %)	27 (17 %)	1.5 (0.84 – 2.97)	72 (46 %)	1.1 (0.72 – 1.65)	2.06	0.4	141 (45 %)	171 (55 %)	1.1 (0.82 – 1.54)	0.53	0.5
	Case	48 (31 %)	36 (23 %)		72 (46 %)				132 (42 %)	180 (58 %)
TGFB1(rs1800472)	Genotypes ˃	CC	CT	OR (95 % CI)	TT	OR (95 % CI)			C	T	OR (95 % CI)
	Controls	46 (29 %)	40 (26 %)	0.3 (0.19 – 0.61)	70 (45 %)	0.3 (0.20 – 0.57)	21.02	P < 0.001***	132 (42 %)	180 (58 %)	0.4 (0.31 – 0.58)	28.016	P < 0.001***
	Case	86 (55 %)	26 (17 %)		44 (28 %)				198 (63 %)	114 (37 %)
TP53(rs1042522)	Genotypes ˃	GG	GC	OR (95 % CI)	CC	OR (95 % CI)			G	C	OR (95 % CI)
	Control	87 (56 %)	31 (20 %)	2.44 (1.41 – 4.23)	38 (24 %)	1.57 (0.91 – 2.72)	10.52	0.005**	205 (66 %)	107 (34 %)	1.50 (1.09 – 2.07)	6.05	0.01**
	Case	61 (39 %)	53 (34 %)		42 (27 %)				175 (56 %)	137 (44 %)
MTHFR (rs180133)	Genotype ˃	CC	CT	OR (95 % CI)	TT	OR (95 % CI)			C	T	OR (95 % CI)
	Controls	29 (19 %)	16 (10 %)	1.35 (0.64 – 2.87)	111 (71 %)	0.39 (0.13 – 0.43)	20.692	P < 0.001***	74 (24 %)	238 (76 %)	0.42 (0.30 – 0.60)	24.378	P˂ 0.001 ***
	Case	48 (31 %)	36 (23 %)		72 (46 %)				132 (42 %)	180 (58 %)
GSTP1 (rs1695)	Genotype ˃	AA	AG	OR (95 % CI)	GG	OR (95 % CI)			A	G	OR (95 % CI)
	Control	88 (56 %)	22 (14 %)	1.74 (0.94 – 3.23)	46 (30 %)	1.08 (0.65 – 1.80)	3.218	0.2	198 (63 %)	114 (37 %)	1.12 (0.81 – 1.54)	0.436	0.5
	Case	78 (50 %)	34 (22 %)		44 (28 %)				190 (61 %)	122 (39 %)

P > 0.05 = Non significant; P < 0.01 = Significant (**); P < 0.001 = Highly significant (***); OR = Odd ratio; 95 % CI = 95 % confidence intervals

We calculated observed heterozygosity (HO) and expected heterozygosity (HE) for Hardy Weinberg equilibrium at rs8099917 in IL28B gene for both case and control groups. Analysis of our results indicated higly significant deviation from Hardy Weinberg equilibrium law for both control (X² = 82.545, P < 0.001) and cases (X² = 72.001, P < 0.001) at rs8099917 in IL28B gene indicating genetic diversity in both studied groups (Table 2, Table 3).

Table 2.

Genotype and allelic frequencies, heterozygosity value, and Chi-square value at rs8099917 in IL28B, rs1800470 and rs1800472 in TGF-β1, rs1042522 in TP53, rs180133 in MTHFR and rs1695 in GSTP1 gene among cases and controls enrolled during present study. P-value represents the output of Hardy-Weinberg equation calculated for cases and controls.

Gene/SNP	Treatments ˅	Genotypic frequency			Allelic frequency		Chi square value	P-Value
IL-28B (rs8099917)	Genotypes ˃	GG	GT	TT	G	T
	Controls (HO)	87 (88 %)	20 (10 %)	49 (2 %)	118 (38 %)	194 (62 %)
	HE	60.31	73.37	22.31	0.62	0.37	82.545	P < 0.001***
	Case (HO)	67 (43 %)	25 (16 %)	64 (41 %)	153 (49 %)	159 (51 %)
	HE	40.51	77.971	37.51	0.51	0.49	72.001	P < 0.001***
TGF-β1 (rs1800470)	Genotypes ˃	CC	CT	TT	C	T
	Controls (HO)	57 (37 %)	27 (17 %)	72 (46 %)	141 (45 %)	171 (55 %)
	HE	31.86	77.27	46.86	0.45	0.54	66.02	P < 0.001***
	Case (HO)	48 (31 %)	36 (23 %)	72 (46 %)	132 (42 %)	180 (58 %)
	HE	27.92	76.15	51.92	0.42	0.57	43.37	P < 0.001***
TGF-β1 (rs1800472)	Genotypes ˃	CC	CT	TT	C	T
	Controls (HO)	46 (29 %)	40 (26 %)	70 (45 %)	132 (42 %)	180 (58 %)	35.19	P < 0.001***
	HE	27.9	76.15	51.92	0.57	0.42
	Case (HO)	86 (55 %)	26 (17 %)	44 (28 %)	198 (63 %)	114 (37 %)	64.006	P < 0.001***
	HE	62.82	72.34	20.83	0.37	0.63
TP53(rs1042522)	Genotypes ˃	GG	GC	CC	C	T
	Controls (HO)	87 (56 %)	31 (20 %)	38 (24 %)	207 (66 %)	107 (24 %)
	HE	67.35	70.30	18.35	0.66	0.34	48.75	P < 0.001***
	Case (HO)	61 (39 %)	53 (34 %)	42 (27 %)	303 (56 %)	137 (44 %)
	HE	49.07	76.44	30.07	0.56	0.44	14.8	P < 0.001***
MTHFR (180133)	Genotypes ˃	CC	CT	TT	C	T
	Controls (HO)	29 (19 %)	16 (10 %)	111 (71 %)	74 (24 %)	238 (76 %)
	HE	8.7	56.4	90.7	0.23	0.76	80.74	P < 0.001***
	Case (HO)	48 (31 %)	36 (23 %)	72 (46 %)	132 (42 %)	180 (58 %)
	HE	27.9	76.1	51.9	0.42	0.57	43.367	P < 0.001***
GSTP1 (1695)	Genotypes ˃	AA	AG	GG	A	G
	Controls (HO)	88 (56 %)	22 (14 %)	46 (30 %)	198 (63 %)	114 (37 %)	75.55	P < 0.001***
	HE	62.8	72.3	20.8	0.63	0.37
	Case (HO)	78 (50 %)	34 (22 %)	44 (28 %)	190 (61 %)	122 (39 %)	45.89	P < 0.001***
	HE	57.9	74.3	23.9	0.61	0.39

P < 0.001 = Highly-significant (***)

HO = Observed heterozygosity and HE = Expected heterozygosity

Table 3.

