
==== Front
Hum Vaccin Immunother
Hum Vaccin Immunother
Human Vaccines & Immunotherapeutics
2164-5515
2164-554X
Taylor & Francis

39254005
10.1080/21645515.2024.2399382
2399382
Version of Record
Research Article
Novel Vaccines
Genomic insights into mRNA COVID-19 vaccines efficacy: Linking genetic polymorphisms to waning immunity
M.-J. HSIEH ET AL.
HUMAN VACCINES & IMMUNOTHERAPEUTICS
Hsieh Min-Jia a
Tsai Ping-Hsing b *
Chiang Pin-Hsuan c
Kao Zih-Kai d
Zhuang Zi-Qing c
Hsieh Ai-Ru e
Ho Hsiang-Ling f g
Chiou Shih-Hwa b h
Liang Kung-Hao b i j k *
https://orcid.org/0000-0002-4124-1949
Chen Yu-Chun a c h l *
a Department of Family Medicine, Taipei Veterans General Hospital , Taipei, Taiwan
b Department of Medical Research, Taipei Veterans General Hospital , Taipei, Taiwan
c Big Data Center, Taipei Veterans General Hospital , Taipei, Taiwan
d Department of Information Management, Taipei Veterans General Hospital , Taipei, Taiwan
e Department of Statistics, Tamkang University , New Taipei, Taiwan
f Department of Pathology and Laboratory Medicine, Taipei Veterans General Hospital , Taipei, Taiwan
g Department of Biotechnology and Laboratory Science in Medicine, National Yang Ming Chiao Tung University , Taipei, Taiwan
h School of medicine, National Yang Ming Chiao Tung University , Taipei, Taiwan
i Biosafety level 3 laboratory, Taipei Veterans General Hospital , Taipei, Taiwan
j Institute of Biomedical Informatics, National Yang Ming Chiao Tung University , Taipei, Taiwan
k Institute of Food Safety and Health Risk Assessment, National Yang Ming Chiao Tung University , Taipei, Taiwan
l Department of Family Medicine, Taipei Veterans General Hospital Yuli Branch , Hualien, Taiwan
CONTACT Yu-Chun Chen yuchn.chen@gmail.com Department of Family Medicine, School of Medicine, National Yang Ming Chiao Tung University, No. 155, Sec. 2, Linong St. Beitou Dist, Taipei City 112304, Taiwan.
Ping-Hsing Tsai figatsai@gmail.com.
Kung-Hao Liang khliang@vghtpe.gov.tw Department of Medical Research, Taipei Veterans General Hospital, No.201, Sec. 2, Shipai Rd., Beitou District, Taipei 11217, Taiwan.
* Ping-Hsing Tsai, Kung-Hao Liang, and Yu-Chun Chen equally contributed and corresponded to this study.

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© 2024 The Author(s). Published with license by Taylor & Francis Group, LLC.
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https://creativecommons.org/licenses/by-nc/4.0/ This is an Open Access article distributed under the terms of the Creative Commons Attribution-NonCommercial License (http://creativecommons.org/licenses/by-nc/4.0/), which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. The terms on which this article has been published allow the posting of the Accepted Manuscript in a repository by the author(s) or with their consent.

ABSTRACT

Genetic polymorphisms have been linked to the differential waning of vaccine-induced immunity against COVID-19 following vaccination. Despite this, evidence on the mechanisms behind this waning and its implications for vaccination policy remains limited. We hypothesize that specific gene variants may modulate the development of vaccine-initiated immunity, leading to impaired immune function. This study investigates genetic determinants influencing the sustainability of immunity post-mRNA vaccination through a genome-wide association study (GWAS). Utilizing a hospital-based, test negative case-control design, we enrolled 1,119 participants from the Taiwan Precision Medicine Initiative (TPMI) cohort, all of whom completed a full mRNA COVID-19 vaccination regimen and underwent PCR testing during the Omicron outbreak. Participants were classified into breakthrough and protected groups based on PCR results. Genetic samples were analyzed using SNP arrays with rigorous quality control. Cox regression identified significant single nucleotide polymorphisms (SNPs) associated with breakthrough infections, affecting 743 genes involved in processes such as antigenic protein translation, B cell activation, and T cell function. Key genes identified include CD247, TRPV1, MYH9, CCL16, and RPTOR, which are vital for immune responses. Polygenic risk score (PRS) analysis revealed that individuals with higher PRS are at greater risk of breakthrough infections post-vaccination, demonstrating a high predictability (AUC = 0.787) in validating population. This finding confirms the significant influence of genetic variations on the durability of immune responses and vaccine effectiveness. This study highlights the importance of considering genetic polymorphisms in evaluating vaccine-induced immunity and proposes potential personalized vaccination strategies by tailoring regimens to individual genetic profiles.

