Polymorphisms of IFN signaling genes and FOXP4 influence the severity of COVID-19

Author:

Zhang Feng,Zhou Pingping,Wang Liangliang,Liao Xinzhong,Liu Xuejie,Ke Changwen,Wen Simin,Shu Yuelong

Abstract

Abstract Background The clinical manifestations of COVID-19 range from asymptomatic, mild to moderate, severe, and critical disease. Host genetic variants were recognized to affect the disease severity. However, the genetic landscape differs among various populations. Therefore, we explored the variants associated with COVID-19 severity in the Guangdong population. Methods A total of 314 subjects were selected, of which the severe and critical COVID-19 patients were defined as “cases”, and the mild and moderate patients were defined as “control”. Twenty-two variants in interferon-related genes and FOXP4 were genotyped using the MassARRAY technology platform. Results IFN signaling gene MX1 rs17000900 CA + AA genotype was correlated with a reduced risk of severe COVID-19 in males (P = 0.001, OR = 0.050, 95%CI = 0.008–0.316). The AT haplotype comprised of MX1 rs17000900 and rs2071430 was more likely to protect against COVID-19 severity (P = 6.3E-03). FOXP4 rs1886814 CC genotype (P = 0.001, OR = 3.747, 95%CI = 1.746–8.043) and rs2894439 GA + AA genotype (P = 0.001, OR = 5.703, 95% CI = 2.045–15.903) were correlated with increased risk of severe COVID-19. Haplotype CA comprised of rs1886814 and rs2894439 was found to be correlated with adverse outcomes (P = 7.0E-04). FOXP4 rs1886814 CC (P = 0.0004) and rs2894439 GA + AA carriers had higher neutralizing antibody titers (P = 0.0018). The CA + AA genotype of MX1 rs17000900 tended to be correlated with lower neutralizing antibody titers than CC genotype (P = 0.0663), but the difference was not statistically significant. Conclusion Our study found a possible association between MX1 and FOXP4 polymorphisms and the severity of COVID-19. Distinguishing high-risk patients who develop severe COVID-19 will provide clues for early intervention and individual treatment strategies.

Funder

Guangdong Basic and Applied Basic Research Foundation

National Natural Science Foundation of China

Shenzhen Science and Technology Innovation Program

Publisher

Springer Science and Business Media LLC

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