Genotype frequency distribution among cases and controls for various combinations at rs8099917 in IL28B, rs1800470 and rs1800472 in TGF-β1, rs1042522 in TP53, rs180133 in MTHFR and rs1695 in GSTP1 and their possible association with Ischemic stroke.

Genotype	Control	Case	OR (95 % CI)	Chi-Square value	P-value
TGF-β1rs1800470and IL28B rs8099917
TGF-β1 (CC) and IL28B (GG)	35	23	1 (Ref)
TGF-β1(CC) IL28B (GT)	4	9	3.42 (0.93 – 12.55)
TGF-β1(CC) IL28B (TT)	18	16	0.39 (0.18 – 0.87)
TGF-β1 (CT) and IL28B (GG)	17	16	1.05 (0.49 – 2.28)
TGF-β1 (CT) IL28B (GT)	3	5	1.77 (0.40 – 7.77)	10.328	0.243
TGF-β1 (CT) IL28B (TT)	7	15	1.28 (0.48 – 3.44)
TGF-β1(TT) and IL28B (GG)	35	28	0.37 (0.19 – 0.73)
TGF-β1(TT) IL28B (GT)	13	11	1.05 (0.44 – 2.53)
TGF-β1(TT) IL28B (TT)	24	33	1.63 (0.85 – 3.13)
TGF-β1rs1800470and TP53 rs1042522
TGF-β1(CC) andTP53(CC)	13	14	1 (Ref)
TGF-β1(CC) TP53 (GC)	11	14	1.18 (0.41–3.39)
TGF-β1(CC) TP53 (GG)	33	20	0.47 (0.18–1.19)
TGF-β1(CT) andTP53(CC)	11	8	1.2 (0.37–3.84)
TGF-β1(CT) TP53 (GC)	4	11	3.78 (0.97–14.74)	18.208	0.02*
TGF-β1(CT) TP53 (GG)	12	17	0.52 (0.18–1.48)
TGF-β1(TT) andTP53 (CC)	16	20	0.88 (0.33–2.36)
TGF-β1(TT) TP53 (GC)	14	28	1.6 (0.66–3.89)
TGF-β1(TT) TP53 (GG)	42	24	0.29 (0.13–0.64)
TGF-β1rs1800470and TGF-β1rs1800472
TGF-β1(CC) and TGF-β1(CC)	16	25	1 (Ref)
TGF-β1(CC) TGF-β1(CT)	17	7	0.26 (0.09–0.74)
TGF-β1(CC) TGF-β1(TT)	24	16	1.62 (0.64–4.12)
TGF-β1(CT) and TGF-β1(CC)	7	25	5.35 (1.95–14.65)	26.453	0.001***
TGF-β1(CT) TGF-β1(CT)	9	7	0.22 (0.07–0.72)
TGF-β1(CT) TGF-β1(TT)	11	4	0.47 (0.13–1.68)
TGF-β1 (TT) and TGF-β1(CC)	23	36	4.30 (1.74–10.61)
TGF-β1(TT) TGF-β1(CT)	14	12	0.55 (0.20–1.54)
TGF-β1(TT) TGF-β1(TT)	35	24	0.80 (0.37–1.74)
TGF-β1rs1800470and MTHFR rs180133
TGF-β1 (CC) and MTHFR(CC)	14	21	1.00 (Ref)
TGF-β1 (CC) MTHFR (CT)	5	9	1.20 (0.33–4.34)
TGF-β1 (CC) MTHFR (TT)	38	18	0.32 (0.13–0.76)
TGF-β1 (CT) and MTHFR (CC)	5	21	2.80 (0.85–9.17)
TGF-β1 (CT) MTHFR (CT)	2	9	3.00 (0.56–16.01)	43.24	P < 0.001***
TGF-β1 (CT) MTHFR (TT)	20	6	0.20 (0.06–0.62)
TGF-β1 (TT) and MTHFR (CC)	10	26	1.73 (0.64–4.69)
TGF-β1 (TT) MTHFR (CT)	9	12	0.89 (0.30–2.66)
TGF-β1 (TT) MTHFR (TT)	53	34	0.43 (0.19–0.95)
TGF-β1 rs1800470 and GSTP1 rs1695
TGF-β1 (CC) and GSTP1(AA)	28	21	1 (Ref)
TGF-β1 (CC) GSTP1(AG)	13	12	1.23 (0.47–3.24)
TGF-β1 (CC) GSTP1(GG)	16	15	1.01 (0.41–2.49)
TGF-β1 (CT) and GSTP1(AA)	18	16	0.95 (0.39–2.29)
TGF-β1 (CT) GSTP1(AG)	1	9	10.10 (1.19–86.02)	9.666	0.289
TGF-β1 (CT) GSTP1(GG)	8	11	0.15 (0.05–0.44)
TGF-β1 (TT) and GSTP1(AA)	42	41	0.71 (0.35–1.45)
TGF-β1 (TT) GSTP1(AG)	8	13	1.66 (0.58–4.73)
TGF-β1 (TT) GSTP1(GG)	22	18	0.50 (0.22–1.16)
TGF-β1 rs1800472 and GSTP1 rs1695
TGF-β1(CC) and GSTP1(AA)	26	40	1 (Ref)
TGF-β1(CC) GSTP1(AG)	4	17	2.76 (0.83–9.13)
TGF-β1(CC) GSTP1(GG)	16	29	0.43 (0.20–0.94)
TGF-β1(CT) and GSTP1(AA)	24	16	0.36 (0.16–0.80)
TGF-β1(CT)GSTP1(AG)	7	7	1.50 (0.47–4.78)	26.142	P < 0.001***
TGF-β1(CT) GSTP1(GG)	9	3	0.33 (0.08–1.33)
TGF-β1(TT) and GSTP1(AA)	38	22	1.74 (0.85–3.58)
TGF-β1(TT) GSTP1(AG)	11	10	1.57 (0.58–4.22)
TGF-β1(TT) GSTP1(GG)	21	12	0.63 (0.27–1.50)
TGF-β1 rs1800472 and IL28B rs8099917
TGF-β1(CC) and IL28B (GG)	20	34	1 (Ref)
TGF-β1(CC) IL28B (GT)	7	14	1.18 (0.41–3.40)
TGF-β1(CC) IL28B (TT)	19	38	1.18 (0.54–2.57)
TGF-β1(CT) and IL28B (GG)	21	14	0.39 (0.16–0.94)
TGF-β1(CT) IL28B (GT)	6	4	0.39 (0.10–1.56)	26.531	P < 0.001***
TGF-β1(CT) IL28B (TT)	13	8	0.36 (0.13–1.02)
TGF-β1(TT) and IL28B (GG)	46	19	0.24 (0.11–0.52)
TGF-β1(TT) IL28B (GT)	7	7	0.59 (0.18–1.92)
TGF-β1(TT) IL28B (TT)	17	18	0.62 (0.26–1.48)
TGF-β1 rs1800472 and MTHFR rs180133
TGF-β1 (CC) and MTHFR(CC)	9	42	1 (Ref)
