How we're finally cracking the secrets of what makes us sick

We first mapped the human genome over a decade ago and can now sequence an individual’s genetic code faster and cheaper than ever. Yet critics argue that we’re getting “diddly-squat” from the hype and the billions of dollars poured into understanding these building blocks of life.

There’s reason to think that now, the increasing availability of genetic information and our improved ability to process it are transforming our understanding of the genetic causes of disease.

In the millions of letters that make up our genome, we know there are answers to big questions about who we are and why our bodies sometimes fail us.

Our genes help define the characteristics that make us individuals. Just one or sometimes a few can define physical traits like freckles or whether or not we’ll get an illness like Huntington’s disease. Scientists suspect that — along with environmental factors — a combination of many genes is largely responsible for traits like intelligence and our susceptibility to diseases like cancer, Alzheimer’s, and autism.

The more complex these interactions get, the less we understand them.

But what we know is “just growing every day,” says Dr. Emily Conley, a neuroscientist at personal genomics company 23andMe. “Five years ago we didn’t know half of what we know now, ten years ago we knew practically nothing, so, predict where we will be in ten years.”

Scientists are now harnessing technology to decode our genomes, figure out how to treat tricky illnesses, and provide genetic information directly to curious consumers. That’s means we’re moving beyond collecting all this genomic data about ourselves — we’re finally learning how to use it.

Decoding big data

Beyond My Ken/Wikimedia CommonsThe Icahn Institute at Mt. Sinai.

For the data in our genome to be useful, we need to process not just the 3 billion base pairs of DNA that make up each person’s genome, but the genomes of many people. The Precision Medicine Initiative is starting with the genomes of at least one million people.

That’s a mind-boggling amount of data already, and we’ll eventually end up needing even more: “many millions” of genomes, says Dr. Eric Schadt, founding director of the Icahn Institute for Genomics and Multiscale Biology at Mount Sinai.

We’re still figuring out how to decode it all.

At Mount Sinai, scientists are trying to “collect as much information on as many patients as we can, integrate it, build predictive models from it, and then derive from those models more refined diagnoses, risk assessments, and plans for treatment than has been possible before,” Schadt told Nature Biotechnology in 2012.

The team at Icahn is collecting genetic information from patients and trying to incorporate that data with everything from their clinical history to the bacteria on and around them in order to develop predictive models that will calculate how a disease will affect a person.

“The technology advances [that make that possible] have been astounding,” says Schadt. “The ability to generate sequencing data has moved at a super-Moore’s law rate,” faster than even computing technology, he says. The supercomputers we have now can process genetic information in ways that would have been “just impossible 10 years ago.”

Scientists all over the country are pushing for new ways to understand genomic data.

At 23andMe, researchers are collecting data from the more than one million people who have spit into a tube, sent their genetic material to the company to learn more about themselves, and consented to have their genetic information used for research.

The genomics company’s in-house research center is combining that information with surveys that their customers answer to try to tease out the relationship between genetics and environment for complex diseases. They recently launched their own drug development center, which Conley says should have a leg up, since they are basing what they know on human data, not just animal models or other external factors. And the company’s many academic and corporate research partners are also tapping into that wealth of information.

Making sense of the links within such vast stores of data will require technologies that are only now becoming powerful enough to help.

Deep Genomics, a startup run by Brendan Frey, is leveraging artificial intelligence to help decode the meaning of the genome.

Specifically, the company is using deep learning: the process by which a computer takes in data and then, based on its extensive knowledge drawn from analysing other data, interprets that information.

Deep Genomics Brendan FreyCourtesy Roberta BakerFrey, in the yellow shirt, is leveraging artificial intelligence to help decode the meaning of the genome.

Deep Genomics’ learning software is developing the ability to predict the effects of a particular mutation based on its analyses of hundreds of thousands of examples of other mutations — even if there’s not already a record of what those mutations do. They’re trying to build not just a Rosetta Stone that explains an as yet largely inscrutable body of text, but a way to predict how a tiny change in the letters will create something new.

Transforming treatment through sequencing

Genome sequencing is already playing a role in some treatments, according to Dr. Heidi Rehm, an associate professor of pathology at Harvard and the director of labs dealing with genetics and disease at Partners HealthCare and the Broad Institute.

“Today it’s primarily being used to diagnose rare diseases and guide care of those patients,” she says. For people with an illness that’s so rare that only one in a few thousand people have it, just the ability to potentially find the cause of that disease is a huge step.

That sometimes requires comparing patients on different sides of the globe. A doctor in India might have a patient with a rare disease and an idea for a gene that’s responsible for it that they can’t explain without comparing it to a similar patient in, say, the United States. To solve that problem, the Global Alliance for Genomics and Health is trying to standardize ways for researchers to safely share genomic data with each other, no matter where they are, according to Rehm, who is on the steering committee for that group.

And treatments for rare diseases help more than the people suffering from those illnesses.

In many cases, the treatments for a rare disease “end up being relevant to more common and milder forms of

diseases,” Rehm says. As an example, she says that understanding the genetic pathways behind the connective tissue disorder known as Marfan syndrome led to drugs that could treat other serious heart problems.

Outside of rare diseases, she says, researchers are using genetic tests to identify the mutations that are driving tumour growth in cancers, so doctors can choose treatments that they know will affect specific tumours. And more and more, she says, doctors are sequencing the genes of bacteria to find the agent causing an infectious disease — no trivial thing, since identifying the source might indicate a potential cure.

Mount Sinai is pioneering many of those efforts.

“We made the very conscientious decision to focus primarily on the highly actionable parts of the genome” for now, says Schadt, in order to be able to provide clinically relevant treatment information to patients about everything from cancer to rare disease to how they will react to certain medications. At the same time, they have built a system that will allow them to incorporate new discoveries about the genome as they are made.

Opening up health information to consumers

Right now, there are still limits on the useful information that a healthy individual can get from having their genome sequenced.

After working with the FDA, 23andMe is now allowed to send anyone who pays the $US199 fee information about whether they carry the genes that could pass on a limited list of diseases.

And Conley says they’d like to get to the point where they can tell people about genes that might be risk factors for other diseases, as well as those that indicate a person might respond to specific medications in certain ways. In the US, however, they aren’t allowed to provide that information yet.

Part of the problem with genetic indicators of disease up to this point is that we’ve identified genes that might pose a risk based on sick patients, says Rehm. Because of that, we might know that a gene is connected to Alzheimer’s or Parkinson’s, but we don’t know how common it is in the healthy population, and we don’t want people to think they are “sick” if it turns out to be a much more common gene: one that indicates a slightly higher risk for a particular disease but doesn’t actually cause it.

Today, efforts like the Precision Medicine Initiative and research at a wide variety of companies and institutions are helping us gain a much more complete understanding of the genomes of healthy and sick people alike. That, in turn, will help us crack the complex genetic causes of disease — and reveal new interventions and treatments along the way.

For critics unsure about whether genomic medicine is just another buzzword that hasn’t delivered, it’s possible that until now, the time had not yet come.

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