No Genetically Predicted Association Between Human Papillomavirus and COVID-19: A Mendelian Randomization Analysis in European Ancestry Population

Sha-Sha Tao,1– 3,* Man Ge,1,2,* Yi-Fan Cai,4,* Shu-Zhen Xu,1,2 Yi-Sheng He,1,2 Yi-Qing Xu,1,2 Tian He,1,2 Xiao-Fan Dai,5 Xiao Hu,6 Peng Wang,6 Hai-Feng Pan1,2 1Department of Epidemiology and Biostatistics, School of Public Health, Anhui Medical University, Hefei, Anhui, People’s Republic of China; 2Inflammation and Immune Mediated Diseases Laboratory of Anhui Province, Hefei, Anhui, People’s Republic of China; 3Preventive Medicine Experimental Teaching Center, School of Public Health, Anhui Medical University, Hefei, Anhui, People’s Republic of China; 4Department of Clinical Medicine, Anhui Medical University, Hefei, Anhui, People’s Republic of China; 5Department of Public Utilities Management, School of Health Service Management, Anhui Medical University, Hefei, Anhui, People’s Republic of China; 6Teaching Center for Preventive Medicine, School of Public Health, Anhui Medical University, Hefei, Anhui, People’s Republic of China*These authors contributed equally to this workCorrespondence: Hai-Feng Pan, Department of Epidemiology and Biostatistics, School of Public Health, Anhui Medical University, Hefei, Anhui, People’s Republic of China, Email [email protected]; [email protected] Peng Wang, Teaching Center for Preventive Medicine, School of Public Health, Anhui Medical University, Hefei, Anhui, People’s Republic of China, Email [email protected]: Previous studies reveal that coronavirus disease 2019 (COVID-19) infection accelerates the progression of Human papillomavirus (HPV)-related diseases, but the results remain controversial. We conducted a bidirectional two-sample Mendelian randomization (MR) study to evaluate the causal association between HPV infection and COVID-19 using genome-wide association study (GWAS) summary data from European ancestry populations.Patients and Methods: Genetic summary data of HPV infection and COVID-19 were derived from the public GWAS meta-analysis and the COVID-19 host genetics initiative GWAS, respectively. The causal link between HPV infection and COVID-19 was evaluated by MR analysis with inverse variance weighting (IVW), MR-Egger, and weighted median methods. Additional MR Pleiotropy RESidual Sum and Outlier (MR-PRESSO) and MR-Egger regression methods were used to identify the potential pleiotropy of the instrumental variables (IVs). Bonferroni correction was used to account for the issue of multiple comparisons, leading to a statistically significant P-value of less than 0.004 (0.05/2*3*2).Results: There were no significantly causal links of HPV-16 or HPV-18 infection with COVID-19 infection, hospitalized COVID-19, or severe COVID-19 (all P> 0.05). Furthermore, no significant causal effect of all three types of COVID-19 on HPV-16 and HPV-18 was observed in the reverse MR analyses (all P> 0.05). MR-Egger regression and MR-PRESSO global test did not find the presence of horizontal pleiotropy between IVs of HPV infection and COVID-19.Conclusion: This study shows that COVID-19 infection does not affect the risk of HPV-16/18 infection, nor does HPV-16/18 infection increase COVID-19 infection risk. It highlights the need to maintain routine health management and no change to HPV prevention strategies.Keywords: HPV, COVID-19, mendelian randomization, causal relationship, genetic association

Authors

Institutions

Publication Details

Journal
SHILAP Revista de lepidopterología
Published
2026-01-01
Primary Topic
Cervical Cancer and HPV Research
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

No Genetically Predicted Association Between Human Papillomavirus and COVID-19: A Mendelian Randomization Analysis in European Ancestry Population

Pan HF, Dai Xf, Cai YF, He YS et al.
SHILAP Revista de lepidopterología
Cervical Cancer and HPV Research
article