TGF-β1 (CC) MTHFR (CT)	7	17	0.52 (0.17–1.64)
TGF-β1 (CC) MTHFR (TT)	30	27	0.37 (0.16–0.85)
TGF-β1 (CT) and MTHFR(CC)	8	12	1.67 (0.56–4.94)
TGF-β1 (CT) MTHFR (CT)	5	6	0.80 (0.21–3.05)	50.335	P < 0.001***
TGF-β1 (CT) MTHFR (TT)	27	8	0.25 (0.11–0.57)
TGF-β1 (TT) and MTHFR(CC)	12	14	3.94 (1.39–11.15)
TGF-β1 (TT) MTHFR (CT)	4	7	1.50 (0.39–5.75)
TGF-β1 (TT) MTHFR (TT)	54	23	0.24 (0.13–0.46)
TGF-β1 rs1800472 and TP53 rs1042522
TGF-β1 (CC) andTP53 (CC)	11	23	1 (Reference)
TGF-β1 (CC)TP53(GC)	9	32	1.70 (0.64–4.49)
TGF-β1 (CC) TP53 (GG)	27	31	0.32 (0.15–0.67)	30.799	P < 0.001***
TGF-β1 (CT) andTP53 (CC)	11	9	0.71 (0.25–2.00)
TGF-β1 (CT)TP53(GC)	9	10	1.36 (0.47–3.91)
TGF-β1 (CT) TP53 (GG)	20	7	0.32 (0.13–0.81)
TGF-β1 (TT) andTP53 (CC)	18	10	1.59 (0.60–4.22)
TGF-β1 (TT) TP53 (GC)	11	11	1.80 (0.64–5.08)
TGF-β1 (TT) TP53 (GG)	40	23	0.58 (0.31–1.08)
TP53 rs1042522 and IL28B rs8099917
TP53 (CC) and IL28B (GG)	24	18	1 (Ref)
TP53 (CC) IL28B (GT)	5	6	1.6 (0.42–6.08)
TP53 (CC) IL28B (TT)	9	18	1.67 (0.61–4.57)
TP53 (GC) and IL28B (GG)	15	20	0.67 (0.27–1.66)
TP53 (GC) IL28B (GT)	5	8	1.2 (0.34–4.29)	16.975	0.03*
TP53 (GC) IL28B (TT)	11	25	1.42 (0.56–3.62)
TP53 (GG) and IL28B (GG)	49	29	0.26 (0.12–0.56)
TP53 (GG) IL28B (GT)	10	11	1.86 (0.65–5.33)
TP53 (GG) IL28B (TT)	28	21	0.68 (0.30–1.56)
TP53 rs1042522 and MTHFR rs180133
TP53 (CC) and MTHFR (CC)	9	21	1 (Ref)
TP53 (CC) MTHFR (CT)	3	11	1.57 (0.35–7.01)
TP53 (CC) MTHFR (TT)	28	10	0.09 (0.03–0.26)
TP53 (GC) and MTHFR (CC)	6	24	11.2 (3.42–36.72)
TP53 (GC) MTHFR (CT)	6	9	0.38 (0.1–1.39)	47.621	P < 0.001***
TP53 (GC) MTHFR (TT)	17	20	0.78 (0.28–2.15)
TP53 (GG) and MTHFR (CC)	14	23	1.39 (0.5–3.88))
TP53 (GG) MTHFR (CT)	7	10	0.87 (0.25–3.01)
TP53 (GG) MTHFR (TT)	66	28	0.29 (0.12–0.71)
TP53 rs1042522 and GSTP1 rs1695
TP53 (CC) and GSTP1 (AA)	22	21	1 (Ref)
TP53 (CC) GSTP1 (AG)	4	13	3.4 (0.95–12.11)
TP53 (CC) GSTP1 (GG)	14	8	0.17 (0.06–0.49)
TP53 (GC) and GSTP1 (AA)	20	23	2.01 (0.86–4.69)
TP53 (GC) GSTP1 (AG)	4	13	2.83 (0.79–10.08)	22.771	0.004**
TP53 (GC) GSTP1 (GG)	5	17	1.04 (0.33–3.33)
TP53 (GG) and GSTP1 (AA)	46	34	0.22 (0.10–0.46)
TP53 (GG) GSTP1 (AG)	14	8	0.77 (0.27–2.21)
TP53 (GG) GSTP1 (GG)	27	19	1.23 (0.53–2.84)
IL28B rs 8099917 and GSTP1 rs1695
IL28B (GG) and GSTP1 (AA)	54	41	1 (Ref)
IL28B (GG) GSTP1 (AG)	9	7	1.02 (0.35–2.97)
IL28B (GG) GSTP1 (GG)	24	19	1.01 (0.49–2.09)
IL28B (GT) and GSTP1 (AA)	14	9	0.81 (0.32–2.05)
IL28B (GT) GSTP1 (AG)	3	10	5.19 (1.34–20.07)	11.615	0.169
IL28B (GT) GSTP1 (GG)	3	6	0.6 (0.14–2.54)
IL28B (TT) and GSTP1 (AA)	20	28	0.7 (0.35–1.41)
IL28B (TT) GSTP1 (AG)	10	17	1.21 (0.50–2.92)
IL28B (TT) GSTP1 (GG)	19	19	0.59 (0.28–1.25)
IL28B rs8099917 and MTHFR rs180133
IL28B (GG) and MTHFR (CC)	13	30	1 (Ref)
IL28B (GG) MTHFR (CT)	10	12	0.52 (0.20–1.39)
IL28B (GG) MTHFR (TT)	64	25	0.33 (0.16–0.69)
IL28B (GT) and MTHFR (CC)	4	9	5.76 (1.63–20.39)
IL28B (GT) MTHFR (CT)	1	5	2.22 (0.24–20.55)	41.654	P < 0.001***
IL28B (GT) MTHFR (TT)	15	11	0.15 (0.06–0.38)
IL28B (TT) and MTHFR (CC)	12	29	3.29 (1.22–8.84)
IL28B (TT) MTHFR (CT)	5	13	1.08 (0.33–3.48)
IL28B (TT) MTHFR (TT)	32	22	0.26 (0.12–0.55
MTHFR rs180133 and GSTP1 rs1695
MTHFR (CC) and GSTP1 (AA)	17	33	1 (Ref)
MTHFR (CC) GSTP1 (AG)	3	19	3.26 (0.83–12.78)
MTHFR (CC) GSTP1 (GG)	9	16	0.28 (0.11–0.74)
MTHFR (CT) and GSTP1 (AA)	12	12	0.56 (0.21–1.52)
MTHFR (CT) GSTP1 (AG)	1	6	6.00 (0.67–53.55)	43.102	P < 0.001***
MTHFR (CT) GSTP1 (GG)	4	12	0.50 (0.14–1.77)
MTHFR (TT) and GSTP1 (AA)	59	33	0.19 (0.09–0.40)
MTHFR (TT) GSTP1 (AG)	19	9	0.85 (0.33–2.22)
MTHFR (TT) GSTP1 (GG)	33	16	1.02 (0.46–2.29)

