Machine learning illuminates genetic links between blood cells and disease

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Scientists from the Cambridge Baker Systems Genomics Initiative have used machine learning to create genetic predictors of blood cell traits, such as white blood cell counts, that are linked to chronic disease. The research, published today in the journal Cell Genomics, identified shared genetic architecture between blood cell traits and various common diseases, including coronary artery disease. Senior author Professor Michael Inouye, Munz Chair of Cardiovascular Prediction and Prevention at the Baker Institute, said the findings could pave the way for novel, personalized methods to better predict, prevent and treat a variety of conditions, including heart disease, the world's biggest killer. Blood cells play essential roles in a variety of biological processes that keep our bodies working well. Blood cell traits--such as the number of cells and the proportions of different types--are among the most common tests in healthcare.

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