Biomedical researchers and clinicians are generating an enormous, ever-expanding trove of digital data through DNA sequencing, biomedical imaging, and by replacing a patient’s medical chart with a lifelong electronic medical record. What can be done with all of this “Big Data”?
Besides being handy for patients and doctors, Big Data may provide priceless raw material for the next era of biomedical research. Today, I want to share an example of research that is leveraging the power of Big Data.
NIH-funded researcher Atul Butte of Stanford University recently mined mountains of existing data to find new links among genes, diseases, and traits. In this instance, traits are defined as any detectable physical or behavioral characteristic, such as cholesterol levels or other blood chemistries; bone density; or body weight. Butte reasoned that a trait that was closely linked to a disease through specific genes might be useful as a predictive marker of disease risk.
To discover these new links, he tapped into the VARiants Informing MEDicine (VARIMED) database, a resource that he began building in 2008 to interpret the clinical consequences of DNA variation in patients . To create VARIMED, Butte and his colleagues read scientific papers on human genetics—including many genome-wide association studies (GWAS), which identify common genetic variants that are associated with disease risk—and noted the genes, variations, and traits mentioned in each paper and the connections between them. Over the years, the privately funded database grew; today it contains findings from more than 9,000 studies.
In their most recent study, Butte’s team examined the genetic architecture of each disease—all of the genetic variations that influence disease risk—and made a list of the gene-disease pairs.
The views, opinions and positions expressed by these authors and blogs are theirs and do not necessarily represent that of the Bioethics Research Library and Kennedy Institute of Ethics or Georgetown University.