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F1000Research

2046-1402

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10.12688/f1000research.167074.2

Research Article

Articles

The Genetic Association of Single Nucleotide Polymorphisms of SERPINE1 (rs6092) and IFNAR 2 (rs1051393, rs2229207) Genes Is Related to Post Covid-19 Respiratory Syndrome

[version 2; peer review: 1 approved, 1 not approved]

Ariani

Yulia

Conceptualization Investigation Supervision Writing – Original Draft Preparation Writing – Review & Editing https://orcid.org/0000-0002-6640-8530 a 1 Muhiardi

Indra

Formal Analysis Investigation Writing – Original Draft Preparation Writing – Review & Editing 1 Gunawan

Indra

Formal Analysis Investigation Writing – Original Draft Preparation Writing – Review & Editing 1 Ramadhani

Nadhifa Tazkia

Methodology Writing – Original Draft Preparation Writing – Review & Editing https://orcid.org/0009-0006-5348-4119 1 Risti

Shafa Talitha

Methodology Writing – Original Draft Preparation Writing – Review & Editing 1 Sutarto

Riyadi

Investigation Writing – Original Draft Preparation Writing – Review & Editing 2 Susanto

Agus Dwi

Conceptualization Investigation Supervision Writing – Original Draft Preparation Writing – Review & Editing 2 3 1Department of Medical Biology, University of Indonesia, Central Jakarta, Jakarta, 10430, Indonesia 2Persahabatan National Respiratory Hospital, East Jakarta, Jakarta, 13230, Indonesia 3Department of Pulmonology and Respiratory Medicine, University of Indonesia, Central Jakarta, Jakarta, 10430, Indonesia

a yulia.ariani@ui.ac.id

No competing interests were disclosed.

9 5 2026

2025

1292

29 4 2026

2026

This is an open access article distributed under the terms of the Creative Commons Attribution Licence, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Background

Coronavirus Disease 2019 (COVID-19), caused by SARS-CoV-2, is a global pandemic. Severe cases may develop post-COVID-19 respiratory syndrome, characterized by symptoms persisting ≥4 weeks. In this study, we aim to identify the association of post-COVID-19 respiratory syndromes with variants in the SERPINE1 and IFNAR2 genes which are responsible for regulating fibrinolysis and immune response against viral infection, respectively.

Methods

We recalled 85 COVID-19 positive subjects who were originally admitted to Persahabatan Central General Hospital, Jakarta, in 2020—2021. We performed TaqMan genotyping on clinically relevant variants at rs1051393 and rs2229207 in IFNAR2 and rs6092 in SERPINE1 and conducted statistical analysis to identify the association of these variants with the clinical manifestations of post-COVID-19 respiratory syndrome.

Results

We found that patients with post-COVID-19 respiratory syndrome had greater disease severity at admission (p = 0.02) and during hospitalization (p = 0.004), along with longer hospital stays (p = 0.0001), possibly due to poorer radiological findings at admission (p = 0.013) and discharge (p = 0.001). Variants in SERPINE1 and IFNAR2 showed significant associations: rs1051393 ( IFNAR2) with fever (p = 0.037), and rs2229207 with chest pain (p = 0.047), higher potassium (p = 0.001), chloride (p = 0.047), and pCO ₂ (p = 0.030). Additionally, rs6092 ( SERPINE1) was linked to increased basophils (p = 0.027) and eGFR (p = 0.036). After Benjamini–Hochberg correction, only disease severity during hospitalization (padj = 0.021), radiological findings at admission (padj = 0.052) and discharge (padj = 0.008), hospitalization duration (padj = 0.00016), and the association of rs2229207 with potassium levels (padj = 0.003) remained significant.

Conclusion

Our findings suggested the potential contribution of genetic predispositions in the SERPINE1 and IFNAR2 genes to the emergence of post-COVID-19 respiratory symptoms, providing additional consideration in treatment management to improve the outcomes of patients positive for COVID-19.

covid-19 single nucleotide polymorphisms Covid-19 respiratory syndrome SERPINE1 IFNAR2

Riset Inovasi dan Indonesia Maju–Lembaga Pengelola Dana Pendidikan

36/IV/KS/06/2022

This study was supported by the Riset Inovasi dan Indonesia Maju–Lembaga Pengelola Dana Pendidikan (RIIM LPDP) grant (Grant No. 36/IV/KS/06/2022).

The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Revised Amendments from Version 1

In this revised version of the manuscript, we have addressed the comments and suggestions provided by the reviewers. The revisions include improvements in language and clarity, as well as the addition of further data analyses. Specifically, we have incorporated additional genetic model analyses, including dominant, codominant, overdominant, and recessive models. Furthermore, we have conducted multivariate analyses across all study parameters to provide a more comprehensive evaluation. Revisions have also been made to the Introduction and Discussion sections to enhance clarity and strengthen the interpretation of the findings. In addition, the tables presenting statistical results have been revised. Methodological improvements have also been implemented, including a clearer justification for the use of saliva samples, refinement of the statistical analysis procedures, and more detailed inclusion criteria for study participants to ensure greater comprehensiveness.

Introduction

Coronavirus Disease 2019 (COVID-19) was a global pandemic of respiratory illness caused by SARS-CoV-2. ¹ ^, ² According to the Ministry of Health of the Republic of Indonesia in March 2022, the number of confirmed COVID-19 cases reached 5,891,022, with 154,221 reported deaths, while 5,658,238 patients had recovered. ³ However, individuals recovered from COVID-19 may develop post-COVID-19 respiratory syndrome, characterized by persistent respiratory symptoms lasting ≥4 weeks after the initial onset. ⁴ Zhou et al. (2020) described that among 55 COVID-19 patients, 14.5% experienced shortness of breath that persisted for up to three months, ⁵ while another study revealed that 45.5% of 183 patients experienced shortness of breath for up to four weeks. ⁶ Another study reported that among patients with post-COVID-19 respiratory syndrome, 15.5% experienced persistent cough, 11.2% reported shortness of breath, and 3.8% experienced sore throat. ⁷

The emergence of post-COVID-19 symptoms is often preceded by severe COVID-19 symptoms, such as cytokine storms and dysregulated coagulation that may result in excessive blood clotting. ⁷ ^– ⁹ Previous studies have highlighted the association between dysregulated PAI-1, the main inhibitor of fibrinolysis enzymes important for removing clot formation, with severe and critical COVID-19 conditions. ¹⁰ ^– ¹² A report by Fricke-Galindo et al. (2022) disclosed that rs6092 in SERPINE1 was associated with higher levels of P-selectin and tPA in subjects carrying GA + AA genotypes in severe COVID-19 patients, which may indicate the contribution of genetic variants to the impaired fibrinolysis in severe COVID-19. ¹¹ Similarly, variants in the IFNAR2 have been reported to be associated with significantly high mortality risk in subjects with severe COVID-19, as higher soluble IFNAR2 levels were also observed among the surviving patients. ¹³ However, whether the genetic predisposition in SERPINE and IFNAR2 is associated with post-COVID-19 respiratory symptoms is still unclear and requires further confirmation.

Here, we investigated the association of rs6092 in SERPINE1, and rs1051393 and rs2229207 in IFNAR2 with prolonged respiratory symptoms and blood markers in 85 subjects of COVID-19 in Indonesia. We indicated significant associations of variants in SERPINE1 and IFNAR2, in which rs1051393 in IFNAR2 was significantly associated with fever, while rs2229207 was associated with chest pain, higher potassium and chloride levels in blood analysis, as well as higher pCO ₂ level. We also note that rs6092 in SERPINE1 was associated with elevated levels of basophils and eGFR in blood analysis. Our findings suggested the potential contribution of genetic predispositions in the SERPINE1 and IFNAR2 genes to the emergence of post-COVID-19 respiratory symptoms, providing additional consideration in treatment management to improve the outcomes of patients with COVID-19.

Methods Study population

The ethical approval of this study was issued by the Health Research Ethics Committee, Faculty of Medicine, Universitas Indonesia (KET-872/UN2.FI/ETIK/PPM.00.02/2023) and by the Health Research Ethics Committee of Persahabatan General Hospital (98/KEPK-RSUPP/06/2023). This was a retrospective study involving subjects who were admitted to Persahabatan General Hospital (RSUP) Jakarta between 2020 and 2021. The study population consisted of 42 COVID-19 patients without respiratory syndrome as controls and 43 cases of post-COVID-19 respiratory symptoms who consented to be recalled in 2024 to provide their saliva samples. Subjects classified as cases if at least one of the following criteria was met: hospitalization duration of ≥28 days; or < 28 days with pulmonary fibrosis; or < 28 days with no improvement of radiological findings and at least one persistent respiratory symptom (cough, dyspnea, malaise, chest pain, or sore throat) at the time the patient was discharged. ⁴ The control population consists of subjects with none of these criteria.

The cases in our study were classified as mild, moderate, severe, and critical, in which mild was characterized by symptoms without evidence of viral pneumonia or hypoxia, fever, cough, fatigue, myalgia, or anosmia/ageusia, with SpO ₂ >95% on room air. Moderate cases were defined by pneumonia, fever, cough, dyspnea, or tachypnea, without features of severe pneumonia, with SpO ₂ >93% on room air. Severe patients were indicated by pneumonia accompanied by at least one of the following: respiratory rate > 30 breaths/minute, severe respiratory distress, or SpO ₂ <93% on room air. Critical subjects were those with acute respiratory distress syndrome (ARDS), sepsis or septic shock, or other conditions requiring life-support interventions such as mechanical ventilation or vasopressor therapy.