KEYWORDS

COVID-19
mRNA-based vaccines
waning immunity
genetic polymorphisms
long-term memory CD8+ T cells
Academia Sinica 10.13039/501100001869 40-05-GMM AS-GC-110-MD02 and 236e-1100202 National Development Fund, Executive Yuan NSTC 111-3114-Y-001-001 Taipei Veterans General Hospital 10.13039/501100011912 V111E-002-1 V112E-001-1 V113E-002-1 V113E-002-4 The work was supported by the Academia Sinica [40-05-GMM, AS-GC-110-MD02 and 236e-1100202]; National Development Fund, Executive Yuan [NSTC 111-3114-Y-001-001]; Taipei Veterans General Hospital [V111E-002-1, V112E-001-1, V113E-002-1, V113E-002-4].
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pmcIntroduction

Since 2019, the emergence of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has led to a widespread and rapid global pandemic characterized by its high infectivity and challenges in implementing effective protective measures. This has resulted in more than 775 million reported cases and 7 million fatalities worldwide. Scientists have been actively working to create vaccines to reduce hospitalizations and deaths.1–3 Among various types of vaccines, mRNA-based COVID-19 vaccines have been widely distributed because of their rapid production and stable structure.4,5 Vaccine induced immunity can be simplified to five stages. First, vaccine mRNA engulfed by innate immune cells and degraded. Second, the degraded particles were translated into antigenic proteins which induce further adaptive immunity. Third, the antigens were broken down to small particles and recognized by MHC proteins on T cells. Fourth, cytotoxic T cells kill infected cells. Helper T cells prompt B cells to generate antibodies that can neutralize pathogens in the bloodstream and trigger macrophages to engage in phagocytosis.6 MRNA vaccines have demonstrated effectiveness in preventing severe disease and deaths, with minimal safety concerns apart from temporary local and systemic adverse effects.7,8

While current mRNA vaccines are believed to offer protection against most mutated strains with varying efficacy,9 studies have indicated a decline in vaccine-induced immune protection over time post-vaccination. Research has shown a rapid increase in neutralizing antibodies in the initial weeks after vaccination, followed by a decline within 3 to 6 months.9–11 Additionally, cellular immune responses against SARS-CoV-2 have been observed to decrease over time, despite the presence of sustained memory cells and affinity maturation.10,12 Thus, booster doses have been suggested to consolidate protection against the virus as immune wanes over time.

However, despite receiving same amounts of vaccine, some individuals or populations demonstrate accelerated immune decline post-vaccination in immunity.13–15 Studies have shown that genetic polymorphisms can influence how the immune system responds to vaccines. It has been found that human leukocyte antigen (HLA) gene variants are associated with the efficacy of SARS-CoV-2 vaccines in various ethnic groups.15 Additionally, genetic alterations in immune-regulating elements have been linked to the varied persistence of immunity among individual’s post-vaccination.16–19

Mechanisms under immune waning had been under debate.11 While it was previously thought that antibodies decrease and the virus mutates to evade immunity, some had proposed poor ability of the adaptive immune system to recognize antigens of new virus variants.9,11,20 We hypothesize that certain gene variants might modulate the development of vaccine-initiated immunity and cause impaired immune function such as less amounts, shorter life, or impaired function of immune related cells or proteins.12 If so, both under complete vaccination regimen, those with impaired immunity would develop breakthrough infection earlier while those with intact immunity might be protected when encountering viral invasions. Using GWAS analysis, the present study aims to identify gene polymorphisms in molecular process of mRNA vaccines-initiated immunity which are associated with immune sustainability following three doses of mRNA vaccines.

Materials and methods

Study design

This is a test negative case-control study combined with genome-wide association study (GWAS) analysis. We included participants who completed the full mRNA COVID-19 vaccination regimen and had COVID-19 testing due to encountering COVID-like symptoms during the outbreak of the Omicron variant in Taiwan, aiming to assess the genetic variants that may influence the sustainability of vaccine-induced immunity (Figure 1 and Table 1). The study was approved by the Research Ethics Committee of Taipei Veterans General Hospital, Taipei, Taiwan (IRB no. 2023–06-020BC). Figure 1. GWAS flowchart of participant selection.

Breakthrough group and protected group have received three doses of mRNA vaccine and genotyped by the TPMI project. Sample matching was performed according to their age, gender and comorbidity. Detailed characteristics were also listed in Table 1.

Table 1. Comparison of demographic and comorbidity profiles between protected and breakthrough groups post-vaccination (n = 708).

Characteristics	Protected group
(n = 472)	Breakthrough group
(n = 236)	p value	
Demographics	 	 	 	
Age	53.82 (17.7)	53.54 (18.0)	<.001	
Male, no. (%)	172 (36.4)	93 (39.4)	.440	
Follow-up duration, mean (SD)	60.23 (37.85)	92.00 (35.88)	<.001	
Comorbidity	 	 	 	
Myocardial infarction (%)	4 (0.85)	2 (0.85)	1.000	
Heart failure (%)	16 (3.39)	7 (3.00)	.760	
Cardiovascular disease (%)	40 (8.47)	17 (7.20)	.560	
Dementia (%)	18 (3.81)	7 (2.97)	.560	
Chronic obstructive pulmonary disease (%)	57 (12.08)	20 (8.47)	.150	
Rheumatologic disease (%)	29 (6.14)	8 (3.39)	.120	
Diabetes (%)	77 (16.31)	41 (17.37)	.720	
Severe renal disease (%)	9 (1.91)	8 (3.39)	.220	

