Fellows Feature: Chris DeBoever

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Chris DeBoever is a CEHG postdoctoral fellow in the labs of Carlos Bustamante and Manuel Rivas. He is a graduate of Harvey Mudd College (BS, Mathematical Biology) and the University of California San Diego (PhD, Bioinformatics and Systems Biology). Chris uses genomics approaches to investigate the genetics of complex phenotypes and diseases in humans.

Can you tell us a bit about yourself, personally and professionally? 

I’m a postdoctoral researcher working with Drs. Carlos Bustamante and Manuel Rivas. I grew up in Orange County and have spent a lot of time exploring the wilderness in California, especially the deserts and mountains of Southern California. I completed a B.S. in Mathematical Biology at Harvey Mudd College and a PhD in Bioinformatics and Systems Biology at UC San Diego with Kelly Frazer. My PhD focused on the genetic regulation of gene expression and splicing in both cancer and stem cells. I also have an interest in public policy and research ethics, stemming from my time at Harvey Mudd, that I was able to explore a bit during my PhD, which included a trip to China as part of a delegation representing the AAAS. I enjoy playing and listening to music, camping, and traveling. After defending my PhD in May, I took a few months off to travel to Thailand and Europe before starting at Stanford in October.

Can you tell us about your current research and what you want to achieve with it? 

My research is focused on investigating the role of genetic variation in human disease and other phenotypes. I am approaching this problem by developing statistical methods and applying them to data from large biobank and genetic association studies. As the cost of sequencing and genotyping continues to decrease, we are going to have access to much more genetic data than ever before. However, our ability to obtain genetic data is outpacing our ability to recruit and methodically phenotype study participants. Instead, we will look to mining data from electronic health records, surveys, or even phones or wearables to gather phenotypic information that we can use in concert with genetic information for exploring the genetic factors that affect various phenotypes. I am working with data from projects like the NHGRI’s Genome Sequencing Program and the UK Biobank, which are collecting genetic data at a scale much larger than previous efforts. I am developing methods using these data sets that will be useful as we move toward even wider adoption of genotyping and sequencing.

Besides using genetic information and phenotype data from healthcare records to conduct research on the genetic causes of disease, I am also interested in feeding back results into the healthcare system. For example, we can construct genetic risk models for different diseases and see whether that information is useful in identifying people that have increased risk for disease. I think that the efforts to study the genetics of disease and integrate genetic information in the clinic are complimentary. For instance, we can use genetic risk models to identify people who have a high risk for disease but who are in fact healthy. It may be that these people have a protective genetic variant that protects them from disease. These types of variants can be really useful for identifying drug targets. We can also use this information to update our risk models. I am excited to see what we will be able to learn about how genetics contributes to disease risk over the next few years.

How did you end up at Stanford? What first got you interested in genetics and science?

I went to college thinking that I wanted to study math or physics. However, I took a required introductory biology course taught by David Asai (now at HHMI) and Stephen Adolph that really piqued my interest in biology. This was in 2007, so next-generation sequencing was just emerging, and it seemed like an exciting time in computational biology. I remember thinking that the field seemed like the “wild west,” because there was so much opportunity to investigate questions that had been difficult to look at in the past. I enjoyed my computer science classes as well, so computational biology seemed like a natural fit. I did research in college with Eliot Bush and really enjoyed it, so I decided I’d like to continue on to a PhD.

What are your future plans? 

I am hoping to apply for faculty positions and start my own laboratory following my postdoc. I enjoy doing science and exploring questions that we don’t yet know the answers to. I also enjoy teaching and mentoring, so I look forward to working with my own trainees. I’d like to be involved with industry at some level as well. As I mentioned above, I think that there are a lot of interesting problems related to how we implement genetics and genomics in clinical care, and some of those questions are best approached by partnering with companies, hospitals, insurers, and other healthcare stakeholders.

Tell us what you do when you aren’t working on research. Do you have hobbies? Special talents? Other passions besides science? 

I’ve played guitar for the last 10 years or so, and I enjoyed listening to many different types of music and discovering new artists. I enjoy recreational mathematics and reading. I love to sneak in an online course here or there about subjects outside of my research to continue learning about new areas.

Were there specific people in particular to whom you would attribute your professional success?

I’ve had a lot of supporters over the years that I owe thanks to. My family has been very supportive and I’ve certainly had many great teachers and professors. My high school math teacher, Barbara De Roes, was really great at encouraging my interest in math. I was privileged to receive an amazing undergraduate education at Harvey Mudd College. Mudd professors are very dedicated to teaching and provide a great environment for undergraduate research. I also really enjoyed my PhD with Kelly Frazer at UCSD; we had the complimentary strengths and points of view that you really want for a good mentor-mentee relationship.

What advice would you offer to other grad students or postdocs who are considering pursuing a similar educational and career path as you? 

I’d suggest taking a lot of math, especially the stuff that some of us don’t get in high school. Linear algebra, probability, and statistics play an outsized role in genetics (and many other fields). Fluency in these areas is really useful.

Similarly, start coding early. Hack together projects, even if they don’t have anything to do with science or academics. I’ve learned a lot about how to code and how to manage a codebase through little pet projects. As a computational scientist, you often act as software developer, tester, and maintainer, so it’s important to be able to do those things effectively so you have time to devote to the scientific questions.

Can you speak a bit to the role you see CEHG playing on Stanford campus?

Genetics is a very interdisciplinary field. The problems that I am interested in exist at the intersection of biology, computer science, and statistics, so it is crucial that I take interdisciplinary approaches. This often means communicating with people in other laboratories or fields to share expertise and gain knowledge. Stanford is a very collaborative place, and CEHG helps to facilitate those collaborations. There is such a wide variety of research going on amongst CEHG fellows and associated labs that it’s easy to find someone to chat about a problem you’re facing in your research.

 

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