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genome-wide association studies

Mining the Big Data Mountain

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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.


Exploring the Complex Genetics of Schizophrenia

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Illustration of a human head showing a brain and DNA

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 [1]. 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 [2]. 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.


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