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Genome-EHR Matching Leads To New Disease Associations

CHICAGO (GenomeWeb) – An international team of biomedical informatics researchers has mapped some new associations between human leukocyte antigen mutations and specific diseases, courtesy of data matching between phenotypes and electronic health records.

The researchers posted their data on a freely available website and described their work in a paper published today in the journal Science Translational Medicine.

PheWAS is shorthand for phenome-wide association study, which is what the investigators conducted with phenotypic and deidentified EHR data on 28,839 people from a biobank at Vanderbilt University Medical Center and another 8,431 patients from Marshfield Clinic in Wisconsin. They also performed four-digit HLA sequencing on more than 3,100 people of European ancestry at Vanderbilt and at Murdoch University in Perth, Australia, to confirm their initial data.

Notably, the study found new associations between HLA mutations and multiple sclerosis and cervical cancer. "Cervical cancer is one of the more interesting ones," said corresponding author Joshua Denny, director of the Center for Precision Medicine within the Vanderbilt Genetics Institute. While some research has shown an elevated risk of cervical cancer in women whose mother or sister had the disease, it usually is linked to environmental factors.

The researchers also observed "previously unidentified" links between nonstandard HLA and a variety of conditions across multiple organs and systems, such as gastrointestinal hemorrhage, nodular lymphoma, and atherosclerosis of the extremities. "We found clusters of autoimmune diseases," Denny said, among them rheumatoid arthritis, celiac disease, and MS.

Additionally, their work confirmed earlier research that tied HLA to higher risk of one common autoimmune disease, namely type 1 diabetes. "The large numbers of subjects studied also allowed evaluation of 'subphenotypes' such as those indicating specific T1D complications or manifestations," the researchers wrote.

While the visualization of the associations is a breakthrough, Denny said there is plenty more to be learned. "This is a lot of people, but it's relatively small compared to what is coming," he said.

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