Statistical analysis
We initially conducted cross-sectional logistic models in the
observational models (Figure 1). To investigate whether a factor is a
causal factor for asthma, we conducted MR two-stage least squares (2SLS)
and MR sensitivity analyses for all relationships. MR 2SLS analysis is a
fundamental method in MR for both binary and continuous variables of
exposure and outcomes22. MR sensitivity analyses (MR
inverse-variance weighted, Egger [slope], and weighted median
method) were used to support the validity of the causal inference from
the MR analysis23. To elucidate whether various
factors predict incident asthma in later life, we also applied a
discrete-time hazard model (DTHM). The DTHM is a survival analysis model
that enables the estimation of hazard ratios (HRs) and 95% confidence
intervals (CIs) when an event occurrence may be considered in a discrete
period; baseline information on these factors was collected on
disease-free individuals at age 11, and the outcomes were incident
asthma at ages 12 and 17. The observational models and DTHM were
adjusted for several potential confounders, such as age, sex, parental
education, and family income. Odds ratios (ORs), HRs, and beta values
were estimated using the IQR increase of the factors.
The GRS for a risk factor can be considered a genetic proxy. To compare
the effect of these factors on asthma, as identified from observational
and MR analyses, we further utilized the GRS to calculate the predicted
prevalence of asthma. We computed the sample mean per quintile for each
of the GRS to serve as the new dataset for prediction by logistic
regression. To estimate the predicted probability of the GRS of an
obesity-related factor, we included the means per quintile for asthma.
All model regression and sensitivity analyses were performed using R
software and R package (ivreg) MR 2SLS analysis (version 3.3.2, RStudio,
Inc., Boston, MA, USA).