Literature DB >> 18767201

Meta-analysis of Mendelian randomization studies incorporating all three genotypes.

Tom M Palmer1, John R Thompson, Martin D Tobin.   

Abstract

In Mendelian randomization a carefully selected gene is used as an instrumental variable in the estimation of the association between a biological phenotype and a disease. A study using Mendelian randomization will have information on an individual's disease status, the genotype and the phenotype. The phenotype must be on the causal pathway between gene and disease for the instrumental-variable analysis to be valid. For a biallelic polymorphism there are three possible genotypes with which to compare disease risk. Existing methods select two of the three possible genotypes for use in a Mendelian randomization analysis. Multivariate meta-analysis models for Mendelian randomization case-control studies are proposed, which extend previous methods by estimating the pooled phenotype-disease association across both genotype comparisons by using the gene-disease log odds ratios and differences in mean phenotypes. The methods are illustrated using a meta-analysis of the effect of a gene related to collagen production on bone mineral density and osteoporotic fracture. Copyright 2008 John Wiley & Sons, Ltd.

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Year:  2008        PMID: 18767201     DOI: 10.1002/sim.3423

Source DB:  PubMed          Journal:  Stat Med        ISSN: 0277-6715            Impact factor:   2.373


  3 in total

Review 1.  Insight into rheumatological cause and effect through the use of Mendelian randomization.

Authors:  Philip C Robinson; Hyon K Choi; Ron Do; Tony R Merriman
Journal:  Nat Rev Rheumatol       Date:  2016-07-14       Impact factor: 20.543

2.  Bayesian methods for meta-analysis of causal relationships estimated using genetic instrumental variables.

Authors:  Stephen Burgess; Simon G Thompson; S Burgess; S G Thompson; G Andrews; N J Samani; A Hall; P Whincup; R Morris; D A Lawlor; G Davey Smith; N Timpson; S Ebrahim; Y Ben-Shlomo; G Davey Smith; N Timpson; M Brown; S Ricketts; M Sandhu; A Reiner; B Psaty; L Lange; M Cushman; J Hung; P Thompson; J Beilby; N Warrington; L J Palmer; B G Nordestgaard; A Tybjaerg-Hansen; J Zacho; C Wu; G Lowe; I Tzoulaki; M Kumari; M Sandhu; J F Yamamoto; B Chiodini; M Franzosi; G J Hankey; K Jamrozik; L Palmer; E Rimm; J Pai; B Psaty; S Heckbert; J Bis; S Anand; J Engert; R Collins; R Clarke; O Melander; G Berglund; P Ladenvall; L Johansson; J-H Jansson; G Hallmans; A Hingorani; S Humphries; E Rimm; J Manson; J Pai; H Watkins; R Clarke; J Hopewell; D Saleheen; R Frossard; J Danesh; N Sattar; M Robertson; J Shepherd; E Schaefer; A Hofman; J C M Witteman; I Kardys; Y Ben-Shlomo; G Davey Smith; N Timpson; U de Faire; A Bennet; N Sattar; I Ford; C Packard; M Kumari; J Manson; Debbie A Lawlor; George Davey Smith; S Anand; R Collins; J P Casas; J Danesh; G Davey Smith; M Franzosi; A Hingorani; D A Lawlor; J Manson; B G Nordestgaard; N J Samani; M Sandhu; L Smeeth; F Wensley; S Anand; J Bowden; S Burgess; J P Casas; E Di Angelantonio; J Engert; P Gao; T Shah; L Smeeth; S G Thompson; C Verzilli; M Walker; J Whittaker; A Hingorani; J Danesh
Journal:  Stat Med       Date:  2010-05-30       Impact factor: 2.373

3.  Efficient design for Mendelian randomization studies: subsample and 2-sample instrumental variable estimators.

Authors:  Brandon L Pierce; Stephen Burgess
Journal:  Am J Epidemiol       Date:  2013-07-17       Impact factor: 4.897

  3 in total

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