Kipper Fletez-Brant, a computational biologist with our Therapeutics division, thought he’d be a physicist one day.
Growing up in Phoenix, Arizona, Kipper escaped the heat reading “hard-sci-fi novels” with mind-bending plots woven with currently understood science about things like dark matter, wormholes, and the bending of the space-time continuum.
“But I realized at some point I liked living things and physics wasn’t the best fit,” he said.
Physics is sometimes referred to as the philosopher’s science. So perhaps it was appropriate that instead of a hard science, Kipper tackled other kinds of hard questions and studied philosophy as an undergraduate at St. John’s College.
After four years of wrestling with the eternal questions of life, he wanted something different.
“I wasn’t keen on the fact that there aren’t answers in philosophy,” Kipper said.
And that in turn motivated him to “think about science again.”
After graduation, while doing clerical work for an ophthalmologist, a doctor who noticed his aptitude and interest suggested he consider bioinformatics.
“I learned that was a way to put math and biology together,” Kipper said. “There was just one catch. I hadn’t studied either.”
It’s what motivated him to go back to school and break into the field. What followed was a Masters in Biotechnology giving him crash courses in computer science and molecular biology. He went on to work in a genetics lab and then spent time at the National Institutes of Health’s Vaccine Research Center, working on data related to vaccine trials. Work that he’s been thinking about on a daily basis since the pandemic.
“Our mission was to understand, on a molecular level, what changes vaccines caused, so that we could make better, more effective vaccines,” Kipper said.
The mission was important but Kipper wanted to lead research himself, and so he returned to school, but this time for a Ph.D. in Human Genetics from the McKusick-Nathans Institute of Genetic Medicine at Johns Hopkins University’s School of Medicine and a Masters in Statistics from the School of Public Health.
Now three years into his work at 23andMe, Kipper is a critical part of the team of scientists and computational biologists looking for potential drug targets. One of the projects that Kipper is working on is a research effort called the Black Representation in Genomic Research Study (BRGR) in which scientists are studying the genetics of gene expression with the aim of improving diversity in biomedical research. Ultimately, the aggregated and de-identified RNA and whole-genome sequencing data from the project will be made available to qualified researchers through the NIH’s dbGap. The hope is that this will assist those developing more effective drugs that can treat conditions and diseases affecting members of the Black community who have been underrepresented in traditional research.
It’s just one of many examples of what excites Kipper about working at 23andMe. For a scientist still fascinated by the potential of genetics to lead to novel therapies, he said being part of 23andMe’s Therapeutics division offers rare opportunities.
“The genetic and phenotypic data at 23andMe is unparalleled,” Kipper said. “There is simply no other resource that compares to the 23andMe database in terms of the scale of phenotypes surveyed or size of the cohort.”
As part of an occasional series about scientists working at 23andMe, we sat down with Kipper to talk more about computational biology, 23andMe, and what it’s like being a scientist in 23andMe’s Therapeutics division.
Where did you grow up?
I grew up in Phoenix, AZ. I remember when it hit 122F in the early 90s. It grounded airplanes, many people had heatstroke. We didn’t have AC at the time, so my mom sprayed us with the hose (this motivated my parents to get AC)
What did you study as an undergrad?
I studied philosophy at St. John’s College, a small liberal arts college in Annapolis, Maryland. (It is directly across the street from the United States Naval Academy).
Did you always want to be a scientist?
Intermittently. As a child, I thought I might want to do physics because I read a lot of “hard” sci-fi where they talked about the physics of things like space travel. But I realized at some point I liked living things and physics wasn’t the best fit. I did philosophy because at least I thought about difficult things a lot, but I wasn’t keen on the fact that there aren’t answers in philosophy, which motivated me to think about science again.
Was your interest inspired by a person or a book or an experience?
After undergrad, I was working as an administrative assistant in a North Baltimore suburb for an ophthalmology office, and one of the doctors told me I should look at bioinformatics. I learned that was a way to put math and biology together. The catch was I hadn’t studied either. But that motivated me to look around for ways to break into the field.