Sample collection and preparation

DNA was obtained from 2 mL of saliva to prevent the feeling of discomfort during sample retrieval. Saliva was collected in a sterile 10 mL tube filled with preservative solution, then immediately transported to the laboratory for DNA extraction using Zymo Miniprep Kit (Zymo Research, Irvine, CA) according to the manufacturer’s instructions with modification. Quantification of the DNA samples was performed using Qubit Fluorometer (Thermo Fisher Scientific, Waltham, MA), while the purity was evaluated with Varioskan LUX multimode microplate reader (Thermo Fisher Scientific, Waltham, MA).

SNP genotyping

The genotypes of rs6092, rs1051393, and rs2229207 were evaluated with the TaqMan Assay (Applied Biosystems, Waltham, MA) using normalized DNA samples at 5 ng/μL. About 4 μL of the DNA samples were mixed with 12.5 μL of 2x TaqPath ProAmp Master Mix, 1.25 μL of TaqMan SNP Genotyping Assay, and 7.25 μL of nuclease-free water (NFW) in a 96-well plate. The thermal cycling was done in Applied Biosystem 7500 Real Time PCR (Applied Biosystems, Waltham, MA). The genotype of each variant was assigned by the autocalling feature of the TaqMan Genotyper Software v1.7.1, and then the analysis results were exported in a table file.

Data analysis

Data in this study were analyzed using SPSS v25.0. Statistical analysis of the demographic data, study population, and disease severity classification was assessed using Pearson’s Chi-Square test, while the analysis of blood laboratory results, arterial blood gas, and clotting blood factors was performed using the Mann-Whitney U test, Kruskal-Wallis, or the Independent t-test. We conducted linear and logistic regression to evaluate the association of rs6092, rs1051393, and rs2229207 with post-COVID-19 respiratory symptoms and blood laboratory parameters. To account for multiple comparisons, p-values were adjusted using the Benjamin–Hochberg (BH) false discovery rate (FDR), in which the global FDR correction was applied across all SNP–phenotype tests. Clustered FDR correction was conducted within predefined clinical and laboratory domains, including symptoms, electrolytes, complete blood count (CBC) parameters, liver function tests, renal function markers, and blood gas analyses. An adjusted p-value of <0.05 was considered significant.

Results Demographic comparisons

We examined the demographic and hospitalization history of patients with post-COVID-19 respiratory syndrome and control subjects ( Table 1). The average age of patients with post-COVID-19 respiratory syndrome was higher than control, although the difference was not significant (51.23 vs. 47.33, p = 0.178). Despite no significant differences in terms of sex, year of admission, or body mass index (BMI), we noted significant differences in disease severity upon admission, in which we observe that more COVID-19 patients with respiratory syndrome were classified as severe to critical conditions, while the majority of patients with no respiratory syndrome were mostly had moderate severity ( p = 0.02) ( Table 1). However, this difference was no longer significant after adjusting for BH-correction ( Table 2).

Table 1. Demographic data of COVID-19 subjects with and without respiratory syndromes.

	Case	Control	p-value
Age	51.23 ± 13.388	47.33 ± 13.094	0.178 *
Sex	M: 34, F: 9, n = 43	M:29, F: 13, n = 42	0.292 **
Year of Admission	2020: 21, 2021: 22, n = 43	2020: 18, 2021: 24, n = 42	0.580 **
BMI	26.30 (23.59-28.53)	26.81 (24.05-34.19)	0.401 ***
Severity on Admission
Mild	2 (4.7%)	0 (0.0%)	0.023
Moderate	17 (39.5%)	30 (71.4%)
Severe	17 (39.5%)	11 (26.2%)
Critical	6 (14.0%)	1 (2.4%)
Radiology on Admission
Normal or Near Normal	2 (4.7%)	6 (14.3%)	0.013
Reversible Lesion	36 (83.7%)	36 (85.7%)
Irreversible Lesion (Fibrosis)	5 (11.6%)	0 (0.0%)
Hospitalization Time	16.00 (11.00–20.00)	9.50 (6.75–12.00)	0.0001
The Worst Clinical Severity Throughout Hospitalization
Mild	0 (0.0%)	0 (0.0%)	0.004
Moderate	16 (37.2%)	28 (66.7%)
Severe	19 (44.2%)	12 (28.6%)
Critical	8 (18.6%)	2 (4.8%)
End of Radiology Series
Normal or Improvement with Near Normal result	1 (2.3%)	10 (31.3%)	0.001
Improvement, not Normal	33 (25.6%)	22 (68.8%)
Stationary	15 (34.9%)	0 (0.0%)
Deteroriation	16 (37.2%)	0 (0.0%)
Discharge Outcome
Cured	40 (93%)	41 (97.6%)	0.32 ***
Cured with Complication	3 (7%)	1 (2.4%)
Symptoms
Fever	32 (74.4%)	31 (73.85)	0.949 **
Cough	35 (81.4%)	32 (76.2%)	0.557 **
Malaise	6 (14.0%)	6 (14.3%)	0.965 **
Sorethroat	2 (4.7%)	2 (4.8%)	1 ^
Dyspnoea	36 (83.7%)	29 (69%)	0.111 **
Chest Pain	1 (2.3%)	2 (4.8%)	0.616 ^

T-test.

Chi-Square.

***

Man-Whitney.

^{^}

Fisher-Exact Test.

Table 2. Benjamini-Hochberg adjustment for multiple testing of demographic data.

	p-value	BH-adjusted p-value
Severity on Admission	0.023	0.074
Radiology on Admission	0.013	0.052
Hospitalization Time	0.0001	0.0016
The Worst Clinical Severity Throughout Hospitalization	0.004	0.021
End of Radiology Series	0.001	0.008

Subjects with post-COVID-19 respiratory syndrome had the highest disease severity during the course of hospitalization, in which we noted that these patients were further deteriorating towards the more severe and critical status ( p = 0.004; p _adj = 0.021). This data was also supported by other clinical findings, in which we noted that patients with post-COVID-19 respiratory syndrome had significantly worse radiological examination upon admission ( p = 0.013; p _adj = 0.052) and at the end of hospitalization ( p = 0.001; p _adj = 0.008). Moreover, we observed that patients with post-COVID19 respiratory syndrome had longer hospitalization compared to patients with no respiratory syndrome ( p = 0.0001; p _adj = 0.00016), although these differences may have been contributed by the inherently different classification between the case and control groups. In addition, we found that most patients in both groups had fever, cough, and malaise, but no significant differences were observed ( Table 1).

Genotype distributions

The allele and genotype frequencies of rs6092, rs1051393, and rs2229207 are summarized in Table 3. We found that 17.60% of individuals carry GG and 82% have GA genotypes for rs6092 (G > A). Meanwhile, genotype frequencies were 16.7% TT, 34.7% TG, and 48.6% GG for rs1051393 (T > G), while for rs2229207 (T > C), TT, TC, and CC genotypes were observed in 74.10%, 21.20%, and 4.70% of participants, respectively. The most frequent genotypes were GA for rs6092, GG for rs1051393, and TT for rs2229207. Allele frequencies were 58.82% G and 41.18% A for rs6092; 34% T and 66% G for rs1051393; and 84.7% T and 15.30% C for rs2229207. Hardy–Weinberg equilibrium (HWE) analysis demonstrated that all SNPs were in equilibrium, with χ ² values of 0.055 for rs6092 ( df = 1, critical value = 3.41), 0.223 for rs1051393 ( df = 2, critical value = 5.99), and 1.354 for rs2229207 ( df = 2, critical value = 5.99), all below their respective critical values. These results suggested that the genotype distributions were consistent with HWE, supporting the genetic stability and reliability of the study population.

Table 3. Allele and genotype frequencies of the rs6092, rs105393, and rs2229207.

SNPs	Allele 1 Frequency	Allele 2 Frequency	Genotype of Homozygote Wild-Type	Genotype of Heterozygote	Genotype of Homozygote Mutant
rs6092 (G > A)	58.82% (G)	41.18% (A)	17.60% (GG)	82.00% (GA)	0% (AA)
rs105393 (T > G)	34% (T)	66% (G)	16.70% (TT)	34.70% (TG)	48.60% (GG)
rs2229207 (T > C)	84.7% (T)	15.3% (C)	74.10% (TT)	21.20% (TC)	4.70% (CC)

Table 4. Association of rs6092, rs105393, and rs2229207 with subject classification.

SNPs	Genotype	Case	Control	X ²	p-value
rs6092 (G > A)	GG	8 (18.60%)	7 (16.70%%)	0.055	1
rs6092 (G > A)	GA	35 (81.40%)	35 (83.30%)	0.055	1
rs1051393 (T > G)	TT	5 (15.20%)	7 (17.90%)	0.223	0.894
	TG	11 (33.30%)	14 (35.90%)
	GG	17 (51.50%)	18 (46.20%)
rs2229207 (T > C)	TT	30 (69.80%)	33 (78.60%)	1.354	0.508
	TC	10 (23.30%)	8 (19.00%)
	CC	3 (7.00%)	1 (2.40%)

Our data showed no significant differences in genotype distributions of rs6092, rs1051393, and rs2229207 between patients with and without post-COVID-19 respiratory syndrome ( p > 0.05). Genetic association analyses across various inheritance models indicated that none of rs6092, rs1051393, or rs2229207 were significantly associated with post-COVID-19 respiratory syndrome after correction for multiple comparisons ( Table 5). Consistently, genotype distributions for rs6092, rs1051393, and rs2229207 did not differ between the two groups, with cut-off p-values <0.05 and 95% CI, indicating no association of genotypes and post-COVID-19 respiratory syndrome in our study ( Table 6).

Table 5. Genetic association analyses across inheritance models.

