Abstract
BackgroundIn two-sample Mendelian randomization (MR) studies, sex instrumental heterogeneity is an important problem needed to address carefully, which however is often overlooked and may lead to misleading causal inference.MethodsWe first employed cross-trait linkage disequilibrium score regression (LDSC), Pearson’s correlation analysis, and the Cochran’s Q test to examine sex genetic similarity and heterogeneity in instrumental variables (IVs) of exposures. Simulation was further performed to explore the influence of sex instrumental heterogeneity on causal effect estimation in sex-specific two-sample MR analyses. Furthermore, we chose breast/prostate cancer as outcome and four anthropometric traits as exposures as an illustrative example to illustrate the importance of taking sex heterogeneity of instruments into account in MR studies.ResultsThe simulation definitively demonstrated that sex-combined IVs can lead to biased causal effect estimates in sex-specific two-sample MR studies. In our real applications, both LDSC and Pearson’s correlation analyses showed high genetic correlation between sex-combined and sex-specific IVs of the four anthropometric traits, while nearly all the correlation coefficients were larger than zero but less than one. The Cochran’s Q test also displayed sex heterogeneity for some instruments. When applying sex-specific instruments, significant discrepancies in the magnitude of estimated causal effects were detected for body mass index (BMI) on breast cancer (P = 1.63E-6), for hip circumference (HIP) on breast cancer (P = 1.25E-20), and for waist circumference (WC) on prostate cancer (P = 0.007) compared with those generated with sex-combined instruments.ConclusionOur study reveals that the sex instrumental heterogeneity has non-ignorable impact on sex-specific two-sample MR studies and the causal effects of anthropometric traits on breast/prostate cancer would be biased if sex-combined IVs are incorrectly employed.
Highlights
In the literature of causal inference in observational studies, Mendelian randomization (MR) is a novel statistical method to establish causal relationship between an exposure and an outcome by leveraging genetic variants as instrumental variables (IVs) (Lawlor et al, 2008; Sheehan et al, 2008)
Due to the limitation of data sharing and participant privacy concern, individual-level genome-wide association studies (GWASs) datasets are often not accessible; instead, publicly available summary-level statistics are employed in practice, which brings one great benefit that the exposure and the outcome are not required to be measured on the same set of individuals, leading to the so-called twosample MR study (Lawlor, 2016)
We revealed that the causal effects of anthropometric traits on both breast and prostate cancers would be to some extent changed when using sex-specific IVs compared with those generated with sex-combined instruments
Summary
In the literature of causal inference in observational studies, Mendelian randomization (MR) is a novel statistical method to establish causal relationship between an exposure and an outcome by leveraging genetic variants as instrumental variables (IVs) (Lawlor et al, 2008; Sheehan et al, 2008). The two-sample MR is considerably powerful and flexible and appears technically straightforward to undertake Due to those reasons, the past few years have witnessed the rapid development and application of MR for causal inference in genetics and epidemiology (Hartwig et al, 2017b; Davies et al, 2018; Zeng and Zhou, 2019a; Yu et al, 2020; Yuan et al, 2020; Liu et al, 2021).
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