Study population

Participants were selected from the Taiwan Precision Medicine Initiative (TPMI) cohort at Taipei Veterans General Hospital. TPMI is a joint endeavor orchestrated by Academia Sinica in partnership with 15 leading medical centers across Taiwan, aiming to compile a comprehensive database of clinical and genetic data for one million Taiwanese individuals (https://tpmi.ibms.sinica.edu.tw/www/en/). This study enrolled eligible individuals who had completed the full mRNA COVID-19 vaccination regimen – consisting of two initial doses followed by a booster – and who were subsequently referred for PCR testing at Taipei Veterans General Hospital by healthcare providers due to COVID-like symptoms. The study period was November 2021 to May 2022, when the Omicron variant outbreak in Taiwan. During the period, individuals developed COVID-like symptoms were demanded to visit health institutions for PCR testing, providing a natural experimental environment to evaluate vaccine effectiveness.21 Each participant’s follow-up period extended from the administration of their last booster dose to the date of their PCR test.

Assessment of COVID-19 infection

Participants were categorized into the breakthrough group and the protected group based on the results of their PCR tests as previous study.22 A positive test result indicated that, despite receiving the full vaccine regimen, the participants were more susceptible to viral invasions and developed breakthrough infection. On the other hand, a negative test result in the protected group indicated successful immunological responses to the virus. The PCR tests were administered under the regulation of the public health system and were available only to symptomatic patients who were evaluated by a healthcare provider and deemed suspected cases of COVID-19. By exclusively including PCR tests conducted for medically evaluated symptoms, and excluding tests performed for non-medical reasons (such as travel in which people have to do self-paid testing), we aimed to minimize potential biases introduced by asymptomatic individuals. The immune sustainability was evaluated by the time from vaccination to development of breakthrough infection.

Genetic sampling and genotyping

The TPMI project employed the SNP array derived from the Axiom Genome-Wide Taiwan Biobank 2.0 Array Plate (https://www.twbiobank.org.tw/), which underwent Calibration through the utilization of whole-genome sequencing data from patients who were also subjected to SNP array analysis. In addition to genetic information, the TPMI project incorporated clinical data encompassing diagnoses, medication history, and biochemical measurements. Commencing in 2017, the TPMI initiated a cohort study involving 103,106 individuals. In accordance with the project protocol, a minimum of 5 mL of peripheral blood was carefully obtained and stored in vacutainers supplemented with Ethylenediaminetetraacetic Acid. Genomic DNA was then isolated from mononuclear cells and processed in groups to reduce potential operational biases.

Data matching and quality control

To eliminate the confounding factors, the eligible cohort were matched with the risk age, gender, and comorbidity profiles with a ratio of 1:2 between breakthrough and protected groups. Quality control were performed with exclusions of incomplete data (geno >0.05, mind > 0.1), gender discrepancies (check-sex), incomplete SNPs (MAF <0.01), markers with unusual genetic patterns (hwe <0.00001), and closely related individuals (IBD >0.125) using PLINK 1.9.

Analyzing association between genetic polymorphisms and immune sustainability

To assess the genes affecting immune sustainability after 3 doses of mRNA vaccination, Cox regression models (using Python with CoxPHFitter package version 0.28.0) were used to analyze the association between SNPs and breakthrough infection. The significant SNP variants (p < .001) were used to establish the PRS using PLINK and a predictive model using Stata version 17 for the risks and the incidence rate of breakthrough infection. Moreover, the notable variations were subsequently subjected to bioinformatic scrutiny, as detailed in Supplementary Material 2 available at the DOI 1 0.6084/m9.figshare.26095021.

Gene mapping and selection

Variant annotation and prediction of variant effects were conducted using VEP (The Ensembl Variant Effect Predictor) version,23 and the results are detailed in Supplementary Material 1, available in the publicly accessible repository with the DOI 1 0.6084/m9.figshare.26095021. After filtering out non-significant variants (p < .0001) in the analysis of Cox regression, we obtain 112 genes for further systemic analysis. The genes that have been impacted are detailed in the Supplementary Material 2 provided in the same repository.

Regulatory network construction

To establish a link with widely recognized immune-related genes, we compiled gene sets from the Molecular Signatures Database (MSigDB). This database was developed collaboratively by UC San Diego and the Broad Institute.24 We used the gene set of “GOBP Antiviral innate immune response (M40535),” “GOBP B cell activation (M10657),” “GOLDRATH Naïve vs. effector CD8 T cell (M3036),” “GOLDRATH Naive vs. memory CD8 T cell (M3039),” “GOMF MHC complex (M18641, M26641, and M18976),” “GOBP Viral translation termination reinitiation (M24631),” and “GOBP IRES dependent viral translational initiation (M24630).” In this study, a total of 112 genes exhibiting variants with a significance level of p value less than 0.0001 were considered. Among these genes, 37 were specifically identified as having regulatory region variants. A regulatory network was then established based on protein-protein interaction annotations, utilizing the STRING website for calculations. Subsequently, the network was visualized through the application of Cytoscape.25

Polygenic risk score analysis

In this research, the Polygenic Risk Score (PRS) was computed utilizing the ‘score’ function available in PLINK 1.9. This approach entails aggregating the weighted influences of genetic variations according to their magnitudes of effect. PLINK 1.9 has the ability to automatically address situations in which the alleles associated with the effect and non-effect are reversed between different datasets. The Cox-vaccine PRS was utilized in this study to predict the prognosis of SARS-CoV-2, a score derived from 794 genetic variants associated with vaccine efficacy as identified through Cox regression analysis. The selection criteria entail a p-value less than 0.001 and an r2 greater than 0.005.