SNP	Minor/Major Allele	Genetic Model	Genotype Frequency (Cases) n (%)	Genotype Frequency (Controls) n (%)	OR (95% CI)	p-value
rs6092	A/G	Dominant	-	-	0.91 (0.28–2.96)	0.869
		- G/G	8 (18.2%)	7 (16.7%)	1.00 (Reference)	-
		- A/G + A/A	36 (81.8%)	35 (83.3%)	0.91 (0.28–2.96)	0.869
		Codominant	-	-	-	0.874 ^‡
		- G/G	8 (18.2%)	7 (16.7%)	1.00 (Reference)	-
		- A/G	36 (81.8%)	35 (83.3%)	0.91 (0.27–3.01)	0.874
		- A/A	0 (0%)	0 (0%)	-	-
		Recessive	-	-	-	-
		- G/G + A/G	44 (100%)	42 (100%)	1.00 (Reference)	-
		- A/A	0 (0%)	0 (0%)	-	-
		Overdominant	-	-	1.12 (0.34–3.68)	0.852
		- A/G	36 (81.8%)	35 (83.3%)	1.12 (0.34–3.68)	0.852
		- G/G + A/A	8 (18.2%)	7 (16.7%)	1.00 (Reference)	-
rs1051393	G/T	Dominant	-	-	1.05 (0.29–3.85)	0.941
		- T/T	5 (14.7%)	7 (17.9%)	1.00 (Reference)	-
		- T/G + G/G	29 (85.3%)	32 (82.1%)	1.05 (0.29–3.85)	0.941
		Codominant	-	-	-	0.965 ^‡
		- T/T	5 (14.7%)	7 (17.9%)	1.00 (Reference)	-
		- T/G	11 (32.4%)	14 (35.9%)	0.98 (0.24–4.03)	0.973
		- G/G	18 (52.9%)	18 (46.2%)	1.12 (0.28–4.52)	0.872
		Recessive	-	-	0.76 (0.30–1.94)	0.569
		- G/G	18 (52.9%)	18 (46.2%)	0.76 (0.30–1.94)	0.569
		- T/T + T/G	16 (47.1%)	21 (53.8%)	1.00 (Reference)	-
		Overdominant	-	-	1.12 (0.42–3.02)	0.818
		- T/G	11 (32.4%)	14 (35.9%)	1.12 (0.42–3.02)	0.818
		- T/T + G/G	23 (67.6%)	25 (64.1%)	1.00 (Reference)	-
rs2229207	C/T	Dominant	-	-	1.82 (0.60–5.47)	0.289
		- T/T	31 (70.5%)	33 (78.6%)	1.00 (Reference)	-
		- T/C + C/C	13 (29.5%)	9 (21.4%)	1.82 (0.60–5.47)	0.289
		Codominant	-	-	-	0.584 ^‡
		- T/T	31 (70.5%)	33 (78.6%)	1.00 (Reference)	-
		- T/C	10 (22.7%)	8 (19.0%)	1.85 (0.58–5.89)	0.300
		- C/C	3 (6.8%)	1 (2.4%)	1.21 (0.07–22.02)	0.898
		Recessive	-	-	1.00 (0.06–17.33)	>0.999
		- C/C	3 (6.8%)	1 (2.4%)	1.00 (0.06–17.33)	>0.999
		- T/T + T/C	41 (93.2%)	41 (97.6%)	1.00 (Reference)	-
		Overdominant	-	-	0.53 (0.18–1.61)	0.263
		- T/C	10 (22.7%)	8 (19.0%)	0.53 (0.18–1.61)	0.263
		- T/T + C/C	34 (77.3%)	34 (81.0%)	1.00 (Reference)	-

^‡

p-value for the overall (omnibus) test of the codominant model (df = 2).

Table 6. Analysis of the Odds Ratio (OR) between genotypes and subject classification.

SNPs	Genotype	Case	Control	OR 95% CI	p-value
rs6092 (G > A)	GG	8 (18.60%)	7 (20.00%)	1.143 (0.374–3.493)	1
rs6092 (G > A)	GA	35 (81.40%)	35 (83.33%)	1.143 (0.374–3.493)	1
rs1051393 (T > G)	TT	5 (15.15%)	7 (17.95%)	0.909 (0.226–3.661)	1
	TG	11 (33.33%)	14 (35.90%)	0.909 (0.226–3.661)	1
	TT	5 (15.15%)	7 (17.95%)	0.756 (0.201–2.846)	0.937
	GG	17 (51.52%)	18 (46.15%)	0.756 (0.201–2.846)	0.937
rs2229207 (T > C)	TT	30 (69.77%)	33 (78.57%)	0.727 (0.254–2.085)	0.744
	CT	10 (23.26%)	8 (19.05%)	0.727 (0.254–2.085)	0.744
	TT	30 (69.77%)	33 (78.57%)	0.303 (0.030–3.073)	0.356
	CC	3 (6.98%)	1 (2.38%)	0.303 (0.030–3.073)	0.356

Variants in the IFNAR2 gene were associated with fever and chest pain

In this study, we performed statistical analyses to identify associations of rs6092, rs1051393, and rs2229207 with post-COVID-19 respiratory syndromes ( Table 7). Our analysis indicates an association of IFNAR2 rs1051393 with fever ( p = 0.037), in which further logistic regression analysis showed that rs1051393 was significantly associated with fever in two genetic models. While the recessive model indicated that an individual who carries the recessive genotype has higher odds of fever (OR = 4.42, 95% CI: 1.30–15.07, p = 0.018), the over-dominant model showed lower odds of fever (OR = 0.144, 95% CI: 0.029–0.726, p = 0.019). In this case, the codominant model did not show a statistically relevant association for either genotype comparison (heterozygote vs wild-type: OR = 4.17, 95% CI: 0.568–30.597, p = 0.160; homozygote vs wild-type: OR = 0.502, 95% CI: 0.109–2.310, p = 0.376). Our analysis also indicates an association of rs2229207 IFNAR2 in patients with chest pain ( p = 0.047), although no relevant association of this rs2229207 with chest pain was revealed in our subsequent logistic regression analysis.

Table 7. Association of rs6092, rs105393, and rs2229207 with symptoms of post-COVID-19 respiratory syndrome.

Symptom		rs6092 SERPINE1		rs1051393 IFNAR2			rs2229207 IFNAR2
Symptom		GG	GA	TT	TG	GG	TT	CT	CC
Fever	Yes	10	53	9	23	22	46	14	3
	No	5	17	3	2	13	17	4	1
	p-value	0.468 *		0.07 ** /0.037 *			0.708 **
Cough	Yes	12	55	10	19	28	51	13	3
	No	3	15	2	6	7	12	5	1
	p-value	1.000 **		0.864 *			0.430 **
Malaise	Yes	11	62	10	23	28	53	17	3
	No	4	8	2	2	7	10	1	1
	p-value	0.212 **		0.427 **			0.509 **
Sorethroat	Yes	1	3	0	1	3	3	1	0
	No	14	67	12	24	32	60	17	4
	p-value	0.547 **		0.238 **			0.924 **
Dyspnoe	Yes	11	54	10	18	26	48	13	4
	No	4	16	2	7	9	15	5	0
	p-value	0.745 **		0.751 *			0.812 **
Chest Pain	Yes	1	2	0	1	2	2	0	1
	No	14	68	12	24	33	61	18	3
	p-value	0.446 **		0.433 **			0.564 ** /0.047 *

Pearson Chi-Square.

Fisher’s Exact Test.

Basophil and eGFR levels were associated with rs6092 of the SERPINE1 gene

We identified significant differences in basophil levels ( p = 0.027) and eGFR ( p = 0.036) for rs6092 of the SERPINE1, in which rs6092 was associated with higher levels of basophil and lower eGFR in blood. Meanwhile, we also note that rs2229207 of the IFNAR2 was significantly associated with potassium ( p = 0.001) and chloride levels ( p = 0.047), in which we observed that individuals with the CC genotype had higher potassium and chloride in blood workup ( Table 8). Blood gas analyses also revealed significant differences in CO ₂ partial pressure ( p = 0.030) in rs2229207, with individuals carrying TC and CC genotypes having higher pCO ₂ ( Table 9). However, many of these associations did not withstand the global Benjamini–Hochberg FDR correction. Only rs2229207, which was associated with potassium levels, persisted after clustered Benjamini–Hochberg FDR adjustment ( p _adj = 0.012) ( Table 11). Furthermore, our analysis on coagulation and inflammatory factors revealed no relevant association with rs6092, rs1051393, and rs2229207 in all subjects ( Table 10).

Table 8. Analysis of blood laboratory parameters with rs6092, rs105393, and rs2229207.