P < 0.05 = Least significant (*); P < 0.01 = Significant (**); P < 0.001 =Highly significant (***): OR=Odd ratio; 95 % CI = 95 % confidence intervals

3.2. Genotypic and allelic frequency at rs1800470 in TGF-β1 gene and their association with Ischemic stroke

T-ARMS PCR based amplification of rs1800470 in TGF-β1 gene resulted in the generation of 355 base pairs fragment by the outer primer pair in all the enrolled subjects. While the primers specific for homozygous wild (CC) genotype and homozygous mutant (TT) genotype amplified products of 180 and 123 base pairs respectively. Subjects having heterozygous genotype (CT) at rs1800470 amplified both 180 and 123 base pair amplicons along with 355 base pair fragment amplified by outer primers.

Data analysis revealed that at rs1800470 mutant (TT) genotype was more common (46 %) in controls followed by homozygous wild (CC, 37 %) and heterozygous (CT, 17 %) respectively. While for the Ischemic stroke patients, the genotype pattern at rs1800470 was: mutant (TT, 46 %) > wild (CC, 31 %) > heterozygous (CT, 23 Chi square test analysis revealed that the genotypic frequency at rs1800470 varied non-significant (P = 0.4) when compared between Ischemic stroke patients and control group. For allelic frequency among the controls, it was observed that T (polymorphic) allele was more common (55 %) followed by C (wild type) allele (45 %). Similar trend was observed in Ischemic stroke patients enrolled during present study as T allele was common (58 %) followed by C allele (42 %). Chi square test results revealed that allelic frequency at rs1800470 varied non-significant (P = 0.5) upon comparison between Ischemic stroke cases and controls (Table 1).

We calculated observed (HO) and expected heterozygosity (HE) for Hardy–Weinberg equilibrium at rs1800470 in TGF-β1 gene for both case and control groups. Analysis of our results indicated a significant deviation from Hardy Weinberg equilibrium law for both control (Chi square = 66.02, P < 0.001) and cases (Chi square = 43.37, P < 0.001) at rs1800470 in TGF-β1 gene indicating genetic diversity in both studied groups (Table 2).

3.3. Genotypic and allelic frequency at rs1800472 in TGF-β1 gene and their association with Ischemic stroke

When single nucleotide polymorphism SNP rs1800472 in TGF-β1 gene was amplified through Tetra ARMS PCR, the outer primers generated 313 base pairs for all the enrolled subjects. While the primers specific for homozygous wild (CC) genotype and homozygous mutant (TT) genotype amplified products of 213 and 143 bp respectively. Subjects having heterozygous genotype (CT) at rs1800472 amplified both 213 and 143 bp products.

For the control group, it was observed that homozygous mutant (TT) genotype was most frequent (45 %) followed by wild (CC, 29 %) and heterozygous (CT, 26 %), respectively. While for the Ischemic stroke patients, the genotype pattern at rs1800472 was: wild type (CC, 55 %) > mutant (TT, 28 %) > heterozygous (CT, 17 %). Chi square test results revealed that the genotypic frequency varied highly significant (P < 0.001) when compared between Ischemic stroke patients and control group: Wild genotype (CC) was more common in cases while controls had more frequent polymorphic (TT) and heterozygous (CT) genotypes indicating that subjects wild genotype were more susceptible to develop Ischemic stroke. For allelic frequency, it was observed that T (polymorphic) allele was most frequently detected (58 %) in control group followed by C (wild type) allele (42 %). While opposite trend was observed in Ischemic stroke patients as C allele was frequent (63 %) followed by T allele (37 %). Chi square test results revealed that allelic frequency varied significant (P < 0.001) when compared between control and cases. It was observed that cases had higher wild allele (C) while controls had higher polymorphic allele (T) (Table 1).

