Arbel Harpak is a CEHG graduate fellow in Jonathan Pritchard’s lab. He holds BSc degrees in Mathematics and Physics, and MS degrees in Biology and Statistics. In his PhD research, Arbel asks how mutational mechanisms and natural selection shape genetic variation.
Can you tell us a bit about yourself, personally and professionally?
I grew up in Israel. My family moved around quite a bit, but always kept a stone’s throw away from the beaches of the Mediterranean Sea, where I spent a lot of my time. I was very much into math and into wildlife from a young age, but hadn’t really thought of being an academic; no one in my family was, and playing outside with a ball of some shape or form filled up most of my schedule nicely. I only started my B.Sc. at 23, after a long military service, a short stint in industry, and some travel in Asia. While it sometimes feels like a late start compared to my fellow grad students, I am happy to have these bits of life experience outside Academia.
I started my bachelor studies in Mathematics and Philosophy at the Hebrew University of Jerusalem, and, later on, added Physics to the mix. I deeply enjoyed my Math studies. I never knew—and probably never would have known without my Math education—how it feels to fundamentally and thoroughly understand your object of study. This feeling was a source of real joy for me.
My first research experience was in studying bacterial colony growth dynamics with the Physicist Nathalie Balaban during undergrad. This exposure to Biological research led to my M.Sc. work with Guy Sella (now at Columbia University). In my Masters, I developed theoretical expectations for genetic diversity in evolutionary experiments with micro-organisms, and conducted an experiment in the Balaban lab that tested these expectations. During this time, I got to read lots of inspiring research coming out of Stanford Evolution and Genetics labs; this led me to apply to join Stanford Biology’s Eco/Evo track.
How did you first become interested in genetics and science?
My Masters was probably the first time I started to seriously think about a career in research, mostly thanks to Guy Sella, who made the introduction to Population Genetics extremely exciting. He helped me form a reading list, starting from popular science books about evolution (e.g. Charles Darwin, Sean Carroll, Jerry Coyne and Neil Shubin) advancing into the fundamentals of Population Genetics and Coalescent theory (e.g. John Gillespie, Dick Hudson, Allen Orr, R.A. Fisher, Simon Tavaré, Charlesworths) and eventually contemporary research papers. He encouraged me to spend my first 4-5 months only reading, simulating evolutionary dynamics and periodically updating him on what I learned. In these conversations, he always thoughtfully guided me, but also had genuine interest in my own (probably quite naïve, in retrospect) thoughts. For me, this was a great experience that filled me with excitement for Population Genetics research.
Can you tell us about your current research and what you want to achieve with it?
Most of my doctoral work has been the study of mutational mechanisms, and how they shape human genetic variation. For example, analyzing >120K human exomes in the ExAC dataset, I found that many positions in the human genome were segregating mutations of more than one independent origin. This revealed substantial mutation rate heterogeneity even within what is considered a single mutation type.
What I like most about this work is being able to use large genomic variation datasets and Statistics to make discoveries about basic genetic mechanisms. The extent of mutational heterogeneity can be hard to quantify in direct experiments because of their limited sample sizes—but these huge genetic variation datasets contain evidence from a very long experiment: our own evolution.
I also work on the evolution of gene families. I helped develop riboHMM, a method with which we identified thousands of novel coding sequences in the human genome—some of which had surprising functions. For example, some of the novel sequences function as a regulatory “sink” of gene expression: they occupy Ribosomes and thereby reduce the expression of other genes. In more recent work, I estimated the rate of genetic exchange between gene duplicates. This exchange is a driver of >20 diseases and is also thought to be a central force behind the “concerted evolution” of gene duplicates—a curiously low level of divergence between duplicate genes that is sometimes observed long after their duplication.
What kind of responses have you gotten to your research/findings?
People are often excited to hear about my NYC rats research. This is a collaboration with Pleuni Pennings, Nandita Garud, Noah Rosenberg, Dmitri Petrov and Jason Munshi-South. New Yorkers (and people of other cities of course) are vividly aware that rats have adapted to urban environments impressively; we wanted to know whether this adaptation has a genetic basis. We have collected rats from abandoned lots around New York City and are scanning their genomes for unusual signs of adaptation. I love talking to people about this research because it illustrates evolution beautifully in comprehensible timescales, and with natural phenomena that one can see even in the most urban setting.
What happens next in the process of discovery?
