Michael Snyder on Using Data to Keep People Healthy

<p>Having helped to bring big data to genomics through the lab techniques he invented, such as RNA-Seq, the Stanford molecular biologist Michael Snyder is focused today on how to use data from devices to increase the human healthspan. Some cars have as many as 400 sensors, Snyder notes. "And you can't imagine driving your car around without a dashboard...Yet here we are as people, which are more important than cars, and we're all running around without any sensors on us, except for internal ones." To Snyder, smart watches and other wearable devices should become those sensors, feeding information to our smartphones, which can then be "the health dashboard for humans and just let us know how our health is doing."  (You can sign up to participate in the Snyder lab's study of wearables and COVID-19 at <a href="https://innovations.stanford.edu/wearables">https://innovations.stanford.edu/wearables</a>.)</p><p>Snyder has been chair of Stanford’s Department of Genetics since 2009 and is director of the Stanford Center for Genomics and Personalized Medicine. He has a BA in chemistry and biology from the University of Rochester (1977) and a PhD from Caltech (1982), where he studied with the molecular biologist Norman Davidson. He did a postdoc at Stanford from 1982 to 1986 and then went to teach at Yale in the Department of Molecular, Cellular, and Developmental Biology from 1986 to 2009, when he moved back to Stanford. </p><p>At Yale, Snyder and his lab helped to develop many of the tools undergirding functional genomics, including RNA-Seq, one of the two pillars of transcriptomics (alongside microarrays). Snyder is also known in the world of personalized medicine for having discovered through genomic analysis of his own blood that he was at high risk for Type 2 diabetes, which he later did develop, but controlled through exercise and diet. That work to create an “integrated personal omics profile” (iPOP) was later described in a 2012 <a href="https://www.cell.com/fulltext/S0092-8674%2812%2900166-3">Cell article</a>. Eri