Electronic health records may aid in identification of undiagnosed genetic diseases

New methods of uncovering rare and undiagnosed genetic diseases through electronic medical data could help identify and treat causes of illnesses in patients diagnosed with heart failure, stroke, infertility and kidney failure.

Researchers at Vanderbilt University Medical Center have found a way to search the records to identify these diseases in large populations, so treatments can be tailored to the underlying cause.

“We started with a simple idea: look for a cluster of symptoms and diseases to find an undiagnosed underlying disease,” Josh C. Denny, MD, MS, professor of biomedical informatics and medicine and director of the Center for Precision Medicine, said in a press release from Vanderbilt University Medical Center. “Then we got really excited when we saw how we could systematize it across thousands of genetic diseases to figure out the impact of millions of genetic variants.”

There were 14% of patients with genetic variants affecting the kidney and had kidney transplants while 10% with another variant required liver transplants. According to the press release, if the underlying genetic cause had been identified, those transplants might have been avoided.

The new method, developed by Denny and colleagues, creates a phenotype risk score to find patterns of symptoms that may be caused by an underlying genetic variant. By merging traditional resources with newer data mining techniques, the authors assigned scores to 21,701 individuals based on how well their symptoms matched descriptions of 1,204 different genetic diseases. The resulting score is high for individuals who are a close match and is low for individuals who lack key features of the disease.

This method also led to the discovery of 18 associations between genetic variants and high phenotype risk scores. Most of the associations were for variants that have not previously been described.

The study, which was published in Science, showed outcomes in electronic health records can aid in deciding if a variant might be related to an underlying disease.

“Phenotype risk scoring can easily be applied in any electronic medical record system that is linked to DNA,” study co-author Lisa A. Bastarache, MS, said in the release. “Our work looked at only a small sample of the human genome, about 6,000 variants. The opportunity for additional discoveries using this method is huge.” - by Jake Scott

References:

Bastarache LA, et al. Science. 2018: 10.1126/science.aal4043

https://news.vanderbilt.edu/2018/03/15/study-spots-undiagnosed-genetic-diseases-in-ehr/

Disclosures: The study was supported by grants from the NIH (LM010685, LM011939, LM007359, HG004603, HG006378, HG008672, HG008341, RR024975, TR000445, GM114128, GM115305, GM120523, HL133786, HG009086). Nephrology News & Issues was unable to determine any other relevant financial disclosures.