Lately, my work has focused on highly-polygenic (“complex”) traits. Although most diseases and other traits are polygenic—i.e. affected by many different sites in the genome—most of our contemporary models and tools are best-fitted for a Mendelian world. Together, with lab mates who have already made significant discoveries in this area—Yair Field, Nasa Sinott-Armstrong and Evan Boyle—we are analyzing genome-wide association studies (GWAS) to learn about the genetic architecture of polygenic traits, and study this architecture’s implications on human evolution.
Are you enjoying your time at Stanford?
Stanford is like Disneyland for academics. I can’t think of anywhere else I’d rather be for my Ph.D.
What is it like working with your current lab advisor and his lab?
More than anyone, my advisor, Jonathan Pritchard, is the person that shaped me as a scientist and has been a role model for me. He is a man of few, selected words who usually teaches by example: he is nearly always the quickest to identify the right questions to ask and the first to make sharp inferences. He strikes a balance between being constantly productive and engaged in everyone’s projects, and still managing to come up with innovations completely out of left field. I think a big part of this is his habit of occasionally zooming out—and taking the time to truly think and rethink about data critically.
Were there other people to whom you would attribute your professional success?
Outside of research, I learnt everything I know from Tami and Eric Taylor.
Professionally? that’s such a long list. I am grateful to Stanford faculty that help with key advice along the way, notably Noah Rosenberg, Dmitri Petrov, Christina Curtis, Hunter Fraser and Marc Feldman.
Another large group that I owe a debt of gratitude to are my past and present fellow lab members. Just to give a few examples: I learned what true scholarship looks like from Ziyue Gao, Eilon Sharon, Jamie Blundell and Alison Feder; how to allow data to whisper secrets in your ear from Evan Boyle, Kelley Harris and Eyal Elyashiv; how high productivity and always making yourself available to help others can go hand in hand from Pleuni Pennings, Anand Bhaskar and Nasa Sinott-Armstrong; how to do statistical modelling that is revealing rather than hiding the signal in the data from David Golan, Anil Raj and Doc Edge; how to effectively communicate science from Emily Glassberg, Harold Pimental and Yang Li; this list goes much longer—but I should probably spare CEHG blog readers at this point.
Where do you see yourself professionally in the next 5 or 10 years?
I aspire to start my own research group and combine research with teaching. I would like to lead a group that investigates a biological question with whatever means necessary—a lab that is not too comfortable in its comfort zone. I am also quite passionate about teaching. I got to work as a high-school and college Math teacher to fund myself through my B.Sc. studies, and found it to be highly fulfilling. While I truly enjoy the footwork of applying statistical analysis to questions in Genetics and Evolution, I believe that the greatest impact I could have on the field is in teaching, advising and mentoring future generations; that is just how exponential functions work.
As a CEHG fellow, can you speak a bit to the role you see CEHG playing on Stanford campus?
I am proud to be a part of the CEHG community. CEHG brings together labs with shared interests from many departments at Stanford. Evolgenome seminars provide exposure to cool work and encourage interdisciplinary connections. I am particularly grateful for the generous CEHG fellowship—it allowed me to pursue my deep interest in the adaptation to urban environments in NYC rats and in polygenic trait evolution during the last year of my Ph.D.
CEHG’s core values include “interdisciplinary research” and “collaboration”. Can you speak to the ways your work has embodied these values or to their importance to your future work or past experience? How do these values align with your own approach to science?
In CEHG symposium 2016, I got to hear a talk by Christina Curtis and ask her many questions about it after. This conversation boosted a new research direction for me, and ignited brainstorming meetings with Christina Curtis and Zheng Hu and Ruping Sun from her cancer genomics lab. These meetings naturally developed into a collaboration with the Curtis lab in applying Population Genetics to better understand tumor progression. I feel quite fortunate to have been at the right place and time (= CEHG symposium 2016) that made this interdisciplinary connection possible.
What advice would you offer to other grad students or postdocs who are considering pursuing a similar educational and career path as you?
If I had to identify one necessary but not sufficient condition for success in grad school, it is being excited about your research. It doesn’t have to be the research question; it could be the method or the people that you get to work with. Academic research careers can be very unrewarding externally, so if you are not internally fueled, grad-school crises might last years instead of weeks.
Other than that, I think it’s important to set high standards for yourself in what you want to excel at. For me, often times this means doing things painfully slowly—but from scratch and by myself.
Lastly, let me also confuse you with some contradictory-sounding piece of advice: get the most out of your colleagues’ experience, and give back as often as you can. I have personally learned that having science buddies—or better yet, science BFFs—is the single strongest determinant of my happiness in my work.