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Blog / 11.30.20

#WhyIScience Q&A: A computational biologist discusses her shift from environmental fieldwork to biomedical research

Allison Dougherty, Broad Communications
Credit : Allison Dougherty, Broad Communications
Grace Tiao helps lead gnomAD as Associate Director for Computational Genomics in the Medical and Population Genetics Program at Broad.
By Kelsey Tsipis
Grace Tiao talks about her roundabout route to the Broad and the power of effective teamwork.

A three-month stint as a research assistant studying microbes in Antarctica was the perfect combination of swashbuckling adventure and science for Grace Tiao. It was her first job after graduating from Harvard University in 2008 with a joint bachelor’s degree in English and in History and Science.

It was also Tiao’s first exposure to computational biology—a field where she would come to dedicate the next decade of her career. That experience drove her to earn a second bachelor’s degree, in mathematics and statistics, at the University of Oxford as a Rhodes Scholar. She has been applying her talents as a computational biologist at the Broad Institute of MIT and Harvard ever since.

Tiao was first hired as a researcher in the Broad’s Cancer Genome Analysis Platform, where she worked on a project to identify hereditary cancer-predisposing genes. She then joined a team of Broad scientists leading an effort to aggregate exome and genome sequence data from investigators around the world into a large database, known as the Genome Aggregation Database, or gnomAD.

Now, as Associate Director for Computational Genomics in the Medical and Population Genetics Program at Broad, Tiao helps lead gnomAD, which has grown into the world's largest public collection of germline human genomes and exomes and is used to interpret which genetic variants are linked to human disease.

In this #WhyIScience Q&A, Tiao shares how she navigated her roundabout route into computational biology, what advice she has for others interested in the field, and the lessons she learned from Antarctica.

What prompted your transition from environmental fieldwork to computational biology?

When I joined the Antarctica expedition as a research assistant, the one condition my PI required of me was to move to New Zealand for the rest of the year to do follow-up lab work on the samples that we were collecting. While I was doing wet lab work there, other members of the lab were starting to work with this new, next-generation sequencing technology. I was looking over people's shoulders and quickly realized that this was going to be an incredibly powerful tool, and that it was the kind of big, technological breakthrough that ushers in a new scientific paradigm.

I got really excited about it and felt an instinctive attraction to genomic data. I wanted to work with this data, but because I didn't have the educational background to do that in any meaningful way, I knew I needed to go back to school and acquire an intellectual framework that would allow me to really engage.

What have you found to be the starkest contrast between environmental fieldwork and computational biology?

In field work, you have this elemental contact with your study subject. You can feel and taste and touch and smell the things that you're studying. You're actually living inside of your subject of study.

With computational biology, things are much more abstract. You really have to love the ideas, the concepts, the hypotheses. Your data are very separate from you and your specific operating environment. 

Another contrast that’s been made very clear this year with the pandemic is that with computational work, there's no such thing as a snow day. Because we’re not tied to a particular, local work environment, we just keep working (as long as we’re healthy and there’s internet). If the data don't get analyzed, it's not because weather conditions were really severe or somebody had to get airlifted out because they had an emergency. The responsibility for the success of our analyses lies largely with us. I think many of us have been feeling that extra responsibility this year — it’s a burden as well as an enormous benefit and blessing.

What do you enjoy most about your current work?

My favorite part of my job is working with the gnomAD team. Growing up, I was a big fan of a particular detective book series called the McGurk Mysteries. It was about a rag-tag group of neighborhood kids who would solve mysteries together. Each person had his or her own particular gift. There was a kid called The Nose who had a very sensitive sense of smell. There was another kid who was the tech nerd for the group. The group was named after McGurk, a kid who had no extraordinary talent but who acted as the leader, which in retrospect, as an adult, I now recognize was also a gift.

As a kid, I thought the ultimate pinnacle in life would be to have a strong enough gift to belong on a team like that. And amazingly, that’s exactly what I do now—I work on a team where everyone comes with their own gift. I work with people who work harder than me, who are more expert than me, who are more creative than me, and we solve mysteries together.

I will also add that it is extremely exciting and motivating to know that the quality and scope of the gnomAD data matter very much, in a very practical and material way, for the clinical care of patients. Knowing that our science is being used both for basic biological research and in the clinic is incredibly motivating. It's a great privilege to be able to be part of a project like that.

What advice would you give someone interested in pursuing a career in computational biology?

I would recommend laying down your intellectual foundation as solidly as you can before you start a job. Computational biology encompasses three major disciplines: biology, mathematics, and computer science. Most computational biologists have a strength in one or maybe two of those disciplines, but very rarely in all three. While it's tough, it's something that I would encourage people to aim for, because once you're on the job, there's not a lot of time to pick up foundational concepts.

Do you think you need a PhD to build that foundation?

In terms of acquiring the breadth of knowledge and tools you need to do excellent science, I think an undergraduate or master’s degree can provide a solid intellectual foundation if you're intentional about it. 

That said, I think getting a PhD offers opportunities for professional development, recognition, and growth that are a little bit different than getting commensurate research experience through work. There are lots of softer, less tangible aspects to getting a PhD that are genuinely useful for a career — it helps boost your credibility for leadership positions, for example. And understandably, in academia, academic credentials matter. A senior figure in the field of computational biology once told me that academia is built around the PhD. I think if you’re considering a traditional academic career in computational biology, you need to get a PhD. 

I do think career opportunities are more flexible on the industry side for folks without a PhD. At least, that’s what my colleagues and friends in industry tell me! 

Ultimately, I think career success should be measured based on the quality and impact of your work, not on your academic credentials or pedigree. A field or industry that is capable of assessing that work should provide talented people all the opportunities they deserve, regardless of whether they have PhDs. This is currently much more the case, I think, with the tech industry than with the biotech industry, but my feeling is that as the biotech industry grows over the next few years and decades, market dynamics will reward people without PhDs who contribute real value to this field. Talent is always scarce — I know this, because I’m currently trying to hire computational scientists to join my team! — and people who do great work will continue to be sought after. 

What lessons have you carried with you from your time in Antarctica?

Team dynamics make the difference between success and failure. Even as an individual on a team, you have enormous agency over team dynamics. It is truly something that you can affect positively.

Another lesson from Antarctica is that taking risks and letting go of the need to fully control your environment or your trajectory opens up a lot of doors and gives you the opportunity to reap enormous rewards in your career. I would encourage people to do that. I have to remind myself to do that!