Impact of nonrandom selection mechanisms on the causal effect estimation for two-sample Mendelian randomization methods

Abstract

Nonrandom selection in one-sample Mendelian Randomization (MR) results in biased estimates and inflated type I error rates only when the selection effects are sufficiently large. In two-sample MR, the different selection mechanisms in two samples may more seriously affect the causal effect estimation. Firstly, we propose sufficient conditions for causal effect invariance under different selection mechanisms using two-sample MR methods. In the simulation study, we consider 49 possible selection mechanisms in two-sample MR, which depend on genetic variants (G), exposures (X), outcomes (Y) and their combination. We further compare eight pleiotropy-robust methods under different selection mechanisms. Results of simulation reveal that nonrandom selection in sample II has a larger influence on biases and type I error rates than those in sample I. Furthermore, selections depending on X+Y, G+Y, or G+X+Y in sample II lead to larger biases than other selection mechanisms. Notably, when selection depends on Y, bias of causal estimation for non-zero causal effect is larger than that for null causal effect. Especially, the mode based estimate has the largest standard errors among the eight methods. In the absence of pleiotropy, selections depending on Y or G in sample II show nearly unbiased causal effect estimations when the casual effect is null. In the scenarios of balanced pleiotropy, all eight MR methods, especially MR-Egger, demonstrate large biases because the nonrandom selections result in the violation of the Instrument Strength Independent of Direct Effect (InSIDE) assumption. When directional pleiotropy exists, nonrandom selections have a severe impact on the eight MR methods. Application demonstrates that the nonrandom selection in sample II (coronary heart disease patients) can magnify the causal effect estimation of obesity on HbA1c levels. In conclusion, nonrandom selection in two-sample MR exacerbates the bias of causal effect estimation for pleiotropy-robust MR methods.

Causal associations of sleep traits with cancer incidence and mortality

“…This indicates that there is no causal relationship between insomnia symptoms and pan-cancer risk and that insomnia symptoms might be related to the increased pan-cancer incidence by interfering with multiple factors. Many conventional observational studies suggest that the evening chronotype is associated with an increased risk of some types of cancer ( Erren et al, 2015 ; Hurley et al, 2019 ; Richmond et al, 2019 ; Wang J. et al, 2021 ; Sun et al, 2021 ; Von Behren et al, 2021 ; Xie et al, 2021 ; Costas et al, 2022 ; Peeri et al, 2022 ; Yu et al, 2022 ; Yuan et al, 2023 ). However, only a few studies analysed the causal association between chronotype and cancer risk ( Richmond et al, 2019 ; Wang J. et al, 2021 ; Sun et al, 2021 ; Yu et al, 2022 ; Yuan et al, 2023 ).…”

“…Many conventional observational studies suggest that the evening chronotype is associated with an increased risk of some types of cancer ( Erren et al, 2015 ; Hurley et al, 2019 ; Richmond et al, 2019 ; Wang J. et al, 2021 ; Sun et al, 2021 ; Von Behren et al, 2021 ; Xie et al, 2021 ; Costas et al, 2022 ; Peeri et al, 2022 ; Yu et al, 2022 ; Yuan et al, 2023 ). However, only a few studies analysed the causal association between chronotype and cancer risk ( Richmond et al, 2019 ; Wang J. et al, 2021 ; Sun et al, 2021 ; Yu et al, 2022 ; Yuan et al, 2023 ). Our study explored the correlations and causal associations between evening chronotype and pan-cancer risk using multivariable Cox proportional hazard and MR analyses simultaneously.…”

Causal Associations of Sleep Traits with Cancer Incidence and Mortality

“…Many conventional observational studies have suggested that the evening chronotype is associated with an increased risk of some cancers 2,8-10,29−35 . However, only a few studies have analyzed the causal association between chronotype and cancer risk 2,9,32, 33,35 , and few studies have simultaneously explored the causal relationship between de nite evening/morning chronotypes and pan-cancer incidence using their respective SNPs. Our study explored the correlations and causal associations between chronotype and pan-cancer risk using multivariable Cox proportional hazard and MR analyses, which could illustrate the causal relationship and provide better convincing power.…”

Metabolic Traits and Risk of Ischemic Stroke in Japanese and European Populations: A Two-Sample Mendelian Randomization Study

“…However, the majority of the existing relevant MR studies were carried out in Europeans and mainly focused on central obesity-related indicators [18][19][20]. Although it is advocated to apply multiple MR methods in two-sample MR studies [21], previous studies mainly focused on the results from inversevariance weighted (IVW), weighted median (WM), penalized weighted median (PWM), and MR-Egger methods [15][16][17]22,23]. Based on this, our study aimed to use summary-level data from the Japanese population to elucidate the association between metabolic traits and IS by employing both two-sample and multivariable MR. Additionally, to mitigate bias arising from pleiotropy, our findings were verified by various MR methods.…”