Parameters	rs6092 SERPINE1		p-value	rs1051393 IFNAR2			p-value	rs2229207 IFNAR2			p-value
	GG (n = 15)	GA (n = 70)		TT (n = 12)	TC (n = 25)	GG (n = 35)		TT (n = 63)	TC (n = 18)	CC (n = 4)
	Mean ± SD/Median (Q1-Q3)	Mean ± SD/Median (Q1-Q3)		Mean ± SD/Median (Q1-Q3)	Mean ± SD/Median (Q1-Q3)	Mean ± SD/Median (Q1-Q3)		Mean ± SD/Median (Q1-Q3)	Mean ± SD/Median (Q1-Q3)	Mean ± SD/Median (Q1-Q3)
Sodium (Na), mmol/L	135.9 ± 3.3	133.9 ± 4.01	0.071 *	133.7 ± 4.1	134.7 ± 3.7	133.8 ± 4.6	0.689 **	134.3 ± 4.3	133.6 ± 2.8	136.8 ± 2.5	0.344 **
Potassium (K), mmol/L	3.9 (3.5–4.5)	3.9 (3.5–4.3)	0.560 ***	3.9 (3.5–4.2)	3.8 (3.6–4.2)	3.9 (3.7–4.4)	0.476 ^†	3.8 (3.5–4.1)	4.2 (3.9–4.6)	4.6 (4.5–4.8)	0.001 ^†
Chloride (Cl), mmol/L	102.1 ± 4.6	99.8 ± 4.2	0.063 *	99.7 ± 3.8	99.4 ± 4.4	100.8 ± 5.1	0.457 **	100.4 ± 4.2	98.9 ± 4.4	104.8 ± 4.2	0.047 **
Hemoglobin (Hb), g/dL	13.9 (11.6–15.0)	13.7 (12.5–14.4)	0.493 ***	14.3 (13.5–15.3)	12.9 (11.6–14.9)	13.8 (12.8–14.4)	0.257 ^†	13.7 (12.4–14.5)	13.9 (12.7–14.3)	14.4 (11.3–15.2)	0.766 ^†
Leukocytes (Leu), 10 ³ /μL	8.80 (6.28–9.78)	7.74 (5.97–10.1)	0.397 ***	7.12 (4.97–9.43)	7.74 (6.45–8.96)	8.23 (5.73–9.99)	0.369 ^†	7.78 (6.10–9.20)	7.96 (5.67–10.2)	9.79 (7.39–13.9)	0.518 ^†
Aspartate Transaminase (AST), U/L	273 (226–286)	230 (185–309)	0.440 ***	205 (183–268)	263 (185–351)	253 (207–295)	0.718 ^†	234 (187–298)	267 (179–304)	243 (235–291)	0.849 ^†
Neutrophils (Neu), %	81.0 (67–84.1)	78.6 (67.0–85.8)	0.708 ***	78.6 (67.1–81.5)	79.1 (74.2–83.9)	77.9 (66.7–86.4)	0.704 ^†	78.3 (67.0–84.2)	81.1 (70.3–90.4)	77.4 (60.3–86.0)	0.521 ^†
Eosinophils (Eos), %	0.0 (0.0–0.3)	0.0 (0.0–0.3)	0.747 ***	0.0 (0.0–0.2)	0.0 (0.0–0.2)	0.0 (0.0–0.3)	0.973 ^†	0.0 (0.0–0.4)	0.0 (0.0–0.1)	0.2 (0.1–2.6)	0.115 ^†
Basophils (Bas), %	0.1 (0.0–0.1)	0.1 (0.0–0.2)	0.027 ***	0.1 (0.0–0.2)	0.1 (0.0–0.2)	0.1 (0.1–0.2)	0.807 ^†	0.1 (0.0–0.2)	0.1 (0.1–0.2)	0.2 (0.1–0.4)	0.884 ^†
Lymphocytes (Lym), %	12.8 (8.6–17.4)	13.4 (8.6–22.9)	0.475 ***	13.2 (9.7–17.6)	13.4 (10.8–17.9)	11.4 (6.7–23.0)	0.663 ^†	14.0 (9.1–22.9)	13.0 (6.0–22.4)	6.7 (3.8–12.1)	0.156 ^†
Monocytes (Mo), %	6.7 ± 3.4	7.3 ± 5.5	0.546 *	9.2 ± 4.6	6.8 ± 2.9	7.0 ± 3.7	0.165 **	7.4 ± 3.6	6.4 ± 3.7	8.1 ± 3.7	0.483 **
NLR	6.18 (3.86–9.0)	5.76 (3.01–10.15)	0.936 ***	5.08 (4.04–8.44)	5.90 (4.34–7.49)	6.05 (2.85–10.8)	0.837 ^†	5.42 (3.06–9.03)	6.19 (3.14–15.1)	6.83 (3.10–17.2)	0.685 ^†
OT, U/L	50.0 (31.0–70.0)	44.0 (27.0–71.0)	0.583 ***	46.0 (32.0–70.0)	40.0 (28.0–74.0)	45.0 (27.0–69.0)	0.981 ^†	44.0 (26.0–70.0)	48.0 (35.0–74.0)	40.0 (25.0–56.0)	0.606 ^†
PT, U/L	53.0 (39.0–67.0)	43.0 (28.0–88.0)	0.870 ***	41.0 (33.0–71.0)	40.0 (28.0–100)	47.0 (33.0–84.0)	0.976 ^†	41.0 (28.0–88.0)	58.0 (38.0–87.0)	62.0 (34.0–90.0)	0.599 ^†
Blood Urea Nitrogen (BUN), mg/dL	15.4 (10.28–20.07)	13.6 (10.28–17.52)	0.556 ***	13.1 (9.1–15.2)	13.1 (10.2–18.3)	13.6 (11.2–17.3)	0.476 ^†	13.8 (10.3–17.8)	11.9 (10.3–16.8)	23.6 (11.2–31.1)	0.575 ^†
Creatinine (Cr), mg/dL	0.9 (0.7–0.9)	0.9 (0.8–1.1)	0.498 ***	0.9 (0.8–1.1)	0.8 (0.7–1.1)	0.9 (0.8–1.1)	0.801 ^†	0.9 (0.8–1.0)	0.9 (0.8–1.1)	1.0 (0.9–1.2)	0.654 ^†
eGFR, mL/min/1.73 m ²	95.0 (92.0–108)	87.0 (72.0–105)	0.036 *	92.0 (75.0–105)	92.0 (76.0–108)	91.0 (78.0–100)	0.849 ^†	92.0 (77.0–107)	91.0 (76.0–103)	91.0 (68.0–93.0)	0.666 ^†

Independent T-Test.

^**

ANOVA.

^***

Mann-Whitney.

^{^†}

Kruskal-Wallis.

Table 9. Analysis of blood gas analyses parameter with rs6092, rs105393, and rs2229207.

Parameters	rs6092 SERPINE1		p-value	rs1051393 IFNAR2			p-value	rs2229207 IFNAR2			p-value
	GG ( n = 15)	GA ( n = 70)		TT ( n = 12)	TC ( n = 25)	GG ( n = 35)		TT ( n = 63)	TC ( n = 18)	CC ( n = 4)
	Mean ± SD/Median (Q1-Q3)	Mean ± SD/Median (Q1-Q3)		Mean ± SD/Median (Q1-Q3)	Mean ± SD/Median (Q1-Q3)	Mean ± SD/Median (Q1-Q3)		Mean ± SD/Median (Q1-Q3)	Mean ± SD/Median (Q1-Q3)	Mean ± SD/Median (Q1-Q3)
Bicarbonate (HCO ₃), mmol/L	21.8 ± 2.4	21.6 ± 3.4	0.806	22.5 ± 2.6	22.0 ± 2.9	21.1 ± 3.7	0.426 **	21.4 ± 3.1	22.5 ± 3.8	22.5 ± 3.8	0.378 **
O ₂ Saturation (O ₂_Sat), %	96.8 (91.2–99.4)	98.3 (95.9–99.4)	0.286 ***	98.0 (96.7–98.6)	98.5 (96.1–99.5)	97.7 (95.1–99.4)	0.511 ^†	98.2 (96.0–99.4)	97.8 (92.8–99.4)	97.7 (79.0–99.7)	0.726 ^†
pCO ₂, mmHg	34.4 ± 4.3	33.4 ± 6.2	0.549 *	34.9 ± 5.2	33.6 ± 5.1	32.9 ± 7.1	0.697 **	32.7 ± 5.1	36.7 ± 7.7	35.3 ± 6.3	0.030 **
pH	7.4 ± 0.0	7.4 ± 0.0	0.463 *	7.414 ± 0.040	7.421 ± 0.048	7.414 ± 0.046	0.796 **	7.420 ± 0.041	7.394 ± 0.049	7.408 ± 0.025	0.084 **
pO ₂, mmHg	92.9 (66.4–131.5)	102.5 (79.1–144.5)	0.500 ***	93.1 (81.0–114)	102.5 (79.8–183)	96.3 (73.6–126)	0.424 ^†	100 (78.4–146)	99.9 (77.8–133)	118 (63.1–172)	0.911 ^†
Standard HCO ₃, mmol/L	22.9 ± 2.15	23.0 ± 2.5	0.887 *	23.7 ± 2.0	23.4 ± 2.4	22.7 ± 2.7	0.478 **	23.0 ± 2.4	23.4 ± 2.9	23.3 ± 2.5	0.785 **
Total CO ₂, mmol/L	22.8 ± 2.5	22.9 ± 4.0	0.989 *	24.4 ± 4.1	23.0 ± 3.0	22.1 ± 3.9	0.262 **	22.7 ± 3.7	23.6 ± 4.0	23.6 ± 4.0	0.615 **

Independent T-Test.

^**

ANOVA.

^***

Mann-Whitney.

^†

Kruskal-Wallis.

Table 10. Analysis of coagulation and inflammatory factors with rs6092, rs105393, and rs2229207.

Parameters	rs6092 SERPINE1		p-value	rs1051393 IFNAR2			p-value	rs2229207 IFNAR2			p-value
	GG ( n = 15)	GA ( n = 70)		TT ( n = 12)	TC ( n = 25)	GG ( n = 35)		TT ( n = 63)	TC ( n = 18)	CC ( n = 4)
	Median (Q1-Q3)	Median (Q1-Q3)		Median (Q1-Q3)	Median (Q1-Q3)	Median (Q1-Q3)		Median (Q1-Q3)	Median (Q1-Q3)	Median (Q1-Q3)
D-dimer, ng/mL	820 (420–2060)	610 (360–1070)	0.440 *	560 (315–1455)	880 (430–1280)	600 (420–880)	0.317 †	580 (360–1280)	650 (420–1040)	1955 (1345–18630)	0.059 ^†
C-Reactive Protein (CRP), mg/L	78.7 (60.1–94.8)	75.5 (21.6–114.2)	0.881 *	88.2 (28.0–131)	81.5 (41.3–109)	75.8 (13.3–108)	0.733 †	73.4 (21.6–107)	96.6 (54.1–159)	73.9 (34.2–113)	0.284 ^†

Mann-Whitney.

