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Multivariable Mendelian randomization: the use of pleiotropic genetic variants to estimate causal effects.

Burgess S, Thompson SG - Am. J. Epidemiol. (2015)

Bottom Line: A conventional Mendelian randomization analysis assesses the causal effect of a risk factor on an outcome by using genetic variants that are solely associated with the risk factor of interest as instrumental variables.In this paper, we propose an extension of Mendelian randomization that uses multiple genetic variants associated with several measured risk factors to simultaneously estimate the causal effect of each of the risk factors on the outcome.Subject to these assumptions, we demonstrate that triglyceride-related pathways have a causal effect on the risk of coronary heart disease independent of the effects of low-density lipoprotein cholesterol and high-density lipoprotein cholesterol.

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Causal directed acyclic graph illustrating multivariable Mendelian randomization in associations between variants G1, G2, and G3, risk factors X1 and X2, and outcome Y. Confounders U1 and U2 are assumed to be unknown. A) Risk factors are causally independent (no causal effects between X1 and X2); B) risk factors are causally dependent (X1 has a causal effect on X2).
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KWU283F3: Causal directed acyclic graph illustrating multivariable Mendelian randomization in associations between variants G1, G2, and G3, risk factors X1 and X2, and outcome Y. Confounders U1 and U2 are assumed to be unknown. A) Risk factors are causally independent (no causal effects between X1 and X2); B) risk factors are causally dependent (X1 has a causal effect on X2).

Mentions: the variant is conditionally independent of the outcome given the risk factors and confounders.


Multivariable Mendelian randomization: the use of pleiotropic genetic variants to estimate causal effects.

Burgess S, Thompson SG - Am. J. Epidemiol. (2015)

Causal directed acyclic graph illustrating multivariable Mendelian randomization in associations between variants G1, G2, and G3, risk factors X1 and X2, and outcome Y. Confounders U1 and U2 are assumed to be unknown. A) Risk factors are causally independent (no causal effects between X1 and X2); B) risk factors are causally dependent (X1 has a causal effect on X2).
© Copyright Policy - creative-commons
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4325677&req=5

KWU283F3: Causal directed acyclic graph illustrating multivariable Mendelian randomization in associations between variants G1, G2, and G3, risk factors X1 and X2, and outcome Y. Confounders U1 and U2 are assumed to be unknown. A) Risk factors are causally independent (no causal effects between X1 and X2); B) risk factors are causally dependent (X1 has a causal effect on X2).
Mentions: the variant is conditionally independent of the outcome given the risk factors and confounders.

Bottom Line: A conventional Mendelian randomization analysis assesses the causal effect of a risk factor on an outcome by using genetic variants that are solely associated with the risk factor of interest as instrumental variables.In this paper, we propose an extension of Mendelian randomization that uses multiple genetic variants associated with several measured risk factors to simultaneously estimate the causal effect of each of the risk factors on the outcome.Subject to these assumptions, we demonstrate that triglyceride-related pathways have a causal effect on the risk of coronary heart disease independent of the effects of low-density lipoprotein cholesterol and high-density lipoprotein cholesterol.

View Article: PubMed Central - PubMed

Show MeSH
Related in: MedlinePlus