Facebook Pixel Code
Banner image
About the Institute

Profile of Lisa Strug

Dr. Lisa Strug
Dr. Lisa Strug

Dr. Lisa Strug, PhD

  • Scientist, Child Health Evaluative Sciences
  • Assistant Professor, Public Health Sciences

1. Where are you from?\Where did you study?
I was born and raised in Halifax, Nova Scotia. My graduate studies were conducted at the University of Toronto and at Johns Hopkins in Baltimore, Maryland. I did my Master in biostatistics at Johns Hopkins University and then completed my PhD, also in biostatistics, at U of T. I moved to New York shortly after for a post-doctoral fellowship in statistical genetics at Columbia University. I stayed on at Columbia as a faculty member in the Department of Biostatistics for two years before returning to Canada to take on my research position at SickKids in 2007.

2. What are you researching right now?
We have three research studies going on at the moment in my lab, all related to finding the genes that cause disease. The first is a study looking for genes which modify the course of cystic fibrosis (CF) lung disease; the second a study trying to find the genes which cause rolandic epilepsy (RE); and lastly a research program developing new statistical methods to identify disease genes that can be applied to any disease.

In our CF studies, although we know the gene that causes CF, the severity of the lung disease varies in each individual. We can attribute this, in part, to other genes and those are the ones we are trying to identify. Identifying them will allow us to tailor treatments to individuals depending on their particular DNA profile, and implement earlier interventions for some in highly susceptible groups.

In RE, a child’s seizures remit in adolescence. For this reason, many refer to this type of epilepsy as benign and often there is no treatment administered. We are finding, however, that these children with RE, along with their unaffected siblings, are experiencing more difficulties with speech and reading once they enter school. Identifying the genes that cause RE will provide invaluable information about the epilepsy, as well as these common developmental problems.

The field of genomics is a rapidly evolving field, with new technologies able to produce more and more detailed information about an individual’s genome every day. As a consequence, statistical methods to summarize and understand that information must also be developed, which is what we do in my lab as the focus of the third study. Our hope is that these new methods will realize the full potential of the technological advances, and bring us closer to identifying the disease causing genes.

3. Who is your all-time favourite scientist, and why?
I would have to say that Dr. Ronald Aylmer (R.A.) Fisher is the scientist who has had the greatest impact on my field and my own statistical research. He was a statistician, a developmental geneticist and he has made significant contributions in many other areas. He has had a huge impact on the field of statistics – it is difficult to find a branch of statistics that doesn’t refer to his work in some way. Not to mention that he is a proverbial giant as he always seemed to have an interesting quote or point of view on virtually every aspect of the science. His name is also synonymous with some key statistical theories. He founded the technique of maximum likelihood which is a widespread technique – and key to my own research in the foundations of statistics – which is used for fitting a statistical model to data, and providing estimates for the model's parameters.

4. What in your opinion is the single most important scientific breakthrough, and why?
I would have to say Watson and Crick’s discovery of the double helix as the structure of DNA. Through their work they changed our understanding of heredity and hereditary diseases.

5. What are your interests outside the lab?
I have two young daughters, a 2½-year-old and a five-month-old. I enjoy spending as much time as I can with them. I have recently taken up jogging, which I am really starting to enjoy as a way to clear my mind. My husband and I got into theatre while living in New York City, so we try to take in shows whenever we get the opportunity. I also enjoy reading non-fiction, biographies in particular. I really enjoyed the Richard Feynman biographical accounts, and I have read a lot of books on different scientific figures.

6. Why science?
I like puzzles. I like to think analytically, take a problem and then try to attack it from all kinds of different angles to come up with an explanation. It is so gratifying when you figure out a solution. I guess I enjoy the challenge. This is why the field genetics is an interest for me because unlike in a lot of other sciences, you can actually find out if you were right!

7. Why SickKids?
When I was a graduate student, I worked at SickKids as a research assistant and I was inspired by the work done here. As a professor in New York, I was studying childhood-onset diseases and I was looking to return to Toronto. SickKids seemed like a good fit. I consider myself fortunate to have the opportunity to work at such a world-class institution and continue my research unabated with the support of the institution.

8. What is the most controversial question in your field right now?
In statistics, my particular research area is measures of evidence in the foundations of statistics. We ask the question, how do we properly assess the strength of our statistical evidence? Every scientific field uses statistics to some degree and most use the common approach of reporting the p-value – this is seen by some as the gold standard in statistical measurement. You report a p-value and the assumption is that if you have a p-value that is smaller than my p-value, you have stronger evidence than I do. The controversy lies in this method. I am a member of the community that argues against the legitimacy of the p-value as a valid measure of evidence. I would suggest that it is influenced by too many variables – such as the size of your study – and a measure of the strength of evidence should be calibrated to mean the same thing under variable circumstances. I develop statistical methodology based on an alternative measure of evidence, the likelihood ratio, that I believe is properly calibrated.

September 2009

View scientific profile »»