Internal and external validation

In the development of the PRS model, we employed a methodology based on constructing matched pairs. Each pair was carefully matched by age, sex, and gender to ensure internal validity and minimize confounding variables. This matched dataset formed the basis for building the initial PRS model. To assess external validity and ensure that our model could generalize across different subsets of data, we subsequently applied the model to the entire, preserved dataset. This approach allowed us to evaluate the model’s effectiveness in predicting breakthrough infections across a broader demographic range, maintaining a rigorous check against overfitting by verifying performance on a completely independent set. The entire process was structured to affirm the model’s predictive reliability and its potential applicability in broader epidemiological settings.

Statistical analysis

In this study, continuous variables were summarized using mean values along with standard deviations (SD), and group comparisons were conducted using Student’s t-test. Categorical variables were presented as counts and percentages, with group differences analyzed using the Chi-square test. All p-values were two-tailed, and a p-value less than 0.001 was considered statistically significant. The analyses were conducted using R 4.1.2 (R Core Team, 2023).

Results

Characteristics of participants

To investigate the genetic determinants associated with vaccine efficacy decline, our objective is to identify individuals exhibiting distinct COVID symptoms. The present study encompassed a cohort of 5,780 patients who sought medical care at Taipei Veterans General Hospital (TPEVGH) between November 2021 and May 2022. These individuals were recruited as part of the TPMI project, which aims to conduct genetic profiling and gather comprehensive medical histories at TPEVGH. Individuals who had been administered three doses of mRNA vaccines were selected based on their PCR test results and lack of medical history within one month after the PCR test to minimize the possibility of including inaccurate negative results. The selection process for the GWAS study participants is illustrated in Figure 1. Among the genotyped individuals, 1,145 had completed a full course of mRNA vaccination between November 2021 and May 2022, with 26 participants being excluded due to inadequate genotyping quality. Of the remaining 1,119 PCR-qualified participants who passed quality control, there were 236 breakthrough cases and 883 protected cases. To mitigate potential confounding effects of age, gender, and comorbidity profiles, a 1:2 sample matching was conducted based on these factors. Ultimately, the study comprised 238 breakthrough cases and 462 protected cases, with a total of 982,741 SNPs analyzed. The demographic details of the participants are outlined in Table 1. After conducting data matching, the mean ages of the breakthrough and protected groups were found to be 53.82 and 53.54, respectively, with a p-value of 0.840. Similarly, the percentage of males in the breakthrough and protected groups were 36.4% and 39.4%, respectively, with a p-value of 0.440. These results suggest that biases related to age, gender, and disease status were effectively addressed through the matching process. Furthermore, no statistically significant differences were detected between the breakthrough and protected groups (p < .001) in terms of various health indicators including myocardial infarction (0.85% vs. 0.85%, p = 1.000), heart failure (3.39% vs. 3.00%, p = .760), cardiovascular disease (3.81% vs. 2.97%, p = .560), chronic obstructive pulmonary disease (12.08% vs. 8.47%, p = .150), rheumatologic disease (6.14% vs. 3.39%, p = .120), diabetes (16.31% vs. 17.37%, p = .720), and severe renal disease (1.91% vs. 3.39%, p = .220).

The breakthrough group shows a higher prevalence of significant and hazardous genetic polymorphisms

In our study, we performed a comparative analysis between the breakthrough and protected groups utilizing PLINK 1.9. This study utilized defined criteria such as genotype call rate (geno >0.05), individual call rate (mind >0.1), minor allele frequency (MAF <0.01), identity by descent (IBD >0.125), and Hardy-Weinberg equilibrium (hwe <0.00001) for the analysis. Subsequently, the outcomes were assessed through Cox regression analysis. The detailed p value and positive coefficient (coef) were provided in the available data link. After Cox regression, filtered variants whose p value are less than 0.001 were selected for further characterization. We visualized all filtered variants across the genome and accounted the variant amount on each chromosome (Figure 2a). Meanwhile, its Manhattan plot of the Cox results is presented in Figure 2b. The scatter plot analysis utilizing Cox regression and p-values indicated that most of the SNPs significantly associated with breakthrough infection (p < 10−4) were hazard variants which had coefficients greater than zero (Figure 2c). This implies that individuals in the breakthrough group may have an elevated susceptibility to contracting COVID-19 infection. Based on the consequence annotation provided by Ensembl, a total of 910 variants with a significance level below 0.001 were analyzed, resulting in the identification of 743 genes affected by these variants. Among these, 5.5% were classified as novel variants (50 out of 910). These variants were found to influence a total of 743 genes, encompassing 4,012 transcripts and 242 regulatory features (Figure 2d). The consequence of vaccine waning associated variants identified intron variant (52%), non-coding transcript variant (19%), upstream gene variant (7%), downstream gene variant (7%), NMD transcript variant (7%), intergenic variant (4%), regulatory region variant (3%), and non-coding transcript exon variant (1%) (Figure 2e). The findings suggest that genetic variations may significantly impact the efficiency of transcription in intronic regions or the functionality of RNA. Figure 2. The characterization of genetic variants associated with vaccine effectiveness decreasing after three doses.

