The goal of sequencing a human genome for $1,000 is well within reach but that is just the beginning of the story. With increasingly lower DNA sequencing costs, more and more researchers are generating large amounts of genome sequence data.
With this data, researchers need to use many data analysis tools to detect genetic patterns for underlying common diseases in order to individualize treatments. Many sequence analysis tools have been developed and are publically available, but their use is often limited by the lack of experts who can install and use the tools.
As one component of the new Genome Sequencing Program, the National Human Genome Research Institute (NHGRI) has awarded six researchers approximately $4 million in FY 2012 to create robust, well-documented, and well supported computer software programs to use to analyze genome sequence data that could be used outside of large genome sequencing centers.
NHGRI plans to invest a total of $20 million over the next four years to make existing computational tools more generally accessible and to speed up the ability of investigators to analyze genome sequence data. The teams of project researchers, funded through cooperative agreements are part of a newly established “iSeqTools network”.
The FY 2012 research awards went to:
- Boston College and the University of Michigan for $1 million to produce robust software tools and workflows for variant identification and functional assessment
- The University of southern California Los Angeles for $345,000 to produce robust and portable workflow-based tools for mRNA and genome resequencing
- The Broad Institute, Cambridge Massachusetts for $1 million to develop Genome Analysis Toolkit to provide for high throughput sequence analysis
- Washington University St. Louis for $805,000 to produce robust toolkits and the GeMS turnkey computational framework for high throughput variant discovery and interpretation
- Harvard Medical School for $448,000 to produce accurate genome structural variation analysis with Genome STRiP using large-scale sequence data
- Scripps Tranlational Science Institute, La Jolla, California for $382,000 to develop the Scripps Genome ADVISOR an annotation and distributed variant interpretation service
As the tools are developed within the program, this information will be available to the public and freely available.
NHGRI is also awarding $25 million over the next four years to the “Electronic Medical Records and Genomics” (eMERGE) network to demonstrate that patients’ genomic information located in EMRs can be used to improve their care.
The awardees include Vanderbilt University Medical Center ($772,000), Group Health Cooperative and University of Washington ($823,000), Northwestern University ($762,000), Geisinger Weis Center for Research ($841,000), Essentia Institute for Rural Health ($773,000), Mayo Clinic, Rochester, ($788,000), and the Mount Sinai School of Medicine ($847,000).
The first phase of eMERGE which wrapped up last year, demonstrated that data about disease characteristics in EMRS and patient’s genetic information can be used in large genetic studies. So far, the eMERGE network has been able to identify genetic variants associated with dementia, cataracts, HDL, peripheral arterial disease, white blood cell count, type 2 diabetes, and cardiac conduction defects.
In the next phase, researchers will identify genetic variants associated with 40 more disease characteristics and symptoms, using genome-wide association studies across the entire eMERGE network. DNA from about 32,000 participants will be analyzed in each study.
Genome-wide association studies are a powerful approach that researchers can use to study hundreds of thousands of genetic variants in people with and without certain health conditions to identify genes that cause or contribute to diseases.
eMERGE researchers will then use the genomic information in clinical care. With patient consent, researchers may use information about genetic variants involved in drug response to adjust patient medications. In addition, eMERGE researchers who discover patients harboring genetic variants associated with diseases such as diabetes or cardiovascular disease will be able to intervene to prevent, diagnose, and/or treat such diseases.