Why did you get two master’s degrees (in Statistics and Biotechnology) and was there something that changed to prompt you to get your Ph.D. in human genetics?
I somehow talked my way into my first masters, in Biotechnology at Johns Hopkins, despite having a degree in philosophy and no scientific training. I was essentially making up for lost time. I studied computer science and molecular biology during this degree, while also interning as a laboratory technician in a molecular genetics lab. It really set me up for success, and after graduating I took a job as a data analyst at the National Institutes of Health’s (NIH) Vaccine Research Center, mainly working on datasets that came from participants in vaccine trials. Our mission was to understand, on a molecular level, what changes vaccines caused so that we could make better, more effective vaccines.
Why did you leave the NIH?
While the NIH was a great job with really smart people, I knew I wanted to lead research projects, and I did not have the training for that. I also missed working in genetics and genomics, and so I enrolled in the Human Genetics program at the McKusick-Nathans Institute of Genetic Medicine at the Johns Hopkins University School of Medicine. While there, I knew I needed serious training in statistics to match the biological training from my Ph.D. program, so I did my Ph.D. work with Kasper Hansen, a professor in the Department of Biostatistics in the School of Public Health, and also did a master’s degree in biostatistics. In addition, I was a fellow of the Maryland Genetics, Epidemiology and Medicine (MD-GEM) training program, where I also trained in genetic epidemiology.
What are the kinds of questions or problems you like to study?
I really like difficult problems that don’t have clear answers and make you think in several disciplines at once. At the NIH, I developed a tool for doing quality control of vaccine data that uses statistical concepts typically used in geology. While a Ph.D. student, I worked on statistical methods to find genetic variants that impact what 3D structure DNA actually takes when it is in a cell.
What attracted you to working for 23andMe’s Therapeutics team?
At the NIH I was working very closely with clinical research, and I knew that after my Ph.D. I wanted to return to more translational work. 23andMe, where everything is based on genetics, was a natural choice for me. And to date, I have used every part of my training, from molecular genetics to epidemiology to statistics, to answer questions about how to take our genetic findings and identify drug targets using them.
Could you explain a little bit about your job analyzing functional genomics data and how it plays a role in characterizing potential drug targets?
At 23andMe, everything we do starts with genetics. We know which genetic variants are associated with which diseases. The problem is that knowing that some genetic variant is associated with a disease often tells you nothing about how the variant causes disease, nor what you might do about it. The Computational Biology group helps generate ideas about how diseases are caused by genetic variation, and which genes might be good drug targets, by using our genetic data in conjunction with other biological data, such as the gene expression profiling we did in the BRGR study. Specifically, we often ask questions about whether particular variants affect the expression or function of possible target genes — maybe a particular gene is over-expressed in some diseases with an unmet medical need, for example.
What is unique about working at 23andMe either in terms of the kind of data you’re working with or the culture?
The genetic and phenotypic data at 23andMe is unparalleled. There is simply no other resource that compares to the 23andMe database in terms of the scale of phenotypes surveyed or the size of the cohort. Many GWAS that we run yield signals seen in no other cohort. In this context, drug development is very exciting, in that how to use genetic information to hone in on a drug target is a topic of active research in the community. 23andMe Therapeutics is breaking ground on hard questions and is one of the leaders in this space.
How is what you’re doing now different from what you were doing at St. Johns, or different from other jobs you’ve had?
My role at 23andMe is closer to the drug/therapeutic development process than any other job I’ve had, which was a huge draw for me, to be honest. At Johns Hopkins, I worked on questions that were focused on understanding basic biology, and at the NIH I was downstream of the development process. Here I am actively helping the Therapeutics team find new targets.
What are your hopes for the work you are doing?
I am optimistic that we will find “the best” way to use genetics to inform drug discovery. Our comprehensive dataset allows us to target a wide variety of diseases, and I am hopeful we can make an impact on many of them and the lives of people living with those diseases.