(a) The global distribution of identified mutations throughout the genome. (b) The Manhattan plot depicting the statistical significance, as represented by p-values, of single nucleotide polymorphisms (SNPs) examined for their correlation with vaccine efficacy. (c) The scatter plot compares the significance of the coefficients of the assayed SNPs. The positive coefficient (coef) indicates a high risk of contracting a COVID-19 infection. The red box highlighted the SNPs positively associated with breakthrough infection with a P-value smaller than 10−4. (d) The annotation of significant variants in the Ensembl database. (e) The pie chart depicting the consequences of these mutations, specifically those with a p value less than 0.0001.

Genetic variants may coordinate antiviral immune response against SARS-CoV-2

In the adaptive immune system of humans, the functions of T cells and B cells are crucial in combating external pathogens like viruses, bacteria, and fungi. Recent study has reported that long-term memory T cells could affect the response of vaccinated individuals even immunized with the BNT162b2 mRNA vaccine.26 To deep explore the relationship with human immunity, we collected several major immune related gene sets from Molecular Signatures Database (MSigDB).24 We used the gene set of “Antiviral innate immune response,” “B cell activation,” “Effector CD8 T cell,” “Memory CD8 T cell,” “MHC complex,” “Viral translation,” and “IRES dependent viral translation.” To reduce the association of less significant variant in the protein-protein regulatory network, we only used variants whose p value less than 0.0001. Therefore, a total of 112 genes were used to construct the connection with indicated immune related gene sets. In the context of the antiviral immune response network, we emphasize the regulatory region variants because recent study reported regulatory region variants are crucial for transcriptional expression. Nodes were differentiated by size, where larger nodes indicated regulatory region variants and smaller nodes represented other variants. Only nodes directly associated with assigned activities were retained. Emphasis was placed on genes with regulatory roles by labeling them with larger nodes. Through functional enrichment analysis using STRING calculation (https://string-db.org/.), it was determined that CBFA2T3, DOP1B, VEGFC, TRPV1, CD247, RPTOR, MYH9, CCL16, RPS29, and LIPG are associated with the antiviral immune response (Figure 3a). Furthermore, associations were observed between CBFA2T3, MYH9, EHMT1, TRPV1, VEGFC, RPTOR, CD247, PUS10, CMYA5, CCL16, and RPS29 with B cell activation (Figure 3b). In the context of T cell gene networks, our study specifically examined effector T cells and memory T cells. Our analysis revealed that TRPV2, CEP290, RPTOR, MYH9, CD247, RPS, and EHMT1 are shared genes between both T cell subtypes. Notably, we found that memory T cells exhibit a higher number of uniquely affected genes, such as CCL16, ANXA9, LIPG, CMYA5, PUS10, and VEGFC (Figure 3c). The discovery of more genes linked to memory CD8 T cells has been suggested as a key reason why certain groups have struggled to achieve complete protection. The development of immunity necessitates eukaryotic translation and identification by immune cells subsequent to the delivery of mRNA into the cells. This prompted an inquiry into whether the distinction between the breakthrough and protected groups stems from variations in immune cell recognition or viral translation. Consequently, our investigation commenced to explore the relationship between variants and eukaryotic translation. The regulatory system linked to the Major Histocompatibility Complex (MHC), essential for pathogen detection in the adaptive immune system, showed the participation of genes like SOX5, CPNE1, RANBP9, SYTL2, SLC30A10, LRGUK, PACRG, ZBTB16, FGF2, RPS29, REL, MYH9, CD247, ADAMTS2, and EFEMP1. (Figure 3d) On the other hand, we specifically chose gene sets related to viral translation to enhance the significance of the gene network. The regulatory network of viral translation comprised of RPS29, RPTOR, MARK4, and EXOC3L2 (Figure 3e). Moreover, the regulatory framework governing IRES-dependent viral translation involves ZFHX3, RBM12, NAV1, RPS29, and RPTOR. Subsequently, an analysis was performed on the protein-protein regulatory network associated with the innate immune response, B cells, effector cells, and memory T cells. This analysis indicates that memory T cells could potentially contribute to the phenomenon of vaccine waning. Figure 3. Variant-affected genes regulate the activity fighting against SARS-CoV-2.

(a) The gene regulatory network revealed that genes affected by variants play a role in combating SARS-CoV-2 by regulating activities related to antiviral immune response, B cell activation, effector T cells, and memory T cells. (b) We also examined their connection to the MHC complex formation to assess their association with recognizing foreign pathogens. (c) We focused on the connection between variant-affected genes and the translation of external mRNA. The nodes outlined by a prominent red border correspond to the designated gene category. In Figures 3a,b the nodes with red borders signify a collection of multiple genes that exhibit the same biological functional categories. In contrast, Figure 3c depicts individual genes within the network. It is noteworthy that the color filling of the nodes lacking borders varies in shades of red, reflecting their degree of connectivity.

