Jackson researchers use computing power to predict disease-related genes

Date: December 15, 2010
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In research that could fast-track determining which genes are associated with a disease or physical trait, Assistant Professor Matt Hibbs and colleagues at The Jackson Laboratory and Princeton University have demonstrated a computing technique that “mines” genetic data for relevant genes.  

Using modern computing horsepower and the huge amounts of genetic and genomic data generated by high-throughput technologies, Hibbs used computing algorithms to analyze gene relationships on a genome-wide scale, construct functional gene networks and identify genes associated with diseases and other phenotypes.

As proof of concept for his approach, Hibbs explored genes associated with bone mineral density, one of several important factors in osteoporosis, the disease that leads to bone fractures and deterioration. His analysis predicted two genes, called Timp2 and Abcg8, that had never before been associated with osteoporosis.

Testing mice lacking either Timp2 or Abcg8, Hibbs found changes in bone density and structure, indicating that the genes are likely related to osteoporotic fractures. The results validate Hibbs’ approach, which will provide a valuable complement to existing genomic research techniques.


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Guan et al.: Functional genomics complements quantitative genetics in identifying disease-gene associations. PLoS Computational Biology, November 2011, doi:10.1371/journal.pcbi.1000991



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