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Mendelian randomisation: using genetics to determine causality

Published on February 4, 2014   35 min

A selection of talks on Genetics & Epigenetics

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0:00
Today, I'm going to talk about using genetics to determine causality of a biomarker in cardiovascular disease using an approach known as Mendelian randomization.
0:13
I'm going to touch on a number of points in this talk, including why we need to use this approach and what exactly is Mendelian randomization. I will then go on to discuss in more detail the different components of this approach, including the biomarker-disease association and the selection of the most appropriate SNPs to use as our genetic instrument. I will then go on to give some examples where this approach is being used to infer causality, as well as to examine drug targets. Finally, I will end with some of the limitations of this approach. First, let's start with why we should use Mendelian randomization in cardiovascular disease.
0:53
Cardiovascular disease is a complex disease. There are many contributing aetiological factors and risk factors with several interacting pathological pathways where risk factors tend to cluster together. This makes it difficult to tease apart which factors are causal or which pathway should be targeted for treatment. Therefore, better evidence for causality is required since this can lead to delays in potential new therapies. One approach to infer causality is to use Mendelian randomization.
1:29
We can use this approach to answer two types of questions. Firstly, is the relationship between a biomarker and disease causal? And secondly, to examine drug targets. What are the consequences for biomarkers or disease risk when you specifically modulate the drug target?

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Mendelian randomisation: using genetics to determine causality

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