Data in Biotech Podcast: The Evolution of Genomic Analysis

“We can finally sequence genomes at scale. But now we have to ask: assuming we line every single person up on the planet and they’re holding their medical records in their hands, and we sequence every single individual and have those records cataloged, how much of human disease can we explain? This is a metric that we call population-attributable risk. There are many ways in which you can do the underlying calculation, and we all want that number to be 80, 90, 95 percent. But in reality, that number is more on the order of 14 to 16 percent. Which means that even if we fully understand the human genome, there is a massive amount of information that is missing. It’s akin to solving a puzzle in which you only have 14 percent of the pieces. It’s virtually impossible, especially for a complex puzzle such as the human body.

We became very interested in that other side of the puzzle – beyond the genome, looking at those types of risk factors that are not captured in heritability from your mother and father, but that come from the world in which we live. Everything we eat, drink, smell, smoke, and the way we live our life… and that we know has huge implications for human disease. That information is continually evolving and dynamic through life and is not encoded in your genomic sequence; rather, it’s encoded in small molecule and large molecule chemistry (proteins, metabolites, lipids).

The idea behind these orthogonal data assets, whether it be metabolomics, lipidomics, or proteomics, is to complement genomics for drug development. And having large data in itself is not the solution. It’s about having the right type of data and having that right type of data at scale.”