Analysis of results indicated significant deviations from Hardy Weinberg Law for both control (x² = 35.19, P < 0.001) and for case (x² = 66.006, P < 0. 001) indicating genetic diversity at rs1800472 in TGFB1 gene in both studied genetic groups for screened SNP (Table 2).

3.4. Genotypic and allelic frequency at rs1042522 in TP53 gene and their association with Ischemic stroke

Tetra ARMS PCR based amplification of rs1042522 in TP53 gene resulted in generation of 493 base pairs amplicon by outer primer pair in all enrolled subjects. While the primers specific for homozygous wild (GG) genotype and homozygous mutant (CC) genotype amplified fragments of 200 and 247 base pairs respectively. Subjects having heterozygous genotype (GC) at rs 1042522 amplified both 233 and 290 bp amplicons.

For control group, it was observed that wild (GG) genotype was most frequent (56 %) followed by homozygous mutant CC (24 %) and heterozygous GC (20 %) respectively. While for the ischemic stroke patients, the genotype pattern at rs1042522 was: wild type (GG, 39 %) > heterozygous (GC, 34 %) > mutnat (CC, 27 %). Chi square test results revealed that the genotypic frequency varied significantly (P = 0.005) at rs1042522 when compared between ischemic stroke patients and control group indicating that heterozygous (GC) and mutant genotypes (CC) was more common in cases and had 2.44 and 1.57 times higher risk of developing ischemic stroke, respectively. While wild (GG) combination was safe as it was more frequent in controls. For allelic frequency, it was observed that G (wild type) allele was more frequently detected (66 %) in control group followed by C (mutant type) allele (34 %). Similar trend was observed in Ischemic stroke patients as G allele was frequent (56 %) followed by C allele (44 %) but the patients had significantly higher proportion of mutant allele (P = 0.01) than controls indicating that cases with mutant allele has 1.5 times higher risk of ischemic stroke than controls (Table 1).

Analysis of our results indicated a significant deviation from Hardy-Weinberg Law for both control (x² = 48.75, P < 0.001) and case (x² = 14.8, P < 0. 001) at rs 1042522 in TP53 gene indicating genetic diversity in both studied groups (Table 2).

3.5. Genotypic and allelic frequency at rs180133 in MTHFR gene and their association with Ischemic stroke

T-ARMS PCR based amplification of rs180133 in MTHFR gene resulted in the generation of 407 base pairs fragment from the outer primer pair in all the enrolled subjects. While the primers specific for homozygous wild (CC) and homozygous mutant (TT) genotype amplified products of 273 and 190 base pairs respectively. Subjects having heterozygous genotype (CT) at rs180133 amplified both 273 and 190 base pair amplicons along with 407 base pair fragment amplified by outer primers.

Data analysis revealed that at rs180133 mutant (TT) genotype was more common (71 %) in controls followed by homozygous wild (CC, 19 %) and heterozygous (CT, 10 %) respectively. While for the Ischemic stroke patients, the genotype pattern at rs180133 was: mutant (TT, 46 %) > wild (CC, 31 %) > heterozygous (CT, 23 %). Chi square test analysis revealed that the genotypic frequency varied highly significant (P ˂ 0.001) when compared between Ischemic stroke patients and control group. Cases had significantly higher proportion of wild and heterozygous genotypes and subjected having CT genotype had 1.35 times higher risk of having ischemic stroke than controls (Table 1). For allelic frequency among the controls, it was observed that T (mutant type) allele was more common (76 %) followed by C (wild type) allele (24 %). Similar trend was observed in Ischemic stroke patients enrolled during present study as T allele was common (58 %) followed by C allele (42 %). Chi square test results revealed that allelic frequency at rs180133 varied significant (P ˂ 0.001) between the enrolled subjects. Upon comparison, it was observed that cases had higher “C” while controls had higher polymorphic “T” allele indicating that this polymorphism has reduced the incidence of stroke among the controls (Table 1).

Analysis of our results indicated a significant deviation from Hardy Weinberg equilibrium law for both control (X² = 80.74, P < 0.001) and cases (X² = 43.367, P < 0.001) at rs180133 in MTHFR gene indicating genetic diversity in both studied groups (Table 2).

3.6. Genotypic and allelic frequency at rs1695 in GSTP1 gene and their association with Ischemic stroke

Tetra ARMS PCR based amplification of rs1695 in GSTP1 gene resulted in generation of 476 base pairs amplicon by outer primer pair in all enrolled subjects. While the primers specific for homozygous wild (AA) genotype and homozygous mutant (GG) genotype amplified fragments of 233 and 290 base pairs respectively. Subjects having heterozygous genotype (AG) at rs1695 amplified both 233 and 290 bp amplicons.

For the control group, it was observed that wild (AA) genotype was most frequent (56 %) followed by homozygous mutant GG (30 %) and heterozygous AG (14 %) respectively at rs1605. While for the Ischemic stroke patients, the genotype pattern at rs1695 was: wild type (AA, 50 %) > mutant (GG, 28 %) > heterozygous (AG, 22 %). Chi square test results revealed that the genotypic frequency varied non-significantly (P = 0.2) when compared between Ischemic stroke patients and control group indicating that none of the genotypes at rs1695 was associated with the incidence of ischemic stroke during present investigation (Table 1). For allelic frequency, it was observed that A (wild type) allele was most frequently observed (63 %) in control group followed by G (mutant type) allele (37 %). Similar trend was observed in Ischemic stroke patients as A allele was frequent (61 %) followed by G allele (39 %). Chi square test results revealed that allelic frequency varied non-significantly (P = 0.5) between control and cases. Upon comparison, it was observed that both cases and control had higher wild allele (A) (Table 1).

