Career Interview: Jay Shendure, MD, PhD

Associate Professor of Genome Sciences
University of Washington

ASHG: If you could go back to when you were a trainee, what is one piece of advice you would give yourself for your current career?

Dr. Shendure: I’m pretty happy with how things turned out, so I probably wouldn’t advise my younger myself to do anything differently lest it inadvertently turn out for the worse. That being said, one thing that I do regret from graduate school is that I didn’t get out to more meetings (e.g., Biology of Genomes, ASHG). It just wasn’t the culture of our lab to attend these, and probably I got more work done as a consequence.

But looking back, I think that my perspective at the time was much narrower than it might have been, and also you realize over time what a social process science is, well beyond the interactions within any one lab.

ASHG: What are your favorite and least favorite parts of your job?

Dr. Shendure: The parts of the job that still allow me to intersect with the science are by far the most fun. This includes of course interacting with my wonderful lab, reading and writing papers, and believe it or not, writing grants.

For better or worse (and mostly for worse), there is a lot of administrative overhead that comes with the territory (meetings, conference calls, forms, budgets, etc.). It takes a lot of effort just to keep such things from overwhelming the more meaningful parts of the job.

ASHG: What do you think the future holds for the field of genetics?

Dr. Shendure: Of course the future of genetics includes sequencing millions or perhaps billions of whole genomes, but I’ll be disappointed if generating and sifting through that data is all our field is capable of. The exercise of gene finding is a finite exercise or at least subject to the law of diminishing returns.

I think that we need to be increasingly thinking beyond gene finding and onwards to connecting the dots between genetic implication and functional understanding. This is the case at the level of loci (e.g., identifying causal variants underlying genome-wide associations), genes (e.g., understanding not only that mutations in gene X underlie a given phenotype, but also the how and why of it), and variants (e.g., variants of uncertain significance).

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