Until now, determining genetic risk for type 1 diabetes has been limited to people with well-documented and well-known risk profiles.
But with machine learning tools created by researchers at the University of California, San Diego (La Jolla) and their colleagues, scientists hope they can cast an even wider net.
People with type 1 diabetes cannot produce insulin, a hormone responsible for regulating blood sugar levels and supplying cells with glucose for energy. Therefore, an external source is required.
Research published in a scientific journal on April 30th natural genetics Beyond existing genetic risk scores, we detail findings from the researchers’ machine learning model, T1GRS.
The UC San Diego team behind the paper includes co-senior author Kyle Galton, associate professor of pediatrics at the School of Medicine. Emily Griffin, postdoctoral fellow in the Galton lab. Carolyn McGrail is a former graduate student in the Galton lab and currently a senior associate at LEK Consulting. TJ Sears is a postdoctoral researcher and former graduate student in the lab of co-senior author Hannah Carter, associate professor of medicine.
Other collaborators include Alexandra Ghaben and Parul Kudtarkar from UC San Diego. Patrick Smadbeck of the Broad Institute of MIT and Harvard University. Jason Frannick of Harvard Medical School, the Broad Institute, and Boston Children’s Hospital;
T1GRS evaluates interactions between genes beyond known high-risk mutations. As a result, scientists will be able to better assess genetic risk scores beyond the more obvious cases.
“Previous research has primarily focused on the highest-risk information, which captures the subset of individuals who are likely to develop type 1 diabetes,” Galton said. “Part of the innovation of our study is that we are now able to capture more complex relationships and better predict whether a wider range of individuals will develop type 1 diabetes.”
The machine learning model was developed using data from 20,000 people with type 1 diabetes who have European ancestry and approximately 800,000 patients without European ancestry. Scientists have identified 79 genetic loci, or locations of genes on chromosomes, that qualify as risk variants. Of this group, 13 had no previous association with type 1 diabetes.
The scientists also used data from 29,000 people to map the region on chromosome 6, called the major histocompatibility complex (MHC), that is most genetically associated with type 1 diabetes. By doing so, we were able to discover novel mutant strains associated with type 1 diabetes that affect gene activation and immune function.
Type 1 diabetic patients were divided into four different groups: MHC-driven, MHC-enhanced, T-cell-enhanced, and pancreatic-enhanced.
“There is a ‘block’ of collaborative genetic information in the MHC that is extremely rich in people with type 1 diabetes,” Griffin said in a statement. “Having them doesn’t mean you’ll get diabetes, but it does mean you’re very unlikely to get diabetes if you don’t have them.”
Galton said: la jolla light Researchers hope to combine genetic and biomarker data to better understand type 1 diabetes risk. Other goals include avoiding misdiagnosis, better management of the disease, identifying candidates for clinical trials, and advancing preventive therapies.
“There is a lot of type 1 diabetes that is not covered by very high-risk genetic variants at very early ages,” Galton says. “Our tool helps us expand the set of individuals across the disease spectrum for which we can predict disease.”
“As new treatments are developed and new clinical trials are created and are successful, I think that will ultimately help develop permanent preventive treatments that people can receive,” he added. “I think accurate early prediction is important for any disease, and type 1 diabetes is definitely one of them.” ♦