Analysis of our results indicated a significant deviation from Hardy Weinberg equilibrium law for both control (X² = 75.55, P < 0.001) and cases (X² = 45.89, P < 0.001) at rs1695 in GSTP1 gene indicating genetic diversity in both studied groups (Table 2).

3.7. Association of demographic factors with the incidence of Ischemic stroke

When the studied demographic factors were compared between the case and controls enrolled during present study, Chi square test results revealed that non-smokers (P < 0.01) had significantly higher incidence of Ischemic stroke than smokers. While male and female subjects (P = 1) having different age ranges (P = 0.9) were equally susceptible to develop Ischemic stroke (Table 4).

Table 4.

Analysis of the studied demographic parameters and their association with Ischemic stroke. P-value indicates the results of chi square test calculated for each parameter.

Parameters	Category	Control (N = 156)	Cases (N = 156)	OR (95 % CI)	Chi square value	P-value
Age (Years)	15–30 31–45 46–60 61–75 Above 76	03 37 64 42 10	04 32 63 44 13	1 (Ref) 0.65 (0.32–1.34) 1.14 (0.63–2.05) 1.06 (0.55–2.05) 1.24 (0.46–3.33)	0.95	0.9
Gender	Male Female	92 64	92 64	1 (Ref) 1 (0.63–1.59)	0.000	1
Smoking	Yes No	67 89	47 109	1 (Ref) 1.75 (1.11–2.77)	5.53	0.01**

P > 0.05 = Non-significant, P ≤ 0.01 = Significant (**); OR = Odd ratio; 95 % CI = 95 % confidence intervals

4. Discussion

Globally, stroke is the most common cause of disability and is the second leading cause of death. A number of mono and polygenic factors has been associated with ischemic stroke genes but polygenic etiologies are more common (Yoshimoto et al., 2025). Stroke incidence is increasing in younger populations. According to a recent study, more than 60 % of stroke affected subjects are having age less than 70 years while 16 % subjects are even less than 50 years old (Alabduljabbar et al., 2025). This trend underscores the importance of investigating genetic predispositions associated with the ischemic stroke.

Analysis of our data revealed that genotypes at rs8099917 in IL-28B gene were significantly associated with the disease. Cases had significantly higher polymorphic genotype (TT) as well as allele (T) than controls during this investigation (Table 1). Genotype and allelic frequency at this SNP has never been investigated in relation with ischemic stroke. Hence, this SNP further investigation to be considered as a potential genetic marker for ischemic stroke. However, Chen et al (Chen et al., 2012a). had reported that T (polymorphic) allele and homozygous polymorphic (TT) genotype were linked to an increased risk of schizophrenia, a serious mental health condition, in Han Chinese population.

During the present investigation both wild (C) and polymorphic (T) genotypes at rs1800470 in TGF-β1 gene were not associated with ischemic stroke (Table 1). There are a few studies available in literature where the genotypes at rs1800470 have been investigated in relation to ischemic stroke. Contrary to our observations, Tao et al (Tao et al., 2010). had reported that Han Chinese subjects, especially females, carrying TT genotype at rs1800470 were susceptible to ischemic stroke. Similarly, Kim and Lee (Kim and Lee, 2006) had reported that Korean subjects having polymorphic TT genotype at rs1800470 were susceptible to both ischemic stroke and vascular dementia. Contrary to this observation, Peng et al (Peng et al., 2011). had found that the frequencies of CC wild genotype and C allele were significantly higher in the cerebral infraction group as compared to enroll from Chinese population.

This SNP rs1800472 is located in exon 5 of TGF-β1 gene and changes the amino acid from threonine to isoleucine at position 263 (Tao et al., 2010). A significant association was observed between rs1800472 in TGF-β1 gene and ischemic stroke during present investigation. Interestingly, we found that nucleotide change from “C” to “T” at position 788 in TGF-β1 gene resulted in decreased incidence of ischemic stroke as the majority of controls were carrying this polymorphism. While the wild CC genotype was associated with increased disease incidence (Table 1). This SNP has been reported to be associated with cardio-vascular disorders and coronary heart disease in various ethnic groups (Chen et al., 2012b) but has never been investigated with reference to ischemic stroke. Hence, this study is adding to existing knowledge that rs1800472 decreases the risk of ischemic stroke among screened Pakistani population.

Single nucleotide polymorphism rs1042522 (CGC goes to CCC) is located in exon 4 and it is one of the most commonly screened SNP in the TP53 gene that leads to an Arginine to Proline amino acid substitution at amino acid position 72 (Ounalli et al., 2023). During the present study, we observed that rs1042522 in TP53 gene was associated with the disease incident and subjects suffering from ischemic stroke had more frequent heterozygous (GC) and polymorphic genotypes (CC) at rs1042522. Our results are in agreement with the only study available in literature in which Liu et al (Liu et al., 2021). had documented that genotypes at rs1042522 in p53 was associated with the recurrence risk of ischemic stroke under the dominant model in Chinese population and the patients carrying the CG + GG genotype showed an increased ischemic stroke recurrence risk. These limited studies warants that this SNP should be explored more frequently in ischemic stroke cases having various ethnic backgrounds.

During present investigation, we observed a signification association between rs180133 in MTHFR gene with ischemic stroke. It was observed that polymorphic genotype (TT) was more common in control group making it a safe combination. On the other hand cases had more frequent wild (CC) or heterozygous (CT) genotypes at rs180133 making them associated with the disease incidence among the screened population (Table 1). Our results are in agreement with those of Kumar et al (Kumar et al., 2016). as they found that the risk of ischemic stroke was 1.9 times greater in Indian subjects having TT and CT genotypes at rs180133. Our results are in partial agreement with El-Khawaga et al (El-Khawaga et al., 2024). as they had documented that CT genotype at rs180133 increased threefold while TT (polymorphic) genotypes increased the risk of having ischemic stroke to fourfold among the screened Egyptian population. Contrary to our findings, Huang et al (Huang et al., 2022). (reported that the Chinese subjects having polymorphic TT genotype had three times higher risks of having ischemic stroke than those having wild (CC) genotype. Mazdeh et al (Mazdeh et al., 2021). and Escobedo et al (Escobedo et al., 2018). had also found that polymorphic TT genotype at rs180133 substantially increased the risk of stroke than the wildtype (CC) genotype in Iranian and Mexican populations respectively. These diverse genotyping results at rs180133 in MTHFR makes it a worth screening candidate gene for estimation of ischemic stroke incidence among global populations.

