big data
Creative Minds: Looking for Common Threads in Rare Diseases
Posted on by Dr. Francis Collins
Valerie Arboleda
Credit: UCLA/Margaret Sison Photography
Four years ago, Valerie Arboleda accomplished something most young medical geneticists rarely do. She helped discover a rare congenital disease now known as KAT6A syndrome [1]. From the original 10 cases to the more than 100 diagnosed today, KAT6A kids share a single altered gene that causes neuro-developmental delays, most prominently in learning to walk and talk, plus a spectrum of possible abnormalities involving the head, face, heart, and immune system.
Now, Arboleda wants to accomplish something even more groundbreaking. With a 2017 NIH Director’s Early Independence Award, she will develop ways to mine Big Data—the voluminous amounts of DNA sequence and other biological information now stored in public databases—to unearth new clues into the biology of rare disorders like KAT6A syndrome. If successful, Arboleda’s work could bring greater precision to the diagnosis and potentially treatment of Mendelian disorders, as well as provide greater clarity into the specific challenges that might lie ahead for an affected child.
Creative Minds: Building Better Computational Models of Common Disease
Posted on by Dr. Francis Collins
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.
Cardiometabolic Disease: Big Data Tackles a Big Health Problem
Posted on by Dr. Francis Collins
More 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.
Big Data and Imaging Analysis Yields High-Res Brain Map
Posted on by Dr. Francis Collins
Caption: Map of 180 areas in the left and right hemispheres of the cerebral cortex.
Credit: Matthew F. Glasser, David C. Van Essen, Washington University Medical School, Saint Louis, Missouri
Neuroscientists have been working for a long time to figure out how the human brain works, and that has led many through the years to attempt to map its various regions and create a detailed atlas of their complex geography and functions. While great progress has been made in recent years, existing brain maps have remained relatively blurry and incomplete, reflecting only limited aspects of brain structure or function and typically in just a few people.
In a study reported recently in the journal Nature, an NIH-funded team of researchers has begun to bring this map of the human brain into much sharper focus [1]. By combining multiple types of cutting-edge brain imaging data from more than 200 healthy young men and women, the researchers were able to subdivide the cerebral cortex, the brain’s outer layer, into 180 specific areas in each hemisphere. Remarkably, almost 100 of those areas had never before been described. This new high-resolution brain map will advance fundamental understanding of the human brain and will help to bring greater precision to the diagnosis and treatment of many brain disorders.
International “Big Data” Study Offers Fresh Insights into T2D
Posted on by Dr. Francis Collins
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.
The study, led by Michael Boehnke at the University of Michigan, Ann Arbor, Mark McCarthy at the University of Oxford, England, and David Altshuler, until recently at the Broad Institute, Cambridge, MA, involved more than 300 scientists in 22 countries.
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