Ani Manichaikul: Statistical Geneticist

courtesy of STEM to the Sky

Were you always interested in genetics growing up?

When I was a junior in high school, I remember being interested more in the physical sciences rather than biology. At the time, I didn’t think I was that interested in biology, but I grew up pretty close to the NIH. I happened to find a mentor at the National Institute on Deafness and Communication Disorders who was doing studies on deafness and communication disorders, but he himself was a geneticist. It was through him that I got exposure to the field of genetics. He showed me that genetics was a mathematical field. From that experience, I got a chance to analyze some of the datasets, which was really cool for me. It wasn’t exactly at that moment that I decided that’s what I wanted to do, but those were the experiences that made me interested in genetics.

When it was time to decide on what I was going to do after college, I decided to go to graduate school. At the beginning of graduate school, I was thinking of what I wanted to focus on and remembered this great research experience I had. I didn’t really see myself as somebody doing lab experiments, so I decided that I could go into genetics. Genetics requires a lot of data analysis, so I saw it as a good fit for me.

What was your favorite class in college?

I got my Ph.D. in biostatistics, and one of my favorite classes that I took in college was an introduction to probability course. I was really fascinated by the stories and anecdotes the professor told about how statistics are incorporated into everyday life. He would explain how statisticians can determine the next best stock picks based on the top 10 outperforming stocks from the past year. They can predict that certain stocks will probably be going down due to the fact that they went up too much last year. He would tell us that you can get in a helicopter on top of the highway between Berkeley and Stanford at rush hour and look at the patterns of the cars moving during a traffic jam. Those patterns of cars may look random, but if you know statistics, you can actually model the way the cars are moving.

Is there anything that surprised you about your current role or field?

I’ve actually been surprised over time to see the way my field has been evolving. Things have gone better for this field than I would have expected in a lot of ways. Over the past 10 years, we’ve been doing a lot of what we call Genome-Wide Association Studies (GWAS). The first of these studies was first published in 2005. At the time people thought it was a long shot that they were going to examine thousands or millions of individual DNA variations across hundreds or thousands of people, and maybe using this type of approach, they would be able to find regions of DNA variation that were associated with disease. At the time, it had never been done before, so people weren’t sure if it would work. It turns out that it worked really well.

We have been doing this over and over again to millions of people for the past 10 or 15 years now. Although we have found DNA variations that are predictive of disease and have confirmed them with repetition, actually translating those discoveries into knowledge that’s useful for doctors or developing drugs has been a lot more challenging.

In terms of my role, I used to imagine that my job was to be an expert at the science part, and that’s it. What was initially surprising was that a lot of my role is about interacting with large numbers of people, managing those relationships, and making sure that we communicate what’s going on in certain projects in order to do the science that we want to do. These projects require a lot of people’s skills and input, so we need to continually communicate with all the different members of our team.

What advancements do you foresee happening in the future of genomics?

One of the things we would love to do is find a way to use all those DNA variants that we’ve identified and turn them into improvements in health care treatment and prevention. There are already a few examples of that happening. For example, some drugs for kidney disease prevention and lipid-lowering medications have already been approved. Drugs and medications that people didn’t have before the GWAS era are now available through discovery studies and geneticists have been able to translate research into new drugs and therapies. We would love to see more examples of actual treatments that come out of the research that we’re doing.

Besides that, we are now able to apply precision medicine to people’s entire DNA sequence to identify biologically relevant genes that we can use to develop treatments for everybody. The other aspect is developing personalized treatments where you look at an individual’s genome and say, based on your particular DNA sequence, there is a specific type of treatment that would work better for you than it would for other people. We’re not quite there yet, but we do have pretty good progress in terms of an area that we call genetic risk score.

Geneticists in my field are currently working to improve genetic risk predictions to be more accurate for non-European ancestry populations. One of the ways we are doing this is by collecting more DNA samples from diverse ancestry individuals. If we do more genetic studies in diverse ancestries, then we can also build better genetic risk prediction models for multi-ethnic people.

What is the most rewarding aspect of your role?

Right now, we are expanding what we’re doing to more diverse ancestry individuals. We have interesting technical or methodological advancements by combining both gene expression and DNA variation together from diverse ancestry people in order to improve personalized health care for more diverse ancestry individuals.

It is also rewarding to do research with people that I am mentoring, such as graduate students, and postdocs. I’ve come to realize it’s really exciting to advance science, but at the same time, science advances slowly. In order to keep science alive and growing, there’s only so much I can do as an individual. To increase my contribution and give back to the field of research, I can accomplish more by mentoring other people so that they can develop their careers and their scientific ideas.