Validation of the polygenic risk score in population before matching

The receiver operating characteristic curve (ROC) and the area under the curve (AUC) were commonly utilized metrics27 to illustrate the performance of a classifying model, plotted with the sensitivity against one minus specificity at various thresholds. The ROC developed from the 794 statistically significant genetic variants (p-value <.001, R2 <0.05) in GWAS exhibited an AUC value of 0.787 (Figure 4a), indicating a good predictability of the PRS model. Furthermore, to validate the predictability of the PRS, we conducted comparative analyses using the eligible cohort before matching as the validating population (n = 1,119). The prevalence of breakthrough infection was higher among those with higher PRS compared to those with a lower PRS (59.14% vs. 8.45%). And the incidence rates were higher among the high risk individuals all over the study period, meaning a faster development of new cases (Figure 4b and Table 2). Moreover, PRS indicated the durability of immunity, that we found earlier breakthrough infection among those with more risky genetic variants, i.e. higher PRS (mean infection day: 124.99 [SD = 17.73] vs. 77.81 [SD = 32.20]) (Table 2). Notably, the delay of infection in low-PRS individuals was about 14 weeks, consistent with previous findings that vaccine-induced immunity can last 3–6 months,27–29 indicated a more complete immune induced protection in the group. Taken together, the high PRS group have damaged immune protection after three-time mRNA vaccine administration. Figure 4. Patients with a high PRS are more susceptible to COVID-19 even after vaccination.

(a) The predictive performance of PRS in determining susceptibility to COVID-19 was evaluated using the ROC curve. The ROC curve with AUC 0f 0.787 indicated a good predictability of the PRS. (b) Incidence rates by weeks among high versus low PRS individuals before matching: Those with high PRS developed breakthrough infection earlier, and with a faster acceleration (indicated by the sleeper slope), while most cases occurred before 20 weeks after vaccination. On the other hand, those with low PRS had rarely cases until the 14th week after vaccination, indicating a persistent protection from vaccine-induced immunity.

Table 2. Numbers and incidence rates of COVID-19 infections post-vaccination stratified by polygenic risk score (PRS) in the validating population (n = 1,1119).

Time interval
after vaccination (weeks)	Validating population with lower 75% PRSa
(n = 840)	Validating population with highest 25% PRSa
(n = 279)	
No. of COVID-19 infections	Incidence ratesb	No. of COVID-19 infections	Incidence rateb	
0 - 4	0	0.00	18	2.65	
5 - 8	1	0.06	23	4.55	
9 - 12	0	0.00	38	10.84	
13 -16	9	1.40	70	46.82	
>16	61	24.82	16	120.94	
aThe PRS categorization is based on a predetermined threshold, dividing the population into lower 75% and higher 25% PRS, reflecting their genetic predisposition to COVID-19 infections post-vaccination.

bIncidence rates of COVID-19 infections are calculated and expressed per 1,000 person-days. This standardization allows for equitable comparison across groups with varying sizes.

Discussion

In this retrospective GWAS analysis, we reported the gene variants potentially modulate antigenic protein translation, B cells memory and T cells functions which subsequently reduced immune sustainability against SARS-CoV-2 after 3 doses of mRNA vaccination. Moreover, the corresponding PRS had high predictability on the risk of breakthrough infection after the 3rd mRNA vaccination. Those with higher risk scores, i.e. immune dysfunction, had higher risks of breakthrough infection and higher incidence rates. We developed PRS to predict breakthrough infections and to evaluate efficacy of mRNA vaccines. It has been reported the immune protection after COVID-19 vaccination lasts around a few weeks to months, with an average of 3 to 6 months.27–29 In the current investigation, it was observed that the frequency of occurrences among individuals with lower polygenic risks began to increase after the third month (Figure 4b). This trend aligns with prior research findings. On the other hand, among those with immune dysfunction, i.e. with higher polygenic risks, the infection started since the first month after vaccination and with an elevating incidence rate after (Figure 4b). The findings validated the prediction of the PRS and provided evidence for the broken immunity against SARS-CoV-2 under genetic mutation. Furthermore, the genetic impact was persistent even after the breakthrough infection started, based on increasing the incidence rate during the study period. To our knowledge, there had been no previous study discussing the application of gene polymorphisms on precise vaccination. We proposed the possibility to provide personal vaccination suggestions by recognizing gene mutations and the indicating stages of vaccine induced immunity.

In our investigation, a number of genes were pinpointed that are recognized for their association with the efficacy of SARS-CoV-2 vaccines, such as CD247, TRPV1, MYH9, CCL16, and RPTOR. CD247 is a constituent of the T-cell receptor complex that participates in antigen recognition and signal transduction. Variations in CD247 have the potential to impact T-cell activation and function, thereby influencing the strength of immune responses.30 TRPV1 is an ion channel primarily involved in pain and heat perception. Studies indicate that TRPV1 also regulates the function of immune cells, including dendritic cell and macrophage activation. This indicates that TRPV1 may play a crucial role in regulating immune reactions and inflammatory processes.31 The MYH9 protein, a myosin heavy chain, is thought to play a critical role in the infection of human pulmonary cells by SARS-CoV-2. Genetic variations in MYH9 could impede SARS-CoV-2 infection, potentially affecting the severity of infection and the efficacy of vaccines.32 CCL16 is a chemokine that regulates the migration and positioning of immune cells. Variants in CCL16 may influence the clustering of immune cells at sites of infection, thereby impacting the efficiency of immune responses and vaccine protection.33,34 RPTOR is a component of the mTOR complex involved in cell proliferation and metabolism.35 The mTOR signaling pathway plays a crucial role in the proliferation and differentiation of T-cells and B-cells. Moreover, RPS29 is a ribosomal protein essential for protein synthesis.36 Mutations in this ribosomal protein are suggested to affect fundamental cellular processes, including protein synthesis and the functioning of immune cells. Genetic variations in these genes have the potential to influence immune responses through diverse mechanisms, including T-cell and B-cell activation, immune cell migration and positioning, and intracellular signal transduction and metabolic pathways. Additional investigation is necessary to acquire a more comprehensive comprehension of the precise functions of these genes in the efficacy of vaccines.

