Being told you are genetically predisposed to disease is scary – what does it even mean? Genetics and disease are highly complex and this often means it is hard to navigate whether there is high risk of disease or not. Beyond this, our genetic data can feel personal. So how do we ensure our genetic data is translated responsibly by healthcare providers?
Cosmos spoke to molecular geneticist Dr Fayeza Khan and Professor Michael Inouye, director of the Cambridge Baker Systems Genomics Initiative and Munz Chair of Cardiovascular Prediction & Prevention, Baker Heart & Diabetes Institute, about the complex biology and ethics of genetic disease.
Let’s talk about genetics and disease
Have you ever read an article with a title like: “Study shows these genes mean you’re at risk of X disease?” It sounds scary – but it doesn’t usually portray genetics and risk of disease accurately.
The sequencing of the human genome kickstarted a leap toward personalised medicine using genetic information about an individual’s personal risk of disease. It has contributed phenomenally to our understanding of human disease.
One of the benefits of analysing genetics is to assess the risk a person has of developing a disease. These types of diseases are partially caused by changes to the DNA – called variants – of multiple genes, and may manifest later in life.
To better understand this risk, patients may be given Polygenic Risk Scores (PRS) based on mutations, and other non-genetic risk factors.
But despite the goldmine of information in our DNA, there are challenges to using and translating this data. Hearing about genetic risk, especially if inaccurately explained, can cause anxiety and distress for patients and lead to bold generalisations about marginalised groups of people.
To make sure we use and talk about genetic data as best we can, proper frameworks need to be in place to ensure genetic data is analysed and translated well.
Benefits, risks and gaps in genetic analysis
According to a new report, published in Nature Medicine, there are three main points to consider when using genetic data responsibly: establishing benefits, mitigating risks, and closing gaps.
These need to be considered together to improve risk prediction and diagnostics and minimise biases in the data. They will also help to improve risk communication in patients, to give them the best, most accurate information possible to avoid distress and anxiety.
What are the benefits?
PRS have incredible potential to predict and prevent disease. A single genome-wide test for one individual costs as little as $45 AUD ($25USD), can be used for their whole life, and can provide scores for multiple diseases, such as diabetes and breast cancer.
By predicting an individual’s risk of a certain disease compared to the rest of the population using PRS and other risk factors, medical professionals may be able to help patients make lifestyle choices or prescribe medicines that prevent the disease from manifesting in the first place.
Genetics can also be used in conjunction with other tests as a more refined diagnostic tool. For example, differentiating between type 1 and type 2 diabetes can be very difficult and lead to unintended complications, but genetic data can bolster diagnosis.
PRS can also be used to predict the progression or recurrence of disease. Here, certain risk scores may show the likelihood of recurrent coronary artery disease, which may help refine mitigation strategies and prompt risk-reducing behaviour.
Finally, each extra genome analysed can contribute to our understanding of the population as a whole. For example, population-scale genomic data can reveal trends in ages and other demographics of people who are at risk of breast cancer, especially where there is no family history of it.
This could, in turn, prompt improved screening of breast cancer and optimise frequency of mammograms.
Clearly the benefits of genetic screening are enormous, but they do not come without risk.
What are the risks?
When it comes to PRS, there are some risks to patients and the general population that need to be acknowledged and mitigated, and most of these are centred around the use and translation of this complex field.
Incorrect information
There are many things that affect our risk of disease, including genetics, lifestyle and age. This makes assessing and quantifying PRS complex, and sometimes incorrect information is relayed to a clinician or patient.
For example, “false positives”, where a person is wrongly categorised as high risk, could lead to unnecessary clinical action and emotional distress in the patient. Here, it is important to emphasise that PRS is not a solid “yes/no” score and can be imprecise due to the incredibly complex nature of genetics, environment and disease.
Of course, this issue isn’t isolated to PRS – it is a challenge for any medical test one would have – and is necessary in communicating risk of disease in general.
On the other hand, there may also be risk of “false negatives”, where certain mutations and correlations are unknown. Much of the genetic data collected to date has been from people of European descent, but this isn’t representative of all people, and genetic risk factors in people of African or Asian descent may go unnoticed, or lead to a false-positive, because they haven’t been well-researched.
Miscommunication of correct information
Even when data is informative and accurate, things can sometimes be lost in translation.
PRS assesses many genes across the genome, and presents the risk of a disease developing later in life compared to other people in the population. Unfortunately, this can easily be conflated with unavoidable disease caused by only one or very few mutations (ie cystic fibrosis). This means that learning about a risk of disease can feel downright terrifying, especially if the uncertainty of PRS isn’t properly conveyed.
New methods of communication, such as showing an individual their risk on a bell curve chart, are being trialled to establish the best way to talk about risk of disease, but extra measures will be necessary to ensure communications done sensitively and equitably across all ethnicities.
Societal risks
There are two risks as a social level; Use of genetic information to discriminate, and general audiences may be used to thinking of genetics as destiny but PRS is not deterministic.
Historically, medical information has been used to discriminate against marginalised groups, so there need to be robust frameworks in place to prevent this.
Disease is also complex, so clinicians must convey that PRS is not deterministic, which also requires extra training and programs to help change attitudes. Without this, genetic information is at risk of being used irresponsibly to perpetuate incorrect and harmful ideologies.
To ensure this doesn’t happen, practices around PRS delivery require input and guidance from social and behavioural scientists.
Finally, the language of science and genetics often has individual, precise meanings that are unclear to the general public. Many new scientific words were introduced into media during the COVID-19 pandemic, which caused confusion instead of clarifying answers, especially for people from linguistically diverse backgrounds. Use of this language, and use of genomic data, may also be misconstrued to perpetuate racist or sexist rhetoric, so clear, well-explained consultations are necessary to mitigate this harm.
The Gaps
Despite the massive burst of genomic data over the last few decades, there are still knowledge gaps, which need to be filled to confidently assess risk and remove bias.
As discussed before, many of these gaps concern people of non-European descent. We need to embrace diversity. To do this, we need to prioritise and allocate resources to address this gap to ensure a robust and accurate dataset for comparison. This data is critical to improve transparency, accuracy, and public trust in genomic analysis.
Finally, communication and regulation of PRS needs to evolve alongside its technical growth. These frameworks need to be designed to properly explain what PRS is in an effective way, and to make sure the data is used and translated safely, equitably and responsibly.