This month??was published that gives hope to millions across the UK suffering from rare diseases ¨C which affect 6 per cent of the population.

The study, led by Genomics England and Queen Mary University of London, showed that reading people¡¯s complete DNA code, known as whole-genome sequencing, is the most effective way to quickly find the cause of their medical problems. The approach worked well across a broad range of rare diseases, including intellectual disabilities and disorders of vision and hearing.

Additionally, the results illustrated that this approach allows scientists to uncover conditions that would not have been detectable with existing testing.

It is undoubtedly a landmark moment in helping people who typically must wait years for a diagnosis, let alone begin treatment. It also means substantial long-term savings for the NHS, which are especially critical considering the pressures on the service created by Covid-19, as well as the fact that people are living longer than ever before. It has never been more important to deliver treatments to patients in a cost-effective way and to get a maximum return for every pound invested in healthcare. A faster diagnosis through whole-genome sequencing will be a major factor in this.

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It goes without saying that no single organisation or person could have achieved this alone. Scientific strides of this magnitude can only happen when the best and brightest from a range of different disciplines and organisations come together with a united aim.

The pivotal moment for this pilot of whole-genome sequencing at scale came in 2013, when Genomics England ¨C a company established and owned by the Department of Health and Social Care ¨C partnered with nine hospitals across the country and the National Institute for Health Research¡¯s BioResource, which puts willing medical research participants in touch with scientists. Hundreds of clinicians, scientists and informaticians had to work together to build the systems to recruit the 4,660 participants, collect their blood samples, perform the sequencing and store the results, along with the rest of their clinical data, in a secure, high-performance computer centre.

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Other researchers could then work collaboratively in this environment to find the diagnoses. For example, Queen Mary researchers developed revolutionary software to help discover the cause of a rare disease patient¡¯s condition, searching among the millions of variants in every genome. But such a needle-in-a-haystack search would never succeed without a wealth of background clinical data, the gathering of which required us to work closely with the clinicians looking after the patients. Only then were we armed with the tools to solve the conundrum.

Great collaboration allows us to deliver game-changing work like this at a significant scale. And now that we have the infrastructure in place, our plan is to continue to build on it, incorporating new knowledge from researchers worldwide so we can help as many people as possible to get a diagnosis.

The very best version of this project in the future will be a global coalition of healthcare professionals and researchers. Together, they will leave no stone unturned in searching for opportunities to help those with rare diseases, including developing innovative therapies where we can.

To solve a global issue, we need to look at a global solution, and that means taking advantage of all the medical brilliance we have on every continent. We cannot hope to bring the benefits of whole-genome sequencing to people thousands of miles away without leaning heavily?on the local expertise and knowledge that their scientists, doctors, researchers and organisations have.

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In the dark days of the pandemic, we have had some flashes of light in the wealth of scientific achievements that have improved health outcomes for so many. It is important that we do not lose the valuable momentum gained. We must keep working tirelessly and, most importantly, keep working together ¨C for the benefit of patients everywhere.

Damian Smedley is professor in computational genomics at Queen Mary University of London.