^†

Kruskal-Wallis.

Table 11. Benjamini-Hochberg adjustment for multiple testing of rs6092, rs105393, and rs2229207 with clinical and laboratory parameters.

SNPs	p-value	BH-adjusted p-value (Global/Clustered)
SERPINE1 rs6092
Bas	0.027	0.864/0.243
eGFR	0.036	0.576/0.108
IFNAR2 rs1051393
Fever	0.037	1.184/0.222
IFNAR2 rs2229207
Chest Pain	0.047	0.501/0.282
K	0.001	0.032/0.003
pCO ₂	0.030	0.48/0.21
Cl	0.047	0.376/0.07

Table 12. Logistic regression analysis for symptoms of post Covid-19 respiratory syndrome.

	Chest Pain OR (95% CI), p	Fever OR (95% CI), p
IFNAR2 rs2229207 (CT vs TT)	23.11 (0.39–1384.6), 0.133	–
IFNAR2 rs2229207 (CC vs TT)	0.00 (0.00–.), 0.999	–
IFNAR2 rs1051393 (TG vs TT)	–	1.84 (0.40–8.47), 0.436
IFNAR2 rs1051393 (GG vs TT)	–	7.55 (1.43–39.95), 0.017
Age on admission	1.01 (0.91–1.12), 0.857	1.00 (0.96–1.05), 0.871
Sex	0.37 (0.02–5.80), 0.480	0.77 (0.18–3.37), 0.726
Severity on admission	0.17 (0.01–3.09), 0.230	2.29 (0.85–6.18), 0.102
Year of admission	1.28 (0.07–23.23), 0.869	0.51 (0.15–1.73), 0.278

Table 13. Logistic regression analysis of rs6092, rs105393, and rs2229207 with clinical symptoms across genetic inheritance models.

Predictor	Fever OR (95% CI), * p* (Codominant)	Fever OR (95% CI), * p* (Recessive)	Fever OR (95% CI), * p* (Overdominant)
rs1051393
Codominant (Heterozygote vs Wild-type)	4.170 (0.568–30.597), 0.160	-	-
Codominant (Homozygote vs Wild-type)	0.502 (0.109–2.310), 0.376	-	-
Recessive	-	4.420 (1.297–15.065), 0.018	-
Overdominant	-	-	0.144 (0.029–0.726), 0.019
Covariates
Age	1.003 (0.958–1.050 0.048), 0.899	1.004 (0.960–1.050), 0.873	1.003 (0.958–1.049), 0.910
Sex	1.417 (0.321–6.254), 0.645	1.361 (0.325–5.695), 0.673	1.419 (0.325–6.198), 0.641
Severity	2.411 (0.919–6.322), 0.074	2.279 (0.893–5.814), 0.085	2.347 (0.897–6.142), 0.082
Year	2.196 (0.653–7.385), 0.204	2.194 (0.672–7.165), 0.193	2.176 (0.654–7.236), 0.205

Abbreviations: OR = odds ratio; CI = confidence interval.

Notes: Covariate estimates are from the codominant model. Similar estimates were observed in other models. Bold values indicate statistical significance (p < 0.05). All models showed good fit (Hosmer-Lemeshow test p > 0.05 for all models).

Table 14. Multiple linear regression analyses for significant laboratory panels.

All models adjusted for year of admission, age on admission, sex, and severity on admission. TT is the reference for IFNAR2, and GG is the reference for SERPINE1.

Predictor	eGFR B (95% CI), * p*	Basophil % B (95% CI), * p*	Potassium B (95% CI), * p*	Chlorida B (95% CI), * p*	pCO ₂ B (95% CI), * p*
rs6092	−0.120 (−25.665, 7.118), 0.264	0.111 (−0.201, 0.588), 0.332	-	-	-
rs2229207	-	-	0.317 (0.122, 0.596), 0.003	0.107 (−0.836, 2.513), 0.322	0.240 (0.266, 4.850), 0.029
Covariates	-	-	-	-	-
Age on admission	−0.224 (−0.972, −0.024), 0.040	−0.089 (−0.016, 0.007), 0.438	0.040 (−0.008, 0.012), 0.700	−0.015 (−0.074, 0.064), 0.887	−0.021 (−0.104, 0.085), 0.842
Sex	0.186 (−1.760, 26.771), 0.085	−0.028 (−0.386, 0.301), 0.805	−0.219 (−0.611, −0.015), 0.040	0.119 (−0.927, 3.291), 0.268	−0.197 (−5.537, 0.237), 0.071
Severity on admission	0.152 (−2.578, 15.079), 0.163	0.048 (−0.167, 0.258), 0.673	0.057 (−0.138, 0.238), 0.600	−0.265 (−2.948, −0.284), 0.018	0.579 (−1.312, 2.334), 0.579
Year of admission	0.050 (−9.410, 15.362), 0.640	−0.032 (−0.341, 0.256), 0.778	0.040 (−0.208, 0.309), 0.699	−0.203 (−3.596, 0.059), 0.058	0.110 (−1.199, 3.804), 0.303

Abbreviations: B = unstandardized regression coefficient; CI = confidence interval.

Note: “–” indicates the variable was not included in that model. All VIF values were < 1.2, indicating no multicollinearity.

Our subsequent multivariable linear regression analysis (Table 14) showed that rs2229207 genotype was significantly associated with potassium levels (B = 0.317, 95% CI: 0.122–0.596, p = 0.003) after adjusted for age, sex, BMI, and severity, in which we noted an interesting finding that female individuals was independently associated with lower potassium levels (B = −0.219, 95% CI: −0.611 to −0.015, p = 0.040). To clarify the mode of inheritance, we performed further analysis of rs6092 and rs2229207 genotypes under different inheritance models ( Table 15). Our analysis revealed that rs6092 was significantly associated with basophil, eGFR, potassium, chloride, pCO ₂, pH, and D-dimer in blood across dominant, codominant, and over-dominant models (all p < 0.05). While for rs2229207, potassium showed consistent significant associations across all genetic models ( p < 0.05), with additional significant associations observed for pCO ₂, pH, chloride, and D-dimer in specific models ( Table 15).

Table 15. Association of rs6092 and rs2229207 Genotypes with Laboratory Parameters in Different Inheritance Models.

SNPs	Genetic Model		n	p-value
SNPs	Genetic Model		n	Basophil	eGFR	K	Cl	pCO ₂	pH	D-dimer
rs6092	Dominant	GG	15	0.027 *	0.036 ***	0.56 ***	0.63 *	0.549 *	0.463 *	0.44 ***
	Dominant	GA/AA	70	0.027 *	0.036 ***	0.56 ***	0.63 *	0.549 *	0.463 *	0.44 ***
	Codominant	GG	15	0.027 *	0.036 ***	0.56 ***	0.63	0.549 *	0.463 *	0.44 ***
		GA	70
		AA	-
	Recessive	GG/GA	85	-	-	-	-	-	-	-
	Recessive	AA	0	-	-	-	-	-	-	-
	Overdominant	GA	70	0.027 *	0.036 ***	0.56 ***	0.63	0.549 *	0.463 *	0.44 ***
	Overdominant	GG/AA	15	0.027 *	0.036 ***	0.56 ***	0.63	0.549 *	0.463 *	0.44 ***
rs2229207	Dominant	TT	63	0.851 ***	0.443 ***	0.001 ***	0.722 *	0.032 *	0.031 *	0.211 ***
	Dominant	TC/CC	22	0.851 ***	0.443 ***	0.001 ***	0.722 *	0.032 *	0.031 *	0.211 ***
	Codominant	TT	63	0.884 ^†	0.666 ^†	0.001 ^†	0.047 **	0.793 **	0.084 **	0.059 ^†
		TC	18
		CC	4
	Recessive	CC	4	0.62 ***	0.467 ***	0.009 ***	0.033 *	0.561 *	0.785 *	0.019 ***
	Recessive	TT/TC	81	0.62 ***	0.467 ***	0.009 ***	0.033 *	0.561 *	0.785 *	0.019 ***
	Overdominant	TC	18	0.955 ***	0.655 ***	0.02 ***	0.139 *	0.054 *	0.03 *	0.902 ***
	Overdominant	TT/CC	67	0.955 ***	0.655 ***	0.02 ***	0.139 *	0.054 *	0.03 *	0.902 ***

Independent T-Test.

^**

ANOVA.

^***

Mann-Whitney.

^†

Kruskal-Wallis.

