Statistical Analysis
We used the inverse variance-weighted (IVW) model as the main
statistical method [33]. In sensitivity analyses, the weighted
median method [34], MR-Egger method [35], Maximum likelihood,
and Mendelian Randomization Pleiotropy RESidual Sum and Outlier
(MR-PRESSO) method [36] were performed to examine the consistency of
associations and detect possible pleiotropy. The weighted median method
provides a causal estimate when more than 50% of the weight in the
analysis comes from valid instrumental variables [34]. The MR-Egger
regression mode is able to detect pleiotropy by its intercept, but it
compromises statistical power after pleiotropy is corrected [35].
The MR-PRESSO method, can detect and adjust for horizontal pleiotropy by
outlier removal [36]. Cochran’s Q statistics were calculated to test
for heterogeneity produced by different genetic variants in the IVW
analyses.
Multivariable MR analysis was further applied to assess whether any
association of domain-specific lifestyle behaviors with OC subtypes
could be affected by potential confounders, including BMI and smoking.
We also conducted a 2-step multivariable MR analysis in the analysis to
adjust for BMI and smoking initiation. The associations were deemed
significant associations at a strict Bonferroni corrected P -value
below 0.00048 (correcting for 13 exposures and 8 outcomes), and
associations with P -value > 0.00048 and
< 0.05 were regarded as suggestive associations. All
statistical tests were 2-tailed and performed using the TwoSampleMR
[32], MR-PRESSO [36], and Mendelian randomization [37]
packages in the R software (version 4.0.2; R Foundation for Statistical
Computing, Vienna, Austria).