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Printable Handouts
Navigable Slide Index
- Introduction
- Outline
- Cardiovascular disease
- Answering two questions
- Why use Mendelian randomisation analysis?
- What is MR?
- Mendelian randomisation principles
- Randomized control trial vs. MR
- Unidirectional information flow
- Common genetic variation
- Mendelian randomisation model (1)
- Mendelian randomisation model (2)
- Biomarker-disease association
- Where to obtain effect size from
- Obtaining reliable estimates
- Importance of large studies and meta-analyses (1)
- Importance of large studies and meta-analyses (2)
- Selection of SNPs
- Mendelian randomisation: the early days
- Sources of genetic instruments
- GWAS and gene-centric microarrays
- Selecting SNPs
- Choosing the most suitable SNPs
- Using CRP as an example
- Whitehall II study
- Association between CRP and number of traits
- Potential confounders
- IBC human CVD BeadChip (Cardiochip)
- SNP identification (1)
- Genetic model
- SNP identification (2)
- Effect size
- Genetic instrument specificity
- CRP-associated SNPs specificity
- Specificity
- The question of 'non-specificity'
- The right tool for the job
- Applications of MR: causality of biomarkers
- Environmental exposures
- Is alcohol causal in esophageal cancer?
- ALDH2 genotype
- Alcohol study findings
- Association between alcohol and cancer
- Endogenous biomarkers
- Evidence on the link between cholesterol and CHD
- Is LDL-C causal in CHD?
- Genotype, biomarker and event rate (1)
- Genotype, biomarker and event rate (2)
- Genotype, biomarker and event rate (3)
- Randomized control trial of statins
- Circulating proteins
- Is CRP causal in coronary disease?
- Genotype, biomarker and event rate (4)
- CRP SNP and level associations
- Genotype, biomarker and event rate (5)
- CRP SNP, level and risk
- Genotype, biomarker and event rate (6)
- Applications of MR: drug targets
- Clinical relevance
- Torcetrapib – a CETP inhibitor
- Illuminate trial results
- Randomized control trial vs. MR study findings
- Confounders/limitations
- Limitations (1)
- Limitations (2)
- Confounders
- Thank you
Topics Covered
- Why do we use MR
- What is MR-biomarker/disease association
- Selection of SNPs
- Applications of MR
- Confounders/limitations
Links
Series:
Categories:
Therapeutic Areas:
Talk Citation
Shah, T. (2014, February 4). Mendelian randomisation: using genetics to determine causality [Video file]. In The Biomedical & Life Sciences Collection, Henry Stewart Talks. Retrieved December 10, 2024, from https://doi.org/10.69645/FCYI9246.Export Citation (RIS)
Publication History
Financial Disclosures
- Dr. Tina Shah has not informed HSTalks of any commercial/financial relationship that it is appropriate to disclose.
A selection of talks on Genetics & Epigenetics
Transcript
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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?