Previous research has demonstrated that both direct SARS-CoV-2 infection or mRNA vaccination can elicit CD8+ T cell responses,37,38 underscoring the significant role of T cells in combating COVID-19. Furthermore, aside from their recognized role in coordinating adaptive immune responses, the significance of memory T cells in enhancing the effectiveness of vaccines has been emphasized. Recent research has shown that inactive vaccines can induce the generation of memory B cells specific to SARS-CoV-2, which are crucial for the rapid and effective initiation of immune responses to combat viral infections.39 However, the current lack of molecular evidence or identified candidate genes, particularly due to challenges in recruiting a sufficient number of participants who have received three doses of mRNA vaccine, poses a limitation. Another research has demonstrated that effector and memory CD8 T cells are essential components in the immune defense against SARS-CoV-2.37–39 Effector T cells are specifically designed to carry out immune responses, whereas memory T cells exhibit quick reactivity upon encountering the same antigen again. One notable characteristic of memory T cells is their ability to remain present in the body for an extended period following the initial infection, with the potential to last for prolonged periods, spanning from months to years. Consequently, it could be postulated that discrepancies in memory T cell responses might contribute to vaccine waning, as deficient memory T cells are incapable of retaining the memory of foreign pathogenic viral antigens.

Variations in non-coding regions of the genome can significantly impact disease susceptibility.40 This includes variants in regulatory regions, upstream and downstream regions, as well as intronic regions. This research focuses on regulatory region variants as primary targets for assessing vaccine efficacy due to their increasing significance in predicting functional consequences. Actually, we have already examined other consequent variants but their connections to the indicated functions are not very satisfied (data not shown). Regulatory variants play a crucial role in determining susceptibility to immune responses.41 SNPs in these regions can potentially influence the transition of T cells between inactive and activated states. This transition may affect the clinical response to infections such as SARS-CoV-2. Additionally, regulatory region variants exhibit greater cell specificity compared to other non-coding variants.42 Consequently, these findings underscore the importance of investigating regulatory regions in understanding vaccine effectiveness.

mRNA COVID-19 vaccines are uniquely engineered to translate mRNA into the SARS-CoV-2 spike protein, thereby eliciting a specific immune response.43 Although this mechanism sets them apart from other vaccines such as those for influenza and yellow fever, the influence of genetics on the duration of immune protection spans across the innate, cellular, and humoral immune systems for all these vaccines.44 In the innate immune system, genetic variations affect how vaccine antigens are initially recognized and responded to across all three vaccine types. In cellular immunity, differences in genes related to antigen presentation, like the MHC genes, play a crucial role in the effectiveness of T cell responses to vaccine antigens. In humoral immunity, genetic factors shape both the initial production and longevity of antibodies in response to COVID-19 vaccines and determine antibody specificity in the context of influenza and yellow fever vaccines.

Clinical implications

We proposed the possibility to provide personal vaccination suggestions by recognizing gene mutations and the indicating stages of vaccine induced immunity. The evaluation might be started from screening the immune sustainability to a breakthrough infection with the PRS model. For those susceptible to infection and with faster immune waning, genetic analysis could be used to point out in what stages gene dysfunction exists for certain individual. Firstly, individuals failing to translate vaccine-mRNA to antigenic spike protein, that leads to failure in further cascades to induce immune, might be more suitable for vaccines that skip mRNA translation such as protein subunit vaccines. Secondly, those with impaired B cell memory might have short immune sustainability from antibodies and require booster doses more frequently. Thirdly, for those with T cell dysfunctions and incomplete immune protection consequently, high-dose vaccines or vaccines with adjuvant which causes stronger immune responses could be tried. Finally, genes mutation in antigen recognition on MHC might indicate dysfunctions in both adaptive and innate immune systems, no matter using vaccines based on which platforms.

