Creative Minds: Building Better Computational Models of Common Disease

Hilary Finucane

Hilary Finucane

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 [1], providing a strong start to what’s shaping up to be a rewarding career in computational biology.

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Cardiometabolic Disease: Big Data Tackles a Big Health Problem

Cardiometabolic risk lociMore and more studies are popping up that demonstrate the power of Big Data analyses to get at the underlying molecular pathology of some of our most common diseases. A great example, which may have flown a bit under the radar during the summer holidays, involves cardiometabolic disease. It’s an umbrella term for common vascular and metabolic conditions, including hypertension, impaired glucose and lipid metabolism, excess belly fat, and inflammation. All of these components of cardiometabolic disease can increase a person’s risk for a heart attack or stroke.

In the study, an international research team tapped into the power of genomic data to develop clearer pictures of the complex biocircuitry in seven types of vascular and metabolic tissue known to be affected by cardiometabolic disease: the liver, the heart’s aortic root, visceral abdominal fat, subcutaneous fat, internal mammary artery, skeletal muscle, and blood. The researchers found that while some circuits might regulate the level of gene expression in just one tissue, that’s often not the case. In fact, the researchers’ computational models show that such genetic circuitry can be organized into super networks that work together to influence how multiple tissues carry out fundamental life processes, such as metabolizing glucose or regulating lipid levels. When these networks are perturbed, perhaps by things like inherited variants that affect gene expression, or environmental influences such as a high-carb diet, sedentary lifestyle, the aging process, or infectious disease, the researchers’ modeling work suggests that multiple tissues can be affected, resulting in chronic, systemic disorders including cardiometabolic disease.

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International “Big Data” Study Offers Fresh Insights into T2D

World map

Caption: This international “Big Data” study involved hundreds of researchers in 22 countries (red).

It’s estimated that about 10 percent of the world’s population either has type 2 diabetes (T2D) or will develop the disease during their lives [1]. Type 2 diabetes (formerly called “adult-onset”) happens when the body doesn’t produce or use insulin properly, causing glucose levels to rise. While diet and exercise are critical contributory factors to this potentially devastating disease, genetic factors are also important. In fact, over the last decade alone, studies have turned up more than 80 genetic regions that contribute to T2D risk, with much more still to be discovered.

Now, a major international effort, which includes work from my own NIH intramural research laboratory, has published new data that accelerate understanding of how a person’s genetic background contributes to T2D risk. The new study, reported in Nature and unprecedented in its investigative scale and scope, pulled together the largest-ever inventory of DNA sequence changes involved in T2D, and compared their distribution in people from around the world [2]. This “Big Data” strategy has already yielded important new insights into the biology underlying the disease, some of which may yield novel approaches to diabetes treatment and prevention.

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Flipping a Genetic Switch on Obesity?

Illustration of a DNA switchWhen 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 [1]. 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 [2].

Now, NIH-funded researchers reporting in The New England Journal of Medicine [3] 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.

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Mining the Big Data Mountain

Cartoon of three men mining mountains of data

Credit: Chris Jones, NIH

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.

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