Evidence map›Paper›PMID 35583096›Full record

ArticleGenetic epidemiology2022

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

Siyang Cai, April Hartley, Osama Mahmoud, Kate Tilling, Frank Dudbridge

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Article in Genetic epidemiology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 25 papers, 1 of them a synthesis that pooled it.

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25citing papers in PubMed, 1 pooled it
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1 · What the graph read from it

What it found

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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

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3 · Its place in the literature

Who cites it

25 citing papers in PubMed, 1 synthesis or guideline pooled it.

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4 · The record

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5 · Who and what money

Authors and funding

5 authors.

Siyang CaiDepartment of Health Sciences, University of Leicester, Leicester, UK.
April HartleyMRC Integrative Epidemiology Unit, University of Bristol, Bristol, UK.
Osama MahmoudDepartment of Mathematical Sciences, University of Essex, Colchester, UK.
Kate TillingMRC Integrative Epidemiology Unit, University of Bristol, Bristol, UK.
Frank DudbridgeDepartment of Health Sciences, University of Leicester, Leicester, UK.ORCID 0000-0002-8817-8908

Funding

Medical Research Council MC_UU_00011/1Medical Research Council MC_UU_00011/3Medical Research Council MR/S037055/1
6 · The paper itself

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.

Indexed as

Genome-Wide Association StudyMendelian Randomization AnalysisBiasHumansLeast-Squares AnalysisWaist-Hip Ratioascertainment biasindex event biasMendelian randomisationselection bias

Identifiers

PMID35583096
PMCPMC9544531

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.