It currently takes about 24 hours, and costs about 1,000 pounds, to sequence a human genome today.
That might seem expensive or cheap depending on how you look at it but, for some perspective, it is worth considering how much it cost to sequence the first human genome a little over a decade ago:
$2.7 billion and approximately 13 years.
That was the cost of the Human Genome Project — the first ever completed sequence of the human genome.
The Human Genome Project required the effort of over 2,800 researchers and the participation of several governments and took place from 1990 to 2003.
A lot has changed since that first sequencing, and genome sequencing today generally costs a little over $1,000 and takes about 24 hours to complete.
Genetic testing is a lot cheaper than it used to be, too; according to the National Science Foundation, genetic testing is now so affordable that at least 1 in 25 American adults now know their ancestry thanks to cheap, at-home DNA tests. (Also read: How is machine learning affecting genetic testing?)
The cheapest DNA testing kits now cost less than $100 according to Top10.com, and experts are predicting that both genome sequencing and DNA testing will be much cheaper in the years to come.
It is important to realize, however, that genetic testing and genome sequencing won’t be disrupted until the use of big data is adopted by the industry.
According to the National Science Foundation report earlier referenced, genetic testing has a data problem that needs to be addressed before the industry can grow: while human genes is 99% similar, the 1% variation in our genes each contains 4 to 5 million single nucleotide polymorphisms (SNPs) — which is quite a lot of data to process for a single person.
A single human genome sequence will generate about 200 gigabytes of data, with analysis of the data generating an additional 100 gigabytes of data; that’s about 300 gigabytes of data for just one person.
Now imagine the amount of data involved for millions or billions of people.
Not only is it very expensive to store and process this data, but analyzing and making any sense of this data becomes a lot more complicated unless big data is involved.
How Big Data Is Going to Change Genetic Testing
Big data will improve the accessibility and usability of genomic data
While genetic testing is a lot more cheaper today than it was a decade ago, the reality is that not much has been accomplished in the way of the usefulness of this data.
A lot of medical institutions and experts in the medical industry have no idea about what to do with genomic data even if the data is given to them.
In an article on The Medical Futurist, Dr. Berci Meskó related the challenges he experienced in having his DNA data used by medical experts he gave them to; despite having done different genetic tests with at least four different genetic testing companies, none of the medical professionals he presented the data to could make any use of it.
In some locations, they couldn’t even add his DNA data to his medical records.
This might not seem like much of a big deal until you realize that a significant percentage of health challenges could be addressed with knowledge of an individual’s DNA data; this data contains key information such as a patient’s family health history, their sensitivity to certain classes of drugs, and health conditions they could have — knowledge of which can significantly impact the solutions presented to patients and how personalized and effective these solutions are.
Big data makes it easy to integrate and synthesize DNA data with a patient’s medical records automatically and in a way that makes clear what treatment options would work for them.
Big data will ensure more effective and less dangerous health therapies for patients
One of the major ways big data will change genetic testing is that it will make interpretation of genomic data easier in such a way that it is easy to present patients with therapeutic options with less severe side effects.
Laboratories such as the Munich Leukemia Laboratory are already researching this concept: by sequencing known hot-spots from certain patients using Next-Generation Sequencing technology that can analyze billions of DNA strands in parallel, they could cross reference data from tens of thousands of cases, isolate the most effective therapy with less severe side effect based on data from other patients, and then use this information to treat new patients.
The major benefit of big data in approaches used by labs such as the Munich Leukemia Laboratory lies not just in analysing the data, but in its interpretation; since the human DNA contains so much data, careful and precise interpretation is required to ensure that only the right therapy is suggested, and this is only possible with big data.
Big data will make it a lot easier to diagnose deadly illnesses and diseases from genetics data
Several studies are already showing potential in the use of genetic testing and big data to diagnose certain forms of deadly diseases, such as cancer.
A particular study looks at how genomics can be used to deal with cancer: there is a form of often-fatal leukemia that can usually be treated successfully with a full bone-marrow transplant. The only problem with this transplant is that it can result in complications that can in themselves be fatal.
As such, only people with the deadliest forms of this leukemia are advised to get this transplant due to the potential risk of complications; unfortunately, it is impossible to manually predict those with the deadliest form of this leukemia.
By sequencing the genomes of 1,500 people who had this cancer to determine the mutations causing these cancers and then seeing how mutations correlated with outcomes, however, clinicians were able to more accurately determine which patients have a deadlier form of the leukemia and as a result should get the transplant.
The application of big data in genetic testing is still nascent but the potential is endless.
As genetic testing becomes a lot more available and affordable to the average human, it becomes even more important to ensure that genomic data is of actual practical use when it comes to addressing human medical issues. Big data makes this possible.