Othman Laraki on achieving the long-tail distribution of genetic insights

The O'Reilly Radar Podcast: Color Genomics, genetic testing access, and the future of precision medicine.

By Jenn Webb
July 14, 2016
DNA Molecule display, Oxford University. DNA Molecule display, Oxford University. (source: Christian Guthier on Flickr)

This week, I chat with Othman Laraki, co-founder of Color Genomics. We chat about challenges and opportunities in genetic testing, the future of precision medicine, and the hurdles medicine and health care are currently facing (and how we can overcome them).

Here are some highlights:

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Genetics testing for everyone

Genetics, we felt, had come to a point where there was an opportunity to have a very big impact by essentially mixing some of the best of the biology world with software—in many ways, genetics had started to become, in part, a software problem.

It felt like it was starting to be possible to build products that made genetics accessible to a much broader population by both dropping costs as well as increasing access, so making this information more accessible to a much broader population in a scalable way.

… For example, one of the things we did that we’re very proud of is we created this program called the Every Woman Program, where whenever someone buys a test from Color, they can also contribute to fund testing for someone who can’t afford it. Then we work with a number of cancer centers, for example at UCSF and the University of Washington, Morehouse in Georgia, and a number of others, where each one of those centers works with underprivileged populations, and they can provide tests for free for people who can’t afford it but who the doctors think should get tested.

Opportunities in machine learning

One of the big opportunities for machine learning in genetics, for example, is around the interpretation of the effects of specific genetic changes. Right now, there are set of guidelines or processes that are used by the industry around the interpretation of how a specific mutation impacts a gene. It’s a structured process that’s very labor intensive, but it’s one of those areas where over time is going to become something that’s very heavily solved by machine learning because there’s a lot of data that can be used to train a model instead of purely running it in a manual way. The industry is going to evolve quite a bit over the next few years and machine learning is going to have very substantial impact there.

Using the full data set of the human body

Each one of us is carrying and generating a tremendous amount of data in our daily lives, whether it’s our genome, our microbiome, etc., etc. So far, the link between that data and health practice had been through the path of research and translation to a few proxies, essentially, where researchers collect a lot of data, they do a research study, it turns into a set of conclusions, and that over time gets turned into a few rules that get introduced into medical practice. If someone’s lipid levels are at this level, etc., then you draw these kinds of conclusions.

Now, we’re coming to the point where the amount of data that a doctor will be able to use in a real way to make medical decisions is going to be the full data set of our bodies, which is very exciting and can have a very big impact.

Long-tail distribution of genetic insights

In some ways, I feel right now we’ve come to this point where there’s been enough data and science behind us that we can already create a lot of value, and that allows the bootstrapping of doing things at a massive scale that really takes us to that long-tail distribution of insights around how genetics work and how the body works.

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