Relating the Geno to the Pheno
Health data analytics seek to unravel the biology behind Alzheimer’s and Schizophrenia
Finding links between genetic markers and the end phenotype can be difficult – especially with complex diseases, such as Alzheimer’s and Schizophrenia, where a multitude of other environmental factors affect disease development. In an attempt to close the gap, researchers from the University of Pittsburgh and Pfizer have announced a collaboration that aims to create a statistical model that relates brain scan data to genetic profiles (1). We spoke with Kayhan Batmanghelich, principle investigator and Assistant Professor in the Department of Biomedical Informatics in the School of Medicine, to tell us more about the collaboration.
How did the collaboration come about?
The idea for the collaboration stemmed from conversations I had during my post-doc at MIT. Pfizer happened to have an office close to where I worked and, one day, I bumped into one of their researchers. We got chatting and decided that it might be a good idea to work together.
What is the goal of the collaboration?
When people do Genome Wide Association Analysis (GWAS), there are so many things that happen in between the genetics and the diagnosis – and one of the modalities that can fill that gap is imaging data. When you image brain anatomy, you measure the variation in the tissue. For example, in Alzheimer’s disease, we know that the cortical thinning and loss of gray matter tissue in areas related to the memory area manifest as the symptoms of the disease. Now, with MRI images we can measure this and use it as a surrogate for the disease. The idea is to take the variation data from the MRI scans and relate it to the underlying genetic and clinical observational data, with the ultimate aim of developing an algorithm that explains causal relationships between them. We hope it will provide a deep insight into the underlying biology of the diseases.
Why collaborate?
In recent times, the budget of the NIH has remained constant while the number of scientists has increased, meaning that budget per capita has decreased. It’s important to think outside the box to obtain other sources of funding. When we presented the project to Pfizer, they confirmed that they had similar issues that needed resolving. Why not combine resources and make things more efficient? I think there’s a trend towards greater openness and collaboration in research that will inevitably lead to more innovation on the industry side, as well as greater opportunities for academia in terms of funding.
What are the main challenges when collaborating with industry?
In general, when you’re working with pharma there can be strings attached to the data in terms of restrictions on publication. It’s understandable, but if industry wants to work with academia more frequently, there will need to be a more relaxed approach. On the university side, there can be a tendency towards protecting intellectual property and I think that could also be a little more relaxed to encourage collaboration with industry.
- University of Pittsburgh, “Pitt, Pfizer Team Up on Health Data Analytics”, (2016). Available at: bit.ly/2juQnHX. Last accessed January 11, 2017.
Over the course of my Biomedical Sciences degree it dawned on me that my goal of becoming a scientist didn’t quite mesh with my lack of affinity for lab work. Thinking on my decision to pursue biology rather than English at age 15 – despite an aptitude for the latter – I realized that science writing was a way to combine what I loved with what I was good at.
From there I set out to gather as much freelancing experience as I could, spending 2 years developing scientific content for International Innovation, before completing an MSc in Science Communication. After gaining invaluable experience in supporting the communications efforts of CERN and IN-PART, I joined Texere – where I am focused on producing consistently engaging, cutting-edge and innovative content for our specialist audiences around the world.
