- Researchers have sequenced the genomes of many patients suffering from common multigene diseases, looking for shared mutations in their control regions. The trouble is that these studies yield hundreds of mutations, most of them benign. So the team designed a computer program that could learn the difference between mutations that are likely to affect gene activity levels and those that likely won’t.
- To test, the team worked to predict the impact of mutations in the control regions for two pigment-related genes in mouse melanocytes (skin pigment cells). They then selected 40 mutations with different levels of predicted impact and tested their effect in melanocytes grown in the laboratory. When they measured, they found that there was a strong correlation between the prediction and the actual change.
- Beer says: “The next step is to collect cells from patients with these autoimmune diseases, test their gene activity levels and find out if our predictions were right. If so, it should help us determine how the activity is being perturbed and how we can fix it.” The same process can theoretically be repeated on many other diseases, providing timesaving insights for each.
AI program predicts key disease-associated genetic mutations for hundreds of complex diseases
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