Let's talk about lab water
Let's talk about lab water
Genetics and genomics research has the potential to transform modern medicine, offering ways to prevent and treat some of the most complex diseases. In fact, we’re already seeing how these therapeutics can change patient lives for the better.
For example, in June 2023, the FDA approved the first gene therapy treatment of pediatric patients with Duchenne muscular dystrophy (DMD), meeting an urgent, and previously unmet, medical need for this devastating condition.
However, several significant challenges stand in the way, preventing dedicated genomics and genetics scientists from advancing the field further.
Here, we summarize four of the most pressing challenges barring the path to genetics and genomics success.
One of the most pressing challenges facing geneticists and geneticists relates to their samples — more specifically, the quantity, quality, and diversity of biospecimens available for research.
Sample diversity is garnering increasing attention as a crucial challenge with wide-ranging consequences in genetics and genomics research. In short, most current genomics and genetics studies are still heavily focused on populations with European ancestry. As a result, huge swathes of the world’s population could miss out on the transformative benefits of genetics and genomics research, such as faster and earlier diagnosis, and more effective therapies.
Many researchers across the world struggle to obtain enough biospecimens for their research. That’s problematic, as genetics and genomic studies may require thousands of samples to achieve statistical significance. Drivers of this challenge include donor recruitment difficulties, the need for expertise in handling biospecimens, and studies where even potential donors are scarce — for instance, in the case of rare diseases.
Sample quality is another big issue (which can also compound sample quantity challenges). It goes without saying that high quality samples are crucial for successful genomics and genetics research — without them, researchers risk producing unreliable and inaccurate results, and drawing erroneous conclusions.
Ensuring the required level of sample quality, however, is no easy feat. Sample collection, preparation, and storage, and DNA extraction are all highly complex and error-prone processes, where even the smallest mistakes can lead to degraded, poor-quality samples that can derail the most promising research.
Thanks to the steadily declining cost of (and greater access to) high-throughput screening technologies, genomics and genetics research now generates gargantuan amounts of data. In fact, genomics research could produce as much as 2–40 exabytes of data by 2025. And it’s not just the volume of data that can cause issues — genomics data is often in a variety of different formats.
It’s no surprise, then, that one of the key challenges is effectively managing this research output. Researchers must be able to capture, store, and organize huge volumes of genetic and genomic data in a way that ensures it’s still accessible, usable, and shareable, which demands advanced (and potentially costly) IT infrastructure and tools, as well as dedicated IT expertise.
Unfortunately, as adoption of low-cost sequencing tools increases, the amount of data generated is only set to grow, which means this challenge is only set to worsen.
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The sheer volume, complexity, and heterogeneous nature of genetics and genomics data mean that data analysis is challenging, too.
To succeed in unlocking deep insights from genetics and genomics data, labs need access to huge amounts of computing power, as well as considerable expertise in biostatistical and computational methods.
Often, though, labs won’t have staff with the right multi-disciplinary know-how. While training existing staff is an option, it can be time-consuming and expensive. Tracking down and securing external, appropriately skilled bioinformaticians is similarly difficult and costly.
Make no mistake, genetics and genomics research has already yielded a range of successes when it comes to disease prevention, diagnosis, and treatment. However, translating genetics and genomics research into the clinic is generally still difficult.
There are several reasons for this, not least the fact that successful clinical translation demands integration of several disciplines across both academia and industry — from epidemiology to bioinformatics — which is far from straightforward. Clinical implementation of genetic and genomic innovations is also complex: clinicians may lack the required knowledge, convoluted new protocols may deter patients, and clinical facilities may need to heavily invest in new technologies and infrastructure to make adoption feasible.
Without a doubt, genetics and genomics research is an exciting field, and with transformative potential. But before we can unlock this potential, researchers must overcome tough challenges — from biospecimen quality and diversity to Big Data difficulties, and a significant clinical translation gap.
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