Relaxing parametric assumptions for non-linear Mendelian randomization using a doubly-ranked stratification method

PLOS Genetics · 4 authors, 3 centres

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Simulation studies showed the method can produce unbiased estimates when standard assumptions are violated, and an illustrative application found a positive effect of alcohol intake on systolic blood pressure, particularly at higher consumption levels.

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It is designed to relax the strict parametric assumptions of linearity and homogeneity required by the standard residual method, which can lead to biased estimates if violated. The method involves two ranking steps: first stratifying by the genetic instrument, then sub-stratifying by exposure level within those groups. Simulation studies demonstrated that the doubly-ranked method yielded unbiased stratum-specific estimates and appropriate coverage rates even with non-linear or heterogeneous instrument-exposure effects, and was robust to coarsened exposure data. An illustrative application to UK Biobank data suggested a positive effect of alcohol intake on systolic blood pressure, with evidence of a stronger effect at higher consumption levels.

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research methods

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ORIGINAL PAPER

PMC10343089

Relaxing parametric assumptions for non-linear Mendelian randomization using a doubly-ranked stratification method

PLOS Genetics

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ABSTRACT

Abstract Non-linear Mendelian randomization is an extension to standard Mendelian randomization to explore the shape of the causal relationship between an exposure and outcome using an instrumental variable. A stratification approach to non-linear Mendelian randomization divides the population into strata and calculates separate instrumental variable estimates in each stratum. However, the standard implementation of stratification, referred to as the residual method, relies on strong parametric assumptions of linearity and homogeneity between the instrument and the exposure to form the strata. If these stratification assumptions are violated, the instrumental variable assumptions may be violated in the strata even if they are satisfied in the population, resulting in misleading estimates. We propose a new stratification method, referred to as the doubly-ranked method, that does not require strict parametric assumptions to create strata with different average levels of the exposure such that the instrumental variable assumptions are satisfied within the strata. Our simulation study indicates that the doubly-ranked method can obtain unbiased stratum-specific estimates and appropriate coverage rates even when the effect of the instrument on the exposure is non-linear or heterogeneous. Moreover, it can also provide unbiased estimates when the exposure is coarsened (that is, rounded, binned into categories, or truncated), a scenario that is common in applied practice and leads to substantial bias in the residual method. We applied the proposed doubly-ranked method to investigate the effect of alcohol intake on systolic blood pressure, and found evidence of a positive effect of alcohol intake, particularly at higher levels of alcohol consumption.

CITATION EXPORT

Haodong Tian, Amy M Mason, Cunhao Liu et al.. Relaxing parametric assumptions for non-linear Mendelian randomization using a doubly-ranked stratification method. PLOS Genetics. (2023). https://doi.org/10.1371/journal.pgen.1010823

AUTHORS

4 · 3 CENTRES

Haodong Tian, Amy M Mason, Cunhao Liu, Stephen Burgess

1MRC Biostatistics Unit, School of Clinical Medicine, University of Cambridge, Cambridge, United Kingdom
2British Heart Foundation Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge, United Kingdom
3University Hospital of the Canton Vaud (CHUV), SWITZERLAND