Jonathan Haines, PhD, discusses the newly-renamed Department of Population and Quantitative Health Sciences and the significance of data analysis on precision medicine.
Jonathan L. Haines, PhD, is Mary W. Sheldon MD Professor of Genomic Sciences, chair of the Department of Population and Quantitative Health Sciences, and director of the Institute for Computational Biology at Case Western Reserve University School of Medicine.
His research focuses on using statistical computational approaches to identify genetic variants and their modifiers affecting human diseases, especially of the eye and nervous system.
Q. What prompted your dual interests in genomics and data analysis to address human disease?
A. I was always interested in biology and fortunately good at math and numbers. I got hooked on genetics in high school; inheriting one gene from mom and another from dad. It seemed so simple and yet powerful. The binary nature of both genetics and computers seemed a natural fit. Being able to combine genetics and computation to help understand why things go wrong in human disease has always been very motivating.
Q. Last month, the department you chair underwent a name change. What is the significance of the shift from the Department of Epidemiology and Biostatistics to the new Department of Population and Quantitative Health Sciences?
A. The department has a long history of growth and change, and the name change is the latest expression of that evolving heritage. In addition to the longstanding focus on epidemiology and biostatistics, over the last few years researchers in the department have branched out into such areas as bioinformatics and computational biology, genetics, health-outcomes research, community health, and public health. The new name reflects this evolving mission and heightened cross-disciplinary collaboration. The added clarity brought about by the name also helps department members better serve CWRU faculty members who use its services, which include helping design research efforts and analyzing data. The department supports and teams with virtually every department in the School of Medicine and other schools within the University including nursing, dental medicine, business, and the Mandel School of Applied Social Sciences.
Q. The wide scale use of statistical and data analysis has had an enormous effect on the rise of precision medicine. What are your views on this relationship?
A. The point of precision medicine is to deliver the best possible health care to each person. Statistical and data analysis, which falls under the umbrella of data science, has really made precision medicine possible. Finding out what’s best for an individual almost always requires also knowing what happens to other individuals. Being able to compare across individuals, and populations, makes it possible to pinpoint what’s best for each person.
Q. The range of projects that you and your colleagues in the Department of Population and Quantitative Health Sciences engage in is wide and varied. Can you give us some representative examples of this research diversity?
A. We are directly involved in, and support our CWRU colleagues in dozens of research projects in a given timeframe. Some recent ones include:
Q. What are some promising innovations and developments in quantitative health science that offer hope for improving population and individual health?
A. There are almost too many to mention! On the educational front, we are developing new tracks in our existing programs and new master’s and PhD programs in clinical and health informatics. There is a need for more people who really understand how to use population and quantitative techniques on biomedical data.
On the service front, we are combining and harmonizing our statistical and informatics core and support activities to better serve the CWRU community.
On the research front, we are working to harmonize and integrate electronic health record data across institutions, making them available and easily useable for biomedical research. We are working on ways to connect these data to genomic, imaging, community, and population data so that we can have a real 360-degree look at the causes and outcomes of health and disease. We are also developing better ways to analyze these connected data through statistical and computational algorithms and novel ways of visualization. It’s a very exciting time!