Multivariable analysis revealed a different inheritance model for rs6092 and rs2229207

Multivariable linear regression analysis was performed to evaluate the associations of rs6092 and rs2229207 with clinical parameters after adjusting for age, sex, severity, and year of admission ( Table 16). Our data revealed that rs6092 was not significantly relevant to eGFR (B = −0.120; 95% CI: −25.665 to 7.118; p = 0.264) and basophil percentage (B = 0.111; 95% CI: −0.201 to 0.588; p = 0.332). In contrast, rs2229207 was significantly associated with increased pCO ₂ levels under the dominant model (B = 2.417; 95% CI: 0.614 to 6.347; p = 0.018) and showed significant effects on pH under both dominant (B = −0.228; 95% CI: −0.043 to −0.001; p = 0.040) and over-dominant models (B = 0.219; 95% CI: 0.00008 to 0.046; p = 0.049). Furthermore, rs2229207 was strongly associated with potassium levels across dominant (B = 0.280; 95% CI: 0.099 to 0.701; p = 0.010), codominant (B = 0.317; 95% CI: 0.122 to 0.596; p = 0.003), and recessive models (B = −0.255; 95% CI: −1.398 to −0.109; p = 0.022), as well as with chloride (recessive model: B = −0.249; 95% CI: −9.456 to −0.772; p = 0.022) and D-dimer levels (recessive model: B = −0.333; 95% CI: −12976.164 to −2869.870; p = 0.003). Among covariates, age was negatively associated with eGFR (B = −0.224; 95% CI: −0.972 to −0.024; p = 0.040), while sex was associated with potassium levels (B = −0.219; 95% CI: −0.611 to −0.015; p = 0.040), and disease severity showed a negative association with chloride (B = −0.265; 95% CI: −2.948 to −0.284; p = 0.018). Multicollinearity diagnostics indicated no significant multicollinearity with all VIF < 1.2.

Table 16. Multiple linear regression analysis of rs6092, rs105393, and rs2229207 with laboratory parameters across inheritance models.

Predictor	eGFR B (95% CI), * p*	Basophil % B (95% CI), * p*	Chloride B (95% CI), * p*	pCO ₂ B (95% CI), * p*	pH B (95% CI), * p*	D-dimer B (95% CI), * p*	Potassium B (95% CI), * p*
rs6092
Dominant	−0.120 (−25.665, 7.118), 0.264	0.111 (−0.201, 0.588), 0.332	-	-	-	-	-
Codominant	−0.120 (−25.665, 7.118), 0.264	0.111 (−0.201, 0.588), 0.332	-	-	-	-	-
Overdominant	−0.120 (−25.665, 7.118), 0.264	0.111 (−0.201, 0.588), 0.332	-	-	-	-	-
rs2229207
Dominant	-	-	-	2.417 (0.614, 6.347), 0.018	−0.228 (−0.043, −0.001), 0.040	-	0.280 (0.099, 0.701), 0.010
Codominant	-	-	0.107 (−0.836, 2.513), 0.322	-	-	-	0.317 (0.122, 0.596), 0.003
Recessive	-	-	−0.249 (−9.456, −0.772), 0.022	-	-	−0.333 (−12976.164, −2869.870), 0.003	−0.255 (−1.398, −0.109), 0.022
Overdominant	-	-	-	-	0.219 (0.00008, 0.046), 0.049	-	−0.175 (−0.601, 0.064), 0.112
Covariates
Age on admission	−0.224 (−0.972, −0.024), 0.040 ¹	−0.89 (−0.016, 0.007), 0.438 ²	−0.015 (−0.074, 0.064), 0.887 ³	−0.011 (−0.099, 0.090), 0.921 ⁴	0.155 (−0.0002, 0.001), 0.155 ⁴	0.150 (−21.824, 136.281), 0.154 ⁵	0.040 (−0.008, 0.012), 0.700 ⁶
Sex	0.186 (−1.760, 26.771), 0.085 ¹	−0.028 (−0.386, 0.301), 0.805 ²	0.119 (−0.927, 3.291), 0.268 ³	−0.181 (−5.306, 0.430), 0.095 ⁴	−0.036 (−0.025, 0.018), 0.743 ⁴	−0.077 (−3297.891, 1537.075), 0.471 ⁵	−0.219 (−0.611, −0.015), 0.040 ⁶
Severity on admission	0.152 (−2.578, 15.079), 0.163 ¹	0.048 (−0.167, 0.258), 0.673 ²	−0.265 (−2.948, −0.284), 0.018 ³	0.068 (−1.238, 2.363), 0.536 ⁴	−0.004 (−0.014, 0.013), 0.971 ⁴	0.124 (−632.107, 2378.494), 0.252 ⁵	0.057 (−0.138, 0.238), 0.600 ⁶
Year of admission	0.040 (−9.410, 15.361), 0.634 ¹	−0.032 (−0.341, 0.256), 0.778 ²	−0.203 (−3.596, 0.059), 0.058 ³	1.110 (−1.384, 3.604), 0.378 ⁴	−0.114 (−0.028, 0.009), 0.295 ⁴	0.001 (−2073.143, 2099.927), 0.990 ⁵	0.040 (−0.208, 0.309), 0.699 ⁶

Abbreviations: B = unstandardized regression coefficient; CI = confidence interval; eGFR = estimated glomerular filtration rate; pCO ₂ = partial pressure of carbon dioxide.

Note: “–” indicates the variable was not included in that model. Values in bold indicate statistical significance (p < 0.05). All VIF values were < 1.2, indicating no multicollinearity. Covariate values were derived from different genetic models:

from model with rs6092 Dominant for eGFR;

from model with rs6092 Dominant for Basophil %;

from model with rs2229207 Codominant for Chloride;

⁴

from model with rs2229207 Dominant for pCO ₂ and pH;

⁵

from model with rs2229207 Recessive for D-dimer;

⁶

from model with rs2229207 Codominant for Potassium.

Discussion

COVID-19 patients with higher disease severity have an increased risk of developing post-COVID-19 respiratory syndrome. ⁷ In unadjusted analyses, our study identified a significant association between higher disease severity upon admission ( p = 0.023) and during the course of hospitalization ( p = 0.004) within subjects with post-COVID-19 respiratory syndromes. However, we noted that only the disease severity during the course of hospitalization remained significant after adjusting for multiple comparisons using the Benjamini-Hochberg method ( p _adj = 0.021). The severe symptoms in COVID-19 patients are often associated with a cytokine storm. This storm occurs when SARS-CoV-2 infects the lung tissue via the ACE2 receptor and induces an immune response that releases pro-inflammatory cytokines, such as IL-6, GM-CSF, and IFN-γ. ¹⁴ This abundant release led to the infiltration of macrophages and neutrophils into the lung tissue. ¹⁵ ^, ¹⁶ The persistence of the virus in tissues and a dysfunctional immune response during the acute phase can impair the enzymatic function of the ACE2 receptor and worsen respiratory symptoms. Histopathological studies have demonstrated that SARS-CoV-2 can persist in tissues for up to 230 days, potentially triggering repeated immune responses, which lead to the emergence of prolonged symptoms. ¹⁷ ^, ¹⁸

The post-COVID-19 respiratory syndrome refers to respiratory symptoms that persist for at least four weeks following COVID-19 infection. ⁴ In our study cohort, we found a higher prevalence of poor pulmonary radiological examination upon admission ( p = 0.013) and at the end of hospitalization ( p = 0.001) in cases. Additionally, we noted that subjects with post-COVID-19 respiratory syndrome had significantly longer hospitalization compared to control subjects. This finding might be attributed to the poor radiological examination, as we observed a significantly higher prevalence of fibrosis in subjects with post-COVID-19 respiratory syndrome. This finding is consistent with previous reports that individuals infected with COVID-19 who developed acute respiratory distress syndrome (ARDS) and require intubation during the acute phase have a threefold increased risk of developing irreversible pulmonary fibrosis as the main irreversible lesion. ¹⁹

The irreversible lesion in the lungs of COVID-19 patients is potentially the result of hyperinflammation due to the downregulation of ACE2 and upregulation of angiotensin II, which had a negative impact on cells. The hyperinflammation in patients with COVID-19 is reported due to high cytokine release and immunothrombosis which activates the blood-clotting factors such as PAI-1. ¹⁷ ^, ²⁰ Previous studies also described that the infiltration of macrophages and neutrophils led to the expression of transforming growth factor-beta (TGF-β) and fibroblast activation. These fibroblasts differentiate into myofibroblasts, which produce extracellular matrix (ECM) components, such as collagen and fibronectin. ²¹ ^, ²² The increased formation of ECM impaired the repair of the alveolar epithelium, increased extracellular collagen matrix formation, and led to the loss of normal lung architecture. ¹⁹ Additionally, mechanical ventilation may further aggravate this condition, leading to ventilator-induced lung injury (VILI), a known contributor to pulmonary fibrosis. ²³

As we described previously, the incidence of pulmonary fibrosis might be attributed to the dysfunctional expression of blood-clotting factors in the lung. ²⁴ ^, ²⁵ The human SERPINE1 gene encodes the plasminogen activator inhibitor 1 (PAI-1), which inhibits tissue plasminogen activator (tPA) by converting plasminogen to plasmin. ²⁶ Previous studies have indicated an association between the rs6092 of the SERPINE1 gene and coagulation abnormalities in COVID-19 patients. ¹¹ Moreover, another study reported that the high level of D-dimer and the inhibition of plasmin prevent fibrinolysis, leading to the excessive clotting in COVID-19 patients. ²⁶ Despite no association of rs6092 with the clinical symptoms of post-COVID-19 respiratory syndrome, we observed a significantly higher level of basophils and lower eGFR.

