Polygenic risk scores and genomics are revolutionising the preventative medicine and healthcare landscape. Here I provide a quick overview of polygenic risk scores, its benefits and effects on the future of genomics as well as the Life Sciences & Healthcare industry.
Hi there, my name is Alan, and if you’re watching this video, you’re probably trying to find out the latest emerging Life Sciences Trends. Well, this year, GlobalData ranked genomics as the biggest trend within Life Sciences, but what is genomics and what are the key technologies surrounding it? Keep watching to find out.
Here at life sciences trends, we explain the latest emerging trends in simple language, without the complex jargon. So what is genomics? Well, simply put, genomics is a study of all of an organism or a person’s genes, aka their genome. Now, this includes both the non-protein expressing regions called the introns, and also the protein expressing regions called the exons.
And scientists can use a person’s DNA and genes to find out various different things about people and patients. For example, they can use DNA to detect if there is an undiagnosed cancer that hasn’t been detected yet, or to determine the risk that an individual has to certain diseases. Now genomics is currently a $28 billion market globally, and is projected to grow at a compound annual growth rate of 16.4% from 2023 to 2030.
Now, although there are many advances within genomics, I’ll be focusing specifically on three main areas. The first being polygenic risk scores, the second being liquid biopsies, and thirdly, gene editing. And over the next few videos, I’ll be explaining and highlighting these key technologies in detail, starting with polygenic risk scores today.
So what are polygenic risk scores? First, in order to understand what polygenic risks are, you need to first understand the relationship between genes and diseases. Now some diseases are caused or heavily influenced by the presence of a single gene mutation. Take breast cancer, for example, people who have a BRCA1 or BRCA2 gene mutation can have up to a 70% chance of getting breast cancer by the age of 80.
Now, this is 57% higher than the average woman in the US who have a lifetime chance of only 13% of getting breast cancer. But this effect is even more pronounced in the case of single gene disorders, where the presence of a single gene mutation alone can cause the disease.
A good example of this is Huntington’s disease, which is an autosomal dominant disorder, which means that someone only needs to inherit one copy of the defective gene to have the disorder. And by the way, single gene disorders are also called monogenic disorders or Mendelian disorders. So if you hear any of these terms, it’s simply referring to a disease which is caused by a mutation in a single gene.
Now, although this is the case of single gene disorder, Most diseases, including breast cancer, are actually influenced by tens to thousands of different single gene mutations across several different genes. We therefore call these diseases polygenic, poly, referring to many, and genic, referring to genes. In addition, these diseases are also influenced by environmental factors.
Both of these combined mean that these diseases are called complex diseases. Now since complex diseases are influenced by various different gene mutations, innovative companies have determined ways to calculate through algorithms the accumulative risk that individuals have to certain diseases based on their genome alone.
These are called polygenic risk scores. For example, concerning coronary artery disease. A 2018 study by Khera et al. determined that 8% of the population studied, had a three times threefold, increased chance of getting coronary artery disease based on their genome and polygenic risk scoring. And more recently, Genomics Plc determined that the highest risk population for cardiovascular disease have a 40% lifetime chance of getting the disease.
This was determined to be 10 times greater than the lowest risk population. Similarly, within that afore mentioned Khera et al. study, it was determined that 3.5% of the population studied had a three times or greater risk of getting type two diabetes. Now, why is this important?
Well, I’m not sure about you but me personally. If I knew that I had a three times greater risk than normal of having type two diabetes or heart disease, I definitely would be looking to adapt my lifestyle choices accordingly to make sure I minimise that risk as much as possible. And this brings me to the next chapter of polygenic risk scores, what are the benefits?
So what are the key benefits of polygenic risk scores? Well, currently genomics in healthcare is highly reactive. This means that it’s mainly used as a diagnostic tool once people are already very sick and suffering from illness and diseases. Common use cases are for rare diseases in children to help diagnose and determine which diseases they’re suffering from or for cancer patients to help determine which treatments would be the most appropriate and the most successful.
