Not so long ago, Hilary Finucane was a talented young mathematician about to complete a master’s degree in theoretical computer science. As much as she enjoyed exploring pure mathematics, Finucane had begun having second thoughts about her career choice. She wanted to use her gift for numbers in a way that would have more real-world impact.
The solution to her dilemma was, literally, standing right by her side. Her husband Yakir Reshef, also a mathematician, was developing a new algorithm at the Broad Institute of MIT and Harvard, Cambridge, MA, to improve detection of unexpected associations in large data sets. So, Finucane helped the Broad team with modeling biomedical topics ranging from the gut microbiome to global health. That work led to her co-authoring a paper in the journal Science , providing a strong start to what’s shaping up to be a rewarding career in computational biology.
When weight loss is the goal, the equation seems simple enough: consume fewer calories and burn more of them exercising. But for some people, losing and keeping off the weight is much more difficult for reasons that can include a genetic component. While there are rare genetic causes of extreme obesity, the strongest common genetic contributor discovered so far is a variant found in an intron of the FTO gene. Variations in this untranslated region of the gene have been tied to differences in body mass and a risk of obesity . For the one in six people of European descent born with two copies of the risk variant, the consequence is carrying around an average of an extra 7 pounds .
Now, NIH-funded researchers reporting in The New England Journal of Medicine  have figured out how this gene influences body weight. The answer is not, as many had suspected, in regions of the brain that control appetite, but in the progenitor cells that produce white and beige fat. The researchers found that the risk variant is part of a larger genetic circuit that determines whether our bodies burn or store fat. This discovery may yield new approaches to intervene in obesity with treatments designed to change the way fat cells handle calories.
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.
Credit: Jonathan Bailey, National Human Genome Research Institute, NIH
Schizophrenia is one of the most prevalent, tragic, and frustrating of all human illnesses, affecting about 1% of the human population, or 2.4 million Americans . Decades of research have failed to provide a clear cause in most cases, but family clustering has suggested that inheritance must play some role. Over the last five years, multiple research projects known as genome-wide association studies (GWAS) have identified dozens of common variations in the human genome associated with increased risk of schizophrenia . However, the individual effects of these variants are weak, and it’s often not been clear which genes were actually affected by the variations. Now, advances in DNA sequencing technology have made it possible to move beyond these association studies to study the actual DNA sequence of the protein-coding region of the entire genome for thousands of individuals with schizophrenia. Reports just published have revealed a complex constellation of rare mutations that point to specific genes—at least in certain cases.