Combining Mendelian randomization with the sibling comparison design.
Mendelian randomization
bias
causal inference
sibling comparison
Journal
Statistics in medicine
ISSN: 1097-0258
Titre abrégé: Stat Med
Pays: England
ID NLM: 8215016
Informations de publication
Date de publication:
11 Dec 2023
11 Dec 2023
Historique:
revised:
09
10
2023
received:
24
04
2022
accepted:
21
11
2023
medline:
11
12
2023
pubmed:
11
12
2023
entrez:
11
12
2023
Statut:
aheadofprint
Résumé
Mendelian randomization (MR) is a popular epidemiologic study design that uses genetic variants as instrumental variables (IVs) to estimate causal effects, while accounting for unmeasured confounding. The validity of the MR design hinges on certain IV assumptions, which may sometimes be violated due to dynastic effects, population stratification, or assortative mating. Since these mechanisms act through parental factors it was recently suggested that the bias resulting from violations of the IV assumptions can be reduced by combing the MR design with the sibling comparison design, which implicitly controls for all factors that are constant within families. In this article, we provide a formal discussion of this combined MR-sibling design. We derive conditions under which the MR-sibling design is unbiased, and we relate these to the corresponding conditions for the standard MR and sibling comparison designs. We proceed by considering scenarios where all three designs are biased to some extent, and discuss under which conditions the MR-sibling design can be expected to have less bias than the other two designs. We finally illustrate the theoretical results and conclusions with an application to real data, in a study of low-density lipoprotein and diastolic blood pressure using data from the Swedish Twin Registry.
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Subventions
Organisme : The Swedish Research Council
ID : 2020-01188
Informations de copyright
© 2023 The Authors. Statistics in Medicine published by John Wiley & Sons Ltd.
Références
Riaz H, Khan M, Siddiqi T, et al. Association between obesity and cardiovascular outcomes: a systematic review and meta-analysis of mendelian randomization studies. JAMA Netw Open. 2018;1(7):e183788.
Zhan Y, Song C, Karlsson R, et al. Telomere length shortening and Alzheimer disease-a Mendelian randomization study. JAMA Neurol. 2015;72(10):1202-1203.
Vaucher J, Keating B, Lasserre A, et al. Cannabis use and risk of schizophrenia: a Mendelian randomization study. Mol Psychiatry. 2018;23(5):1287-1292.
Hernán M, Robins J. Instruments for causal inference: an epidemiologist's dream? Epidemiology. 2006;17(4):360-372.
Glymour M, Tchetgen Tchetgen E, Robins J. Credible Mendelian randomization studies: approaches for evaluating the instrumental variable assumptions. Am J Epidemiol. 2012;175(4):332-339.
Brumpton B, Sanderson E, Heilbron K, et al. Avoiding dynastic, assortative mating, and population stratification biases in Mendelian randomization through within-family analyses. Nat Commun. 2020;11(1):1-13.
Greenland S. Quantifying biases in causal models: classical confounding vs collider-stratification bias. Epidemiology. 2003;14(3):300-306.
Cole S, Platt R, Schisterman E, et al. Illustrating bias due to conditioning on a collider. Int J Epidemiol. 2010;39(2):417-420.
Sjölander A, Frisell T, Öberg S. Sibling comparison studies. Annu Rev Stat Appl. 2022;9:71-94.
Dixon P, Hollingworth W, Harrison S, Davies N, Smith G. Mendelian randomization analysis of the causal effect of adiposity on hospital costs. J Health Econ. 2020;70:102300.
Frisell T, Öberg S, Kuja-Halkola R, Sjölander A. Sibling comparison designs: bias from non-shared confounders and measurement error. Epidemiology. 2012;23(5):713-720.
Zagai U, Lichtenstein P, Pedersen N, Magnusson P. The Swedish twin registry: content and management as a research infrastructure. Twin Res Hum Genet. 2019;22(6):672-680.
Greenland S, Pearl J, Robins J. Causal diagrams for epidemiologic research. Epidemiology. 1999;10(1):37-48.
Causality PJ. Models, Reasoning, and Inference. 2nd ed. New York: Cambridge University Press; 2009.
Reid N, Brockman D, Elisabeth Leonard C, Pelletier R, Khera AV. Concordance of a high polygenic score among relatives: implications for genetic counseling and cascade screening. Circ Genom Precis Med. 2021;14(2):e003262.
Sjölander A, Öberg S, Frisell T. Generalizability and effect measure modification in sibling comparison studies. Eur J Epidemiol. 2022;37:461-476.
Otsuka T, Takada H, Nishiyama Y, et al. Dyslipidemia and the risk of developing hypertension in a working-age male population. J Am Heart Assoc. 2016;5(3):e003053.
Valdes-Marquez E, Parish S, Clarke R, et al. Relative effects of LDL-C on ischemic stroke and coronary disease: a Mendelian randomization study. Neurology. 2019;92(11):e1176-e1187.
Palmer T, Lawlor D, Harbord R, et al. Using multiple genetic variants as instrumental variables for modifiable risk factors. Stat Methods Med Res. 2012;21(3):223-242.
R Core Team. R: A Language and Environment for Statistical Computing. Vienna, Austria: R Foundation for Statistical Computing; 2021. https://www.R-project.org/
Zetterqvist J, Sjölander A. Doubly robust estimation with the R package drgee. Epidemiol Methods. 2015;4:69-86. doi:10.1515/em-2014-0021
Zetterqvist J, Vansteelandt S, Pawitan Y, Sjölander A. Doubly robust methods for handling confounding by cluster. Biostatistics. 2016;17(2):264-276.
Gordon M, Lumley T. forestplot: Advanced Forest Plot Using ‘grid’ Graphics. R Package Version 2.0.1; 2021.