Take 10 with... Tayaza Fadason

1. Describe your research topic to us in 10 words or less.

I investigate the genetics of human diseases.

2. Now describe it in everyday terms!

Our genes and the environment have a complex interplay that determines whether we end up with a disease or not. It’s important to understand the different role of each of these components, and then look at how they interact. My research focuses on which part of the DNA is involved in disease and what the consequences are of changes in the DNA. I have developed a method that will give us a more complete understanding of what genetic changes do in association with diseases.

3. What are some of the day-to-day research activities you carry out?

Day to day I read papers, write computer programme to make what we do reproducible to other people, and figure out how computers can process the big data we’re working with. I’m an early career researcher so I also participate in student supervision.

4. What do you enjoy most about your research?

It’s hard to compare the different aspects of research. Brainstorming ideas or analysing data are both interesting when I’m doing it. Discussing problems with my teammates to find potential solutions is very exciting. I only have my own perspective - discussing with others brings something else to the table.

5. Tell us something that has surprised or amused you in the course of your research.

When I started my PhD the goal was clearly defined and I thought when I finished that would be “it”. Turns out it was just the tip of the iceberg! Although I had identified certain genes that are affected in particular diseases, I then had to know exactly what biological processes are affected. This is different in different tissues, and even though all of our cells that contain DNA have the same composition of DNA, the genes that are turned on or off are different in each cell, which is what gives them their speciality. With the data we have right now, it’s difficult to know which tissues we should take into account, whether there might be these changes in the DNA, and how to apply this at an individual level to predict if person A is going to end up with a disease. It amuses me that I was presumptuous enough to think the work would be done at the end of my PhD!

6. How have you approached any challenges you’ve faced in your research?

The first thing I do is to look for an alternative approach, because it’s possible that someone has done something similar. Maybe not on the same problem, but the same idea or approach. The other thing is to talk to people – we bring all sorts of challenges to our group meetings for discussion.

7. What questions have emerged as a result?

Most of the scientific research that has been done is skewed population-wise. It’s mostly on male Caucasians so the question now is: how can we translate that to other population groups or sexes? And the other question is, how do we make sense of this at an individual level? For complex disorders, which I specialise in, it’s difficult to predict what will happen because there are so many environmental factors. And because there are so many variables, the big thing I’m trying to figure out is what to work on. What’s most feasible? What will have the most impact? I want to prioritise those factors and pick one to study for the next few years of my life. It is important that I don’t climb the wrong tree in terms of focusing on something that ends up having very low impact on people’s health. That’s why it’s so important to use all available data in the process of hypothesis generation. It’s difficult to divorce yourself from what you’ve done in your PhD, but I’m making a conscious effort to try to do that and take a step back.

8. What impact is your research having or what impact do you hope it will have?

My research has shown the importance of long-range regulatory interactions in the human body as it relates to disease and other phenotypes. People used to disregard long-range interactions because we didn’t understand them, but the method I developed during my PhD allows us to better understand this regulatory interaction because it’s distant from the gene. Now we have methods to actually look at it and weed out those that happen by chance and those that we think are potentially consistent across different life cycles of a cell or different tissues – so you prioritise those.

I hope this will help in understanding how diseases occur together. Say for example, someone has obesity and it’s associated with genetic variant A, but there’s another genetic variant B that has been associated with Type 2 Diabetes. At face value you’d think those are two different things, but actually when you look at the whole genome and the long-range interactions, more often than not you see that genetic variant A and genetic variant B control the same genes underneath. That understanding will have an impact on comorbidity diseases: we’ll be able to look at the patient as a whole because it’s the same processes that are affected. This will influence how patients are treated and the drugs that are prescribed.

9. If you collaborate across the University, or outside the University, who do you work with and how does it benefit your research?

I work with the Department of Statistics, whose staff bring experience in human genetics and population studies. I’ve worked with Dr John Ingram on Type 2 diabetes and obesity and with Professor Tony Merriman on gout. We help them understand the genetic associations of disease and they help us apply our research, which is mostly computational biology, to the real world. Recently I’ve begun a collaboration with Professor Cathy Stinear on cerebral vascular disease, particularly stroke, and how we can find new drugs and try to predict recovery, because stroke is common in the elderly and our elderly population is growing so it’s an important area to tackle.  Collaborators bring a unique set of experience that is different from mine: they are specialists in their field, and they often have direct access to patients, whereas I am trying to understand the basic mechanisms that happen within a cell using computational methods.

10. What one piece of advice would you give your younger, less experienced research self?

Be patient. It’s easy to want to rush things and try to meet deadlines, for example to publish. We’re trying to solve complex problems so it’s very possible that the hypothesis might be wrong in the first few attempts.