Adjusting for collider bias in genetic association studies using instrumental variable methods

Siyang Cai, April E Hartley, Osama Mahmoud, Kate M Tilling, Frank Dudbridge*

*Corresponding author for this work

Research output: Contribution to journalArticle (Academic Journal)peer-review

8 Citations (Scopus)
94 Downloads (Pure)

Abstract

Genome-wide association studies have provided many genetic markers that can be used as instrumental variables to adjust for confounding in epidemiological studies. Recently, the principle has been applied to other forms of bias in observational studies, especially collider bias that arises when conditioning or stratifying on a variable that is associated with the outcome of interest. An important case is in studies of disease progression and survival. Here, we clarify the links between the genetic instrumental variable methods proposed for this problem and the established methods of Mendelian randomisation developed to account for confounding. We highlight the critical importance of weak instrument bias in this context and describe a corrected weighted least-squares procedure as a simple approach to reduce this bias. We illustrate the range of available methods on two data examples. The first, waist–hip ratio adjusted for body-mass index, entails statistical adjustment for a quantitative trait. The second, smoking cessation, is a stratified analysis conditional on having initiated smoking. In both cases, we find little effect of collider bias on the primary association results, but this may propagate into more substantial effects on further analyses such as polygenic risk scoring and Mendelian randomisation.
Original languageEnglish
Pages (from-to)303-316
Number of pages14
JournalGenetic Epidemiology
Volume46
Issue number5-6
Early online date18 May 2022
DOIs
Publication statusPublished - 3 Aug 2022

Bibliographical note

Funding Information:
Siyang Cai and Frank Dudbridge are supported by the MRC (MR/S037055/1). Kate Tilling and April Hartley are part of the MRC Integrative Epidemiology Unit (IEU) at the University of Bristol, which is supported by the MRC (MC_UU_00011/1 and MC_UU_00011/3).

Publisher Copyright:
© 2022 The Authors. Genetic Epidemiology published by Wiley Periodicals LLC.

Keywords

  • ascertainment bias
  • index event bias
  • mendelian randomisation
  • selection bias

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