Study spots undiagnosed genetic diseases in EHR
Patients diagnosed with heart failure, stroke, infertility, and kidney failure could actually be suffering from rare and undiagnosed genetic diseases.
And now researchers at Vanderbilt University Medical Center have found a way to search genetic data in electronic health records to identify these diseases in large populations so treatments can be tailored to the actual cause of the illness.
The implications for the findings reported March 15 in the journal Science are broad and numerous—14% of patients with genetic variants affecting the kidney had kidney transplants and 10% with another variant required liver transplants. If their genetic cause had been diagnosed, those transplants might have been avoided.
The new method, developed by Denny, Lisa Bastarache, MS, lead data scientist with VUMC’s Center for Precision Medicine, and a team of collaborators, creates a phenotype risk score to find patterns of symptoms that may be caused by an underlying genetic variant—including some genetic variants whose effects were previously unknown.
The authors theorized that many patients currently diagnosed with issues such as heart failure, stroke, infertility, or kidney failure might actually be suffering from a rare genetic disease. If that underlying disease could be identified, it may have a specific treatment preventing the symptoms from recurring or getting worse.
By merging traditional resources with newer data mining techniques, the authors assigned scores to 21,701 individuals based on how well their list of symptoms fit the clinical description of each of 1,204 different genetic diseases. The resulting phenotype risk score is high for individuals who are a close match and low for individuals who lack keys features of the disease.
The researchers found 18 associations between genetic variants and high phenotype risk scores. Some are well known to geneticists, such as two variants that cause cystic fibrosis, but most of the associations were for variants that have not previously been described.
Individuals for the study were drawn from BioVU, one of the largest repositories of its kind linking DNA samples to de-identified electronic health records. The team then replicated their results at a second biobank at the Marshfield Clinic and confirmed them through tests in labs at VUMC and the University of Oklahoma.
The research also provides an important insight into the nature of disease inheritance. Until now, physicians have assumed that genetic diseases called “recessive” require two mutations (one from each parent) to become symptomatic. However, the researchers found that only one variant was enough for some diseases to impact a patient’s health.
As genetic testing becomes more common, there is a growing need to understand the impact of genetic variants. Only a fraction of the rare genetic variants found in human beings are well understood.
This study shows that looking at outcomes in electronic health records can be helpful in deciding if a variant might be disease-associated.