The increase in blood basophils might be the response to combat the presence of SARS-CoV-2, as basophils are responsible for the innate and adaptive immunity by releasing IL2RA and IL2RG. ²⁷ Despite more relevant evidence being required to explain the correlation of higher basophils in the blood of subjects with post-COVID-19 respiratory syndrome and pulmonary fibrosis, basophils-rich blood has been reported to increase the likelihood of lower extremity deep venous thrombosis (LEDVT) by 2.7 times in patients with spontaneous intracerebral hemorrhage (sICH), potentially by increasing the level of the enhanced factor II plasma coagulant. ²⁸ ^, ²⁹ rs6092 remains a potential candidate marker with severe clinical manifestations in COVID-19 patients, as this variant caused an amino acid change of Ala15Thr located within the central hydrophobic core of the PAI-1 signal peptide. The Ala15Thr substitution increases hydrophobicity and enhances the propensity for α-helix formation, both of which are critical for proper signal peptide function. ³⁰

The ability of the immune system to combat viral infection is also contributed to by the expression of interferons. The interferon alpha and beta receptor 2, which is crucial in the antiviral response, is encoded by the IFNAR2 gene. When SARS-CoV-2 infects the cells, lymphocytes release interferons that bind to IFNAR2 receptors. This binding activates the Janus Kinase (JAK) pathway, resulting in the phosphorylation of STAT1 and STAT2. These transcription factors, in combination with IRF9, form the ISGF3 complex, which enters the nucleus and activates genes involved in the antiviral response, including MX1, OAS, and IRF7. ³¹ ^, ³² Previous studies have demonstrated a relationship between variations in IFNAR2 and severe COVID-19 symptoms. ¹³ In our study, both of the examined variants in the IFNAR2 gene were associated with clinical parameters, in which rs1051393 was significantly associated with fever ( p = 0.037), while the rs2229207 was associated with chest pain ( p = 0.047) and higher potassium ( p = 0.001), chloride ( p = 0.047), and pCO ₂ ( p = 0.030).

To the best of our knowledge, this is the first study to report the associations of rs1051393 with fever and rs2229207 with chest pain. Notably, our findings also suggest an increasing trend across TT, CT, and CC genotypes of the rs2229207 and blood potassium (B = 0.317, 95% CI: 0.122 to 0.596, p = 0.003). The abnormalities in potassium levels were also described in different cohorts of studies. Ashammari et al. reported that 34 out of 72 COVID-19 patients with heart failure had higher potassium levels, which was attributed to acute kidney injury and the use of angiotensin receptor-neprilysin inhibitors. ³³ Interestingly, another report by Amin et al. found that hyperkalemia was observed in only 12% of COVID-19 patients, but was significantly more common among older subjects and in those with chronic kidney disease or diabetes mellitus. This study also showed that patients with hyperkalemia were more likely to be admitted to the ICU and had a longer duration of hospitalization. ³⁴

Despite limited evidence of biological relevance to the severity of COVID-19, the association of IFNAR2 variants with COVID-19 cases has been described elsewhere. A study by Nhung et al. in a Vietnamese population also found an association between rs2229207 and the risk of contracting COVID-19. ³⁵ GWAS data have also demonstrated that the IFNAR2 locus is also known to play a role in determining severe clinical manifestations of COVID-19, with one such variant being rs2236757, which is located in a non-coding region. ¹³ ^, ³⁶ ^, ³⁷ In addition to its association with COVID-19, the rs2229207 has also been linked to chronic hepatitis patients, in which an amino acid substitution leads to a reduced expression of the IFNAR2 protein on the cell membrane, potentially disrupting interferon signaling. ³⁸ ^, ³⁹ Both rs1051393 and rs2229207 were missense variants in the IFNAR2 receptor. These structural changes may interfere with the antiviral mechanism and reduce the expression of the IFNAR2 protein on the cell membrane, potentially disrupting interferon signaling. ⁴⁰

In our study, we did not observe a significant association of genotype distribution with control or cases. We noted that the minor allele frequency (MAF) for rs6092 in our study was 41%. The value is higher compared to other studies, which reported to be at 10.88% from 786 individuals and 7—11% as documented in NCBI dbSNP. This discrepancy may be attributed to the relatively limited sample size of our study population, which may not be fully representative when compared to global population databases. While for the rs1051393, we noted the MAF for the G allele at 34.03%, consistent with the previous study by Fricke-Galindo et al., which reported the MAF at 28—32%. ¹¹ We also found that the MAF for the C allele of rs2229207 was 15.29%. This result aligns with a prior investigation by Malvestiti et al. (2026), which documented an MAF of 8–10% for rs2229207 in a sample of 487 individuals. ¹³ When compared to the NCBI dbSNP, the allele frequencies for both rs1051393 and rs2229207 in our study are relatively consistent with those observed in other Asian populations, which range from 42–44% for rs1051393 and 16–18% for rs2229207. Although the frequency of the T allele for rs1051393 in our study is slightly lower, the overall allelic distribution pattern remains comparable, suggesting a shared genetic background with Asians.

Our data has demonstrated possible contributions of genetic predisposition on the SERPINE1 and IFNAR2 genes to the emergence of post-COVID-19 respiratory syndromes but are subject to several limitations. While we observed significant associations respiratory symptoms with disease classification in our cohort, only disease severity during hospitalization, radiological examination results upon admission and at the end of hospitalization, and duration of hospitalization remained significant after applying the BH-correction. Moreover, we also note that only rs2229207 withstand the BH-correction after adjusting for age, sex, BMI, and disease severity. Additionally, the modest sample size would limit the statistical power in this study and restrain our analysis to explore the contribution of genetic variants on disease outcomes.

Conclusion

Our data showed that subjects with post-COVID-19 respiratory syndrome had significantly higher disease severity upon admission and during hospitalization. These patients had a longer hospitalization, which might be due to poor radiological examination upon admission and at the end of hospitalization. We argue that these were predisposed by genetic factors, in which we reported that rs1051393 was associated with fever, while rs2229207 was associated with chest pain, higher blood potassium, chloride, and pCO ₂. We also suggested that the association of rs6092 with higher basophil levels in blood might potentially contribute to the risk of blood clotting in cases with pulmonary fibrosis. However, due to modest sample size, only disease severity during hospitalization, radiological examination results upon admission and at the end of hospitalization, duration of hospitalization, and the association of rs2229207 with blood potassium levels remained significant after applying the Benjamini-Hochberg correction.

Ethical consideration

All patients involved in this study have agreed, and the study has passed the ethical review, based on the Letter of Approval of the Research and Health Ethics Committee of the Faculty of Medicine, University of Indonesia and Dr. Cipto Mangunkusumo National Central General Hospital (FKUI-RSCM), Jakarta: KET-872/UN2. F1/ETIK/PPM.002.02/2023. This research was also supported by the Persahabatan National Respiratory Hospital, Jakarta (98/KEPK-RSUPP/06/2023).

Data availibility statement

Figshare: The Genetic Association of Single Nucleotide Polymorphisms of SERPINE1 (rs6092) and IFNAR2 (rs1051393, rs2229207) Genes Is Related to Post Covid-19 Respiratory Syndrome. https://doi.org/10.6084/m9.figshare. 30338875. ⁴¹

This project contains the following underlying data: •

Long Covid-19_F1000_dataset.xlsx – This file contains research data including: demographic information, clinical data, laboratory data, and genotyping results.

The data are available under the terms of the Creative Commons Attribution 4.0 International (CC BY 4.0) license.

Acknowledgement

This research is supported by the funding from the National Research and Innovation Agency (BRIN) through the Riset Inovasi dan Indonesia Maju (RIIM) grant (Contract no. 36/IV/KS/06/2022) Funding Year 2025.

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10.5256/f1000research.199019.r483188

Reviewer response for version 2

Verma

Shrikant

1 Referee https://orcid.org/0000-0002-8356-8448 1Era University, Lucknow, India

Competing interests: No competing interests were disclosed.

22 5 2026

2026

This is an open access peer review report distributed under the terms of the Creative Commons Attribution Licence, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

recommendation

approve

The author has successfully addressed my concerns, and I have no further questions.

Is the work clearly and accurately presented and does it cite the current literature?

Yes

If applicable, is the statistical analysis and its interpretation appropriate?

Yes

Are all the source data underlying the results available to ensure full reproducibility?

Yes

Is the study design appropriate and is the work technically sound?

Yes

Are the conclusions drawn adequately supported by the results?

Partly

Are sufficient details of methods and analysis provided to allow replication by others?

Yes

Reviewer Expertise:

Molecular Biology and Genetics

I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard.

10.5256/f1000research.184154.r441148

Reviewer response for version 1

Saba

Abdullah Al

1 Referee 1University of Dhaka, Dhaka, Dhaka Division, Bangladesh

Competing interests: No competing interests were disclosed.

7 1 2026

2026

recommendation

reject

The authors investigated the association of rs1051393 and rs2229207 within the IFNAR2 gene and rs6092 within the SERPINE1 gene with post COVID-19 respiratory syndrome. No significant association was observed between post COVID-19 respiratory syndrome and these variants. However, the variants were significantly associated with certain COVID-19 symptoms and clinical parameters. The paper contains major errors. The following points should be addressed by the authors.

Introduction section

Methods section

In the context of COVID-19, the time period of infection is very important, as patients exhibited different symptoms depending on the SARS-CoV-2 variants with which they were infected. Therefore, the timeframe of infection represents a potential confounder in this analysis. No such consideration has been addressed in this study.

Additionally, the authors should justify why saliva samples were collected instead of blood samples.

Results section

Discussion section

Is the work clearly and accurately presented and does it cite the current literature?

Partly

If applicable, is the statistical analysis and its interpretation appropriate?

Are all the source data underlying the results available to ensure full reproducibility?

Yes

Is the study design appropriate and is the work technically sound?

Are the conclusions drawn adequately supported by the results?

Are sufficient details of methods and analysis provided to allow replication by others?

Partly

Reviewer Expertise:

Genetics and Genomics, Bioinformatics, Immunology, Population Genetics

I confirm that I have read this submission and believe that I have an appropriate level of expertise to state that I do not consider it to be of an acceptable scientific standard, for reasons outlined above.

Ariani

Yulia

Medical Biology, University of Indonesia, Depok, West Java, Indonesia

Competing interests: No competing interests were disclosed.