No Genetically Predicted Association Between Human Papillomavirus and COVID-19: A Mendelian Randomization Analysis in European Ancestry Population

Pan HF, Dai Xf, Cai YF, He YS, Tao SS, Ge M, Xu YQ, Wang P, Xu SZ, He T, Hu X
article en

Abstract

Sha-Sha Tao,1– 3,* Man Ge,1,2,* Yi-Fan Cai,4,* Shu-Zhen Xu,1,2 Yi-Sheng He,1,2 Yi-Qing Xu,1,2 Tian He,1,2 Xiao-Fan Dai,5 Xiao Hu,6 Peng Wang,6 Hai-Feng Pan1,2 1Department of Epidemiology and Biostatistics, School of Public Health, Anhui Medical University, Hefei, Anhui, People’s Republic of China; 2Inflammation and Immune Mediated Diseases Laboratory of Anhui Province, Hefei, Anhui, People’s Republic of China; 3Preventive Medicine Experimental Teaching Center, School of Public Health, Anhui Medical University, Hefei, Anhui, People’s Republic of China; 4Department of Clinical Medicine, Anhui Medical University, Hefei, Anhui, People’s Republic of China; 5Department of Public Utilities Management, School of Health Service Management, Anhui Medical University, Hefei, Anhui, People’s Republic of China; 6Teaching Center for Preventive Medicine, School of Public Health, Anhui Medical University, Hefei, Anhui, People’s Republic of China*These authors contributed equally to this workCorrespondence: Hai-Feng Pan, Department of Epidemiology and Biostatistics, School of Public Health, Anhui Medical University, Hefei, Anhui, People’s Republic of China, Email [email protected]; [email protected] Peng Wang, Teaching Center for Preventive Medicine, School of Public Health, Anhui Medical University, Hefei, Anhui, People’s Republic of China, Email [email protected]: Previous studies reveal that coronavirus disease 2019 (COVID-19) infection accelerates the progression of Human papillomavirus (HPV)-related diseases, but the results remain controversial. We conducted a bidirectional two-sample Mendelian randomization (MR) study to evaluate the causal association between HPV infection and COVID-19 using genome-wide association study (GWAS) summary data from European ancestry populations.Patients and Methods: Genetic summary data of HPV infection and COVID-19 were derived from the public GWAS meta-analysis and the COVID-19 host genetics initiative GWAS, respectively. The causal link between HPV infection and COVID-19 was evaluated by MR analysis with inverse variance weighting (IVW), MR-Egger, and weighted median methods. Additional MR Pleiotropy RESidual Sum and Outlier (MR-PRESSO) and MR-Egger regression methods were used to identify the potential pleiotropy of the instrumental variables (IVs). Bonferroni correction was used to account for the issue of multiple comparisons, leading to a statistically significant P-value of less than 0.004 (0.05/2*3*2).Results: There were no significantly causal links of HPV-16 or HPV-18 infection with COVID-19 infection, hospitalized COVID-19, or severe COVID-19 (all P> 0.05). Furthermore, no significant causal effect of all three types of COVID-19 on HPV-16 and HPV-18 was observed in the reverse MR analyses (all P> 0.05). MR-Egger regression and MR-PRESSO global test did not find the presence of horizontal pleiotropy between IVs of HPV infection and COVID-19.Conclusion: This study shows that COVID-19 infection does not affect the risk of HPV-16/18 infection, nor does HPV-16/18 infection increase COVID-19 infection risk. It highlights the need to maintain routine health management and no change to HPV prevention strategies.Keywords: HPV, COVID-19, mendelian randomization, causal relationship, genetic association

SHILAP Revista de lepidopterología
Government of Western Australia Department of Health (AU), Cancer Research And Biostatistics (US), Faculty of Public Health (GB)
Good health and well-being
Openalex Percentile: Top 93%
Cervical Cancer and HPV Research
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.