During the present study, none of the genotypes at rs1695 was associated with the incidence of Ischemic stroke during present investigation (Table 1). Genotypes at rs1695 has never been investigated with reference to ischemic stroke but it has been documented by Polonikov et al (Polonikov et al., 2012). did not found any association between genotypes at rs1695 and hypertension in Russian population. Similary, no association of rs1695 was found with migraine susceptibility among South-Eastern European Caucasian population (Papasavva et al., 2023) and with schizophrenia in Chinese population (Gao et al., 2017). This limited information needs more screening of rs1695 in various populations in order to establish their potential association with ischemic stroke. We believe the differences in genotyping results compared to other studies may be explained by ethnic variation. Allele frequencies and linkage disequilibrium structures are known to differ across populations, and such genetic diversity can influence the distribution of SNPs as well as their association with disease phenotypes. Therefore, the variations observed in our cohort likely reflect population-specific genetic architecture rather than methodological (van Dyke et al., 2009).

Hardy-Weinberg equilibrium is calculated to estimate the heterozygosity value in a population and to calculate their genetic diversity (Abramovs et al., 2020). During the present study, Hardy-Weinberg equilibrium was found disturbed in cases and controls for all the screened SNPs and the observed and expected heterozygosity of cases as well as controls significantly differed from each other (Table 2). The observed deviations appear to result from substantial differences in genotype distribution between cases and controls, which likely reflect true biological variation rather than technical error. Given that our cohort represents a specific ethnic population, these deviations may also be explained by underlying genetic background differences. Therefore, we believe the results reflect genuine genotype-phenotype correlations within this study population. A number of factors are known to disturb the Hardy-Weinberg equilibrium including natural selection, nonrandom mating, mutations, genetic drift and gene flow (Graffelman et al., 2017) and these factors are also applicable in the population that was enrolled in the present study.

It has been an established fact that results generated in the studies that deals with individual SNP cannot be a good disease predictor as this SNP will alters the function of only that particular gene. While for a normal physiological response, usually several genes has to play their normal role (Arshad et al., 2023). A single SNP has thus a modest influence in the disease progression and the same biological event is more greatly affected by SNPs in different genes and the disease progression also depends upon how these SNP combinations interact (Ijaz et al., 2024). During current investigation, we found that certain genotypic combinations at the screened SNPs significantly increased the risk to develop ischemic stroke among enrolled subjects. All the combinations where a particular genotype had significantly higher frequency among the controls were considered protective, while those genotypic combinations having higher frequency among cases were considered as a risk factor of developing ischemic stroke (Table 3). Our results indicated that subjects having heterozygous (CT) at rs1800470 in TGF-β1and heterozygous GC genotype at s1042522 in TP53 (OR = 3.78), heterozygous (CT, OR = 5.3) and polymorphic (TT, OR = 4.3) at rs1800470 in TGF-β1 and wildtype (CC) at rs1800472 in TGF-β1, heterozygous (CT, OR = 8.87) and polymorphic (TT, OR = 8.67) at rs1800470 in TGF-β1 and wildtype (CC) at rs180133 at MTHFR wildtype (CC, OR = 2.76) at rs1800472 in TGF-β1and heterozygous (AG) at re1695 at GSTP1, wildtype (CC, OR = 1.18) at rs1800472 in TGF-β1 and heterozygous (GT) at rs8099917 in IL-28B, wildtype (CC, OR = 3.948) at rs1800472 in TGF-β1 and wildtype (CC) at rs180133 at MTHFR, wildtype (CC, OR = 3.948) at rs1800472 in TGF-β1 and heterozygous GC genotype at rs1042522 in TP53, polymorphic (GG, OR = 1.86) at s1042522 in TP53 and heterozygous (GT) at rs8099917 in IL-28B, heterozygous (GC) genotype at s1042522 in TP53 and wildtype (CC, OR = 11.2) at rs180133 at MTHFR, wildtype (CC, OR = 3.4) and heterozygous (GC, OR = 2.83) at rs1042522 in TP53 and heterozygous (AG) at rs1695 at GSTP1, heterozygous (GT, OR = 5.76) and polymorphic (TT, OR = 3.29) at rs8099917 in IL-28B and wildtype (CC) at rs180133 at MTHFR and those having wildtype (CC, OR = 3.26) and heterozygous (CT, OR = 6) at rs180133 in MTHFR and heterozygous (CT) at rs1695 in GSTP1 had higher incidence of ischemic stroke (Table 3). In a similar study, Liu et al (Liu et al., 2021). had reported that two polymorphisms, rs1042522 in p53 and rs2027701 in LINC-ROR, were jointly associated with ischemic stroke recurrence. They found that subjected having heterozygous (CG) or polymorphic ( GG) rs1042522 and polymorphic “GG” phenotype at rs2027701 in Chine population had higher recurrence of ischemic stroke. Similarly, during a haplotype analysis conducted by Peng et al (Peng et al., 2011)., it was observed that the frequency of the-509T (intronic polymorphism)/+ 869 C (rs1800470) combined genotypes in TGF-β1 was significantly higher in the cerebral infraction group of Chinese population than in controls. Mazdeh et al (Mazdeh et al., 2021). had also reported that CT haplotype at rs1801131 and rs180133 respectively in MTHFR gene increases the risk of ischemic stroke in Iranian population. Zhou et al (Zhou et al., 2014). had als found that carriers of the MTHFR CTTCGA (at rs180133,-rs12121543,-rs9651118,-rs2274976,-rs13306553-and rs1801131 respectively) in Chinese population had significantly lower risk of having ischemic stroke than those with the CTTTGA haplotype

