Introduction to Mendelian Randomization

Feb 27, 2020

Hello everyone,

My project will use Mendelian Randomization and R to assess whether a relationship exists between sleep habits (chronotype, duration, etc.) and lung cancer. In 2019, California signed a state law that would delay school start times past 8:30 A.M with the intention of improving teenagers’ health. Though the impact of the law is controversial at the moment, this link between sleep and health is worth investigating.

One way to determine cause and effect between sleep duration and lung cancer experimentally would be to separate subjects into groups, enforce a certain amount of sleep each night for each group, and then compare lung cancer development across groups. However, this is neither practical or ethical.

Observational studies and surveys do not suffice either due to confounding effects. A confounding variable affects both the independent and dependent variable, which can induce a false association between the two. For instance, if a statistician were to study the relationship between expensive running shoes and 100-meter times, he might find that more expensive shoes are correlated with faster times, and may conclude that expensive shoes cause people to run faster. However, this is incorrect, as confounding variables are not taken into account. Elite runners are more likely to invest in expensive shoes. Thus, the expensive shoes are just associated with elite runners and are not actually responsible for the faster times.

Mendelian Randomization circumvents these confounding effects by using genetic variants (i.e. single-nucleotide polymorphisms (SNPs)) as instrumental variables (IVs) in studying the relationship between a risk exposure (chronotype, sleep duration, etc.) and an outcome (lung cancer). Because genetic markers are fixed at birth, they are less likely to be related to potential confounders. We identify SNPs known to be strongly associated with the risk exposure and do not directly affect the outcome. If we find an association between the IVs and the outcome, then we can conclude that the risk exposure must have an impact on the outcome.


2 Replies to “Introduction to Mendelian Randomization”

  1. Thomas E. says:

    This kind of research is much needed in today’s day and age. Keep up the good work!

  2. Lieselotte D. says:

    Considering how little sleep I know some of our classmates get, this research will be very interesting. I can’t wait to see what else you do.

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