Researchers at the Simons Center for Data Analysis (SCDA) have established how genes work together within 144 different human tissues and cell types in carrying out those tissues' functions. They demonstrated how computer science and statistical methods can work together to aggregate and analyze very large genomic big data collections.
The researchers, led by Olga Troyanskaya, collected and integrated data from about 38,000 genome-wide experiments, and used integrative computational analysis to isolate the functional genetic interconnections contained in datasets for various tissue types. They combined the datasets to identify statistical associations between genes and diseases that would otherwise be undetectable. The resulting technique, called network guided association study (NetWAS), integrates quantitative genetics with functional genomics to increase the power of genome-wide association studies and identify genes underlying complex human diseases.
"Our approach mined these big data collections to build a map of how genetic circuits function in the podocyte cells, and in many other disease-relevant tissues and cell types," Troyanskaya says.
The researchers also created the Genome-scale Integrated Analysis of Networks in Tissues (GIANT), an online resource enabling users to explore networks such as NetWAS. "With GIANT, researchers studying Parkinson's disease can search the substantia nigra network, which represents the brain region affected by Parkinson's, to identify new genes and pathways involved in the disease," says SCDA's Aaron K. Wong.
From Simons Foundation
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Abstracts Copyright © 2015 Information Inc., Bethesda, Maryland, USA
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