And by the way, if you ever heard the word, oncology, oncology simply means the study of cancer, and therefore, oncology patients would be patients who are suffering from cancer. Now, although these are very helpful and impactful, they don’t do much to actually prevent these diseases from occurring in the first place.
And this is the beauty of polygenic risk scores. From a once in a lifetime test, we can determine the predispositions that people have to certain diseases and really help to intervene to either treat these diseases very early. Or better yet, prevent these diseases from occurring in the first place by adapting the lifestyle and choices that these patients make to prevent them from realising this risk.
Essentially, it allows you to shift from being reactive to being proactive and to enter the realm of preventative medicine where you are going far beyond simply treating diseases and actually preventing them from occurring in the first place through adaptation of lifestyle choices. This is greatly important because although guidelines do exist for high risk populations, many of these high risk populations are actually hidden to healthcare systems.
For example, someone could have no family members with breast cancer, but still be deemed high risk based on their polygenic risk score. Now, NICE guidelines recommend that these high risk population of women for breast cancer should be screened annually each year from age 40 onwards through a mammogram, and this is in contrast to what’s commonly available to most women through the NHS, which is a screening every three years from the ages of 50 to 71.
But how would these populations and their healthcare systems otherwise know that they are at high risk for these diseases, unless they have family members who suffered from the same disease in the past, most likely they will only find out when they’ve contracted the disease. At which point it will be much harder to treat.
Now, moving on, there are some single gene mutations that can greatly increase the risk of developing some common diseases. However, these tend to be the minority of cases. A good example of this is in a case of type two diabetes. Now there is a single gene missense mutation, which alone has a 5x increased chance of developing type two diabetes.
However, within the afore mentioned Khera et al. study, it was determined that this was present in only 0.1% of the population. Simply put most of the people who have type two diabetes do not have this single gene mutation. However, the presence of populations with high Polygenetic risk profiles are much, much higher.
This is well illustrated with coronary artery disease, where the populations of people, which were determined to have a polygenic risk score conferring to a threefold increased chance of the disease was a 20 times greater population size than those who have single gene mutations conferring to the same level of risk.
And this could mean that for some disease areas we could be increasing our exposure and visualization of people who have a high risk to diseases, by up to 20x through polygenic risk scores alone.
So what does this mean for the future of genomics. Well the future widespread application of polygenic risk scores means that people who have a high risk of certain diseases such as breast cancer, for example, will no longer be hidden to healthcare systems, and finally these populations will be able to get the increased attention that they need and that’s required to make sure that we can treat and diagnose these diseases far earlier, or in best case scenarios, prevent these diseases from occurring in the first place.
This more proactive stance is the future of genomics, and there’s reason to believe that within the next 10 to 20 years, genomics will be ubiquitous within the healthcare landscape and will permanently revolutionize the preventative healthcare and preventative medicine landscape.
And this is not just hopes and dreams already right now as we speak, some companies are running trial ones in collaboration with the NHS, to help determine the effectiveness of polygenic risk scoring in detecting the risk that people have for cardiovascular disease.
Likewise, UK just launched, its Our Future Health Initiative. Its largest campaign by far to study 5 million people from the perspective of preventative medicine and detecting disease from various different backgrounds. And of course, polygenic risk scoring is one of the technologies being examined.
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Now to conclude there’s three key takeaways from this video. Firstly, most diseases are very complex, which means that they are influenced by various different gene mutations as well as environmental factors. The accumulative risk of all of these different gene mutations are called the polygenic risk score.
Secondly, polygenic risk scores allow us to unveil, a large, hidden population of people who have high risk of certain diseases. And lastly, polygenic risk scoring is shifting genomic medicine to a far more proactive stance where we are shifting from not only diagnosing diseases, but also preventing them from occurring in the first place.
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