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Research Abstracts Online
January 2008 - March 2009

University of Minnesota Twin Cities
College of Food, Agricultural, and Natural Resource Sciences
Department of Animal Science

PI: Yang Da

Detection of SNP Epistasis Effects in Large-scale Genome-wide Associate Studies

This group’s parallel computing tool was applied to the Framingham heart study for detecting epistasis effects of 500,000 SNP markers on four quantitative measures related to hypertension. This work demonstrates that parallel computing tools are capable of analyzing pairwise epistasis effects for any existing large-scale genome-wide association studies in a timely manner. The computer program has been developed to extend the method of detecting epistasis effects to account for sibling correlation. Based on the previous work by this group on methods for detecting epistasis effects, a new and general quantitative genetics approach for detecting complex epigenetic effects was developed, including imprinting, gene-gene, gene-sex and gene-environment interactions. Epigenetics refers to changes in gene expression that do not involve changes in the underlying DNA sequence of the organism and includes epistasis effects. Work is in progress to develop parallel computing tools to implement this new approach.

Group Member

Li Ma, Graduate Student