The National Institute of General Medical Sciences, part of the National Institutes of Health, has awarded a grant of nearly $1.8 million to Jackson Laboratory to develop new computational tools that can analyze vast amounts of genomic data and give insights into how various genes interact in complex diseases.
The work, which aligns with the nation’s Precision Medicine Initiative, seeks to find the large number of genes seen in complex diseases such as heart disease, multiple cancer types, Alzheimer’s and diabetes, determine how these genes interact, and which ones have a propensity to cause disease, says Greg Carter, PhD, an associate professor at Jackson Laboratory in Bar Harbor, Maine.
“Not only do individual genes contribute to risk for a given disease, but studies of laboratory mice and other model organisms have also shown that genetic variants can interact; the effect of one gene variant can depend on whether other gene variants are also present,” he adds.
The lab uses newer molecular level data provided by various NIH projects. The research being done looks at which genes are “expressed” in patients, meaning, which genes are turned on or off in a given organ, which can help researchers find variants that could cause disease.
The new computational tools being developed are in the early stage and are expected to help researchers better work with large data sets, according to Carter. These include improved algorithms and visual web-based interfaces to enable researchers to see more information when analyzing data and assessing outcomes.
For example, instead of just noting which genes being researched are important, the computational tools can help assess how the genes interact and which combinations of interacting genes contribute to good or bad outcomes, Carter explains.
“The big problem with precision medicine is we don’t know how genes are interacting in a complex way, so this helps to better understand disease down to the individual patient level.”
(This article appears courtesy of our sister publication, Health Data Management)
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