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dc.contributor.authorGrover S
dc.contributor.authorStein CM
dc.contributor.authorZiegler A
dc.contributor.authorDel Greco M F
dc.date.accessioned2019-04-01T08:40:03Z
dc.date.available2019-04-01T08:40:03Z
dc.date.issued2017
dc.identifier.isbn978-1-4939-7
dc.identifier.urihttp://dx.doi.org/10.1007/978-1-4939-7274-6_29
dc.identifier.urihttp://hdl.handle.net/10863/9164
dc.description.abstractConfounding and reverse causality have prevented us from drawing meaningful clinical interpretation even in well-powered observational studies. Confounding may be attributed to our inability to randomize the exposure variable in observational studies. Mendelian randomization (MR) is one approach to overcome confounding. It utilizes one or more genetic polymorphisms as a proxy for the exposure variable of interest. Polymorphisms are randomly distributed in a population, they are static throughout an individual’s lifetime, and may thus help in inferring directionality in exposure–outcome associations. Genome-wide association studies (GWAS) or meta-analyses of GWAS are characterized by large sample sizes and the availability of many single nucleotide polymorphisms (SNPs), making GWAS-based MR an attractive approach. GWAS-based MR comes with specific challenges, including multiple causality. Despite shortcomings, it still remains one of the most powerful techniques for inferring causality.en_US
dc.languageEnglish
dc.language.isoenen_US
dc.relation
dc.rights
dc.titleMendelian Randomizationen_US
dc.typeBook chapteren_US
dc.date.updated2019-03-28T13:35:44Z
dc.publication.titleStatistical Human Genetics
dc.language.isiEN-GB
dc.description.fulltextnoneen_US


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