NIH commits $49M to fuel fusion of genomic data and EHRs

The U.S. National Institutes of Health (NIH) has allocated $48.6 million to fuel the integration of genomic information into electronic health records (EHRs). The NIH is making the cash available in grants to more than 10 research teams, each of which is tasked with investigating a different aspect of the process.

Brigham and Women's Hospital and Baylor College of Medicine have snagged the biggest pieces of the pot. The organizations will evenly share $16.9 million over the next four years in return for acting as the main sequencing and genotyping centers. Cash is already allocated for Brigham and Baylor, but the rest of the grants are dependent on funding being available. Collectively, the grants are intended to equip EHRs and healthcare for the predicted era of precision medicine.

"The newly funded projects are focused on discovering genes and gene variants with clinical implications by using the latest sequencing technologies to examine rare and common variants suspected to relate to disease risk and treatment effects," NIH's Dr. Rongling Li said in a statement. "The other important component of these grants is implementing what researchers learn about these gene variants into medical settings to improve patient care."

The NIH is focusing its research agenda on around 100 clinically relevant genes. The biomedical research organization has enlisted the support of 10 other teams to help with the initiative, each of which is in line to snag a slice of the $40 million not allocated to Brigham and Baylor. Vanderbilt University School of Medicine is set to pick up the largest of the 10 grants--$4.2 million--in return for acting as the coordinating center for the whole initiative.

Columbia University, Geisinger Health System, Mayo Clinic and a handful of other big-name health organizations make up the rest of the grant recipients. Mayo Clinic is looking at the genetics of hypercholesterolemia and colorectal cancer, while Geisinger will play with DNA and EHR data to probe the causes of a pair of diseases.

- read the release
- and GenomeWeb's take (reg. req.)

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