17 4 2026

Introduction section

The authors did not provide an explicit literature review regarding the association of SERPINE1 and IFNAR2 gene variants with post COVID-19 respiratory syndrome in the Introduction section. Moreover, the rationale behind the selection of these two genes and the specific variants rs1051393, rs2229207, and rs6092 is not adequately justified. Response: We thank the reviewer for this valuable comment. Regarding this point, the authors consider that the selected variants may have potential pathomechanistic roles. Therefore, in this study, we attempted to investigate their association with post–COVID-19 respiratory syndrome. This rationale has now been explicitly stated in the Introduction section.

Methods section

The number of study participants is too low. The authors did not provide a priori sample size calculation or a post hoc power analysis. As a result, this study most likely lacks sufficient statistical power to support the conclusions drawn. Moreover, the case and control groups are confusing and poorly defined. The control group is not clearly delineated, which is misleading. The criteria based on which study subjects were classified as cases according to PDPI should be supported with appropriate references from WHO or CDC guidelines. Response: This was a retrospective study involving patients who experienced respiratory syndrome after COVID-19. The subjects were patients treated at the Persahabatan Central General Hospital (RSUP), Jakarta, Indonesia, from 2020-2021. The inclusion criteria for this research were divided into case and control. Patients were classified to be in the case population if at least one of the following criteria was met: Hospitalization duration of ≥ 28 days; or < 28 days with pulmonary fibrosis; or < 28 days with no improvement of radiological findings and at least one persistent respiratory symptom (cough, dyspnea, malaise, chest pain, or sore throat) at the time the patient was discharged. The control population consists of patients who met none of the case criteria. The exclusion criteria of this study were patients who did not present with any clinical symptoms, didn’t give consent, or, although they met the inclusion criteria, had passed away at the time of the study. We examined the medical records of COVID-19 patients from that period, did a total sampling, and got 85 participants (43 subjects and 42 control subjects).

Additionally, the authors should justify why saliva samples were collected instead of blood samples. Response: This section has been added to the manuscript, explicitly stated in the method section.

Results section

There are inconsistencies in the tables. In Tables 7 and 8, commas are used instead of decimal points in the reported p-values. Also in table 1, commas are used instead of decimal points in some instances. In Table 1, the word “symptoms” is misspelled as “symptomps.” Response: The inconsistency in the use of commas and dots for decimal numbers has been corrected in Tables 7 and 8. And then, the misspelling has been corrected explicitly in Table 7 of the manuscript.

The authors should have conducted logistic regression analyses to investigate the association between the genetic variants and post COVID-19 respiratory syndrome. Furthermore, linear regression analyses should have been performed to assess the association between the variants and quantitative clinical parameters. Both linear and logistic regression analyses should be adjusted for potential confounding variables. Response: Thank you for this valuable comment. We agree that the timeframe of infection may act as a potential confounder due to differences in SARS-CoV-2 variants. Therefore, we included the infection period as a covariate in the regression analysis to control for its potential confounding effect. The results have been updated accordingly and are now described in the revised manuscript (Methods and Results sections).

The authors conducted genotypic association analyses of the variants with post COVID-19 respiratory syndrome. However, genetic inheritance models such as codominant, dominant, recessive, and over-dominant models should have been applied to identify the model that best explains the data. Response: Thank you for this important suggestion. We agree that applying multiple genetic inheritance models provides a more comprehensive understanding of the association.

Therefore, we have performed analyses under codominant, dominant, recessive, and over-dominant models for all investigated SNPs. The corresponding results have been added to the revised manuscript, and the interpretation has been updated accordingly.

Discussion section

The Discussion section should be rewritten. The authors should discuss findings from similar studies and compare their results accordingly. The genotypic frequencies of rs1051393, rs2229207, and rs6092 should be compared with frequencies reported in other populations using data from the 1000 Genomes Project, the NCBI ALFA dataset, and other relevant resources. Finally, the strengths and limitations of the study should be clearly stated. Response: We have thoroughly revised the Discussion section by incorporating comparisons with previous studies and publicly available datasets (NCBI) for the genotypic and allelic frequencies of the investigated SNPs.

10.5256/f1000research.184154.r443744

Reviewer response for version 1

Verma

Shrikant

1 Referee https://orcid.org/0000-0002-8356-8448 1Era University, Lucknow, India

Competing interests: No competing interests were disclosed.

6 1 2026

2026

recommendation

approve-with-reservations

The authors should clearly specify the inclusion and exclusion criteria in the methodology section to ensure reproducibility and clarify the study population.

The Introduction section requires further improvement. The authors should provide a clearer background, highlight the knowledge gap, and explicitly state the study’s rationale and objectives.

The manuscript requires language improvement. The authors should revise the text for clarity, grammar, and readability to ensure that the scientific content is clearly communicated.

Since multiple variables were analysed, did the authors apply a Bonferroni correction or another method to adjust for multiple comparisons? Please clarify this in the data analysis section.

Is the work clearly and accurately presented and does it cite the current literature?

Yes

If applicable, is the statistical analysis and its interpretation appropriate?

Yes

Are all the source data underlying the results available to ensure full reproducibility?

Yes

Is the study design appropriate and is the work technically sound?

Yes

Are the conclusions drawn adequately supported by the results?

Partly

Are sufficient details of methods and analysis provided to allow replication by others?

Yes

Reviewer Expertise:

Molecular Biology and Genetics

I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard, however I have significant reservations, as outlined above.

Ariani

Yulia

Medical Biology, University of Indonesia, Depok, West Java, Indonesia

Competing interests: No competing interests were disclosed.

17 4 2026

Authors mention ‘Severity on Admission’ in Table 1. Please provide a clear description of the criteria used for categorization in the methodology section, including clinical, laboratory, or supportive intervention parameters, to ensure reproducibility and clarity. Response: We divided patients’ severity into mild, moderate, severe, and critical. Mild disease is characterized by symptoms without evidence of viral pneumonia or hypoxia, such as fever, cough, fatigue, myalgia, or anosmia/ageusia, with SpO₂ >95% on room air. Moderate disease is defined by clinical signs of pneumonia, including fever, cough, dyspnea, or tachypnea, without features of severe pneumonia, with SpO₂ >93% on room air. Severe disease (severe pneumonia) is characterized by pneumonia accompanied by at least one of the following: respiratory rate >30 breaths/minute, severe respiratory distress, or SpO₂ <93% on room air. Critical disease includes patients with acute respiratory distress syndrome (ARDS), sepsis or septic shock, or other conditions requiring life-support interventions such as mechanical ventilation or vasopressor therapy.

Did the authors verify the minor allele frequency (MAF) of the reported SNPs? Please provide the source or database used, and mention whether the frequencies were consistent with the studied population. Response: In this study, three single nucleotide polymorphisms (SNPs) were analyzed, namely rs6092 in the SERPINE1 gene, rs1051393 in the IFNAR2 gene, and rs2229207 in the IFNAR2 gene. The first SNP analyzed was rs6092 in the SERPINE1 gene. Information on the minor allele frequency (MAF) of this SNP was obtained from the dbSNP database. After verification using the dbSNP database for rs6092 in the SERPINE1 gene, with the reference allele G and the alternative allele A, the minor allele frequency (MAF) was found to be 0.078 (7.8%) in the Asian population and 0.115 (11.5%) in the Southeast Asian population. Based on these MAF data, the rs6092 SNP shows a relatively high frequency in Asian populations, making it a relevant candidate for further investigation. The second SNP analyzed was rs1051393 in the IFNAR2 gene. The minor allele frequency (MAF) data for this variant were obtained from the dbSNP database. The second SNP analyzed was rs1051393 in the IFNAR2 gene. The minor allele frequency (MAF) data for this variant were obtained from the dbSNP database. The third SNP analyzed was rs2229207 in the IFNAR2 gene. for SNP rs2229207 in the IFNAR2 gene, the reference allele is T and the alternative allele is C. In the general Asian population, the minor allele frequency (MAF) of the C allele ranges from approximately 16–17%, indicating that this SNP is relevant and may be considered for inclusion in studies conducted in Indonesia. The three SNPs selected for this study have been verified and are suitable for investigation in the Indonesian population.

The authors should clearly specify the inclusion and exclusion criteria in the methodology section to ensure reproducibility and clarify the study population. Response: The inclusion criteria for this research were divided into case and control. Patients were classified to be in the case population if at least one of the following criteria was met: Hospitalization duration of ≥ 28 days; or < 28 days with pulmonary fibrosis; or < 28 days with no improvement of radiological findings and at least one persistent respiratory symptom (cough, dyspnea, malaise, chest pain, or sore throat) at the time the patient was discharged. The control population consists of patients who met none of the case criteria. Patients who did not present with any clinical symptoms were excluded from the study.

The Introduction section requires further improvement. The authors should provide a clearer background, highlight the knowledge gap, and explicitly state the study’s rationale and objectives. Response: The Introduction has been revised to clarify the background, explicitly define the knowledge gap, and clearly state the study rationale and objectives.

The Discussion section also needs further improvement. The authors should provide a deeper interpretation of the findings, compare results with existing recent literature, discuss potential mechanisms, and address study limitations. Response: The discussion section has been revised for improvement about deeper interpretation and compare the result with other studies before.

The manuscript requires language improvement. The authors should revise the text for clarity, grammar, and readability to ensure that the scientific content is clearly communicated. Response: The manuscript has been carefully revised to improve the language, grammar, clarity, and readability throughout the text.

Since multiple variables were analysed, did the authors apply a Bonferroni correction or another method to adjust for multiple comparisons? Please clarify this in the data analysis section. Response: To adjust for multiple comparisons in the data analysis, we applied the Benjamini–Hochberg correction. Since this study involves genetic association analysis, this method is considered appropriate for controlling the false discovery rate.