Limitations

This study encompassed several limitations that are important for interpreting the findings and guiding future research. First, potential selection bias arose from utilizing a cohort primarily from a specific geographic region and healthcare setting, which may limit the generalizability of the results. The genetic diversity and environmental exposures that varied across populations could significantly influence immune responses to vaccination and virus exposure. Second, our reliance on PCR results to identify breakthrough infections, focusing predominantly on symptomatic individuals, may underrepresent the prevalence and impact of asymptomatic SARS-CoV-2 cases. Furthermore, the absence of serological testing in our study design restricted our ability to distinguish between symptomatic and subclinical infections as well as the differentiation between cellular, humoral and innate immune mechanisms. However, this issue should be minimalized by introducing test negative case-control design which reduces unmeasured confounding due to health care-seeking behavior. Third, our analysis lacked detailed PCR data to differentiate between breakthrough infections caused by various SARS-CoV-2 strains. This limitation hampered our ability to ascertain whether observed breakthroughs were primarily due to impaired immunity or the influence of different viral variants, which is crucial for understanding how genetic polymorphisms may interact with vaccine efficacy against diverse strains. Fourth, this study focused exclusively on mRNA vaccine responses, raising questions about the applicability of identified SNP associations to non-mRNA COVID-19 vaccines, which utilize different protein translation mechanisms. The limited availability of comparative data across different vaccine technologies restricted our analysis, potentially affecting the comprehensiveness and applicability of our findings to other vaccine types. Fifth, a critical gap in our methodology was the absence of longitudinal measurements of serological markers, which precluded the direct correlation of SNP profiles with the duration of immune responses over time (waning antibody level) and at the same time reduced our ability to elucidate the underlying biological mechanisms, such as innate or adaptive immune responses, or cellular or humoral immune responses. Sixth, the generalization of PRS should be used with caution even under the internal and external validation in the present study design, as the population of external validation included both the preserved (n = 411) and training data (n = 708). To better validate the predictability of the PRS, more rigorous external validation including breakthrough cases retained outside the training set or more data from other centers should be performed.

Future works

Future research should prioritize expanding study cohorts to include diverse populations and multi-center studies to enhance the generalizability of findings and reduce selection bias. This approach will ensure that the genetic diversity and environmental exposures typical in different regions are adequately represented, providing a more comprehensive understanding of vaccine efficacy across various demographics. Additionally, extending research to include non-mRNA COVID-19 vaccines will allow for the assessment of SNP associations across different vaccine platforms, addressing whether the findings are applicable beyond mRNA technologies. Moreover, to elucidate the detailed immune functions of influential SNPs, targeted serological assays with longer observation could provide biological evidence other than bioinformatic information. Lastly, considerable classifications of infected versus non-infected cases, as well as breakthrough infection versus new variants related infection may be vital for understanding how genetic polymorphisms interact with vaccine efficacy. The comprehensive approaches will enhance our knowledge of genetic factors influencing vaccine response and inform more effective vaccination strategies on precise vaccination.

Conclusions

We investigate the gene polymorphisms associated with decreased immune sustainability by GWAS analysis. Gene mutations on the modulations of antigenic protein translation, B cells memory and T cells functions could cause susceptibility to shorten immune protection and faster breakthrough infections. The recognized gene dysfunctions and produced PRS score could be used to provide personal vaccination suggestions in the era of precision medicine.

Supplementary Material

Supplemental_Material_2.docx

Supplemental_Material_1.docx

Acknowledgments

We thank all the participants and investigators from Taiwan Precision Medicine Initiative. This study was funded by Academia Sinica (40-05-GMM, AS-GC-110-MD02 and 236e-1100202), and National Development Fund, Executive Yuan (NSTC 111-3114-Y-001-001). This study is based in part on data from the Big Data Center, Taipei Veterans General Hospital (BDC, TPEVGH). The interpretations and conclusions contained herein do not represent the position of Taipei Veterans General Hospital. The article processing charge was supported by an intramural grant of Taipei Veterans General Hospital (V111E-002-1, V112E-001-1, V113E-002-1, V113E-002-4). We would like to acknowledge the support provided by the Hsu Chin-De Memorial Foundation. The funders, including the Hsu Chin-De Memorial Foundation, had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript; or in the decision to publish the results.

This study is a collaborative work led by Prof. Dr. Yu-Chun Chen, Dr. Ping-Hsing Tsai, and Dr. Kung-Hao Liang. Prof. Dr. Yu-Chun Chen, a professor at the Department of Family Medicine, National Yang Ming Chiao Tung University and the director of the Big Data Center at Taipei Veterans General Hospital, is a distinguished physician-scientist committed to advancing healthcare through innovative applications of data science. His work has significantly contributed to understanding disease epidemiology, treatment efficacy, and healthcare policy, ultimately aiming to improve patient care and medical outcomes through big data applications and AI-based healthcare tools. Dr. Ping-Hsing Tsai, a researcher at Taipei Veterans General Hospital, is a biomedical scientist with significant contributions in cellular signaling, gene reports, and genomic medicine. His research has led to valuable insights into cellular processes and genetic mechanisms, reflecting his dedication to understanding complex biological systems and improving human health. Dr. Kung-Hao Liang, a chief researcher at Taipei Veterans General Hospital, focuses on liver diseases, viral hepatitis, RNA interference, statistical genomics, and bioinformatics. His significant work includes research on bladder cancer biomarkers, hepatitis B, and hepatocellular carcinoma, as well as the development of novel biosensor platforms for detecting viral infections.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Abbreviations

PRS Polygenic risk score

GWAS Genome-wide association studies

SNP Single nucleotide polymorphisms

TPMI Taiwan Precision Medicine Initiative

Data availability statement

The available data supporting our findings are available in figshare (https://doi.org/10.6084/m9.figshare.26095021.v1).

Supplementary material

Supplemental data for this article can be accessed on the publisher’s website at https://doi.org/10.1080/21645515.2024.2399382
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