In a new study, researchers from the University of Oxford found that the amount of influence our genes have on disease susceptibility declines as we get older. The results are published in PLoS Genetics.
Age and disease risk
Human beings are living longer than ever before. It’s estimated that by 2050 there will be 434 million people in the world aged 80-years and over. This aging global population provides scientists with increasing opportunities to explore exactly what happens to the human body as it becomes older, and how this varies between different people and populations.
As our bodies age, we are more likely to become sick. Our susceptibility to a whole host of human diseases increases, including diabetes, chronic obstructive pulmonary disease and dementia. For many diseases, the probability that we will develop the condition is influenced by our genetic code. Recent advances in next-generation sequencing technologies, including whole-genome sequencing and exome-sequencing, provide the ability to read and analyze our DNA code to calculate disease risk scores. “Our understanding of genetic risk has reached the point where – using polygenic risk scores – we can identify individuals at many-fold higher risk than the population average. Importantly, genetics can identify at-risk individuals years – or even decades – before symptoms appear, enabling preventative approaches (improved lifestyle, increased screening and prophylactic drugs) to reduce the disease burden,” Gil McVean, professor of statistical genetics at the Nuffield Department of Medicine, University of Oxford, told Technology Networks.
What is a polygenic risk score?
To understand what a polygenic risk score is, we need to review some DNA basics. Our DNA comprises nucleotide bases, often referred to as “chemical building blocks”: adenine (A), thymine (T), cytosine (C) and guanine (G). These nucleotide bases exist in specific sequences – genes – and the collection of genes that we possess in our body is known as our genome. Each human’s genome is almost identical, but there are some differences. These differences – known as genetic variants – arise in different ways, such as a subtle change in the letters of our DNA code. Sometimes these changes do not have any impact on our body, but in some cases, a variant can increase or decrease the likelihood that we will develop a disease. A polygenic risk score represents the total number of variants that an individual has and can be used to predict disease susceptibility.
Best practice for using genetic risk scores
Other factors – such as sex, age or ethnicity – can influence the power of these predictive scores. In their latest work, McVean and colleagues focused on age. They wanted to know whether there are specific windows of time in an individual’s life where their genetic information is more relevant for predicting disease risk. “This will be important in determining what interventions an individual may be given at different points during their life,” McVean explained.
To conduct the study, the researchers utilized the UK Biobank resource, a biomedical database that contains in-depth health and genetic information from 500,000 UK participants. “These individuals were recruited about 15 years ago, so there is a lot of data on what diseases they have had over the years,” McVean said. “Our approach was to adapt some of the methods that have been developed to study how risk changes over time using such prospective data within the epidemiological sphere to study genetic risk, which arises from the combined effects of hundreds or thousands of variants across the genome.”
Genetic risk scores more useful for predicting disease early in life
Using the BioBank data, the team focused on an individual’s risk of developing 24 common diseases that are linked to genetics, including but not limited to rheumatoid arthritis, Type I and Type II diabetes mellitus, osteoarthritis and myocardial infarction.
The team’s major finding was that the risk we inherit from our parents for common diseases is typically more important for predicting diseases earlier in life, as opposed to later in life. “Put another way, genetics is a very strong factor among those who are unfortunate to get a disease early in life, but a much weaker factor among those who get a disease later in life,” McVean said. “Even when the impact of genetic risk arising from a single variant is low, by sharing information across variants in a principled statistical manner, we can detect patterns in how genetic risk changes through life,” he added.
The study results have implications for how society chooses to use genetic information for predicting disease risk. “We should focus efforts on identifying risks in young individuals, long before anyone is getting the disease, but early enough that those at risk can be identified and helped appropriately,” McVean emphasized.
A limitation to the work is its use of UK participants only, a population that is primarily white British, which must be considered when interpreting the findings. McVean said, “The findings should be replicated in cohorts of different races or ethnicities across the world. Among the different ethnic groups within UK Biobank, we found little evidence for the result not generalizing, but the sample size is small.”
Next steps: “Constellations” of diseases
In terms of the next steps, McVean and colleagues have a two-pronged approach for advancing their research. Firstly, they will look to adapt their methodology such that it focuses on studying the onset of multiple diseases at the same time, rather than on a one-by-one basis. “By studying such “constellations” of diseases, we hope to uncover whether the occurrence of a disease at different times in life is accompanied by distinct patterns in associated biomarkers or co-occurring diseases. By doing so, we hope to understand the biology underpinning disease at different ages better, and to enable better “early-detection” approaches,” he said.
Secondly, age is just one way in which we vary from one another, there are of course other factors that may influence inherited risk. Exploring the potential impact of such factors is another focus for the team in this area of research.
Gil McVean was speaking to Molly Campbell, Science Writer for Technology Networks.
Reference: Jiang X, Holmes C, McVean G. The impact of age on genetic risk for common diseases. PLoS Genet. 2021. 17(8): e1009723. doi: 10.1371/journal.pgen.1009723.
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