2.4 Statistical analysis
The inverse variance-weighted (IVW) mode was applied as the primary
approach, which used meta-analysis to combine the SNP-specific Wald
ratio estimates for each IV and obtained an overall estimate of the
effect17. Three other methods,
MR-Egger18, weighted-median19, and
weighted mode20approaches were used as supplements.
Heterogeneity was evaluated by Cochrane’s Q
statistic21. The horizontal pleiotropy was accessed by
the intercept of MR‐Egger regression, which should not be significantly
different from 0 (i.e., P > 0.05)18.
We also used the Mendelian
randomization pleiotropy residual sum and outlier (MR-PRESSO) global
test to identify and fix horizontal pleiotropic
outliers22.
The
multivariate IVW model with Lasso penalization was further used to
evaluate the independent causal effect of related diseases on severe
COVID-19. The Lasso penalization
could shrink the coefficients of the invalid variables to zero,
preventing overfitting23.
In multivariate MR analysis,
phenotypes with comparable traits could be treated as controls for each
other to identify the predominant phenotypes24.
Odds ratios (OR) and 95% confidence intervals (CI) were used to
describe the causal impact of exposures on outcomes. To correct for the
multiple testing, the Bonferroni
adjusted P values of significance were
< 0.0167
(α = 0.05/3 exposure factors) and
< 0.0125 (α = 0.05/4 exposure factors) in multivariate MR
analyses for the three allergic diseases and the four asthma subtypes,
respectively. P<0.0167/0.0125 was
regarded as strong evidence of
causality, while
0.0167/0.0125<P<0.05 was considered suggestive
evidence of causality. In addition,
other statistical tests had a
two-sided design, with the threshold of statistical significance set at
P<0.05. The R language (version 4.2.2) was used to select IVs,
perform statistical analyses, and visualize our findings.