The UK BiLEVE and Mendelian randomisation: using multivariable instrumental variables to address "damned if you, damned if you don't" adjustment problems

Benjamin Woolf; Dipender Gill; Hannah Sallis; Marcus R Munafò

doi:10.1186/s13104-023-06434-8

The UK BiLEVE and Mendelian randomisation: using multivariable instrumental variables to address "damned if you, damned if you don't" adjustment problems

Benjamin Woolf^*, Dipender Gill, Hannah Sallis, Marcus R Munafò

^*Corresponding author for this work

Research output: Contribution to journal › Article (Academic Journal) › peer-review

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Abstract

OBJECTIVE: To explore the use of multivariable instrumental variables to resolve the "damned if you do, damned if you don't" adjustment problem created for Mendelian randomisation (MR) analysis using the smoking or lung function related phenotypes in the UK Biobank (UKB).

RESULT: "damned if you do, damned if you don't" adjustment problems occur when both adjusting and not-adjusting for a variable will induce bias in an analysis. One instance of this occurs because the genotyping chip of UKB participants differed based on lung function/smoking status. In simulations, we show that multivariable instrumental variables analyses can attenuate potential collider bias introduced by adjusting for a proposed covariate, such as the UKB genotyping chip. We then explore the effect of adjusting for genotyping chip in a multivariable MR model exploring the effect of smoking on seven medical outcomes (lung cancer, emphysema, hypertension, stroke, heart diseases, depression, and disabilities). We additionally compare our results to a traditional univariate MR analysis using genome-wide analyses summary statistics which had and had not adjusted for genotyping chip. This analysis implies that the difference in genotyping chip has introduced only a small amount of bias.

Original language	English
Article number	157
Number of pages	7
Journal	BMC Research Notes
Volume	16
Issue number	1
DOIs	https://doi.org/10.1186/s13104-023-06434-8
Publication status	Published - 25 Jul 2023

Bibliographical note

Funding Information:
Benjamin Woolf is funded by an Economic and Social Research Council (ESRC) South West Doctoral Training Partnership (SWDTP) 1 + 3 PhD Studentship Award (ES/P000630/1). BW, HMS and MRM are all members of the Medical Research Council (MRC) Integrative Epidemiology Unit at the University of Bristol (MC_UU_00011/7).

Funding Information:
This project was conducted using UK Biobank application no. 15,825. UK Biobank was established by the Wellcome Trust medical charity, Medical Research Council, Department of Health, Scottish Government and the Northwest Regional Development Agency. It has also had funding from the Welsh Government, British Heart Foundation, Cancer Research UK and Diabetes UK. UK Biobank is supported by the National Health Service (NHS). UK Biobank is open to bona fide researchers anywhere in the world.

Funding Information:
This work was carried out using the computational facilities of the Advanced Computing Research Centre, University of Bristol - http://www.bris.ac.uk/acrc/. We are very grateful to Kate Tilling for providing feedback on the simulation methods and interpretation of the study.

Publisher Copyright:
© 2023, The Author(s).

Keywords

Humans
Genome-Wide Association Study
Smoking/genetics
Heart Diseases
Pulmonary Emphysema
United Kingdom

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

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AU - Sallis, Hannah

AU - Munafò, Marcus R

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N2 - OBJECTIVE: To explore the use of multivariable instrumental variables to resolve the "damned if you do, damned if you don't" adjustment problem created for Mendelian randomisation (MR) analysis using the smoking or lung function related phenotypes in the UK Biobank (UKB).RESULT: "damned if you do, damned if you don't" adjustment problems occur when both adjusting and not-adjusting for a variable will induce bias in an analysis. One instance of this occurs because the genotyping chip of UKB participants differed based on lung function/smoking status. In simulations, we show that multivariable instrumental variables analyses can attenuate potential collider bias introduced by adjusting for a proposed covariate, such as the UKB genotyping chip. We then explore the effect of adjusting for genotyping chip in a multivariable MR model exploring the effect of smoking on seven medical outcomes (lung cancer, emphysema, hypertension, stroke, heart diseases, depression, and disabilities). We additionally compare our results to a traditional univariate MR analysis using genome-wide analyses summary statistics which had and had not adjusted for genotyping chip. This analysis implies that the difference in genotyping chip has introduced only a small amount of bias.

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KW - Genome-Wide Association Study

KW - Smoking/genetics

KW - Heart Diseases

KW - Pulmonary Emphysema

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DO - 10.1186/s13104-023-06434-8

M3 - Article (Academic Journal)

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SN - 1756-0500

VL - 16

JO - BMC Research Notes

JF - BMC Research Notes

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M1 - 157

ER -

The UK BiLEVE and Mendelian randomisation: using multivariable instrumental variables to address "damned if you, damned if you don't" adjustment problems

Abstract

Bibliographical note

Keywords

UN SDGs

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