tetano
Editor, Senior Moderator
Influenza Other Respir Viruses
. 2026 Oct;20(10):e70334.
doi: 10.1111/irv.70334.
Xiao Xue 1 , Yaning Feng 1 2 , Yu-Ping Lin 1 , Pui-Ching Chan 1 , Hon-Cheong So 1 3 4 5 6 7 8
Affiliations Expand
Background: Influenza remains a significant global health burden, potentially leading to hospitalization and mortality. Although previous studies have explored genetic susceptibility to influenza infection, the genetic basis of influenza hospitalization and its relationship with respiratory diseases like COVID-19 remain poorly understood.
Methods: We performed a binary GWAS of hospitalized influenza using UK Biobank data (UKBB; 1037 cases/404,108 controls) and then meta-analyzed results with the FinnGen GWAS, yielding 11,171 cases and 782,400 controls. A time-to-event GWAS in UKBB was also performed to identify genetic factors underlying the timing (age) of first influenza hospitalization. p-values from both analyses were integrated by the harmonic mean p-value (HMP) method to enhance association detection. Further differentiation and colocalization analyses with COVID-19 investigated their distinct and shared genetic features. An integrative framework combining functional annotations, multi-omics analyses, and phenome-wide scans prioritized key risk and differential genes.
Results: The binary GWAS meta-analysis identified three loci (ST6GAL1, AASDHPPT, SPATS2L) associated with hospitalization risk. The time-to-event GWAS recognized UGT2B4 as linked to hospitalization timing. HMP analysis, which combines evidence across the binary and time-to-event analyses, identified ST6GAL1 (p = 3 × 10-9) and UGT2B4 (p = 3.63 × 10-8) as genome-wide significant; however, each signal was driven predominantly by one component test, ST6GAL1 by the binary meta-analysis (meta p = 1.5 × 10-9), and UGT2B4 by the time-to-event analysis (SPACox p = 1.82 × 10-8), rather than showing independent support from both analyses. Differentiation analysis identified 29 loci and two tissues distinguishing influenza from COVID-19, whereas analysis of shared association signals (cofdr) highlighted candidate shared genetic factors, including ST6GAL1 and ICAM5 (gene-level PPA ≥ 0.9).
Conclusions: This study uncovers the genetic architectures of hospitalized influenza. It also highlights genetic differences and similarities with COVID-19, providing hypothesis-generating insights that may inform future functional and therapeutic research.
. 2026 Oct;20(10):e70334.
doi: 10.1111/irv.70334.
Genome-Wide Association Studies of Hospitalized Influenza Identify Four Risk Loci and Reveal Shared and Unique Genetic Factors Compared With COVID-19
Xiao Xue 1 , Yaning Feng 1 2 , Yu-Ping Lin 1 , Pui-Ching Chan 1 , Hon-Cheong So 1 3 4 5 6 7 8
Affiliations Expand
- PMID: 42885252
- DOI: 10.1111/irv.70334
Abstract
Background: Influenza remains a significant global health burden, potentially leading to hospitalization and mortality. Although previous studies have explored genetic susceptibility to influenza infection, the genetic basis of influenza hospitalization and its relationship with respiratory diseases like COVID-19 remain poorly understood.
Methods: We performed a binary GWAS of hospitalized influenza using UK Biobank data (UKBB; 1037 cases/404,108 controls) and then meta-analyzed results with the FinnGen GWAS, yielding 11,171 cases and 782,400 controls. A time-to-event GWAS in UKBB was also performed to identify genetic factors underlying the timing (age) of first influenza hospitalization. p-values from both analyses were integrated by the harmonic mean p-value (HMP) method to enhance association detection. Further differentiation and colocalization analyses with COVID-19 investigated their distinct and shared genetic features. An integrative framework combining functional annotations, multi-omics analyses, and phenome-wide scans prioritized key risk and differential genes.
Results: The binary GWAS meta-analysis identified three loci (ST6GAL1, AASDHPPT, SPATS2L) associated with hospitalization risk. The time-to-event GWAS recognized UGT2B4 as linked to hospitalization timing. HMP analysis, which combines evidence across the binary and time-to-event analyses, identified ST6GAL1 (p = 3 × 10-9) and UGT2B4 (p = 3.63 × 10-8) as genome-wide significant; however, each signal was driven predominantly by one component test, ST6GAL1 by the binary meta-analysis (meta p = 1.5 × 10-9), and UGT2B4 by the time-to-event analysis (SPACox p = 1.82 × 10-8), rather than showing independent support from both analyses. Differentiation analysis identified 29 loci and two tissues distinguishing influenza from COVID-19, whereas analysis of shared association signals (cofdr) highlighted candidate shared genetic factors, including ST6GAL1 and ICAM5 (gene-level PPA ≥ 0.9).
Conclusions: This study uncovers the genetic architectures of hospitalized influenza. It also highlights genetic differences and similarities with COVID-19, providing hypothesis-generating insights that may inform future functional and therapeutic research.