Mendelian randomization: Using genes as instruments for making causal inferences in epidemiology

By Deborah Lawlor, R. Harbord, J. Sterne, Nicholas J. Timpson, George Davey Smith. Statistics in Medicine, 2008

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View Paper: https://www.semanticscholar.org/paper/7767fce8d28a1f73aa7232f7fbba39673c1ba0dc

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Citations in the corpus

Avoiding bias from weak instruments in Mendelian randomization studies. (2011, background): “Mendelian randomization uses genetic variants (G) which are associated with the phenotype (X) to estimate a causal effect [3].”
Effect of long-term exposure to lower low-density lipoprotein cholesterol beginning early in life on the risk of coronary heart disease: a Mendelian randomization analysis. (2012, background): “Each of these polymorphisms is allocated randomly at the time of conception in a process sometimes referred to as Mendelian randomization (6).”
Mendelian randomization of blood lipids for coronary heart disease (2014, positive): “Moreover, at a population level the randomized allocation of parental alleles at conception tends to balance confounding factors among groups of differing genotypes.(9,10) Where a polymorphism is”
Low-density lipoproteins cause atherosclerotic cardiovascular disease. 1. Evidence from genetic, epidemiologic, and clinical studies. A consensus statement from the European Atherosclerosis Society Consensus Panel (2017, background): “whether an observed association between an exposure and an outcome is likely to be causal.(23) Numerous variants in multiple genes have been reported to be associated with lower LDL-C levels.”
Causal Relationship between Obesity and Vitamin D Status: Bi-Directional Mendelian Randomization Analysis of Multiple Cohorts (2013, methodology): “The genetic associations, unlike the directly observed associations for vitamin D intake/status, should be less prone to confounding by lifestyle and socio-economic factors and be free from reverse causation as genotypes are invariant and assigned at random before conception [20].”
Remnant cholesterol as a causal risk factor for ischemic heart disease. (2013, methodology): “mined levels of lipoproteins are present from birth and are therefore generally not confounded by other risk factors (13,14).”
An atlas of genetic influences on human blood metabolites (2014, background): “Amino acid Carbohydrate Cofactor or vitamin Energy Lipid Nucleotide Peptide Xenobiotic Unknown 7 SLC5A6 (14) GCKR (15) SRD5A2 (16)”
Modelling local gene networks increases power to detect trans-acting genetic effects on gene expression (2016, methodology): “Our approach is also related to Mendelian randomization [48, 49] and methods to reconstruct (small) directed acyclic graphs for multi-trait GWAS [50].”