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Genes produce proteins that keep human body functioning and healthy, but genes that code for protein make up less than 2 percent of DNA. Researchers from Princeton University and the Flatiron Institute’s Center for Computational Biology in New York City have introduced a method to link variations in non-coding DNA to the operation of genes. Using machine learning, the researchers created a computational method, called ExPecto, that reads sections of DNA and predicts how that segment will alter the activation and deactivation of genes throughout the body.
Researchers computed the effects of more than 140 million mutations on tissues throughout the body. The researchers identified mutations potentially responsible for increasing the risk of several immune-related diseases, including chronic hepatitis B virus (HBV) infection and Crohn’s disease. The researchers cautioned that their method is a long way from diagnosing disease; more work is needed to better understand the mechanisms of genetic operation and the balance between genetic and environmental causes of disease.
DNA contains genes that serve as blueprints for building proteins, the workhorse molecules of human bodies responsible for carrying out ferrying oxygen, communicating with other cells and fighting infections. Protein-coding sequences of DNA make up less than 2 percent of the human genome. All of these genes are present in cells throughout the body. This ubiquity means that protein-encoding genes vital to brain function, for instance, also exist in the digestive tract, lying dormant.
Genes are switched on and off by the other 98 percent of the genome, the noncoding portion that doesn’t code for proteins. Most genetic mutations are found in this noncoding region. A mutation is essentially a genetic typo-an addition, deletion or alteration in the genomic sequence. Mutations in the noncoding region can sometimes cause genes to switch on or off in the wrong part of the body at the wrong time, increasing the risk of diseases like cancer.
Identifying the specific mutation responsible is difficult because the noncoding portion of DNA is so large. Previous studies compared the genomes of many individuals with a given disease, searching for mutations the individuals had in common. This approach, however, becomes increasingly tricky for rarer mutations. Furthermore, strings of DNA are sometimes inherited in large clusters, so scientists struggle to pinpoint which particular piece of genetic code is the troublemaker.
The researchers used ExPecto to predict the mutations that contribute to Crohn’s disease, chronic HBV infection and Behçet’s disease and discovered that ExPecto’s predicted candidate was a more promising potential contributor to the disease than those proposed by previous studies.