Genetic Information Might Help Us Understand Lifestyle Risks for Disease

Genetic Information Might Help Us Understand Lifestyle Risks for Disease

Genetic variants predict biological risk for disease, but they might also be able to tell us about heritable behavioural risk factors


Powered by Guardian.co.ukThis article titled “Genetic information might help us understand lifestyle risks for disease” was written by Suzi Gage, for theguardian.com on Monday 22nd February 2016 13.16 UTC

When you read articles claiming that there are a number of genes that predict a disease, usually this information comes from genome-wide association studies (GWAS). These hypothesis-free analyses use large numbers of people, and look for associations between a disease and genetic variations in many hundreds of thousands of locations across the genome.

But knowing that there are 108 genetic locations associated with schizophrenia isn’t necessarily that useful unless you know what these genetic variants actually do. GWAS are a research tool, a way of helping us understand a disease. Sometimes it’s known that a variant codes for a protein, and then the function of that gene is easier to understand, but at the moment the biological function of a lot of genetic variants just isn’t known, limiting the use of GWAS as a research tool.

It has been assumed that genetic variants identified by GWAS represent direct, biological risk factors for a disease – the “nature” rather than “nurture” end of risk. My colleagues and I have recently published a paper in which we suggest that there may be more information than that contained within the results.

When a GWAS of lung cancer was conducted, one genetic variant that was strongly associated with lung cancer was a known variant located within an area of chromosome 15 called the “nicotine acetylcholine gene receptor cluster”. This variant has been shown to be linked to the number of the cigarettes smoked per day, in people who smoke. While it might be the case that the gene also independently increases the risk of lung cancer, as well as heaviness of smoking, it seems far more likely that this variant is identified in the GWAS for lung cancer because smoking causes lung cancer.

There’s another good example in the alcohol literature. A genetic variant has been identified that predicts alcohol consumption. It codes for a protein that breaks down toxic metabolites of alcohol into non-toxic ones. If you don’t have much of this protein, levels of of toxins can build up, causing you to feel ill and flushed. The variant isn’t very common in European populations, but is more common in populations of East Asian ancestry. In these groups, GWAS of high blood pressure identified this particular alcohol-related genotype. But it’s not seen in GWAS of high blood pressure in European populations. This is fairly strong evidence that alcohol consumption might contribute to high blood pressure.

These findings led to an interesting thought: perhaps, as well as confirming causal associations we already know (like smoking and lung cancer), it might help us to identify other causal risk factors for disease. For example, the same smoking variant was also identified in a recent GWAS of schizophrenia – might this mean that smoking is a causal risk factor for the disease? Possibly, although further testing would be needed to confirm this, as its still possible this could be shared genetic architecture.

At the moment, the main limitation in interpreting GWAS in this way is that there aren’t very many genetic predictors of modifiable risk factors identified. But this is changing. For example, we know that cannabis use is at least partly heritable, suggesting there is a genetic component, but as yet no variants have been identified. When these variants start being found, the information in GWAS may open up to us.

And why is this useful? Well, it’s much easier to help people change their lifestyle behaviours than it is to alter their genetics!

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