A variety of risk factors has been reposted to be associated with ischemic stroke. These risk factors for stroke range from common ailments like diabetes mellitus (Fischer et al., 2006), atrial fibrillation, hypertension, aneurysms (De Stefano et al., 2021), hyperlipidaemia, arterio venous malformation (Chen et al., 2012b), heart-gastrointestinal-psychiatric disease (She et al., 2022), arthritis (Huang et al., 2025) and smoking to factors observed in otherwise seemingly healthier individuals like antiphospholipid syndrome, vasculitides, procoagulopathies and Moyamoya disease (Nomani et al., 2017). El-Khawaga et al (El-Khawaga et al., 2024). observed that ischemic stroke patients exhibited significantly higher levels of systolic blood pressure, cholesterol, high density lipoprotein and low-density lipoprotein while lower diastolic blood pressure, triglycerides, and very low-density lipoprotein than controls. Our findings also highlight the importance of considering gene-environment interactions, particularly in relation to smoking, which emerged as a significant risk factor in this study. Cigarette smoke induces oxidative stress, endothelial dysfunction, and chronic inflammation, all of which contribute to vascular injury and atherogenesis (Fukuoka et al., 2018, Uddin et al., 2008, Wang et al., 2024). These processes may interact with genetic variants in pathways regulating oxidative stress response (e.g., GSTP1), vascular remodeling (TGF-β1), apoptosis and inflammation (TP53), and homocysteine metabolism (MTHFR). Individuals carrying risk alleles in these genes may be more susceptible to the deleterious vascular effects of smoking, thereby experiencing an elevated risk of ischemic stroke. This gene-environment interplay could partly explain the stronger association observed between smoking and stroke in our cohort. Similar to our observations, Fukuoka et al (Fukuoka et al., 2018). and Uddin et al (Uddin et al., 2008). had also documented that smokers were at higher risk to develop the ischemic stroke than non-smoker in Japan and Bangladesh respectively. It was documented that duration of smoking habit was directly proportional to stroke incidence. Wang et al (Wang et al., 2024). had also reported a similar observation during a survey that involved subjects from 32 countries confirming that globally smokers had higher incidence of ischemic stroke. In contrast to these observations, Bejot et al (Béjot et al., 2014). found that the current smokers had no association with the incidence of ischemic stroke in subjects enrolled from Dijon, France but the former smokers do showed an association. Furthermore, we did not find any association of age and gender with ischemic stroke during this study. In contrast to our observation, Ojha et al (Ojha et al., 2020). had reported that the incidence of stroke increased with age in Indian population and the 46–60 years age group, especially females, were most susceptible to stroke injury. Liu et al (Liu et al., 2021). found that age above 55 and stroke subtype were associated with ischemic stroke recurrence and diabetes mellitus was a potential risk factor for ischemic stroke in Chinese population. While Kim and Lee (Kim and Lee, 2006) documented that age, gender, hyperlipidemia and smoking habit were not associated with ischemic stroke in Korean population. In agreement with our findings, Talebi et al (Talebi et al., 2014). had also documented that the incidence of ischemic stroke was same for both male and females enrolled from Dijon, France but the former smokers do showed an association.

The small sample size is a limitation of this study as the higher sample size makes the results better predictor and more reliable. Although, we targeted 5 genes and 6 SNPs in this investigation, the number can be enhanced in future studies as many other genes and SNPs are reported to be associated with ischemic stroke in various ethnic groups globally. However, the present study is valuable as these SNPs have never been screened in local population with reference to ischemic stroke. Another limitation of our study is that allele frequencies were not compared with global reference databases such as 1000 Genomes or gnomAD. Such comparisons would have provided broader context for our findings, and we plan to include this analysis in future investigations.

5. Conclusion

In conclusion, ischemic stroke is a serious health issue in Pakistan, and reporting population-specific genetic data is essential to identify SNPs that may contribute to disease development. Our findings suggest that rs8099917 in IL-28B, rs1800472 in TGF-β1, rs1042522 in TP53, and rs180133 in MTHFR may play a significant role in the progression of ischemic stroke, whereas rs1800470 in TGF-β1 and rs1695 in GSTP1 showed no association. Smoking was also identified as an important risk factor. We acknowledge several limitations of this pilot study, including the relatively small sample size, the limited recruitment period, and the absence of functional validation of the identified variants. Nevertheless, these preliminary results provide valuable insights into the genetic basis of ischemic stroke in this population. Larger, multi-center studies with functional analyses will be needed to confirm and extend these findings.

CRediT authorship contribution statement

Adil Khan: Writing – review & editing, Software. Saba Shakoor: Writing – original draft, Software, Formal analysis. Burhan Pasha: Writing – review & editing, Resources, Investigation. iqbal furhan: Writing – review & editing, Supervision, Formal analysis, Conceptualization. Kam-Hong Kam: Writing – review & editing, Methodology, Conceptualization. Chien-Chin Chen: Writing – review & editing, Software, Conceptualization. Muhammad Latif: Writing – review & editing, Supervision, Resources, Conceptualization. Rashid Mehmood: Writing – review & editing, Formal analysis. Ayesha Nasir: Writing – review & editing, Formal analysis. Mehnaz Akhtar: Writing – review & editing, Formal analysis. Alisha Batool: Writing – review & editing, Formal analysis. Maryam Akhlaq: Writing – review & editing, Formal analysis. Abass Kasim: Writing – review & editing, Software. Meesam Ali: Writing – review & editing, Formal analysis.

Ethical approval

All the experimental details and subject handling procedures were approved by the Institutional Review Board of Nishter Medical University via letter number 212133/NMU (2024).

Consent to participate

Informed oral consent was obtained from all the subjects enrolled in this study.

Consent to publish

Authors give their consent to the publisher to publish this manuscript upon acceptance.

Declaration of Competing Interest

The authors declare no competing interests with anyone.

Appendix A. Supplementary material

Supplementary material

Data availability

All the data associated with this project are presented in this manuscript.