When the cost of genome-wide genotyping for precision medicine is the same as an x-ray

When it comes to improving clinical practice and patient care, the role of implementing precision medicine is becoming abundantly clear. However, from a health economic perspective, there is also tremendous opportunity to use ‘Big Data’ that underpins precision medicine to make top-grade, innovative healthcare more accessible and cost-effective. Unlocking the potential of precision medicine in healthcare could not only ensure high-quality, personalised care for every individual but could also save the NHS valuable money and resources while doing so.

Genotyping arrays are more cost-effective than ever

For several decades, there has been a large amount of research surrounding one of the biggest sources of ‘Big Data’: the human genome. As a result, genotyping arrays have become increasingly more efficient, allowing up to two million single nucleotide polymorphisms (SNPs) across the whole genome to be analysed simultaneously and then, by using information about how these SNPs group together in populations, extending this number to 20 million.

The cost of obtaining this genome-wide information has fallen dramatically over the last few years to a cost that is comparable to a standard chest X-ray, so the issue of realising the clinical potential of using this genome Big Data in practice changes from one of “can we afford it?”  to “how do we do it?”.

There is also whole genome sequencing (WGS) in which every single base pair across the 23 chromosomes is read, providing even larger amounts of data, including rare or unique mutations, for an individual. While WGS remains considerably more expensive and challenging to process than genome-wide genotyping the technology is rapidly developing with costs similarly falling rapidly.

Efficient genotyping increases accessibility of human genome data

Moreover, for a significant proportion of the Scottish population, this data already exists. Over 280,000 people across Scotland have been recruited in the SHARE project, which conserves leftover blood samples used for routine healthcare purposes for genotyping for research. Uniquely, participants in SHARE agree that, where considered clinically relevant, specific genetic information can be incorporated into their medical record. 72,000 of these participants are located in Tayside, with genome-wide data already collected for over 1 in 10 adults in Tayside and this number is continually growing as more individuals are genotyped. This data is securely stored within the Data Safe Havens across Scotland, including Dundee’s Health Informatics Centre, and can be accessed for research.

Precision medicine can save lives and money

This gradually expanding availability of population-wide genomic data can be pivotal in the development of innovative healthcare solutions. It can then be compiled to establish predictive models for disease prevention, diagnosis and treatment. Routinely applying this precision medicine methodology can not only enhance patient care but can help to inform health economic decisions, with the potential of saving more money for the NHS in the long-term.

A prime example of this was with Clopidogrel pharmacogenetics. Clopidogrel is widely used to prevent further strokes, but for about 25% of patients clopidogrel does not work optimally because of a particular gene they carry and so, when routinely treated with clopidogrel after a stroke, they are still at higher risk of more strokes.  In this type of patient, only about 15 need to have their treatment switched to an alternative treatment, indicated by their genotype, to prevent a recurrent stroke.  On average one stroke costs the NHS approximately £30,000 over 5 years so it is clear that making medicines more effective in this way can be very cost effective.

Now that the cost of genotyping has fallen so significantly and to a level that healthcare payers are willing to pay in other settings, we are getting ever closer to realising the potential for elevated, cost-effective healthcare.  The case for adopting precision medicine and the use of ‘Big Data’ in routine clinical practice and patient management is becoming increasingly compelling.